Monoclonal antibody therapy efficacy can be boosted by combinations with other treatments: Predictions using an integrated Alzheimer's Disease Platform.

For many years, clinical research in Alzheimer's disease (AD) has focused on attempts to identify the most explicit biomarker, namely amyloid beta. Unfortunately, the numerous therapies that have been developed have failed in clinical practice. AD arises as a consequence of multiple factors, and as such it requires a more mechanistic analytical approach than statistical modeling. Quantitative systems pharmacology modeling is a valuable tool for drug development. It utilizes in vitro data for the calibration of parameters, embeds them into physiologically based structures, and explores translation between animals and humans. Such an approach allows for a quantitative study of the dynamics of the interactions between multiple factors or variables. Here, we present an overview of the quantitative translational model in AD, which embraces current preclinical and clinical data. The previously published description of amyloid physiology has been updated and joined with a model for tau pathology and multiple intraneuronal processes responsible for cellular transport, metabolism, or proteostasis. In addition, several hypotheses regarding the best correlates of cognitive deterioration have been validated using clinical data. Here, the amyloid hypothesis was unable to predict the aducanumab clinical trial data, whereas simulations of cognitive impairment coupled with tau seeding or neuronal breakdown (expressed as caspase activity) matched the data. A satisfactory validation of the data from multiple preclinical and clinical studies was followed by an attempt to predict the results of combinatorial treatment with targeted immunotherapy and activation of autophagy using rapamycin. The combination is predicted to yield better efficacy than immunotherapy alone.