At A Glance
Name: Robert Temple
Title: Director of the Office of Medical Policy, Associate Director for Medical Policy, and Director of the Office of Drug Evaluation I — US Food and Drug Administration Center for Drug Evaluation and Research
Background: Acting Director and Director of the Office of Drug Research and Review, FDA — 1982-1995; Director of the Division of Cardio-Renal Drug Products, FDA — 1976-1982; Assistant Director of the Bureau of Drugs, FDA — 1974-1976; Clinical Associate and Chief Clinical Associate — Clinical Endocrinology Branch of the US National Institute of Arthritis, Metabolism, and Digestive Diseases, National Institutes of Health — 1969-1972
Education: BA from Harvard College, Boston, Mass. —1963; MD from New York University School of Medicine — 1967
Robert Temple oversees the FDA office responsible for the regulation of cardio-renal, oncologic, and neuropharmacologic/psychopharmacologic drug products. He also has a long-standing interest in the design and conduct of clinical trials, a subject he has written much about, and appeared recently on “CDER Live!” with four other FDA officers and a representative of Novartis Pharmaceuticals to discuss the FDA’s Critical Path initiative, a broad streamlining strategy aimed at remedying the slowing process of drug development and approval. Pharmacogenomics Reporter caught up with Temple this week to ask him more about the initiative and his thoughts on the drug pipeline.
How would you describe the Critical Path?
Critical Path is a response to the perception that, despite the tremendous advances in understanding biology, the practical outcome in terms of drugs is at least so far, unimpressive. And the perception here and to some extent the perception at the NIH is that people aren’t as good at translation as they need to be.
So Critical Path includes a wide range of things that might help the thing work better. I’d say that’s what it is. And it includes Janet [Woodcock’s] initiative on manufacturing — to bring it up to date, make it simpler, and in fact better. It includes looking at what we need for certain kinds of early studies to get into humans — you know, maybe what we’ve usually asked for is realistic for regular IVs, but maybe for a microdose-IV, to get an idea of where the drug goes. It includes an attempt to make our requirements clear where they’re not — write guidance, resolve certain kinds of things. And the two parts of it I find most interesting, it includes attempts to make studies more efficient — by which I generally mean more likely be able to show something, if there is anything. And the fundamental mechanism that I’ve been interested in for many years is the concept of enrichment — trying to find a population in whom success is more likely.
Some of it is trivial, we have [methods] to screen out responders — people do that sort of thing all the time. You try to find people with the disease that fits the pharmacology of your drug. Just to take an obvious example — if you’re studying an ionotropic agent, you don’t use people with diastolic dysfunction, that’ll make it worse.
But those are obvious, people do them automatically. But it turns out, partly on a genomic basis, but partly on a next-level basis.
There are more differences between people who look alike than we ever imagined. And in some cases, those differences could predict who’s a responder — which is very important to identify — or who is likely to have the events of interest. The industry tend to think of these things as identifying responders as the most important thing, and that is the most glamorous in some way — find a drug that is targeted to particular individuals. But people don’t realize how much the success of a lot of cardiovascular interventions has depended on identifying high risk people, and doing the study first in them. My favorite example is the first drug that ever showed a survival effect in heart failure was enalapril, and that was done in a study called the CONSENSUS study, which looked at Class 3 and 4 people with heart failure — those people had a disease worse than cancer, their mortality at six months was 40 percent, and this trial of enalapril studied 263 people in a mortality study, and succeeded like gangbusters.
The general point being, if you get a lot of events early and your drug works, you’ll be able to see it. This kind of thing has happened in others — the first really successful lipid-lowering trial, the 4S trial, was done in people with cholesterol of 260 who had had a previous heart attack, and they had a spectacular event rate and the results were very good. And gradually, people have — having established benefit in people who are at very high risk — moved down into various lower-risk categories, fairly secure that things were going to work out OK, because they had the data from the more severe people.
How do you think Critical Path will affect pharma and diagnostic companies that deal in genomics?
The other thing that genomic or other measurements might give you is who is going to respond, and the recent example of [Tarceva] is really interesting. The drug was done in a large, randomized trial for a general population of people who were [receiving] third-line [therapy for] lung cancer. But the company also got EGFR data on a third of the patients, and characterized them as positive or negative based on a widely available test. I don’t know about tests, but that’s my understanding. It turned out that if you looked at that population, the people who were EGFR positive — which makes sense because the drug is directed at the EGFR and the tyrosine kinase associated with it — overall, the effect was nice, there was about a two-month median survival increase. You know, good effect, looks pretty good.
In the EGFR-positive people, the median increase in survival was seven months, the curves separated hugely, and in the EGFR-negative people — not a large enough sample size to say this definitively — there was no effect at all.
We’re short of being absolutely convinced of this because they didn’t get an EGFR status on everybody.
But the ability to choose a population that is going to respond by a fairly simple test, is just a huge advance. Now this is not a terribly toxic drug, but it isn’t cheap, and people who are paying for it would probably like to know who it works in. So it represents a spectacular advance.
Moreover, in some sense they were just lucky. That’s because EGFR positivity is present in about half of the population, maybe about 55 percent. Just suppose it was present in only 20 percent of the population? Then this big general study might’ve failed, they might not have seen improved survival, but the effect in the 20 percent would’ve been huge.
So, one of the things we need to do is convey to people a number of possibilities that so far have not been done. For example, it’s OK to do your study in a general population, and you might want to, but it might be OK to make your primary endpoint be effect in the people with the appropriate receptor — that is a subset, a prospectively defined subset. Well, companies are nervous about doing that without permission, so we need to define how to go about doing that.
Many of the participants of the CDER Live program spoke about “the flat drug pipeline,” what do you think the reason is for that?
I don’t know. And we don’t know the reason. This is something we hear from companies. There’s some increase in INDs. And there are a lot possible reasons, and I don’t know what they are. One hypothesis going around is that all the “low-hanging fruit” has been picked, we have treatments for all the obvious things.
I’m not entirely persuaded by that. Because there are still some things that are not very satisfactorily treated. When you need an analgesic, it’s narcotics or anti-inflammatory drugs. That’s not entirely satisfactory, those drugs have problems.
It may be that, in the past it was OK to develop 4, 5, 6, 7, 8, 9, 10 versions of a molecule, and now third-party payers don’t make it worthwhile once you’re passing three or four, so there are fewer copies.
I’m not sure.
It’s a widespread perception in the industry.