Translational Bioinformatics Approaches to Drug Development.

Significance: A majority of therapeutic interventions occur late in the pathological process, when treatment outcome can be less predictable and effective, highlighting the need for new precise and preventive therapeutic development strategies that consider genomic and environmental context. Translational bioinformatics is well positioned to contribute to the many challenges inherent in bridging this gap between our current reactive methods of healthcare delivery and the intent of , particularly in the areas of drug development, which forms the focus of this review.

Recent Advances: A variety of powerful informatics methods for organizing and leveraging the vast wealth of available molecular measurements available for a broad range of disease contexts have recently emerged. These include methods for data driven disease classification, drug repositioning, identification of disease biomarkers, and the creation of disease network models, each with significant impacts on drug development approaches.

Critical Issues: An important bottleneck in the application of bioinformatics methods in translational research is the lack of investigators who are versed in both biomedical domains and informatics. Efforts to nurture both sets of competencies within individuals and to increase interfield visibility will help to accelerate the adoption and increased application of bioinformatics in translational research.

Future Directions: It is possible to construct predictive, multiscale network models of disease by integrating genotype, gene expression, clinical traits, and other multiscale measures using causal network inference methods. This can enable the identification of the "key drivers" of pathology, which may represent novel therapeutic targets or biomarker candidates that play a more direct role in the etiology of disease.