Accelerating Drug Discovery with Symbolic-Numerics in SciML
Wellcome Trust
Boston University
Pharmaceutical companies understand the need for modeling stochasticity, by which we can improve the reliability and effectiveness of drugs across the whole population. Our 2020 review in Trends in Pharmacological Sciences, one of the highest impact journals in pharmacology, argued that deterministic models that ignore stochasticity bias clinical and pre-clinical trial analyses. The quantitative systems pharmacology (QSP) groups within drug companies are the core units that model and predict patient outcomes, dramatically reducing the research and development timeline by predicting which drugs will fail before clinical trials begin. These groups have recently adopted stochastic dynamical models in order to better achieve their goals. Discussing with CJ Musante, Global Head of Pharmacology at Pfizer, the main reason that stochastic models are not more widely used is due to a lack of software that addresses the needs of QSP modelers. Stochastic models are more predictive of patient outcomes, but computationally expensive. Moreover, turnaround times needed by QSP teams are too short to make use of stochastic models with current software. This proposal addresses this problem, proposing to build software tooling which automates the construction and solution of stochastic models in a way that is orders of magnitude faster than what exists today.
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