A New Hybrid Agent-Based Model to Evaluate Antibody-Drug Conjugates in Solid Tumors
Creators: name: Menezes, Bruna
Publisher: name: My University
Year: 2021
Subjects: subject: antibody-drug conjugate
subject: agent-based model
subject: pharmacokinetics and pharmacodynamics
subject: drug-delivery
subject: distribution and efficacy prediction
subject: solid tumors
subject: Chemical Engineering
subject: FOS: Chemical engineering
subject: Engineering

description: Antibody-drug conjugates (ADCs) are a type of targeted therapy that combines the specificity of an antibody and a cytotoxic payload agent connected by a linker. The development of new ADCs has improved in the last few years with new ADCs being approved by the FDA and many others in the pipeline, but the failure rate for ADCs in solid tumors is still nearly 90%. One of the major problems is the limited ability of the ADC to penetrate into the tumor tissue; for example, the FDA-approved ADC Kadcyla® (T-DM1 targeted against HER2+ breast cancer) is localized only on tumor cells close to the blood vessel even at the clinical dose. As a result, many cells in the tumor do not receive any drug, influencing efficacy. In the clinic, it has also been found that the efficacy of T-DM1 and other ADCs is closely related to the expression of receptors per cell, making tumors with low receptor expression resistant to the treatment. Besides these challenges, several additional aspects of the tumor environment can influence efficacy such as variability in cancer cell dynamics (proliferation and death), blood vessel dynamics (angiogenesis), heterogeneous receptor expression, and intrinsic resistance. New approaches to improve ADC treatment, such as coadministration of the ADC with its unconjugated antibody, fractionated dosing, and bystander payloads, have been proposed to overcome these challenges that limit the efficiency of ADCs as cancer therapeutics. These approaches have overlapping contributions to efficacy, and analyzing them experimentally is a difficult and time-consuming task that requires an impractical and unethical number of in vivo experiments. Developments in the field of computational modeling methods have enabled the development of more sophisticated and mechanistic models that can capture the complexity of the tumor environment to predict ADC behavior in vivo. In this thesis, I describe the use of a new hybrid agent-based model framework to mechanistically capture pharmacokinetics and pharmacodynamics of ADC treatment in solid tumors. I first describe the development of this new model that combines a commonly used deterministic Krogh cylinder tumor model with a stochastic agent-based model and show how it captures systemic, intratumoral, and single-cell dynamics that impact the overall drug efficacy. I also demonstrate how this model can be used to inform different dosing regimens, such as coadministration of ADC with its unconjugated antibody and fractionated dosing, by predicting tumor inhibition efficiency for each. I further extend the model capabilities to capture heterogeneity in receptor expression and intrinsic resistance by changing cell properties. I compare how different ADC dosing regimens influence the efficacy of ADCs containing bystander payloads, which possess the inherent ability to compensate for tumor heterogeneity. Finally, I apply this model to elucidate the in vivo behavior of a complex ADC currently under clinical evaluation in the complex tumor microenvironment of patient-derived tumors. In summary, I demonstrate a new approach to evaluate the pharmacokinetics and pharmacodynamics of ADCs from the perspective of varying dosing regimens, mechanism of action, and tumor microenvironments that can guide future experiments and improve the overall development of this drug class.