PhD Candidate Johns Hopkins University Baltimore, Maryland, United States
Background: Antibody-Drug Conjugates (ADCs) consist of a monoclonal antibody (mAb) attached to cytotoxic small molecules (warheads) via chemical linkers. The mAb targets a tumor antigen, so the warhead can kill tumor cells while sparing healthy tissue. However, despite hundreds of clinical trials, only 12 ADCs have been approved for use by the FDA as cancer treatments. Quantitative systems pharmacology (QSP) models go beyond traditional PK/PD models to integrate known mechanisms with data at multiple scales, supporting the development of improved ADCs and cancer therapies. Methods: We have expanded upon our QSP model of cellular and intracellular mechanisms specific to ADCs with pyrrolobenzodiazepine (PBD) warheads, developing a compartmental model to predict in vivo efficacy and incorporating the PK/PD of the ADCs in mice. Model parameters were estimated from literature, or calibrated and validated using experimental data on in vitro cytotoxicity and in vivo tumor growth inhibition provided by AstraZeneca for anti-BCMA and anti-HER2-PBD ADCs. Results: Following model parameterization, we conducted local sensitivity analyses at different time points and receptor expression levels to determine the effect on the tumor cell population. At both the in vitro and in vivo scales, the system was most sensitive to parameters for cell killing and DNA crosslinking, as well as ADC uptake, endosomal exit, and recycling, suggesting that both warhead potency and non-specific ADC uptake are important considerations that influence ADC efficacy. Using this multi-scale model, we simulated ADC efficacy under various ADC design scenarios and examined effects specific to both the anti-BCMA and anti-HER2 ADCs at the in vitro and in vivo scales. Notably, we built a warhead tracking feature into the model, enabling us to visualize changes in PBD warhead location and recent history at multiple scales. Conclusion: We have developed a multi-scale, mechanistic QSP model describing the efficacy of PBD-ADCs to enable in vitro to in vivo translation. This model integrates cellular-level mechanisms specific to PBD-based ADCs with in vivo mouse PK/PD, using parameters from literature or fit to AZ experimental data. By incorporating relevant experimental data, this computational model can be further developed to create a human clinical model to run virtual clinical trials.
