Recent advancements in computational methods and their applications in biology have resulted in explosion of biological knowledge by way of mapping and exploration of human and other useful genomes. This led to generation of tera bytes of genetic/ protein data, which is becoming increasingly difficult to collect and analyse.
Setbacks in drug discovery research, absence of potential blockbusters and patent expiry of existing drugs, resulted in R&D pressure, which in turn is compelling the pharma-biotech industry to adapt to newer methods that increase drug research productivity, accelerate the drug discovery process and reduce R&D expenditure.
Computational biology is promising to revolutionize biotech and pharmaceutical discovery research by reducing the time and cost for development of useful technologies, products and processes. Further, large scale genome sequencing and analysis, structural and functional genomics initiatives involve intense data driven protocols and hence require novel approaches to process and simulate voluminous data for analyzing and solving the biological complexity. Addressing such inten-sive and voluminous data requires not only powerful and system intelligent software but has to be highly user friendly and biologically sensitive platforms.
For example, prior to genomics revolution, only about 500 gene-based drug targets were identified and used in the drug development. However, with the advent of bio-computational methods, there is practically 6 to 20 fold increase in drug targets. This has resulted in the need for more efficient and accurate methods to elucidate new drug targets. It is not just the increase in the drug targets but the rationale and precision with which these tools are working, makes them most sought after subjects in the discovery research today.
Overall computational biology appears to have taken centre-stage for speeding up the discovery research and for providing more rationale opportunities to combat dreaded diseases. Computational biology programmes are now custom designed to address intense bio-researcher needs and provide solutions for the ever increasing complexity in pharmaceutical and bio-computational research.
GLOBAL R&D TRENDS
If one looks at the past 5-8 years global R&D expenditures figures of top-10 pharmaceutical, bio-pharmaceutical and biotech companies, it is noted that i) R&D expenditures are growing at 17% CAGR on gross sales, ii) current R&D expenditure is close to USD 32 -35 billion and iii) industry is approximately spending 4-6 % of this is on computational biology and system intelligent informatics initiatives. Similarly, crop genetics industry has expenditure on R&D of transgenics alone estimated at USD $6 billion.
Globally, the industry is actively supporting such spending patterns on technologies, which can help in speeding up discovery research and cut short the project-product development time. Computational biology has demonstrated strong research dominance with double-digit annual growth rates, as more and more biotech and pharmaceutical firms are investing in the latest state of art bio-computational / R&D informatics platforms.
COMPUTATIONAL BIOLOGY MARKETS
Computational biology and genomic information technologies segment is currently growing at 14.8% CAGR annually and account close to ~USD $2 billion.
Computational biology markets are broadly classified into three groups on the basis of the proximity and relationship they maintain within the actual R&D project-product development process. They are i) data suppliers (companies supplying information and knowledge from the proprietary databases and offering access either through subscriptions or fee for use business models), ii) anlaytical software providers (companies like HGPL involved in the development of analytical computational tools and bio-software platforms) and iii) enterprise system providers (companies that couple enabling technologies/ hardware supporting infrastructure).
If one looks more carefully at the above table, it is well understood that analytical software segment is the most potential and promising one showing an upward surge and dominance over others.
TECHNOLOGY CHALLENGES
Despite strong market growth and acceptability amongst the global R&D centres, the general perception of the pharmaceutical industry is still cautious and at times skeptical. The argument from the industry being i) the software is restricted for use to only specialized end user groups, ii) the software is not as user-friendly as they are required to be. Further, as pharmaceutical companies that have invested heavily in computational tools in the human genome project are yet to see tangible returns, there exists a natural skepticism about their efficacy.
In order to meet the challenges, computational biology companies have to quantify their productivity increments through wet lab collaborations and demonstrate the benefits on paper. The need of the hour for computational biology companies is to generate success stories by working with real time research problems & projects. Additionally, one has to handle the researcher inability to learn, understand and implement the complex computational biology software. Hence the most critical and demanding need is to develop and facilitate user friendly and ready to use and implement software protocols.
Computational biology is heated up with mergers and acquisitions, causing changes in the competitive structure and market dynamics. Companies such as DoubleTwist have exited the market, while a few, such as Compugen, have moved up the value chain by offering their own drug discovery programmes.
Lured by the discovery prospects and market opportunities, companies are busy integrating and setting up complete discovery pipeline, there by translating themselves into bio-pharmaceutical shops. Definitely licensing, collaborative research and royalty sharing have become the industry buzz words today. Such business & revenue models are firing the market growths for these computational biology companies. For example, with royalty and milestone payment agreements which not only ensure steady cash flows but also create strategic partnerships between computational biology vendors and discovery companies.
- (The author is head-Marketing and Operations, Helix Genomics Pvt. Ltd. (HGPL), Hyderabad)