I’m not sure about this, but …

Personalized medicine is intended to add several layers of information into the treatment decision to make it more likely that a patient will perform as desired during treatment or perhaps more accurately, that we’ll know how a patient will perform during treatment. But personalized medicine is an incredible uphill battle, as many companies and organizations have seen over the last decade.

Randall C Willis
A thousand years ago, when I still worked in a laboratory, Ihad the pleasure of being the wet biochemist for two biophysicists, one of whomwas so completely focused on mathematical gymnastics with Fourier transformsthat he considered proteins merely an artifact of protein NMR—something to betolerated.
One day, the two of us were talking in the lab—to this day,I don't know why that was allowed to happen—and he asked me what the averageyield of a particular protein prep was. Very matter-of-factly, I told him theaverage yield was the total yield of all experiments, divided by the number ofexperiments performed to reach that total. He simply stared at me, unblinkingfor several minutes, while his brain tried to absorb what had to be theglibbest answer he'd ever heard in his life. And yet, I wasn't trying to beglib.
What the mathy-physicist could not grasp, because the "bio"in his title was something forced on him, was that unlike math—and to someextent, chemistry—biology is an art, not a science. It is not precise, butrather chaotic. And despite the best efforts of practitioners to make it fitthe scientific method of, "do the same thing, you'll get the same result,"anything that relies on a biological system is likely to give you a (hopefullyonly slightly) different answer every time you ask it the same question.
And as it was with my biophysicist boss, the basicimprecision is incredibly frustrating to biomedical researchers, physicians,patients and finance managers.
As I research clinical trials and preclinical research formy articles for Drug Discovery Newsor any of my other medical writing, I am always surprised at how much effortgoes into the statistical analysis of a trial, as it seems any statistics thatarise from a study become largely moot once the study is completed.
Now, please: This is not meant as a criticism orcondemnation of clinical trial specialists or statisticians. These people dotheir damnedest to ensure that the trials are as fair and reflective of actualclinical practice as possible, but in an attempt to actually complete the studywithout too many confounding factors to accommodate, things get left out.
At the end of the day, you learn that in this study, 75percent of patients on Drug X showed an efficacy endpoint, while only 53 percentof patients on placebo (or Drug Y) demonstrated that endpoint and that thesefindings were statistically relevant.
Congratulations, Drug X has been approved. Now let's put itinto the real-world patient population—the equivalent of teaching your child toswim in a kiddie pool and then throwing her into the Colorado River duringflood season.
Watch the rocks! We'll see you downstream … we hope.
Patient populations and patients are not the same thing.When you give an individual patient Drug X, he is unlikely to be 75-percenttreated. He responds or he doesn't, or he inhabits that pharmacologic limbosomewhere between the two. Confounding factors such as comorbidities, complianceand lifestyle that were not part of the original trial very much complicate thereal world.
The problem is, the patient expects to be better aftertaking the drug and comes out a little (or a lot) disappointed on the otherend. He can't understand the imprecision of medicine because that's not howwe've sold it.
Can we make medicine more precise? Yes. Personalizedmedicine is intended to add several layers of information into the treatmentdecision to make it more likely that a patient will perform as desired duringtreatment or perhaps more accurately, that we'll know how a patient willperform during treatment.
But personalized medicine is an incredible uphill battle, asmany companies and organizations have seen over the last decade. Attempts tomodel human disease and drug response are fraught with knowledge gaps throughwhich you could float a cruise ship, and many an organization has faded intothe woodwork, exhausted from attempts to make these models practically useful.Sure, we get more data every day, but have you seen how big they're makingcruise ships these days?
This is not an indictment of personalized medicine, or evena suggestion that it is a waste of time. I personally believe that to move inany other direction would be foolish and un-Hippocratic. This is, however, awarning that at the end of the day, even with the most precise models of thehuman condition, there will still likely be an unsettling degree of imprecisionin our efforts to treat individuals. These are biological entities notelectromagnetic pulses, and thus are prone to do what they will no matter howsmart we think we are.
The sooner we understand that and make it part of ourdiscussions with clinicians, patients and investors, the better it will be foreveryone.
Randall C. Willis isthe features editor of ddn. He hasworked at both ends of the pharmaceutical industry, from basic research tomarketing, and has written about biomedical science for almost two decades.

Randall C Willis

Subscribe to Newsletter
Subscribe to our eNewsletters

Stay connected with all of the latest from Drug Discovery News.

November 2022 Issue Front Cover

Latest Issue  

• Volume 18 • Issue 11 • November 2022

November 2022

November 2022