Unlike previous editorials (1–4), this one contains a table, deals with science policy, and pertains to just one country. I apologize for this last deviation because Physiology intends to serve a world-wide audience. Nonetheless, readers from outside the USA may resonate with some of the issues.
Between 1998 and 2003, the US Federal Government did something truly extraordinary: it doubled the budget of the National Institutes of Health . . . spurred on by scientific societies—as well as by rank-and-file scientists and their friends and relatives—who communicated to Congress the importance of biomedical research. These efforts and the good will of Congress and the President brought about a series of five annual increases of ~15%—an overall increase of 99.2% (see Table).
Alas, the promise of the so-recently completed doubling has left an empty feeling with many independent investigators, who have seen NIH “pay lines”1 descend into the doldrums that prompted the call for the doubling in the first place. What went wrong? 9/11 for one . . . it diverted funds to non-NIH programs and even diverted some NIH funds to bioterrorism programs that could have been funded by other agencies. However, even after 9/11, the Government completed the final 2 years of the 5-year doubling. Afterward, the growth of the NIH budget decelerated markedly before taking a step backward in 2006.
But the slumping NIH budget is only part of what went wrong. Another factor has been a major increase in the number of grant applications. After a long slide (itself the result of the previous funding squeeze, see below), the annual number of applications hit bottom in 1998. However, this number rose by more than 40% from 1998 to 2005. Thus, to some extent, the enemy is us! Other things being equal, more applications translate to lower pay lines. On the other hand, magically eliminating the additional 40% of applications would only raise a 10 percentile pay line to the 14 percentile, still far short of the 25 percentile.
But there is still one more piece to the pay-line puzzle: the allocation of NIH dollars, sometimes mandated by Congress, and often following the advice of committees of independent investigators. The fraction of the NIH budget devoted to research by independent investigators (Table 1⇓) steadily fell from 1998 to 2003. Conversely, spending for other programs including “big science”—the sequencing of genomes, clinical trials, and other costly and lengthy projects—steadily rose. Where does one draw the line between shifting funds to big science and yet maintaining a healthy portfolio of independent-investigator research? When the NIH is afloat in money (e.g., pay lines ≥25th percentile for research by independent investigators), such a shift may make sense. An example is the sequencing of the human genome, which has been invaluable. But what about sequencing the squid genome, which I personally would love to see? Before addressing this question, let us examine the value of investigator-initiated research and the dangers of interrupting it for even a couple of years.
The scientific engine that drives translational research—and that drives big science as well—is the independent investigator. It is the independent investigator who trains the next generation of researchers. Moreover, discoveries almost always come about when bright independent investigators stumble over unexpected findings and then sort them out. Such stumbling is unpredictable. The bigger the discovery, the more unpredictable. As unnerving as it may seem, the best way to invest in discovery is to fund the best independent investigators and turn them loose to stumble.
The cost of interrupting support to an independent laboratory is enormous. For an established independent investigator, interruptions in funding lead not just to a halt in work but to a loss in personnel and institutional memory. During a funding lapse, it is difficult and often impossible to generate preliminary data. The longer the lapse, the older is the “old” preliminary data. After a couple of years, the lapsed investigator is no longer competitive, and the lapse becomes permanent (hence the aforementioned slide in NIH applications). These events also send a devastatingly strong message to the potential future generation that witnesses them—undergraduates, graduate students, and postdoctoral fellows all ask whether this is the life they wish to lead. For a new investigator who misses the pay line after several attempts, the episode aborts a promising career. For society and the sponsoring academic institution, the costly and lengthy investment in training a young scientist is lost.
So, what about the squid genome? I think it can wait until the pay lines rise above the 25th percentile. When push comes to shove, it is better to mothball a few robots than to gut the roster of established independent investigators and to lose a generation of new independent investigators. Of course, we can do more than just postponing the sequencing of the squid genome. We could advise the NIH to scale back big science immediately. As shown in the Table, although the NIH budget doubled between 1998 and 2003, the amount for independent-investigator research rose by less than 72%. The amount for other programs rose by 119%. Had those percentages been reversed, the NIH could have funded 7,800 additional grants in 2005, equivalent to 1,560 new 5-year grants each year, increasing the number of funded grants by 25%. When Congress eventually raises NIH funding—and when pay lines again exceed the 25th percentile—then we could prioritize the deferred big science and begin funding what is at the top of that list. But if we—the US biomedical community—fail to make the immediate adjustments necessary to keep independent investigator research healthy, we may soon look back and realize that the translational pipeline is empty and that big science is playing to an empty house.
↵1 The percentile score (0 percentile being the unattainable best) below which a grant application must score in order to merit funding. For some NIH institutes, these pay lines have sunk to the neighborhood of the 10th percentile, after having been comfortably over the 20th percentile during the actual doubling.
- © 2006 Int. Union Physiol. Sci./Am. Physiol. Soc.