Phase I oncology clinical trials often comprise a limited number of patients representing different disease subtypes who are divided into cohorts receiving treatment(s) at different dosing levels and schedules. Here, we leverage a previously developed quantitative systems pharmacology model of the anti-CD20/CD3 T-cell engaging bispecific antibody, mosunetuzumab, to account for different dosing regimens and patient heterogeneity in the phase I study to inform clinical dose/exposure-response relationships and to identify biological determinants of clinical response. We developed a novel workflow to generate digital twins for each patient, which together form a virtual population (VPOP) that represented variability in biological, pharmacological, and tumor-related parameters from the phase I trial. Simulations based on the VPOP predict that an increase in mosunetuzumab exposure increases the proportion of digital twins with at least a 50% reduction in tumor size by day 42. Simulations also predict a left-shift of the exposure-response in patients diagnosed with indolent compared to aggressive non-Hodgkin's lymphoma (NHL) subtype; this increased sensitivity in indolent NHL was attributed to the lower inferred values of tumor proliferation rate and baseline T-cell infiltration in the corresponding digital twins. Notably, the inferred digital twin parameters from clinical responders and nonresponders show that the potential biological difference that can influence response include tumor parameters (tumor size, proliferation rate, and baseline T-cell infiltration) and parameters defining the effect of mosunetuzumab on T-cell activation and B-cell killing. Finally, the model simulations suggest intratumor expansion of | 1135

Quantitative systems pharmacology (QSP) models are often implemented using a wide variety of technical workflows and methodologies. To facilitate reproducibility, transparency, portability, and reuse for QSP models, we have developed gQSPSim, a graphical user interface–based MATLAB application that performs key steps in QSP model development and analyses. The capabilities of gQSPSim include (i) model calibration using global and local optimization methods, (ii) development of virtual subjects to explore variability and uncertainty in the represented biology, and (iii) simulations of virtual populations for different interventions. gQSPSim works with SimBiology‐built models using components such as species, doses, variants, and rules. All functionalities are equipped with an interactive visualization interface and the ability to generate presentation‐ready figures. In addition, standardized gQSPSim sessions can be shared and saved for future extension and reuse. In this work, we demonstrate gQSPSim’s capabilities with a standard target‐mediated drug disposition model and a published model of anti‐proprotein convertase subtilisin/kexin type 9 (PCSK9) treatment of hypercholesterolemia.