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Why Depression Studies So Often Fail:  Don’t Blame “Placebo Response”

Posted by Brook White on Thu, Jun 29, 2017 @ 02:34 PM
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Jack Modell, Vice President and Senior Medical OfficerJack Modell, Vice President and Senior Medical Officer, is a board-certified psychiatrist with over 35 years of experience in clinical research, including 20 years conducting trials, teaching, and providing patient care in academic medicine, and 15 additional years of experience in clinical drug development (proof of concept through market support), medical affairs, successful NDA filings, medical governance, drug safety, compliance, and management within the pharmaceutical and CRO industries. Jack has authored over 50 peer-reviewed publications across numerous medical specialties and has lead several successful development programs in the neurosciences. Jack is a key opinion leader in the neurosciences and is nationally known for leading the first successful development of preventative pharmacotherapy for the depressive episodes of seasonal affective disorder.

Prior to joining the pharmaceutical and contract research organization industries, I was in clinical practice for twenty years as a psychiatrist and medical researcher.  And something I noticed very early on among my patients with major mental illnesses, particularly those with severe depression and psychotic disorders, was that they did not generally get better – at least not for more than a day or two – by my simply being nice to them, treating them with ineffective medications (e.g., vitamins when no vitamin deficiency existed), seeing them weekly for office visits, or by providing other so-called supportive interventions that did not directly address the underlying illness.  To be clear, this is not to say that kindness and supportive therapy are not critical to the patient-physician relationship (“The secret of the care of the patient is in caring for the patient” [Frances Weld Peabody, 1927]), but rather that kindness and support alone rarely make a biologically based illness substantially improve or disappear. 

With this background, I vividly recall my surprise upon being asked shortly after I joined the pharmaceutical industry:  “Can you help us figure out how to decrease the nearly 50% placebo-response rate we see in antidepressant trials for major depressive disorder?”  “Fifty percent?” I replied, incredulously.  “There’s no way that 50% of patients in a true major depressive episode get better on placebos or just by seeing the doctor every couple of weeks!”  “Seriously?” was the reply, and they showed me voluminous data supporting their figure.

I spent the next few years trying to figure out this apparent paradox.  Not surprisingly, the answer turned out to be multifactorial.  After careful review of internal and external data, as well as published explanations for high “placebo response rates” in clinical depression trials (much of which also applies to clinical trials in general), the following three factors emerged as being of particular importance because they are easily mitigated by proper trial design, thorough research staff training, and meticulous oversight of study conduct.

(1)  Subjects being admitted into clinical trials often had depressive symptoms, but did not truly meet criteria for major depressive disorder.  Examples include subjects with personality disorders whose symptoms wax and wane considerably with external factors (e.g., family or job stress), subjects with depressive symptoms in response to a particular stressor (not of sufficient severity or duration to meet formal criteria for a major depressive episode and likely to abate with the passage of time), and subjects who – for various reasons – may feign or exaggerate symptoms for the purpose of seeking attention or gaining access to a clinical trial.  Unlike the patients I encountered in my clinical practice, subjects with these presentations often do improve with supportive interventions and placebo. 

Recruitment of truly depressed subjects is made even more difficult by the widespread availability of reasonably effective medication options. Patients in the throes of a major depressive disorder, who sometimes have difficulty even making it through the day, are rarely keen to commit to the additional efforts, uncertainties, and treatment delays involved with a clinical trial when an inexpensive prescription for an effective generic antidepressant can now be filled in a matter of minutes. Indeed, as more and more generally safe and effective medications have become approved and readily available for a variety of illnesses, the motivation for patients to join clinical trials in the hope of finding an effective treatment has correspondingly decreased.

(2) The second factor is somewhat difficult to discuss because it sometimes provokes an understandable defensive response in clinical investigators.  Consciously or unconsciously, many investigators and clinical raters inflate or deflate clinical ratings to enable the subject to gain entry into, or remain enrolled in, a clinical trial.  Most commonly, this is done by subtly – and sometimes not so subtly – coaching subjects on their answers, or when subject responses or findings seem to fall in between scale severity ratings, by rounding up or down to a rating that is more likely to qualify the subject for the trial. 

The effect of this practice is diagrammed in the following figures, specific examples of which can be seen in these references.1-3 In Figure 1, the white bell-shaped distribution is the expected distribution in severity rating scores of an unselected clinical population presenting for clinical trial participation, let’s say with a mean score at shown at X̄n. Not uncommonly, what we see in clinical trials in which a certain scale severity score is required for study entry (depicted by the vertical light blue line, with a score to the right of it required for entry) is not the expected right half of this bell-shaped distribution, but rather a distribution like that shown by the orange curve, which is essentially the right-half of the bell-shaped distribution with a large proportion of subjects whose ratings fell short of required severity for study entry (to the left of the blue line) “pushed” to the right, over the blue line, so that the subjects now qualify for study inclusion, with the mean of those thus selected shown at X̄s.

Figure 1

depression-fig-1.jpg

At the first follow-up visit, when raters (and subjects) now have little incentive to influence rating scores to meet a pre-specified criterion, the scores of the entire included population are free to relax towards their true values and assume the pre-selection and more normally distributed pattern.  Moreover, subjects and investigators, expecting that the onset of treatment should coincide with at least some clinical improvement, may bias rating scores during this period to reflect this expectation even though the signs and symptoms of the illness may have yet to show true change.  During this same time, any actual clinical improvement will also result in the rating score mean shifting leftward (white arrow, figure 2), but because the measured change – from the initial X̄s of the selected population to the new mean (X̄n1; orange arrow, figure 2) – is generally much greater than a true treatment effect during this period, any real changes are obscured and the ability to detect a true drug-placebo difference may be lost.  While this early “improvement” in rating scores for subjects in clinical trials may appear to be a “placebo effect” and is often confused with it, this apparent improvement is instead the result of artificially inflated scale scores regressing back to their original true distribution, in combination with whatever actual treatment and placebo effects may have occurred during this time.  Unfortunately, the introduction of non-qualified subjects to the study and rater bias will continue to hamper detection of actual drug-placebo differences throughout the course of the study.

Figure 2

depression-fig-2.jpg

(3) Finally, investigators and site staff often do not fully understand the true objective of the clinical trial:  it should never, for example, be “to show treatment efficacy” or to show that a product is “safe and well tolerated,” but rather, to test the null hypothesis of no treatment difference or to estimate likely treatment effect, as well as to faithfully and objectively record all adverse effects that may emerge during treatment.  Likewise, investigators and site staff often fail to understand the importance of complete objectivity and consistency in performing clinical ratings, the intention behind and importance of every inclusion and exclusion criterion (necessary for their proper interpretation and application), and the destructive effect on the outcome and scientific integrity of the trial that even well-intended efforts to include subjects who are not fully qualified can have.  

Each of these three factors can skew both drug and placebo trial populations and results, making it appear that subjects “improved” well beyond what would have resulted had there been strict adherence to protocol requirements and objective assessment of study entry and outcome measures.

What, then, can be done to prevent these problems from sabotaging the results of a clinical trial?  Foremost are thorough and meticulous investigator and rater education and training.  All too often, perfunctory explanations of the protocol and clinical assessment tools are provided at investigator meetings, and “rater training” takes the form of brief demonstrations of how the rating scales are used and scored, without actually testing raters to be certain that they fully understand how the scales are to be used and interpreted, including understanding scoring conventions, criteria, and necessary decision-making.4  Even seemingly sound training has marked limitations both immediately and as training effects deteriorate during conduct of the trial.4-7 

Training of the research staff must include not only a review of the protocol design and study requirements, but detailed explanations about why the trial is designed exactly as it is, the importance of strict adherence to study inclusion and exclusion criteria, and the necessity for complete honesty, objectivity, and consistency in conducting the clinical trial and in performing clinical assessments.  Detailed training on the disease under study is also important to ensure that all site staff have a complete understanding of the intended clinical population and disease being studied so that they can assess subjects accordingly.  And, as noted above, rater training must include not only education on the background, purpose, characteristics, and instructions for each scale or outcome measure used, but trainers, as well as investigators and raters, should be tested for adequate understanding and proficiency in use of each of these measures. 

Meticulous monitoring during the course of the study is also essential to ensure continued understanding of, and compliance with, protocol requirements, as well as accurate and complete documentation of study procedures and outcomes.  Study monitors and others involved with trial oversight should review data during the course of the trial for unexpected trends in both safety and efficacy data, and not simply for identification of missing data or isolated datum outliers.  Unexpected trends in safety data include adverse event reporting rates at particular sites that are much higher or lower than median reporting rates, and vital signs that are relatively invariant or favor certain values over time.  Unexpected trends in efficacy data include changes in closely related outcome measures that are incongruent – for example, objective and subjective ratings of a similar outcome differing considerably in magnitude or direction, that are much larger or smaller at particular sites than those observed at most sites, that occur in relatively fixed increments, and that show unusually similar patterns or values across subjects. 

Finally, and perhaps most importantly, is that no matter how well-informed or well-intentioned investigators and raters might be, humans simply cannot match computers in objectivity and consistency, including of the kind needed to make assessments based on subject responses to questions in clinical trials.  Unless being programmed to do so, a computer cannot, for example, coach a subject on how to respond, nor would it inflate or deflate ratings based on feelings, expectations, response interpretations, or desired outcomes.  A computer faithfully asks the same questions every time, following the same algorithm, and records responses exactly as provided by the subject.  Indeed, several studies have shown that computerized assessments of entry criteria and outcome measures in clinical trials – in particular interactive voice response systems (IVRS) and interactive web response systems (IWRS) – provide data of quality and signal-detection ability that meet and often exceed that obtained by human raters.1,3,7,8,9  For these reasons, strong consideration should also be given to using IVR and/or IWR systems for assessing study entry criteria and endpoints that allow such use.  

The author acknowledges John H. Greist, MD, for his outstanding research and input regarding these important findings and considerations.

References

  1. Greist JH, Mundt JC, Kobak K.  Factors contributing to failed trials of new agents:  can technology prevent some problems.  J Clin Psychiatry 2002;63[suppl 2]:8-13.
  2. Feltner DE, Kobak KA, Crockatt J, Haber H, Kavoussi R, Pande A, Greist JH.  Interactive Voice Response (IVR) for Patient Screening of Anxiety in a Clinical Drug Trial.  NIMH New Clinical Drug Evaluation Unit, 41st Annual Meeting, 2001, Phoenix, AZ.
  3. Mundt JC, Greist JH, Jefferson JW, Katzelnick DJ, DeBrota DJ, Chappell PB, Modell JG.  Is it easier to find what you are looking for if you think you know what it looks like?  J Clinical Psychopharmacol 2007;27:121-125.
  4. Kobak KA, Brown B, Sharp I, Levy-Mack H, Wells K, Okum F, Williams JBW.  Sources of unreliability in depression ratings.  J Clin Psychopharmacol 2009;29:82-85.
  5. Kobak KA, Lipsitz J, Billiams JBW, et. al.  Are the effects of rater training sustainable?  Results from a multicenter clinical trial.  J Clin Psychopharmacol 2007;27:534-535.
  6. Kobak KA, Kane JM, Thase ME, Nierenberg AA.  Why do clinical trials fail.  The problem of measurement error in clinical trials:  time to test new paradigms?  J Clin Psychopharmacol 2007;27:1-5.
  7. Greist J, Mundt J, Jefferson J, Katzelnick D.  Comments on “Why Do Clinical Trials Fail?”  The problem of measurement error in clinical trials:  time to test new paradigms?  J Clin Psychopharmacol 2007;27:535-536.
  8. Moore HK, Mundt JC, Modell JG, Rodrigues HE, DeBrota DJ, Jefferson JJ, Greist JH.  An Examination of 26,168 Hamilton Depression Rating Scale Scores Administered via Interactive Voice Response (IVR) Across 17 Randomized Clinical Trials.  J Clin Psychopharmacol 2006;26:321-324.
  9. http://www.healthtechsys.com/publications/ivrpubs2.html 

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Working In Vulnerable Patient Populations: Research in the Cognitively Impaired

Posted by Brook White on Wed, May 25, 2016 @ 11:07 AM
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research in dementia, cognitively impairedResearch involving cognitively impaired patients requires researchers to take extra precautions to protect these patients. There are a number of reasons a patient may be considered cognitively impaired—psychiatric illness, a medical or neurological disorder which may temporarily impair decision making , and dementia to name a few. According to the New York Times, the population of Americans over the age of 65 will nearly double by the middle of the century—a trend which has been referred to as the graying of America. With an increasingly elderly population, more potential research subjects will suffer from various forms of cognitive impairment and there will be a greater demand for treatments for age-related conditions. The likely result is the need to incorporate larger numbers of cognitively impaired patients in research studies.

As researchers, we need to consider how we simultaneously protect patients who are cognitively impaired or may become cognitively impaired during a study with the need to include this population as part of our research. Unlike children, pregnant women, and prisoners, the current Code of Federal Regulations for the Protection of Human Subjects (45 CFR Part 46) does not contain specific guidance for the protection of subjects with cognitive impairment in clinical trials.

Informed Consent

One key issue related to cognitively impaired subjects is informed consent.  Many of the items discussed here are related to consent.  If a research subject has substantial cognitive impairment, they cannot provide informed consent.  In this case, a legally authorized representative (LAR) must provide consent on their behalf.  The standard for an LAR to provide consent is substituted judgment—what is in the best interest of the subject and what would the subject want.  If possible, the subject should assent.  In situations where a subject refuses to provide assent, the subject should generally not be included in the study.

Deciding if a Patient is Cognitively Impaired

Dementia is often a matter of degree, and in mild forms of cognitive impairment, a patient may still be capable of providing consent.  How do we decide when a patient is competent to consent and when an LAR is needed?  This certainly isn’t a settled matter, but one option is to include an assessment which would be performed by the investigator as part of the screening process to make this determination.  The assessment should check the patients understanding of potential risks, potential benefits, that participation is voluntary, and what will be involved in participating (study procedures, additional visits). 

Consent is a Process, Not a One Time Event

In many cases involving dementia, a patient’s decision making ability may degrade over the course of a study.  It may be that a patient is capable of providing informed consent at the beginning of a study, but is not as the study progresses.  This is something investigators should keep in mind.  You may want to plan for reassessment later in the study.

Identifying LARs

There is not a single standard for determining who can act as an LAR.  Some states have enacted laws specifying who can act as LAR, but the laws are not all the same.  In states where there isn’t a law, identifying an LAR can be trickier.  In other countries and cultures, views on who can make decisions for the cognitively impaired vary greatly from common views in the US.  Make sure at the start of the trial that you are aware of local laws and standards related to LARs.

Limiting Risk

Study designs should take extra care in limiting risk exposure in studies which will be using cognitively impaired patients, particularly when there is little or no potential for direct benefit. In most cases, studies involving these patients that don’t offer the potential of direct benefit should be limited to those involving minimal risk.

In conclusion, because formal guidance on conducting clinical research in subjects with cognitive impairment has not yet been standardized or incorporated into law, it is important to take extra care to protect these patients. We’d be interested in knowing how other organizations are responding to this issue. Is this a topic your organization is actively addressing? If so, please share in the comments.

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5 Lessons Learned Conducting ADHD Clinical Trials

Posted by Brook White on Mon, Jun 17, 2013 @ 09:53 AM
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Clinical Project Director Kristen SnipesInformation for this article was contributed by Kristen Snipes, a Project Director at Rho with extensive experience managing attention deficit hyperactivity disorder (ADHD) trials, including the recent successful completion of a phase III laboratory classroom study.

1.  Selection of a clinically meaningful endpoint is critical.

I’ve found that at the beginning of a study a lot of time is spent deciding what rating scale will be used, and yet not nearly enough time is spent determining what the precise endpoint will be. Particularly in a study that will be used as part of a marketing application, defining a clinically meaningful primary end point can mean the difference between success and failure. Picking the best end point requires consulting with key opinion leaders, regulatory experts, statisticians, and a medical director experienced in clinical trials. Depending on the phase of development, a special protocol assessment with FDA may be advisable.

2.  Use of online ADHD assessment tools can reduce stress on parents and sites while increasing data quality.

Using an online assessment tool, parents are able to complete assessments on their own time and don’t have to worry about getting paper assessment forms returned to the site. Making things easier for parents prevents drop-outs, which can cause timeline delays and data issues. Sites’ efforts are reduced because they don’t have to follow up with parents to get the forms, scoring is completed automatically by the system reducing variability in data and they don’t have to transcribe the data from the forms into an electronic data capture (EDC) system. This removes the possibility of data entry errors leading to higher quality data.

ADHD Laboratory Classroom Studies3.  When conducting laboratory classroom studies, short visit windows may create scheduling headaches that must be carefully managed.

The laboratory classroom portion of the study typically is conducted on a Saturday. If you have a two day visit window around the classroom day, the visit must occur between Thursday and Monday. This often means the visit must occur on Thursday, Friday, or Monday. Taking into account school and work schedules, this can create a stressful situation for parents. In some cases, a narrow visit window can’t be avoided. If so, you must stay on top of this issue to avoid both protocol deviations for visits outside the window and drop-outs because parents can’t make it to the visits.

4.  Use of centralized, experienced sites will help you stay on schedule.

Ideally, you want to use sites with experience in ADHD trials and EDC that have a proven track record on both patient enrollment and data quality. Sites with prior experience on ADHD trials know what it takes to enroll subjects with this indication. They will have some ideas about what type and how much advertising is needed and how to incentivize parents to participate (e.g. providing snacks for after school study visits). They also are better able to estimate how many patients they can enroll and how quickly. Conversely, delays in enrollment typical of new sites can be costly and will lead to delays in the overall timeline.

Training and consistency are keys for the laboratory classroom studies which utilize raters to assess frequency of behavior. You need to ensure that the sites are rating in a similar manner to ensure reduced variability by center. Holding a study-wide training may be more expensive but will be worth the money spent when your final analysis is complete.

It is also important to select sites with a proven track record of delivering high quality data. Experience with EDC is an important factor here. Poor data quality can increase costs by driving up the number of queries and the time spent on site resolving problems. Data quality also can delay timelines as additional effort may be necessary to clean the data prior to database lock.

5.  Patient diaries aren’t all they are cracked up to be.

We see a lot of interest in using patient diaries to collect information for ADHD studies. Patient diaries allow collection of information at any time directly from patients. It sounds like a great idea; however, what you usually end up with is a lot of dirty data. It can be very difficult to draw meaningful conclusions from what you have, but once you have it, you will have to do something to address it.

When collecting dosing information, keep in mind what you plan to use it for in your analysis. Is your goal strictly compliance? If so, can you gather this from your accountability records or number of tablets returned? Reducing the burden on patients and sites helps to ensure useful, qualify data that can tell the story you want in your clinical study report (CSR).

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Conducting CNS Clinical Trials? Overview and Considerations of the New Tools from NIH for Assessment of Neurological and Behavioral Function

Posted by Brook White on Tue, Apr 16, 2013 @ 09:29 AM
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neuropsychological assessments in clinical trialsIn recent years the National Institutes of Health (NIH) has undertaken several initiatives intended to advance neuroscience research by means of a multi-institute collaboration entitled “the Blueprint for Neuroscience Research.” Some of these initiatives involve standardization and sharing of cutting-edge technologies such as neuroimaging and genomics. Another of their initiatives, the NIH Toolbox, is a set of standardized neurobehavioral assessments that is useful to a broad research audience.   This article summarizes the development-of and uses-for the NIH Toolbox, and provides considerations for its current and future utility in drug development1.

Many central nervous system (CNS) clinical trials, as well as those in other therapeutic areas, require neurological or behavioral assessments.  Currently, investigators use a number of different assessments to assess the same construct, making it difficult to understand data across multiple studies.  In an effort to resolve this problem in 2004, the NIH formed a coalition to create a toolbox of neurological and behavioral assessments.  The coalition included multiple divisions at NIH and more than 250 scientists at more than 80 institutions.  The goals of the coalition were:

  • To develop and validate a set of standardized, psychometrically sound tools for neurobehavioral constructs
  • To be able to measure the same construct across the life span
  • To create tools that are royalty-free, and as close to cost-free as possible (most assessments currently in use are proprietary and often costly)
  • To create tools that are efficient to administerTo provide measures that facilitate the pooling of data across many studies

In October of 2012, the coalition released the toolbox to the public.  It includes:

  • Four domain level batteries
  • English and Spanish versions
  • 34 supplemental instruments
  • Training materials for administration of the assessments
  • Public data from the studies conducted during the development of the tools

The batteries are fully normalized for ages 3-85, and are essentially free to use.  Each domain takes 30 minutes to administer.  From the toolbox website, you can access the domain level batteries, supplemental assessments, and training materials including videos showing the assessments being conducted.

The four domains covered by the assessments are cognition, motor, sensation, and emotion. The cognitive domain includes working memory (short term buffer), executive function (planning and organizing), episodic memory (acquisition and retrieval), language, processing speed, and attention.  The motor domain includes standing balance, strength, dexterity, and speed/endurance.  The sensation domain includes audition, olfaction, pain, taste, vestibular, and vision.  The emotional domain includes psychological well-being, social relationships, stress and self-efficacy, and negative affect.

What will be the impact of the NIH toolbox on CNS clinical trials and the development of new treatments for CNS disorders?  They are not designed to capture pathology, and so far, they have been used primarily in health subjects. Therefore, the NIH Toolbox is unlike earlier initiatives such as the ECDEU assessment manual for psychopharmacology published in 1976, which had a profound effect on drug development through the late 1980s.  However, because of its focus on function in healthy subjects, the NIH Toolbox may provide a more precise and quantitative concept of “normal” against which pathology can be measured. As such, it will inevitably have utility throughout clinical research and drug development. 

It may take some time to gain broad acceptance in the CNS community.  For now, it seems risky to use one of these assessments alone as a primary end point for a clinical trial, and if you are considering doing so, you should probably proactively pursue agreement from the FDA. Another consideration is how you would use the assessments in a trial.  Most of the assessments are aimed at benchmarking a state of wellness.  While that doesn’t provide a direct measurement of disease state, these tests may be valuable in showing clinical significance.  

Have you used or considered using any of these assessments yet?  If so, share your experiences in the comments section.

For more information, see the NIH Toolbox website www.nihtoolbox.org where the assessments and training videos can be found.

1Information for this article was contributed by Nancy Yovetich, Ph.D., Senior Research Scientist, and Herbert Harris, M.D., Ph.D., Medical Director at Rho, Inc.  These contributors were not involved in the development or validation of the NIH Toolbox.  One of the authors did attend the launch of the NIH Toolbox in Bethesda, MD.  Both Dr. Yovetich and Dr. Harris have extensive backgrounds in clinical research and in psychology/psychiatry.

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FDA Issues Draft Guidance on Developing Drugs for Treatment of Early Stage Alzheimer’s Disease

Posted by Brook White on Mon, Mar 25, 2013 @ 09:23 AM
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Herbert Harris, Rho Medical DirectorThe following article was contributed by our medical director, Herbert Harris, MD, PhD. 

On February 7, the FDA issued a proposal designed to assist companies developing new treatments for patients in the early stages of Alzheimer’s disease, before the onset of noticeable (overt) dementia.

Although we have an enormous amount of information about the underlying molecular pathophysiology of Alzheimer’s disease, translating this knowledge into effective new treatments has been exceedingly difficult. Part of this difficulty arises from the slowly progressive nature of the disorder. We have known for many decades that the accumulation in the brain of a protein known as amyloid is a central part of this process. Abnormal accumulation of amyloid triggers many other biochemical processes that lead to neuronal cell death and dysfunction that cause cognitive deterioration characteristic of the disease. This understanding has led to the development of many drugs that have the potential to prevent or oppose the abnormal accumulation of amyloid. However, these new drugs have typically been tested in patients in whom cognitive impairments are already fairly far advanced. Yet in recent years, advances in imaging technology and neuropathology have indicated that amyloid accumulation may begin years, or even decades before the appearance of measurable cognitive deficits. Such findings imply that interventions targeting amyloid accumulation are unlikely to show significant clinical benefits if they are not used until cognitive deficits have manifested. Instead, medicines that target amyloid accumulation and other fundamental molecular processes should probably be introduced well in advance of the onset of cognitive changes in order to be optimally effective. This understanding has led to a fundamental rethinking of the methods and strategies for drug development in Alzheimer's disease. Recognizing these new challenges that face the field, the FDA has developed a draft guidance document for the development of drugs to treat early stages of Alzheimer's disease. The guidance identifies a number of critical drug development issues and has indicated potential solutions that could move the field forward. In an accompanying press release, Russell Katz, M.D., Director of the Division of Neurology Products at the FDA’s Center for Drug Evaluation and Research noted: “The scientific community and the FDA believe that it is critical to identify and study patients with very early Alzheimer’s disease before there is too much irreversible injury to the brain. It is in this population that most researchers believe that new drugs have the best chance of providing meaningful benefit to patients.”

developing drugs for Alzheimer's patientsPerhaps the most problematic issue is that of identifying appropriate patient populations to study. Conventional clinical trials involving Alzheimer therapeutics typically enroll patients who meet criteria for a mild to moderate level of dementia as measured by various cognitive tests. Currently, there are a number of diagnostic entities that have been defined so as to capture patient populations at an early stage. These include Mild Cognitive Impermanent (MCI) and prodromal Alzheimer's disease. However, these diagnoses still depend on identification of some level of cognitive dysfunction. To identify patients at even earlier stages may require the use of genetic and other biomarkers. In developing their industry guidance, the FDA has acknowledged the potential importance of conducting trials in enriched populations defined by combinations of clinical findings and biomarkers. Unfortunately, to date, no biomarkers have been identified with sufficient predictive power. However, a great deal of progress is being made in this area.

The development of treatments for early stage Alzheimer's disease may also require the development of innovative outcome measures. Conventional studies of mild to moderate Alzheimer's disease typically employ cognitive testing used in combination with either a functional or global outcome measure as a co-primary endpoint. In the FDA guidance, it is acknowledged that in early stage Alzheimer's subjects, there may be little or no functional impairment. Therefore, it is recognized that in some cases the use of a co-primary outcome measure may be impractical. However, it is noted that as patients progress to later stages in which both functional and cognitive impairment begin to manifest, it may be appropriate to use composite scales that capture elements of function and cognition. The Clinical Dementia Rating Scale–Sum of Boxes score, which is been validated in patients whose level of impairment does not meet the threshold of frank dementia, is given in the guidance as an example of such a scale,. In the draft guidance, the possibility was also raised that a treatment might obtain approval under the accelerated approval mechanism based on effects demonstrated on an isolated cognitive measure. It was noted that in this scenario a sponsor might be required to demonstrate sustained global effects as a post-marketing condition.

The draft guidance contains an extensive discussion of the topic of biomarkers as primary and secondary outcome measures. It is noted that the use of a biomarker as a primary efficacy endpoint is a theoretical possibility under the accelerated approval mechanism, but there is currently no biomarker for which there is sufficient evidence to justify its use as a proxy for clinical preventive Alzheimer's disease. The draft guidance states “until there is widespread evidence-based argument in the research community that in effect on the particular biomarker is reasonably likely to predict clinical benefit, we will not be in a position to consider approval based on the use of a biomarker as a surrogate outcome measure in Alzheimer's disease (at any stage of illness).”

While many issues such as the potential role of biomarkers will have to await scientific development within the field, the development of an industry guidance document represents an important step that will focus the energies of the research community and enable much-needed progress in Alzheimer research. The agency is currently seeking public comments on the draft guidance. It is likely that they will begin finalization of the document next month. The FDA proposal is part of U.S. Department of Health and Human Services initiative known as the National Plan to Address Alzheimer’s Disease. This calls for both the government and the private sector to intensify efforts to treat or prevent Alzheimer’s and related dementias and to improve care and services.

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