Interview

'Bioinformatics was embraced as a weeder'

Technology is trying to demystify drug discovery with bioinformatics solutions. Anuradha Acharya, CEO, Ocimum Biosolutions uncovers the advantages of these solutions in conversation with Katya Naidu.

What is the importance of bioinformatics in the drug discovery process?

R&D investment in pharma industry is acknowledged as high at levels of $800 million for a typical lead to drug conversion. Bioinformatics was embraced as a weeder that reduces time and effort spent in screening of leads. With advances in genomics and sequencing target identification is no longer a problem. But target validation still remains a challenge, and is the current focus of bioinformatics solutions. Bioinformatics helps in reducing the time and also on reducing the number of targets that have to be validated in the lab. By enabling researchers to find out sooner that their hoped-for compound is not working out, bioinformatics can steer them towards more promising candidates.

What are the various solutions that can be employed at various stages of drug discovery?

Drugs usually act on either cellular or genetic chemicals in the body, known as targets, which are believed to be associated with disease. Scientists use a variety of techniques to identify and isolate a target and learn more about its functions and how these influence disease. 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 point for refinement of the chemical structures.

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 modelled computationally using any of several different techniques. These techniques attempt to reproduce the researchers' understanding of how to choose likely compounds built into a software package that is capable of modelling a very large number of compounds in an automated way. Once a number of lead compounds have been found, computational techniques have been very successful in refining the molecular structures to give a greater drug activity and fewer side-effects.

What are some of the computational techniques that help in drug discovery?

Quantitative Structure Activity Relationships (QSAR) is a computational technique which is used to detect the functional group in a compound in order to refine the drug. This can be done using QSAR that 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.

One needs 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.

What are the solutions that Ocimum is working on at present?

Keeping in pace with current trends in field we, at Ocimum, have developed a novel siRNA design tool called iRNAchek. The siRNA technology itself has two primary questions answered before it can deliver the promised therapeutic application with reduced investments in target to drug discovery cycle. Firstly, the parameters that influence design of ideal siRNAs, and secondly, a transfection technology that delivers siRNA to even systemic target site. Current high focus of the industry has been the design of silencing arrays that generate silencing profiles for pathways, diseases, and metabolism. Although the success of any chip depends on identification of its probes, most organisations design no more than three siRNAs per gene to make experimentation practical and economical.

The difficulty in screening for such few drug candidates from the hundreds of targets identified for any typical mRNA has necessitated recognition of features that weed out ineffective siRNAs. Ocimum Biosolutions has launched iRNAchek to address this issue at a time when key focus of research has been to identify key criteria for successful siRNA. With iRNAchek, Ocimum Biosolutions has provided the RNAi community with an easy means to design test and analyse siRNAs for success criteria.

What are the other areas of drug discovery that you are targeting at?

Expression analysis is yet another key processes in drug discovery. Ocimum caters to array requirements at both oligo design and data analysis levels with their tools, Oligostar and Genowiz. Genowiz, our microarray data analysis package, has a number of options for statistical and biological analysis to ease the process of target identification. Genowiz comes with features like functional classification, pathway analysis and annotation supplements that aid researchers in deriving biologically significant knowledge from expression data. The tool maintains latest data from metabolic, regulatory, signal transduction and disease pathways to generate organism based interaction maps of genes. Expression data when mapped onto these pathways gives further insight into pathways and steps influenced by affected genes. Studying pathways in relation to expression data helps in identification of possible drug targets as they provide an insight into how rate limiting steps are being effected. Modelling of predicted pathways is yet another solution.

How can modelling metabolic pathways help predict them?

They help in bringing down the number of possible choices and also see large number of pathways and potential unknown ones too. Simulation and modelling of pathways helps in analysing reaction kinetics of pathways. Concentrations of substrates, catalysts, their time of action, feedback reactions, intermediate pathway steps play an important role in drug discovery. Mathematical models constructed based on dynamic changes in metabolite concentrations over time and stages enable pathway prediction.

What are the limitations of bioinformatics solutions?

A key limitation to drug discovery remains our incomplete biological understanding of disease, the ability to recognise targets linked with validity to disease. Also most of the bioinformatics software screens one to five million small molecules using high-throughput screening. These small libraries can often lead to the generation of only a few ligands of low affinity, or no ligands at all.