#107

Описание: In this podcast, Christian Soschner interviews Jack Scannell on the topic of drug development efficiency. Scannell begins by sharing his background and how he discovered erooM's Law, which discusses the decline in drug development productivity. He identifies factors that led to this decline, such as the better-than-the-Beatles problem, higher regulatory hurdles, and the misindustrialisation of science. Scannell then discusses how drug development efficiency has improved since 2010 and shares tips on speeding up early-stage research and development.

He also talks about the technical and managerial components required to improve the speed of drug development and shares a case study on how to improve the predictive quality of models. He discusses the interplay of different forces in complex problem-solving and emphasizes how model quality is crucial to ensuring high R&D quality.

Scannell also talks about making the pharma value chain more effective, the appropriate success measure for drug R&D, and the role of artificial intelligence in the drug discovery process. He explains the limiting factors of AI in biology and the availability of capital in drug development. Scannell concludes the podcast by discussing the economics of the research model and the common goods problem and the role of public funds in validating R&D model quality. He emphasizes the importance of truth-seeking over progression-seeking behavior and prioritizing scientific integrity in drug development.

The podcast includes memorable quotes and timestamps, as well as links to relevant resources, including papers on the prospects of artificial intelligence in drug R&D and the AstraZeneca article on truth-seeking versus progression-seeking behavior.

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📖 Memorable quotes:

(11:41) “Between 1950 and 2010, Drug Development saw a halving time: Every nine years, the number of drugs developed per billion dollars spent halved.”
(24:58) “4 Reasons for the Decline of Drug Development Productivity: Better Than the Beatles Problem, Higher Regulatory Hurdles, Throw Money at it Problem, Misindustrialisation of Science
(26:30) “Metformin is the Pharmaceutical Equivalent to the Beatles”
(45:15) “In early drug discovery, predictive validity, the degree to which your model output correlates with human clinical utility is critically important”
(01:19:31) “If the only tool you have to measure the height of the fruit is the rate at which you are picking fruit, and you notice that the rate of picking fruit is declining, you will always blame the low hanging fruit problem.”
(01:34:15) “When capital is freely available, the average quality is going down”

⏰ Timestamps:

(00:00) Introduction: Uncovering Drug Development Efficiency Secrets
(06:30) Background Jack Scannell: Pharma Journey
(09:15) Finding erooM’s Law: Playful Experiment Leads to Discovery
(12:00) Inversion of Output Efficiency: More Capital, Fewer Drugs
(13:15) Best Key Figure for R&D Productivity?
(20:24) Factors Behind R&D Productivity Decline - 1950 to 2010
(25:30) The Beatles Inform Drug Development
(28:34) The Regulatory Problem
(30:00) Throwing Money at the Problem & Misindustrialisation of Science
(31:00) Turnaround in Drug Development Efficiency from 2010
(38:30) Speeding Up Early-Stage R&D
(45:45) Technical & Managerial Components to Improve Speed
(47:10) Improving Predictive Quality of Models - Alzheimer's Case Study
(57:00) Interplay of Different Forces in Complex Problem Solving
(01:02:30) Model Quality Matters the Most for High R&D Quality
(01:12:45) Making Pharma Value Chain More Effective
(01:18:00) Appropriate Success Measure for Drug R&D?
(01:21:05) Role of AI in Drug Discovery Process
(01:29:32) Limiting Factor of AI in Biology?
(01:31:00) Availability of Capital a Limiting Factor in Drug Development?
(01:39:00) Economics of Research Model - Common Goods Problem
(01:47:00) Role of Public Funds to Validate R&D Model Quality
(01:52:00) Truth Seeking Over Progression Seeking Behaviour: Prioritizing Scientific Integrity in Drug Development.