Monday, November 28, 2005

Benefits of Bioinformatics Technology in Drug Discovery

as written Francisca T .D Nyachoto

Bioinformatics technology allows researchers to analyze the terabytes of data produced by the Human Genome Project. Gene sequence databases ,gene expression databases, protein sequence databases and related analysis tools all help to determine whether and how a particular molecule is directly involved in a disease process.That in turn helps find ew and better drug targets(1). This is essentially the essence of using bioinformatics in drug discovery; identifying and validating targets

Bioinformatics thus has definite significant advantages over traditionally expensive and time consuming ‘wet lab’ research methods because computational tools give the most predictive and accurate information about genes and proteins with regards to mediating aspects of drug action.(12).The pharmaceutical industry has embraced genomics as a source of drug targets .Bioinformatics tools help by validating the potential drug targets and determining which ones are the most suitable for entry into the drug development pipeline.

It is interesting to note that while the traditional process of drug discovery uses synthetic organic molecules which are then tested in animals or whole organ preparations, nowadays with increased understanding of molecular biology , molecular target approach is used.Here virtual High-Throughput Screening (vHTS) is employed. This involves the screening of protein targets against databases of small molecule to determine the ones which fit into the target.The compounds which fit are then further tested thus allowing researchers to only work with promising leads only. This results in cutting costs and time for the research process.Virtual High-Throughput Screening enables millions of compounds to be screened daily.

In computer aided drug design research one often knows the genetic sequence of multiple organisms or the amino acid sequence of proteins from several species. It is very useful to determine how similar or dissimilar the organisms are based on gene or protein sequences. 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. There are many bioinformatic sequence analysis tools that can be used to determine the level of sequence similarity.

Determining the 3-D structure of proteins is an important aspect of drug design. 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. 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. 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. 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.Examples of tools used in homology modeling are Modeller, and Swiss-Model Repository(10)

A common activity in biopharmaceutical companies is the search for drug analogues. Starting with a promising drug molecule, one can search for chemical compounds with similar structure or properties to a known compound. There are a variety of methods used in these searches, including sequence similarity, 2D and 3D shape similarity, substructure similarity, electrostatic similarity and others. A variety of bioinformatic tools and search engines are available for this work.(10)

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. This usually involves a series of modifications to the primary structure (scaffold) and secondary structure (moieties) of the compound. This process can be enhanced using software tools that explore related compounds (bioisosteres) to the lead candidate. OpenEye’s WABE is one such tool. 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.(10)

Drug-receptor interactions occur on atomic scales. 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. 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.(10)

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. The key characteristics for drugs are Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) and efficacy—in other words bioavailability and bioactivity. Although these properties are usually measured in the lab, they can also be predicted in advance with bioinformatics software.(10)

CADD methods and bioinformatics tools offer significant benefits for drug discovey programs which are discussed briefly below:

Costs
The Tufts Report suggests that the cost of drug discovery and development has reached $800 million for each drug successfully brought to market. Many biopharmaceutical companies now use computational methods and bioinformatics tools to reduce this cost burden. Virtual screening, lead optimization and predictions of bioavailability and bioactivity can help guide experimental research. Only the most promising experimental lines of inquiry can be followed and experimental dead-ends can be avoided early .Growth of the bioinformatics market is primarily attributed to its increased usage in the pharmaceutical industry. The application of bioinformatics in drug discovery and development is expected to reduce the annual cost of developing a new drug by 33 percent, and the time taken for drug discovery by 30 percent.(7) That’s a valuable proposition in the global drug discovery market expected to be worth $25.1 billion in 2006. The global bioinformatics market is forecast to grow to $3 billion in 2010 from its current $1.4 billion, for a compound annual growth rate (CAGR) of 15.8 percent, says BCC Research. Analysis software and services should drive this growth, rising to $1.2 billion in 2010 from $450 million in 2005.(9)

Time-line
The predictive power of CADD can help drug research programs choose only the most promising drug candidates. By focusing drug research on specific lead candidates and avoiding potential “dead-end” compounds, biopharmaceutical companies can get drugs to market more quickly.(10)

Insight
One of the non-quantifiable benefits of CADD and the use of bioinformatics tools is the deep insight that researchers acquire about drug-receptor interactions. Molecular models of drug compounds can reveal intricate, atomic scale binding properties that are difficult to envision in any other way. 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. This is an intangible benefit that can help design research programs(10).
CADD and bioinformatics together are a powerful combination in drug research and development. 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.(10).

Bioinformatics thus clearly allows exploitation of the data that is available and this together with increased understanding of molecular biology and the molecular basis of disease greatly improves the drug discovery process.The data from the Human Genome Project has availed great opportunities for drug discovery and streamlining the choice of targets to support the drug discovery pipeline.The methods outlined above which are used in computer aided drug design mean that finding an attractive target is not an issue. The only concern is validating those targets to come up with the ones likely to succeed and here bioinformatic tools are ‘the saviour’

Bioinformatics tools can be used to gather all the necessary information about potential targets.This information includes nucleotide and protein sequencing, homologue mapping,function prediction,pathway information, disease associations,variants,structural information,gene and protein expression data and species distribution among others.The accumulation of this information into databases about potential targets means pharmaceutical companies can save themselves much time , effort and expense exerting bench efforts on targets that will ultimately fail.(11).

As compared to the traditional method of drug discovery where a compound with potential pharmacological activity is isolatedthen tested on animals and subsequently in people during clinical trials, using bioinformatics tools it is now easy to start with the compound which specifically targets proteins. Thus the whole process is no longer on a trial and error basis like the traditional approach. This is the way to go for pharmaceutical companies. Now armed with the resources from information technology and the human genome data , it only makes good economic sense to invest in the bioinformatics sector and help make it work to their advantage.No more do scientists have to work hard coming up with a lead only for it to fail when finally tested in humans , resulting in incurring losses. Using bioinformatics is more like a marketing aspect where one asseses the needs of the consumer and then comes with a product to meet those needs, instead of making a product first and then imposes it onto the consumer hoping that it will meet their needs.Surely the first method is the winner and bioinformatics presents such a unique opportunity.

Although bioinformatics is considered to have generated a lot of excitement and yet failed to deliver what it promised, that does not remove the clear advantages bioinformatics brings to drug discovery. Bioinformatics may be considered to be a discipline in it’s infancy and as such it needs timeto grow and really get organized. It is important to note that at the end of it , it is the patient buying prescription drugs from a chemist who has to enjoy the benefit of medicine which works and has minimal side effects at most importantly at an affordable price. The pharmaceutical industry should never lose this focus as it is easy to only consider productivity and profitability at the expense of the patient.

It is exciting to think of the possibilities that bioinformatics may bring forth in terms of finding drugs for conditions such as cancer, AIDS, tuberculosis.The benefit for the industry is obviously in cutting costs and speeding up the process to get new drugs onto the market.For the common man on the street the major benefits would be of course drug affordability as well as access to life saving drugs in a shorter time..Many lives have been lost due to unavailability of such drugs and embracing bioinformatics may reduce the loss and greatly improve the lives of humanity.

1. Drug Companies on Speed www.cio.com/archive/101501/speed.html

2. Bioinformatics web-Comprehensive educational resource on Bioinformatics www.geocities.com/bioinformaticsweb3, Di.Masi,J.A,Hansen,R.W: Grabowski,H.G.,200

3, The price of innovation;new estimates of drug development costs,Journal of Health Economics 22,151-185

4. Clinical costs skyrocket to new levels.Phil Webster, Pharma-Biotech Research Analyst (Published 09/12/04 Source:Frost and Sullivan www.inpharm.com/External/InpH/1,2580,1-3-128917

5. Pharmaceutical Industry, Heal thyself. David Handelsman www.sas.com/news/feature/his/mar05dev.html

6. Genomics Provides Crucial Tools for Breaking Bottlenecks in Drug Discovery and Development.www.healthtech.com/newsarticles/issueII_1.asp

7. IT Use for Drug Dscovery to Rise.Stacey Lawrence. June 29,. 2005 www.cioinsight.com/article2/0,1540,1832630,00.asp

8. What is Bioinformatics http://smi-webistanford-edu/people/altman/bioinformatics.html

9. Faststats: Drug Discovery- Drug Discovery buzz www.redherring.com/Article.aspx?a=12388~hed=faststats , June 19,2005

10. Bioinformatics in Computer-Aided Drug Design by Dr Richard M.Casey(Published May 10,2005) www.b-eye-network.com/view/852

11. Bioinformatics and drug discovery www.ebi.ac.uk/2can/disease/genes11.html

12. www.hypography.com/topics/Bioinformatics_100147.cfm

13. www.biochem.northwestern.edu/holmgren/Glossary/Definitions/Def-B/Bioinformatics.html ( 27-07-04)

14. Intergration of automation,bioinformatics and proteomics spawns a new approach. Stanley Belkowski,PhD, and Sergey Ilyin,PhD

15. www.ddd.mag.com

16. www.activemedia-guide.com/pharmaceutical_industry.htm (Industry Analysis,Pharmaceuticals Industry, source: US Business Reporter, 17/11/01)

17. Pharmaceutical Industry: Sector overview ;Practicing the most stark acts of corporate inhumanity www.corpwatch.org.

This information has been published by the International Biopharmaceutical Association www.ibpassociation.org . Please note this information does not give any medical advice.

The project is sponsored by Kriger Research Center - CRO and Training Services ( www.kriger.com ) , ClinQua CRO (www.clinqua.com ), Biorole Free Resumes Data Base (www.biorole.com ) and Altaspera Designs ( www.altaspera.com )

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Benefits of Bioinformatics Technology in Drug Discovery

as written Francisca T .D Nyachoto

Bioinformatics technology allows researchers to analyze the terabytes of data produced by the Human Genome Project. Gene sequence databases ,gene expression databases, protein sequence databases and related analysis tools all help to determine whether and how a particular molecule is directly involved in a disease process.That in turn helps find ew and better drug targets(1). This is essentially the essence of using bioinformatics in drug discovery; identifying and validating targets

Bioinformatics thus has definite significant advantages over traditionally expensive and time consuming ‘wet lab’ research methods because computational tools give the most predictive and accurate information about genes and proteins with regards to mediating aspects of drug action.(12).The pharmaceutical industry has embraced genomics as a source of drug targets .Bioinformatics tools help by validating the potential drug targets and determining which ones are the most suitable for entry into the drug development pipeline.

It is interesting to note that while the traditional process of drug discovery uses synthetic organic molecules which are then tested in animals or whole organ preparations, nowadays with increased understanding of molecular biology , molecular target approach is used.Here virtual High-Throughput Screening (vHTS) is employed. This involves the screening of protein targets against databases of small molecule to determine the ones which fit into the target.The compounds which fit are then further tested thus allowing researchers to only work with promising leads only. This results in cutting costs and time for the research process.Virtual High-Throughput Screening enables millions of compounds to be screened daily.

In computer aided drug design research one often knows the genetic sequence of multiple organisms or the amino acid sequence of proteins from several species. It is very useful to determine how similar or dissimilar the organisms are based on gene or protein sequences. 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. There are many bioinformatic sequence analysis tools that can be used to determine the level of sequence similarity.

Determining the 3-D structure of proteins is an important aspect of drug design. 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. 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. 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. 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.Examples of tools used in homology modeling are Modeller, and Swiss-Model Repository(10)

A common activity in biopharmaceutical companies is the search for drug analogues. Starting with a promising drug molecule, one can search for chemical compounds with similar structure or properties to a known compound. There are a variety of methods used in these searches, including sequence similarity, 2D and 3D shape similarity, substructure similarity, electrostatic similarity and others. A variety of bioinformatic tools and search engines are available for this work.(10)

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. This usually involves a series of modifications to the primary structure (scaffold) and secondary structure (moieties) of the compound. This process can be enhanced using software tools that explore related compounds (bioisosteres) to the lead candidate. OpenEye’s WABE is one such tool. 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.(10)

Drug-receptor interactions occur on atomic scales. 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. 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.(10)

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. The key characteristics for drugs are Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) and efficacy—in other words bioavailability and bioactivity. Although these properties are usually measured in the lab, they can also be predicted in advance with bioinformatics software.(10)

CADD methods and bioinformatics tools offer significant benefits for drug discovey programs which are discussed briefly below:

Costs
The Tufts Report suggests that the cost of drug discovery and development has reached $800 million for each drug successfully brought to market. Many biopharmaceutical companies now use computational methods and bioinformatics tools to reduce this cost burden. Virtual screening, lead optimization and predictions of bioavailability and bioactivity can help guide experimental research. Only the most promising experimental lines of inquiry can be followed and experimental dead-ends can be avoided early .Growth of the bioinformatics market is primarily attributed to its increased usage in the pharmaceutical industry. The application of bioinformatics in drug discovery and development is expected to reduce the annual cost of developing a new drug by 33 percent, and the time taken for drug discovery by 30 percent.(7) That’s a valuable proposition in the global drug discovery market expected to be worth $25.1 billion in 2006. The global bioinformatics market is forecast to grow to $3 billion in 2010 from its current $1.4 billion, for a compound annual growth rate (CAGR) of 15.8 percent, says BCC Research. Analysis software and services should drive this growth, rising to $1.2 billion in 2010 from $450 million in 2005.(9)

Time-line
The predictive power of CADD can help drug research programs choose only the most promising drug candidates. By focusing drug research on specific lead candidates and avoiding potential “dead-end” compounds, biopharmaceutical companies can get drugs to market more quickly.(10)

Insight
One of the non-quantifiable benefits of CADD and the use of bioinformatics tools is the deep insight that researchers acquire about drug-receptor interactions. Molecular models of drug compounds can reveal intricate, atomic scale binding properties that are difficult to envision in any other way. 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. This is an intangible benefit that can help design research programs(10).
CADD and bioinformatics together are a powerful combination in drug research and development. 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.(10).

Bioinformatics thus clearly allows exploitation of the data that is available and this together with increased understanding of molecular biology and the molecular basis of disease greatly improves the drug discovery process.The data from the Human Genome Project has availed great opportunities for drug discovery and streamlining the choice of targets to support the drug discovery pipeline.The methods outlined above which are used in computer aided drug design mean that finding an attractive target is not an issue. The only concern is validating those targets to come up with the ones likely to succeed and here bioinformatic tools are ‘the saviour’

Bioinformatics tools can be used to gather all the necessary information about potential targets.This information includes nucleotide and protein sequencing, homologue mapping,function prediction,pathway information, disease associations,variants,structural information,gene and protein expression data and species distribution among others.The accumulation of this information into databases about potential targets means pharmaceutical companies can save themselves much time , effort and expense exerting bench efforts on targets that will ultimately fail.(11).

As compared to the traditional method of drug discovery where a compound with potential pharmacological activity is isolatedthen tested on animals and subsequently in people during clinical trials, using bioinformatics tools it is now easy to start with the compound which specifically targets proteins. Thus the whole process is no longer on a trial and error basis like the traditional approach. This is the way to go for pharmaceutical companies. Now armed with the resources from information technology and the human genome data , it only makes good economic sense to invest in the bioinformatics sector and help make it work to their advantage.No more do scientists have to work hard coming up with a lead only for it to fail when finally tested in humans , resulting in incurring losses. Using bioinformatics is more like a marketing aspect where one asseses the needs of the consumer and then comes with a product to meet those needs, instead of making a product first and then imposes it onto the consumer hoping that it will meet their needs.Surely the first method is the winner and bioinformatics presents such a unique opportunity.

Although bioinformatics is considered to have generated a lot of excitement and yet failed to deliver what it promised, that does not remove the clear advantages bioinformatics brings to drug discovery. Bioinformatics may be considered to be a discipline in it’s infancy and as such it needs timeto grow and really get organized. It is important to note that at the end of it , it is the patient buying prescription drugs from a chemist who has to enjoy the benefit of medicine which works and has minimal side effects at most importantly at an affordable price. The pharmaceutical industry should never lose this focus as it is easy to only consider productivity and profitability at the expense of the patient.

It is exciting to think of the possibilities that bioinformatics may bring forth in terms of finding drugs for conditions such as cancer, AIDS, tuberculosis.The benefit for the industry is obviously in cutting costs and speeding up the process to get new drugs onto the market.For the common man on the street the major benefits would be of course drug affordability as well as access to life saving drugs in a shorter time..Many lives have been lost due to unavailability of such drugs and embracing bioinformatics may reduce the loss and greatly improve the lives of humanity.

1. Drug Companies on Speed www.cio.com/archive/101501/speed.html

2. Bioinformatics web-Comprehensive educational resource on Bioinformatics www.geocities.com/bioinformaticsweb3, Di.Masi,J.A,Hansen,R.W: Grabowski,H.G.,200

3, The price of innovation;new estimates of drug development costs,Journal of Health Economics 22,151-185

4. Clinical costs skyrocket to new levels.Phil Webster, Pharma-Biotech Research Analyst (Published 09/12/04 Source:Frost and Sullivan www.inpharm.com/External/InpH/1,2580,1-3-128917

5. Pharmaceutical Industry, Heal thyself. David Handelsman www.sas.com/news/feature/his/mar05dev.html

6. Genomics Provides Crucial Tools for Breaking Bottlenecks in Drug Discovery and Development.www.healthtech.com/newsarticles/issueII_1.asp

7. IT Use for Drug Dscovery to Rise.Stacey Lawrence. June 29,. 2005 www.cioinsight.com/article2/0,1540,1832630,00.asp

8. What is Bioinformatics http://smi-webistanford-edu/people/altman/bioinformatics.html

9. Faststats: Drug Discovery- Drug Discovery buzz www.redherring.com/Article.aspx?a=12388~hed=faststats , June 19,2005

10. Bioinformatics in Computer-Aided Drug Design by Dr Richard M.Casey(Published May 10,2005) www.b-eye-network.com/view/852

11. Bioinformatics and drug discovery www.ebi.ac.uk/2can/disease/genes11.html

12. www.hypography.com/topics/Bioinformatics_100147.cfm

13. www.biochem.northwestern.edu/holmgren/Glossary/Definitions/Def-B/Bioinformatics.html ( 27-07-04)

14. Intergration of automation,bioinformatics and proteomics spawns a new approach. Stanley Belkowski,PhD, and Sergey Ilyin,PhD

15. www.ddd.mag.com

16. www.activemedia-guide.com/pharmaceutical_industry.htm (Industry Analysis,Pharmaceuticals Industry, source: US Business Reporter, 17/11/01)

17. Pharmaceutical Industry: Sector overview ;Practicing the most stark acts of corporate inhumanity www.corpwatch.org.

This information has been published by the International Biopharmaceutical Association www.ibpassociation.org . Please note this information does not give any medical advice.

The project is sponsored by Kriger Research Center - CRO and Training Services ( www.kriger.com ) , ClinQua CRO (www.clinqua.com ), Biorole Free Resumes Data Base (www.biorole.com ) and Altaspera Designs ( www.altaspera.com )

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