Challenges in Clinical Trial for Small Patient Populations

Could Seamless Adaptive Designs Advance Clinical Trial Design?

A Strategic Clinical Innovation Organization (SCIO) opportunity.

Reducing time to market is, has been, and will be a top priority in pharmaceutical drug development.  Bringing valuable medicines to patients sooner increases the value of the drug to the parent company.  Adaptive seamless designs can, in principle, help reduce this development time and increase efficiency of the development process. What is a seamless adaptive design?

A seamless design is a clinical trial design which combines into a single trial objectives which are traditionally addressed in separate trials, for example combination of dose selection with confirmation into one trial. An adaptive seamless design is a seamless trial in which the final analysis uses data from patients enrolled before and after the combination of measurements.  With the added flexibility of seamless designs, comes added complexity.  One example would be a seamless adaptive phaseII/III design addressing objectives normally achieved through separate phase II and phase III trials.  These phase II/III adaptive trials are confirmatory in nature, as opposed to the seamless adaptive trials in early development, which are essentially exploratory.  The first stage of a seamless adaptive phase II/III trial might be similar to a late phase II trial with a control group and several treatment groups (for example, different dose levels of the same treatment).  Results are examined at the end of the first stage, and one or more of the treatment groups are selected to continue, along with the control group in the trial’s second stage.  The final analysis comparing the selected groups with the control will use data from the continuing groups from both stages of the trial.

There are three main potential advantages of seamless adaptive designs: they shorten the clinical development program by eliminating the time lag between phase II and phase III trials; they lead to greater efficiency in the use of data from both stages, which might mean that fewer patients are required to obtain the same quality of information; and they enable the earlier acquisition of long-term safety data, gathered through continued follow up of patients from the first stage.

However, careful consideration should be given to the feasibility for a seamless design for a particular program since not all programs can use seamless development, and even if two programs can use seamless development, one might be better suited than the other.  Feasibility considerations include, for example, the length of follow-up time for the endpoint used for selection compared with duration of enrollment.  Shorter follow up will be more conducive to their use, whereas a relative long endpoint follow-up period will tend to go against that. Programs that do not involve complex treatment regimens might lend themselves better to this approach.  Also, to maintain a trial’s integrity, the processes by which interim data are examined and selection decisions are made and implemented must be considered carefully.  Current conventions that restrict knowledge of interim results in ongoing trials should be respected to avoid compromising the interpret-ability of trial results.  In some cases the decision being made at the selection point of a seamless design will be one for which a sponsor’s perspective might be relevant and for which the sponsor traditionally had been responsible, raising the question for its involvement in the monitoring process.  Most importantly, in any type of adaptive design, the right expertise must be applied to all decisions.

A few final thoughts: adaptive seamless designs have an ability to improve the development process by reducing time lines for approval; statistical methods must be available to account for adaptive trial designs; extra planning is necessary to implement an adaptive seamless design protocol; and benefits should be carefully weighed against the challenges of such designs before implementation.  With this in mind, a Strategic Clinical Innovation Organization (SCIO) expertise would be advisable to assist in addressing the difficulties of the adaptive trial design. The rigorous scientific and strategic plan development expertise that a SCIO provides could be applied as needed to all decisions of adaptive seamless design. Such expertise would help, for example, in connecting the primary endpoint with the selected patients, and product mechanism of action with doses and treatment time.

What do you think?

How Good is FAST?

By Mirella Zulueta

There are two bills in Congress at the moment related to the process of drug approval, specifically aiming to speed up the approval of new drugs.  These two bills –FAST (Faster Access to Specialized Treatments) and TREAT (Transforming the Regulatory Environment to Accelerate Access to Treatments) would essentially mediate that drugs are approved faster for serious disease conditions with no better treatment options.  Such path would very much follow the precedent of the HIV/AIDS drug approval model.  Language in FAST and TREAT states “that FDA should apply the accelerated approval and fast track provisions to the greatest extent possible to help expedite the development and availability to patients of treatments for serious or life-threatening diseases or conditions while maintaining appropriate safety and effectiveness standards for such treatments”.  Key stakeholders may have different views on these proposals.

The FDA is less concerned with the pathway itself than it is with safety, and in its view the rate-limiting factor is getting safe and effective drugs developed.  Both FAST and TREAT specify the need to include early clear surrogate endpoints to predict benefit.  According to Dr. Janet Woodcock, Director of the Center for Drug Evaluation and Research at the FDA, "The real problem is the paucity of worked-up endpoints, either as surrogates or as clinical endpoints that could be used for accelerated approval.  It is not that we don't accept them - it is that they don't exist.  People need to develop more interim endpoints and more surrogates."1  

The need to identify valid new surrogate markers as well as novel clinical studies designs that would support accelerated approvals is apparent.  It will take time and disruptive thinking to reach and implement consensus around new clinical and surrogate endpoints.  Language in FAST encouraging the application of "modern scientific tools earlier in the drug development cycle" would likely expand the kind of data FDA reviewers consider acceptable for supporting accelerated approval.

Biotech companies support FAST.  In the past, an accelerated approval process stimulated an explosion of investment in drugs to treat HIV/AIDS and cancer. The expectation now is that increased regulatory certainty and faster development times would stimulate investment, which in turn will ultimately increase the number of breakthrough drugs available to patients.

While biotech companies have endorsed FAST enthusiastically, pharmaceutical companies have not and remain mostly opposed to FAST.1  What are the possible explanations for the divide?  The shorter time drug approvals require, the less that smaller biotechs would need funding for clinical trials and research from big pharma.  While pharmaceutical companies have been more willing to undertake those partnerships rather than spend more for in-house R&D, it may have come at a price to the biotechs, i.e.,  a cut of their potential sales.  It is certainly a possibility that it would be to big pharma’s advantage to continue to resource to biotechs as generators of novel drugs and innovation, rather than give the smaller biotech companies the ability to launch drugs on their own if given the option to have their drugs tentatively approved after comparatively inexpensive phase II studies.

Independently of who takes a novel drug to the end through an accelerated approval, companies would have to do very careful post-marketing surveillance, and drugs could be pulled from the market if they didn't live up to their pre-approval expectations.
Lastly, from the patient’s perspective faster approval of new drugs for severe conditions and rare diseases without better treatment would seem quite desirable.

Different stakeholders, different opinions … key questions remain as open ended at this point:  Is FAST likely to dramatically increase the number of new drugs developed or approved? Who will benefit from this process, biotechs, pharma, or the patient?

1. Biocentury, March 12, 2012

Longer Stretch to the Finish Line

Disorders of the central nervous system (CNS) account for the most frequent health disruptions afflicting our society as a whole today:  Alzheimer's, migraine headaches, stroke, addictions, depression, autism, panic, epilepsy... And yet, drugs developed to treat CNS diseases take about 35% longer to complete clinical trials and receive regulatory approval compared to other new prescription medicines.1  The complications of the pathologies and figuring the science behind them, the high costs of clinical studies, and the common clinical failures pose a daunting panorama.  Not surprisingly, we are witnessing an apparent exodus of Big Pharma companies from the CNS drug development field (e.g., AstraZeneca cutting 2,200 R&D jobs, and slashing neuroscience in restructuring).

Joseph DiMasi, director of economic analysis at the Tufts Center for the Study of Drug Development1 seems to believe that "Despite the longer and more costly development associated with CNS drugs, the CNS new product pipeline is among the richest in the R&D-based drug industry, in the short term because there are a lot of drugs in the pipeline."  Biomarker development continues as well. And the search for Alzheimer's disease biomarkers goes on, with a number of biomarkers beginning to emerge.  Recently, a team from Washington University School of Medicine in St. Louis have published2 the detection of biomarkers in the cerebrospinal fluid of people with very mild or mild Alzheimer's disease.  They measured the biomarkers visinin-like protein-1 (VILIP-1), tau, p-tau181 and Aβ42 (amyloid beta), as well as tracking changes in mental activity and memory with annual assessments.  VILIP-1 measures damage to brain cells, and tau and Aβ42 reflect the plaques starting to form in the brain.  Alzheimer's disease in people with higher levels of these biomarkers, particularly VILIP-1, progressed more quickly.  In patients with early symptoms of Alzheimer's disease, VILIP-1 and other biomarkers could be very useful in predicting the course of the disease and in evaluating new treatments in clinical trials.

Any runner knows that maintaining a focus on the finish line is a tremendous support to actually attaining it.  CNS biomarker and new drug development is a very special and important race.  How can the key stakeholders maintain their focus on the finish line?  How can they be incentivized to remain in this seemingly long stretch?

1. http://csdd.tufts.edu
2. Tarawneh R., Lee J.-M., Ladenson J.H., Morris J.C., Holtzman D.M.  CSF VILIP-1 predicts rates of cognitive decline in early Alzheimer disease. Neurology WNL.0b013e318248e568; published ahead of print February 22, 2012.

Cost vs. Value: Can We Have Both?

By Mirella Zulueta

More than ever new drug developers today have to have a very realistic attitude towards drug development.  Biotech and pharmaceutical companies are aggressively slashing prices in order to win business in a hyper-competitive industry.  In order to make a study worth their efforts, these organizations always have one eye on the bottom line, potentially sacrificing value and quality while seeking to build in added costs along the way in order to maintain profitability.  In this environment what’s needed is disruptive thinking.  The kind that results in high-risk, unconventional, patient-oriented approaches leading to significant breakthroughs.

Not too surprisingly, a virtual approach model to drug development has seen the light in the last few years since its initial conception by Steven Burril in the early 1990s.  This model thrives on biotech and companies that are knowledge-based organizations.  In parallel, biotech and pharma freelancers-based model is springing up and using the depth, the knowledge and the complexity of a global community of experts.  Some of the advantages for any company of drawing from freelancers include: a) a lesser overall cost; b) access to the best and most experienced individuals; c) flexibility;  and d) an ever increasing wide network to resource from.

However, this operating model also presents a number of potential challenges, including an essential need for the core team to work together, in a tightly coordinated, time-efficient and cost-efficient fashion; a necessity to cover a large variety of areas; immediate and focused responsiveness to each project evolution, and of course constant communication with all experts involved. In addition, budgeting of this kind of model may present its own challenges.

The key to making the freelancer model a success might be the inclusion of what I am naming “an organizing center” of the global community of niche-specific freelance experts. Such organizing organization would be shaped as a boutique clinical development firm; a one-stop shop with a collection of elite services provided by a team of individuals that work seamlessly with companies, freelance experts, CROs etc.  A boutique-style service company tending to the goals of reduced cost and increased value for its clientele would provide tailored solutions, low overhead associated cost, fast responsiveness, and an advance, state of the art, comprehensive array of transitional services that would foster efficient collaborations and long term relationships.

So could we have both reduced cost and increased value through boutique shopping?

What’s in a name?

By Mirella Zulueta

Zuckerman and Milne at the Tufts Center for the Study of Drug Development (CSDD) have published results from interviews conducted in 2009- 2010 with 20 individuals from 13 biopharmaceutical companies to understand the current and future state of personalized medicine.1  The definition of personalized medicine used in those interviews was:  “A medical intervention (i.e., drug, biologic or vaccine) tailored by the physician to the genetic, genomic or proteomic characteristics of individual patients, or subpopulations, drawing on data gathered from a variety of sources (including individual genetic variation, differences in molecular-level and cellular-level disease processes, health states, behavioral and environmental determinants, and response to treatment). The personalized medicine will contain such information on the label.  It is possible the approved labeling for such a product may suggest the use of a diagnostic to select patients for treatment or to monitor efficacy or safety.1”  It certainly is a comprehensive definition that is slowly but surely moving towards realization.  One of the observations from the CSDD study was that on average up to half of a company’s pipeline projects had associated biomarkers, but only less than 10% of projects had identified specific target populations or companion diagnostics.  Also, all respondents interviewed noted that biomarker research is done for compounds before they enter clinical development, and that the primary intent of biomarker development was to provide more information about products internally, not for prescribing or monitoring a marketed product.  These observations expose a very apparent gap in the necessary incorporation of biomarkers in clinical trial design and implementation.  Beyond the scientific and cost problems, respondents of the interview considered that utilizing pharmacogenomic data was challenging because of a lack of regulatory guidance.  Although progress has been made, such as the release of a draft guidance by the FDA on pharmacogenomics in early-phase clinical studies, many companies felt that they were unable to use pharmacogenomic data in a regulatory approval package until pathways are better defined.

Given the scientific barriers, economic toll, regulatory evolution, and commercial uncertainties associated to personalized medicine, what are the incentives for biopharmaceutical companies to develop personalized medicines as defined above?  

The obvious invitation is for pharmaceutical and biotechnology companies to take a long-term investment view.  And, importantly, to refocus on creating differentiated clinical outcome value during the drug discovery and clinical trial process.  The drugs in development today will be entering markets with more competitors, more pricing pressures as well as higher standards for better clinical outcomes.  The driving force should be clinical value, supported by practical innovative models for risk-sharing along the way.

The expectation is that over the next few decades, more personalized medicines will be created.  In order to support this and reap the benefits of personalized medicine, all stakeholders should work together to help re-shape and create well aligned incentive structures.

1. Rachael Zuckerman & Christopher-Paul Milne.  Market watch: Industry perspectives on personalized medicine. Nature Reviews Drug Discovery 11, 178 (March 2012).

Refocusing and Regrouping

Drug development is a complex endeavor, in which cost-effective basic research is only the initial step in the critical pre-IND strategic planning continuum.  We would argue that one action recently taken by some pharmaceutical companies towards increasing long-term cost-effectiveness, is the concentration of research and development activities in “bioscience clusters”.  For decades, pharmaceutical companies centered their drug research and development operations in campuses gated from the outside world, the idea being to locate scientists near manufacturing plants for the most part.  But lately, companies are moving to microenvironments rich in top universities, health care institutions and pharmaceutical and biotech competitors.  The motivation behind these -short-term costly- moves is the belief that real time, immediate access to knowledge, talent, resources, infrastructure, etc., will increase the odds of innovation and, ultimately, better products will follow.  Boston/Cambridge, New York/New Jersey and California’s Bay Area are currently the three best areas for bioscience clustering.  In Boston and Cambridge, Mass., construction began in 2011 on four new developments for the pharmaceutical industry, and at least one more project is planned for 2012.  In Cambridge, Mass, Biogen Idec. is building 495,000 square feet of new offices, and Novartis International AG is surely staying and expanding with two new buildings planned.1

We would argue that critical to the ability to offset the short-term high cost of the establishment of operations in bioclusters is the reaching out to the expert pool in the cluster and the taking advantage of the real opportunity of face-to-face interactions.  In clusters, like in any self-sustaining system, interconnectors and integrators are key.  In this regard, the BBCR team has been part of a very unique microenvironment in the Boston/Cambridge cluster for over two decades, which has unfolded into a rich network of connections and participations in top scientific institutions (e.g., MIT, Harvard, Boston University, University of Massachusetts, etc.), as well as in top patient care organizations (e.g., Beth Israel Medical Center, Massachusetts General Hospital, Brigham and Women's Hospital, etc.) and pharmaceutical collaborators.

1. A Biotech Building Room. The Wall Street Journal. Property Report. November 23, 2011, C8.

The Art of Practicing Personalized Medicine

By Mirella Zulueta

Practicing medicine, like art, is not “black and white”.  Believing that personalized medicine is the panacea to the generally deficient block-buster based medicine seems to me to be a “black and white” belief.  The reality of personalized medicine presents with many tonalities.  I am referring to personalized medicine defined as a tailored approach to patient treatment based on the molecular analysis of genes, proteins, and metabolites.  This approach to treatment has generated much excitement in the last decade, and continues to do so but qualified by much more caution.  Reality has shown that few personalized medicine tests have achieved high clinical adoption to date.  What are the challenges to the development and acceptance of personalized medicine, and how can they be overcome?

I continue to support  an “integrated” approach to conceptualize and implement novel strategies that inevitably must consider the perspective of at least four  key groups of stakeholders:  patients, payors, providers and pharmaceutical/diagnostic companies.  In this post I open up the discussion on the perspective of the pharmaceutical and diagnostic companies.

Many pharmaceutical and diagnostic companies are now attempting to use biomarkers to assist R&D.  In some cases, some biomarkers will be developed as companion diagnostics –tests to assess a patient’s likelihood of responding positively to a drug, or of having side effects.  In 2007, 16 of the top 20 pharmaceutical companies had R&D programs in which 30 to 50 percent of drugs in development have an associated biomarker program.  By contrast, fewer than 10 percent of drugs with current biomarker programs will be launched with a companion diagnostic over the next five to ten years.

Companion diagnostics can, in principle, aid by decreasing clinical trial size, reducing attrition, reducing time to market of a new drug, and potentially by enhancing commercial performance of a drug.  However, many companies are moving slower than predicted/expected towards the application of biomarkers and companion diagnostics. A “cautious approach” to companion diagnostics is now palpable, versus the excitement of earlier years.  Scientific and clinical factors present some limitations to the pace of development and implementation.  In some diseases, poor understanding of underlying molecular mechanisms or a lack of identified molecular markers associated with the disease presents an insufficient knowledge situation to select biomarkers rationally at the early stages of clinical development.  In other areas, there is not a large clinical need for companion diagnostics.  And yet, in other disease areas, companies are moving slowly despite scientific advances.

It seems that companion diagnostics might do little to improve development productivity in real ways.  In many cases, they might actually increase overall cost and delay development.  With respect to clinical trials, Phase II studies often need to be larger when companion diagnostics are used.  In practice and in some cases, trials often would have to be designed to include several potential candidate biomarkers in Phase II.  And “marker-negative” patients would also have to be included for a proper Phase III study.

In terms of incentives for companies to pursue companion diagnostics, potential commercial benefits from potential increased market share and pricing power could be imagined, especially in later-to-market players in crowded markets with typically pricing flexibility.

I would argue that “practical” reasons (i.e., higher commercial value, lower business risk, high scientific potential) exist in some disease areas more than in others that would drive investing in personalized medicine.  A complete and thorough analysis would be needed to ascertain the validity of such statement.  What are those disease areas more amenable for successful personalized medicine?

What is the promise and what is the reality?  

Strategic Innovation in Trial Design drives Precision Medicine

By Claudio Carini

The practice of medicine today remains largely empirical; physicians generally rely on pattern matching to establish the diagnosis based on a combination of the patients’ medical history, physical examination and laboratory data. Thus, the treatment is often based on physician past experience with similar patients so typically a blockbuster medicine gets prescribed for the typical patient for that specific disease. According to this paradigm, treatment decision is driven by trial and error and the patient occasionally becomes the victim of unpredictable side effects, or poor or no efficacy for a drug that works in the majority of people affected by that disease.

Biomarkers are going to enable a shift from empirical medicine to precision medicine. It is conceivable that in the immediate future, we will be moving away from the concept of “one size fits all” but rather shifting to personalized medicine (meaning the right medicine, for the right patient, at the right dose, at the right time).  As a result of it, we will improve patient care and reduce health care cost.

The scientific community is aware that the utility of a specific treatment varies across the population.  Patients differ by several parameters:  

1. The early vs late stage of the disease
2. Slow vs fast metabolizers 
3. Good responders vs poor responders

What is driving more and more the acceptance of precision medicine is the fear and need to reduce healthcare cost. Payers have an increased cost; pharmaceutical industry has a decreased market share; patients and physicians strive for drugs that are more efficacious and safer. All these have brought awareness and a sense of urgency in the community that the trial and error medicine is not anymore viable. Precision medicine may, indeed, represent the solution by improving diagnosis, treatment and prognosis.

A growing body of evidence shows that precision medicine will definitively bring value to the industry, reducing healthcare costs by ensuring a more efficacious and safer treatment. The overgrowing interest in “precision medicine” a term that couples established clinical-pathological indexes with state of art molecular profiling to create diagnostic, prognostic and therapeutic strategies tailored for specific group of patients.

The real challenge for precision medicine will be to compile, interpret, the flux of information at the paste that they are developed by research. Precision medicine is no longer just a blip on the horizon but soon will become reality and expected to achieve a rapid acceleration in the identification and development of next generation drugs.

Precision medicine will mean a departure from traditional clinical trial frameworks, to Phase III trials focusing on a specific group of patients and that can be only achieved via a strategic innovative approach in early drug development and trial design. This narrowed focus should facilitate and enhance clinical and economic effectiveness.  The shift towards a deeper understanding of disease based on molecular biology will inevitably need a reclassification of disease states incorporating the new molecular knowledge. The entire taxonomy of diseases will need to be changed to incorporate the new acquired molecular knowledge on health and diseases.

In conclusion, precision medicine should ensure that a patient gets the right treatment, at the right time, at the right dose with minimal or no side effects.  This ultimately will change how medicine will be practiced and taught in future. Despite this big promise delivered by precision medicine, the expectations must be realistic: precision medicine will not happen overnight.

The End of The Beginning in Novel Clinical Study Strategies

By Mirella  Zulueta

The traditional approach to drug development separates clinical development into sequential distinct phases, in which progress is measured at discrete milestones.  We argue that the effectiveness of the clinical development can be improved by adopting a more integrated model that increases flexibility and maximizes accumulated knowledge.  Central to this model is the re-focusing of the pharmaceutical industry efforts on strategy and design –the beginning- and the integration of post-marketing studies with patient self-reports of efficacy and side effects -the end.

The end.  In this time and age, the Internet and expanding virtual communications have transformed the way information is shared and used, and impacted tremendously new developments in global health surveillance, including e-Health networks, disease surveillance, dissemination of information and alerts in real-time.  Internet monitoring is considered an important source of ‘‘leading edge’’ data.  Specific Internet communities and website forums exist for patients who use a variety of drugs, and an increasing number of patients now resource to Internet discussion boards for medical queries.  A few companies have emerged with services to empower patients to freely access and share medical information.  For example, users can search reports filed with the FDA covering more than 4,500 prescription drugs.  In addition, the adverse-event reporting system for drugs (AERS) helps the FDA monitor side effects in the real world.  Databases like this are rapidly growing with not only companies and manufacturers entering data but also patients, physicians and family members.  Even with the obvious limitations of data jumbling and inconsistencies, consumers looking for information can base some conclusions on online discussion boards and freely available databases.

The beginning.  The other part of the model, development and design, where pharmaceutical re-focusing is called upon, is a science and an art of its own.  As part of clinical strategy efforts, companies need to be open to novel tools, including modeling, simulation, and adaptive designs.  Thus, in a first phase of exploratory development, modeling would be a key feature of a more integrated approach.  Biological modeling can be used to understand genetic, biochemical and physiological networks as well as pathways and processes of disease and pharmacotherapy.  Pharmacological modeling can be used to guide clinical trial design, dose selection and development strategies.  Finally, statistical modeling can be used to assess development strategies and trial design in populations.  These three types of modeling should be used throughout the development process to maximize their impact and synergies.  In this exploratory phase, modeling and simulation can help refine dose selection and study design. The use of all available information is crucial for effective decision making.  Modeling and simulation approaches can be used to represent dose-response and time-response behavior of safety and efficacy endpoints.  Furthermore, these approaches can be combined with methods to provide continuous flow of information across different phases of development.  For example, preclinical data can be used to construct models and to provide prior information on model parameters.  Likewise, the results from a proof of concept study can be used to form prior distributions for a similar model to be used in a subsequent dose design study.