Mechanistic Pharmacodynamic Modeling for Drug Combination Responses

ABSTRACT – MECHANISTIC PHARMACODYNAMIC MODELING FOR DRUG COMBINATIONS Most industries simulate design options before implementation, but this is rarely possible in the pharmaceutical and medical industries. An important gap is unbiased drug combination response predictions, which is experimentally impractical. A long-term vision of our lab is improving drug development and precision medicine by building “mechanistic pharmacodynamic models” that can simulate drug combination responses. Such models infuse pharmacology concepts with physics and engineering approaches to describe causal, quantitative, and dynamic mechanisms underlying drug response. A foundational premise is that capturing (i) mechanistic, causal network structure, (ii) dose-response, (iii) dynamics, and (iv) cell-cell variability is necessary to improve many combination response predictions. Here, we study how drug combinations affect single-cell proliferation and death fates by merging theoretical and experimental innovation. The first project builds on our recent and one of the most comprehensive mechanistic models for regulation of single-cell proliferation and death dynamics. We will leverage our involvement with a recent LINCS consortium effort that generated a deep molecular characterization of perturbation response dynamics, including dose responses to 8 drugs. We will integrate network biology with mechanistic models using new approaches to obtain candidate models that are consistent with this dataset, and experimentally test drug combination predictions for the 8 drugs. This will for the first time address the prediction of a comprehensive set of drug combination responses across varied mechanisms of action relying on causal biochemical reasoning and also identify novel mechanisms of signaling and drug response through iterative model refinement and experimental validation. The second project builds on our recently developed experimental approach for fluorescence multiplexing called MuSIC. We propose that MuSIC can enable high-dimensional genetic interaction screening in single mammalian cells, which is not yet possible but would be transformative. We will test the approach by evaluating genetic interactions between a recently curated set of 667 gene targets of 1,578 FDA-approved drugs. This work will nominate new network structures not only for use in the first project, but also more generally. The third project also leverages the above mechanistic model but pivots across cell lines with Cancer Cell Line Encyclopedia data for 1,132 cell lines and 24 drugs. An innovative and foundational feature of our model is that it ingests multi-omic data to create a cell line-specific context through “initialization”. We will generate 1,132 model variants with cell line-specific profiles and evaluate predictive capacity for single and prioritized drug combination responses. This project will establish performance of the current models, identify critical modeling gaps for improving predictions, suggest new potentially effective drug combinations, and elucidate mechanisms underlying synergy. Overall, these projects will produce next-generation pharmacodynamic models that move towards filling the drug combination prediction gap that hinders drug development and precision medicine.