Assessing the Impact of Tissue Target Concentration Data on Uncertainty in In Vivo Target Coverage Predictions

Understanding pharmacological target coverage is fundamental in drug discovery and development as it helps establish a sequence of research activities, from laboratory objectives to clinical doses. To this end, we evaluated the impact of tissue target concentration data on the level of confidence in tissue coverage predictions using a site of action (SoA) model for antibodies. By fitting the model to increasing amounts of synthetic tissue data and comparing the uncertainty in SoA coverage predictions, we confirmed that, in general, uncertainty decreases with longitudinal tissue data. Furthermore, a global sensitivity analysis showed that coverage is sensitive to experimentally identifiable parameters, such as baseline target concentration in plasma and target turnover half-life and fixing them reduces uncertainty in coverage predictions. Overall, our computational analysis indicates that measurement of baseline tissue target concentration reduces the uncertainty in coverage predictions and identifies target-related parameters that greatly impact the confidence in coverage predictions.