A Step-by-Step Workflow for Performing In Silico Clinical Trials With Nonlinear Mixed Effects Models

In silico clinical trials (ISCT) are computational frameworks that employ mathematical models to generate virtual patients and simulate their responses to new treatments, treatment regimens, or medical devices via simulations mirroring real-world clinical trials. ISCTs are an important component of the model-informed drug development (MIDD) framework for optimizing therapies, treatment personalization, informing regulatory decisions, and accelerating overall drug development by enhancing R&D productivity. However, the emergence of complex models, such as quantitative systems pharmacology (QSP) models, presents significant challenges for their effective implementation. Guidelines for conducting ISCTs have been published to address these challenges, focusing on algorithms and credibility frameworks for generating plausible virtual patients and calibrating virtual populations. However, it is not straightforward to apply existing workflows to models where parameter distributions and correlations are estimated using nonlinear mixed effects (NLME) population fitting approaches, a common practice in the pharmaceutical industry when individual-patient-level data is available. Here, we illustrate a modeling workflow for conducting ISCTs with NLME models, detailing key considerations, methods, and challenges at each step. We demonstrate the practical implementation of this workflow through two examples to showcase its broad applicability: (1) a simple model predicting tumor growth in response to chemotherapy and (2) a more complex mechanistic QSP model of hepatitis B virus infection that captures the physiological mechanisms underlying treatment response with standard-of-care therapies.