APPLICATIONS OF BIOINFORMATICS IN DRUG DISCOVERY AND PROCESS

APPLICATIONS OF BIOINFORMATICS IN DRUG DISCOVERY AND PROCESS RESEARCH Dr. Basavaraj K. Nanjwade M. Pharm. , Ph. D Associate Professor Department of Pharmaceutics JN Medical College KLE University, Belgaum- 590010 20/03/2008 Dept. of Pharmaceutics 1

Bioinformatics n Application of CS and informatics to biological and Drug Development science n Bioinformatics is the field of science in which biology, computer science, and information technology merge to form a single discipline. n 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 20/03/2008 Dept. of Pharmaceutics 2

Bioinformatics Hub 20/03/2008 Dept. of Pharmaceutics 3

Bioinformatics Tools The processes 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 following path. 1. 2. 3. 4. 5. 6. 7. 8. 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 20/03/2008 Dept. of Pharmaceutics 4

Bioinformatics Tools n Identify Target Disease: - 1. 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. 2. Target identification alone is not sufficient in order to achieve a successful treatment of a disease. A real drug needs to be developed. 20/03/2008 Dept. of Pharmaceutics 5

Bioinformatics Tools n Identify Target Disease: - 3. This drug must influence the target protein in such a way that it does not interfere with normal metabolism. 4. Bioinformatics methods have been developed to virtually screen the target for compounds that bind and inhibit the protein. 20/03/2008 Dept. of Pharmaceutics 6

Bioinformatics Tools Study Interesting Compounds: 1. One needs to identify and study the lead compounds that have some activity against a disease. 2. These may be only marginally useful and may have severe side effects. 3. These compounds provide a starting point for refinement of the chemical structures. n 20/03/2008 Dept. of Pharmaceutics 7

Bioinformatics Tools n Detect the Molecular Bases for Disease: - 1. 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. 2. This is often modeled computationally using any of several different techniques. 20/03/2008 Dept. of Pharmaceutics 8

Bioinformatics Tools n Detect the Molecular Bases for Disease: - 3. Traditionally, the primary way of determining what compounds would be tested computationally was provided by the researchers' understanding of molecular interactions. 4. A second method is the brute force testing of large numbers of compounds from a database of available structures. 20/03/2008 Dept. of Pharmaceutics 9

Bioinformatics Tools n Rational drug design techniques: - 1. 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. 2. Many different algorithms have been used for this type of testing, many of which were adapted from artificial intelligence applications. 20/03/2008 Dept. of Pharmaceutics 10

Bioinformatics Tools n Rational drug design techniques: - 3. The complexity of biological systems makes it very difficult to determine the structures of large biomolecules. 4. Ideally experimentally determined (x-ray or NMR) structure is desired, but biomolecules are very difficult to crystallize 20/03/2008 Dept. of Pharmaceutics 11

Bioinformatics Tools n Refinement of compounds: - 1. 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. 20/03/2008 Dept. of Pharmaceutics 12

Bioinformatics Tools n Refinement of compounds: - 2. 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. 20/03/2008 Dept. of Pharmaceutics 13

Bioinformatics Tools n Quantitative Structure Activity Relationships (QSAR): - 1. Computational technique should be used to detect the functional group in your compound in order to refine your drug. 2. 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. 3. This information can then be used to suggest new chemical modifications for synthesis and testing. 20/03/2008 Dept. of Pharmaceutics 14

Bioinformatics Tools n Solubility of Molecule: - 1. 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. 2. The ability to get a drug to the correct part of the body is an important factor in its potency. 20/03/2008 Dept. of Pharmaceutics 15

Bioinformatics Tools n Solubility of Molecule: - 3. Ideally there is a continual exchange of information between the researchers doing QSAR studies, synthesis and testing. 4. 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. 20/03/2008 Dept. of Pharmaceutics 16

Bioinformatics Tools n Drug Testing: - 1. 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. 2. 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. 3. In the clinical trials, additional side effects may be found and human dosages are determined. 20/03/2008 Dept. of Pharmaceutics 17

Structure Prediction flow chart 20/03/2008 Dept. of Pharmaceutics 18

Computer-Aided Drug Design (CADD) n Computer-Aided Drug Design (CADD) is a specialized discipline that uses computational methods to simulate drug-receptor interactions. n CADD methods are heavily dependent on bioinformatics tools, applications and databases. As such, there is considerable overlap in CADD research and bioinformatics. 20/03/2008 Dept. of Pharmaceutics 19

Bioinformatics Supports CADD Research n Virtual High-Throughput Screening (v. HTS): - 1. Pharmaceutical companies are always searching for new leads to develop into drug compounds. 2. One search method is virtual high-throughput screening. In v. HTS, protein targets are screened against databases of small-molecule compounds to see which molecules bind strongly to the target. 20/03/2008 Dept. of Pharmaceutics 20

Bioinformatics Supports CADD Research n Virtual High-Throughput Screening (v. HTS): - 3. If there is a “hit” with a particular compound, it can be extracted from the database for further testing. 4. With today’s computational resources, several million compounds can be screened in a few days on sufficiently large clustered computers. 5. Pursuing a handful of promising leads for further development can save researchers considerable time and expense. e. g. . ZINC is a good example of a v. HTS compound library. 20/03/2008 Dept. of Pharmaceutics 21

Bioinformatics Supports CADD Research n Sequence Analysis: - 1. In CADD research, one often knows the genetic sequence of multiple organisms or the amino acid sequence of proteins from several species. 2. It is very useful to determine how similar or dissimilar the organisms are based on gene or protein sequences. 20/03/2008 Dept. of Pharmaceutics 22

Bioinformatics Supports CADD Research n Sequence Analysis: - 3. With this information one can infer the evolutionary relationships of the organisms, search for similar sequences in bioinformatic databases and find related species to those under investigation. 4. There are many bioinformatic sequence analysis tools that can be used to determine the level of sequence similarity. 20/03/2008 Dept. of Pharmaceutics 23

Bioinformatics Supports CADD Research n Homology Modeling: - 1. Another common challenge in CADD research is determining the 3 -D structure of proteins. 2. Most drug targets are proteins, so it’s important to know their 3 -D structure in detail. It’s estimated that the human body has 500, 000 to 1 million proteins. 3. However, the 3 -D structure is known for only a small fraction of these. Homology modeling is one method used to predict 3 -D structure. 20/03/2008 Dept. of Pharmaceutics 24

Bioinformatics Supports CADD Research n Homology Modeling: - 4. In homology modeling, the amino acid sequence of a specific protein (target) is known, and the 3 -D structures of proteins related to the target (templates) are known. 5. Bioinformatics software tools are then used to predict the 3 -D structure of the target based on the known 3 -D structures of the templates. 6. MODELLER is a well-known tool in homology modeling, and the SWISS-MODEL Repository is a database of protein structures created with homology modeling. 20/03/2008 Dept. of Pharmaceutics 25

Bioinformatics Supports CADD Research n Similarity Searches: - 1. A common activity in biopharmaceutical companies is the search for drug analogues. 2. Starting with a promising drug molecule, one can search for chemical compounds with similar structure or properties to a known compound. 3. There a variety of methods used in these searches, including sequence similarity, 2 D and 3 D shape similarity, substructure similarity, electrostatic similarity and others. 4. A variety of bioinformatic tools and search engines are 4. A variety of available for this work 20/03/2008 Dept. of Pharmaceutics 26

Bioinformatics Supports CADD Research n Drug Lead Optimization: - 1. When a promising lead candidate has been found in a drug discovery program, the next step (a very long and expensive step!) is to optimize the structure and properties of the potential drug. 2. This usually involves a series of modifications to the primary structure (scaffold) and secondary structure (moieties) of the compound. 20/03/2008 Dept. of Pharmaceutics 27

Bioinformatics Supports CADD Research n Drug Lead Optimization: - 3. This process can be enhanced using software tools that explore related compounds (bioisosteres) to the lead candidate. Open. Eye’s WABE is one such tool. 4. Lead optimization tools such as WABE offer a rational approach to drug design that can reduce the time and expense of searching for related compounds. 20/03/2008 Dept. of Pharmaceutics 28

Bioinformatics Supports CADD Research n Physicochemical Modeling: - 1. Drug-receptor interactions occur on atomic scales. 2. To form a deep understanding of how and why drug compounds bind to protein targets, we must consider the biochemical and biophysical properties of both the drug itself and its target at an atomic level. 3. Swiss-PDB is an excellent tool for doing this. Swiss-PDB can predict key physicochemical properties, such as hydrophobicity and polarity that have a profound influence on how drugs bind to proteins. 20/03/2008 Dept. of Pharmaceutics 29

Bioinformatics Supports CADD Research n Drug Bioavailability and Bioactivity: - 1. Most drug candidates fail in Phase III clinical trials after many years of research and millions of dollars have been spent on them. And most fail because of toxicity or problems with metabolism. 2. The key characteristics for drugs are Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) and efficacy—in other words bioavailability and bioactivity. 3. Although these properties are usually measured in the lab, they can also be predicted in advance with bioinformatics software. 20/03/2008 Dept. of Pharmaceutics 30

Benefits of CADD n Cost Savings: - 1. The Tufts Report suggests that the cost of drug discovery and development has reached $800 million for each drug successfully brought to market. 2. Many biopharmaceutical companies now use computational methods and bioinformatics tools to reduce this cost burden. 20/03/2008 Dept. of Pharmaceutics 31

Benefits of CADD n Cost Savings: - 3. Virtual screening, lead optimization and predictions of bioavailability and bioactivity can help guide experimental research. 4. Only the most promising experimental lines of inquiry can be followed and experimental deadends can be avoided early based on the results of CADD simulations. 20/03/2008 Dept. of Pharmaceutics 32

Benefits of CADD Time-to-Market: - 1. The predictive power of CADD can help drug research programs choose only the most promising drug candidates. 2. By focusing drug research on specific lead candidates and avoiding potential “dead-end” compounds, biopharmaceutical companies can get drugs to market more quickly. 20/03/2008 Dept. of Pharmaceutics 33

Benefits of CADD n Insight: - 1. One of the non-quantifiable benefits of CADD and the use of bioinformatics tools is the deep insight that researchers acquire about drugreceptor interactions. 2. Molecular models of drug compounds can reveal intricate, atomic scale binding properties that are difficult to envision in any other way. 20/03/2008 Dept. of Pharmaceutics 34

Benefits of CADD n Insight: - 1. When we show researchers new molecular models of their putative drug compounds, their protein targets and how the two bind together, they often come up with new ideas on how to modify the drug compounds for improved fit. 2. This is an intangible benefit that can help design research programs. 20/03/2008 Dept. of Pharmaceutics 35

CADD n CADD and bioinformatics together are a powerful combination in drug research and development. n An important challenge for us going forward is finding skilled, experienced people to manage all the bioinformatics tools available to us, which will be a topic for a future article. 20/03/2008 Dept. of Pharmaceutics 36

Research Achievements n Software developed n Bioinformatics data base developed n Traditional medicine research tools developed 20/03/2008 Dept. of Pharmaceutics 37

Software developed 1. SVMProt: Protein function prediction software http: //jing. cz 3. nus. edu. sg/cgi-bin/svmprot. cgi 2. INVDOCK: Drug target prediction software 3. Mo. Vi. ES: Molecular vibrations evaluation server http: //ang. cz 3. nus. edu. sg/cgi-bin/prog/norm. pl 20/03/2008 Dept. of Pharmaceutics 38

Bioinformatics database developed 1. Therapeutic target database http: //xin. cz 3. nus. edu. sg/group/cjttd/ttd. asp 2. Drug adverse reaction target database http: //xin. cz 3. nus. edu. sg/group/drt/dart. asp 3. Drug ADME associated protein database http: //xin. cz 3. nus. edu. sg/group/admeap. asp 4. Kinetic data of biomolecular interactions database http: //xin. cz 3. nus. edu. sg/group/kdbi. asp 5. Computed ligand binding energy database http: //xin. cz 3. nus. edu. sg/group/CLi. BE. asp 20/03/2008 Dept. of Pharmaceutics 39

Traditional medicine research tools developed 1. Traditional medicine information database 2. Herbal ingredient and content database 3. Natural product effect and consumption info system 4. Traditional medicine recipe prediction and validation system 5. Herbal target identification system 20/03/2008 Dept. of Pharmaceutics 40

THANK YOU E-mail: bknanjwade@yahoo. co. in 20/03/2008 Dept. of Pharmaceutics 41