How startups can fight "Eroom's Law": interview with Jack Scannell скачать в хорошем качестве

How startups can fight "Eroom's Law": interview with Jack Scannell

Eroom's Law ("Moore's Law" backwards) states that the cost of developing a new drug has increased exponentially in the last several decades -- despite despite dramatic improvements in technology like DNA sequencing, high-throughput screening and combinatorial chemistry. In the last few years, it seems like we've reversed this trend -- drug development is getting cheaper. To understand how, I talked to Jack Scannell, who coined the term in a landmark 2012 paper: https://www.nature.com/articles/nrd3681 Jack is currently researching how we can improve the efficiency of drug development by developing models with more "predictive validity": ie that better predict what a drug will do in humans. Jack's "predictive validity" reading list: Eroom’s Law Scannell JW, Blanckley A, Boldon H, Warrington B. (2012) Diagnosing the decline in pharmaceutical R&D efficiency. Nature Reviews Drug Discovery https://www.nature.com/articles/nrd3681 Ringel M, Scannell JW, Baedeker M, Shulze U (2020) Breaking Eroom’s Law. Nature Reviews Drug Discovery https://www.nature.com/articles/d4157... What predictive is and why it matters Scannell, J. W., & Bosley, J. (2016). When quality beats quantity: Decision theory, drug discovery, and the reproducibility crisis. PLoS ONE, 11(2) https://doi.org/10.1371/journal.pone.... Some clear thinking on models Sams-Dodd, F. (2006). Strategies to optimize the validity of disease models in the drug discovery process. Drug Discovery Today https://doi.org/10.1016/j.drudis.2006... Why conventional hypothesis testing often fails and how to do better Chuang-Stein, C., Kirby, S., French, J., Kowalski, K., Marshall, S., Smith, M. K., … Beltangady, M. (2011). A Quantitative Approach for Making Go/No-Go Decisions in Drug Development. Therapeutic Innovation & Regulatory Science https://doi.org/10.1177/0092861511045... Colquhoun, D. (2014). An investigation of the false discovery rate and the misinterpretation of p-values. Royal Society Open Science https://doi.org/10.1098/rsos.140216 Towards model evaluation Ferreira, G. S., Veening-Griffioen, D. H., Boon, W. P. C., Moors, E. H. M., De Wied, C. C. G., Schellekens, H., & Van Meer, P. J. K. (2019). A standardised framework to identify optimal animal models for efficacy assessment in drug development. PLoS ONE https://doi.org/10.1371/journal.pone.... Baudy, A. R., Otieno, M. A., Hewitt, P., Gan, J., Roth, A., Keller, D., … Proctor, W. R. (2020). Liver microphysiological systems development guidelines for safety risk assessment in the pharmaceutical industry. Lab on a Chip https://doi.org/10.1039/c9lc00768g And lots of great resources from the CAMERADES collaboration, particularly around statistical and experimental hygiene http://www.dcn.ed.ac.uk/camarades/ Inspiration from other disciplines, in terms of the “philosophy” of model evaluation and methods for eliciting and using subjective judgements from experts How to measure and communicate uncertainty in scientific results: Funtowicz, S. O., & Ravetz, J. R. (1990). Uncertainty and Quality in Science for Policy https://doi.org/10.1007/978-94-009-06... Turning experts’ subjective opinions into rating scales (one example of a variety of methods): Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9–26 https://doi.org/10.1016/0377-2217(90)...