Drugs

Enrichment Design Studies should Enhance Signals of Effectiveness

by Robert Temple, M.D., Deputy Center Director for Clinical Science

Robert Temple, M.D. caricature
Enrichment design studies for evaluating certain treatments or drugs, is not really a novel idea. Enrichment is an attempt to find a study population in which the effect of a drug can be most readily demonstrated -- if, in fact, the drug is effective. People have been using approaches like this since clinical trials were first conducted. There are many practical enrichment maneuvers that are common in clinical trials. In fact, CDER has just published a draft guidance document to help people better understand the topic. 

There are basically three kinds of enrichment: noise reduction, prognostic and predictive. Enrichment won’t save a drug that doesn’t work, but it will help find one that does.   

Noise Reduction

Noise reduction is one of the variety of ways researchers try to include people who can be measured precisely and correctly, and whose disease is stable, so if they have a drug effect it can be detected. 

For example, at the start of trials, it’s common to have a period of treatment with a placebo when testing antidepressants or other drugs where there is a large placebo response. If you can eliminate people who have a significant placebo response, then the difference between active treatment and the placebo will be obvious. If everybody’s disease goes away in the placebo group, then there is no effectiveness to show. 

Sometimes measurements can be done precisely in people, and sometimes they cannot. You can screen a population for an antihypertensive trial to eliminate people whose blood pressure is very variable. That variability will make enough noise so you may not be able to show what you want to show for the drug.  Another example is to try to identify people who will comply with the therapy -- it’s not always easy to do, but makes a difference when you can.  

Prognostic Enrichment

The second major method of enrichment is called prognostic enrichment. This mostly applies to studies where you are trying to show that a drug reduces a bad outcome, like heart attack or death. In order to succeed, you need a population that has a reasonable number of these events. If they’re too healthy, the group won’t have any events and your drug will look like it doesn’t do anything. Similarly, in early studies of drugs to treat symptoms, people often try to do the early studies of a drug in a population that’s reasonably sick, so there is something to improve.

My favorite example is the first study of an angiotensin converting enzyme inhibitor in heart failure that showed success in reducing death rates. It was called the CONSENSUS Study and was done with a drug called enalapril, in a population with New York Heart Association Class IV (very severe) heart failure. The NYHA Functional Classification provides a simple way of ranking the extent of heart failure. It places patients in one of four categories based on how much they are limited during physical activity. Class IV patients are unable to do any physical activity without discomfort; the more the activity, the more the discomfort. These were very sick people. In fact, the mortality rate during the six months of the trial was more than 50 percent.

In this study of only 253 patients, a tiny study compared to most mortality studies in heart disease, it was possible to show that enalapril decreased mortality by about 40 percent. The study was able to show results in a small number of patients because the event rate was so high. 

It’s important to recognize that the study group didn’t necessarily have a bigger effect from the drug than other people. But since the study group had lots of events, it showed -- with a relatively small population -- that the drug helped.   

Predictive Enrichment

The third kind of enrichment is predictive enrichment -- trying to find a population that responds to the particular drug in question. One way to do this is by testing a population first, showing that they responded, then take the drug away and randomly allocate those that receive the alternative treatments. You now have a population that is very capable of showing that the drug works. It doesn’t tell you about the effect in the overall population, but it does tell you that in at least some population the drug really works. In many cases, this is the first thing you want to know. 

But even more exciting is the possibility of choosing (without initial treatment) patients who can respond by finding a genetic or physiological characteristic that predicts response to a particular therapy.

There have been some recent examples of this. Many have to do with cancer which is, in some sense, a genetic disease. It has become possible to identify particular characteristics on the surface of the cancer cell or sometimes genetic characteristics that predict whether a particular kind of drug will treat that tumor. 

A classic example is the drug herceptin. When used for breast cancer, it works in people who have the HER 2 receptor but it doesn’t work nearly as well in people who don’t have that receptor. Early studies of the drug studied patients with tumors that had the HER receptor. 

Another example is a recently-approved drug for that reverses the genetic defect that causes cystic fibrosis. The drug, Kalydeco, works in only a small fraction of the people (about 4%) with the disease who have a particular genetic abnormality. While the drug works in only four percent of patients with cystic fibrosis, it has a dramatic effect in that population.  A study of an unselected population, in 90% of whom the drug would have little effect, would probably have failed.

There are also several new treatments for Hepatitis C. They work faster and better than the previous treatments, but only work in patients with Hepatitis C Type 1; they don’t work nearly as well in Types 2 and 3 but there are other drugs under development to treat these conditions. 

These examples are all called predictive enrichment because they are based on the expectation that a certain population will respond much better than an unselected population.

Now there are some issues with predictive enrichment. One is that you always believe the characteristic you use to enrich predicts the good responders; it may not do this as well as you hope. So it’s very important to characterize the test that leads you to select those patients; then see whether it’s true that patients with the characteristic always (or most of the time) respond, and that patients without the characteristic don’t respond very much. 

An issue to consider in any enrichment design is how much you need to study the people who don’t have the enrichment characteristic. This is something to be worked out over time. But thinking about the question, “Have I picked the population that is most likely to be able to show an effect?” is important. Sometimes called individualization of therapy, this approach has shown some drugs to be dramatically effective in targeted populations. Enrichment design studies help you reach this kind of individualization. 

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Robert Temple, M.D., Deputy Director for Clinical Science

Robert Temple serves as CDER’s Deputy Center Director for Clinical Science and also Acting Deputy Director of the Office of Drug Evaluation I (ODE-I). He has served in this capacity since the office's establishment in 1995. 

Dr. Temple received his medical degree from the New York University School of Medicine in 1967. In 1972 he joined CDER as a review Medical Officer in the Division of Metabolic and Endocrine Drug Products. He later moved into the position of Director of the Division of Cardio-Renal Drug Products. 

In his current position, Dr. Temple oversees ODE-1 which is responsible for the regulation of cardio-renal, neuropharmacologic, and psychopharmacologic drug products. Dr. Temple has a long-standing interest in the design and conduct of clinical trials. He has written extensively on this subject, especially on choice of control group in clinical trials, evaluation of active control trials, trials to evaluate dose-response, and trials using “enrichment” designs.

Page Last Updated: 12/12/2014
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