Computational Epitope Prediction and Screening Precision Antibody Therapeutics for Alzheimer's Disease

Abstract

Using Molecular Dynamic simulations, we predicted the epitopes on Aβ oligomers, which would be distinct from those in either Aβ monomer or Aβ fibril. Starting from the predicted epitopes, 66 antibodies are raised by passive immunization in mice, and their binding to Aβ monomer, oligomer and plaque, along with preferential binding to Aβ protein in the cerebrospinal fluid and brain homogenate from deceased Alzheimer Disease (AD) patients vs healthy controls are measured experimentally. These experimental measurements provide up to eight different screening criteria to select lead candidates for clinical stages of drug development. The screening criteria do not perfectly correlate with each other and there is uncertainty in the criteria values as well as their importance/weights, making the selection of lead candidates a nontrivial maximum-likelihood ranking problem. In other fields, this problem is referred to generally as multiple criteria decision making or MCDM, and has been applied in various fields including finance and economics, energy policy, and environmental science, but not—at least to our knowledge—to the problem of screening candidate drug therapeutics. We employ a stochastic MCDM method named “SMAA-TOPSIS” to screen and rank the measured large cohort of the antibodies, in order to find lead candidate therapeutics. We obtain distributions of candidate rankings due to uncertainty in screening measurements, as well as the user-defined weight of importance attributed to each screening criterion. In choosing lead candidates, we propose a quantity “topness” as the most robust measure of ranking. Our hope is that this method may enable more systematic screening of candidate therapeutics when it becomes difficult intuitively to process multi-variate screening data that distinguishes candidates, so that additional candidates will be exposed as potential leads, increasing the likelihood of success in downstream clinical trials.