American baseball player
POPULARITY
2 episodes with Derek Lowe
2 episodes with Derek Lowe
3 episodes with Derek Lowe
3 episodes with Derek Lowe
2 episodes with Derek Lowe
2 episodes with Derek Lowe
2 episodes with Derek Lowe
2 episodes with Derek Lowe
2 episodes with Derek Lowe
2x Executive of the Year joins the show today ... Expos Magic, game day with Derek Lowe, the magic between Pedro and Varitek ... and the mitochondria?
The best conversations about the Red Sox this week from The Greg Hill Show, Jones & Keefe, and WEEI Afternoons. Sam Kennedy joins The Greg Hill Show to talk about Lucas Giolito's pending return and when we might see top prospect Roman Anthony called up. Rob Bradford makes his weekly appearance on Jones & Keefe, Alex Cora joins WEEI Afternoons, and Derek Lowe makes a surprise visit to The Greg Hill Show.
The best conversations about the Red Sox this week from The Greg Hill Show, Jones & Keefe, and WEEI Afternoons. Sam Kennedy joins The Greg Hill Show to talk about Lucas Giolito's pending return and when we might see top prospect Roman Anthony called up. Rob Bradford makes his weekly appearance on Jones & Keefe, Alex Cora joins WEEI Afternoons, and Derek Lowe makes a surprise visit to The Greg Hill Show.
The best conversations about the Red Sox this week from The Greg Hill Show, Jones & Keefe, and WEEI Afternoons. Sam Kennedy joins The Greg Hill Show to talk about Lucas Giolito's pending return and when we might see top prospect Roman Anthony called up. Rob Bradford makes his weekly appearance on Jones & Keefe, Alex Cora joins WEEI Afternoons, and Derek Lowe makes a surprise visit to The Greg Hill Show.
Brandon and Tyler are joined by Derek Lowe - former All Star and World Series Champion, live from the LPGA Tournament of Champions and the Lake Nona Country Club.
Tom Caron and Alex Speier welcome former Red Sox manager Terry Francona for an insightful discussion about his journey in baseball. Francona reflects on his decision to return to managing, sharing how taking a year off rejuvenated him. He opens up about feeling like he let people down when he retired and what drives him now as he leads the Reds. Terry shares his thoughts on the impact of the 2004 documentary, his comfortable clubhouse atmosphere, and how his experiences over the past two decades have shaped him. He recounts the challenges of coming to Boston, including managing the weight of the city's history and keeping pace with Theo Epstein. Francona also discusses pivotal moments from the 2004 season, including the trade deadline moves and Game 3 against the Yankees, where Tim Wakefield made significant sacrifices for the team. He reflects on the influence of key players like Keith Foulke and Derek Lowe, as well as the evolution of David Ortiz's expectations. With candid anecdotes about managing the pressures of Boston, relationships with players like Kevin Cash, and the surprising journeys of former players now in managerial roles, this episode is a must-listen for Red Sox fans. Don't miss this engaging conversation featuring Tom Caron and Alex Speier on 310 To Left! GET NESN 360: https://nesn.com/download-the-nesn-app/ Subscribe on YouTube: https://www.youtube.com/NESN Twitter: https://twitter.com/NESN Facebook: https://www.facebook.com/NESN/ Instagram: https://www.instagram.com/nesn TikTok: https://www.tiktok.com/@nesn Twitch: https://twitch.tv/nesn/ Learn more about your ad choices. Visit megaphone.fm/adchoices
October 20, 2004 Game 7 of the ALCS began at 8:30 in the evening at Yankee Stadium. After winning the first three and loosing the next three, the Yankees were sending Kevin Brown to the mound to face Derek Lowe for the Red Sox. Johnny Damon, suffering through a 3 for 29 slump decided to play aggressively leading off the game with a single to left and a stolen base, but was thrown out at home trying to score on a Manny Ramirez base hit. The very next pitch, however, was lined into the right-field bleachers by David Ortiz to give Boston a 2--0 advantage. In the second inning, the Sox loaded the bases against Brown, causing Yankees manager Joe Torre to remove him and put in Javier Vázquez to face Johnny Damon. Damon hammered Vázquez' first pitch into the right-field seats for a grand slam. The rout was on. Damon, who also added an upper deck two-run blast in the fourth, had three hits in the game. Boston also enjoyed a solid performance from their starting pitcher, Derek Lowe, who allowed only one run and one hit in six innings of work, and was never even intended to be a starter in the postseason. He pitched game seven on just two days of rest. Pedro Martinez relieved Lowe in the seventh inning, receiving loud chants of "Who's Your Daddy?" which intensified as he gave up two runs. He eventually raised the velocity of his fastball to the mid-90s and shut down the rally. At 12:01 a.m., on October 21, Rubén Sierra hit a groundball to second baseman Pokey Reese, who threw to first baseman Doug Mientkiewicz to finish the unprecedented comeback. "Not many people get the opportunity to shock the world. We came out and did it," Boston first baseman Kevin Millar said. "You know what? We beat the Yankees. Now they get a chance to watch us on the tube."The Red Sox won 10--3 and became the first team in Major League Baseball history to win a seven-game series after losing the first three games.
October 20, 2004 Game 7 of the ALCS began at 8:30 in the evening at Yankee Stadium. After winning the first three and loosing the next three, the Yankees were sending Kevin Brown to the mound to face Derek Lowe for the Red Sox. Johnny Damon, suffering through a 3 for 29 slump decided to play aggressively leading off the game with a single to left and a stolen base, but was thrown out at home trying to score on a Manny Ramirez base hit. The very next pitch, however, was lined into the right-field bleachers by David Ortiz to give Boston a 2--0 advantage. In the second inning, the Sox loaded the bases against Brown, causing Yankees manager Joe Torre to remove him and put in Javier Vázquez to face Johnny Damon. Damon hammered Vázquez' first pitch into the right-field seats for a grand slam. The rout was on. Damon, who also added an upper deck two-run blast in the fourth, had three hits in the game. Boston also enjoyed a solid performance from their starting pitcher, Derek Lowe, who allowed only one run and one hit in six innings of work, and was never even intended to be a starter in the postseason. He pitched game seven on just two days of rest. Pedro Martinez relieved Lowe in the seventh inning, receiving loud chants of "Who's Your Daddy?" which intensified as he gave up two runs. He eventually raised the velocity of his fastball to the mid-90s and shut down the rally. At 12:01 a.m., on October 21, Rubén Sierra hit a groundball to second baseman Pokey Reese, who threw to first baseman Doug Mientkiewicz to finish the unprecedented comeback. "Not many people get the opportunity to shock the world. We came out and did it," Boston first baseman Kevin Millar said. "You know what? We beat the Yankees. Now they get a chance to watch us on the tube."The Red Sox won 10--3 and became the first team in Major League Baseball history to win a seven-game series after losing the first three games.
October 20, 2004 Game 7 of the ALCS began at 8:30 in the evening at Yankee Stadium. After winning the first three and loosing the next three, the Yankees were sending Kevin Brown to the mound to face Derek Lowe for the Red Sox. Johnny Damon, suffering through a 3 for 29 slump decided to play aggressively leading off the game with a single to left and a stolen base, but was thrown out at home trying to score on a Manny Ramirez base hit. The very next pitch, however, was lined into the right-field bleachers by David Ortiz to give Boston a 2--0 advantage. In the second inning, the Sox loaded the bases against Brown, causing Yankees manager Joe Torre to remove him and put in Javier Vázquez to face Johnny Damon. Damon hammered Vázquez' first pitch into the right-field seats for a grand slam. The rout was on. Damon, who also added an upper deck two-run blast in the fourth, had three hits in the game. Boston also enjoyed a solid performance from their starting pitcher, Derek Lowe, who allowed only one run and one hit in six innings of work, and was never even intended to be a starter in the postseason. He pitched game seven on just two days of rest. Pedro Martinez relieved Lowe in the seventh inning, receiving loud chants of "Who's Your Daddy?" which intensified as he gave up two runs. He eventually raised the velocity of his fastball to the mid-90s and shut down the rally. At 12:01 a.m., on October 21, Rubén Sierra hit a groundball to second baseman Pokey Reese, who threw to first baseman Doug Mientkiewicz to finish the unprecedented comeback. "Not many people get the opportunity to shock the world. We came out and did it," Boston first baseman Kevin Millar said. "You know what? We beat the Yankees. Now they get a chance to watch us on the tube."The Red Sox won 10--3 and became the first team in Major League Baseball history to win a seven-game series after losing the first three games.
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: A primer on why computational predictive toxicology is hard, published by Abhishaike Mahajan on August 19, 2024 on LessWrong. Introduction There are now (claimed) foundation models for protein sequences, DNA sequences, RNA sequences, molecules, scRNA-seq, chromatin accessibility, pathology slides, medical images, electronic health records, and clinical free-text. It's a dizzying rate of progress. But there's a few problems in biology that, interestingly enough, have evaded a similar level of ML progress, despite there seemingly being all the necessary conditions to achieve it. Toxicology is one of those problems. This isn't a new insight, it was called out in one of Derek Lowe's posts, where he said: There are no existing AI/ML systems that mitigate clinical failure risks due to target choice or toxicology. He also repeats it in a more recent post: '…the most badly needed improvements in drug discovery are in the exact areas that are most resistant to AI and machine learning techniques. By which I mean target selection and predictive toxicology.' Pat Walters also goes into the subject with much more depth, emphasizing how difficult the whole field is. As someone who isn't familiar at all with the area of predictive toxicology, that immediately felt strange. Why such little progress? It can't be that hard, right? Unlike drug development, where you're trying to precisely hit some key molecular mechanism, assessing toxicity almost feels…brutish in nature. Something that's as clear as day, easy to spot out with eyes, easier still to do with a computer trained to look for it. Of course, there will be some stragglers that leak through this filtering, but it should be minimal. Obviously a hard problem in its own right, but why isn't it close to being solved? What's up with this field? Some background One may naturally assume that there is a well-established definition of toxicity, a standard blanket definition to delineate between things that are and aren't toxic. While there are terms such as LD50, LC50, EC50, and IC50, used to explain the degree by which something is toxic, they are an immense oversimplification. When we say a substance is "toxic," there's usually a lot of follow-up questions. Is it toxic at any dose? Only above a certain threshold? Is it toxic for everyone, or just for certain susceptible individuals (as we'll discuss later)? The relationship between dose and toxicity is not always linear, and can vary depending on the route of exposure, the duration of exposure, and individual susceptibility factors. A dose that causes no adverse effects when consumed orally might be highly toxic if inhaled or injected. And a dose that is well-tolerated with acute exposure might cause serious harm over longer periods of chronic exposure. The very definition of an "adverse effect" resulting from toxicity is not always clear-cut either. Some drug side effects, like mild nausea or headache, might be considered acceptable trade-offs for therapeutic benefit. But others, like liver failure or birth defects, would be considered unacceptable at any dose. This is particularly true when it comes to environmental chemicals, where the effects may be subtler and the exposure levels more variable. Is a chemical that causes a small decrease in IQ scores toxic? What about one that slightly increases the risk of cancer over a lifetime (20+ years)? And this is one of the major problems with applying predicting toxicology at all - defining what is and isn't toxic is hard! One may assume the FDA has clear stances on all these, but even they approach it on a 'vibe-based' perspective. They simply collate the data from in-vitro studies, animal studies, and human clinical trials, and arrive to an approval/no-approval conclusion that is, very often, at odds with some portion of the medical comm...
Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: A primer on why computational predictive toxicology is hard, published by Abhishaike Mahajan on August 19, 2024 on LessWrong. Introduction There are now (claimed) foundation models for protein sequences, DNA sequences, RNA sequences, molecules, scRNA-seq, chromatin accessibility, pathology slides, medical images, electronic health records, and clinical free-text. It's a dizzying rate of progress. But there's a few problems in biology that, interestingly enough, have evaded a similar level of ML progress, despite there seemingly being all the necessary conditions to achieve it. Toxicology is one of those problems. This isn't a new insight, it was called out in one of Derek Lowe's posts, where he said: There are no existing AI/ML systems that mitigate clinical failure risks due to target choice or toxicology. He also repeats it in a more recent post: '…the most badly needed improvements in drug discovery are in the exact areas that are most resistant to AI and machine learning techniques. By which I mean target selection and predictive toxicology.' Pat Walters also goes into the subject with much more depth, emphasizing how difficult the whole field is. As someone who isn't familiar at all with the area of predictive toxicology, that immediately felt strange. Why such little progress? It can't be that hard, right? Unlike drug development, where you're trying to precisely hit some key molecular mechanism, assessing toxicity almost feels…brutish in nature. Something that's as clear as day, easy to spot out with eyes, easier still to do with a computer trained to look for it. Of course, there will be some stragglers that leak through this filtering, but it should be minimal. Obviously a hard problem in its own right, but why isn't it close to being solved? What's up with this field? Some background One may naturally assume that there is a well-established definition of toxicity, a standard blanket definition to delineate between things that are and aren't toxic. While there are terms such as LD50, LC50, EC50, and IC50, used to explain the degree by which something is toxic, they are an immense oversimplification. When we say a substance is "toxic," there's usually a lot of follow-up questions. Is it toxic at any dose? Only above a certain threshold? Is it toxic for everyone, or just for certain susceptible individuals (as we'll discuss later)? The relationship between dose and toxicity is not always linear, and can vary depending on the route of exposure, the duration of exposure, and individual susceptibility factors. A dose that causes no adverse effects when consumed orally might be highly toxic if inhaled or injected. And a dose that is well-tolerated with acute exposure might cause serious harm over longer periods of chronic exposure. The very definition of an "adverse effect" resulting from toxicity is not always clear-cut either. Some drug side effects, like mild nausea or headache, might be considered acceptable trade-offs for therapeutic benefit. But others, like liver failure or birth defects, would be considered unacceptable at any dose. This is particularly true when it comes to environmental chemicals, where the effects may be subtler and the exposure levels more variable. Is a chemical that causes a small decrease in IQ scores toxic? What about one that slightly increases the risk of cancer over a lifetime (20+ years)? And this is one of the major problems with applying predicting toxicology at all - defining what is and isn't toxic is hard! One may assume the FDA has clear stances on all these, but even they approach it on a 'vibe-based' perspective. They simply collate the data from in-vitro studies, animal studies, and human clinical trials, and arrive to an approval/no-approval conclusion that is, very often, at odds with some portion of the medical comm...
In this electrifying episode of "Missing the Point," hosts Michael Marcangelo, Rayshawn Buchanan, and Bob Kelly dive deep into the most iconic and impactful trades in Boston sports history. Whether you're a fan of the Red Sox, Celtics, Bruins, or Patriots, this episode is packed with memorable moments and game-changing deals that have shaped the landscape of Boston sports. Join us as we celebrate the monumental 20th anniversary of Nomar Garciaparra's trade to the Chicago Cubs, and journey through historic moves like the Red Sox's shocking trade of Babe Ruth to the Yankees, which began the infamous 86-year "Curse of the Bambino." We also cover Kevin Garnett's transformative trade to the Celtics, Pedro Martinez's pivotal move to Boston, and the strategic trades that brought Curt Schilling, Cam Neely, and other superstars to the city. In this episode, we break down each trade, discussing its significance, the players involved, and the long-term impact on the teams and the city. Our hosts provide insightful analysis and engaging commentary, making this a must-listen for any Boston sports fan.
#sports #football #bsketball#golf #fishing #baseball #motivation #athlete #bhfyp #love #follow #like #life #themasters #mastes #tigerwoods #golflife #peronsaltraining #fitness #laketahoe
On this week's edition of Inside the (Rob) Parker, Rob discusses Aaron Judge and Juan Soto being on a historic start to the season for the New York Yankees, the chances that Shohei Ohtani becomes the leading vote-getter for the upcoming MLB All-Star Game and his biggest gripe with the upcoming London Series between the New York Mets and the Philadelphia Phillies. Plus, 2x MLB All-Star and World Series champion Derek Lowe swings by to discuss his participation in the upcoming 2024 Century Championship golf tournament, why the Yankees' pitching staff might actually be the best part of their team, the parity around the Majors, what's gone wrong for the Texas Rangers so far this season and much more! Finally, we've got appearances by MLBBro.com managing editor JR Gamble, analytics guru Anthony Masterson and gambling expert David Gascon. Subscribe and download all of the latest Inside the Parker podcasts and follow Rob on Twitter!! #OddCouple See omnystudio.com/listener for privacy information.
“I'm a short-term pessimist and a long-term optimist” about the potential for AI to transform drug development, Derek Lowe, author of the In the Pipeline blog, told BioCentury Washington Editor Steve Usdin on the latest BioCentury Show. Lowe's skepticism about claims that AI will rapidly transform the field is driven by a belief that the “problems that we have in the drug industry that we want to solve are almost inversely proportional to the ability of AI to solve them." Lowe also discussed the Biosecure Act and U.S. reliance on Chinese contract manufacturing and development organizations; talked about why he hopes and believes the Supreme Court will rule for FDA in litigation over the abortion drug mifepristone; and explained his view that approval of Aduhelm to treat Alzheimer's disease was one of FDA's worst decisions. The BioCentury Show is now available as an audio podcast. The Show, featuring BioCentury one-on-one with an industry KOL, is available on Apple, Spotify and wherever you listen to your favorite podcasts and in video podcast format on BioCentury's YouTube channel. View full story: https://www.biocentury.com/article/65242700:00 - The BioCentury Show Podcast 01:51 - Lowe's Blog05:30 - Biosecure Act09:53 - AI & Drug Discovery17:33 - FDA, The Abortion Drug, & Aduhelm22:05 - Lessons from COVID
John Smoltz was elected to the National Baseball Hall of Fame in 2015 after a 21-year career, 20 of which were spent with the Atlanta Braves. Smoltz is currently the lead analyst for MLB Coverage on FOX Sports. Smoltz is an 8-time All-Star and a 1995 World Series champion. He won the NL CY Young Award in 1996 with a blistering 24-8 record. After an arm injury, he converted to a reliever in 2001 and set NL saves record in 2002 with 55. He is the only pitcher in history to record 200 career wins,150 saves, and 3,000 strikeouts. Smoltz is participating in the Invited Celebrity Classic which is a nationally televised PGA Tour Champions competition that features 78 PGA Tour Champions and 40 sports and entertainment stars. The event occurs between April 19-21, 2024, at Las Colinas Country Club in Irving, Texas. It is live on the Golf Channel all three days. Golfers include Vijay Singh, Justin Leonard, Retief Goosen, Lee Janzen, and Colin Montgomerie. Other sports athletes include Smoltz teammates Tom Glavine and Greg Maddux. Former Sports with Friends guests Derek Lowe, Mark Mulder, and Pudge Rodriguez. Also, former players Jon Lester, Joe Carter, Kevin Millar, Tony Romo, Albert Pujols, Brian Urlacher, Adam Thielen, and Robbie Gould. In this episode, Smoltz talks about how the younger fans know him from his announcing days on FOX. He also talks about the evolution of the sport, and what works and what doesn't. He talks extensively about playing for Bobby Cox and the role Golf played both during his baseball career and in the years since he retired.
What causes Alzheimer's? The main theory is that it's due to a build-up of amyloid plaques in the brain. But some scientists think that's hopelessly wrong, and that a hidebound belief in the amyloid hypothesis is stopping us from finding a cure.In this episode of The Studies Show, Tom and Stuart talk about the amyloid hypothesis of Alzheimer's, ask whether all the hype over the three recent Alzheimer's drugs (“a momentous breakthrough!”) is justified, and look at some ways we could do better research on dementia.The Studies Show is supported by the i, the UK's smartest daily newspaper. You can get a money-off deal on your digital subscription—which includes full access to all Stuart's science writing—by following this special podcast link.The Studies Show is brought to you by Works in Progress, the online magazine about science, technology, and human progress. If you're a listener to The Studies Show, it's a dead-cert that you'll love Works in Progress - and it's all available for free. Find the magazine at this link.Show notes* The Mini-Mental State Examination (MMSE) - the test Stuart quotes at the start* NHS list of Alzheimer's symptoms* List and discussion of possible theories for the cause of Alzheimer's* Chris Hemsworth interview about finding out he's at high genetic risk of Alzheimer's* Potential clues about the origin of Alzheimer's from Down Syndrome* Sharon Begley's STAT article on “how an Alzheimer's ‘cabal' thwarted progress toward a cure for decades”* Science investigation of potential fraud in the original Aβ*56 study* Explanation of why it's bad, but not devastating for the amyloid hypothesis* Independent panel urges the FDA not to approve Aducanumab - but they do so anyway* Derek Lowe's highly sceptical discussion of the “disgraceful” approval* Stuart's sceptical article in the i on Lecanemab (link to the trial itself)* BBC article on the “momentous breakthrough”* “16 cautionary notes” on Lecanemab* Stuart's sceptical article in the i on Donanemab (link to the trial itself)* And Stuart's Twitter thread on “clinically meaningful” effects in Alzheimer's* BBC More or Less episode discussing the problems with measuring the effect of an Alzheimer's drug* How do the new Alzheimer's drugs work in theory? One potential explanation* Paper by Elliot Tucker-Drob on how we measure dementia and how we forget about individual differences while doing soCreditsThe Studies Show is produced by Julian Mayers at Yada Yada Productions. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thestudiesshowpod.com/subscribe
September14, 1994 - The remainder of the baseball season is canceled by actingcommissioner BudSelig after 34 days of theplayers' strike. The last 50 games of the season and post season were cancelleddue to the strike called by the Players Association and their leader Don Fehr.The World Series would not be played for the first time in 90 years. The strikewas finally ended by a ruling from future Chief Justice Sonia Sotomayor. September14, 1923: Red Sox first baseman George Burns completes an unassisted tripleplay against the Indians as he gathers in a Frank Brower line drive, tags RubeLutzke coming from first, andbeats Riggs Stephenson back to second. September14, 1980, in a 10 - 7 win over the Cubs, Lee Mazzilli homers to break a droughtfor Mets. It is the team's first homer in 175 2/3 innings, going back to August26 when Claudell Washington homered. This would be the longest drought for therest of the century. September14, 1990: Mariner Ken Griffey, Sr. and his son, Ken Griffey, Jr., become thefirst father and son to hit homers in the same major league game. Theback-to-back blasts are given up by Angel hurler Kirk McCaskill. September14, 2002, Derek Lowe wins his 20th game as the Red Sox beat the Orioles, 6 - 4.Lowe becomes the first pitcher in history to win 20 games the season aftersaving 20. He is also the first to record at least 40 saves and later win 20games. Dennis Eckersley and John Smoltz did it the other way around.
Today, you'll learn about how metal was discovered to be able to heal itself, a potential cure for sensitive teeth, and the truth about sports supplements. Find episode transcripts here: https://curiosity-daily-4e53644e.simplecast.com/episodes/healing-metals-sensitive-teeth-sports-supplementsHealing Metals “Metals Have the Intrinsic Ability to Heal Themselves, New Research Finds.” by Alexander Beadle. 2023.https://www.technologynetworks.com/applied-sciences/news/metals-have-the-intrinsic-ability-to-heal-themselves-new-research-finds-376457“Autonomous healing of fatigue cracks via cold welding.” by Christopher M. Barr, et al. 2023.https://www.nature.com/articles/s41586-023-06223-0Sensitive Teeth “Mineral-Building Lozenge Offers Long-Term Fix for Tooth Sensitivity.” by Paul McClure. 2023.https://newatlas.com/science/dental-lozenge-rebuilds-lost-tooth-minerals/“Biomimetic Dentin Repair: Amelogenin-Derived Peptide Guides Occlusion and Peritubular Mineralization of Human Teeth.” by Deniz T. Yucesoy, et al. 2023.https://pubs.acs.org/doi/10.1021/acsbiomaterials.2c01039Sports Supplements“Presence and Quantity of Botanical Ingredients With Purported Performance-Enhancing Properties in Sports Supplements.” by Pieter A. Cohen, MD. 2023.https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2807343“Nearly 90% of herbal sports supplements are mislabeled. One in ten contain prohibited substances.” by Tibi Puiu. 2023.https://www.zmescience.com/medicine/nearly-90-of-herbal-sports-supplements-are-mislabeled-one-in-ten-contain-prohibited-substances/“What's In Those Supplements Again?” by Derek Lowe. 2023.https://www.science.org/content/blog-post/what-s-those-supplements-againFollow Curiosity Daily on your favorite podcast app to get smarter with Calli and Nate — for free! Still curious? Get exclusive science shows, nature documentaries, and more real-life entertainment on discovery+! Go to https://discoveryplus.com/curiosity to start your 7-day free trial. discovery+ is currently only available for US subscribers.
MLB All Stars Joe Mauer, Jon Lester & Derek Lowe said hello from the American Century Championship at the beautiful Lake Tahoe Edgewood resort & golf course!Minnesota Twins legend, Joe Mauer, spoke on what he's been up to in his recent retirement & how he's been raising his kids. Joe also reflected on his career & reminisced on who were some of his most difficult pitchers to face.Red Sox champions, Derek Lowe & Jon Lester, gave us some insight on their golf games & shared fun stories on who the locker room pranksters were from their careers!For video footage:Joe Mauer - https://youtu.be/PmHOsC5Vsis?si=DWJgAYm__F21K-bsDerek Lowe - https://youtu.be/dx9RjSBrnAs?si=XqhLHXIFDsDdfyL_Jon Lester - https://youtu.be/lYLfrXpz58w?si=eY_3uTUY0SNI1Yw-For more exclusive content, follow the Jim on Base Show on social media (Twitter/Instagram/TikToK): @JimonBaseShow
Derek Lowe pitched in the major leagues from 1997-2013. He is a 2-time All-Star, and a member of the Boston Red Sox 2004 World Series Championship Team. He threw a no-hitter on April 27, 2002. He was the American League Saves leader in 2000 and also was the NL Wins Leader in 2006. He is a member of the Boston Red Hall of Fame. Lowe is an avid golfer and is participating in this year's American Century Championship celebrity golf tournament in Lake Tahoe. In this episode, Lowe discusses being part of one the most lopsided trade in baseball history, where he was Jason Varitek were traded from the Seattle Mariners to the Red Sox for Heathcliff Slocumb. He also talked openly about his pitching style not working in today's game. Lowe was a ground ball pitcher who threw consistently at 88-92 MPH, while most pitchers today throw 98+. He also explained his reasons for not joining social media.
HOUR 3: Tyreek Hills plans to retire after 2024 to do what? Should we believe it? Derek Lowe, 5x All-Star and 2004 World Series Champion, drops in to the Mothership. Is Fernando Tatis Jr actually good for baseball?
Dr. Derek Lowe is a medicinal chemist, author, and blogger. He is currently Director in Chemical Biology and Therapeutics at the Novartis Institutes for Biomedical Research (NIBR). Dr. Lowe's work in drug discovery has spanned multiple decades with tenures at Schering-Plough, Bayer, and Vertex Pharmaceuticals. In addition to his industry work, Dr. Lowe authors a popular blog covering topics in drug discovery; check it out here: In the Pipeline.
In this conversation, Daniel Belkin and Mitch Belkin interview Derek Lowe, PhD, about drug discovery, clinical trials, drug patents, Alzheimer's disease, the FDA, and his blog “In The Pipeline”. They discuss the potential role for machine learning in pharmaceutical development, whether Big Pharma spends excessively on marketing, and much much more. Who is Derek Lowe?Derek Lowe, PhD, is a medicinal chemist who works in preclinical drug discovery. He received a PhD in organic chemistry from Duke University and completed a Humboldt Fellowship in Germany for his post-doc. His blog about the pharmaceutical industry “In The Pipeline” has been continuously operating since 2002. Follow us at @ExMedPod Subscribe to our Youtube channelConsider supporting us on Patreon
Welcome to the Instant Trivia podcast episode 656, where we ask the best trivia on the Internet. Round 1. Category: African Americans 1: Crispus Attucks, called the first American to die for independence, was killed in this 1770 incident. the Boston Massacre. 2: This Harlem-born singer-dancer-actor was a member of the 1960s "Rat Pack" with Frank and Dino. Sammy Davis, Jr.. 3: An award for African-American authors and illustrators is named for this wife of Martin Luther King Jr.. Coretta Scott King. 4: "I cannot offer you money, position or fame", he said when he offered George Washington Carver a job at Tuskegee. Booker T. Washington. 5: 8 years before she won Olympic gold, this gymnast was a 2004 Virginia State champion. Gabby Douglas. Round 2. Category: 4-Letter Words 1: Bacall's last word on how to whistle. blow. 2: From Greek for "single", a single man who lives in a monastery. monk. 3: This word commonly follows cuff or missing. link. 4: A boy who bears a bride's train, or a bellboy calling out a name. page. 5: A collection of tales by Nathaniel Hawthorne were "Twice" this. Told. Round 3. Category: Authors' Native Lands 1: Godot waiter Samuel Beckett. Ireland. 2: "The Remains of the Day" novelist Kazuo Ishiguro. Japan. 3: Gabriel Garcia Marquez, chronicler of a death foretold. Colombia. 4: Celebrator of imperialism Rudyard Kipling. India. 5: Deptford trilogist Robertson Davies. Canada. Round 4. Category: Let's Eat Italian! 1: Served before the pasta, it's an appetizer plate of meats, cheese, fish and vegetables. antipasto. 2: Long, very thin pasta called capellini is also known by this "divine" name. angel hair pasta. 3: This blue-veined cheese is named for a suburb of Milan, not a snake-haired monster. Gorgonzola. 4: From the Italian for "knot of wood", these potato dumplings may have cheese or spinach in them. gnocchi. 5: It's a layered ice cream dessert usually containing chopped fruits and nuts. spumoni. Round 5. Category: Sports 2004 1: These big cats gave the Patriots all they could handle in Super Bowl XXXVIII. the (Carolina) Panthers. 2: All "Hale" this 58-year-old who won his fourth Senior PGA championship by a str--a shot. Hale Irwin. 3: He was the winning pitcher in the clinching game of all 3 series in the Red Sox' magical run. Derek Lowe. 4: Women from this country won tennis' Wimbledon and French and U.S. Opens. Russia. 5: This team's Chauncey Billups was MVP of the NBA Finals. the Detroit Pistons. Thanks for listening! Come back tomorrow for more exciting trivia! Special thanks to https://blog.feedspot.com/trivia_podcasts/
Two-time All Star and World Series champ Derek Lowe joins the pod to break down the upcoming MLB playoffs and to take issue with everybody pitching around Aaron Judge during his chase for 62 home runs. Lowe, a top golfer on the celebrity circuit, also recaps his stunning win over legendary LPGA pro Annika Sorenstam in the Hilton Grand Vacations Tournament of Champions last January. Plus, the boys make NFL Week 5 picks, which leads to a spirited debate about a certain New York Giants quarterback.As always, check GolfDigest.com for our full array of gambling content, including picks from our anonymous caddie, Pat Mayo of DraftKings/Mayo Media Network; Rick Gehman of RickRunGood.com; Brandon Gdula of numberFire/FanDuel; and Lee Alldrick of FanShareSports.
In a trade that's become a punchline almost since it was completed, 25 years ago this week the Mariners sent Jason Varitek and Derek Lowe to the Red Sox for Heathcliff Slocumb. But was the trade actually so lopsided? Or did the M's get exactly what they bargained for? Mike and Bill look back at what led to one of the best trades in Boston history and its aftermath. Plus, happy birthday to Tom Burgmeier and Art Nehf. This episode is powered by Stathead.com.
Grey Mirror: MIT Media Lab’s Digital Currency Initiative on Technology, Society, and Ethics
In this episode, researcher, writer, editor and PhD student in psychiatric genetics Saloni Dattani joins us to talk about her view of the world in different movements she tracks and how she does the research process. It is possible to make progress against the huge problems we are all facing in the world by sharing new and underrated ideas of amazing thinkers. Why don't we? Because research and data isn't accessible. Saloni works through different fields making that knowledge accessible and understandable for us all. We dive deep into science communication and how to make it better, what brings Saloni into the research space, how sub communities interact and collaborate, the importance of learning in real time from amazing people on twitter and why we should make use of that in the current academic publishing system. Stay tuned! SUPPORT US ON PATREON: https://www.patreon.com/rhyslindmark JOIN OUR DISCORD: https://discord.gg/PDAPkhNxrC Who is Saloni Dattani? Saloni Dattani is a founding editor of Works in Progress and an editor at Stripe Press. She is also a PhD student in psychiatric genetics at King's College London , science writer, and researcher at Our World in Data. Dattoni is also the founder of a new Substack newsletter called Scientific Discovery. Topics: Welcome Saloni Dattani to The Rhys Show!: (00:00:00) About Saloni & goal of this episode: (00:01:15) Catalyzing moment in childhood that Made Saloni curious and eager to learn: (00:02:06) What influenced Saloni to be so curious: (00:03:54) Evolutionary lense from a memetic perspective & sub communities interacting and collaborating: (00:05:51) About memetic phrases & cheems mindset: (00:08:11) The goal of Saloni's new “Scientific Discovery” weekly newsletter: (00:10:38) 1st newsletter on effective vaccines against respiratory syncytial virus: (00:12:25) Why RSV vaccine is effective according to Saloni: (00:14:17) How mRNA vaccine worked: (00:17:03) What brings you into research spaces and how do you go about it: (00:18:37) How #scicomm should be done in 20 years: (00:23:40) Learning from amazing people on twitter to share: (00:28:42) About Work In Progress: how Saloni does the process, finds the right people and edits: (00:33:47) How can we help with the memeplexes that people fall into in regards to covid reporting: (00:38:13) About Our World In Data: (00:45:04) Are meta science, Twitter and getting a PhD underrated or overrated?: (00:50:48) Mentioned resources: Horrible Science (series of books): https://en.wikipedia.org/wiki/Horrible_Science “Evolve or Die (Horrible Science)” book by Phil Gates: https://www.amzn.com/dp/1407105353 Swole Dog vs. cheem: https://knowyourmeme.com/memes/swole-doge-vs-cheems Derek Lowe: https://www.science.org/blogs/pipeline Virological: https://virological.org/ Steven Pinker: https://en.wikipedia.org/wiki/Steven_Pinker Connect with Saloni Dattani: Twitter: https://twitter.com/salonium Work in Progress: https://www.worksinprogress.co/ Stripe: https://stripe.com/es-us Our World in Data: https://ourworldindata.org/
We find out that Mattlanta might be a huge diva. So that's a huge reason why Front Office Los Medina is in for Chernoff. Dimino takes us through the art of “calling in sick.” Chris also says that we need to take the night off as fans of this team. We get into some birthdays and it gets weird right off the bat. See omnystudio.com/listener for privacy information.
2004 World Series champion and Red Sox Hall of Famer Derek Lowe joins Joe Castiglione and Will Flemming from the booth in in Fort Myers.
Welcome back to the Season 2 premiere of Neurotech Pub!In this episode, host and Paradromics CEO Matt Angle sits down with fellow Founder/CEOs Carolina Aguilar, Brian Pepin, and Kunal Ghosh to talk shop about building cutting edge neurotech companies from the ground up. We dive deep into business strategies, the neurotech fundraising landscape, emerging therapeutics, and more. We also provide an insider's view of the intersections of data, pharma, and med devices that are shaping the future of healthcare. Pour yourself a cold one and settle in! Check out full video with transcript here: Check out video and a full episode transcript here. 00:00 | Updates & News >> INBRAIN Neuroelectronics raised a $17M Series A >> Rune Labs raised a $22.8 Million Series A >> Inscopix Launched Cloud-Based Platform for Data Management and Analysis2:15 | Meet the panel and pick up a book1:54 | Jester King Brewery 2:25 | Rune Labs 2:50 | Neurostimulator for deep brain stimulation therapy 3:23 | INBRAIN Neuroelectronics 4:11 | Inscopix 5:24 | Ursula K. Le Guin's 'The Dispossessed' 6:19 | Yuval Noah Harari's 'Sapiens: A Brief History of Humankind' 6:32 | Daniel G. Miller's 'The Tree of Knowledge' 6:40 | Jiddu Krishnamurti's 'The Book of Life' 7:34 | Barack Obama's 'A Promised Land,' ‘Dreams from my Father,' & ‘The Audacity of Hope' 7:56 | Karl Popper's 'The Open Society and Its Enemies'9:25 | Venture Capital in Neurotech34:44 | Business Strategy in Neurotech40:32 | Tom Oxley, CEO, Synchron 43:58 | Dr. Thomas Insel 44:06 | Mindstrong Mental Health Care 44:35 | Aduhelm controversy 52:25 | Galvani Bio 59:39 | Percept Neurostimulator 1:00:32 | Neuromodulation and the future of treating brain disease 1:07:21 | Software as a Medical Device FDA Guidance1:09:12 | State of Animal Model Systems1:14:28 | α-Synuclein in Parkinson's Disease 1:18:01 | Alto Neuroscience 1:18:36 | Flatiron Foundation 1:18:45 | Gaurdent Health 1:19:03 | Melanoma Trends & Rates1:21:41 | The Pharma-Data-Device Ecosystem 1:21:42 | Frank Fischer, Chairman of Neuropace 1:22:28 | Neurotech Pub Season 1, Episode 9 1:26:35 | Roche acquisition of Flatiron Health & merger with Foundation Medicine 1:27:12 | Companion Diagnostics 1:28:29 | Adhulem and PET imaging 1:29:09 | Resignations at the FDA over Alzheimer's Drug 1:29:32 | Derek Lowe's take on the Aducanumab Approval, FDA Committee Votes, Halting the Aducanumab Trials, & The FDA Advisory Committee Briefing Document on Aducanumab 1:31:39 | Donanemab receives breakthrough therapy designation in 2021 1:36:58 | Mapping the Frontal-Vagal Pathway 1:37:09 | The Human Connectome Project 1:40:07 | Teal Organizations and Holacracy 1:41:18 | Society for Neuroscience 1:44:37 | Affymetrix (Thermo Fisher Scientific) 1:44:39 | IlluminaWant more?Follow Paradromics & Neurotech Pub on Twitter Follow Matt, Brian, Carolina, & Kunal on Twitter
There was a lot of golf being played this weekend! Akshay Bhatia wins on the Korn Ferry Tour PGA Tour Champions first event - Mitsubishi Electric Championship at Hualalai, Miguel Angel Jiménez wins in a playoff over Australian Steven Alker LPGA Tour: Tournament of Champions at Lake Nona Danielle Kong takes over in the final round. Derek Lowe beats Anika in a playoff to win the celeb division. DP World Tour: Thomas Pieters gets back in the winners circle at the Abu Dhabi HSBC Championship Tyrrell Hatton does not like the 18th hole at Yas Links PGA Tour: Hud Swafford wins The American Express with a hot putter! Jon Rahm was not a fan of the course setup. Equipment: Stealth review Jordan and Weston Hudson Swafford WITB Driver: TaylorMade Stealth (9 degrees) Shaft: Project X HZRDUS Smoke 3-wood: Ping i25 (14 degrees) Shaft: Aldila Rogue Silver 125 MSI 80 TX 5-wood: Ping i25 (18 degrees) Shaft: Aldila Tour Blue 85 Irons: PXG 0311 ST Gen3 (4, 5) 0311 ST Gen4 (6-9) Shaft: True Temper Dynamic Gold Tour Issue X100 Wedges: PXG 0311 Sugar Daddy (46-10, 50, 56-13), Vokey Design SM8 WedgeWorks (60-T) Shafts: True Temper Dynamic Gold Tour Issue X100 Putter: Scotty Cameron Phantom X 7.5 tour prototype Ball: Titleist Pro V1
On this week's episode, Daily Sun Sports Editor Nick Feely, senior writers Jeff Shain and Cody Hills and staff writer Drew Chaltry cover a thrilling weekend of playoff football (1:25). Then, Cody and Drew offer updates on The Villages and Wildwood high school sports teams (40:30), and Jeff recaps Danielle Kang and Derek Lowe's exciting finishes at Lake Nona (53:20).
In episode 13 we talk about the four Red Sox no hitters from the 2000s that include Hideo Nomo, Derek Lowe, Clay Buchholz and Jon Lester. We also touch upon some of the other great pitching performances in Red Sox history including Roger Clemens' 20 strikeout game. Follow us on social media Twitter & Instagram @nasppodcast
Host George McNeilly with Hilton Grand Vacations Tournament of Champions winner Danielle Kang, runner up Brooke Henderson and Derek Lowe, the celebrity division winner who held off Hall of Famer Annika Sorenstam in a playoff on her home course.
La désinformation sur les vaccins contre le Covid-19 continue de faire des ravages. Alors que l'on observe une très forte proportion de non-vaccinés parmi les cas d'infection les plus sévères, le professeur Didier Raoult est venu cette semaine apporter de l'eau au moulin des antivax, reprochant aux vaccins d'avoir « augmenté l'épidémie ». Une déclaration qui ne s'appuie pourtant sur aucune démonstration scientifique. Ce sont des arguments totalement fallacieux qui ont conduit cette semaine Didier Raoult à multiplier les contre-vérités, à la fois sur la chaîne YouTube de l'Institut hospitalo-universitaire de Marseille et dans une émission radio grand public, avec à la clef des millions de vues et de partages sur les réseaux sociaux. « Comment expliquer que c'est dans les pays où l'on a le plus vacciné qu'il y a le plus de cas » ?, interroge le professeur Raoult. Interrogation purement rhétorique débouchant sur la conclusion que le vaccin « a augmenté l'épidémie ». Le questionnement est à ce point biaisé qu'il suffit d'en inverser les termes pour corriger le raisonnement : c'est plus souvent dans les pays où les populations étaient les plus menacées, les plus affectées, que les campagnes de vaccination ont été les plus intenses, et non l'inverse. Des allégations non fondées Didier Raoult s'appuie sur un autre argument pour incriminer la vaccination. Selon lui, une proportion importante des vaccinés testés positifs le seraient dans un intervalle de temps très court après l'injection. Une observation sans portée scientifique, car aucunes données statistiques ne permettent à l'heure actuelle de quantifier ce phénomène à grande échelle. Didier Raoult part donc d'un faux constat. À partir du moment où la population vaccinée est devenue largement majoritaire, comme en France, lorsque la campagne de rappel a été lancée, étant donnée la contagiosité extrême du variant Omicron, il n'y a rien d'étonnant à ce que l'on observe de nombreux cas positifs chez des personnes venant de recevoir une dose. Mais rien ne permet d'établir un lien de cause à effet. L'hypothèse non vérifiée de l'infection facilitée par les anticorps Le professeur Raoult se réfère néanmoins à l'hypothèse des anticorps facilitants, un phénomène de complication que l'on a pu observer par exemple dans le cas de la dengue. Les chercheurs se sont penchés sur ce problème désigné par l'acronyme ADE (antibody-dependant enhancement). Mais les conclusions de leurs travaux ne vont pas dans le sens des déclarations du professeur Raoult. Loin d'être négligée, cette piste a été étudiée, comme le relate l'article de Stéphane Korsia-Meffre, paru le 3 novembre 2020 sur le site médical Vidal, mis à jour le 10 janvier 2022 : « Après un an d'administration des vaccins contre la Covid-19, sur plus de quatre milliards de personnes à travers le monde, aucun événement de maladie aggravée associée à la vaccination n'a été observé parmi les personnes infectées malgré la vaccination. Au contraire, lors d'infections perthérapeutiques (« breakthrough infections », malgré le vaccin), la masse des données pointent vers une réduction de la sévérité des symptômes chez les personnes vaccinées. » Autrement dit, ce qui peut se produire dans le cas de la dengue, par exemple, n'est pas forcément transposable au Covid-19. Aucune publication n'a pu établir le fait qu'en vaccinant on augmenterait l'infection, au lieu de l'éviter ou l'atténuer. C'est même le contraire qui a été prouvé, comme on peut le lire sur le blog de Derek Lowe pour le magazine Science, où plusieurs études sont analysées. Les travaux entrepris ont montré que si ce mode d'infection par anticorps facilitants avait pu se produire in vitro-en laboratoire, c'est tout le contraire in vivo, rien de tel n'a pour l'instant été détecté, ni chez l'animal, ni chez l'homme. Et toute l'expérience accumulée avec les campagnes de vaccinations menées dans le monde montre bel et bien que les vaccins permettent de réduire considérablement les cas les plus graves. Début janvier en France, sur un million de non-vaccinés, on enregistrait chaque jour en moyenne 26 personnes en réanimation, moyenne tombant à 1,5 chez les vaccinés. Graphiques à l'appui, les Décodeurs du Monde montrent, dans l'édition du 4 janvier 2022, que « la majorité des patients en réanimation sont bien non vaccinés », dans un article qui met en garde contre l'interprétation parfois erronée des chiffres de la Drees (Direction de la recherche, des études, de l'évaluation et des statistiques). Faire le buzz coûte que coûte Difficile de comprendre ce qui justifie une telle prise de position de la part de quelqu'un qui a pu être considéré comme un grand scientifique français, un grand praticien. Mais force est de constater que Didier Raoult multiplie les erreurs depuis l'émergence de ce coronavirus, qu'il a dès le début présenté comme « l'infection respiratoire la plus facile à traiter », comme le rappellent Ariane Chemin et Gilles Rof dans leur enquête pour le Monde intitulée « Jusqu'où ira Didier Raoult, l'idole des antivax qui s'accroche à son poste ? ». Malheureusement, plus personne ne parle aujourd'hui de son protocole à base d'hydroxychloroquine, hors des cercles conspirationnistes. Plus ça va, plus Didier Raoult s'enferre dans l'argumentaire, de celui qui - seul contre tous - connaît la solution. Il est vrai que cette posture qu'il adopte sur les plateaux de certaines émissions en recherche d'audience lui ont permis de recueillir plus de deux millions de vues sur sa chaîne YouTube. Quelle que soit la justesse de ses observations sur la puissance des grands laboratoires, source de potentiels conflits d'intérêt, la crédibilité de ses assertions est aujourd'hui bien entamée sur le plan scientifique.
Big pharma is out for big pharma, but that doesn't mean we don't benefit from drugs, vaccines and treatments – AIDS was a death sentence until pharma came up with drugs called protease inhibitors that allowed people with HIV to live out their lives. Now there's Paxlovid - a protease inhibitor to fight Covid-19. The biggest downside of this drug is there isn't enough of it to go around. I'll be talking about that drug and more with medicinal chemist Derek Lowe, who is the author of Science Magazine's In the Pipeline blog. It's a wonderful, critical, objective look at the science of pharmaceuticals. The bottom line is you don't ever have to blindly trust anyone to tell you a drug works. What matters is that they can show how drugs work – and provide reproducible data. “Follow the Science" is produced, written, and hosted by Faye Flam, with funding by the Society for Professional Journalists. Today's episode was edited by Seth Gliksman with music by Kyle Imperatore. If you'd like to hear more "Follow the Science," please like, follow, and subscribe!
Surely the biggest development in the last year is the record speed with which vaccines were discovered for the Covid-19 virus that shut down the entire world. I spoke to medicinal chemist and science blogger Derek Lowe about the many issues surrounding drug discovery like regulatory constraints, funding, and many other issues in the industry.Enjoy the conversation with Derek Lowe - and the transcript is available for paid subscribers. This is a public episode. Get access to private episodes at www.ideasuntrapped.com/subscribe
In the midst of the baseball playoffs, who better to talk to than a three-time MLB General Manager? Dan Duquette oversaw the Montreal Expos, the Boston Red Sox and the Baltimore Orioles. And he has very strong opinions. The Astros, in his view, should not be considered the World Champions of 2017, and the same scandal tag – stealing signs and banging on trash cans - still hangs over Red Sox Manager Alex Cora. A native New Englander, Duquette grew up having his heart broken by the Red Sox of 1967, '75, '78 and '86 – so he was thrilled to help build the 2004 World Series Championship team by signing Manny Ramirez (the World Series MVP,) Pedro Martinez, Tim Wakefield, Johnny Damon and bringing both Jason Varitek and Derek Lowe from Seattle. Dan says he would take a look if another opportunity came along.
The prevalence of Covid-19 even in highly vaccinated countries such as Israel has led to a scary rumor that vaccines are actually making the disease worse. That has happened before with other vaccines - sometimes antibodies can actually turn traitor and help the virus through something called antibody dependent enhancement. Medicinal chemist Derek Lowe has been getting lots of questions about this from concerned readers of his Science Magazine pharmaceutical blog In The Pipeline. In this episode he explains how antibody dependent enhancement works, where it's happened before, and why it's almost certainly not happening with our Covid-19 vaccines. “Follow the Science" is produced, written, and hosted by Faye Flam, with funding by the Society for Professional Journalists. Today's episode was edited by Seth Gliksman with music by Kyle Imperatore. If you'd like to hear more "Follow the Science," please like, follow, and subscribe!
https://astralcodexten.substack.com/p/highlights-from-the-comments-on-aducanumab These are highlights from the comments of Adumbrations Of Aducanumab, Details Of The Infant Fish Oil Story, and discussion of those posts elsewhere. C_B writes: I agree with this post's overall point that the FDA is not, on average, too lax, and that the Atlantic article's take that the aducanumab approval is a sign of them being too lax is a bad take. That said, I think the beginning of this article really undersells how uniquely bad the aducanumab approval is. It's not just "pretty unclear whether it actually treats Alzheimers." Nobody in the field thinks there's any serious possibility that it treats Alzheimers. - Here's Derek Lowe talking about it: https://blogs.sciencemag.org/pipeline/archives/2021/06/08/the-aducanumab-approval - The FDA's advisory committee doesn't think it treats Alzheimers: https://alzheimersnewstoday.com/2020/11/11/fda-committee-votes-aducanumab-trial-data-fail-support-alzhimers-treatment-benefit/ - The trial was halted for futility: https://www.reuters.com/article/us-biogen-alzheimers/biogen-eisai-scrap-alzheimer-drug-trials-idUSKCN1R213G - The details of the "positive results" are textbook p-hacking of exactly the sort that the whole replication crisis has been about. It's a post-hoc subgroup analysis where the subgroup was selected based on similarity to the patients who had the most positive results; i.e., trivially guaranteed to show "positive" results via group selection. You can read more details in the statistical reviewer's comments in the advisory committee's document (PDF, starting on p. 174): https://www.fda.gov/media/143502/download
Joey Devine is a comedian, writer and host of the Roundball Rock podcast. We talk about a nasty sinker (two of them actually) from Derek Lowe, closing a game against the A's in the 2003 playoffs. A sad moment in Joey's life. Also discussed: Stevie Nicks, Josh Reddick's attention span, the truly wild 2003 playoffs, the uncancellable Pedro Martinez, Johnny Damon's concussion, cancelling Ken Macha, striking out looking (to end the All Star Game (in a tie!)), Moneyball, Mark Ellis, Steph Curry, Robert Horry, and Billy Beane. Lowe's 9th inning: https://www.youtube.com/watch?v=HTTcgyHwk2o Joey: https://twitter.com/JoeyDevine Justin: https://twitter.com/routinelayup Walk-up music: Edge of Seventeen, by Stevie Nicks Email ideas: andthepitchpodcast@gmail.com --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app
Joey Devine is a comedian, writer and host of the Roundball Rock podcast. We talk about a nasty sinker (two of them actually) from Derek Lowe, closing a game against the A's in the 2003 playoffs. A sad moment in Joey's life. Also discussed: Stevie Nicks, Josh Reddick's attention span, the truly wild 2003 playoffs, the uncancellable Pedro Martinez, Johnny Damon's concussion, cancelling Ken Macha, striking out looking (to end the All Star Game (in a tie!)), Moneyball, Mark Ellis, Steph Curry, Robert Horry, and Billy Beane. Lowe's 9th inning: https://www.youtube.com/watch?v=HTTcgyHwk2o Joey: https://twitter.com/JoeyDevine Justin: https://twitter.com/routinelayup Walk-up music: Edge of Seventeen, by Stevie Nicks Email ideas: andthepitchpodcast@gmail.com --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app
There are philosophical reasons that political conservatives are more likely to cheer for experimental Covid-19 drugs - hydroxychloroquine and Ivermectin – and reasons that political liberals are more likely to cheer for vaccines. But you can't always get what you want. In this episode, medicinal chemist Derek Lowe, author of the In the Pipeline blog, gets into the molecular workings of things to explain why it's easier to fight a viruses with vaccines than with drugs. He explains why it's worth continuing to search for better drugs for Covid-19, and to test the much-publicized anti-parasitic drug Ivermectin. But there are scientific reasons that it's a big long shot, and any miracle cure for Covid-19 is highly unlikely. “Follow the Science" is produced, written, and hosted by Faye Flam, with funding by the Society for Professional Journalists. Today's episode was edited by Seth Gliksman with music by Kyle Imperatore. If you'd like to hear more "Follow the Science," please like, follow, and subscribe!
This week Harry sits down with Vangelis Vergetis, the co-founder and co-executive director of Intelligencia, a startup that uses big data and machine learning to help pharmaceutical companies make better decisions throughout the drug development process. Vergetis argues that if you put a group of pharma executives in a conference room, then add an extra chair for a machine-learning system, the whole group ends up smarter—and able to make more accurate predictions about which drug candidates will succeed and which will fail.Bringing better analytics into the pharma industry has been an uphill battle, Vergetis says. One survey by McKinsey, his former employer, showed that financial services companies were the most likely to adopt AI and machine learning tools; the least likely were the building and construction trades. But just one rung up from the bottom was healthcare and pharmaceuticals. "The impact that AI could have on health care is "enormous," Vergetis says. "It's in the trillions. But in terms of AI adoption, we are right above construction—and no offense to construction, but it's not the most innovative industry."But with the proper data, machine learning algorithms can help drug makers form far more accurate predictions about the probability that a new drug will perform well in Phase I clinical trials, or whether a drug that's succeeded in Phase I should be advanced to Phase II. "For years we've seen the productivity of R&D declining in our space in pharma and biotech, and I refuse to accept that," Vergetis says. "In the era of a lot of data becoming available, in the era of us being able to use techniques like machine learning to do something with that data, there's gotta be a way to reverse that trend."Please rate and review MoneyBall Medicine on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:• Launch the “Podcasts” app on your device. If you can't find this app, swipe all the way to the left on your home screen until you're on the Search page. Tap the search field at the top and type in “Podcasts.” Apple's Podcasts app should show up in the search results.• Tap the Podcasts app icon, and after it opens, tap the Search field at the top, or the little magnifying glass icon in the lower right corner.• Type MoneyBall Medicine into the search field and press the Search button.• In the search results, click on the MoneyBall Medicine logo.• On the next page, scroll down until you see the Ratings & Reviews section. Below that, you'll see five purple stars.• Tap the stars to rate the show.• Scroll down a little farther. You'll see a purple link saying “Write a Review.”• On the next screen, you'll see the stars again. You can tap them to leave a rating if you haven't already.• In the Title field, type a summary for your review.• In the Review field, type your review.• When you're finished, click Send.• That's it, you're done. Thanks!Full TranscriptMoneyBall Medicine - Vangelis Vergetis TranscriptHarry Glorikian: I'm Harry Glorikian, and this is MoneyBall Medicine, the interview podcast where we meet researchers, entrepreneurs, and physicians who are using the power of data to improve patient health and make healthcare delivery more efficient. You can think of each episode as a new chapter in the never-ending audio version of my 2017 book, “MoneyBall Medicine: Thriving in the New Data-Driven Healthcare Market.” If you like the show, please do us a favor and leave a rating and review at Apple Podcasts. Harry Glorikian: My guest today is Vangelis Vergetis, the co-founder and co-executive director of Intelligencia. It's big-data analytics startup focused on the pharmaceutical industry. And the argument Vergetis makes to potential clients is that you can take any group of 10 drug development experts in a conference room, and make them a lot smarter by adding an eleventh chair for a machine-learning system.Of course, there's always an art to deciding which drug candidates should advance to clinical trials; which Phase 1 trials should advance to Phase 2; and so on. Decisions that like are risky and expensive, and you can't make them without having a lot of old-fashioned experience and instinct around the table.Even so, sometimes the experts are biased and the experience doesn't apply. And there's only so much data they humans can keep in their heads. And let's be honest: if decision makers at the big drug companies were that smart and talented, they'd have more home runs and fewer strikeouts.Vergetis argues that we've got the historical data and the computing power today to make far more informed predictions about which drug programs to push forward. And if more drug companies used those tools, he thinks, it might reverse the decline in R&D productivity.In the conversation you're about to hear, we talked about how Vergetis and his co-founder Dimitrios Skaltsas started Intelligencia; how they built their own datasets; how they work with clients; and why it is that he and I think a lot alike—to the point of using the same MoneyBall metaphor when we talk about transforming drug discovery and healthcare.So here's my conversation with Vangelis Vergetis.Harry Glorikian: Vangelis, welcome to the show. Vangelis Vergetis: Thank you. Very good to be here. Harry Glorikian: You know, it's interesting. I was looking at the company and looking at what you guys are doing. And I, I've probably talked to, I don't know, close to 70 experts in different areas of healthcare, drug discovery, computer science you know. Out of all those people, I honestly think you and your company Intelligencia might be the most exact reflection of the argument I was making in my 2017 book MoneyBall Medicine. In fact, I actually think you used the MoneyBall metaphor in your own talks. So I want to start out with having you explain the parallels between your company and what Billy Bean did at the Oakland A's.Vangelis Vergetis: it's very funny. You say this Harry, by the way when we started the company, what is it, three, three and a half years ago now, we had a slide actually. You know, baseball did it in the nineties. Is it about time that healthcare does the same? and going through the MoneyBall analogy. So look, the quick or the easiest way to explain it, right, it's the analogy of how do you pick baseball players and build a winning baseball team and how do you pick drug candidates and development programs and build a winning pipeline?So, you know, back in the day, what baseball did is a lot of experts in a big conference room. And these guys have watched—and I say guys, because yeah, they were primarily guys—they watched, you know, thousands of baseball games each, and they had their own perspectives and views and biases and experience in terms of what's you know, who's a good baseball player and who's not, and who they want on the team and how do they complement each other.And that's how they built a baseball team and, you know, the, the kid comes in and, you know, the chubby kid, I think Jonah Hill, right, and tells Brad Pitt, or Billy Bean in real life, I think we can do this differently. And that's a little bit of the analogy here, look, it's not a perfect analogy, like everything. Right? But the analogy here is how do you go from when you design a clinical trial or when you think about the pros and cons and the risks of a development program, how do you take that conversation from a room full of people, the oncology PhD, the statistician, the person who's developed dozens of drugs in the past and so on, and you inject some data science and machine learning capability into that conversation. There is art in drug development. We'll be the first one to acknowledge that, the same way there's art in baseball. So I would not expect that you know, that room gets replaced by a machine in any shape or form and definitely not in the, in the near or even medium, medium future. But the idea is, you know, if you have 10 people in the room, can you pull up an 11th chair, have the machine learning algorithms, sit at a chair. And provide a very unbiased data-driven perspective into that conversation. So that, that, that's what we do. Harry Glorikian: So we're going to, I want to get into some of the details, but I want to step back and fill in some history here for the people and how Intelligencia got started. If I'm not mistaken, your background is computer science, not biology. Right? Okay. And your co-founder Dimitrios [Skaltsas] is trained in law. So you both spent times at McKinsey, is that where you guys met? Vangelis Vergetis: So we, it's a, it's a good, good both of those good points. So you have a former lawyer—which we don't hold against him, we still like him very, very much—and a former computer scientist or electrical engineer who are running a company in drug development. Like, how does that work? A couple of things. As you, as you rightly pointed out, we met at McKinsey. We were both part of the healthcare practice there. Initially I was in the, in the US. Dimitrios was in Europe. We met 10 years before starting a company just running client projects together. We kept in touch over the years. And at some point, I think it was 2014, Dimitrios moved to New York, moved to the US with McKinsey and took some AI responsibilities. McKinsey was doing some internal AI. I think it was called McKinsey Solutions or something like that.So we became closer when he was in New York. We were both in healthcare for the better part of the last a decade, and we were looking for, what is the opportunity? You know, what's the area in, in drug development or frankly in pharma more broadly, where we believe we can have an impact.And it was partly us thinking through different areas. It was frankly customers or clients coming. We were both at McKinsey and we have done this study over and over again. Right. How do you design a better clinical trial? We had, I had done this, I don't know, two dozen times, maybe more. And clients kept asking McKinsey or us, Hey guys, you know, we understand how you do this and you do it very well, but are you using machine learning? Are you using data? And after saying no for about, you know, 50 times we said, okay, we should stop saying no and just go build the damn business. So here we are. Harry Glorikian: Yeah, no, I know that. I mean, from my days having Scientia Advisors, they ask over and over and over again and you keep it. It's great profitability by the way, but because you sort of know the answer. But you couldn't have picked a harder space though this is not a trivial exercise, especially if you go back to 2014 where some of the data was not even truly available or not in a format or not labeled or, or, or, or, or, or—right, to where we are today.Vangelis Vergetis: We started the company basically in 2018. The biggest challenge, I think you, you, you rightly put it, it's getting your hands on the right data. You need to answer the question you want to answer. And we took that view by the way. And some people go differently and I'll have my biases, my own biases, I'll admit. In a lot of places, what we've seen, particularly some big pharma, because they're sitting on a vast amount of their own data, but whether it's CTMS data or whatever clinical trial data they have, and the exercise they mentally do is okay, I have all this data. What questions can I answer? What can I do? And there's a lot of value there. We can answer a lot of good questions. But sometimes the question you ask needs more data than what you have, and you're kind of force-fitting it a little bit and say, yeah. Okay. But maybe I can answer most of it. Well, not really. So we flipped it. We asked the question, the question is, what is the risk of this clinical development program or the flip side of it? How likely is it that this clinical program or this drug will eventually reach a patient, will eventually receive approval by the FDA and be used by a patient. Then we went there. We said, okay, if that's the question, what data do we need to answer that question? Some of it very easily accessible. Some of it doable, but you need to build data pipelines. You need to clean it up. It's a little bit messy, whatever. Some of it doesn't exist. We've got to build it from scratch. So if you do it the other way and say, what do I have, you'll ignore that piece that says, doesn't exist. I have to build this from scratch. You're going to try to solve the problem with the other stuff.And then you realize it's not enough. So we asked the question and then we went very systematically to get all the data we needed to train the machine learning models. To answer that question. Harry Glorikian: Sounds like a consulting approach. What do we need to fill the two by two? So I totally get it. What are the biggest limitations you see right now from pharma's current method of assessing clinical trial risk? Vangelis Vergetis: Yeah, there is, there's a few and some are bigger. Some are smaller. And it's, it's hard to paint the whole industry with a broad brush, but there are some technical limitations that everybody has like as humanity, as a scientific community. Do we really understand drug biology or biology? Really well, human biology. I don't know. We understand it well enough, but from the, total knowledge, biological knowledge, we probably know this much. That's one challenge and it's a technical challenge or a scientific challenge.A technical challenge is and I think you put your finger on it, data availability. But it goes beyond, can I get my hands on the right data? Is it curated in a particular way? Is it well annotated? Is it labeled? Does it have the same quality? Is it consistent? You know, I, I take data from this genomic database. I pick data from that genomic database. Are they structured the same way? Kind of combine them or how much work do I need to do combine them. Now, it's a solvable problem. You know, the understanding of biology. It is solvable over time, but not immediate. The technical aspect of, can I make data consistent, solvable, is incredibly painful, and very few people have the patience for it or are willing to, I mean, we've killed a lot of brain cells pulling that data together, but we've done it.And then there's a third group, I think, of challenges that I would put in the broader, you know, cultural umbrella. You know, there is the, what I call the “every drug is unique” syndrome. A lot of people out there will say, well, you know, there's so many differences between drugs and programs and all that, there's no way you can use machine learning to estimate the success of this drug. Most of it not true, actually there's that syndrome there is the—and it's actually very interesting in the pharma industry, particularly, or in biotech—here is the “I want to see very quick results. I want to try this AI thing, whatever this AI thing is. Let me try it for two, three months. Show something quick. If I can show us a quick win. Great. If not, I'll throw it away. I don't have the patience for it.” And this is an industry that will easily not even think about investing 10 years and a billion dollars to develop, forget clinical, in the preclinical world, to discover a new target or a new molecule that could cure Alzheimer's or pancreatic cancer or something. So we are an industry that we're very much into putting an enormous amount of resources, time, patience, to discover a drug, but when it comes to incorporating an AI system methodology model that may help us tremendously, we are impatient. “Three months. Let's see what I can do. Oh, no results? Throw it away. I'll never see it again.” And there's a little bit about this, I think in all fairness, companies are getting better. So most of the large pharmas, they have now chief digital officers or chief innovation officers with a whole structure underneath them and mandates and all that. So I don't want to be too, too pessimistic here. Right. There's a lot of effort. And I think the industry at the very least has acknowledged they have a cultural barrier that needs to be overcome. But I don't think we're fully there in how we overcome it. But we're making progress, Harry Glorikian: But it's interesting, right. I look at existing big pharma and the lumbering ways they sort of move forward in fits and starts. And, you know, do I want to disrupt my kingdom to implement this thing? I mean, there's, there's a lot of human psychology that's involved here and a lack of understanding right. Of fully understanding this and what it can do for them in different areas.Then I look at the startups that literally from day one are totally data purpose-built right. Everything they're looking at is, “What's the data. How do I label it? Where are we going to use it? How do I manipulate it?” I mean, literally it is from the ground up. And I always think to myself sooner or later on my bet is that the startup is going to out maneuver the big guy.I mean, Google started from as a purpose-built entity and it's, you know, it, it outstrips most of its competitors and reshapes industries. I always think it's harder to take an existing entity and reprogram its DNA rather than have a predesigned piece of DNA from, from day one. Vangelis Vergetis: Harry it's an incredibly interesting thought, and I don't have an answer for it. And only time will tell. I would expect some pharma companies, whether we're talking about big pharma, you know, the big 10 or, you know, the, the massive guys or some of the, you know, in our industry, it's very funny, like a mid-sized biotech, it's still a $20 billion business. So, but I would bet some of them, to use your words, will adapt, will reprogram their DNA to some degree, a little bit painfully, it's going to be a little bit slow or they're going to have some false starts, but somehow they'll, they'll get there. Some others will just buy and we've seen this in the industry, right? So, interesting startup, I'll just buy them. And a few of these have already happened. We've seen, what is it, Flatiron was bought by, I believe it was Roche, right? Yes. There's many other similar examples. That's probably one of them more, the bigger ones, the more prominent ones. So I would expect this reprogramming of DNA will not fully happen organically. Some of it will happen by big pharma realizing, “Yeah. We need to play, you know, if we, if we're not a data company in a few years from now, we're, we'll be nowhere, right? How do we get there? Let's get our stuff stuff organized, and maybe we'll go make a couple of select acquisitions and eventually we'll get there.”So I think all of these flavors will materialize in some shape or form, and some companies will lose. Some companies will do the investments and put the, hire the right people and make the right acquisitions and, and, and they will continue to grow. Harry Glorikian: Yeah. And I look at it as an analogy to like, if I look at say JP Morgan or Goldman Sachs, I mean, they are the amount of money that they're spending trying to transition to this new capability is, we're not spending the same amount of money in pharma for sure. Right? Not even close. Vangelis Vergetis: I don't know the actual amount of money, because I haven't done the analysis. I haven't seen numbers. But my former employer, McKinsey, has done quite a bit of work. I think it was MGI. So MGI is McKinsey's think tank, it's the McKinsey Global Institute. They had done a lot of work on this. And I remember seeing a chart that I thought was, was mind boggling. Areas that are way ahead in AI, or industries that are way ahead in AI, I would say financial services. So the Goldmans and JP Morgans and Morgan Stanleys and some of the world's high-tech of course, and a few others. Who's at the bottom? I think it was like building materials or construction, which I get it. Second from the bottom? Health care. It was literally that bad.Well, it's true. If you look at the data, the, the sad thing for me the part that we need to think about as an industry, the promise or the impact that AI can have in healthcare. And I'm talking about healthcare more broadly now, including hospitals and payers, not just drug development or a pharma. But the impact that AI can have on health care is enormous. It's in the trillions. But in terms of AI adoption, we are right above construction and no offense to the construction, but it's not the most innovative industry.Harry Glorikian: So, this is why I love investing in this area, because it's such an incredible, I mean, some of the other opportunities are still incredible, don't misunderstand me, but this is at its nascent stage in my mind, where the opportunity is dramatic to sort of move the ball forward. Okay. Which brings me to the next question, which is, you know, and you don't have to name any names or anything like that. Walk us through sort of a real world example of how you help a client in practice. Vangelis Vergetis: Ooh. Maybe I'll give you two examples. You asked for one, I'll give you two. Actually I'm gonna give you more, but let, let's start with that. So where do we typically you know, we work with several flavors of customers, right? So we, we serve some of the largest, you know, top five big pharma companies we serve. Some of the smaller, even private biotechs. And we serve a bunch of the mid sized biotechs or midsize pharma companies. One area that that comes or one example is a specific program. So I'll, I'll pick on an actual example. So a specific, it's a phase two asset on a phase two program. It was a combination program, I believe for pancreatic [cancer] that our client was running. It was the phase two. It had been going on for about a year, I want to say. So it was in the middle of phase two, they were starting to see some interim results.They hadn't published anything. They were starting to see some interim results, but they were still waiting for the phase three to complete. And then there were basically three questions with increasing degrees of difficulty, if you will. Question number one, how likely is it that this program, so this combo, so our molecule with, I believe it was chemo for pancreatic cancer, will eventually reach a patient, will eventually receive regulatory approval by the FDA? That was question number one, which is our bread and butter. This is what our algorithms do. I'll make up the number now. It's a, you know, 13%, which by the way, for pancreatic cancer, phase two, that's not bad. The second question was, okay, now let's start thinking forward. So at the end of phase two, we're able to show ABC, how does that probability change? Because given the interim results we've seen, we have pretty decent conviction we'll be able to show something in that range when it comes to OS or ORR or whatever end points we're measuring. What will our probability to change to. It's 13 now, will it go to 20 or we'll go to zero?What if we managed to show something better or something worse. So in that sense, we're trying to calibrate and say, based on what we show at the end of phase two, how do we make a decision? Should we go to phase three or not? Is it too risky still? And it needs to be derisked further? Or are we comfortable with the risk we're taking, and we're willing to write a, you know, $200 million check to run a phase three program. So we did the simulations, if you will, of the analysis to say, based on what your phase two will show, here's what you should expect your risk to be at the beginning of phase three. That was the second layer. The third layer went even a step further and said, okay, let's assume we are now comfortable moving forward. So the risk is within what we're willing to take given the size of the prize, right? Because if you do get this drug approved, we estimate an enormous commercial potential. So we're willing to take significant risks here. How should we do this? So help us think through how different choices for continuing our development program affect our chances for approval.For example, should we run a smaller phase two-B and then two large phase three trials. Should we scrap the phase two-B and go straight to pivotal phase three and do a much larger trial. And there are different trade offs there that have to do with costs, time and risk. We help them think through from the middle of phase two where they are today, how likely is it that they go approved? How will that evolve once they publish results? And if they decide to move forward, what the best path forward is from a risk point of view. So that's one example. Well, I'll spare you. The second one, I spent too long on the first one. Harry Glorikian: So you've written this machine learning model, right? So, and I want to say there's at least a hundred factors, clinical trial, design outcomes, regulatory process, you know, the biology itself that you mentioned, right? The history. You have to train a model like that. Where did you get the data to train this complex model?Vangelis Vergetis: There's no single. So I wish there was. So we we've been to now dozens of data sources. So I think what I said at the very beginning, right? Some of the data was easy to get. So for example, there is a bunch of data that clinical trials.gov has. Of course we have that, and everybody else has that. That's very easy to get right. Valuable, but very easy to get, which is good.There are some data where you need to, it's publicly available, but you need to spend a lot of time cleaning up and curating. So think of genomic databases, whether it's TCGA or GTX, or, you know, dozens of other genomic databases that needs a lot of analysis and lot of processing and a lot of cleanup before you create features out of that data to put in your machine learning algorithms. So that's a, probably a second group.And a third group that goes back to the point initially that, you know, not all the data you want to answer, the question, is available. So you have to build it yourself. We built it ourselves. So an example, there is clinical trial outcome. So there is no to our knowledge and we looked hard. There is no data you can buy that has in an incredibly consistent, systematic way, all the outcomes of clinical trials in a particular therapeutic area for the last 20 years. So let's say, I mean, I mean, oncology, I'll give you an example. There's been a few thousand trials in the last 20 years. Let's say since 2000, we need to know every end point that this trial measured. How many patients were in each patient cohort or in each arm of the trial. What was the value of that endpoint? What ORR did they achieve? What OS did they achieve? Whatever. When was that? Because sometimes we say, OS, Overall Survival, well, was it measured at six months or 12 months. One layer more of specificity of exactly how the end point was captured. And then you need the number. How many patients survived at the six month mark or whatever it is. So there's all that, all that stuff that you need, and then you need it, not just for the trial or the program you're assessing, that's easy to do, right? It's one program. We can get it from the, from the pharma company themselves. We need it for every single trial that has ever succeeded in the past. And for every single trial that has ever failed. That's how you train a machine learning algorithm. That was very painful. We have a whole team in Athens, actually. So if the name didn't give it up, I'm from Greece originally. I've been in New York for like 25 years now, but I'm from Greece originally. So a lot of the team is based in Greece and part of that team, they're a very highly educated team and, you know, PhDs in biology, oncology, immunology, pharmacology, all the ologies. And that team curates in an incredibly systematic way all that data, before our data engineers and before our machine learning team can take over to build models. Right? So to answer your question in a short way, dozens of data sources, some easy to get some much harder with a lot of processing. And some we had to just create from scratch. Harry Glorikian: I mean, that was just thinking about what you were saying. That, that last piece we were just discussing. I mean, I can imagine to hospitals and to doctors that would be—if you could put that into interesting matrix, they could get an interesting view into these drugs instead of memorizing off the top of their head. It's it, you know, I always find all these discussions with companies that have data. I can think of five other things to do easily. Once you've got the data source. Vangelis Vergetis: We've been discussing internally, both as a team, but also with our advisors and even our customers at this point where they're coming to us on the saying, Hey guys, that's amazing what you have. We'll pay you money. Can we now do this. Can we now do that. And some of that we would love to do and we're entertaining it. Some of it, you know, we, we're still a growing company or, you know, there's 40 of us total in the company. You also don't want to get distracted by too many shiny objects. You know, find the right shiny object and focus on a couple of them, but not too many.So for some of them, we'll say, look, we could do it. We can, we don't have the time. We don't have the bandwidth today. Maybe later. For some of them we would say, yeah, that's incredibly interesting. And we were planning to go there anyway. Let's do it faster together. So we're discussing with one of our customers today about building something that goes beyond risk and starts thinking about the commercial implications of what happens when a drug actually gets approved. So it's not just predicting approval, but can you predict anything in the commercial space, whether that's revenue reimbursement market shares and so on. [musical transition]Harry Glorikian: I want to pause the conversation for a minute to make a quick request. If you're a fan of MoneyBall Medicine, you know that we've published dozens of interviews with leading scientists and entrepreneurs exploring the boundaries of data-driven healthcare and research. And you can listen to all of those episodes for free at Apple Podcasts, or at my website glorikian.com, or wherever you get your podcasts.There's one small thing you can do in return, and that's to leave a rating and a review of the show on Apple Podcasts. It's one of the best ways to help other listeners find and follow the show.If you've never posted a review or a rating, it's easy. All you have to do is open the Apple Podcasts app on your smartphone, search for MoneyBall Medicine, and scroll down to the Ratings & Reviews section. Tap the stars to rate the show, and then tap the link that says Write a Review to leave your comments. It'll only take a minute, but it'll help us out immensely. Thank you! And now back to the show.[musical transition]Harry Glorikian: If you have it to say, what is your defensible advantage, your special sauce? Like, what is it that you're doing for pharma that they can't somehow reproduce for themselves? Vangelis Vergetis: That's a great question, Harry. I will say a couple of things. Some are softer, some are harder. On the softer side, and probably more important by the way, is the persistent focus you know, unrelenting pursuit of what we're here to build. In a larger company, it's too easy to lose focus, budgets, get cut, people, get reassigned, promoted, change departments, move.So it's very hard to get a team together to focus on something for an extended period of time and only do that. So that's probably one thing when, when you compare it to a larger pharma company, right. The, the second thing would be. Bringing together people with very different expertise and experiences.So if you go to our office in Athens—and not the last year, given all the mess, we're all living in with coronavirus—but if you go to our office in Athens either before that, or hopefully very soon, it's a room and you have, you know, the data scientist is sitting here. The oncology PhD is right next to her. Right across is the data engineer. The drug developer is sitting over there. The statistician is there. So it's literally having all those people in one room or in, you know, a series of rooms in one floor, let's say, where they work together on the same topic. And it sounds a little bit mundane and it sounds a little trite, but it makes a difference for the biologist to be listening into, as these computer scientists or data scientists are talking about their models. And I'm sitting here entering all the biological clinical data from this New England Journal of Medicine article that I'm reading. I actually understand how they use it and I can offer an idea. I can say, Hey, actually, I can capture it in a way that will help you guys given what you're discussing. So all those things help.So that's the second element, which is a team of you know, we use diversity in many ways. So a diverse team, not just in the, in the racial or, or, you know any other perspective, but also in experiences and backgrounds. And the third one, which is the more technical one. It's the data we actually do have. It does take an enormous amount of time, a lot of people, an enormous amount of effort to actually build and create the data cube that we have. Nobody else has this. It's incredibly painful but we've done it. So that does set us apart. There are companies out there that are trying to solve the same or very similar questions or answer very similar questions based on a much more limited set of data. And they fall short. They're okay. But they will short of, of our predictive power. Not because they're not doing anything wrong, not because they're not good data scientists, all of those things are fine. They just don't have the data we have. Harry Glorikian: And so that brings me to that next question. In all of these models, there there's little issues, fraught throughout the process…Vangelis Vergetis: Oh my God. There's so many. And some of them are longer. Harry Glorikian: Many, right, that you have to think through. Right. That's why whenever somebody says, oh yeah, I've got the perfect answer, I'm like, it's impossible. Perfect? No, right. So what is the accuracy? I mean, if you said your predictive algorithm, how do you, how do you, first of all, what do you compare it against? And then let me just pick and say, if I will, putting it against a traditional way of making decisions. How do you measure your accuracy? And then do you go back and look at real world evidence versus the system?Vangelis Vergetis: Yeah. So we we've done a few things that are very interesting. There is a standard metric for machine learning. So let's not get too technical or I don't know how technical your audience is. But there's the AUC, which is Area Under the Curve, which means the area under the ROC curve…whatever, there's a metric called AUC. It's pretty much a number between 0.5 and 1. I mean, technically it could be low as 0.5, but that's a silly, so it's a number between 0.5 and 1. The higher it is the more predictive your model is. We are in the high eighties, low nineties, which is, which is incredibly predictive for a problem this nuanced and this hard. If you do image recognition and you use deep learning for image recognition, you get close to 0.999.These are very different problems. So with a standard AUC metric, we score very highly and we've compared that with what others have published in literature. And we are higher than at least what we've seen published. But by others then you do obvious things, right? So, so what do you do, you say, okay, let me take an example of hundred trials or a hundred programs for which my algorithm predicts that they are, let's say in the 20 to 30% success.All right. So my algorithm says all of these hundred fall in the 20 to 30% range. Now let me follow them over time and see what happens. What do you want? Ideally you want 25% of them to succeed, you know, somewhere in the middle. And it most often that's what happens. So when we say zero to 10 on average, let's say 7% of them succeed.When we say 10 to 30 on average, 22% succeed. When we say 30 to 50 on average, 39% succeed. So you do that on a large amount of trials, and then you start gaining confidence that dammit, what this algorithm or what this model is telling me eventually reflects reality. Now, of course, these are averages, right? So there will be trials for which you say 5% and they succeed. Now the obvious thing there to say is, and what we like about this actually, it's a true probability measure. So 5%, what does it mean? Right. I don't need to tell you. 5% means one out of 20 should succeed. Otherwise it's not 5%. If every, if every trial for which you say 5% fails, well, it's not 5%. It's zero. So if you say 5%, you should have one out of 20 succeeding. So you want to see that and you do see that, which is good. Similarly, if you go to a drug developer and you say, you know, 80%, they've never heard a higher number in drug development. Those numbers are rarely exist. So 80% to a drug developer means success. Well, no, it means two out of 10 will fail. Right. So you want to see that you run statistical checks, like the bins that I mentioned, Brier scores, AUC. So you run a bunch of statistical tests and you get very high predictive power. Look, I'll summarize it like this in the beginning of phase two, which is pretty early in drug development, right? So you still have, five, six years of, of development left ahead of you. The predictive power of our algorithms are about 90%. So we can tell you with 90% confidence that the probability that we give you is the right probability. When we tell you 20 it's 20, when we tell you it's 60 it's 60, we don't give you a one-zero estimate, we'll give you a number. And we're 90% confident on that number. Harry Glorikian: That's a pretty bold statement. So I'll, you know, let's, let's think about it here though. Right? So two things, right? Mof this stuff at some point has to be explainable, which is typically an issue in machine learning is the explainability of the model. So how have you designed it in a way where you can be like, yeah. Okay. This is why I got to this answer. Vangelis Vergetis: It's a great point. I wish we could do exactly what you said. But we can come close. So a couple of things, culturally, and for the right reasons, if you go, eh in front of the EVP of R&D in a large pharma company or the head of portfolio, whatever, and you tell them the answer is 42, they're going to throw you out of the room. They want to know, “Where does the 42 coming from? Why are you telling me this? Give me some, I need to know what can I do about it? I need to understand it.” Which it's very human and it's also the right thing. So we run, by design, we run machine learning models that are explainable. And there is explainability work being done in the academic community even for, let's say deep learning models, which are still much less explainable than a random forest or a KNN or, or something like that. So we run explainable machine learning algorithms. We spend a lot of time on explainability.And if one goes on our platform or uses our software, if you look at the number and then you literally click on a thing that says, explain to me why, and you see all the features that contribute to that answer and how important each feature is. So the reason I'm telling you that your probability is 42 is because on the positive side—and I'm making it up for a second, right?—a target that's a gene that's highly expressed in the tissue. You're going after let's say the lung or, or, or the breast or the liver or whatever it is. The cancerous tissue versus the healthy tissue. You've designed a very good trial with the right endpoints. It's well sized with the, the amount of patients you're putting in. You have a biomarker, which is a good thing, blah, blah. And maybe we'll also say on the negative side, by the way you know, as a company, you may not have that much experience in this particular disease area. So I'm dinging you a little bit. And the regulator hasn't said anything special about you, you haven't received any breakthrough or accelerated approval or anything like that. The gene you picked is highly expressed, but there has been zero, it's a first in class indication. If it's a first in class molecule that has been no approvals in the past of that target. So that tells me it's a little more risky than the 20th PD1 in the market. So it will give you all that.And people can do two things with that. One, and perhaps less important, but important. It gives them confidence that they understand why the machine is telling something. They can wrap their head around it and they can get more confident, even though I can tell you, yeah, I've run the statistics and the predictive power is 90%, you want to be able to understand it. You want to touch it. You want to feel it. You want to understand why? So it does that. The second thing it does is you might be able to do something about it. So back to the simulation, right? What do we help our customer? I can maybe assess for you what the difference will be if you use the biomarker versus not. If you have a larger trial with another arm or not. If you use this endpoint versus that endpoint. So you may be able to say, okay, I understand that the probability is 42%, but if I change these three things, can I make it 50? And those eight points in PTRS and probability of approval are massive in terms of NPV or whatever, evaluation you use. Harry Glorikian: That was going to be what I would, one of my next questions is, so you're doing all this. And so do they always act on the data or in some cases, do they make a different decision based on what the model said?Vangelis Vergetis: Both. So, and, and the model is not a black or white model, right? It's not going to tell you do this, or don't do this, or move to phase three or don't move to phase two. I'll give you an example, if you are in oncology if I tell you that this asset has a 80% probability of success versus 60% probably of success. It probably doesn't matter. You're going to move ahead. Anyway. It's high enough and the risk is too low. You might as well do it. So sometimes, you know, at the extreme, it may not make a big difference whether if I tell you it's a 5% probability versus a 3% probability, do you actually care? It's pretty damn low. Now in a lot of cases though, they, they fall somewhere in the gray zone and this is where a lot of other factors come in. So what do we think of that commercial potential. What are our competitors doing? How does it fit broadly with the rest of our pipeline and all of the other assets, both approved and the programs we have out there. So there's a lot of other considerations that go into making a decision, whether I move to phase three or whether I de-risk it, or you know, what I do.But for the most part what we've seen is our customers act on the information. They are able to take that information, enhance their decision-making process and make at the end of the day, a better decision either because they stopped something they should have stopped, they progressed something they should have progressed, or they designed the trial a little bit differently, or they you know, put a program in place that maximizes the potential of the asset they have in their pipeline.So all of those things happen. The last thing I'll say, Harry, and this one is where we see a lot of action as well, is in business development. So while most of our, we're not, most actually, a lot of our work is in R&D. So pharma companies developing their own molecules. We see two more areas where this approach is gaining a lot of steam.Actually one is business development. So as I'm looking not for my own pipeline, but as I'm looking to identify or attract programs out there that I may want to go buy or partner with or in-license and do all sorts of things. So we work with a customer early on phase one and they said, you know, what are the innovative, if you will, first-in-class assets in phase one, so risky stuff for a particular indication, RA or IBD or Parkinson's or pancreatic cancer, whatever it is for the indication that I care about, what are the phase one programs out there that one are scientifically innovative. So I don't want the me-too drugs. I don't want the 21st PD1 in the market, but I want something innovative. And two, can I see that list ranked from a risk point of view or from an attractiveness point of view, you know, some have a 2% chance of approval. Some have a 20% chance of approval. Well, I want to talk about the 20.Yes. And we've, we've helped customers identify molecules and programs like that, where they go and they have a conversation with a biotech in south San Francisco or in Zurich, Switzerland, or in Tokyo or wherever, with that biotech about in-licensing or partnerships or acquisitions or whatever it is. So with that we've seen quite a bit of action.Harry Glorikian: Machine learning takes hold in drug development. What's the big picture outcome. What do you think, you know, how do you think…is it the Intelligencias of the world that are going to change the dynamic? Is it going to be the companies themselves? You know, I believe this is going to have a profound impact on how things are done and what goes forward. Vangelis Vergetis: Here's what I'd love to see Harry, I'd love to see… For years we've seen—and there's some change recently—we've seen the productivity of R&D declining in our space in pharma and biotech. I refuse to accept that. In the era of a lot of data becoming available, in the era of us being able to use techniques like machine learning, to do something with that data, there's gotta be a way to reverse that trend, that declining trend in R&D productivity, and see it going up again. Who benefits? Patients, where they see better drugs reaching them faster and curing disease. And of course the broader community of pharma companies, biotechnology companies and so on. So the, the big picture is I'd love to see the productivity of R&D in our space increase.And AI, whether it's Intelligencia—and I'm hoping, and I'm sure we will, but there we'll be honest there and that's great. We all need to think through, you know, how do we reverse the trend? So in, in pharma or, or in drug development, I see that as the big picture you know, how do I pick the winners? How do I invest behind the winners? How do I make sure I don't create any, you know, biases in that way where I miss some of the drugs that would have existed had I made the right choice and make my R&D dollars and R&D hours and effort much more productive at the end of the day for delivering drugs to people that need them.Harry Glorikian: So I saw you were quoted in a report from a law firm called Orrick that I liked. I think you were paraphrasing Derek Lowe from Novartis where you said, “It is not that AI will replace drug developers. It's that the drug developers who use AI will replace those who don't.” And coming back to the beginning, you know, do you think this is happening across the board in all businesses? Whether it's on experimental drugs or winning baseball teams.Vangelis Vergetis: Yeah. So it's a great question. Look, I think it is happening across all industries but each industry is different. So I think the scale of impact and the scale of adoption to date are very different across industries.We talked about, you know, we used construction as an example earlier. If you think about construction, the impact that AI will have a construction, it's not zero. I know one, a friend and a mentor runs a cement business and their AI. I'm not joking. They're using AI in cement production to make it more environmentally friendly, increased productivity, increased—he'll do all those things. So yeah, there will be impact. But it's going to be less in construction and building materials than it is in healthcare. Or it's going to be built different in, in, in financial services, let's say that, than it is in travel and tourism. Again there are opportunities for machine learning in travel and tourism. Probably less than in banking or financial services broadly or healthcare. To attempt to answer your question, because I don't know, I don't know what the answer is, I can tell you what my bias is or my view. Yes, it will be used across industries, but the scale of impact will be materially different, whether you're in healthcare or in travel.And two, the adoption to date is very different. All this excitement about AI and all this energy and all this impact that it can have, it's fantastic, and it will have it, but let's also be thoughtful here. I think we all are. But you need experts. There's a lot of art and a lot of things that happen. There's art in drug development. There is art in baseball, there's art, in a lot of things. There is instincts, gut feels that humans have. Some of it is bad because it's biased, but some of…he didn't miss it. There's decisions that doctors make every day as they treat patients. Forget drug development, that yes, that can be made better by AI. Maybe they can be guided by AI, but I'm not sure an AI will take over a physician's job and anytime soon.Harry Glorikian: No, I mean, I think the two together always, at least right now, will equate to step wise function up, right? The AI may not miss a piece of data that the physician didn't see. I've been with physicians where they call it and they were missing a piece of data. Had they had that data, that decision would have been different. The machine isn't going to miss that last piece, right, necessarily. And so I think the two together can be much more powerful than any one alone per se.Vangelis Vergetis: Yeah. And it varies a lot by the use case, meaning can a machine read a lung image or can it tell me if this picture is a dog or a cat? Yeah. Probably can do it better than a human or, or equally good, equally well. But in use cases that are much more intricate than, you know, reading looking at an image, whether it's building a baseball team or designing a phase three trial or anything approaching that level of complexity, the two need to come together and will for a long time to come. So I think Derek is right in that sense. Yeah. If, you know, the ones that use drug development will replace the ones that don't, but AI by itself is not going to replace everybody. Not anytime soon. Harry Glorikian: Yep. I agree. Well, listen, it was great to speak to you. I look forward to continuing our conversation, because I can see that there's many areas of overlap. And it's been great. Vangelis Vergetis: Thank you, Harry. I appreciate it. Harry Glorikian: Thank you. Vangelis Vergetis: Bye.Harry Glorikian: That's it for this week's show. You can find past episodes of MoneyBall Medicine at my website, glorikian.com, under the tab “Podcast.” And you can follow me on Twitter at hglorikian. Thanks for listening, and we'll be back soon with our next interview.
In this 79th in a series of live discussions with Bret Weinstein and Heather Heying (both PhDs in Biology), we discuss the state of the world through an evolutionary In this 79th in a series of live discussions with Bret Weinstein and Heather Heying (both PhDs in Biology), we discuss the state of the world through an evolutionary lens. In this episode, we again discuss the research showing that SARS-CoV2’s spike protein is sufficient on its own to cause disease. The authors believe that this is good news with regard to the vaccines currently in circulation; they may be right. We discuss the paper, the authors’ clarification. Then: lab leak hypothesis continues to gain ground. And: Portland erupts in more violence. The Evergreen State College cannot manage to hire a new president—what are they doing wrong, and what does it suggest about the future of higher ed? Finally, robins and crows face-off. Our book, A Hunter-Gatherer’s Guide to the 21st Century, is now available for pre-sale at amazon. Publication date: 9-14-21: https://www.amazon.com/dp/0593086880/... DarkHorse merchandise now available at: store.darkhorsepodcast.org Find more from us on Bret’s website (https://bretweinstein.net) or Heather’s website (http://heatherheying.com). Become a member of the DarkHorse LiveStreams, and get access to an additional Q&A livestream every month. Join at Heather's Patreon. Like this content? Subscribe to the channel, like this video, follow us on twitter (@BretWeinstein, @HeatherEHeying), and consider helping us out by contributing to either of our Patreons or Bret’s Paypal. Looking for clips from #DarkHorseLivestreams? Here are some, updated frequently: @DarkHorse Podcast Clips Theme Music: Thank you to Martin Molin of Wintergatan for providing us the rights to use their excellent music. Q&A Link: https://youtu.be/MwcW6TB327w Mentioned in this episode: Lei et al 2021. SARS-CoV-2 Spike Protein Impairs Endothelial Function via Downregulation of ACE 2. Circulation Research 128(9): 1323–1326. https://www.ahajournals.org/doi/10.1161/CIRCRESAHA.121.318902 Salk Institute news release, modified since May 1: https://www.salk.edu/news-release/the-novel-coronavirus-spike-protein-plays-additional-key-role-in-illness/ Blog post on Science Translational Medicine by Derek Lowe on Spike Protein Behavior: https://blogs.sciencemag.org/pipeline/archives/2021/05/04/spike-protein-behavior Video interview with Uri Manor, one of the authors on the Lei et al 2021 paper: https://www.youtube.com/watch?v=Uydsf51Lzv8 Wade, Support the show (https://www.patreon.com/bretweinstein)
Welcome to Episode 5 of the Backstop Podcast with me, Ovi Muniz. Red Sox Update: This week the Red Sox went 3-3 vs the Twins, Braves and Rays. Alex Cora announced that he will have either 13 or 14 pitchers in his 25-man roster. He currently has a 38-man roster that will more than likely shrink down at the end of this week. The Red Sox are 10-7 in spring training. Jarren Duran, Michael Chavis, and Bobby Dalbec are leading the team in hits with 11 Jonathan Arauz leads the team in doubles with 5. Chavis and dalbec have 5 HRs With a minimum of 30 At Bats, Arauz is batting a .300Dalbec, .324, and Chavis .297. Concerns on the Red Sox roster are JD Martinez and Rafael Devers. Both players are the veterans in the team that have not quite had a great spring training. Franchy Cordero made his debut Saturday going 1-2 in the game. Hunter Renfroe has demonstrated a big presence for the Red Sox. He was interviewed by Peter Abraham from the Boston Globe and stated that he was very optimistic. He knows he struggled last season and is looking forward to making a comeback. Renfroe was released by the Rays following the World Series loss. The Red Sox AAA farm team, the Worcester Red Sox and AA farm team Portland Sea Dogs, will begin their season on May 4. The schedule games will be in one-week intervals. Meaning, they will play their opponents home or away from Tuesday to Sunday. Check the teams website for ticket sales Major League Baseball is scheduled to kick off the 2021 regular season on April 1st. This season will mark the development of having normalcy as our world has faced ambitious challenges with the pandemic and playing the game. While our nation is on the verge to combat the coronavirus, the games will begin with baseball fans. And that is good news for baseball. April 1st is the game opener. The last time the Red Sox opened at Fenway park on this date was 2002. The Red Sox had Pedro Martinez for the game opener who went 3 innings pitched and gave up 7 Earned Runs in a 12-11 loss from the Toronto Blue Jays. The lineup that season had Johnny Damon, Jose Offerman, Nomar Garciapparra, Manny Ramirez, Trot Nixon, Rickey Henderson. Pitchers that season were Derek Lowe, John Burckett, Frank Castillo, Tim Wakefield, and closer Ugueth Urbina. The Red Sox finished 93-69 that season, second to the New York Yankees, which had a 103-58 record. The Anaheim Angels won the world series pennant over the San Francisco Giants four games from the best of 7. This year, the Red Sox will open at FenWay park in a three game series versus the Baltimore Orioles. Eduardo Rodriguez receives the nod to become the Red Sox ace. The Orioles will have John Means as their ace. Rodriguez has been successful against the Orioles in his career. Orioles are batting .177 and the on base percentage is .225. Rodriguez had success against batter Chris Davis and a toss up with Trey Mancini. On the other side, Means has struggled allowing the Red Sox to bat .311, 5 HRs and 12 RBIs. Xander Bogaerts and JD Martinez have made it difficult for Means to stay on the mound. Most of the games will be televised on NESN, the New England Sports Network. NESN is adding three former red sox players, Ellis Burks, Mo Vaughn, and Kevin Youkilis as part of the network's coverage this season. Jonathon Papelbon will also be a special contributor on a weekly basis. Play by play callers will be Dave O'Brien, Jeremy Remy, and Dennis Eckersly. IN the studio, Jim Rice, Tim Wakefield, and Lenny DiNardo.
Topics Discussed:Atlanta Spa ShootingsDerek Lowe & the Covid-19 VaccineOutside of Politics: Daylight Saving TimeThank you for being a part of our community! We couldn't do what we do without you. To become a financial supporter of the show, please visit our Patreon page, purchase a copy of our book, I Think You're Wrong (But I'm Listening), or share the word about our work in your own circles. Follow us on Instagram, Twitter, and Facebook for our real time reactions to breaking news, GIF news threads, and personal content. To purchase Pantsuit Politics merchandise, check out our TeePublic store and our branded tumblers available in partnership with Stealth Steel Designs. To read along with us, join our Extra Credit Book Club subscription.Please visit our website for full show notes and episode resources. See acast.com/privacy for privacy and opt-out information.
Drug discovery chemist and Science Magazine blogger Derek Lowe discusses COVID-19 vaccine manufacturing.
Host: Larry Bernstein. Guests include Dan Gelber, Derek Lowe, Steve Alloy, Robert Pondiscio, and Alan Auerbach.
America has become a two-vaccine nation, but plenty of other companies are continuing to work on new vaccines that could increase supply and accelerate the country's goal of herd immunity. Dan digs into the vaccine pipeline with Derek Lowe, a medicinal chemist and biotech blogger. Learn more about your ad choices. Visit megaphone.fm/adchoices
The coronavirus pandemic is worsening, both in the U.S. and abroad, with cases, hospitalizations and deaths all rising. Dan digs into the state of global vaccine development, including why both the U.S. and China seem to be going it alone, with medicinal chemist and biotech blogger Derek Lowe. Plus, Axios financial correspondent Felix Salmon on U.S. GDP growth. Learn more about your ad choices. Visit megaphone.fm/adchoices
Bradley is joined by Derek Lowe, a medicinal chemist and has worked for major pharmaceutical companies on drug discovery projects against schizophrenia, Alzheimer’s, diabetes, osteoporosis and other diseases. Bradley and Derek discuss how the COVID-19 vaccine trials are designed, what vaccine distribution will look like, and herd immunity.
Drug discovery chemist Derek Lowe joins Adam to break down why producing an effective COVID-19 vaccine is so challenging, the science of how vaccines work, and the issues that arise when pharmaceutical research is conducted for profit. See omnystudio.com/policies/listener for privacy information.
We spoke with Derek Lowe, longtime drug discovery researcher and author of the "In the Pipeline" blog at sciencemag.org See omnystudio.com/listener for privacy information.
Did the FDA suddenly get stringent about new drugs? Is longevity research over-hyped? And what can recovered patients teach us about Covid-19 vaccines?
This week the Chief and the Champ talk with three owners, Statmen, Basura Bulls and The Lakers. Lakers and Statmen seem to have mentally given-up, but will play till the end. Bulls, think they can win it with a few more Saves. Baseball History, Bob Gibson, Pete Rose and Derek Lowe. Wind up with Speed Skating Silver Medalist Eddy Alveraz and Nick Markakis.
Russia announced Tuesday that it has approved a vaccine for COVID-19 and has plans to inoculate health care workers, teachers, and others in the coming months, despite barely starting Phase 3 clinical trials. Axios Re:Cap producer Naomi Shavin digs into the impacts of this announcement on the vaccine race with Derek Lowe, medicinal chemist, author and expert on drug development and the pharmaceutical industry. Learn more about your ad choices. Visit megaphone.fm/adchoices
As new science emerges—informing our understanding of the mode of transmission, the viral structure, the target and the mechanism of action—science communication plays a crucial role in informing public health policies as well as the public’s behavior. In this episode we explore COVID-19 and Drug Discovery with Derek Lowe, a medicinal chemist and author of the drug discovery blog, In the Pipeline. Follow Derek LoweTwitter: @DerekloweEmail: derekb.lowe@gmail.comWebsite: https://blogs.sciencemag.org/pipeline/Follow Desnor Nicole:Instagram: @desnornicoleTwitter: @DesnorCWebsite: desnornicole.comFollow Rutendo ChabikwaTwitter and Instagram: @tedoexFollow Tedoex Media House:Instagram and Twitter: @tedoexmediaWebsite: www.tedoexmediahouse.comMusic:Title: Elipsis Artist: Chad Crouch (https://freemusicarchive.org/music/Ch...)Source: Free Music ArchiveLicense: Creative Commons Attribution-NonCommercial 3.0 International License.Title: Hip Hop Instrumental 2Artist: Ketsa (https://freemusicarchive.org/music/Ketsa)Source: Free Music ArchiveLicense: Creative Commons Attribution-NonCommercial-No Derivatives 4.0 License
Today's guests: Derek Lowe, Medical chemist / drug discovery researcher - covers drug & vaccine development at the "In the Pipeline" blog at sciencemag.org Gaëlle Simard-Duplain, Research Chair in Intergenerational Economics, HEC Montréal Roy Gutterman, journalism professor and director of the Tully Center for Free Speech at New York’s Syracuse University Dr. Chris Lalonde, Professor of Psychology at the University of Victoria / Academic advisor to Rights4Vapers
This week, Donald Trump revealed that he’s been taking hydroxychloroquine to prevent himself from contracting COVID-19 — despite the lack of evidence to support that it can treat or prevent it. In fact, public health experts have warned that the drug can cause potentially harmful side effects. Today, we’re joined by medicinal chemist Derek Lowe to talk about the hype around hydroxychloroquine and whether it’s actually showing any promise in the fight against COVID-19.
Air Date 5/7/2020 Today, Amanda and I discuss the dangerous, pervasive epidemic of hope fueled by desperation spreading throughout the country that is leading people to self-destructive thoughts and actions. Be part of the show! Leave us a message at 202-999-3991 MEMBERSHIP ON PATREON (Get AD FREE Shows & Bonus Content) EPISODE SPONSORS: SHOP AMAZON: Amazon USA | Amazon CA | Amazon UK SHOW NOTES Ch. 1: Dr. Tony Fauci From One Pandemic to Another: Mark Harrington and Peter Staley - EPIDEMIC with Dr. Celine Gounder and Ronald Klain - Air Date 4-10-20 Dr. Celine Gounder talks to Peter Staley and Mark Harrington, members of Act Up, co-founders of the Treatment Action Group, and dedicated HIV/AIDS activists who know firsthand what it is like to live through a large-scale pandemic. Ch. 2: The placebo reject Ch. 3: Waiting For a Game-Changer - On The Media - Air Date 5-6-20 Derek Lowe, the organic chemist behind the science blog In the Pipeline, urges caution. He speaks with Bob about how to report on the so-called "game-changer" drugs, and where he believes reporting on the "race for a cure" falls short. Ch. 4: Profiteering off of the desperation of those with rare diseases Ch. 5: Dr. Leana Wen Discusses the Tortuous and High Stakes Path that Dr.'s Fauci and Birx Must Navigate - The Al Franken Podcast - Air Date 4-5-20 Dr. Wen gives her unique insight into the dilemma that Fauci and Birx face in dealing with an inarguably crazy president. Ch. 6: Strategic lying to save the lives of the irrational and those with whom they come in contact MUSIC (Blue Dot Sessions): Opening Theme: Loving Acoustic Instrumental by John Douglas Orr Tar and Spackle - Plaster The Summit - K2 Closer - American Moon Bicycle Voicemail Music: Low Key Lost Feeling Electro by Alex Stinnent Closing Music: Upbeat Laid Back Indie Rock by Alex Stinnent Produced by Jay! Tomlinson Visit us at BestOfTheLeft.com Support the show via Patreon Listen on Apple Podcasts | Google Podcasts | Spotify | +more Check out the BotL iOS/Android App in the App Stores! Follow at Twitter.com/BestOfTheLeft Like at Facebook.com/BestOfTheLeft Contact me directly at Jay@BestOfTheLeft.com Review the show on Apple Podcasts, Stitcher and Facebook!
Host Mike Petriello and national editor Matt Meyers talk about their favorite ever pitching performances, including the time Clayton Kershaw almost threw a perfect game, when George Brunet had the worst inning ever and Derek Lowe’s big ALCS moment
Host Mike Petriello and national editor Matt Meyers talk about their favorite ever pitching performances, including the time Clayton Kershaw almost threw a perfect game, when George Brunet had the worst inning ever and Derek Lowe's big ALCS moment
Over the past few weeks, the public has been introduced — by way of Gilead Science, and a leaked video of doctors discussing their preliminary trial data — to a new potential therapy for Covid-19. Remdesivir, a broad-spectrum antiviral medication, was cleared by the FDA this week to treat severely ill Covid-19 patients, despite limited preliminary results from a handful of clinical trials. Some in the media initially touted the drug as a potential miracle cure. But as the mounting pressure to cope with an increasingly dire pandemic makes anything less than a silver bullet difficult to swallow, Derek Lowe, the organic chemist behind the science blog In the Pipeline, urges caution. He speaks with Bob about how to report on the so-called "game changer" drugs, and where he believes reporting on the "race for a cure" falls short.
In this episode of The TC & Jerry Podcast, Tom Caron and Jerry Remy discuss the positives and negatives of baseball in Korea. They also discuss MLB's cautious approach to starting their season. Finally, the duo recall a conversation between Remy and former Sox pitcher Derek Lowe on a cross-country team flight.
Over the past few weeks, the public has been introduced — by way of Gilead Science, and a leaked video of doctors discussing their preliminary trial data — to a new potential therapy for Covid-19. Remdesivir, a broad-spectrum antiviral medication, was cleared by the FDA this week to treat severely ill Covid-19 patients, despite limited preliminary results from a handful of clinical trials. Some in the media initially touted the drug as a potential miracle cure. But as the mounting pressure to cope with an increasingly dire pandemic makes anything less than a silver bullet difficult to swallow, Derek Lowe, the organic chemist behind the science blog In the Pipeline, urges caution. He speaks with Bob about how to report on the so-called "game changer" drugs, and where he believes reporting on the "race for a cure" falls short. On the Media is supported by listeners like you. Support OTM by donating today (https://pledge.wnyc.org/support/otm). Follow our show on Instagram, Twitter and Facebook @onthemedia, and share your thoughts with us by emailing onthemedia@wnyc.org.
Guest speakers include Steve Alloy, Dan Yergin, Mark Gunnin, Jordan Shiner, John Lipsky, Eric Klinenberg, Dr. Inderpal Randhawa, Dr. Cyril Wecht, David Salsburg, and Derek Lowe
In this, the last of our short series of coronavirus updates, I will be telling you everything I've found out about the hunt for a coronavirus vaccine - when we can expect it to be ready, the testing that needs to be undergone, and some of the techniques that might be used. With thanks to Derek Lowe of Science Magazine whose blog post on this I used as a main source for many of the details.
It's Way Back Wednesday good people and today we are bringing back The Credit King! Pod'up and take notes...If you spend 3-4 hours per day scouring the internet for travel reward deals and have created a 15 credit-card spreadsheet that is detailed down to the last available dollar, then you and our guest, Derek Lowe, have a few things in common. Did we mention he is not even 30 yet!! Sit back and pod's because who knew credit could be take you around the world first class for less than a hundred dollars...The Credit King is in the building!!! Let's go. Support this show http://supporter.acast.com/2bg. See acast.com/privacy for privacy and opt-out information.
The Work in Sports Podcast - Insider Advice for Sports Careers
Don't try and defeat Applicant Tracking Systems, learn how to work with them! We explain in this edition of the Work In Sports Podcast with Brian ClappHey everybody, I'm Brian Clapp, VP of Content and Engaged Learning with WorkInSports.com and this is the WorkInSports podcast.I have to tell you all, later this afternoon I am interviewing Dan Duquette for this podcast and I'm a little more than fired up. Dan Duquette was the GM of the Expos, the Red Sox and the Baltimore Orioles. He's a 2-time MLB executive of the year. He traded for Pedro Martinez!He signed Manny Ramirez!He traded Heathcliff Slocumb for Derek Lowe and Jason Varitek!As a die-hard Red Sox fan….I'm in mind-blowing don't mess this up, mode right now. I usually prep about 10 questions for each interview subject… with Dan, I wrote 25 questions down in like 10 minutes. And for all you Orioles fans out there, your team may be terrible now, but I'd like to remind you it was he who led them to the playoffs in 2012 for the first time since 1997, and won the American League East in 2014. The guy is a baseball genius. He's going to be talking about his career. But also about a new online course in Baseball Player Development that he's launching with our friends over at Sports Management Worldwide. Can you tell I'm excited?Alright,m tune into that next Wednesday -- this Wednesday is another great interview with Dennis Adamovich, the CEO of the College Football Hall of Fame. We talk a lot about leadership, event management and who he admires in the game today. Really fun stuff, that's on Wednesday so tune in. Alright, so what is on tap for today… fan question!Well, before we get into that...a little perspective and inspiration. I don't appreciate cheesy sayings people put on their wall to motivate themselves. BUT, I absolutely love when someone LIVES in a way that represents truth and perspective.Maybe you've heard this story, maybe not. Sadio Mane, The Liverpool Star from Senegal who earns approximately 10.2 million dollars annually, was spotted by fans carrying a cracked Iphone, and somehow it became a story. People asked him why… why do you a man of great means, suffer through a cracked iPhone, oh the horror.His response is legendary. "Why would I want ten Ferraris, 20 diamond watches and two jet planes? What would that do for the world? I starved, I worked in the fields, I played barefoot, and I didn't go to school. Now I can help people. I prefer to build schools and give poor people food or clothing. I have built schools [and] a stadium; we provide clothes, shoes, and food for people in extreme poverty. In addition, I give 70 euros per month to all people from a very poor Senegalese region in order to contribute to their family economy. I do not need to display luxury cars, luxury homes, trips, and even planes. I prefer that my people receive a little of what life has given me," I love this man.Floyd Mayweather...are you listening? Hat tip to Alina Menuhkin for sharing this on LinkedIn earlier today. Alright...now today's question.It is from Nick in Salisbury, Maryland. “Hi Brian, I've heard you talk tangentially about how to optimize your resume to defeat the Applicant Tracking Systems -- but I haven't heard you specifically address how. So...how do you do it?”Nick, I am going to read and answer your question primarily because you used the word tangentially in it. I found that impressive. I think that is a fair critique, I may have mentioned the importance of defeating the Applicant Tracking systems in passing...but never got into the nuts and bolts of it all. So let's do that!Quick explainer on how ATS systems work.They scan you, and if you don't hit the mark, I never see you.1: Apply only for jobs you are qualified for!2: Don't try any tricks you see on the interwebs that seem like they are bypassing your lack of qualifications. Either you are qualified for the job or not. 3: Do not apply for multiple jobs at the same company 4: Include the right keywords5:...
We talk with Derek Lowe, Medicinal Chemist and author of the blog, "In The Pipeline". He shares his predictions for the next decade in Pharma and Drug Discovery, his job search experience as a postdoc in Germany and his thoughts on the job market now, and we delve into public perceptions of the pharmaceutical industry. True to periodic bagel form, we also had a little fun at the end. Stay tuned!
Why hope for China's new Alzheimer's drug turned into skepticism ? Is three a crowd in CAR-T? And what's a Bionomy?
Episode 69 (nice) with Derek Lowe of Saint Sabrina's in Minneapolis, Minnesota. Derek has 20+ years of experience as a piercer, studio manager, and APP conference attendee. We talk about what goes in to a successful studio, and a successful staff.
In 1990, two thieves dressed as policemen walked into Boston's Gardner museum and walked out with 13 artworks worth half a billion dollars. After 28 years the lost masterpieces have never been recovered. In this week's episode of the Futility Closet podcast we'll describe the largest art theft in history and the ongoing search for its solution. We'll also discover the benefits of mustard gas and puzzle over a surprisingly effective fighter pilot. Intro: In 1938, Italian physicist Ettore Majorana vanished without a trace. Many of the foremost intellectuals of the early 20th century frequented the same café in Vienna. Sources for our feature on the Gardner heist: Ulrich Boser, The Gardner Heist: The True Story of the World's Largest Unsolved Art Theft, 2008. Stephen Kurkjian, Master Thieves: The Boston Gangsters Who Pulled Off the World's Greatest Art Heist, 2015. Michael Brenson, "Robbers Seem to Know Just What They Want," New York Times, March 19, 1990. Peter S. Canellos, Andy Dabilis, and Kevin Cullen, "Art Stolen From Gardner Museum Was Uninsured, Cost of Theft Coverage Described as Prohibitive," Boston Globe, March 20, 1990, 1. Robert Hughes, "A Boston Theft Reflects the Art World's Turmoil," Time 135:14 (April 2, 1990), 54. Peter Plagens, Mark Starr, and Kate Robins, "To Catch an Art Thief," Newsweek 115:14 (April 2, 1990), 52. Scott Baldauf, "Museum Asks: Does It Take a Thief to Catch a Degas?," Christian Science Monitor 89:193 (Aug. 29, 1997), 3. Steve Lopez and Charlotte Faltermayer, "The Great Art Caper," Time 150:21 (Nov. 17, 1997), 74. "Missing Masterpieces," Security 37:6 (June 2000), 14-18. Robert M. Poole, "Ripped From the Walls (And the Headlines)," Smithsonian 36:4 (July 2005), 92-103. Paige Williams, "The Art of the Story," Boston Magazine, March 2010. Randy Kennedy, "20th Anniversary of a Boston Art Heist," New York Times, March 17, 2010. Mark Durney and Blythe Proulx, "Art Crime: A Brief Introduction," Crime, Law and Social Change 56:115 (September 2011). Katharine Q. Seelye and Tom Mashberg, "A New Effort in Boston to Catch 1990 Art Thieves," New York Times, March 18, 2013. Tom Mashberg, "Isabella Stewart Gardner: 25 Years of Theories," New York Times, Feb. 26, 2015. Shelley Murphy, "Search for Artworks From Gardner Heist Continues 25 Years Later," Boston Globe, March 17, 2015. Tom Mashberg, "Arrest by F.B.I. Is Tied to $500 Million Art Theft From Boston Museum, Lawyer Says," New York Times, April 17, 2015. Serge F. Kovaleski and Tom Mashberg, "Reputed Mobster May Be Last Link to Gardner Museum Art Heist," New York Times, April 24, 2015. "New Video in 25-Year-Old Art Heist at Boston's Isabella Gardner Museum," New York Daily News, Aug. 6, 2015. Tom Mashberg, "25 Years After Gardner Museum Heist, Video Raises Questions," New York Times, Aug. 6, 2015. Rodrigue Ngowi and William J. Kole, "2 Suspects in Boston Art Theft Worth $500 Million Are Dead, FBI Says," Washington Post, Aug. 7, 2015. Sarah Kaplan, "Surveillance Video Raises Questions — and Possible Clues — in 25-Year-Old Museum Mystery," Washington Post, Aug. 7, 2015. Justin Peters, "Why Is Stolen Art So Hard to Find?," Slate, Aug. 14, 2015. Erick Trickey, "The Gardner Museum Heist: Who's Got the Art?," Boston Magazine, March 13, 2016. Shelley Murphy and Stephen Kurkjian, "Six Theories Behind The Stolen Gardner Museum Paintings," Boston Globe, March 18, 2017. Graham Bowley, "Gardner Museum Doubles Reward for Recovery of Stolen Masterpieces," New York Times, May 23, 2017. Edmund H. Mahony, "Stubborn Stand-Off Over Stolen Gardner Museum Art Could End With Sentencing of Hartford Gangster," Hartford Courant, Sept. 5, 2017. Katharine Q. Seelye, "Clock Is Ticking on $10 Million Reward in Gardner Art Heist," New York Times, Dec. 26, 2017. Camila Domonoske, "Got the Scoop on the Gardner Museum Art Heist? You Have 4 Days to Earn $10 Million," The Two-Way, National Public Radio, Dec. 27, 2017. Edmund H. Mahony, "Museum Extends $10 Million Reward in Notorious Boston Gardner Museum Art Heist," Hartford Courant, Jan. 11, 2018. Colin Moynihan, "Gardner Museum Extends $10 Million Reward for Information in Art Heist," New York Times, Jan. 11, 2018. Nadja Sayej, "Will Boston's $500m Art Heist Ever Be Solved?," Guardian, Jan. 19, 2018. Leah Silverman, "Suspect in the Isabella Stewart Gardner Museum Heist Sentenced to Four Years in Prison," Town & Country, Feb. 28, 2018. Sarah Cascone, "Paintings Stolen in America's Biggest Art Heist Have Returned to Their Frames -- Thanks to Augmented Reality," Artnet, March 26, 2018. "Learn About the Theft," Isabella Stewart Gardner Museum (accessed April 29, 2018). Listener mail: Derek Lowe, "Understanding Antidepressants -- or Not," Science Translational Medicine, Feb. 12, 2018. Johnathan Frunzi, "From Weapon to Wonder Drug," Hospitalist, February 2007. "Evolution of Cancer Treatments: Chemotherapy," American Cancer Society (accessed May 17, 2018). Augustus De Morgan, A Budget of Paradoxes Reprinted, With the Author's Additions, From the Athenaeum, 1872. Robert and Michele Root-Bernstein, "Medicinal Notes: Honey Works Better Than Cow-Dung," Independent, May 4, 1999. Ole Peter Grell, Paracelsus, 1998. This week's lateral thinking puzzle was contributed by listener Steven Jones. You can listen using the player above, download this episode directly, or subscribe on Apple Podcasts or Google Play Music or via the RSS feed at http://feedpress.me/futilitycloset. Please consider becoming a patron of Futility Closet -- you can choose the amount you want to pledge, and we've set up some rewards to help thank you for your support. You can also make a one-time donation on the Support Us page of the Futility Closet website. Many thanks to Doug Ross for the music in this episode. If you have any questions or comments you can reach us at podcast@futilitycloset.com. Thanks for listening!
In this week’s episode, we speak to Derek Lowe of Saint Sabrina’s. Listen in as we talk about managing a team of employees, how the people you surround yourself with can influence your success and the passion our industry has for its livelihood. This episode sponsored by Other Couture Jewelry, Gold Heart Woodworks and Amory Body Arts. Find out more on the Real Talk : A Piercing Podcast website.
July 22, 2016 Steven talks to former MLB pitcher Derek Lowe at the American Century Championship in Lake Tahoe Nevada.See omnystudio.com/listener for privacy information.
Last year, Paul Sporer of Baseball Prospectus stopped by to chat with Host Gill Alexander around this very time of the MLB season and delivered the goods, spurring on mass down-the-stretch fades of the accidents waiting to happen that were Derek Lowe, Aaron Cook, and Henderson Alvarez. Now comes the 2013 edition at the 100-game mark. Skill position fantasy players are highlighted, as are underachieving starters, before the list of starters destined for trouble (our fade targets) are isolated, on Tuesday's Betting Dork (July 23, 2013).
Ben and Sam try to figure out why more pitchers’ careers don’t look like Derek Lowe’s, then talk about the latest Matt Garza trade rumors.
It's been far too long since I did a Joncast. Always nice to catch up with him. Enjoy as we ride the conversation for nearly an hour covering a wide range of topics. Vacations and how we don't take nearly enough of them The European method of taking a month off in August Our pursuit of happiness and self-inflicting wounds culturally Defining needs vs. wants and how we've become spoiled Swisher and the bro dude stare at the mound Tom Hamilton and what he said Rob Neyer and comparing baseball to a soap opera Derek Lowe and Dusty Baker Liking Craig Calcaterra's blog and writing The gotcha culture and why people shouldn't have an opinion on anything Godwin's law Getting ingrained in a culture and having it ruin the things you love Did we create obsessive compulsive disorder or at least augment its existence? Is sports talk radio a guilty pleasure? Have you ever injured your voice screaming at talk radio? Jim Rome and forcing the audience to eat their own Hiram and why long form is so much better for so many people The Indians and how you could see this coming on the schedule Walk-offs and overtime NHL goals are the best endings in sports Playoff NBA officiating is worse than regular season NBA officiating NHL playoff officiating is better than regular season NHL officiating The nebulous nature of blocking and charging calls USMNT coming to Cleveland St. Louis and how it is a big soccer town Learn more about your ad choices. Visit megaphone.fm/adchoices
The Red Sox split their four game series with the Indians over the weekend, and still stand at 57-59, and 5.5 games out of a playoff spot. Alex explains why this series against the Orioles is undoubtedly the most important series of the year, and why anything less than a sweep should dramatically change how the Red Sox approach the rest of the month of August. Though the Red Sox won on Friday night behind another strong performance from Clay Buchholz, Will Middlebrooks was hit on the wrist by a 96 mph fastball, and may be lost for the season. How cautious should the Red Sox be with Middlebrooks' rehab? On that note, when will David Ortiz return from his achilles injury? Of course, Alex also touches upon the latest beer drinking story involving rehabbing pitcher John Lackey. Was it a valid story to report, or an attempt to generate needless controversy? In the "Around the League" segment, Alex looks at the AL East standings, and the Yankees' acquisition of former Red Sox great Derek Lowe. Manny Machado's terrific big league debut weekend, and the Rays taking care of business against the Twins is touched upon too. Other notes from playoff races around the league are talked about too.
Looking back at the WS. The Awards Season. Hinske and McLouth. And the Derek Lowe trade.…
Episode: 609 - Opening Day Preview: Big Z vs. Derek Lowe, Fan Plans, Offseason Highlights, a Look at the Lineup and Piniella's Lofty Goal (April 5) - Cubscast 2010 Season Podcasts - Cubscast.com - Hosted By: Lou, Sheps & Sneetch
Alex has returned from Cancun and he has a lot on his mind! In this episode, Alex analyzes the recent Red Sox signings of both Brad Penny and Josh Bard, and if the signing of Bard affects anything regarding Jason Varitek and the Red Sox catching situation. Alex also lays to rest the Hanley Ramirez trade rumors, and assures the listeners that even without Mark Teixeira, the Red Sox are a still a very good baseball team. Alex wraps up the show by going "Around the League," and talks about the Teixeira signing from the Yankees' perspective, and what the signing means for baseball both on and off of the field. In addition to that, Alex mulls over the recent free agent speculation on where Manny Ramirez, Derek Lowe, and Ben Sheets may land.
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