Nova's approach to Model-Informed Drug Development

Jules Henri Poincaré (French mathematician, 1854-1912)

Science is facts; just as houses are made of stone, so is science made of facts; but a pile of stones is not a house, and a collection of facts is not necessarily science.

Foreword

This series describes how Nova approaches modeling and simulation for drug discovery and development. It is not a textbook and not a set of recipes for Modeling and Simulation (M&S) or Model-Informed Drug Development (MIDD): it explains an approach rather than a catalogue of methods. The approach looks complicated at first, because the biology is, but it is a systematic way to reach precise, accurate, and meaningful results. Explaining it draws on history, epistemology, reasoning, pharmacology, drug development, and data management, so those threads are presented together rather than one at a time. This is not a scientific paper or a review either, and the bibliography cites a handful of articles rather than covering the literature of the fields it touches.

Nova's approach

Nova starts from a single premise: we do not know what we know. Socrates said "All I know is that I don't know anything." Twenty-four centuries later the problem has inverted. So much knowledge has accumulated that the gap between what we have learned and what remains to be learned is no longer the binding constraint. The constraint is that no human brain can hold the knowledge already available, even within one narrow field[1]

Contents

Part 1. Introductory remarks

Part 2. Reconciling knowledge with data to predict clinical outcomes

Part 3. Nova's modeling and simulation approach

Part 4. Responders: the M&S perspective

Part 5. Examples of Effect Model applications

Part 6. Are M&S outputs real?

Part 7. What Nova's approach can be used for

Part 8. Expected consequences on R&D and the future of MIDD

Part 9. Conclusions

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1. Boissel JP, Amsallem E, Cucherat M, Nony P, Haugh MC. Bridging the gap between therapeutic research results and physician prescribing decisions: knowledge transfer, a prerequisite to knowledge translation. Eur J Clin Pharmacol. 2004;60:609-16 ↩︎