Using Machine Learning Surrogate Modeling for Faster QSP VP-Cohort Generation

Virtual patients (VPs) are widely used within QSP modeling to explore the impact of variability and uncertainty on clinical response. In one method of generating VPs, parameters are sampled from a distribution, protocols are simulated, and the possible VP is either accepted or rejected based on constraints on model output behavior, such as achieving reasonable responses to clinical protocols. The approach works but can be inefficient, i.e., the vast majority of model runs typically do not result in valid VPs.Machine learning (ML) surrogate models offer an opportunity to greatly improve the efficiency of VP creation. Surrogate models are trained using the full QSP model to discriminate between parameter combinations that result in feasible VPs vs. those that do not. Once the surrogate models are developed, parameter combinations can be pre-screened rapidly, and the overwhelming majority of pre-vetted combinations result in valid VPs when tested in the original QSP model.In this webinar, Rosa and MathWorks will present this novel workflow and give a case study example using a psoriasis disease QSP model from the Rosa PhysioPD™ practice and the MATLAB® Regression Learner app to select and optimize the surrogate models. The VPs generated by the surrogate modeling approach are statistically similar to VPs generated using only the original QSP model. We conclude with comparisons of the relative efficiency of the methods, and ideas for expansion of the use of this and other ML methods in QSP modeling.