High-throughout SNPs

Single nucleotide polymorphisms (SNPs) can be used to enhance drug discovery and development. Seizing a prominent role in the biopharmaceutical community, SNPs are binary elements of genetic variability in the human genome and function as signatures for different biological traits, such as susceptibility to disease.

SNPs have enormous commercial potential for the pharmaceutical community. In 1999, the SNP Consortium was founded by 10 of the world’s top pharmaceutical companies—AstraZeneca plc, Bayer AG, Bristol-Myers Squibb Co., Hoffman-La Roche Ltd., Glaxo Wellcome plc, Hoechst Marion Roussel AG/Rhône Poulenc Rorer (Aventis), Novartis AG, Pfizer, Inc., G. D. Searle & Co. (Monsanto), and SmithKline Beecham plc—and is committed to identifying and mapping at least 300,000 SNPs from the human genome and releasing them into the public domain. The consortium’s efforts have put pressure on other companies that want to create proprietary collections of SNPs. Private companies wish to quickly “lock up”, via patent protection, as many SNPs as possible.

The majority of SNPs from the human genome will, however, reside in the public domain. How does the SNP Consortium expect to benefit from its mapping efforts? It is not the SNPs that are of greatest value to drug discovery efforts, but rather the SNP's association with a given disease that will bring the greatest returns to the pharmaceutical industry. Genotyping, the process of creating associations between SNPs and disease, is a key objective for applying information gained from sequencing the human genome.

Genotyping and pharma
SNPs associated with a single disease offer clues about the underlying pathology of the disease state, but they can also differentiate separate populations within what was once considered a single disease. A major focus of the SNP effort is on using small variations in gene sequences as markers for defining populations exhibiting a given phenotype. In other words, cohorts—populations or groups—may be marked using a set of SNPs, and this set of SNPs defines the set of individuals that biologically are indistinguishable with respect to a given trait. Pharmacogenomics is the application of this genetic knowledge by targeting therapies on the basis of genomic composition—the expression of a given haplotype, for example—of an individual. The major drivers for the SNP effort in health care are

Market opportunity
SNPs lack commercial value until they can be “deployed” as binary molecular signatures associated with a biological phenomenon such as enhanced susceptibility to disease.

In the near term, SNPs could potentially help optimize clinical trials through patient stratification. Paul Kelly of ING BARING Furman Selz LLC (1) breaks down the market opportunity for SNP information in clinical trials with these estimates: About 300 compounds reach Phase II clinical trials every year for each of the top 10 pharmaceutical companies. Every year, 30 billion SNP genotypes would be performed for positioning these compounds into appropriate market segments (stratified population). Since one in 100 SNPs is a cSNP (a SNP present in the genes, rather than in intergenic regions), 300 million cSNP genotypes would be performed in targeted analysis by each major pharmaceutical company every year.

In addition to the near-term opportunity for SNP genotyping, it can be deployed in other market segments, including pharmacogenomics (which stratifies patients according to their responder/nonresponder status to a given drug), molecular diagnostics, and predictive medicine. We estimate that these segments represent the long-term upside in the SNP genotyping marketplace.

Software. Informatics tools and algorithms enable data manipulation and mining. Given the amount of data to be manipulated, robust information technology tools are needed.

Reagents. Wet chemistry must keep pace with the information deluge that is likely to result as the private and public genome projects develop more content. As the genome projects—especially SNP identification efforts—progress, the costs associated with analyzing these large numbers of SNPs will grow astronomically unless measures are taken to miniaturize assays and keep consumable volumes low.

Genotyping is the key process by which SNPs are harvested into commercially useful information in the form of biological associations with, for example, diseases. Numerous technologies aspire to be cost-effective and fast enough to handle the deluge of SNPs as they are identified and mapped. Only a few of these technologies, however, have the necessary features for industrial-scale SNP genotyping, such as DNA microarrays, mass spectrometry, and high-efficiency fluorescence polarization.

DNA microarrays
DNA arrays are glass slides or membrane filters containing many immobilized DNA samples. Researchers probe these arrays with labeled cDNAs made from RNA from samples of interest, such as cells from patients. DNA microarrays are best known as tools for monitoring gene expression. Several companies are vying for dominance of this more than $1 billion market opportunity, but the entry costs are significant.

The advantage of microarray technology is that production is relatively well defined and automated, which has made it appealing to those working in the biopharmaceutical community. The downside is that it is “hardwired”—DNA is spotted onto the slide during manufacturing. Researchers who want an uncommon sequence must have it custom-made, usually at high cost. Using DNA microarrays is complex; the assays are heterogeneous and require separation steps, and the data processing is complex. Because sequences of interest must be “transferred” to the company making the arrays, there are also concerns about confidentiality and ownership of genomic content.

Mass spectrometry
Mass spectrometry is an accurate and potentially high-throughput approach to genotyping. It relies on minute differences in molecular mass to identify DNA fragments. It may be possible to bring down individual genotype costs in the future (costs per genotype are currently between $0.50 and $1.00), and the approach is flexible and may be automatable for production-scale genotyping. The downside of the approach is that it is a complicated system and sample preparation steps are potential bottlenecks in the process. Nevertheless, it has potential for future applications.

BY ENAL S. RAZVI and LEV J. LEYTES