Trawling the Genome: Drug Target Identification in the Postgenomic Era

Drug discovery is the mainstay of the pharmaceutical industry. Despite the advent of technologies that enable a deep understanding of diseases at the molecular and biochemical levels, drug target identification remains a bottle neck in the drug discovery process. The number of targets with which approved drugs interact represents only a tiny fraction of the human proteome. In addition, most of the newly identified targets fall into certain privileged protein families, and the discovery of a new class of drug targets is a rare event. While there could be a lot more of druggable proteins encoded by the genome, technologies enabling their discovery are limited. Furthermore, the multifactorial genesis of diseases makes target discovery less straight forward. The target identification strategy is dependent upon the mode of drug discovery. Whereas the forward pharmacology approach relies on identifying drug leads first and then deconvoluting the targets, reverse pharmacology utilizes hypothetical or validated targets as the starting point for lead identification. The unravelling of the human genome and the subsequent revolution in high-throughput omics technologies and data-driven discovery paradigms in biology is impacting the drug discovery enterprise, as any other domain. High-throughput genome editing technologies such as CRISPR-Cas9, transcriptomics tools like RNA-seq, and advancements in mass spectrometry are expected to accelerate target identification. Furthermore, the surge in omics data has provided a better scope for computational and machine-learning interventions in drug discovery. The present chapter reviews the contemporary methods used in drug target identification.