The journey to the impactful application of mathematics in drug discovery
Van Leeuwenhoek Lecture on BioScience.
Neil Benson was educated at the University of East Anglia and the University of Lund, he has a PhD in enzymology.
Neil has 20 years experience of working in modelling and simulation in the pharmaceutical industry (SmithKline Beecham and Pfizer).During this time, he held a number of senior leadership positions, most recently as head of Systems Pharmacology at Pfizer, Sandwich. He was awarded the Pfizer Upjohn Award for innovation in developing dose prediction methodology.
He has extensive experience of using modelling and simulation to address questions of critical importance in drug discovery including clinical dose prediction, optimal target identification and biomarker selection.
From 2011-2015 he was Founder and Director at Xenologiq Ltd, a consultance company focused on the productive application of PKPD and Systems Pharmacology in drug discovery.
At present he serves as the head of Quantitative Systems Pharmacology Operations at Certara (the global biosimulation technology-enabled drug development company).
Despite a decade of analysis, technological advances and changes in the industry, Phase II attrition continues to be the most important and apparently intractable challenge facing drug discovery. (1-3). Arguably, the origin of attrition is the limited understanding of biological complexity and hence the impact of perturbing with a drug. Thus, developing methods to improve our understanding of biological complexity must be at the heart of any solution. To this end, mathematical methods are increasingly being used in drug discovery to enquire into biological systems, with a view to understanding the behavior in a more holistic way. This has taken the form of "omics" approaches, typically characterized by the empirical analyses of large datasets, pharmacokinetic-pharmacodynamic (PKPD) modelling and latterly ordinary differential equation (ODE) based approaches to systems biology and systems pharmacology. This lecture will discuss the author's personal experience in the application of mathematical methods in drug discovery over two decades, review its impact and explore future directions.
1. Kola, L., Landis, J.: Can the pharmaceutical industry reduce attrition rates? Drug Discovery 3 (8), 711-715 (2004)
2. Paul, S.M., Mytelka, D.S., Dunwiddle, C.T. et al: How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nature Reviews, Drug Discovery 9 (3), 203-214 (2010)
3. Hoy, M., Thomas, D.W., Craighead, J.L., Economides, C., Rosenthal, J.: Clinical development success rates for investigational drugs. Nature Biotechnology 32 (1), 40-51 (2014).
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