Quantitative systems pharmacology modeling of pyrrolobenzodiazepine antibody-drug conjugates targeting BCMA

Antibody-drug conjugates (ADCs) are novel therapeutics combining two molecules linked together: an antibody that provides targeting to specific cells, and a cytotoxic drug (warhead) that can deconjugate from the antibody and kill those cells. The warhead by itself would be too nonspecific and toxic; the antibody by itself would be insufficiently effective at killing the cells. As more and more ADCs enter the drug development pipeline, understanding the mechanistic reasons behind efficacy and toxicity is critical to evaluating both successful and failed trials. Here, we have developed a mechanistic computational model of ADCs, and specifically parameterize it using data for MEDI2228, an anti-BCMA antibody conjugated to pyrrolobenzodiazepine (PBD) warheads. We build the model to track not only the concentrations of ADC and its released warhead drug inside and outside the cell, but also to track the recent history of the warhead, so that we can distinguish between pathways for on-target and bystander (nontargeted) cell killing. We show that this effect 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. We also explore the impact of key design parameters of ADCs, including drug to antibody ratio (DAR), warhead potency, and lipophilicity; this analysis demonstrates the balance needed between killing of targeted and nontargeted cells. Using this quantitative systems pharmacology model, we can generate insights for optimization of ADC design and determine which factors are most critical to efficacy and toxicity, leading to more informed and rational development of cancer therapies.