Since I strayed into Genomics for the last issue of NI, I spent a fair amount of time thinking about the pharmacogenomics issue. Your point about antidepressants may be accurate if the increase in drug effect and speed of onset is minor. But if it were significantly better than SSRIs, and it required but one screening test (assuming neither the drug nor the test were priced out of the realm of reason), the 'physician hassle factor' would be much less salient. Schizophrenia and Clozaril may be the best CNS example of how a pharmacogenomic parsing out of those vulnerable to side effects could have completely changed the marketing equation, to a degree that would make it just as attractive for a Big Pharma. FWIW, the following is an excerpt from the October NI pertaining to this hypothetical scenario:
"To understand the potential of pharmacogenomics, consider the numerous currently-available drugs whose effectiveness is offset by concerns regarding side effects. An excellent example in the CNS realm is Novartis’ Clozaril, still generally considered the best antipsychotic drug in terms of its impact upon the cognitive/negative symptoms of schizophrenia, but one which precipitates potentially fatal agranulocytosis in 1% of patients. The utilization of Clozaril has been restricted to second or third-line therapy. If an economical technology had existed to identify a hypothetical genotype associated with a vulnerability to agranulocytosis, Clozaril could have been a dominant antipsychotic drug. Risperdal and Zyprexa would have faced a tougher competitive context, and those antipsychotic drugs under development would have faced a higher hurdle. Had such a pharmacogenomic tool existed when Clozaril was launched, it would have meant at least $5-10 billion in additional sales to Novartis during the 1990s. This is hypothetical, the state of the art has not yet reached the point where such vulnerabilities can be identified. But it suggests an alternative to the gamble of developing a new drug. One could utilize pharmacogenomics to sidestep the shortcomings of currently available medications. The downside of such precision, such as it is, is that there might be fewer blockbuster drugs, if significant portions of the patient population are excluded from the market from the outset. In some disorders, the market might end up fragmented, several drugs each optimal for a specific subgroup. Pharmacogenomics reduces but does not eliminate risk, and it diminishes the likelihood of a single drug dominating an entire disease category. "
NeuroInvestment (Harry Tracy)(www.neuroinvestment.com) |
