Assessing Parameter Impacts on ADC Efficacy and Toxicity: Capabilities and Limitations of Sensitivity Analysis in an Innovative Full Physiologically Based Pharmacokinetic Pharmacodynamic Framework

Objectives: We developed a physiologically based pharmacokinetic pharmacodynamic (PBPK-PD) model to simulate direct and bystander cytotoxic effects of antibody drug conjugates (ADCs) in the tumor microenvironment (TME) and assess their toxicological profiles in various tissues. Local sensitivity analysis (LSA) and global sensitivity analysis (GSA) explored how various parameters impact ADC efficacy and toxicity, with a focus on GSA's effectiveness in identifying non-monotonous parameter influences.Methods: We created an ADC PBPK-PD model by integrating the antibody (Ab) PBPK model [1] and payload (PL) PBPK model [2], updating the model in [3] with PL lymphatic flow. The platform simulates ADC diffusion in the TME by full utilization of the Krogh cylinder [4] and combines existing models [5, 6] for ADC disposition in the TME. LSA and GSA assessed the impact of various parameters on ADC efficacy and toxicity.Results: The results show the model simulates direct cytotoxic and bystander effects vital for ADC efficacy, as well as ADC, Ab, and PL concentrations in tissues for toxicity assessment. LSA reveals design parameters like drug-to-antibody ratio (DAR) affecting efficacy and toxicity linearly, and others like tumor antigen expression and ADC binding affinity impacting non-linearly linked to the binding site barrier (bsb) phenomenon in TME. GSA displays parameters' effects within their linear range and identifies challenges in detecting non-linear relationships.Conclusions: The PBPK-PD model captures whole-body ADC therapy dynamics, stressing the importance of direct and bystander effects in ADC design. LSA shows the model's robustness and predictive ability for ADC safety and efficacy. The study also highlights GSA's limitations, emphasizing the need to consider non-linear parameter effects on ADC outcomes. This modeling work can help refine ADC therapy by identifying the key ADC design features for optimal balance between effectiveness and toxicity for better patient outcomes.Citations: 1. Shah, D.K. and A.M. Betts, Towards a platform PBPK model to characterize the plasma and tissue disposition of monoclonal antibodies in preclinical species and human. J Pharmacokinet Pharmacodyn, 2012. 39(1): p. 67-86.2. Chang, H.P., Y.K. Cheung, and D.K. Shah, Whole-Body Pharmacokinetics and Physiologically Based Pharmacokinetic Model for Monomethyl Auristatin E (MMAE). J Clin Med, 2021. 10(6).3. Chang, H.P., Z. Li, and D.K. Shah, Development of a Physiologically-Based Pharmacokinetic Model for Whole-Body Disposition of MMAE Containing Antibody-Drug Conjugate in Mice. Pharm Res, 2022. 39(1): p. 1-24.4. Weddell, J., et al., Mechanistic Modeling of Intra-Tumor Spatial Distribution of Antibody-Drug Conjugates: Insights into Dosing Strategies in Oncology. Clin Transl Sci, 2021. 14(1): p. 395-404.5. Singh, A.P., S. Sharma, and D.K. Shah, Quantitative characterization of in vitro bystander effect of antibody-drug conjugates. J Pharmacokinet Pharmacodyn, 2016. 43(6): p. 567-582.6. Scheuher, B., et al., Towards a platform quantitative systems pharmacology (QSP) model for preclinical to clinical translation of antibody drug conjugates (ADCs). J Pharmacokinet Pharmacodyn, 2023.