Note: if you are reading the electronic version, add 4 to all my page numbers to find the right one with Acrobat; it numbers in its count the various header pages that aren't given roman numberings in the report. I had initially used the Acrobat numbers, so if any turn out wrong try subtracting 4 from the page number.
One graph I am grappling with is Figure 1 on page 8, which shows the attrition of compounds through the development pipeline. The starting line is marked with 10,000 compounds yielding one final drug at the end. The plot certainly deserves showing up on Junk Charts, as it is not what it could be (and what such an important topic requires). For example, several stages are marked with numbers of compounds, but these label trapezoids with no clarity whether the number represents the start or the end of the stage. I'm guessing that the 10K number is estimating initial screening hits (counting in failed programs). The preclinical trapezoid is labeled "250 compounds" -- so that would be 40:1 hits to something (leads?). I'd better quit -- the more I stare at the graphic the more infuriating I find it.
Figure 6 (p.15) shows the depressing statistics: increasing R&D spending but a flat rate of New Drug Applications (NDAs) and especially NDAs for novel molecules (New Molecular Entities, or NMEs). Personally I'd prefer these as two vertically arrayed graphs & both in the same format (why bars for one but lines for the other?), but it does make the point.
Figure 5 (p.17) is the sort to enrage drug industry critics: 68% of all NDAs are not for NMEs. One thing not made clear in the methodology is whether generic drug applications (ANDAs) or supplementary applications (sNDAs, such as for additional indications) are in these numbers.
It would make no sense to include them, but given the high number of non-NME NDAs in their numbers I am suspicious. I'll confess that I'm not fully conversant in the classification scheme used here (any enlightenment attempts welcome!). For example, where would the next statin fall? Nexium? One wonders whether the classifications are really particularly useful.
In the Internet age, it is a travesty that the report isn't accompanied by computer-readable tables of all the data used. This really wouldn't be very difficult, the data is all public information, and would certainly allow other authors to vet the results or bring in their own analysis methods.
One more complaint: the PDF is apparently set so that copying can't be performed out of it! Aaargh!
One section I was planning to blockquote extensively was the section on translational medicine. The report cites one trouble area (p.27) as
... a shortage of physician-scientists, also known as translational researchers--who possess both medical and research degrees and thus the expertise needed to translate discovery -stage research into safe and effective drugs--was seen by panelists and other experts as a fundamental barrier to increasing the productivity of drug development. ... Experts attribute this shortage to a variety of factors, including lengthy training and relatively lower compensation for physicians who are also scientists, compared to those in clinical practice. In addition, researchers, including those in academia, have noted that academic institutions have not taken the initiative to provide financial incentives, such as scholarships, for medical students to pursue these research interests.
Even prior to reading a related discussion on In The Pipeline, I had been thinking out a different strategy. There should be better incentives for Ph.D.-M.D.s (which I'm pretty sure is what the report was tracking), but any program to create more will take a while, and some in it will choose other careers or interests. If you really want to expand the translational medicine pool, then start thinking about option beyond a very narrow credential list. Perhaps the most obvious would be to develop training programs to add skills to existing M.D.s, without forcing them to go the full Ph.D. route.
Slightly more radical woudl be the notion of developing translational medicine nurse-practioners -- after all, nursing training is very focused on patient care & patient observation, and would therefore be very suitable for careers in clinical medicine. The news is often filled with stories of nurses leaving the profession for various burnout reasons -- perhaps this option would keep some of these highly skilled persons in the field.
Going farther out, a lot of translational medicine is around developing and analyzing biomarkers. Again, nurses have many of the appropriate skills, particularly in observing side-effects that may be biomarkers (such as skin rashes observed both for EGFR inhibitors and bortezomib). Other biomarker development projects involving new assays fall clearly in the domain of med techs -- in my college internship I was in a lab staffed mostly by med techs, and that crew would have made an excellent biomarker pursuit team.
Perhaps the most interesting part of the report is the section of suggestions, beginning on p.35
. Tightly summarized they are:
- Industry-government-academia collaborations to systematically analyze drug failures, develop validated biomarker inventories, and prioritize diseases
- Bigger push in academia for translational medicine specialists (as commented on above)
- FDA incentives & disincentives based on importance of a new drug: innovative medicines get the push, the me-tos discouraged. One proposed method would be basing patent life on the innovativeness & clinical value of a drug
Well, I'm out of steam. Comments?