Chemogenomics Systems Pharmacology Mapping of Potential Drug Targets for Treatment of Traumatic Brain Injury.

Traumatic brain injury (TBI) is associated with high mortality and morbidity. Though the death rate of initial trauma has dramatically decreased, no drug has been developed to effectively limit the progression of the secondary injury caused by TBI. TBI appears to be a predisposing risk factor for Alzheimer's disease (AD), whereas the molecular mechanisms remain unknown. In this study, we have conducted a research investigation of computational chemogenomics systems pharmacology (CSP) to identify potential drug targets for TBI treatment. TBI-induced transcriptional profiles were compared with those induced by genetic or chemical perturbations, including drugs in clinical trials for TBI treatment. The protein-protein interaction network of these predicted targets were then generated for further analyses. Some protein targets when perturbed, exhibit inverse transcriptional profiles in comparison with the profiles induced by TBI, and they were recognized as potential therapeutic targets for TBI. Drugs acting on these targets are predicted to have the potential for TBI treatment if they can reverse the TBI-induced transcriptional profiles that lead to secondary injury. In particular, our results indicated that TRPV4, NEUROD1, and HPRT1 were among the top therapeutic target candidates for TBI, which are congruent with literature reports. Our analyses also suggested the strong associations between TBI and AD, as perturbations on AD-related genes, such as APOE, APP, PSEN1, and MAPT, can induce similar gene expression patterns as those of TBI. To the best of our knowledge, this is the first CSP-based gene expression profile analyses for predicting TBI-related drug targets, and the findings could be used to guide the design of new drugs targeting the secondary injury caused by TBI.

Computational Fragment-Based Drug Design: Current Trends, Strategies, and Applications.

Fragment-based drug design (FBDD) has become an effective methodology for drug development for decades. Successful applications of this strategy brought both opportunities and challenges to the field of Pharmaceutical Science. Recent progress in the computational fragment-based drug design provide an additional approach for future research in a time- and labor-efficient manner. Combining multiple in silico methodologies, computational FBDD possesses flexibilities on fragment library selection, protein model generation, and fragments/compounds docking mode prediction. These characteristics provide computational FBDD superiority in designing novel and potential compounds for a certain target. The purpose of this review is to discuss the latest advances, ranging from commonly used strategies to novel concepts and technologies in computational fragment-based drug design. Particularly, in this review, specifications and advantages are compared between experimental and computational FBDD, and additionally, limitations and future prospective are discussed and emphasized.