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Bioinformatics and drug discovery


Ric Angat

on 9 October 2012

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Transcript of Bioinformatics and drug discovery

Several commercial drug metabolism or drug interaction databases now exist,

MDL Information Systems

University of Washington(Seattle)

Lhasa Limited,
a not-for-profit company based at the University of Leeds BIOINFORMATICS
DRUG DISCOVERY Bioinformatics Tools Study Interesting Compounds is a branch of biological science which deals with the study of methods for storing, retrieval, visualization, prediction and analyzing biological data, such as nucleic acid (DNA/RNA) and protein sequence, structure, function, pathways and genetic interactions. It generates new knowledge that is useful in such fields as drug design and development of new software tools to create that knowledge.
The ultimate goal of the field is to enable the discovery of new biological insights as well as to create a global perspective from which unifying principles in biology can be discerned Bioinformatics Identify Target Disease and Future prospects Conclusion a.) the development of new algorithms and statistics with which to assess relationships among members of large data sets b.) the analysis and interpretation of various types of data including nucleotide and amino acid sequences, protein domains, and protein structures c.) the development and implementation of tools that enable efficient access and management of different types of information 3 major Subclasses Identify target disease
Study Interesting Compounds
Detection the Molecular Bases for Disease
Rational Drug Design Techniques
Refinement of Compounds
Quantitative Structure Activity Relationships (QSAR)
Solubility of Molecule
Drug Testing 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.
Bioinformatics methods have been developed to virtually screen the target for compounds that bind and inhibit the protein. 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 pointfor refinement of the chemical structures 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 Finished 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 Refinement 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.
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. Quantitative Structure Activity Relationships (QSAR) Computational technique should be used to detect the functional group in your compound in order to refine your drug.
QSAR 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. 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. 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. Drug discovery Drug discovery is the process of discovering and designing drugs, which includes target identification, target validation, lead identification, lead optimization and introduction of the new drugs to the public. This process is very important, involving analyzing the causes of the diseases and finding ways to tackle them. Bioinformatics is playing an increasingly important role in nearly all aspects of drug discovery, drug assessment, and drug development. This growing importance lies not only in the role that bioinformatics plays in handling large volumes of data, but also in the utility of bioinformatics tools to predict, analyze, or help interpret clinical and preclinical findings. This report focuses on describing and evaluating some of the newer or more important bioinformatics resources (i.e., databases and software) that are of growing importance to understanding or predicting drug metabolism, especially with respect to the absorption, distribution, metabolism, excretion, (ADME), and toxicity (T) of both existing drugs and potential drug leads. Detailed descriptions and critical assessments of a number of potentially useful bioinformatics/cheminformatics databases and predictive ADMET software tools are provided. Introduction Genomics, proteomics, and metabolomics are profoundly changing the traditional approaches to drug discovery and development. Nowadays, potential drug targets are
increasingly being identified through high-throughput sequencing, through high-throughput microarray or two-dimensional (2D) gel experiments or through large-scale mass spectrometry, and many more. Of course with the increasing use of these high-throughput techniques, scientists are now finding that the greatest challenge lies not with the collection of the data, but with its storage, retrieval, analysis, and interpretation. Problem Overview In order to deal with this ‘‘data explosion,’’ scientists are increasingly turning toward computers and computer scientists to help. This has led to the emergence of two new fields in information science—bioinformatics and cheminformatics. Solution In a similar fashion, cheminformatics is primarily concerned with the handling of chemical data having pharmaceutical or synthetic importance. Because pharmaceutical research typically involves both chemical and biological analyses, a continuing challenge for pharmaceutical researchers is to combine bioinformatics with cheminformatics in and effective and coherent way. bioinformatics largely evolving to a noncommerical, Web-based and open-source model, cheminformatics has evolved to a somewhat more commercial, closed-source, or limited access model. DATABASES SNP Mutation Database Metebolism and Metabolic pathway Disease Drug Metabolism and Drug Interaction Databases Data and databases are key to both bioinformatics and cheminformatics. Without large quantities of easily accessible electronic data, most kinds of data searches would prove to be fruitless, and most kinds of predictive or analytical software could never be developed or tested. Not only is the quantity of biological or chemical data important, but so too is the quality. Generally speaking there are four types of bioinformatics or cheminformatics databases: archival, curated, commercial, and noncommerical. Archival Curated Curated databases are specialized data resources maintained by one or more curators who select, input, or invite only the ‘‘highest-quality’’ data from selected niches. The quality of the data is of the utmost importance, whereas the quantity of data is secondary. In pharmaceutical research, curated databases are generally of greatest interest,because they offer both the high-quality content and the specialized information (primarily with respect to humans) that is needed by most pharmaceutical researchers. Commercial Non-Commercial Archival Curated Archival databases (which are almost always public) accept or include data ‘‘as is’’ from data depositors with relatively modest checking or validation. Their purpose is to be an open-access repository, and their goal is to accumulate as much data as possible. curated databases are specialized data resources maintained by one or more curators who select, input, or invite only the ‘‘highest-quality’’ data from selected niches. The quality of the data is of the utmost importance, whereas the quantity of data is secondary. In pharmaceutical research, curated databases are generally of greatest interest,because they offer both the high-quality content and the specialized information (primarily with respect to humans) that is needed by most pharmaceutical researchers. we will discuss four different types of curated databases that are to believe have a particular importance to drug metabolism research. These include sequence or sequence annotation databases, SNP or mutation databases, general metabolism databases, and finally, drug metabolism/interaction databases. Sequence and Sequence Annotation Database Gene and protein sequence data are now critical to almost all aspects of pharmaceutical research. For instance the sequencing of the human, mouse, and rat genomes has led to the identification of more than 30 families of drug-metabolizing enzymes along with dozens of potentially new protein drug targets Sequence and sequence annotation data play a key role not only in identifying possible protein targets, but also in providing a basis to our understanding of the mechanism or process by which a drug may work, where it may target, or how it might be metabolized. The two primary providers and curators of annotated sequence databases are the

National Centre for Biotechnology Information (NCBI) in Bethesda, Maryland (USA), and the

European Bioinformatics Institute (EBI) in Hinxton-Head (UK). Another class of sequence databases of growing importance in pharmaceutical (especially pharmacogenomic) research are the SNP (single nucleotide polymorphism) and mutation databases. Single nucleotide polymorphisms (SNPs) are DNA sequence variations that occur when a single nucleotide (A,T,C, or G) in the genome sequence is altered. SNPs are highly relevant to drug metabolism. For instance, SNPs and mutations in drug-metabolizing enzymes are known to lead to large differences in drug responses and drug exposure between individual subjects. Some of these responses can prove to be quite toxic, even fatal These adverse drug reactions (ADRs) now account for up to 50% of the reported clinical trial failures for IND (Investigational New Drug) applications. Small molecules account for more than 99% of all FDA-approved drugs and still constitute the vast majority of all IND applications. Interestingly, most small-molecule drugs are designed or chosen to mimic existing small-molecule metabolites. It is also important to remember that most drugs, prodrugs, or xenobiotics are either targeted to, or metabolized by, preexisting metabolic pathways that originally evolved to handle endogenous metabolites. Therefore, the selection of successful drug candidates or drug targets along with an understanding of their metabolic effects and fates is highly dependent on our understanding of an organism’s (both target and host) general metabolism. While general metabolism and pathway databases are playing an increasingly important role in drug development and assessment, another class of databases is emerging that is probably much more relevant to pharmaceutical researchers—the drug metabolism and drug interaction databases. These databases focus much more directly on known drugs or drug metabolites and attempt to link the genomic/proteomic information being gathered about the relevant genes or proteins with the drug compounds themselves. Bioinformatics transforming pharmaceutical research. Not only are these computational techniques having an impact on the early phases of drug discovery, but so too are they having an impact further down the developmental pipeline. From the samples/ methods we've presented:
we have understand how bioinformatics databases can be used to explore and explain the molecular-level properties of newly sequenced genes or variants of genes (SNPs and mutations).
We have also known how other kinds of bioinformatics databases can be used to explain or extend our understanding of xenobiotic metabolism or to identify potential metabolic interference from existing enzymes and pathways.
we have seen how the information being accumulated in these databases can be used to make predictive software that employs powerful machine-learning or artificial intelligence techniques to extract patterns from the data. In the future, we can expect that the scope of predictive or analytical methods in drug metabolism and ADMET will grow, and so to will the score or breadth of information contained in many bioinformatics or cheminformatic databases. It is not unreasonable to expect that in the near future, data will be collected on human physiological, genetic, metabolic, and even epidemiological information.
Given the rapid pace of development in bioinformatics, we can only expect to be continuously surprised by the power of the computer and the innovative ideas coming from the people who program them. Given the importance of host/target metabolism in drug development and assessment, it is little wonder that more drug researchers are finding that metabolic pathway databases can play a role in drug research programs. These blended bioinformatics–cheminformatic databases can provide detailed,organism-specific information (proteins, pathways, chemical structures, etc.) about primary and secondary metabolism for hundreds of different organisms and thousands of different compounds. Another equally comprehensive database is called MetaCyc developed by Peter Karp at the Stanford Research Instititute Most widely known metabolic pathway resource is the Kyoto Encyclopedia of Genes and Genomes or KEGG As a rule, drug metabolism/interaction databases are highly curated, so they tend to be much smaller and less comprehensive than the archival sequence, SNP, and metabolism databases already described. The oldest database is the SNP Consortium database Other useful SNP database are:

Human Genome Variation database
(HGVbase, based in Sweden).


PolySearch (Poly-morphism Search). THE End Reporters:
John Eric Angat
Miguel Ibazeta
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