Bioinformatics in translational drug discovery

Abstract

Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational exploitation of the increasing volumes of data generated during all phases of drug discovery is enabling key challenges of the process to be addressed. Here, we highlight some of the areas in which bioinformatics resources and methods are being developed to support the drug discovery pipeline. These include the creation of large data warehouses, bioinformatics algorithms to analyse 'big data' that identify novel drug targets and/or biomarkers, programs to assess the tractability of targets, and prediction of repositioning opportunities that use licensed drugs to treat additional indications.

Keywords: computational biochemistry; drug discovery and design; genomics.

Figure 1. Translational bioinformatics opportunities in the drug discovery pipeline

A schematic diagram of the drug discovery process. Each phase of the drug discovery pipeline (discovery, clinical and postlaunch) is shown as an orange arrow. Underneath the pipeline, shown as blue rectangles, are the types of ‘big data’ that can be generated in each step of the pipeline. Highlighted below the data types are the potential opportunities to improve the pipeline using bioinformatics techniques. For example, during the discovery phase, the focus is on identifying the druggability of potential target proteins. During the clinical trials, phase personalized medicine and patient selection can be used to better sample and categorize subjects while the use of biomarkers can improve efficacy measurements. Finally, at the post-launch phase of a drug’s life cycle drug safety monitoring and disease subtyping can be used to both improve the quality of life for patients as well as help to identify the opportunities for modified interventions that may be more effective for certain subtypes of a given disease. Adapted from [4], Copyright (2011), with permission from Elsevier.

Figure 2. Prediction of druggable pockets in bromodomains

The acetyl-lysine (KAc) binding pockets of two human bromodomains were identified by DoGSiteScorer. (A) shows the non-druggable KAc binding site of the bromodomain from TRIM24 (PDB: 2YYN_A) (druggability score =0.49). (B) shows the druggable KAc binding site of the bromodomain from BRD1 (PDB: 3RCW_A) ( druggability score =0.68). A score greater than 0.50 is indicative of a druggable pocket [102].

Figure 3. Druggable targets in the DNA damage response

This illustrates the protein–protein interaction network of proteins (derived from the STRING database [114]) involved in the DNA damage response as described in [46]. Each protein is shown as a node/circle with the interaction described as a connecting line. The network is labelled by some of the DDR processes: HR; UR, ubiquitin response; FA, Fanconi anaemia; NER, nucleotide excision repair, CS, chromosome segregation; CR, chromatin remodelling. Nodes coloured dark green indicate a protein for which these is a licenced drug, light green nodes indicate that the protein is a target of a drug in clinical trials. Pink nodes indicate that a protein is predicted to be druggable as it has the features of a good drug target. Each of these proteins have been predicted to be druggable by the at least two of the druggability methods (ligand, structure and network) provided by the canSAR database [84]. Adapted from Supplementary Information (figure) S15 [46]. .