STANFORD – During the past several decades, treatment for a variety of conditions has begun to shift from a “one size fits all” approach to a more personalized strategy. As a result, patients can more often be matched to the best drug for their genetic makeup or the exact subcategory of their disease. This enables physicians to avoid prescribing a medication (or a dosage) that might cause serious side effects in certain populations.
In other words, even among patients who apparently have the same disease and symptoms, the treatment for each one would be determined by various predictive or prognostic tests. Eventually, these tests could extend even to the sequencing of the DNA in an individual patient’s cancer cells, for example.
But, while this high-tech approach could be a boon to patients, it could prove detrimental to drug companies’ bottom lines. The reasons are subtle.
Personalized drug therapy uses biological indicators, or “biomarkers” – such as DNA sequences or the presence or absence of drug receptors – as an indicator of how patients should be treated, as well as to estimate the likelihood that the intervention will be effective. This concept is not new: it has been known for decades, for example, that people who have a genetic deficiency of an enzyme called G6PD can experience severe and precipitous anemia if they are exposed to certain drugs.
Similarly, ethnic groups and individuals vary widely in their ability to clear medications from the bloodstream, owing to differences in the activity of the enzymes that metabolize, or degrade, drugs. That is important because low metabolizers clear certain drugs slowly and have more medication in their systems for longer periods of time than high metabolizers. Thus, the former might be prone to overdose, and the latter to insufficient levels of the same drug.
Prognostic biomarkers have begun to make a big difference in cancer therapy. Drugs such as Erbitux and Vectibix work only in tumors containing the normal version of a gene called KRAS. If mutations of KRAS are present, the drugs are ineffective.
Such mutations explain about 30-40% of cases in which patients fail to respond to these drugs, and mutations in another gene called BRAF could account for another 12%. Knowing this crucial information about a cancer patient’s genes will reduce sharply the number who are unnecessarily subjected to the side effects (and expense) of drugs that will not work.
Improving the efficacy and reducing the side effects of drug therapy will be a boon to doctors, patients, and insurance companies, to be sure, but why should pharmaceutical companies embrace personalized medicine in the long term?
On the positive side, the presence of biomarkers will enable drug companies to perform smaller, better-targeted clinical studies in order to demonstrate efficacy. In any kind of experiment, a fundamental principle is that the greater the number of subjects or iterations, the greater the confidence in the study’s results. Unless the effect of the intervention is profound, small studies generally have large uncertainties in results.
That is where biomarkers make a difference. They can help drug makers to design clinical studies that will show a high “relative treatment difference” between the drug and whatever it is being compared to (often a placebo, but sometimes another treatment).
Thus, when drugs are ultimately approved based on the use of biomarkers, the description of the medication’s approved uses, which is printed on the label, might be more restrictive – that is, it might reduce the size of the patient population for which the drug is intended. For example, a drug broadly approved for “arthritis” – inflammation of joints that may be due to dozens of different disease processes – can be more widely marketed than one approved to treat only the arthritis that accompanies psoriasis or gout.
In reality, however, the situation is more complex. Assessments of safety and efficacy often do not move closely in tandem, so that even if smaller, better-targeted clinical trials offer clear evidence of a drug’s efficacy, regulators might demand far larger studies to provide evidence of the drug’s safety.
Increasingly defensive about accusations that drugs and vaccines are inadequately tested for safety, in recent years safety-obsessed regulators have required massive, hugely expensive, and time-consuming clinical trials designed to detect even very rare side effects. Consider, for example, that before its US approval, a vaccine against rotavirus (a common, sometimes fatal gastrointestinal infection in children) was tested in more than 72,000 children – and another 40,000-plus in post-marketing studies.
On a similar scale, a vaccine to prevent human papilloma virus infection and cervical cancer was tested in almost 30,000 young women. Such studies are very costly, and, by any reasonable standard, the number of patients included in them is grossly excessive.
Thus, the impact of personalized medicine in the short term might be positive at the patient’s bedside, but vast clinical trials to demonstrate the safety of new drugs will impose huge development costs that manufacturers might never recover. (Currently, only about one in five drugs approved by US regulators ever recoup their development costs.) This situation would not be sustainable in the long term.
If society is to derive the maximum benefit from personalized medicine – which will require companies to pursue it – regulators worldwide will need to adopt reasoned and reasonable policies.