Bioinformatics and Drug discovery

From DrugPedia: A Wikipedia for Drug discovery

Bioinformatics approaches incorporate expertise from the biological sciences, computer science, and mathematics. Due to the genome project and the resultant data explosion, it was then imperative to jell different fields of science together to exploit the available data and thus expedite the drug discovery process.

The pharmaceutical industry has embraced genomics as a source of drug targets. It also recognises that the field of bioinformatics is crucial for validating these potential drug targets and for determining which ones are the most suitable for entering the drug development pipeline. The elucidation of the human genome which has an estimated 30,000 to 40,000 genes, presents immense new opportunities for drug discovery and simultaneously creates a potential bottleneck regarding the choice of targets to support the drug discovery pipeline.

Bioinformatics can be thought of as a central hub that unites several disciplines and methodologies.

On the support side of the hub, Information Technology, software applications, databases and computational resources all provide the infrastructure for bioinformatics. On the scientific side of the hub, bioinformatic methods are used extensively in molecular biology, genomics, proteomics, and in CADD research.

The process of drug discovery within the modern scientific context is quite complex, integrating many disciplines, including structural biology, metabonomics, proteomics, and computer science, just to name a few. The process is generally quite tedious and expensive, given the sheer amount of possibilities of drug-to-target interactions in-vivo, and the necessity of successfully passing rigorous pharmacokinetic studies and toxicology assays prior to even being considered for clinical trials (Burbaum). Though a more detailed explanation is offered further into this text, several key components of the drug discovery process include target selection, lead identification, and preclinical and clinical candidate selection. The schematic on the right outlines the steps involved in the drug discovery process.

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Schematic of Drug discovery process

The challenge in the drug discovery process is to find the exact causes of an underlying disease and find a way to negate them or bring them to normal levels. A mechanistic understanding of the nature of the disease in question is essential if we are to elucidate any target-specific remedy for it. While the causes of many documented clinical problems vary greatly in their nature and origin, in some cases, the cause is found at the protein level, involving protein function, protein regulation, or protein-protein interactions. One example of such a disorder would be alkaptonuria, characterized by a defect in the gene coding for the enzyme homogentisic acid oxidase, inhibiting the metabolism of homogentisic acid to maleylacetoacetic acid, within the phenylalanine degradation pathway (Brooker). While the underlying cause of this inborn disease is due to a single gene genetic defect, the clinical manifestations, which include excretion of black urine, are a function of the built up of homogentisic acid resulting from a defective protein enzyme.

Recent advances in applied genomics helped in the target identification process, since it allowed for high throughput screening of expressed genes. However, studies have shown that there is a poor correlation between the regulation of transcripts and actual protein quantities. The reasons for this are that genome analysis does not account for post-translational processes such as protein modifications and protein degradation. Therefore, the methods employed in the drug-discovery process started to shift from genomics to proteomics (Burbaum). Analysis of the dynamic organismal proteome, as opposed to the static genome, will certainly bring a much more accurate approach to identifying not only applicable biomarkers that will aid in diagnosis, but also effective remedies for diseases of varying origins.

Computer-Aided Drug Design (CADD) is a specialized discipline that uses computational methods to simulate drug-receptor interactions. CADD methods are heavily dependent on bioinformatics tools, applications and databases. As such, there is considerable overlap in CADD research and bioinformatics. CADD and bioinformatics together are a powerful combination in drug research and development.

[edit] Drug Design based on Bioinformatics Tools

The process of designing a new drug using bioinformatics tools have open a new area of research. However, computational techniques assist one in searching drug target and in designing drug in silco, but it takes long time and money. In order to design a new drug one need to follow the path described below:

[edit] Identify Target Disease

One needs to know all about the disease and existing or traditional remedies. It is also important to look at very similar afflictions and their known treatments. Target identification alone is not sufficient in order to achieve a successful treatment of a disease. A real drug needs to be developed.This drug must influence the target protein in such a way that it does not interfere with normal metabolism. One way to achieve this is to block activity of the protein with a small molecule. Bioinformatics methods have been developed to virtually screen the target for compounds that bind and inhibit the protein. Another possibility is to find other proteins that regulate the activity of the target by binding and formiong a complex. Study Interesting Compounds: One needs to identify and study the lead compounds that have some activity against a disease. These may be only marginally useful and may have severe side effects. These compounds provide a starting point for refinement of the chemical structures.

[edit] Detect the Molecular Bases for Disease

If it is known that a drug must bind to a particular spot on a particular protein or nucleotide then a drug can be tailor made to bind at that site. This is often modeled computationally using any of several different techniques. Traditionally, the primary way of determining what compounds would be tested computationally was provided by the researchers' understanding of molecular interactions. A second method is the brute force testing of large numbers of compounds from a database of available structures.

[edit] Rational drug design techniques

These techniques attempt to reproduce the researchers' understanding of how to choose likely compounds built into a software package that is capable of modeling a very large number of compounds in an automated way. Many different algorithms have been used for this type of testing, many of which were adapted from artificial intelligence applications. The complexity of biological systems makes it very difficult to determine the structures of large biomolecules. Ideally experimentally determined (x-ray or NMR) structure is desired, but biomolecules are very difficult to crystallize.

[edit] Refinement of compounds

Once you got a number of lead compounds have been found, computational and laboratory techniques have been very successful in refining the molecular structures to give a greater drug activity and fewer side effects. This is done both in the laboratory and computationally by examining the molecular structures to determine which aspects are responsible for both the drug activity and the side effects.

[edit] Quantitative Structure Activity Relationships (QSAR)

This computational technique should be used to detect the functional group in your compound in order to refine your drug. This can be done using QSAR that consists of computing every possible number that can describe a molecule then doing an enormous curve fit to find out which aspects of the molecule correlate well with the drug activity or side effect severity. This information can then be used to suggest new chemical modifications for synthesis and testing.

[edit] Solubility of Molecule

One need to check whether the target molecule is water soluble or readily soluble in fatty tissue will affect what part of the body it becomes concentrated in. The ability to get a drug to the correct part of the body is an important factor in its potency. Ideally there is a continual exchange of information between the researchers doing QSAR studies, synthesis and testing. These techniques are frequently used and often very successful since they do not rely on knowing the biological basis of the disease which can be very difficult to determine.

[edit] Drug Testing

Once a drug has been shown to be effective by an initial assay technique, much more testing must be done before it can be given to human patients. Animal testing is the primary type of testing at this stage. Eventually, the compounds, which are deemed suitable at this stage, are sent on to clinical trials. In the clinical trials, additional side effects may be found and human dosages are determined.