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Small Montana Pharmacogenomics-Software Company Lassoes Big Biopharma, NIH Grants


It might have been better to locate Golden Helix in, say, New Jersey than Bozeman, Montana, admitted Christophe Lambert, founder of the pharmacogenomics software provider.

But despite the Rocky Mountain college town’s considerable distance from the big pharma corridor and other life sciences clusters, Lambert preferred the easygoing lifestyle. And, he said, “we have been able to attract quality people here for that very reason.”

So far, the geographic gamble appears to have paid off. Several weeks ago, the NIH reported giving the company $500,000, part of a $750,000 SBIR grant the firm won last December.

Golden Helix has also had little problem attracting big-name customers for its two software packages — HelixTree, for pharmacogenomics data mining, and ChemTree, for compound selection. AstraZeneca, GlaxoSmithKline, Pfizer, and Schering Plough have been customers.

Golden Helix’s modest success may have much to do with the fact that it’s a wayward seed from the big pharma tree. Lambert, who got his computer science PhD at Duke in 1998, developed the HelixTree software while working as a graduate research assistant at what was then GlaxoWellcome. While he initially worked with researchers to develop cheminformatics methods for analyzing structure-activity relationships, it soon became clear to him “that they could be mapped to the field of genetics, and so I basically made a proposal to Glaxo that I develop that technology for analysis of genetics data.”

After two years of supporting the project, Glaxo made a $1 million-plus bet on the company in March 2001 by taking a 15-percent equity stake in it. Another group of private investors raised additional funding for the company in April 2002, the exact amount of which Lambert would not disclose.

This funding, along with the SBIR grant, has enabled the company to concentrate on further development of HelixTree, which is designed to enable researchers to analyze the relationships between numerous genetic, clinical, and environmental parameters in order to find the subpopulations of patients that respond differently to a particular treatment.

HelixTree uses statistical hypothesis testing as a framework, in segmenting out the data to see if a particular group’s differential response to a treatment is significant, then segmenting that population further to see whether subgroups of that population respond differently to the treatment.

“You essentially have a set of rules that leads to each subpopulation that is very easy to explain and understand and has a statistically sound footing,” Lambert told SNPtech Reporter. “You are female and you are a smoker and you are homozygous 1-1 for gene 354 in this study, [so] you should be excluded from treatment with this particular drug because it elevates blood pressure, or something like that.”

Recently, the company has added to the software haplotype analysis features, enabling researchers to look at multi-gene markers associated with response to a particular treatment.

The software’s decision tree approach is aimed at clinical statisticians, who have typically not had much genetics training. “I’ve heard estimates that the number of clinical statisticians might outweigh the number of statistical geneticists by a factor perhaps 50 to 1,” Lambert said. “If you look at this whole phase of pharmagogenomics, and pharmacogenetics ultimately going to the clinical trial level where there’s a very set procedure of analyzing and submitting data to the FDA, [there’s a] need to bring tools to bear to the clinical statistician where they can make genetics approachable and fit within their workflow.”

This, he explained, “is something that we’ve targeted from the very beginning.”

This clinical pharmacogenomics orientation has drawn notice from the highest levels of the regulatory universe. Lawrence Lesko, chief of the FDA’s Center for Drug Evaluation and Research, invited Double Helix scientists to Washington, DC, in March to present their data-analysis methods to FDA regulators, who are puzzled over how to treat the data.

“They’re interested in hearing the various things that are used in the industry,” he said. And, given that top-tier pharma are using the company’s software, “we’re making an impact on that.”



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