Vasan Kishore, Gysi Deisy Morselli, Barabási Albert-László
Network Science Institute, Northeastern University, Boston, MA, USA.
Department of Statistics, Federal University of Parana, Curtiba, Brazil.
iScience. 2023 Oct 30;26(12):108361. doi: 10.1016/j.isci.2023.108361. eCollection 2023 Dec 15.
The depth of knowledge offered by post-genomic medicine has carried the promise of new drugs, and cures for multiple diseases. To explore the degree to which this capability has materialized, we extract meta-data from 356,403 clinical trials spanning four decades, aiming to offer mechanistic insights into the innovation practices in drug discovery. We find that convention dominates over innovation, as over 96% of the recorded trials focus on previously tested drug targets, and the tested drugs target only 12% of the human interactome. If current patterns persist, it would take 170 years to target all druggable proteins. We uncover two network-based fundamental mechanisms that currently limit target discovery: , leading to the repeated exploration of previously targeted proteins; and , limiting exploration to proteins interacting with highly explored proteins. We build on these insights to develop a quantitative network-based model to enhance drug discovery in clinical trials.
后基因组医学所提供的知识深度带来了研发新药以及治愈多种疾病的希望。为探究这一能力的实现程度，我们从跨越四十年的356403项临床试验中提取元数据，旨在为药物研发中的创新实践提供机制性见解。我们发现传统做法主导着创新，因为超过96%的记录试验聚焦于先前已测试的药物靶点，且所测试的药物仅针对人类相互作用组的12%。如果当前模式持续下去，要针对所有可成药蛋白则需要170年。我们揭示了目前限制靶点发现的两种基于网络的基本机制：一是导致对先前靶向蛋白的重复探索；二是将探索限制在与高度探索蛋白相互作用的蛋白上。基于这些见解，我们构建了一个基于网络的定量模型，以加强临床试验中的药物研发。
