BIOINFORMATICS AND ITS ROLE IN DRUG DISCOVERY

    Bioinformatics is a multidisciplinary field that merges biology, computer science, and statistics to analyze and interpret biological data. Its pivotal role in drug discovery lies in leveraging computational tools and techniques to process vast amounts of biological information, such as DNA sequences, protein structures, and gene expression data. By analyzing this data, bioinformatics helps identify potential drug targets, predict drug interactions, and accelerate the discovery of novel therapeutic molecules, ultimately aiding in the development of safer and more effective drugs. The fields of genomics, proteomics, and metabolomics are significantly altering the conventional methods of drug discovery and development. Presently, potential drug targets are being discovered more frequently using techniques like high-throughput sequencing, microarray experiments, 2D gel experiments, mass spectrometry, and screening of chemical libraries. Bioinformaticians use high-throughput molecular data to make comparisons between individuals exhibiting symptoms and healthy controls in the context of drug discovery. These comparisons serve a number of crucial functions:

1.      Establishing connections between disease symptoms and genetic mutations, epigenetic modifications, and other environmental influencers of gene expression.

2.      Identifying potential drug targets capable of restoring normal cellular function or eliminating aberrant cells, like cancer cells.

3.      Anticipating and refining drug candidates with the capacity to act on specific drug targets, leading to desired therapeutic outcomes while minimizing undesirable side effects.

4.      Evaluating the ecological impact and gauging the potential for drug resistance to ensure both environmental health and effective treatment strategies.

      Bioinformatics made an early impact on drug target discovery by identifying a sequence similarity between the simian sarcoma virus oncogene, v-sis, and platelet-derived growth factor (PDGF) through basic string matching. This revelation not only designated PDGF as a viable cancer drug target but also initiated two novel lines of thought [1]. It suggested that the viral transforming factor might function by converting transient growth factor expression into constant expression, thus highlighting growth factors as potential targets for anti-cancer drug research. This fresh conceptual framework in cancer biology played a crucial role in advancing mechanism-centered approaches to anti-cancer drug development in subsequent years. Bioinformatics plays a crucial role in advancing drug development and discovery, particularly for emerging diseases. It provides a practical and effective approach to designing novel drugs targeted at previously unexplored illnesses.

    The utilization of gene and protein sequence data has become indispensable across various domains of pharmaceutical research. For instance, the sequencing of the genomes of humans, mice, and rats has resulted in the discovery of over 30 families of drug-metabolizing enzymes, along with the identification of numerous potential protein drug targets [2]. Genomic and whole exome sequencing of individuals with inherited disorders has revealed numerous somatic mutations that are linked to genetic diseases and hold promise as potential targets for drug intervention. The primary challenge in bioinformatic studies of somatic mutations revolves around distinguishing disease-causing mutations from the various genetic variations observed between patients and their matched normal controls. Bioinformaticians evaluate mutations for significant gene function impact through three main methods: 1) Identifying mutations that replace conserved amino acids with highly dissimilar ones (e.g., glycine to arginine), 2) Examining mutations in conserved non-coding sequences by comparing human and non-human primate genomes, and 3) Detecting mutations in recognized cellular machinery signals like regulatory motifs, splice sites, and transcription start/stop sites [1,2]. In every tumor/cancer cell, only a small portion of genetic alterations actually contribute to and propel the advancement of the disease. The remaining mutations do not confer any growth benefits and are commonly referred to as passenger mutations. An alternative method, like MutSigCV 18, involves adjusting the assumed mutation background rate by considering the DNA region's replication time and integrating data on gene expression levels. This is especially useful in cancers with elevated mutation rates, where many genetic alterations are unrelated to cancer development. Thus, evaluating the functional significance of these changes becomes valuable [3].   

       In 2019, a novel strain of the Coronavirus surfaced, posing a significant global threat and triggering a widespread pandemic. At the outset, crafting effective drugs to combat these strains proved challenging. With a high mortality rate, there was a pressing need for innovative treatments. This is where bioinformatics plays a vital role, offering valuable assistance. The SARS-CoV-2 pandemic led to significant research efforts aimed at discovering both vaccines and novel antiviral medications. A study explored the effectiveness of the Mpro inhibitor PF-00835231 against Mpro (the main protease enzyme of SARS-CoV-2) and its reported mutants in clinical trials. Various in-silico methods were employed to analyze and contrast the efficacy of PF-00835231 with that of five drugs known to inhibit Mpro [1,4].

    A detailed investigation of metabolites and metabolic processes in biological systems is the focus of the developing discipline of metabolomics, which belongs to the 'omics' sciences. A growing number of practical biomedical applications are entering the mainstream as a result of the ongoing development of metabolomics technologies. The discovery of the protein targets flavin monooxygenase 3 in the liver and bacterial choline TMA-lyase was made possible by the knowledge that trimethylamine (TMA) is a byproduct of choline and carnitine metabolism by gut microbes as well as the knowledge that TMA serves as a precursor to trimethylamine N-oxide (TMAO). These targets have the potential to reduce TMAO levels, indicating a possible route for treating or preventing atherosclerosis [5].

     Databases and datasets play a pivotal role in bioinformatics, serving as repositories of biological information essential for research and analysis. These resources store diverse biological data, such as DNA sequences, protein structures, gene expressions, and more. Researchers can access and mine these databases to uncover patterns, relationships, and insights that aid in understanding complex biological processes, identifying disease markers, and accelerating drug discovery. The significance of these resources lies in their ability to provide a foundation for informed decision-making, hypothesis generation, and the advancement of scientific knowledge in the fields of biology and medicine. The two main organizations responsible for managing annotated sequence databases are the National Centre for Biotechnology Information (NCBI) USA, and the European Bioinformatics Institute (EBI), UK. Each of these organizations employs over 200 staff members and oversees numerous general and specialized sequence databases. These resources are accessible for free on their respective websites [2].

     To further utilize the potential of bioinformatics in drug development, several avenues can be explored. By investing in modern technology and high-performance computing systems, researchers may handle massive datasets more effectively by improving the speed and accuracy of data analysis. In conclusion, bioinformatics stands as a cornerstone in modern drug development and discovery, revolutionizing the way we understand, analyze, and harness biological information. By integrating computational tools, algorithms, and data analysis techniques, bioinformatics enables researchers to expedite the identification of potential drug targets, predict drug interactions, and optimize lead compounds. This synergy of biology and technology has significantly accelerated the drug discovery process, reduced costs, and increased the efficiency of identifying promising candidates for further development.

 

By: Rida Jamal

REFERENCES:

1.       Xia. X, (17 Jun 2017). Bioinformatics and Drug Discovery. (Canada). Bentham Science. 10.2174/1568026617666161116143440

2.       Wishart. D. S, (2005). Bioinformatics in drug development and assessment. (Vancouver, Canada). Taylor & Francis Inc. : 10.1081/DMR-200055225

3.       Wooller. S.K, Benstead-Hume. G, Chen. X, Ali. Y, Pearl. F.M.G. (07 July 2017). Bioinformatics in translational drug discovery. Bioscience Report. https://doi.org/10.1042/BSR20160180

4.       Tutone. M, Almerico. AM. (Dec 2021). Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics. (Italy). Molecules. https://doi.org/10.3390/molecules26247500

5.       Wishart. DS. (11 Mar 2016). Emerging applications of metabolomics in drug discovery and precision medicine. (Alberta, Canada). Macmillan Publishers. 10.1038/nrd.2016.32.