Jason Chang: No financial relationships to disclose
Objectives: Quantitative Systems Pharmacology (QSP) models are increasingly employed in clinical development to enhance the understanding of drug effects and patient variability 1 . A significant challenge when simulating clinical trials is accurately representing patient variability, especially for novel therapies or new patient populations where biological and pharmacological parameter distributions are not well understood. Digital twins offer a promising solution by creating model parameterizations that replicate the physiological and pathological characteristics of each individual patient, allowing for the evaluation of clinical outcomes with limited data2-4. This approach has been successfully applied in the Phase 1 development of T-cell engaging bispecific (TCB) antibodies to characterize clinical dose-response and propose predictive biomarkers2, as well as in TCR-engineered cell therapy to predict T-cell kinetics and explore the role of Tscm biology3.
Methods: We have developed the MATLAB-based gQSPTwin toolbox to automate the digital twin generation workflow, streamlining the process for more efficient use of QSP models during clinical trials. It facilitates the creation of candidate digital twins for each patient, simulates and selects a diverse set of patient-specific digital twins, and provides diagnostic visualizations throughout the workflow.
Results: The gQSPTwin workflow includes key steps such as preparing input files, generating candidate digital twins, visualizing the generation process, simulating digital twins, selecting top digital twins, and visualizing selections via a diagnostic dashboard, culminating in an output log file. We demonstrate the application of the gQSPTwin toolbox through two case studies: TCR-engineered cell therapy and TCB.
Citations: 1. Bai JPF, Liu G, Zhao M, Wang J, Xiong Y, Truong T, Earp JC, Yang Y, Liu J, Zhu H, Burckart GJ. Landscape of regulatory quantitative systems pharmacology submissions to the U.S. Food and Drug Administration: An update report. CPT Pharmacometrics Syst Pharmacol. 2024 Dec;13(12):2102-2110. doi: 10.1002/psp4.13208. Epub 2024 Oct 18. PMID: 39423143; PMCID: PMC11646928. 2. Susilo ME, Li CC, Gadkar K, Hernandez G, Huw LY, Jin JY, Yin S, Wei MC, Ramanujan S, Hosseini I. 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. Clin Transl Sci. 2023 Jul;16(7):1134-1148. doi: 10.1111/cts.13501. Epub 2023 Mar 23. PMID: 36908269; PMCID: PMC10339700. 3. Joslyn LR, Huang W, Miles D, Hosseini I, Ramanujan S. "Digital twins elucidate critical role of Tscm in clinical persistence of TCR-engineered cell therapy". NPJ Syst Biol Appl. 2024 Jan 26;10(1):11. doi: 10.1038/s41540-024-00335-7. PMID: 38278838; PMCID: PMC10817974. 4. Kaddi C, Tao M, Bergeler S, George K, Geerts H, van der Graaf PH, Batista JL, Foster M, Ortemann-Renon C, Zaher A, An Haack K, Zaph S. Quantitative Systems Pharmacology-Based Digital Twins Approach Supplements Clinical Trial Data for Enzyme Replacement Therapies in Pompe Disease. Clin Pharmacol Ther. 2025 Feb;117(2):579-588. doi: 10.1002/cpt.3498. Epub 2024 Dec 4. PMID: 39632463.
