A Bispecific Modeling Framework Enables the Prediction of Efficacy, Toxicity, and Optimal Molecular Design of Bispecific Antibodies Targeting MerTK

Inhibiting MerTK on macrophages is a promising therapeutic strategy for augmenting anti-tumor immunity. However, blocking MerTK on retinal pigment epithelial cells (RPEs) results in retinal toxicity. Bispecific antibodies (bsAbs) containing an anti-MerTK therapeutic and anti-PD-L1 targeting arm were developed to reduce drug binding to MerTK on RPEs, since PD-L1 is overexpressed on macrophages but not RPEs. In this study, we present a modeling framework using in vitro receptor occupancy (RO) and pharmacokinetics (PK) data to predict efficacy, toxicity, and therapeutic index (TI) of anti-MerTK bsAbs. We first used simulations and in vitro RO data of anti-MerTK monospecific antibody (msAb) to estimate the required MerTK RO for in vivo efficacy and toxicity. Using these estimated RO thresholds, we employed our model to predict the efficacious and toxic doses for anti-MerTK bsAbs with varying affinities for MerTK. Our model predicted the highest TI for the anti-MerTK/PD-L1 bsAb with an attenuated MerTK binding arm, which was consistent with in vivo efficacy and toxicity observations. Subsequently, we used the model, in combination with sensitivity analysis and parameter scans, to suggest an optimal molecular design of anti-MerTK bsAb with the highest predicted TI in humans. Our prediction revealed that this optimized anti-MerTK bsAb should contain a MerTK therapeutic arm with relatively low affinity, along with a high affinity targeting arm that can bind to a low abundance target with slow turnover rate. Overall, these results demonstrated that our modeling framework can guide the rational design of bsAbs.