In a brilliant new paper (pdf) (html) Peter Huber draws upon molecular biology, network analysis and Bayesian statistics to make some very important recommendations about FDA policy. Consider the following drugs (my list):
Drug A helps half of those to whom it is prescribed but it causes very serious liver damage in the other half. Drug B works well at some times but when administered at other times it accelerates the disease. Drug C fails to show any effect when tested against a placebo but it does seem to work in practice when administered as part of a treatment regime.
Which of these drugs should be approved and which rejected? The answer is that all of them should be approved; that is, all of them should be approved if we can target each drug to the right patient at the right time and with the right combination of other drugs. Huber argues that Bayesian adaptive testing, with molecular biology and network analysis providing priors, can determine which patients should get which drugs when and in what combinations. But we can only develop the data to target drugs if the drugs are actually approved and available in the field. The current FDA testing regime, however, is not built for adaptive testing in the field.
The current regime was built during a time of pervasive ignorance when the best we could do was throw a drug and a placebo against a randomized population and then count noses. Randomized controlled trials are critical, of course, but in a world of limited resources they fail when confronted by the curse of dimensionality. Patients are heterogeneous and so are diseases. Each patient is a unique, dynamic system and at the molecular level diseases are heterogeneous even when symptoms are not. In just the last few years we have expanded breast cancer into first four and now ten different types of cancer and the subdivision is likely to continue as knowledge expands. Match heterogeneous patients against heterogeneous diseases and the result is a high dimension system that cannot be well navigated with expensive, randomized controlled trials. As a result, the FDA ends up throwing out many drugs that could do good:
Given what we now know about the biochemical complexity and diversity of the environments in which drugs operate, the unresolved question at the end of many failed clinical trials is whether it was the drug that failed or the FDA-approved script. It’s all too easy for a bad script to make a good drug look awful. The disease, as clinically defined, is, in fact, a cluster of many distinct diseases: a coalition of nine biochemical minorities, each with a slightly different form of the disease, vetoes the drug that would help the tenth. Or a biochemical majority vetoes the drug that would help a minority. Or the good drug or cocktail fails because the disease’s biochemistry changes quickly but at different rates in different patients, and to remain effective, treatments have to be changed in tandem; but the clinical trial is set to continue for some fixed period that doesn’t align with the dynamics of the disease in enough patients
Or side effects in a biochemical minority veto a drug or cocktail that works well for the majority. Some cocktail cures that we need may well be composed of drugs that can’t deliver any useful clinical effects until combined in complex ways. Getting that kind of medicine through today’s FDA would be, for all practical purposes, impossible.
The alternative to the FDA process is large collections of data on patient biomarkers, diseases and symptoms all evaluated on the fly by Bayesian engines that improve over time as more data is gathered. The problem is that the FDA is still locked in an old mindset when it refuses to permit any drugs that are not “safe and effective” despite the fact that these terms can only be defined for a large population by doing violence to heterogeneity. Safe and effective, moreover, makes sense only when physicians are assumed to be following simple, A to B, drug to disease, prescribing rules and not when they are targeting treatments based on deep, contextual knowledge that is continually evolving:
In a world with molecular medicine and mass heterogeneity the FDA’s role will change from the yes-no single rule that fits no one to being a certifier of biochemical pathways:
By allowing broader use of the drug by unblinded doctors, accelerated approval based on molecular or modest—and perhaps only temporary—clinical benefits launches the process that allows more doctors to work out the rest of the biomarker science and spurs the development of additional drugs. The FDA’s focus shifts from licensing drugs, one by one, to regulating a process that develops the integrated drug-patient science to arrive at complex, often multidrug, prescription protocols that can beat biochemically complex diseases.
…As others take charge of judging when it is in a patient’s best interest to start tinkering with his own molecular chemistry, the FDA will be left with a narrower task—one much more firmly grounded in solid science. So far as efficacy is concerned, the FDA will verify the drug’s ability to perform a specific biochemical task in various precisely defined molecular environments. It will evaluate drugs not as cures but as potential tools to be picked off the shelf and used carefully but flexibly, down at the molecular level, where the surgeon’s scalpels and sutures can’t reach.
In an important section, Huber notes that some of the biggest successes of the drug system in recent years occurred precisely because the standard FDA system was implicitly bypassed by orphan drug approval, accelerated approval and off-label prescribing (see also The Anomaly of Off-Label Prescribing).
But for these three major licensing loopholes, millions of people alive today would have died in the 1990s. Almost all the early HIV- and AIDS-related drugs—thalidomide among them—were designated as orphans. Most were rushed through the FDA under the accelerated-approval rule. Many were widely prescribed off-label. Oncology is the other field in which the orphanage, accelerated approval, and off-label prescription have already played a large role. Between 1992 and 2010, the rule accelerated patient access to 35 cancer drugs used in 47 new treatments. For the 26 that had completed conventional followup trials by the end of that period, the median acceleration time was almost four years.
Together, HIV and some cancers have also gone on to demonstrate what must replace the binary, yes/ no licensing calls and the preposterously out-of-date Washington-approved label in the realm of complex molecular medicine.
Huber’s paper has a foreword by Andrew C. von Eschenbach, former commissioner of the FDA, who concludes:
For precision medicine to flourish, Congress must explicitly empower the agency to embrace new tools, delegate other authorities to the NIH and/or patient-led organizations, and create a legal framework that protects companies from lawsuits to encourage the intensive data mining that will be required to evaluate medicines effectively in the postmarket setting. Last but not least, Congress will also have to create a mechanism for holding the agency accountable for producing the desired outcomes.