Applications Of Bioinformatics In Drug Discovery And Process

20/03/200820/03/2008 Dept. of PharmaceuticsDept. of Pharmaceutics 11

APPLICATIONS OF APPLICATIONS OF BIOINFORMATICS IN DRUG BIOINFORMATICS IN DRUG DISCOVERY AND PROCESS DISCOVERY AND PROCESS

RESEARCHRESEARCH

Dr. Basavaraj K. Nanjwade Dr. Basavaraj K. Nanjwade M.Pharm., Ph.DM.Pharm., Ph.D

Associate ProfessorAssociate ProfessorDepartment of PharmaceuticsDepartment of Pharmaceutics

JN Medical CollegeJN Medical CollegeKLE University, KLE University,

Belgaum- 590010Belgaum- 590010


BioinformaticsBioinformatics Application of CS and informatics to biological Application of CS and informatics to biological

and Drug Development scienceand Drug Development science

Bioinformatics is the field of science in which Bioinformatics is the field of science in which biology, computer science, and information biology, computer science, and information technology merge to form a single discipline. technology merge to form a single discipline.

The ultimate goal of the field is to enable the The ultimate goal of the field is to enable the discovery of new biological insights as well as discovery of new biological insights as well as to create a global perspective from which to create a global perspective from which unifying principles in biology can be discernedunifying principles in biology can be discerned


Bioinformatics HubBioinformatics Hub


Bioinformatics ToolsBioinformatics Tools The processes of designing a new drug using The processes of designing a new drug using

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

1.1. Identify target diseaseIdentify target disease

2.2. Study Interesting CompoundsStudy Interesting Compounds

3.3. Detection the Molecular Bases for DiseaseDetection the Molecular Bases for Disease

4.4. Rational Drug Design TechniquesRational Drug Design Techniques

5.5. Refinement of CompoundsRefinement of Compounds

6.6. Quantitative Structure Activity Relationships (QSAR)Quantitative Structure Activity Relationships (QSAR)

7.7. Solubility of MoleculeSolubility of Molecule

8.8. Drug Testing Drug Testing


Bioinformatics ToolsBioinformatics Tools

Identify Target Disease:-Identify Target Disease:-

1. One needs to know all about the disease 1. One needs to know all about the disease and existing or traditional remedies. It is and existing or traditional remedies. It is also important to look at very similar also important to look at very similar afflictions and their known treatments. afflictions and their known treatments.

2. Target identification alone is not sufficient 2. Target identification alone is not sufficient in order to achieve a successful treatment in order to achieve a successful treatment of a disease. A real drug needs to be of a disease. A real drug needs to be developed. developed.


Bioinformatics ToolsBioinformatics Tools Identify Target Disease:-Identify Target Disease:-

3. 3. This drug must influence the target This drug must influence the target protein in such a way that it does not protein in such a way that it does not interfere with normal metabolism. interfere with normal metabolism.

4. Bioinformatics methods have been 4. Bioinformatics methods have been developed to virtually screen the target for developed to virtually screen the target for compounds that bind and inhibit the compounds that bind and inhibit the protein.protein.



Study Interesting Compounds:- Study Interesting Compounds:- 1.1. One needs to identify and study the lead One needs to identify and study the lead compounds that have some activity compounds that have some activity

against a disease. against a disease. 2. These may be only marginally useful and 2. These may be only marginally useful and may have severe side effects. may have severe side effects. 3. These compounds provide a starting point3. These compounds provide a starting point for refinement of the chemical structures. for refinement of the chemical structures.



Detect the Molecular Bases for Detect the Molecular Bases for Disease:- Disease:-

1.1. If it is known that a drug must bind to a If it is known that a drug must bind to a particular spot on a particular protein or particular spot on a particular protein or nucleotide then a drug can be tailor nucleotide then a drug can be tailor made to bind at that site.made to bind at that site.

2.2. This is often modeled computationally This is often modeled computationally using any of several different techniques. using any of several different techniques.



Detect the Molecular Bases for Disease:-Detect the Molecular Bases for Disease:-

3. Traditionally, the primary way of 3. Traditionally, the primary way of determining what compounds would be determining what compounds would be tested computationally was provided by tested computationally was provided by the researchers' understanding of the researchers' understanding of molecular interactions. molecular interactions.

4. A second method is the brute force 4. A second method is the brute force testing of large numbers of compounds testing of large numbers of compounds from a database of available structures. from a database of available structures.



Rational drug design techniques:- Rational drug design techniques:- 1. These techniques attempt to reproduce the 1. These techniques attempt to reproduce the

researchers' understanding of how to choose researchers' understanding of how to choose likely compounds built into a software likely compounds built into a software package that is capable of modeling a very package that is capable of modeling a very large number of compounds in an large number of compounds in an automated way. automated way.

2. Many different algorithms have been used 2. Many different algorithms have been used for this type of testing, many of which were for this type of testing, many of which were adapted from artificial intelligence adapted from artificial intelligence applications. applications.



Rational drug design techniques:-Rational drug design techniques:-

3. The complexity of biological systems 3. The complexity of biological systems makes it very difficult to determine the makes it very difficult to determine the structures of large biomolecules. structures of large biomolecules.

4. Ideally experimentally determined (x-ray 4. Ideally experimentally determined (x-ray or NMR) structure is desired, but or NMR) structure is desired, but biomolecules are very difficult to biomolecules are very difficult to crystallize crystallize



Refinement of compounds:- Refinement of compounds:-

1. Once you got a number of lead 1. Once you got a number of lead compounds have been found, compounds have been found, computational and laboratory computational and laboratory techniques have been very successful techniques have been very successful in refining the molecular structures to in refining the molecular structures to give a greater drug activity and fewer give a greater drug activity and fewer side effects. side effects.



Refinement of compounds:-Refinement of compounds:-

2. Done both in the laboratory and 2. Done both in the laboratory and computationally by examining the computationally by examining the molecular structures to determine molecular structures to determine which aspects are responsible for which aspects are responsible for both the drug activity and the side both the drug activity and the side effects. effects.



Quantitative Structure Activity Relationships Quantitative Structure Activity Relationships (QSAR):- (QSAR):-

1. Computational technique should be used to detect 1. Computational technique should be used to detect the functional group in your compound in order to the functional group in your compound in order to refine your drug. refine your drug.

2. QSAR consists of computing every possible number 2. QSAR consists of computing every possible number that can describe a molecule then doing an that can describe a molecule then doing an enormous curve fit to find out which aspects of the enormous curve fit to find out which aspects of the molecule correlate well with the drug activity or side molecule correlate well with the drug activity or side effect severity. effect severity.

3. This information can then be used to suggest new 3. This information can then be used to suggest new chemical modifications for synthesis and testing. chemical modifications for synthesis and testing.



Solubility of Molecule:- Solubility of Molecule:-

1. One need to check whether the target 1. One need to check whether the target molecule is water soluble or readily soluble molecule is water soluble or readily soluble in fatty tissue will affect what part of the in fatty tissue will affect what part of the body it becomes concentrated in. body it becomes concentrated in.

2. The ability to get a drug to the correct part 2. The ability to get a drug to the correct part of the body is an important factor in its of the body is an important factor in its potency. potency.



Solubility of Molecule:-Solubility of Molecule:-

3. Ideally there is a continual exchange of 3. Ideally there is a continual exchange of information between the researchers information between the researchers doing QSAR studies, synthesis and testing. doing QSAR studies, synthesis and testing.

4. These techniques are frequently used and 4. These techniques are frequently used and often very successful since they do not often very successful since they do not rely on knowing the biological basis of the rely on knowing the biological basis of the disease which can be very difficult to disease which can be very difficult to determine. determine.



Drug Testing:- Drug Testing:-

1. Once a drug has been shown to be effective by an 1. Once a drug has been shown to be effective by an initial assay technique, much more testing must be initial assay technique, much more testing must be done before it can be given to human patients. done before it can be given to human patients.

2. Animal testing is the primary type of testing at this 2. Animal testing is the primary type of testing at this stage. Eventually, the compounds, which are stage. Eventually, the compounds, which are deemed suitable at this stage, are sent on to deemed suitable at this stage, are sent on to clinical trials. clinical trials.

3. In the clinical trials, additional side effects may be 3. In the clinical trials, additional side effects may be found and human dosages are determined. found and human dosages are determined.


Structure Prediction flow Structure Prediction flow chartchart


Computer-Aided Drug Design Computer-Aided Drug Design (CADD)(CADD)

Computer-Aided Drug Design (CADD) is a Computer-Aided Drug Design (CADD) is a specialized discipline that uses specialized discipline that uses computational methods to simulate drug-computational methods to simulate drug-receptor interactions. receptor interactions.

CADD methods are heavily dependent on CADD methods are heavily dependent on bioinformatics tools, applications and bioinformatics tools, applications and databases. As such, there is considerable databases. As such, there is considerable overlap in CADD research and overlap in CADD research and bioinformatics. bioinformatics.


Bioinformatics Supports CADD Research Bioinformatics Supports CADD Research Virtual High-Throughput Screening Virtual High-Throughput Screening

(vHTS):-(vHTS):-

1. Pharmaceutical companies are always 1. Pharmaceutical companies are always searching for new leads to develop into drug searching for new leads to develop into drug compounds. compounds.

2. One search method is virtual high-throughput 2. One search method is virtual high-throughput screening. In vHTS, protein targets are screened screening. In vHTS, protein targets are screened against databases of small-molecule compounds against databases of small-molecule compounds to see which molecules bind strongly to the to see which molecules bind strongly to the target. target.


Bioinformatics Supports CADD Bioinformatics Supports CADD ResearchResearch

Virtual High-Throughput Screening (vHTS):-Virtual High-Throughput Screening (vHTS):-

3. If there is a “hit” with a particular compound, it can 3. If there is a “hit” with a particular compound, it can be extracted from the database for further testing. be extracted from the database for further testing.

4. With today’s computational resources, several 4. With today’s computational resources, several million compounds can be screened in a few days million compounds can be screened in a few days on sufficiently large clustered computers. on sufficiently large clustered computers.

5. Pursuing a handful of promising leads for further 5. Pursuing a handful of promising leads for further development can save researchers considerable development can save researchers considerable time and expense. time and expense.

e.g.. ZINC is a good example of a vHTS compound e.g.. ZINC is a good example of a vHTS compound library. library.


Bioinformatics Supports CADD ResearchBioinformatics Supports CADD Research

Sequence Analysis:-Sequence Analysis:-

1. In CADD research, one often knows the 1. In CADD research, one often knows the genetic sequence of multiple organisms or genetic sequence of multiple organisms or the amino acid sequence of proteins from the amino acid sequence of proteins from several species. several species.

2. It is very useful to determine how similar 2. It is very useful to determine how similar or dissimilar the organisms are based on or dissimilar the organisms are based on gene or protein sequences. gene or protein sequences.


Sequence Analysis:-Sequence Analysis:-

3. With this information one can infer the 3. With this information one can infer the evolutionary relationships of the evolutionary relationships of the organisms, search for similar sequences in organisms, search for similar sequences in bioinformatic databases and find related bioinformatic databases and find related species to those under investigation. species to those under investigation.

4. There are many bioinformatic sequence 4. There are many bioinformatic sequence analysis tools that can be used to analysis tools that can be used to determine the level of sequence determine the level of sequence similarity. similarity.




Homology Modeling:-Homology Modeling:-

1.1. Another common challenge in CADD research Another common challenge in CADD research is determining the 3-D structure of proteins. is determining the 3-D structure of proteins.

2. Most drug targets are proteins, so it’s 2. Most drug targets are proteins, so it’s important to know their 3-D structure in important to know their 3-D structure in detail. It’s estimated that the human body detail. It’s estimated that the human body has 500,000 to 1 million proteins. has 500,000 to 1 million proteins.

3. However, the 3-D structure is known for only 3. However, the 3-D structure is known for only a small fraction of these. Homology modeling a small fraction of these. Homology modeling is one method used to predict 3-D structure. is one method used to predict 3-D structure.



Homology Modeling:-Homology Modeling:-

4. In homology modeling, the amino acid sequence of 4. In homology modeling, the amino acid sequence of a specific protein (target) is known, and the 3-D a specific protein (target) is known, and the 3-D structures of proteins related to the target structures of proteins related to the target (templates) are known. (templates) are known.

5. Bioinformatics software tools are then used to 5. Bioinformatics software tools are then used to predict the 3-D structure of the target based on the predict the 3-D structure of the target based on the known 3-D structures of the templates. known 3-D structures of the templates.

6. 6. MODELLERMODELLER is a well-known tool in homology is a well-known tool in homology modeling, and the modeling, and the SWISS-MODELSWISS-MODEL Repository is a Repository is a database of protein structures created with database of protein structures created with homology modeling. homology modeling.


Bioinformatics Supports CADD ResearchBioinformatics Supports CADD Research Similarity Searches:-Similarity Searches:-

1. 1. A common activity in biopharmaceutical A common activity in biopharmaceutical companies is the search for drug analogues. companies is the search for drug analogues.

2. Starting with a promising drug molecule, one can 2. Starting with a promising drug molecule, one can search for chemical compounds with similar search for chemical compounds with similar structure or properties to a known compound. structure or properties to a known compound.

3. There are a variety of methods used in these 3. There are a variety of methods used in these searches, including sequence similarity, 2D and searches, including sequence similarity, 2D and 3D shape similarity, substructure similarity, 3D shape similarity, substructure similarity, electrostatic similarity and others. electrostatic similarity and others.

4. A variety of 4. A variety of bioinformatic tools and search engines are available for this work are available for this work



Drug Lead Optimization:-Drug Lead Optimization:- 1. When a promising lead candidate has been found 1. When a promising lead candidate has been found

in a drug discovery program, the next step (a in a drug discovery program, the next step (a very long and expensive step!) is to optimize the very long and expensive step!) is to optimize the structure and properties of the potential drug. structure and properties of the potential drug.

2. This usually involves a series of modifications to 2. This usually involves a series of modifications to the primary structure (scaffold) and secondary the primary structure (scaffold) and secondary structure (moieties) of the compound. structure (moieties) of the compound.



Drug Lead Optimization:-Drug Lead Optimization:-

3. This process can be enhanced using 3. This process can be enhanced using software tools that explore related software tools that explore related compounds (bioisosteres) to the lead compounds (bioisosteres) to the lead candidate. candidate. OpenEye’s WABE is one such OpenEye’s WABE is one such tool. tool.

4. Lead optimization tools such as 4. Lead optimization tools such as WABEWABE offer offer a rational approach to drug design that can a rational approach to drug design that can reduce the time and expense of searching reduce the time and expense of searching for related compounds. for related compounds.



Physicochemical Modeling:-Physicochemical Modeling:-

1. Drug-receptor interactions occur on atomic scales. 1. Drug-receptor interactions occur on atomic scales.

2. To form a deep understanding of how and why drug 2. To form a deep understanding of how and why drug compounds bind to protein targets, we must consider compounds bind to protein targets, we must consider the biochemical and biophysical properties of both the the biochemical and biophysical properties of both the drug itself and its target at an atomic level. drug itself and its target at an atomic level.

3. Swiss-PDB is an excellent tool for doing this. Swiss-3. Swiss-PDB is an excellent tool for doing this. Swiss-PDBPDB

can predict key physicochemical properties, such as can predict key physicochemical properties, such as hydrophobicity and polarity that have a profound hydrophobicity and polarity that have a profound influence on how drugs bind to proteins. influence on how drugs bind to proteins.



Drug Bioavailability and Bioactivity:-Drug Bioavailability and Bioactivity:-

1. Most drug candidates fail in Phase III clinical trials 1. Most drug candidates fail in Phase III clinical trials after many years of research and millions of after many years of research and millions of dollars have been spent on them. And most fail dollars have been spent on them. And most fail because of toxicity or problems with metabolism. because of toxicity or problems with metabolism.

2. The key characteristics for drugs are Absorption, 2. The key characteristics for drugs are Absorption, Distribution, Metabolism, Excretion, Toxicity Distribution, Metabolism, Excretion, Toxicity (ADMET) and efficacy—in other words (ADMET) and efficacy—in other words bioavailability and bioactivity. bioavailability and bioactivity.

3. Although these properties are usually measured 3. Although these properties are usually measured in the lab, they can also be predicted in advance in the lab, they can also be predicted in advance with bioinformatics software. with bioinformatics software.


Benefits of CADDBenefits of CADD Cost Savings:-Cost Savings:-

1. The Tufts Report suggests that the cost of 1. The Tufts Report suggests that the cost of drug discovery and development has drug discovery and development has reached $800 million for each drug reached $800 million for each drug successfully brought to market. successfully brought to market.

2. Many biopharmaceutical companies now 2. Many biopharmaceutical companies now use computational methods and use computational methods and bioinformatics tools to reduce this cost bioinformatics tools to reduce this cost burden. burden.


Benefits of CADDBenefits of CADD

Cost Savings:-Cost Savings:-

3. Virtual screening, lead optimization and 3. Virtual screening, lead optimization and predictions of bioavailability and bioactivity predictions of bioavailability and bioactivity can help guide experimental research. can help guide experimental research.

4. Only the most promising experimental lines 4. Only the most promising experimental lines of inquiry can be followed and experimental of inquiry can be followed and experimental dead-ends can be avoided early based on dead-ends can be avoided early based on the results of CADD simulations. the results of CADD simulations.



Time-to-Market:-Time-to-Market:-

1. The predictive power of CADD can help 1. The predictive power of CADD can help drug research programs choose only the drug research programs choose only the most promising drug candidates. most promising drug candidates.

2. By focusing drug research on specific lead 2. By focusing drug research on specific lead candidates and avoiding potential “dead-candidates and avoiding potential “dead-end” compounds, biopharmaceutical end” compounds, biopharmaceutical companies can get drugs to market more companies can get drugs to market more quickly. quickly.



Insight:-Insight:-

1. One of the non-quantifiable benefits of 1. One of the non-quantifiable benefits of CADD and the use of bioinformatics tools CADD and the use of bioinformatics tools is the deep insight that researchers is the deep insight that researchers acquire about drug-receptor interactions. acquire about drug-receptor interactions.

2. Molecular models of drug compounds can 2. Molecular models of drug compounds can reveal intricate, atomic scale binding reveal intricate, atomic scale binding properties that are difficult to envision in properties that are difficult to envision in any other way. any other way.


Benefits of CADDBenefits of CADD Insight:-Insight:-

1. When we show researchers new 1. When we show researchers new molecular models of their putative drug molecular models of their putative drug compounds, their protein targets and how compounds, their protein targets and how the two bind together, they often come up the two bind together, they often come up with new ideas on how to modify the drug with new ideas on how to modify the drug compounds for improved fit. compounds for improved fit.

2. This is an intangible benefit that can help 2. This is an intangible benefit that can help design research programs. design research programs.


CADDCADD

CADD and bioinformatics together CADD and bioinformatics together are a powerful combination in drug are a powerful combination in drug research and development. research and development.

An important challenge for us going An important challenge for us going forward is finding skilled, experienced forward is finding skilled, experienced people to manage all the people to manage all the bioinformatics tools available to us, bioinformatics tools available to us, which will be a topic for a future which will be a topic for a future article. article.


Research Achievements Research Achievements

Software developedSoftware developed

Bioinformatics data base developedBioinformatics data base developed

Traditional medicine research tools Traditional medicine research tools developeddeveloped


Software developedSoftware developed

1. SVMProt: Protein function prediction 1. SVMProt: Protein function prediction softwaresoftware

http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cghttp://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgii

2. INVDOCK: Drug target prediction software2. INVDOCK: Drug target prediction software

3. MoViES: Molecular vibrations evaluation 3. MoViES: Molecular vibrations evaluation serverserver

http://ang.cz3.nus.edu.sg/cgi-bin/prog/norm.http://ang.cz3.nus.edu.sg/cgi-bin/prog/norm.plpl


Bioinformatics database developedBioinformatics database developed

1. Therapeutic target database1. Therapeutic target database http://xin.cz3.nus.edu.sg/group/cjttd/ttd.asphttp://xin.cz3.nus.edu.sg/group/cjttd/ttd.asp2. Drug adverse reaction target database2. Drug adverse reaction target database http://xin.cz3.nus.edu.sg/group/drt/dart.asphttp://xin.cz3.nus.edu.sg/group/drt/dart.asp3. Drug ADME associated protein database3. Drug ADME associated protein database http://xin.cz3.nus.edu.sg/group/admeap/admeap.asphttp://xin.cz3.nus.edu.sg/group/admeap/admeap.asp

4. Kinetic data of biomolecular interactions 4. Kinetic data of biomolecular interactions databasedatabase

http://xin.cz3.nus.edu.sg/group/kdbi.asphttp://xin.cz3.nus.edu.sg/group/kdbi.asp5. Computed ligand binding energy database5. Computed ligand binding energy database http://xin.cz3.nus.edu.sg/group/CLiBE/CLiBE.asphttp://xin.cz3.nus.edu.sg/group/CLiBE/CLiBE.asp


Traditional medicine research tools Traditional medicine research tools developeddeveloped

1. Traditional medicine information database1. Traditional medicine information database

2. Herbal ingredient and content database2. Herbal ingredient and content database

3. Natural product effect and consumption 3. Natural product effect and consumption

info systeminfo system

4. Traditional medicine recipe prediction and4. Traditional medicine recipe prediction and

validation systemvalidation system

5. Herbal target identification system5. Herbal target identification system


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Applications Of Bioinformatics In Drug Discovery And Process