Angélique Perrillat-Mercerot (1), Vani Gangwar (1), Abhishek Bhardwaj (1), Kartikeya Aditya Raman (1), Rahul Sing (1), Bhairav Paleja (1)
(1) Vantage Research Inc 16192, Coastal Highway Lewes DE, 19958
Introduction:
Antibody-drug conjugates (ADCs) represent a significant advancement in oncology therapeutics, offering targeted delivery of cytotoxic drugs to tumor cells. Comprehension and modeling of diverse ADC attributes, including drug pharmacokinetics (PK), dynamics of tumor growth inhibition, properties of payloads and linkers, bystander effect, among others, are essential for informed decision-making in the development of these innovative drugs. However, despite a growing body of clinical trials and in silico investigations within targeted therapeutic domains, there remains a notable gap in understanding the translational process required to apply specific insights from the studies focusing on ADC therapy in one condition for another condition, and subsequently predict the efficacy in patients.
Objectives:
The aim of our approach is to establish an ADC platform in order to address this translational gap. More precisely, the objective of this study is to develop and implement a reliable protocol that transforms an in silico approach tailored for breast cancer, leveraging multi-level data involving Trastuzumab deruxtecan (T-DXd) and the associated expression of human epidermal growth factor receptor 2 (Her2), into predictive in silico results for dose-exposure response in gastric cancer.
Methods:
(i) We selected a published pharmacokinetic-pharmacodynamic (PK-PD) model (1) as a base model. This base model already captured drug disposition at cellular level, accounted for antigen expression, drug-antigen interaction, internalization and intracellular payload disposition. It also has a tumor drug distribution module, a tumor growth inhibition (TGI) module and bystander killing by the drug, as a function of payload property. (ii) We expanded it to a minimal physiologically based pharmacokinetic and quantitative system pharmacology (mPBPK/QSP) model adding a module to describe the systemic distribution of both the ADC and drug and implemented it on Simbiology. (iii) The mPBPK/QSP model has been calibrated using preclinical data (2) and clinical data (3) from breast cancer patients who have received treatment with the approved ADC T-DXd (iv) Based on literature data including antigen expression levels, we translated the context of use of the model to gastric cancer. (v) We evaluated the predictions of our model for patients diagnosed with gastric cancer who have received treatment with T-DXd (4)
Results:
The original model designed for breast cancer – T-DXd effectively characterized the PK of both payload and ADC in plasma and tumor tissues. This enabled the accurate portrayal of the responses witnessed in xenograft mice post-ADC administration. Additionally the model was translated to humans. The resulting virtual clinical trial simulations accurately described the response in terms of progression-free survival for T-DXd in the treatment of metastatic breast cancer, accounting for variations in efficacy based on HER2 expression levels. Finally, the model was translated for use in the context of human gastric cancer, demonstrating accurate predictions of T-DXd efficacy, which align closely with available clinical trial data.
Conclusions:
In conclusion, this approach represents a mPBPK/QSP framework with utility across multiple stages of ADC development. The use case shows capability of this in silico approach to consolidate diverse data sources and knowledge to predict ADC outcomes at clinical stage. This platform holds promise for aiding in selecting appropriate patient populations or indications for ADC therapies.
References:
[1] Singh AP, Seigel GM, Guo L, Verma A, Wong GG, Cheng HP, Shah DK. Evolution of the Systems Pharmacokinetics-Pharmacodynamics Model for Antibody-Drug Conjugates to Characterize Tumor Heterogeneity and In Vivo Bystander Effect. J Pharmacol Exp Ther. 2020 Jul;374(1):184-199. doi: 10.1124/jpet.119.262287.
[2] Ogitani Y, Aida T, Hagihara K, Yamaguchi J, Ishii C, Harada N, Soma M, Okamoto H, Oitate M, Arakawa S, Hirai T, Atsumi R, Nakada T, Hayakawa I, Abe Y, Agatsuma T. DS-8201a, A Novel HER2-Targeting ADC with a Novel DNA Topoisomerase I Inhibitor, Demonstrates a Promising Antitumor Efficacy with Differentiation from T-DM1. Clin Cancer Res. 2016 Oct 15;22(20):5097-5108. doi: 10.1158/1078-0432.CCR-15-2822.
[3] Cortés J, Kim SB, Chung WP, Im SA, Park YH, Hegg R, Kim MH, Tseng LM, Petry V, Chung CF, Iwata H, Hamilton E, Curigliano G, Xu B, Huang CS, Kim JH, Chiu JWY, Pedrini JL, Lee C, Liu Y, Cathcart J, Bako E, Verma S, Hurvitz SA; DESTINY-Breast03 Trial Investigators. Trastuzumab Deruxtecan versus Trastuzumab Emtansine for Breast Cancer. N Engl J Med. 2022 Mar 24;386(12):1143-1154. doi: 10.1056/NEJMoa2115022
[4] Shitara K, Bang YJ, Iwasa S, Sugimoto N, Ryu MH, Sakai D, Chung HC, Kawakami H, Yabusaki H, Lee J, Saito K, Kawaguchi Y, Kamio T, Kojima A, Sugihara M, Yamaguchi K; DESTINY-Gastric01 Investigators. Trastuzumab Deruxtecan in Previously Treated HER2-Positive Gastric Cancer. N Engl J Med. 2020 Jun 18;382(25):2419-2430. doi: 10.1056/NEJMoa2004413
Reference: PAGE 32 (2024) Abstr 11212 [www.page-meeting.org/?abstract=11212]
Poster: Drug/Disease Modelling - Oncology