NeoHuman podcast, starring me

Willis NeoHuman

My friend Agah Bahari is interested in everything, which is one of the things that I love about him.

Not that long ago, he decided to indulge his interests by starting something he calls the NeoHuman podcast (which matches nicely with his NeoHuman blog), inviting many of the interesting people he knows to discuss pretty much anything that comes up.

Well, seems he ran out of interesting people and so he invited me to participate…and we talked about anything: biotechnology, pharma, global healthcare, designer babies, creativity, writing, screenwriting, 9/11, marketing, and the novel he and I are writing about his life.

But my favourite part is the question he asks all his guest, which is roughly:

If you met an intelligent alien life-form, what would you describe as the greatest human accomplishment and as the worst human accomplishment?

Never boring, my friend Agah.

Agah-me

(Photo stolen with love from Kelly Brienz Showker)

Substance over volume

ddnews

When you meet someone who does not speak your language, there is a cliché response of talking louder to make yourself understood. There is something within many of us that says if we simply pump up the volume, we can overcome the disconnect.

A couple of months ago, Tufts University released their latest estimates for the average cost of developing a new drug: $2.6 billion (I’ve seen estimates up to $5 billion). Eleven years ago, the same group calculated the costs at $0.8 billion.

Now, every time these estimates arise, the hand-wringing begins over how the costs were calculated, which factors make sense and which are over-reaching. What no one seems to argue, however, is that drugs are less expensive to develop today than they were a decade ago.

So what has this to do with speaking louder?

The same period has seen amazing technological achievements designed to facilitate and accelerate drug discovery and development.

Combinatorial chemistry was heralded as a way to expand compound libraries from hundreds to hundreds of thousands. High-throughput and high-content screening, as well as miniaturization and automation, were lauded as ways to screen all of these compounds faster under the paradigm of “fail early, fail often”. And given the masses of data these technologies would churn out, the informatics revolution was supposed to convert data into knowledge and knowledge into healthcare.

And yet, for all of these improvements in throughput, I question whether we have seen much improvement in the number or quality of drugs being produced. We certainly haven’t made them less expensive.

Please understand, I don’t place any fault in the technologies. These are truly marvels of engineering. Rather, I question the applications and expectations of the technologies.

Almost two years ago, GSK CEO Andrew Witty told a London healthcare conference: “It’s entirely achievable that we can improve the efficiency of the industry and pass that forward in terms of reduced prices.”

The pivotal question here, I believe, is how one defines efficiency.

I wonder how many people simply felt economies-of-scale would improve discovery, much as mass production made Henry Ford a rich man. But drugs are not cars, and where throughput and scale make sense when you have a fully characterized end product, they have their limitations during exploration.

When I was a protein biochemist in an NMR structural biology lab, I spent some time trying to wrap my head around two concepts: precision and accuracy. A 3-Å protein structure is very precise but if the structure isn’t truly reflective of what happens in nature, it is meaningless. A 30-Å protein structure is much less precise, but if it is more accurate, more in tune with nature, then it is likely more useful.

By comparison, I wonder if our zeal to equate efficiency with throughput hasn’t improved our precision at the cost of our accuracy. If you ask the wrong question, all of the throughput in the world won’t get you closer to the right answer.

In researching the DDNews Special Reports over the last couple of years, I have spoken at length to several pharma and biotech specialists about this topic, and many feel that the industrialization of drug discovery and development has underwhelmed if not outright failed. Several have suggested it is time to step back and learn to ask better questions of our technologies.

But getting back to the costs issue.

I know many will rightly point out that the largest expense comes from clinical trials. To address this challenge, new technologies and methodologies are being developed to get the most useful information out of the smallest patient populations.

Here again, however, no one segment of the drug development process stands in isolation, and I think back to the compounds reaching the clinic and question the expense of incremental improvements.

Oncolytics CEO Brad Thompson discussed the challenge in Cancer in the Clinic (June 2014 DDNews).

“If you could double [overall survival], you could show that in a couple of hundred patients. If you want to do a 10-percent improvement, you’re talking thousands of patients to do it to the statistical level that everybody would prefer to see. How do you run a study like that?”

That is a huge difference in financial expenditure that begs the question is an efficacy improvement of just 10 percent of value.

From an individual patient perspective, assuredly. From a pharmacoeconomic perspective, maybe not, and particularly with the growing prevalence of high-cost targeted biologics. Maybe we need to aim for bigger improvements before moving candidates forward, which happens long before the clinic.

Again, I’m not placing blame. The history of any industry is filled with experimentation in different methodologies and technologies. Everyone involved had the best of intentions.

But after a couple of decades of middling results, perhaps it is time to question how and when many of these advancements are applied. Simply yelling at a higher volume doesn’t seem to be enough.

[This piece was originally published in the January 2015 issue of DDNews. A lot has happened in the year since, including some amazing results in the field of immuno-oncology that might just address the demand for high-performance treatments even if only for a select patient population. For more on that, see my June 2015 Special Report “Body, heal thyself”.]

Why models fail us in childhood, on TV and in drug discovery

I know you’re used to me babbling in these pages, but I am sure two of you have wondered, so how does he pay his bills? I know my landlord wonders that.
Below, I have reprinted my latest commentary from DDNews, a magazine for which I write regularly and for which, to my great surprise, the Publishers pay me. Thought it might make a nice change of pace for those tired of the current pace.
DDnews
For those of you who read the article, there is a bonus at the bottom (worst fruit-in-yogurt tagline, EVAR!) 
For a brief period of my childhood, I dabbled in model airplanes and model ships. And by “dabbled,” I mean I spent an inordinate amount of time with my fingers glued together. But aside from the medical agonies of modern chemistry, what struck me most about the exercise was how pale an approximation these models were of the real thing—about the only thing my miniature Spitfire had in common with the WWII fighter was the sheer carnage of the plane as it went from airborne to groundborne in its flight across the room.
More recently, my fascinations have turned to models of a more human variety, such as those found on the catwalks of America’s Top Model. (Let’s face it, after DDNews, I am all about the latest issue of Vogue.) And like the plastic variety described above, these models seem to be at best a glittery approximation of the actual thing.
It’s often hard to believe that I share about 100 percent DNA sequence identity with these mystical creatures. Now, 98 percent coherence with bonobo chimps, I have no problem believing.
All this to say that we are constantly surrounded with models that are poor facsimiles of the real deal upon closer inspection. So, it should probably should come as no surprise that the same is true in medicine and drug discovery.
Last week, I read a story in the newspaper that touted the life-prolonging properties of the diabetes drug metformin; a regular fountain of youth, the headlines implied. And yet, after coursing through the article, I eventually discovered that the rejuvenation experiments were performed on the worm C. elegans, which was shocking for two reasons.
One, I did not realize that there were largely untapped market opportunities in annelid diabetes. And two, the life-extending implications were being made based on a species that wasn’t even a chordate, let alone a mammal.
Now, I appreciate that this is an extreme example, probably overhyped by an eager press officer, but the literature is rife with examples of models that completely failed to live up to expectations when researchers tried to match success in model systems with success in actual humans.
As oncology god Judah Folkman once mused, we have become really good at curing cancer in mice.
Part of the challenge, I think, is that because we cannot experiment directly on humans, or at least not within the editorial reach of DDNews, researchers are often forced to study new compounds or therapeutic modalities on approximations of approximations of approximations of human disease.
We don’t study the impact of irinotecan on human colorectal cancer but rather extrapolate from its effects on an induced form (approximation 1) of murine colon cancer (approximation 2) in mice (approximation 3). Or we study new biologics against a chemically induced inflammation in dogs that bears a passing resemblance to rheumatoid arthritis in humans.
Within the realm of in-vitro models, the advent of technologies like 3D cell culture and microtissues is adding some biological context back into the completely artificial realm of 2D cultures of immortalized cell lines. (For more on this, see the special report “Life moves on” in this issue, on page 21). But in the absence of factors such as tissue vascularization and the like, even these advances result in weak approximations.
The goal of a better, more representative model may be getting a step or two closer, however, with the help of stem cells.
As we’ve reported previously in these pages, and as I am presently hearing at the ISSCR conference in Vancouver, stem cells are giving us enhanced opportunities to study human disease generated from the source material—human cells—potentially down to the scale of the individual patient.
The standard technical limitations of in-vitro analysis hold for stem cells—a lump of microtissue in a microwell dish does not a micro-human make—but we do have the opportunity to limit one or two approximations.
At ISSCR, for example, Daniela Cornacchia and colleagues at Sloan-Kettering and Weill Cornell Medical College describe their efforts to understand the inadvertent de-aging of cells transformed into iPSCs. Even when taken from older patients, the reversion process makes it difficult to use the cells to study late-onset diseases. The group is trying to identify factors that will allow them to induce natural aging into these cultures to improve models of such diseases as dementia.
Similarly, Rohan Nadkarni and Carlos Pilquil of McMaster University are endeavoring to produce 3D lung tissue from iPSCs that contain both conducting and gas-exchange zones mimicking normal lung function. If the model bears out, it may provide an even more realistic platform to study respiratory diseases in vitro.
Until we are in a position where we can do high-throughput human screening—in a 96-well cube farm, perhaps—the search for better model systems must be a priority. And given the challenges of translating preclinical success into clinical success, perhaps it should be a higher priority than the development of new therapeutics.

Look for more on this topic in a special feature on disease modeling in the November issue of DDNews.

Added bonus for blog readers:
#1 on the left photo and #2 on the right photo

#1 on the left photo and #2 on the right photo