Virtual clinical trial simulations using a Quantitative Systems Pharmacology (QSP) model of CDK inhibitors in breast cancer patients
Authors: Trogdon Michael , Braniff Nate , VanArsdale Todd , Shtylla Blerta 
Published: February 2025

Objectives: Breast cancer (BC) is the most commonly diagnosed cancer worldwide[1] and a major area of emphasis for the development of novel treatments. Cyclin Dependent Kinase 4/6 (CDK4/6) inhibitors, in combination with Endocrine Therapy (ET), are a standard of care for ER+/HER2- metastatic BC[2]. Palbociclib was the first CDK4/6i approved in 2015 based on the PALOMA series of clinical trials[3]. In this work, we present a Quantitative Systems Pharmacology (QSP) platform model to support the development of next-generation (NG) CDK inhibitors.Methods: A QSP model was developed with three core components: 1) a mechanistic model of the CDK protein signaling pathway and drug targeting, 2) a model of tumor growth, and 3) connection to clinical efficacy endpoints such as Objective Response Rate (ORR) and Progression Free Survival (PFS). This model integrates both preclinical and clinical data. Specifically, the model was first parameterized with biophysical kinetic binding data for drug-protein and protein-protein binding reactions, in vitro proliferation assays in ER+/HER2- BC cell lines and in vivo xenograft tumor growth inhibition (TGI) data. Next, a virtual population was developed to match the clinical efficacy endpoints for both arms of the Phase 3 PALOMA-3[4] trial by varying key model parameters associated with known or suspected mechanisms of resistance to CDK4/6i[5]. To develop this virtual population, we took a data-driven approach to handle censoring and progression risks beyond target lesion growth such as the development of new lesions.Results: The virtual clinical trial simulation successfully matches the clinical efficacy endpoints of ORR and PFS for both arms of the PALOMA-3 clinical trial. The QSP platform model connects a mechanistic model of CDK protein signaling and inhibition to clinical efficacy. The parameters that are significant in differentiating virtual responders from non-responders align well with previously identified mechanisms of clinical resistance such as CCNE1 expression[6].Conclusions: QSP models can be leveraged to quantitatively explore mechanistic hypotheses around drug mechanism of action and enable systematic extrapolation of optimal dose and regimen for novel therapies. These models also provide an in-silico hypothesis testing framework that integrates preclinical and clinical data sources. The QSP platform model presented here can be used to support the development of next-generation compounds targeting the CDK signaling pathway in metastatic breast cancer including the projection of efficacy in clinically relevant patient populations and dose optimization.Citations: [1] Sung et al., CA Cancer J Clin., 2021) https://doi.org/10.3322/caac.21660[2] (Loibl et al., Lancet, 2021) https://doi.org/10.1016/S0140-6736(20)32381-3[3] (Zhu et al., npj Prec. Onc., 2022) https://doi.org/10.1038/s41698-022-00297-1 [4] (Turner et al., NEJM, 2015) DOI: 10.1056/NEJMoa1505270[5] (Asghar et al., JCO Prec. Onc., 2022) DOI: 10.1200/PO.21.00002[6] (Turner et al., JCO, 2019) DOI: 10.1200/JCO.18.00925