Associate Professor Johns Hopkins University, United States
Introduction: Mechanistic modeling approaches such as QSP (Quantitative Systems Pharmacology) can help us better understand therapeutic molecules, including: why they work or fail; which doses and schedules would be most effective; which patients are more likely to benefit; and identifying biomarkers that would support categorization of patients (into responders and non-responders) and drug/dose selection. Here, we describe our recent studies into antibody-based therapeutics using mechanistic computational models, paired with relevant experimental data, that in each case describe real therapeutic molecules that have been or are being tested for potential efficacy. These include: antibody-drug conjugates (ADCs); and bispecific antibodies (bsAbs).
Materials and
Methods: Each of the models described here is based on a large coupled set of ordinary differential equations, each tracking the concentration of a particular molecule or complex, and with rate terms describing each process. The molecules and processes are different from model to model.
Antibody-drug conjugates (ADCs) comprise a monoclonal antibody base attached to one or more cytotoxic agents via chemical linkers. This strategy enables specific targeting of antigens expressed on the cancer cell surface, resulting in receptor-mediated endocytosis and delivery of toxic payload to the cells of interest while sparing healthy tissues. To study ADCs, we included cellular mechanisms of PBD ADCs, including multiple mechanisms of cellular uptake and release of the conjugate drug. We also designed and incorporated a novel tracking module into this model, which enabled us to identify the recent location history of the cytotoxic conjugate drug payload, which facilitated estimates of both the on-target and off-target (bystander) potential for cell killing. We developed both in vitro (cell culture) and in vivo (tumor) versions of the model for comparison.
Bispecific antibodies (bsAbs) are engineered to have binding sites for two different antigens. This strategy enables the specific targeting of cells that express both antigens, and is hypothesized to decrease off-target binding to cells that do not express both. To study bsAbs, we included detailed multi-step binding processes to permit the comparison of monospecific (but bivalent) to bispecifics. We developed both in vitro (cell culture) and in vivo (tumor) versions of the model for comparison.
Results, Conclusions, and Discussions: We parameterized the ADC model using in vitro experimental data for two ADCs, both of which use the same conjugate cytotoxic payload, pyrrolobenzodiazepine (PBD). One ADC targets B cell maturation antigen (BCMA), intended to treat multiple myeloma, and the other targets human epidermal growth factor receptor 2 (HER2), intended to treat HER2-positive solid tumors. Using the parameterized model, we quantify impact of key design parameters of ADCs, including drug to antibody ratio (DAR), warhead potency, and lipophilicity. This analysis also demonstrates the balance between killing of targeted cells and nontargeted cells. Using warhead tracking, we showed that the bystander effect - the killing of neighboring cells by drug released by other cells - is predicted to be small under in vitro conditions due to dilution in large volumes of media, but likely to form a significant part of both targeted and nontargeted cell killing in vivo, where the extracellular volume has less of a dilution effect. This disparity between in vitro and in vivo model predictions emphasizes how results obtained in one system may not be observed in a second system, and also that we can use mechanistic models to scale from one system to the other. We also showed that the level of bystander killing was lower for the anti-HER2 ADC than for the anti-BCMA ADC, likely due to differences in the level of antigen expression.
The specific application of the bsAb model here is the comparison of a bsAb that targets the receptors for two key tumor-cell-secreted immunocytokines, interleukin-6 (IL-6) and interleukin-8 (IL-8), to the combination of monospecific antibodies that target the same receptors. These cytokines synergize to enhance cancer metastasis in a cell-density dependent manner, and the bsAb has been shown to reduce metastatic burden in preclinical mouse models of cancer. This is important because most current anti-cancer drugs are designed to arrest or reverse tumor growth without directly addressing metastasis. We show that in heterogeneous cell populations (i.e. cells expressing different receptors), there is a selectivity advantage for the bsAb. This selectivity is particularly pronounced at sub-saturating antibody concentrations, which is important for dosing considerations.
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