Building Novel Predictive Networks for high-throughput, in-silico Key Driver Prioritization to Enhance Drug Target Discovery in AMP-AD and M2OVE-AD

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Project Summary We respond to the RFA (AG-17-054) with the goals of 1) applying new computational approaches, i.e. top- down and bottom-up predictive network (predictive network for short) to the existing rich datasets generated by the AMP-AD and M2OVE-AD consortia to discover novel targets that can guide therapy development; 2) performing experimental validation of the novel targets using human cellular models and generating RNA-seq data to systematically validate in-silico prediction; 3) integrating the new RNA-seq data to further improve the predictive network built on existing AMP-AD data and to enhance the quality of the targets. Alzheimer's disease is the most common form of Dementia estimated to affect 36 million people worldwide. This number is expected to rise to 115 million by 2050 unless an effective therapeutic is developed. The AMP-AD Target Discovery and Preclinical Validation and M2OVE-AD programs are large-scale, open science consortia aimed at building a predictive, multi-scale model of AD that better reflects its heterogeneity and at discovering the next-generation therapeutic targets through integrative, data-driven approaches. While the four multi- institutional, multidisciplinary teams in AMP-AD engaged cutting-edge and agnostic analysis efforts to reconstruct the molecular network of the gene, protein, and metabolite in AD and to discover novel therapeutic targets evaluated by multiple model organisms, the reproducibility of the drug targets proposed across all teams is low (1 replicate out of total 80 targets proposed in Sept. 2016) given the considerable complexity of the disease and differences that exist in the data types and the approaches used to generate and analyze the data. This variability undermined the confidence of each candidate target and defocused the efforts of experiment validation. Current key driver screening lacks a high-throughput, in-silico screening component to directly evaluate the efficacy of a target by predicting the downstream molecular phenotype given its perturbation. This gap between target discovery and validation further reduces the overall rate of success, efficacy and efficiency of drug development. In this proposal, we will apply the predictive network pipeline with the in-silico screening component to existing data in AMP-AD in collaboration with all teams in AMP-AD and M2OVE-AD to build causal and predictive networks and to identify and prioritize key drivers, which will be evaluated by experiment validation. The RNA-seq data generated by experiment validation will be used to systematically evaluate the in-silico phenotypic prediction to determine the confidence of proposed therapeutic recipes and to be integrated with the existing predictive networks to enhance drug target discovery.