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Mosunetuzumab, a T-cell dependent bispecific antibody that binds CD3 and CD20 to drive T-cell mediated B-cell killing, is currently being tested in non-Hodgkin lymphoma. However, potent immune stimulation with T-cell directed therapies poses the risk of cytokine release syndrome, potentially limiting dose and utility. To understand mechanisms behind safety and efficacy and explore safety mitigation strategies, we developed a novel mechanistic model of immune and antitumor responses to the T-cell bispecifics (mosunetuzumab and blinatumomab), including the dynamics of Band T-lymphocytes in circulation, lymphoid tissues, and tumor. The model was developed and validated using mosunetuzumab nonclinical and blinatumomab clinical data. Simulations delineated mechanisms contributing to observed cell and cytokine (IL6) dynamics and predicted that initial step-fractionated dosing limits systemic T-cell activation and cytokine release without compromising tumor response. These results supported a change to a step-fractionated treatment schedule of mosunetuzumab in the ongoing Phase I clinical trial, enabling safer administration of higher doses.

The human APOBEC3G is an innate restriction factor that, in the absence of Vif, restricts HIV-1 replication by inducing excessive deamination of cytidine residues in nascent reverse transcripts and inhibiting reverse transcription and integration. To shed light on impact of A3G-Vif interactions on HIV replication, we developed a multi-scale computational system consisting of intracellular (single-cell), cellular and extracellular (multicellular) events by using ordinary differential equations. The single-cell model describes molecular-level events within individual cells (such as production and degradation of host and viral proteins, and assembly and release of new virions), whereas the multicellular model describes the viral dynamics and multiple cycles of infection within a population of cells. We estimated the model parameters either directly from previously published experimental data or by running simulations to find the optimum values. We validated our integrated model by reproducing the results of in vitro T cell culture experiments. Crucially, both downstream effects of A3G (hypermutation and reduction of viral burst size) were necessary to replicate the experimental results in silico. We also used the model to study anti-HIV capability of several possible therapeutic strategies including: an antibody to Vif; upregulation of A3G; and mutated forms of A3G. According to our simulations, A3G with a mutated Vif binding site is predicted to be significantly more effective than other molecules at the same dose. Ultimately, we performed sensitivity analysis to identify important model parameters. The results showed that the timing of particle formation and virus release had the highest impacts on HIV replication. The model also predicted that the degradation of A3G by Vif is not a crucial step in HIV pathogenesis.

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

Modeling and simulation (M&S) is increasingly used in drug development to characterize pharmacokinetic-pharmacodynamic (PKPD) relationships and support various efforts such as target feasibility assessment, molecule selection, human PK projection, and preclinical and clinical dose and schedule determination. While model development typically require mathematical modeling expertise, model exploration and simulations could in many cases be performed by scientists in various disciplines to support the design, analysis and interpretation of experimental studies. To this end, we have developed a versatile graphical user interface (GUI) application to enable easy use of any model constructed in SimBiology® to execute various common PKPD analyses. The MATLAB®-based GUI application, called gPKPDSim, has a single screen interface and provides functionalities including simulation, data fitting (parameter estimation), population simulation (exploring the impact of parameter variability on the outputs of interest), and non-compartmental PK analysis. Further, gPKPDSim is a user-friendly tool with capabilities including interactive visualization, exporting of results and generation of presentation-ready figures. gPKPDSim was designed primarily for use in preclinical and translational drug development, although broader applications exist. gPKPDSim is a MATLAB®-based open-source application and is publicly available to download from MATLAB® Central™. We illustrate the use and features of gPKPDSim using multiple PKPD models to demonstrate the wide applications of this tool in pharmaceutical sciences. Overall, gPKPDSim provides an integrated, multi-purpose user-friendly GUI application to enable efficient use of PKPD models by scientists from various disciplines, regardless of their modeling expertise.Electronic supplementary materialThe online version of this article (10.1007/s10928-017-9562-9) contains supplementary material, which is available to authorized users.