Systems‐based digital twins to help characterize clinical dose–response and propose predictive biomarkers in a Phase I study of bispecific antibody, mosunetuzumab, in NHL

Abstract

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

Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development

“…Continued innovation in QSP virtual trial simulation strategies, as well as more recent efforts using new QSP digital twins workflows has enabled systematic exploration and mapping of biological heterogeneity onto treatment response heterogeneity. 55,55,[60][61][62][63][64][65] QSP modeling has become an important tool to address questions concerning target selection/validation, human efficacious dose projection, rational combination strategies, patient stratification, and response variability. 60 Continued use of QSP to support dose optimization after the start of FIH studies has been enabled from rapid calibration of models with emerging FIH clinical data and methodological improvements enabling quantitative endto-end integration from target engagement all the way to key clinical end points of therapeutic response, such as ORR and progression-free survival.…”

“…Here, we are using the term QSP to more broadly represent both tPK/PD or QSP models. QSP models have been increasingly applied to mechanistically inform drug development and guide regulatory decisions, especially in supporting dose selection in early clinical trials (e.g., first‐in‐human [FIH]) 55,56 . The bottom‐up modeling approach of QSP is particularly valuable in oncology, where early clinical trials often lack predictive biomarkers and indication‐specific efficacy data, and translation of E–R from preclinical in vivo models is fraught with challenges, particularly for immuno‐oncology compounds and modalities 57 .…”

“…This model was validated using cell line data and used to identify an optimal combination dosing schedule used to support the design for a dose‐escalation phase I clinical trial in patients with advanced EGFR mutant lung cancer for study NCT03810807. Continued innovation in QSP virtual trial simulation strategies, as well as more recent efforts using new QSP digital twins workflows has enabled systematic exploration and mapping of biological heterogeneity onto treatment response heterogeneity 55,55,60–65 …”

“…Clinical data generated from NCT02500407 has since confirmed the CRS mitigation of the QSP model‐informed step‐up dosing regimen. Second, QSP simulated digital twins calibrated with phase I data from NCT02500407 were used to support the characterization of dose/E–R relationships and subsequent dose expansion 55 . Clinically calibrated QSP models, as illustrated here, can help explore through incorporation of totality of clinical and preclinical data across mechanistic sources of patient heterogeneity especially when clinical data are limited, as in the case for mosunetuzumab and other phase I studies, and when different dosing regimens are used across cohorts (fixed and step‐up dosing administration of mosunetuzumab).…”

Moving the Needle for Oncology Dose Optimization: A Call for Action

“…Furthermore, the model mechanistically links clinical response rates and relapse or resistance to ADC therapies, which could facilitate dose optimization. In another recent example, Susilo et al 19 leveraged a quantitative systems pharmacology (QSP) model of an anti-CD20/CD3 T-cell engaging bispecific antibody, mosunetuzumab, to account for different dosing regimens and interpatient heterogeneity in the phase I study to identify biological determinants of clinical response and dose/ exposure-response relationships using a novel QSP-derived digital twins approach. Approaches of this nature raise opportunities for multidimensional optimization across the dimensions of dose, patient population, and combination partner-a challenge faced routinely in oncology drug development.…”