Raunak Dutta1, Ms Vani Gangwar1, Dr Komalapriya Chandrasekaran1, Dr Abhijeet Singh1, Mr Rahul Sing1, Ms Varsha Srinivasan1, Dr Rukmini Kumar1
1Vantage Research Pvt Ltd
Title: Translational Modeling of ADC Efficacy and Hematological Toxicity: A QSP Approach for FIH Dose Recommendation with T-DM1 as an Exemplar Author: Raunak Dutta, Vani Gangwar, Komalapriya Chandrasekaran, Abhijeet Singh, Rahul Sing, Varsha Srinivasan, Rukmini Kumar Institution: Vantage Research Pvt Ltd, Chennai, India Objectives: Antibody-drug conjugates (ADCs) have revolutionized cancer therapy by enabling the selective delivery of cytotoxic agents to tumor cells. However, balancing efficacy and toxicity remains challenging, with dose-limiting toxicities (DLTs), particularly hematological adverse events, frequently occurring. We have developed a quantitative systems pharmacology (QSP) model approach to recommend optimal first-in-human (FIH) dose, define the therapeutic window, and predict the risk of DLTs based on preclinical efficacy and hematological toxicity data. This model integrates preclinical pharmacokinetics (PK), and pharmacodynamics (PD) for efficacy and thrombocytopenia (TCP) within a QSP framework, using allometric scaling to facilitate clinical translation. By bridging preclinical and clinical findings, this approach can support novel ADC development. Methods: This approach integrates previously reported PK-efficacy [1] and PK-toxicity [2] models for ADCs within a QSP framework to serve as the foundation. To enable translational efficacy and FIH dose predictions, a two-compartment PK model incorporating absorption, distribution, metabolism, and excretion (ADME), target-mediated drug disposition (TMDD), and deconjugation was developed. This PK model was integrated with a PD framework capturing tumor and systemic drug disposition, growth, and inhibition. The PD module accounts for tumor heterogeneity, antigen expression, drug-antigen complex formation, internalization kinetics, and intracellular payload release for the bystander effect. Efficacy was assessed via tumor-growth inhibition (TGI) to characterize the exposure-response relationship. For modeling hematological toxicity, a repurposed model [2], incorporating the chemotherapy-induced myelosuppression framework by Friberg [3], was utilized to describe ADC-induced TCP. This model captures platelet lifecycle dynamics, systemic payload-mediated platelet depletion, and recovery kinetics, utilizing platelet time-course data to predict TCP in humans. The integrated model was calibrated using Trastuzumab emtansine (T-DM1) preclinical PK data from mice [4], rat [5], and monkey [6]. Efficacy and toxicity calibration included mouse TGI [7], and TCP for multiple preclinical species data [6, 8]. The model was then subsequently scaled to predict clinical efficacy, toxicity outcomes, and FIH dosing. Preclinical PK parameters were translated to the human scenario using standard allometric scaling factors and validated against clinical PK data [9, 10, 11]. Efficacy and toxicity parameters were also scaled and simulations were validated using clinical efficacy [9] and TCP [2] data from T-DM1. This validated framework was further utilized to evaluate the clinical therapeutic index, demonstrating its potential to guide novel ADC development and enable preclinical-to-clinical translation. Results: The QSP model was calibrated to preclinical PK, efficacy, and toxicity, enabling a translational approach to predict clinical outcomes for T-DM1. The model accurately described T-DM1 PK across species and aligned well with observed clinical data when scaled allometrically. TGI was predicted across different dose levels, with model outputs consistent with observed response rates at the clinical dosing regimen. Additionally, TCP dynamics were accurately predicted at the clinical dose level, capturing exposure-dependent platelet depletion and recovery patterns observed in clinical data. By integrating efficacy and toxicity predictions, the model enabled consistent FIH dose prediction and therapeutic window estimation, aligning with reported findings from T-DM1 development. Conclusions: This QSP modeling framework provides a holistic approach to capture the tumor regression and drug-related toxicities for predicting the FIH dose and therapeutic window of ADCs based on preclinical data. This approach can be generalized to other ADCs, supporting model-informed development across different candidates. Moreover, a similar framework can be adapted to predict other hematological toxicities, such as neutropenia, further enhancing its applicability in ADC optimization. These findings underscore the value of QSP modeling in guiding ADC development, optimizing dose selection, and minimizing hematological toxicity risks in early clinical development.
1. Singh AP, Seigel GM, Guo L, et al. Evolution of the Systems Pharmacokinetics-Pharmacodynamics model for Antibody-Drug conjugates to characterize tumor heterogeneity and in vivo bystander effect. Journal of Pharmacology and Experimental Therapeutics. 2020;374(1):184-199. doi:10.1124/jpet.119.262287 2. Bender BC, Schaedeli-Stark F, Koch R, et al. A population pharmacokinetic/pharmacodynamic model of thrombocytopenia characterizing the effect of trastuzumab emtansine (T-DM1) on platelet counts in patients with HER2-positive metastatic breast cancer. Cancer Chemotherapy and Pharmacology. 2012;70(4):591-601. doi:10.1007/s00280-012-1934-7 3. Friberg LE, Henningsson A, Maas H, Nguyen L, Karlsson MO. Model of Chemotherapy-Induced myelosuppression with parameter consistency across drugs. Journal of Clinical Oncology. 2002;20(24):4713-4721. doi:10.1200/jco.2002.02.140 4. Erickson HK, Phillips GDL, Leipold DD, et al. The effect of different linkers on target cell catabolism and Pharmacokinetics/Pharmacodynamics of trastuzumab maytansinoid conjugates. Molecular Cancer Therapeutics. 2012;11(5):1133-1142. doi:10.1158/1535-7163.mct-11-0727 5. Jeon EJ, Han JH, Seo Y, et al. Implementation of systematic bioanalysis of Antibody–Drug conjugates for preclinical pharmacokinetic study of Ado-Trastuzumab emtansine (T-DM1) in rats. Pharmaceutics. 2023;15(3):756. doi:10.3390/pharmaceutics15030756 6. Poon KA, Flagella K, Beyer J, et al. Preclinical safety profile of trastuzumab emtansine (T-DM1): Mechanism of action of its cytotoxic component retained with improved tolerability. Toxicology and Applied Pharmacology. 2013;273(2):298-313. doi:10.1016/j.taap.2013.09.003 7. Ogitani Y, Hagihara K, Oitate M, Naito H, Agatsuma T. Bystander killing effect of DS-8201a, a novel anti-human epidermal growth factor receptor 2 antibody–drug conjugate, in tumors with human epidermal growth factor receptor 2 heterogeneity. Cancer Science. 2016;107(7):1039-1046. doi:10.1111/cas.12966 8. Ait-Oudhia S, Zhang W, Mager DE. A Mechanism-Based PK/PD model for hematological toxicities induced by Antibody-Drug conjugates. The AAPS Journal. 2017;19(5):1436-1448. doi:10.1208/s12248-017-0113-5 9. Krop IE, Beeram M, Modi S, et al. Phase I study of Trastuzumab-DM1, an HER2 Antibody-Drug conjugate, given every 3 weeks to patients with HER2-Positive metastatic breast cancer. Journal of Clinical Oncology. 2010;28(16):2698-2704. doi:10.1200/jco.2009.26.2071 10. Ji D, Shen W, Zhang J, et al. A phase I study of pharmacokinetics of trastuzumab emtansine in Chinese patients with locally advanced inoperable or metastatic human epidermal growth factor receptor 2-positive breast cancer who have received prior trastuzumab-based therapy. Medicine. 2020;99(44):e22886. doi:10.1097/md.0000000000022886 11. Yamamoto H, Ando M, Aogi K, et al. Phase I and pharmacokinetic study of trastuzumab emtansine in Japanese patients with HER2-positive metastatic breast cancer. Japanese Journal of Clinical Oncology. 2014;45(1):12-18. doi:10.1093/jjco/hyu160 12. (T-010) A quantitative systems pharmacology model framework for combined clinical efficacy and hematological toxicity predictions for antibody drug conjugates.: ACOP 20 https://acop2024.eventscribe.net/fsPopup.asp?efp=T1ZGU0NGRkwyMjQ4Mw&PosterID=690528&rnd=0.7055475&mode=posterInfo
Reference: PAGE 33 (2025) Abstr 11418 [www.page-meeting.org/?abstract=11418]
Poster: Drug/Disease Modelling - Oncology