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February 12, 2013

FDA: Enrichment Strategies to Improve Efficiency of Drug Development

Drug Development

At the end of last year, the Food and Drug Administration (FDA) released a draft guidance document designed to help drug companies speed development.  The 42-page guidance outlines “enrichment strategies” meant to improve the efficiency of drug trials such as excluding patients unlikely to demonstrate clinical benefit.  “These are potentially powerful strategies for the pharmaceutical industry because appropriate use of enrichment could result in smaller studies, shortened drug development times, and lower development costs,” wrote Dr. Bob Temple of the FDA, reported by MedPage Today

Interestingly, the guidance comes shortly after FDA announced approving 39 new drugs in 2012, nine more than a year earlier and the most since 1996, suggesting growth prospects for branded drugmakers that have been losing business to patent expirations.  There were eight approvals in December alone.  The approvals included potential blockbuster drugs such as Pfizer and Bristol-Myers Squibb's blood thinner Eliquis, but many are intended for smaller markets, including Vertex Pharmaceuticals' Kalydeco, which treats a particular form of cystic fibrosis.  

The Prescription Drug User Fee Act (PDUFA) “provided critical resources for improving the quality and timeliness of premarket review of drugs,” FDA spokeswoman Sandy Walsh said. 

“The strong number of approvals demonstrates the continuing innovation by biopharmaceutical research companies and commitment to help improve patients’ lives,” said Matt Bennett, a spokesman in Washington for the Pharmaceutical Research and Manufacturers of America, or PhRMA. 

“The sheer number I think supports the correctness in some of the strategy shift of the pharmaceutical companies over the last number of years,” Rick Edmunds, a senior partner at Booz & Co. who leads the consulting firm’s global health-care practice in Washington, said in a telephone interview with Bloomberg News.  The rate “implies pharma growth potential to 2015 and beyond.” 

Closing out the year, FDA approved its first anti-diarrheal drug for HIV/AIDS paitnes—Fulyzaq (crofelemer).  FDA also approved its first drug to treat multi-resistant tuberculosis—Sirturo (bedaquiline).  The agency also approved a new orphan drug for rare cholesterol disorder—Juxtapid (lomitapide) to reduce low-density lipoprotein (LDL) cholesterol, total cholestorl, apolipoprotein B, and non-high-density lipoprotein (non-HDL) cholesterol in patients with homozygous familial hypercholesterolemia (HoFH).   


Clinical trials are not designed to demonstrate the effectiveness of a treatment in a random sample of the general population.  Instead, sponsors use a variety of strategies to select a subset of the general population in which the effect of a drug, if there is one, can more readily be demonstrated. Some of these selection strategies are obvious (e.g., patients are enrolled only if they have the disease that the drug being studied is intended to treat), but there are many more ways in which patients are typically chosen to make detection of a treatment effect more likely.  Examples include selecting patients whose disease does not spontaneously disappear or exhibit a large degree of variability, who are likely to comply with treatment, who are likely to have a high rate of disease progression, or who have some characteristic that suggests they can respond to the treatment.   

All of these selection strategies can be described as enrichment of the study population.  The guidance defines enrichment as “the prospective use of any patient characteristic to select a study population in which detection of a drug effect (if one is in fact present) is more likely than it would be in an unselected population.” 

Among others, demographic, pathophysiologic, historical, genetic or proteomic, clinical, and psychological characteristics have been used for enrichment. Enrichment may also refer to the population to be analyzed within a broader population; that is, a study could include patients both with and without the enrichment characteristic, but the primary analysis would be of the subset with the characteristic, an approach that increases the study’s ability to detect a drug effect, but that can also provide some information about patients without the enrichment characteristic. Although this guidance focuses on enrichment directed at improving the ability of a study to detect a drug’s effectiveness, similar strategies can be used in safety assessments. 

Explaining the Guidance 

Dr. Temple, Deputy Center Director for Clinical Science in FDA’s Center for Drug Evaluation and Research (CDER) explained the guidance on a separate page on FDA’s website.  He explained that 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.  Temple noted that there are basically three kinds of enrichment:  

  1. Strategies to decrease heterogeneity or “noise reduction”: Excluding patients whose disease or symptoms improve spontaneously or whose measurements are highly variable,
  2. Prognostic enrichment strategies: Choosing patients with a greater likelihood of having a disease-related endpoint event or a substantial worsening in condition; and
  3. Predictive enrichment strategies: Choosing patients more likely to respond to the drug due to their physiology or disease characteristic.  Such selection can lead to a larger effect size (both absolute and relative) and permit use of a smaller study population.  Selection of patients could be based on a specific aspect of a patient’s physiology or a disease characteristic that is related in some manner to the study drug’s mechanism, or it could be empiric (e.g., the patient has previously appeared to respond to a drug in the same class. 

The guidance describes and illustrates important enrichment strategies within these categories; discusses study design options for different strategies, including advantages and disadvantages of the various designs; and addresses issues of interpretation of the results of enrichment studies.  The enrichment strategies described in this guidance are discussed primarily in the context of randomized controlled trials. In almost all cases, the strategies affect patient selection before randomization. 

The principal concerns with the use of enrichment strategies relate to the generalizability and applicability of the study results.  When considering use of an enrichment design, it is important toconsider whether the enrichment strategy can be used in practice to identify the patients to whom the drug should be given and whether the drug might be useful in a broader population than will be studied. The extent to which patients who do not meet the selection criteria for enrichment should be studied is therefore a critical consideration. In addition, the accuracy of the measurements used to identify the enrichment population and the sensitivity and specificity of the enrichment criterion in distinguishing responders and non-responders are also critical issues. 

Decreasing Heterogeneity or “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 trails, 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. 

Temple gave the example of 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, which showed a significant survival of 5 months for metastatic breast cancer in patients with high HER 2/neu expressing tumors -- about 25% of all breast cancer patients.  However, it showed less than 2 months' survival improvements in all patients overall. 

The strategy of focusing part of the trial on the specific population of patients with high HER 2/neu-expressing tumors “ultimately supported use of the drug in the marker-selected population despite the significant cardiotoxicity that emerged,” the guidance noted.  “The much smaller mean effect ... that would have been observed in an unselected population and the fact that only about one-fourth of patients would have benefited might have made approval difficult to support in the face of the observed cardiotoxicity of the drug.” 

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.” 


 Temple noted that 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.  

Oncology drugs were high on the FDA’s approval list in 2012 and will likely remain a good area for investors in 2013 as the FDA division that reviews those treatments seems more willing to clear new medicines, Ira Loss, an analyst at Washington Analysis LLC, said in a telephone interview with Bloomberg News. 

The agency approved three drugs to treat a rare blood and bone marrow disease -- chronic myeloid leukemia -- including Cambridge, Massachusetts-based Ariad’s Iclusig, three months ahead of schedule. Roche Holding AG (ROG)’s Perjeta also won approval to work with the Basel, Switzerland-based company’s Herceptin and chemotherapy in patients with a gene mutation associated with aggressive breast cancer. Diabetes drugs still face hurdles, Loss said. 

AstraZeneca Plc (AZN) and Bristol-Myers Squibb Co. (BMY) as well as Johnson & Johnson are attempting to bring diabetes treatments to market that excretes excess blood sugar into patients’ urine. London-based AstraZeneca and Bristol-Myers have been working for a year to answer FDA questions on dapagliflozin. The FDA is scheduled to decide on J&J’s canagliflozin by the end of March. 

On the horizon, FDA may be able to speed the approval of some “breakthrough” drugs under the Food and Drug Administration Safety and Innovation Act (FDASIA), passed by Congress in July 2012.  Pharmaceutical companies can request the agency designate their experimental treatments for serious or life-threatening diseases as breakthrough therapies, which affords them advice and guidance from FDA staff to ensure development is on the right track.  

The FDA received seven breakthrough requests, of which staff granted two, denied one and four were still pending, John Jenkins, director of the FDA’s Office of New Drugs, said at a conference Dec. 10. No drugmaker has revealed its breakthrough status. On the other hand, the FDA has 60 additional days to review drugs as part of the legislation, a provision that took effect in October. Ten and six month reviews are now 12 and eight months in an effort to make drug decisions based on better planning and communication that may eliminate the need for additional questions that reset the review clock, Jenkins said.

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