The Long Road from the Bench to You
September 10, 2009
By Jennifer Phillips, Ph.D.
No other season prompts me to reflect on the passage of time like Fall. The kids are returning to school, the days are growing shorter, and we're beginning the slippery slide toward the end of the year, hastened by the flurry of activities that the coming months will bring. But while everyone is looking ahead to academic and sporting events, and the parade of Holidays that will soon be upon us, I find myself looking back over the past year, examining the discarded days that can never be revisited, marveling at how quickly it all went by, and lamenting, always, that I didn't do nearly as much as I could have with this time.
Part of this seasonal malaise is an occupational hazard: Science is slow, ponderous work. Even though I'm regularly obtaining results from ongoing experiments, none of these are hugely significant in and of themselves. Each individual result informs the design of subsequent experiments, and those results dictate more experiments, and over time the picture of the process being illuminated by all of these findings is clear enough for us to publish our data, which can sometimes inform the design of a whole new set of studies. And while the results of any one of these experiments may be cause for smiles and high-fives (or, occasionally, sobs and 'headdesk's) within the lab, few have been stand-alone, paradigm shifting discoveries. The degree to which any single finding might impact the field is largely informed by the context provided by the other data points surrounding it, which can lend support, relevance and strength to the conclusion.
Accumulating such a body of work, even for very focused, specific research questions, takes time, and obtaining sufficient context for it to be applied to human medicine takes even longer. In the hierarchy of research, illustrated in my last post, the levels at the bottom of the steps are generally thought of as 'basic' research. Basic research can be characterized as 'research for the sake of discovery', or 'pure research', which translates loosely as 'research that is not specifically targeted toward developing a treatment for human disease'. This, of course, is distinct from 'clinical' research, which is all about establishing new treatments to improve human health. As discussed last time, the upper tiers can't exist without the lower ones, and in fact the whole system is more of a continuum than a stepwise process, at least with respect to intent. Most funding proposals for 'basic' research these days won't get a second look unless they include a well developed section on the relevance of the proposed studies to human health. However, identifying a particularly promising research finding and conveying it through the tiers often involves a third category of research, between basic and clinical: translational research. The concept of translational research is fairly straightforward-it acts as a bridge between the lab bench and the patient's bedside, identifying promising discoveries from the former and devising ways to optimize their application to the latter.
Delineation of translational research as a separate entity from either basic or clinical research has gained a lot of popularity in the past several years, as researchers and grant organizations alike try to reconcile their eagerness for finding treatments and cures with their limited budgets. Translational research as a field has become increasingly popular, and the numbers of physicians in training opting for the combined MD/PhD programs that give them experience and insights into the laboratory seems to be at an all-time high. Translational researchers have, in a way, been cast as 'efficiency experts', with the hope that they will be able to accelerate the progress through the research tiers and provide more human health benefits on a shorter time scale.
I should take a moment here to declare that I have infinite respect and admiration for those clinician scientists who are straddling both worlds, and I do believe that they possess a unique set of skills that neither the average basic PhD researcher nor the average MD clinician can offer. That said, I'm not completely sold on the idea that the influx of translational researchers into the breach will significantly impact the speed of scientific progress. Not because these researchers aren't qualified to do the job, or because there's no niche for them to fill, but because of the nature of science itself. Based on what I've already told you about the time scale of research and the winnowing process of data as it proceeds up the chain, I hope at least some of you are also cocking your eyebrows a bit, thinking "if only it were that easy!"
We're not alone in our opinion, either. In an article* appearing in the journal 'Science' last fall, John Ioannidis took a look at the time spans between initial research findings and the application of treatments based on each of those discoveries in human patients. The researchers confined their analysis to the development of high profile therapies, things like cancer drugs, high blood pressure and heart treatments. I'll spare you the blow by blow of the statistics he and his colleagues employed to study the research timeline, but briefly, they identified the year in which a seminal work that eventually led to a human treatment was first published or patented, and then compared that with the year in which the treatment had been established as effective. This latter time point was measured by how many times the paper describing the clinical findings had been cited in other papers-evidence that enough other physicians were obtaining similar results from routine use of the treatment to categorize it as 'widely accepted'. The time between these two points-understood to be the time during which translational researchers were in play--was dubbed 'translational lag' time and the median length of this 'lag' was...24 years.
Ioannidis sums it up as follows:
"Despite a major interest in translational research (1-3), development of new, effective medical interventions is difficult. Of 101 very promising claims of new discoveries with clear clinical potential that were made in major basic science journals between 1979 and 1983, only five resulted in interventions with licensed clinical use by 2003 and only one had extensive clinical use (4)."
Not surprising, given what we've been discussing here over the past month, but hardly encouraging. Ioannidis confirms what we already knew-that there really aren't any shortcuts in this process, and to pretend otherwise is counterproductive:
"Our analysis documents objectively show the long length of time that passes between discovery and translation. As scientists, we should convey to our funders and the public the immense difficulty of the scientific discovery process. Successful translation is demanding and takes a lot of effort and time even under the best circumstances; making unrealistic promises for quick discoveries and cures may damage the credibility of science in the eyes of the public."
This last bit really resonates with me. For various reasons--attempts to phrase new research findings in language the public can appreciate, a wish to give patients hope, or the desire for an exclusive, splashy headline-new findings in science and medicine are often inaccurately presented. I think this ends up working against scientists-and science itself--not only because trust is lost, but because when people hear so many stories about miracle cures and earth-shattering breakthroughs, they tend lose appreciation for the basic reality that I've tried (perhaps laboriously and tediously) to drive home here-that science is a long, hard process, and there are a limited number of shortcuts one can take without compromising quality. Unfortunately, habitual sensationalizing and overstating of findings has misled the public into thinking science can, in the right hands, happen quite rapidly. Thus, the fact that the cures aren't coming out fast and furious lead some to believe that scientists are so insulated from real world problems (aka clinical needs) that they lack the motivation to work faster, or that they are more concerned with their own job security than with the greater good of advancing human health (as was argued, infuriatingly, here).
At the end of all this doom and gloom, however, there are some silver linings. There are, truly, some legitimate reasons to hope that the treatment timeline for Usher syndrome might be shorter than some of the treatments analyzed in the Ioannidis study. For one thing, technical advances over the past decade have definitely improved the speed at which science can happen. The process is still slow, to be sure, but new technologies can definitely enable us to cut time without cutting corners. Perhaps if a similar analysis to the one I've been discussing were published in 2019, it would reveal a diminishing 'transitional lag'. Moreover, we don't yet know where-or when-the starting point will be for such a cure. There is a chance that some already existing piece of information is going to be the one that leads to an improved diagnostic or therapeutic development.
There is even a chance that it's sitting on my lab bench right now, so I'd best get back to work.
D. G. Contopoulos-Ioannidis, G. A. Alexiou, T. C. Gouvias, J. P. A. Ioannidis (2008). Life Cycle of Translational Research for Medical Interventions Science, 321 (5894), 1298-1299
*this article is behind a pay-wall. If you're interested in reading the full text but don't have access, please email me and I'll see what I can do.