Prediction of in Vivo Efficacy for Cell Cycle Inhibitors Using a Multiscale Tumor Growth Model

Cherry Auditorium, Kirk Hall

Model-based drug development (MBDD) has been recognized as a new paradigm and mindset that will change the landscape of drug development. As a part of the wide Quantitative Pharmacology (QP) initiative within Lilly, we developed this cell-cycle based, multi-scale tumor growth model to predict In Vivo efficacy for Lilly cell cycle inhibitors. Cell cycle is a key area of focus in the Lilly oncology portfolio, reflecting essential mechanisms underlying tumor growth. We integrated diverse datasets such as pharmacokinetic data, tumor growth data, in vitro/invo flow cytometry data, and a variety of cell cycle biomarkers such as pTCTPs46, pHH3s10(mitotic marker), TUNEL(apoptotic marker), caspase3(apoptotic marker), phospho-S6, and Ki-67(proliferation marker). Such a model framework provides a platform to simulate experiments and clinical trials, rationalize experimental design, enable better dosing optimization and hypothesis generation. Most importantly, it puts biomarker discovery and dosing regimen optimization on a mechanistic basis. Specifically, we aim to the following goals: 1) Predict dynamic dosing effects: By integrating cell cycle biology into the commonly used PK/PD model, we may be able to predict the effects of varying the dosage frequency on disease progression, thus suggesting optimal dosing regimen based on their mechanisms of action. 2). Explain variability in terms of mechanism: Linking cell cycle biomarker measurements with efficacy may allow us mechanistically understand the patient variability in response, leading to opportunities for tailored therapy. 3) Explore combination therapies. This platform is also applicable for optimal combination therapies for distinct compounds based on their mechanisms of action. In summary, we demonstrated a solid example how systems biology could be applied into traditional discovery biology and PK/PD modeling.