The Economic Value of Bioinformation

The sum total of funds poured into drug research by profit-seeking commercial entities now overshadows the total of government grants for biological research. An increasing fraction of that sum is earmarked for bioinformatics and genomics. While few if any investment decisions of this kind are based on a precise mathematical analysis, an attempt to quantify the economic value of bioinformation may provide some useful insight, and perhaps some focus on our economically motivated research efforts. It is estimated that only about 500 gene products are targeted by currently prescribed drugs. It is forecast that out of the 100 000 or so (estimates still vary widely) human genes discovered through whole-genome sequencing, several thousand will turn out to be good targets. One large class of potential targets are newly sequenced G-protein coupled receptors, which are generally accepted as being amenable to drug development. Can the economic value be calculated for an algorithm that, say, predicts good targets based on their DNA sequence? The key to quantitating the economic value of information is to view the drug discovery process as a gambling or investment process where at each step in the process certain bets (money or other resources that may be purchased) are placed on particular research outcomes. For example, the total investment in the research of a gene that turns out to be a bad drug target is lost, while the investment in the validation of a good target produces multiple returns. Drug discovery is not just about betting on genes. In fact, there are three general stages over a 10-year average duration and a total cost of hundreds of millions of dollars: target discovery and validation; lead discovery and optimization; and clinical phases. Target discovery and validation may be viewed as a betting game where bets are placed on genes, gene products or molecular interactions in the hope that some will be good targets for intervention by small molecules. Lead discovery and optimization is the betting on drug candidates that will act selectively and specifically with the targets to produce the desired effect. Clinical phases are about betting on compounds that are effective and have tolerable side effects in human trials. The bets get more expensive and rewards higher as the process moves forward; methods to predict toxicity or other potential problems prior to clinical trials are of especial value. Drug approval does not signal the end of the betting …