Open-source drug R&D: comment on Munos NRDD
Just a quick comment on “Can open-source reinvigorate drug research?” by Bernard Munos of Eli Lilly, a perspective appearing in September’s NRDD. I commend Munos for tackling this question, which I’ve seen raised in numerous journals and news sources. If you have a subscription to NRDD, take a look at it.
Generally, I think Munos does a good job framing the issues, but the take-home message for me–blink and you miss it in Munos’ Perspective–is that only a small part of drug R&D is amenable to open-source in the computer-software sense of the term. The major barriers to broader applications of open-source in drug R&D are cost and regulation, barriers practically non-existent in the open-source software world.
I did want to call to your attention one part of drug R&D amenable to “open-sourcing” that should be vigorously explored by pharma, as it’s an area with a yawning vacuum of leadership and vision. Munos briefly mentions that clinical experience informs formal drug R&D:
One could argue that there has long been an active, if invisible, collaborative process akin to open-source in drug development, as, for some diseases, half of all prescriptions are for off-label uses. Somehow, physicians share their ideas and experiences informally to uncover novel uses for existing medicines. For instance, oncologists routinely use drugs approved for one kind of cancer to treat other types. In a recent study, DeMonaco found that 59% of drug therapy innovations were discovered by practicing clinicians via field discovery. The way by which physicians uncover these new indications is quick and inexpensive compared with Phase III trials. From an economic and medical standpoint, there would be merit in exploiting these clinical observations and sharing them with physicians as a complement to, or replacement for, some of the traditional clinical development.
There would be merit indeed. These “n of 1″ clinical experiments can be highly informative, as I’ve argued repeatedly to no one in particular in this blog and elsewhere, and they occur much more often than is currently reflected in the clinical literature. Somehow, Munos says, physicians share their ideas informally. Exactly. It was communications infrastructure and vision applied to informal knowledge-sharing that propelled open-source software from niche activity to innovation engine. These same things are needed to harness the largely untapped power of routinely conducted human experiments–yes, off-label prescribing is experimentation.
As every drug researcher should already appreciate, the utility of knowledge from experiments with FDA-approved medicines is applicable to more than just approved medicines. Its applicable to more than just follow-on drugs in the same chemical and therapeutic classes too. These activities potentially inform the entire drug R&D life cycle, from new-target selection to Phase 3 study design. Therefore, this activity should be viewed by potential sponsors as a general fuel to the innovation fire rather than as a therapeutic area-specific activity that must be closely aligned with proprietary research activities to earn support.
I’ve alread mentioned what’s needed to convert a routine clinical activity into a useful open-sourced innovation engine: a communications infrastructure (essentially a user-friendly database and message-board functionality) and leadership to organize it, maintain it and, most importantly, to get the word out. Many parties are capable of providing this leadership and infrastructure: pharma/bio, medical societies, medical publishers, academia, federal governments, WHO, etc. So far, no one has stepped up. Lilly has been a visionary with its quasi open-source InnoCentive and Scienteur programs, perhaps it will lead the way with the off-label documentation initative (OLDI?…nah).
Pharma’s Cutting Edge » A scalable infrastructure to capture real-world clinical experiments said,
June 25, 2007 at 3:52 pm
[…] Last September, I argued in this blog for a data infrastructure, which would be supported by a public-private partnership, capable of capturing real-world, “N of 1″ clinical experiments. Such experiments collectively represent a huge potential repository of clinical scientific evidence. Today, experimental evidence coming from routine clinic encounters is underutilized. Case reports, case series, and retrospective chart reviews, have gained a reputation as weak evidence of causation (typically described as anecdotal evidence) and have thus fallen into disfavor. Too bad, because such anecdotes have historically frequently been the impetus for exploration of a new use for a drug or a signal of an adverse effect that was previously unseen or unrecognized as therapy-related. In contrast, with the rise of managed care and the need for drug and device manufacturers to demonstrate a favorable benefit to risk balance for new therapies, studies that make use of databases linking diagnoses, outcomes and therapies are becoming more common. Such studies are likely to become increasingly important for delivering improved safety surveillance of therapies and would benefit from larger, more demographically diverse patient pools. They would also benefit from a larger amount of clean contextual metadata (data about data). For instance, researchers might now be able to tell when a prescription was first filled by searching PBM or insurance records, but it would be more difficult to ascertain the day the prescribed drug was stopped. However, perhaps the prescribing physician noted in the patient chart (the Electronic Health Record, EHR, in my utopican example) that the drug was stopped 20 days after filling the prescription because of a rash that developed and was restarted after a 30-day off period without reoccurrence of a rash. Charts may be abstracted for smallish studies, but you can’t manually abstract charts on a million patients. Plus, today’s abstractions (e.g. CPT coding) are lossy mechanisms for transforming information into a usable form. […]