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Neuroscience Clinical Trials: An Overview of Challenges and Potential Opportunities

Drug development across all therapeutic areas is fraught with excessive risk, high costs, and low productivity, according to Steven Romano, senior vice president and chief science officer of Mallinckrodt Pharmaceuticals. In this challenging business climate, investment has been shifting to areas with a clear path forward; currently, there is no clear, rational path for many nervous system disorders. Although failed trials in the neuroscience space continue to plague the industry, Daniel Burch, vice president and global therapeutic area head for neuroscience at Pharmaceutical Product Development (PPD), stated that the ultimate failure is not learning from a negative trial the reasons for failure. Drew Schiller, chief technology officer and co-founder of Validic, suggested embracing failure and exploiting it as an avenue to find answers to questions that may not even have been asked. While some of the challenges, barriers, and opportunities discussed at the workshop were specific to neuroscience clinical trials, many were general to trials across therapeutic areas. These topics, listed on the next few pages, are expanded on in the succeeding chapters.

CHALLENGES AND BARRIERS TO NEUROSCIENCE CLINICAL TRIALS¹

• Limited Understanding of the Underlying Biology of Disease. The inaccessibility of the brain makes it challenging to examine through traditional methods such as biopsy. Despite an explosion in basic neuroscience research, particularly in clinical biology and genetics, there are few validated molecular targets for most nervous system disorders. Those that have been identified—mostly for psychiatric diseases such as depression, psychosis, and anxiety—are decades old. The lack of novel and validated targets limits the development of innovative treatments. The lack of compelling biomarkers and new disease models limits investigators’ ability to interrogate the pharmacology of investigational compounds across multiple dimensions (e.g., behavior, functional, electrophysiological, etc.) in proof of concept studies (Romano).
• Understanding How Nosology May Be Constraining Innovation. Several participants noted that progress towards identifying novel and effective treatments for psychiatric diseases may be constrained by reliance on the Diagnostic and Statistical Manual of Mental Disorders, 5th ed. (DSM-5) (American Psychiatric Association, 2013). Despite the perceived shortcomings of the DSM, it is an entrenched system widely used by drug developers, academic researchers, clinicians, regulators, and payers. Moving beyond the DSM will therefore be challenging, and regulators are open to alternatives, but lack efficient mechanisms to do so (Laughren).
• Insufficient Sharing of Data, Knowledge, and Expertise. Despite statements in support of data sharing from multiple sectors (e.g., IOM, 2015; Taichman et al., 2016), some academic and industry scientists continue to resist sharing their data (Rockhold). In addition, the research community is beginning to recognize that it may be time to review and revise the Health Insurance Portability and Accountability Act (HIPAA) restrictions as it continues to impede research (Koski).

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¹ These lists highlight topics discussed throughout this workshop, but should not be construed as reflecting a consensus of workshop participants or any endorsement by the National Academies of Sciences, Engineering, and Medicine or the Forum on Neuroscience and Nervous System Disorders.

• High Failure Rates of Clinical Trials. During the drug development pipeline, thousands of compounds are screened through drug discovery efforts, with hundreds reaching the preclinical space. Of every 10 compounds that make it to human studies, approximately 1 will reach the marketplace. On average, this entire process takes between 10 and 15 years (DiMasi and Grabowski, 2007) (Romano). Clinical trial failures have increased across each phase of development, which continues to negatively impact research and development (R&D) productivity (Pammolli et al., 2011) (Romano).
• High Cost of Clinical Trials Across Therapeutic Areas. The cost of R&D for a new drug is now approaching $2.6 billion (direct cost plus the cost of failure) (TCSDD, 2014), with costs increasing across the entire spectrum of drug development activities. R&D spending from the early 1960s through 2013 has outpaced productivity across all therapeutic areas. These increased costs are attributed in part to the size of and complexity of clinical trials, which involve more stakeholders (Romano).
• Operational Challenges Contributing to Variability Across Sites. The large number of inexperienced investigators (e.g., first-time filers who often may not file again for another trial) conducting trials and the high turnover of even experienced investigators results in increased variability and poorer performance by clinical trial sites (TCSDD, 2015). In addition, protocol noncompliance has grown over the past decade, accounting for nearly half of all site deficiencies (TCSDD, 2015) (Romano).
• Low Yield to Date in the Search for Biomarkers. Several participants noted the lack of validated biomarkers for nervous system disorders; particularly those with predictive power.
• Lack of Clarity on Regulatory Requirements. A few participants stated that companies need more clarity from regulators on what information to collect as part of their clinical trial submissions. For example, data on non-serious adverse events or measures obtained as part of routine clinical care on every patient enrolled in a large pivotal trial are not useful to regulators and expensive to collect (Califf), yet sponsors collect many types of data out of concern that regulators will reject their applications if these data are not included.

OPPORTUNITIES TO IMPROVE NEUROSCIENCE CLINICAL TRIALS

Several workshop participants acknowledged that investigators are starting to address many of above challenges associated with neuroscience clinical trials. Moreover, Romano predicted that an explosion in neuroscience basic research, particularly in clinical biology and genetics, would drive more successful drug development paradigms. He noted that scientists are making progress in recognizing and deconstructing complex behavioral syndromes.

Clarifying the Underlying Biology of Nervous System Disorders

• Identifying endophenotypes—including neuroimaging, functional, neurocircuitry, biochemical, neuroendocrine, cognitive, and neuropsychological endophenotypes—has informed genetic analysis, resulting in a clarification of genetic determinants and identification of relevant targets. For example, in 2012, Buckholtz and Meyer-Lindenberg proposed that common symptoms arise from common circuit dysfunction, suggesting a different way of thinking about potential treatment targets that might be specific to a symptom, but not to a particular condition. Developing more relevant animal models will facilitate translation of fundamental discoveries into effective treatments (Romano).

Achieving the Necessary Evidence Across All Phases of Drug Development

• Demonstrating proof of mechanism is essential to clarifying exposure at the target site and interaction with the pharmacological target, thus providing evidence of relevant and expected pharmacological activity (Romano).
• Demonstrating proof of concept enables optimization of signal detection, and may lead to representative clinical trials (Califf and Romano).
• Rigorously evaluating exposure and response in Phase IIb dose-finding studies is critical, and ensuring continuity between Phase II and Phase III pivotal trials with regard to population characteristics and outcome measures is necessary to allow translation of

Developing Novel Tools, Endpoints, and Statistical Approaches to Improve the Efficiency of Clinical Studies

• Combining whole-genome sequencing and computerized, adaptive self-reports with item response theory and random forest models may enable more accurate assessment of dimensions of psychiatric conditions (Bilder).
• Using multilevel endpoints that enable assessment across multiple domains (e.g., symptoms, neuropsychological, cognitive) may improve linkage of these domains to disability (Bilder).
• Developing biomarkers using unbiased screening approaches and translating these endpoints into a clinical context through partnerships is needed for neuroscience trials (Chen-Plotkin).
• Eliminating silos in neurology and psychiatry, especially at the translational interface, is necessary in order to identify and validate common biomarkers or combinations of biomarkers across disease areas (Jensen).
• Aligning animal models and preclinical studies using common data elements may help to sync studies, allowing investigators to move efficiently from preclinical to clinical trials (Jensen).
• Incorporating causal and network models that demonstrate progression of symptoms over time may improve randomization, sample selection, and choice of interventions (Bilder).
• Standardizing measures, as well as how biospecimens are collected, as demonstrated by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) is important (S. Kapur and Pani).
• Testing novel treatments against comparators rather than placebos will be important to gain a better sense of their potential value in the marketplace compared with the current standard of care (Romano).
• Applying machine learning techniques to clinical trial data may help to identify fraudulent data, which are frequently introduced by underperforming clinical trial sites (de Vries).
• Exploiting innovative enabling technologies, such as virtual reality and the Internet of Things, may help to capture a greater array of data within clinical trials than traditional methods (Reites).

Implementing Operational Changes to Clinical Trials

• Improving the training and capabilities of investigators is critical, particularly for complicated trials that require a more sophisticated skill set among trialists (Koski and Romano).
• Generating data signatures before entry into a study and incorporating existing data streams help contextualize and validate in-trial data (Kieburtz).
• Removing administrative burdens on investigators and other site personnel may help to expand the number of studies that can be conducted without raising costs (Kieburtz).
• Building regionally powerful clinical trial centers in place of existing smaller trial sites would centralize expertise and provide economies of scale (Kieburtz).

Changing the Scope and Scale of Studies

• Using big data approaches—for example, studies that incorporate large genomics databases—has already become the dominant paradigm in some areas such as autism, and has the potential to transform other areas of neuroscience as well, leading to more targeted trials (S. Kapur). However, simple trials should not be overlooked as they may also provide value. The key is to design trials that meet specific and perhaps narrow objectives, with careful consideration of heterogeneity and methods of data analysis (Pencina).
• Recognizing the increasing role that patient advocacy organizations will likely play in trials of the future is important, particularly with regard to the creation of multisite data models and the involvement of technology companies (de Vries).

Building Innovative Research Programs

• Building a more agile research methodology may help to rapidly and efficiently test various technologies and conduct feasibility testing (Rodarte).
• Establishing an accelerator program with domain experts and scientists, where ideas are transformed into proof-of-concept studies, may reduce the length of study development and digital

• health solutions from 12 to 18 months to approximately 90 days (Reites).

• Similar to oncology, integrating clinical trials into routine clinical care will be important for nervous system disorders (Kalali).
• Developing more collaborative programs between pharmaceutical and technology companies would tap into the strengths of both sectors to accelerate therapeutic development (Kalali).
• Adopting methodology from other therapeutic areas with regard to novel designs, recruitment, and assessment may be beneficial for neuroscience trials (Laughren).
• Establishing comprehensive neuroscience centers, akin to the National Cancer Institute’s Comprehensive Cancer Centers, might improve integration between basic science and the clinical enterprise (Nisen).

Collaborating Across Disciplines and Countries

As the complexity of the drug development ecosystem has increased, collaboration among pharmaceutical companies, academia, regulators, patients, payers, and social and business entrepreneurs has increased.

• Facilitating cultural changes regarding how promotions are made and grants are awarded in academic institutions will be needed to facilitate increased collaboration and data sharing (S. Kapur).
• Building international consortia and public‒private partnerships are needed to advance the development of new tools (Chen-Plotkin and S. Kapur).
• Identifying the pioneers who are testing novel paradigms in other fields, and bringing them together with neuroscientists, may help pivot those approaches to the clinical trials space (Reites).
• Creating an environment where innovative companies can come together and, within a rigorous scientific and regulatory framework, determine the best models, algorithms, and digital strategies, might optimally enable drug development and patient management (de Vries).
• Leveraging consumer engagement achieved by electronics companies that have created wearable devices and apps with new models of collecting information, as well as social media platforms that have been used to successfully recruit participants for clinical trials, will be important for trials of the future (Schiller).

Encouraging Increased Data Collection and Sharing to Maximize Learning from Clinical Trials

• Data mining using artificial intelligence algorithms and multivariate statistical techniques enables investigators to identify patterns in large datasets that can be useful for generating hypotheses (Koski).
• With regard to concerns about “phishing,” that is, searching databases for personal information to be used for nefarious purposes, several workshop participants urged reasonableness and social responsibility, commenting that these concerns may be overblown and prevent valuable research (Chiauzzi, Koski, Rockhold, and Snowberg).
• Bringing various stakeholders in academia and industry together is needed to craft interoperable data-sharing mechanisms (Rockhold).
• Developing databases with clinical trial data across therapeutic areas, similar to the FDA’s and Mortara Instruments’ electrocardiogram (ECG) warehouse,² would be beneficial for investigators when assessing a potentially new therapeutic (Laughren).

Making Clinical Trials More Patient-Centric

• While statistical significance is important, it is not enough to determine if a therapeutic will be clinically meaningful to patients. Designing trials where clinical significance is at the forefront (e.g., measuring treatment effects against comparators and not just placebos) is imperative; however, several participants noted that the field needs to further define what it means for a therapeutic to be clinically significant (S. Kapur, Pencina, and Romano).
• Engaging in a sophisticated dialogue with patients about the value of different outcomes and the best way to measure and analyze these outcomes is needed (Kapur).
• Employing mobile and passive monitoring of motion, location, voice, app usage, facial affect, sleep, heart rate, and other characteristics may provide more patient-centric assessments of disease progression and response to treatment (Bilder).

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² For more information, go to https://www.ecgwarehouse.com/index.php (accessed June 2, 2016).

• Building an open research environment, where participants have access to their own data, may help to increase patients’ willingness to engage in a trial (Rodarte).
• When incorporating wearables into a clinical trial, it is important to consider at the design stage how devices will be allocated to participants and what will be done when device manufacturers introduce newer versions or implementations during a trial (Reites).
• Reinforcing to trial participants the nature of the social contract implicit in a clinical trial is critical in order to show that their value in the study is essential, not only for them, but to the broader patient population (Hernandez).
• Ensuring that protocols are simple both for the participants and the practitioners involved may help to decrease confusion and patient attrition (Hernandez).
• Implementing new technologies with a systems perspective helps to ensure they are inter-operable and connectable (Koski).
• Developing new paradigms that reduce patient burden, for example, by taking a sample collection to the participant rather than requiring the participant to come to a central location, may increase patient engagement and decrease attrition (Koski).
• It will be important to adapt our understanding of human subjects’ protection to one that encourages more participation of patients and where the medical encounter becomes part of the research enterprise (Kaufmann).

Meeting Regulatory Requirements

• Innovative technologies may provide a novel approach for providing “substantial evidence” of effectiveness for regulators, if investigators can show that a device generates data that can be quantitatively linked to an element of disease progression (de Vries).
• When conducting trials internationally, consider different regulatory requirements, privacy and other regulations, cultural aspects, and digital enablement (e.g., access to different technologies and social media) (Reites).
• Investigators are encouraged to work with regulators to rethink the process for qualification of drug development tools, which present an obstacle to adoption of new methodologies (Laughren).

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