INVESTING IN CLIENT COLLABORATION

Leveraging Bioinformatics In Drug Discovery & Development

A combination of biology and information technology, the field of bioinformatics comprises all the computational methods needed to analyse large amounts of data generated in high-throughput biological experiments.

The field of bioinformatics caters to molecular biology, structural biology, genomics, and data sciences; its use is becoming ever more essential in nearly all aspects of drug discovery, drug assessment and drug development.

We offer integrated bioinformatics services to biotechnology and pharmaceutical companies across the drug discovery continuum. The scope of our service spans the entire gamut: from building data packages, pathway databases and knowledge bases, to analysis of high throughput data, predictive modelling, structural modelling, mechanistic toxicology and systems biology.

During the year, we entered into a collaboration with a French biotech company to develop and commercialise a novel cholestatic DILI prediction tool. This tool will help pharmaceutical companies predict the impact of a drug on the liver.

5

Predicting protein glycosylation patterns with bioinformatics

Challenges
While manufacturing biologics, one of the challenges is to find the right choice of parameters that would yield the required protein glycosylation profile. This was earlier being determined only by the trial-and-error method, which meant a number of time-consuming laboratory experiments.
The Syngene Approach
The bioinformatics team at Syngene developed a mathematical model to conduct virtual trials and find the optimal parameters to produce the required glycosylation profile.
Outcome
The model not only acts as a surrogate to the real-life experiments, but also suggests smarter short-cuts to limit the number of trials needed to converge to the required end-results.

6

Finding a new indication for a target with bioinformatics

Challenges
A biopharma client was developing dual kinase inhibitors and looking for alternative therapeutic areas for them as a backup in case of problems.
The Syngene Approach
Gene expression data was analysed to identify diseases where the targets were differentially expressed. Protein-disease interaction networks were also examined to identify diseases associated with neighbouring proteins and pathways associated with the targets.
Outcome
The probable mechanism of action was derived for diseases prioritised. This led to the client switching indications for the programme when they ran into efficacy issues for the primary therapeutic area.