Technology-driven biotech companies looking to court the financial community at this year’s BIO-CEO Investor conference in New York were singing a slightly modified tune from that of previous years. While the recent trend at such meetings has been to de-emphasize genomics-driven approaches — especially those that rely on platform-related business models — speakers at this year’s conference distanced themselves from high-throughput technology altogethe: from genomics and proteomics to combinatorial chemistry, high-throughput screening, and even high-performance computing.
The new mantra, apparently, is an approach that foregoes brute force and serendipity in favor of predictive methods that rely on hypothesis-driven experiments. Compugen chairman Martin Gerstel said that the probability of success with high-throughput tools is as slim as buying a lottery ticket, and that the propagation of such methods across the industry is comparable to “buying even more tickets to more lotteries.”
Compugen, along with several other companies presenting at the conference, touted a “rational” approach that places the goal of a better understanding of biological systems over brute-force data-gathering methods. This approach, while smaller in scale than the industrial biology-style methodology of the genomic era, still presents an opportunity for computational tools — even if high-powered number-crunching machines are left out of the picture. Michael French, senior vice president of Entelos, pointed out that although systems-scale computational methods are complex, they still don’t need the IT power required for sequencing the human genome. “The new methodology is in the software,” he said. In response to a question as to why drug discovery companies don’t partner with large IT firms for biological modeling, French said that the methods that Entelos and others use “don’t require a Cray to crunch a number.” Because the approach relies on solving “lots of smaller problems quickly,” distributed systems based on commodity hardware work just fine, he said.
Other companies, such as Array Biopharma, Curagen, and Cyclacel, said they are relying on simulation and predictive modeling to place the vast amounts of data gathered through high-throughput approaches into the proper biological context. Kevin Koch, president and CSO of Array Biopharma, said that companies who invested heavily in genomics or combinatorial chemistry with little payoff in terms of new drug candidates “forgot something critical” in the process. The key issue in biopharma over the next decade, he said, will be “to interpret and make good decisions” based on the wealth of data that is already available.