Drug discovery and development is a complex, high risk, time consuming and potentially highly rewarding process. Pharmaceutical companies literally burn millions of dollar per drug to bring it to the market. The development of a new drug requires a technological expertise, human resources and huge capital investment. It also requires strict adherence to regulations on testing and manufacturing standards before a new drug comes into market and can be used in the general population, in fact, some time it fails to come into market. All these factors just increase the cost for a new chemical entity research and development. Two branches which made positive impact on drug designing process and reduce the overall cost and risk are Bioinformatics and Pharmacogenomics. Their practice in drug designing process made positive effect on overall process and they can accelerate various steps of drug designing, and reduce the cost and over all time. Current note focusses on the role of bioinformatics and pharmacogenomics in drug discovery and development process.

Pharmacogenomics is the study of how the genetic makeup determines the response to a therapeutic intervention. It has the capability to revolutionize the practice of medicine by personalized approach for treatment through the use of novel diagnostic tools. Pharmacogenomic based approaches reduce the trial-and-error approach and restrict the exposure of patients to those drugs which are not effective or are toxic for them. Single Nucleotide Polymorphisms (SNPs) hold the key in defining the risk of an individual's susceptibility to various illnesses and response to drugs. There is an ongoing process of identifying the common, biologically relevant SNPs, in particular those that are associated with the risk of disease and adverse drug reaction. The identification and characterization of these SNPs are necessary before their use as genetic tools. Most of the ongoing SNP related studies are biased deliberately towards coding regions and the data generated from them are therefore unlikely to reflect genome wide distribution of SNPs. To avoid this biasing towards the coding regions SNP, SNP consortium protocol was designed. Though, projects like the HapMap increase credibility and use of SNPs, still there are some concern like the required sample (patient) sizes, the number of SNPs required for mapping, number of association studies, the cost of SNP genotyping, and the interpretation and explanation of results are some of the challenges that surround this field.

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Phylogenetic footprinting is a method for the discovery of regulatory elements in a set of homologous regulatory regions, usually collected from multiple species. It does so by identifying the best conserved motifs in those homologous regions. There are two popular sets of methods-alignment-based and motif-based, which are generally employed for phylogenetic methods. However, serious efforts have lacked to develop a tool exclusively for phylogenetic footprinting, based on either of these methods. Nevertheless, a number of software and tools exist that can be applied for prediction of phylogenetic footprinting with variable degree of success. The output from these tools may get affected by a number of factors associated with current state of knowledge, techniques and other resources available. We here present a critical apprehension of various phylogenetic approaches with reference to prokaryotes outlining the available resources and also discussing various factors affecting footprinting in order to make a clear idea about the proper use of this approach on prokaryotes.

Thiopurine methyltransferase (TPMT) is a cytoplasmic transmethylase present in both prokaryotes and eukaryotes. In humans, it shows its presence in almost all of the tissues, predominantly in liver and kidney. TPMT is one of the important metabolic enzymes of phase II metabolic pathway and catalyzes methylation of thiopurine drugs such as azathioprine, 6-thioguanine and 6-mercaptopurine, which are used to treat patients with neoplasia and autoimmune disease as well as transplant recipients. In this sense, TPMT acts as shield against toxic effect of these drugs. Pharmacogenomic studies revealed that genetic polymorphism in TPMT is responsible for variable and, in some cases, adverse drug reaction. Those human groups who carry variants of TPMT (i.e., [Formula: see text], [Formula: see text], [Formula: see text]) are at high risk, because they are unable to metabolize thiopurine drugs thus becoming a weakness of patients against these drugs. Keeping in the mind the importance of TPMT, this review discusses the existence and distribution of various TPMT variants throughout different ethnic groups and risk of adverse drug reactions to them, and how they can avoid this risk of side effects. The review also highlighted factors responsible for variable reactions of TPMT, how this TPMT polymorphism can be considered in drug designing process to avoid toxic effects, designing precautions against them and more importantly designing personalized medicine.

Identification of potential drug targets is the first step in the process of modern drug discovery, subjected to their validation and drug development. Whole genome sequences of a number of organisms allow prediction of potential drug targets using sequence comparison approaches. Here, we present a subtractive approach exploiting the knowledge of global gene expression along with sequence comparisons to predict the potential drug targets more efficiently. Based on the knowledge of 155 known virulence and their coexpressed genes mined from microarray database in the public domain, 357 coexpressed probable virulence genes for Vibrio cholerae were predicted. Based on screening of Database of Essential Genes using blastn, a total of 102 genes out of these 357 were enlisted as vitally essential genes, and hence good putative drug targets. As the effective drug target is a protein which is only present in the pathogen, similarity search of these 102 essential genes against human genome sequence led to subtraction of 66 genes, thus leaving behind a subset of 36 genes whose products have been called as potential drug targets. The gene ontology analysis using Blast2GO of these 36 genes revealed their roles in important metabolic pathways of V. cholerae or on the surface of the pathogen. Thus, we propose that the products of these genes be evaluated as target sites of drugs against V. cholerae in future investigations.

Our present work focuses on the set of genes, which are involved in primary brain tumors ‐ the glioma pathway. These gliomas are mostly malignant (cancerous) in nature and are difficult to be cured and that’s why they attract the attention of all the workers. To understand the relative functionality of these genes, we analyzed the expression pattern of all genes, using gene expression data, at genomic level, and then to check their universality in all other cancers, we compared their expression levels and patterns in all other types of cancers by using gene expression graphs, and observed their expression levels in all these cancers, whether they are over or under expressed. We found that every gene has its own unique expression pattern and level and on that basis it can be classified. We also found that oncogenes and tumor suppressor genes that were involved in the glioma pathway were showing similar expression patterns in other cancers too but their expression level is low.

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