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Hands on Training Programme in “Drug Discovery and Development through Bioinformatics”

7th –17th, December, 2010(10 DAYS)



Programme Conducted by:

AU-KBC Research Centre,

MIT Campus, Chromepet,

Chennai -  600 044.










Bioinformatics is an interdisciplinary research area, which may be defined as the application of computational and analytical methods to solve biological problems. This exciting area is a relatively young field, and the pace of research is driven by the large and rapidly increasing amount of data being produced. The data generated by the experimental scientists requires annotation and detailed analysis in order to turn it into knowledge which can then be applied to improve health care via, for example, new drugs and gene therapy, medical practices, and food production - all of which are now high-profile issues.

To improve human health, scientific discoveries must be translated into practical applications. Such discoveries typically begin at “the bench” with basic research — in which scientists study disease at a molecular or cellular level — then progress to the clinical level, or the patient's “bedside.” Scientists are increasingly aware that this bench-to-bedside approach to translational research is really a two-way street. Basic scientists provide clinicians with new tools for use in patients and for assessment of their impact, and clinical researchers make novel observations about the nature and progression of disease that often stimulate basic investigations.

Bioinformatics -  A Holistic Approach to Drug Discovery and Development

To develop a new drug conventionally, one has to spend one Billion dollar and 10 –12 years time. There is no guarantee of success in this method, in spite of spending so much time and money. New methods for drug discovery and delivery are receiving considerable attention in the pharmaceutical industry. Trial-and-error discovery methods have been replaced by focused combinatorial chemistry, computer-aided drug design, and other processes. Genomics and Proteomics are complementary technologies of biological research. The potential of these technologies for biomedical revolution will be realized by integrating them into the drug discovery process at every stage from hypothesis generation to clinical evaluation. As biotechnology becomes increasingly important in drug R&D, the application of bioinformatics tools has the potential to drive growth in the worldwide pharmaceuticals drug market from the $240 billion today to $3 trillion by 2020.The forecast value for the worldwide informatics market in the life science sector is between $1.7 and $7 billion by 2012. The modern drug discovery relies heavily on virtual research facilitated by computing power. Hardware and software companies provide an increasing variety of informatics tools for this effort.

Twentieth century biology has been about cataloging the elements of life. Every day, we have a little more of the recipe of life, stretching before us as an almost endless line of As, Gs, Cs, and Ts--forming general sequences common to most living organisms, gene sequences common to most humans, polymorphisms peculiar to small subpopulations. But this static information amounts to little more than a parts catalog, a shopping list for a living organism. A vital thrust of 21st century biology will be the animation of these static parts. After all, a long string of base pair letters is like a long string of letters. It makes for a less interesting read than a telephone directory, and while it tells you how dial up all sorts of important proteins, most sequences alone tell you little more about a person than does their phone number. We can, of course, correlate certain polymorphisms with likely disease outcomes, and we learn more every day. But the more we learn about the importance of these new variables, the more we have to take into consideration when developing clinical strategies, undertaking drug development, and designing clinical trials. And gene sequence, even when linked to functional information, will only be one of many variables to consider in optimally designing therapeutic interventions and treating disease.

In recent years, we have seen an explosion in the amount of biological information that is available. Various databases are doubling in size every 15 months and we now have the complete genome sequences of more than 1000 organisms including human and another 3000 organisms sequence work is progressing. It appears that the ability to generate vast quantities of data has surpassed the ability to use this data meaningfully. The pharmaceutical industry has embraced genomics as a source of drug targets. It also recognizes 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. Recently, there has been a change in the way that medicines are being developed due to our increased understanding of molecular biology. In the past, new synthetic organic molecules were tested in animals or in whole organ preparations. This has been replaced with a molecular target approach in which in-vitro screening of compounds against purified, recombinant proteins or genetically modified cell lines are carried out with a high throughput. This change has come about as a consequence of better and ever improving knowledge of the molecular basis of disease.

All marketed drugs today target only about 500 gene products. 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. The major advances in genomics and sequencing means that finding an attractive target is no longer a problem but finding the targets that are most likely to succeed has become the challenge. The focus of bioinformatics in the drug discovery process has therefore shifted from target identification to target validation.

A lot of factors need to be taken into account concerning a candidate target from a multitude of heterogeneous resources. The types of information that one needs to gather about potential targets include nucleotide and protein sequencing information, homologues, mapping information, function prediction, pathway information, disease associations, variants, structural information, gene and protein expression data and species / taxonomic distribution among others. Different bioinformatics tools can be used to gather this information. The accumulation of this information into databases about potential targets means that the pharmaceutical companies can save themselves much time, effort and expense exerting bench efforts on targets that will ultimately fail. The information that is gathered helps to characterize the different targets into families and subfamilies. It also classifies the behavior of the different molecules in a biochemical and cellular context. Decisions about which families provide the best potential targets are guided by a number of criteria. It is important that the potential target has a suitable structure for interacting with drug molecules. Structural genomics helps to prioritise the families in terms of their 3D structures. 

To develop broad spectrum drugs that are effective against a wide range of pathogenic species while at other times we want to develop narrow spectrum drugs that are highly specific to a particular organism. Comparative genomics helps to find protein families that are widely taxonomically dispersed and those that are unique to a particular organism. For example, when we want to develop a broad-spectrum antibiotic, we are looking for targets that are present in a large number of bacteria yet have no similar homologues in human. This means that the antibiotic will be effective against many bacteria killing them while causing no harm to the human. In order to determine the role our potential drug target plays in a particular disease mechanism we use DNA and protein chips. These chips can measure the amount of transcript or protein expressed by a cell at different times or in different states (healthy versus diseased).

Following on from the genomics explosion and the huge increase in the number of potential drug targets, there has been a move from the classical linear approach of drug discovery to a non-linear and high throughput approach. The field of bioinformatics has become a major part of the drug discovery pipeline playing a key role for validating drug targets. By integrating data from many inter-related yet heterogeneous resources, bioinformatics can help in our understanding of complex biological processes and help improve drug discovery. 

Biomarker discovery provides valuable information that can accelerate drug development and improve modern medicine. Discovery of biomarkers that can predict a medical product's efficacy and toxicity can push drugs faster along the critical path from bench to bedside. Clinicians and hospitals can use biomarkers for early detection of complex diseases, for predicting the course of the disease, and anticipating how it will respond to specific treatments. As knowledge of the human genome advances, organizations are leveraging gene-expression technologies to identify genetic biomarkers.

These days, computers are an integral part of genomics-based drug discovery, helping researchers find drug targets by comparing databases of genomic information with annotations about functional information, by analyzing the data that comes in from various wet lab experiments, and by simply keeping track of the huge amounts of biological data being unearthed in life sciences research. This is the role of bioinformatics, a field that has exploded in importance over the last few years as companies have begun to realize they are drowning in raw data. But now the uses of computers for other parts of the discovery and development process are coming to the fore. Theoretically, researchers could now test virtual drug compounds against virtual protein targets, study the virtual pharmacokinetics of their optimized virtual lead in what amounts to virtual animals, study its effects on virtual organs, design a virtual clinical trial to test assumptions and variances, and even answer some regulatory questions through simulation. Somewhere in that process, a chemist has to actually mix up a compound and conduct some experiment but buckets of silicon are being added to the discovery and development process every day, with the hope that the wet lab will one day become as dry as a sand box.






Translational Research for Drug Discovery
Human Genome Project
Target Discovery & Validation
Lead Generation & Optimization
Drug Discovery and Pharmacogenomics
RNAi and Drug Discovery
Genomic, Proteomic and Metabonomic tools in Drug Discovery & Development
Biomarker Discovery
Molecular Modeling & Docking
Structure-Guided Drug Design
Chemo informatics and Pharmacoinformatics
Medicinal Chemistry
Marine environment as treasure trove for Drug Source
Natural Product Library
Combinatorial Chemistry
Antibiotics Resistance and Drug Discovery
Vaccine development
HTS for Drug Discovery
Data Mining for Drug Discovery
Systems Biology and Drug Discovery
Synthetic Biology
Medical Genomics
Drug Delivery Systems
Patents, IPR, WTO and Drug Discovery


Note: : Please note that there won’t be any wet lab experiments





The workshop is targeted towards Graduate and Post–graduate students in Science, Engineering and Technology / Life Science Scientists / Medicine / Pharma, Bioinformatics and Biotechnology Faculty / PhD scholars / PDF’s. The presentation will be in a tutorial style and participant’s interaction will be strongly encouraged.

For each topic, bird’s eye view will be provided in the beginning and a list of tools and databases used also provided. Important tools in each will be demonstrated with few examples. For all categories, exercise will be given to get hands on experience. Course material will be given in the form of CD to each participant.


Course Plan  

The course will be conducted in 10 sessions at the AU-KBC Research Centre, Chromepet from  

7th –11th, December, 2010
(5 days; 40 hrs)

Morning Session   - 09:00 AM - 01:00 PM

Afternoon Session - 02:00 PM - 06:00 PM 


13th –17th, December, 2010
(5 days; 40 hrs)

Assignments, Exercises, Literature Survey and Guest Lectures 




Dr. G.Ramesh Kumar (AU-KBC)
Dr. Bharath Srinivasan (IIT,Chennai)
Dr. P.Gautam (CBT, Anna University)
Dr. MD.Nair (Chennai)
Ms. T.K.Subazini Vijay (AU-KBC)
Mr. K.Palani Kannan (AU-KBC)
Mr. S.Nagavignesh (AU-KBC)
Mr.CP Rajadurai  (AU-KBC)
Ms.S.Sangeetha (AU-KBC)
Mr.Avinash Ramani (AU-KBC)
Mr.Sameer Hassan , (Ph.D) ( TRC,Chennai)





The course fee is Rupees.Ten Thousand (Rs.10,000/-) only . Please send a Cheque / DD drawn in favour of  The Director, AU-KBC Research Centre along with clear contact details (telephone no. and e-mail address). Cash is also accepted at AU KBC Research Centre- Accounts department.

Last Date for Registration: 1.12.2010. Please note that the number of seats is limited and registration is made on the basis of first come first served.

Participation certificate will be given at the end of the programme. 





Please send your applications along with DD to:  

The Director, 
AU-KBC Research Centre,
MIT Campus,Anna University Chennai,Chromepet,
Chennai - 600 044.
Phone: 2223 2711, 2223 4885,2223 6958 and 2223 6959
E-mail: gramesh@au-kbc.org