Accelerating the Availability of Medications to Pediatric Patients by Optimizing the Use of Extrapolation of Efficacy.

Improving pediatric therapeutic development is a mission of universal importance among health authorities, pharmaceutical companies, academic institutions, and healthcare professionals. Following the passage of legislation in the United States and Europe, we witnessed the most significant advancement yet in pediatric data generation, resulting in added pediatric use information to almost 700 product labels. Tools to accelerate generation of data for the pediatric population are available for use today, and when utilized in accordance with current practices and laws, these tools could increase the amount and timeliness of pediatric information available for clinicians and patients. If we utilize the current laws that allow regulators to incentivize and require evidence generation, apply extrapolation, and utilize modeling and simulation, as well as including adolescents in the pivotal studies alongside adults as appropriate, two strategic goals could be achieved by 2030: (1) reduce the time to pediatric approval by 50%, and (2) renew pediatric labeling information for 15 priority pediatric drugs without patent and/or exclusivity.

Accurate prediction of clinical stroke scales and improved biomarkers of motor impairment from robotic measurements.

OBJECTIVE: One of the greatest challenges in clinical trial design is dealing with the subjectivity and variability introduced by human raters when measuring clinical end-points. We hypothesized that robotic measures that capture the kinematics of human movements collected longitudinally in patients after stroke would bear a significant relationship to the ordinal clinical scales and potentially lead to the development of more sensitive motor biomarkers that could improve the efficiency and cost of clinical trials. MATERIALS AND METHODS: We used clinical scales and a robotic assay to measure arm movement in 208 patients 7, 14, 21, 30 and 90 days after acute ischemic stroke at two separate clinical sites. The robots are low impedance and low friction interactive devices that precisely measure speed, position and force, so that even a hemiparetic patient can generate a complete measurement profile. These profiles were used to develop predictive models of the clinical assessments employing a combination of artificial ant colonies and neural network ensembles. RESULTS: The resulting models replicated commonly used clinical scales to a cross-validated R2 of 0.73, 0.75, 0.63 and 0.60 for the Fugl-Meyer, Motor Power, NIH stroke and modified Rankin scales, respectively. Moreover, when suitably scaled and combined, the robotic measures demonstrated a significant increase in effect size from day 7 to 90 over historical data (1.47 versus 0.67). DISCUSSION AND CONCLUSION: These results suggest that it is possible to derive surrogate biomarkers that can significantly reduce the sample size required to power future stroke clinical trials.

Normative data from linear and nonlinear quantile regression in CANTAB: Cognition in mid-to-late life in an epidemiological sample.

INTRODUCTION: Normative cognitive data can help to distinguish pathological decline from normal aging. This study presents normative data from the Cambridge Neuropsychological Test Automated Battery, using linear regression and nonlinear quantile regression approaches. METHODS: Heinz Nixdorf Recall study participants completed Cambridge Neuropsychological Test Automated Battery tests: paired-associate learning, spatial working memory, and reaction time. Data were available for 1349-1529 healthy adults aged 57-84 years. Linear and nonlinear quantile regression analyses examined age-related changes, adjusting for sex and education. Quantile regression differentiated seven performance bands (percentiles: 97.7, 93.3, 84.1, 50, 15.9, 6.7, and 2.3). RESULTS: Normative data show age-related cognitive decline across all tests, but with quantile regression revealing heterogeneous trajectories of cognitive aging, particularly for the test of episodic memory function (paired-associate learning). DISCUSSION: This study presents normative data from Cambridge Neuropsychological Test Automated Battery in mid-to-late life. Quantile regression can model heterogeneity in age-related cognitive trajectories as seen in the paired-associate learning episodic memory measure.

Continuous event monitoring via a Bayesian predictive approach.

In clinical trials, continuous monitoring of event incidence rate plays a critical role in making timely decisions affecting trial outcome. For example, continuous monitoring of adverse events protects the safety of trial participants, while continuous monitoring of efficacy events helps identify early signals of efficacy or futility. Because the endpoint of interest is often the event incidence associated with a given length of treatment duration (e.g., incidence proportion of an adverse event with 2 years of dosing), assessing the event proportion before reaching the intended treatment duration becomes challenging, especially when the event onset profile evolves over time with accumulated exposure. In particular, in the earlier part of the study, ignoring censored subjects may result in significant bias in estimating the cumulative event incidence rate. Such a problem is addressed using a predictive approach in the Bayesian framework. In the proposed approach, experts' prior knowledge about both the frequency and timing of the event occurrence is combined with observed data. More specifically, during any interim look, each event-free subject will be counted with a probability that is derived using prior knowledge. The proposed approach is particularly useful in early stage studies for signal detection based on limited information. But it can also be used as a tool for safety monitoring (e.g., data monitoring committee) during later stage trials. Application of the approach is illustrated using a case study where the incidence rate of an adverse event is continuously monitored during an Alzheimer's disease clinical trial. The performance of the proposed approach is also assessed and compared with other Bayesian and frequentist methods via simulation.

Building a roadmap for developing combination therapies for Alzheimer's disease.

Combination therapy has proven to be an effective strategy for treating many of the world's most intractable diseases. A growing number of investigators in academia, industry, regulatory agencies, foundations and advocacy organizations are interested in pursuing a combination approach to treating Alzheimer's disease. A meeting co-hosted by the Accelerate Cure/Treatments for Alzheimer's Disease Coalition, the Critical Path Institute and the Alzheimer's Association addressed challenges in designing clinical trials to test multiple treatments in combination and outlined a roadmap for making such trials a reality.

Charting a path toward combination therapy for Alzheimer's disease.

It is acknowledged that progress in combined therapeutic approaches for Alzheimer's disease (AD) will require an unprecedented level of collaboration. At a meeting co-hosted by the Accelerate Cure/Treatments for Alzheimer's Disease Coalition and the Critical Path Institute, investigators from industry, academia and regulatory agencies agreed on the need for combinatorial approaches to treating AD. The need for advancing multiple targets includes recognition for novel adaptive trial designs that incorporate existing and new biomarkers to evaluate drug effects independently and in combination. A combination trial now being planned may test drugs targeting different pathogenic pathways or multiple targets along a common pathway. Collaborations and consortia-based strategies are pivotal for success and a regulatory framework is recommended for success.

Down syndrome and Alzheimer's disease: Common pathways, common goals.

In the United States, estimates indicate there are between 250,000 and 400,000 individuals with Down syndrome (DS), and nearly all will develop Alzheimer's disease (AD) pathology starting in their 30s. With the current lifespan being 55 to 60 years, approximately 70% will develop dementia, and if their life expectancy continues to increase, the number of individuals developing AD will concomitantly increase. Pathogenic and mechanistic links between DS and Alzheimer's prompted the Alzheimer's Association to partner with the Linda Crnic Institute for Down Syndrome and the Global Down Syndrome Foundation at a workshop of AD and DS experts to discuss similarities and differences, challenges, and future directions for this field. The workshop articulated a set of research priorities: (1) target identification and drug development, (2) clinical and pathological staging, (3) cognitive assessment and clinical trials, and (4) partnerships and collaborations with the ultimate goal to deliver effective disease-modifying treatments.

Robotic measurement of arm movements after stroke establishes biomarkers of motor recovery.

BACKGROUND AND PURPOSE: Because robotic devices record the kinematics and kinetics of human movements with high resolution, we hypothesized that robotic measures collected longitudinally in patients after stroke would bear a significant relationship to standard clinical outcome measures and, therefore, might provide superior biomarkers. METHODS: In patients with moderate-to-severe acute ischemic stroke, we used clinical scales and robotic devices to measure arm movement 7, 14, 21, 30, and 90 days after the event at 2 clinical sites. The robots are interactive devices that measure speed, position, and force so that calculated kinematic and kinetic parameters could be compared with clinical assessments. RESULTS: Among 208 patients, robotic measures predicted well the clinical measures (cross-validated R(2) of modified Rankin scale=0.60; National Institutes of Health Stroke Scale=0.63; Fugl-Meyer=0.73; Motor Power=0.75). When suitably scaled and combined by an artificial neural network, the robotic measures demonstrated greater sensitivity in measuring the recovery of patients from day 7 to day 90 (increased standardized effect=1.47). CONCLUSIONS: These results demonstrate that robotic measures of motor performance will more than adequately capture outcome, and the altered effect size will reduce the required sample size. Reducing sample size will likely improve study efficiency.

Down's syndrome and Alzheimer's disease: towards secondary prevention.

A public-private partnership to establish biomarkers of dementia in Down's syndrome could aid the development of preventive therapies for the dementia associated with both Down's syndrome and Alzheimer's disease, based on the apparent common pathogenic role of amyloid precursor protein in the two conditions.

Impaired mitochondrial function in psychiatric disorders.

Major psychiatric illnesses such as mood disorders and schizophrenia are chronic, recurrent mental illnesses that affect the lives of millions of individuals. Although these disorders have traditionally been viewed as 'neurochemical diseases', it is now clear that they are associated with impairments of synaptic plasticity and cellular resilience. Although most patients with these disorders do not have classic mitochondrial disorders, there is a growing body of evidence to suggest that impaired mitochondrial function may affect key cellular processes, thereby altering synaptic functioning and contributing to the atrophic changes that underlie the deteriorating long-term course of these illnesses. Enhancing mitochondrial function could represent an important avenue for the development of novel therapeutics and also presents an opportunity for a potentially more efficient drug-development process.

A bayesian dose-finding design adapting to efficacy and tolerability response.

We propose a new adaptive Bayesian design, explicitly modeling the trade-off between efficacy and tolerability in dose-finding studies. This design incorporates a continuous efficacy variable and a dichotomous tolerability variable. This adaptive design was developed in the context of a drug under development for treatment of major depression, but is easily extended to any setting with a continuous efficacy and a dichotomous tolerability or safety variable. The goal is to identify a target dose that was most efficacious while still being safe. Via simulations under various scenarios we show that our design performs extremely efficiently. Our design incorporates stopping rules, adaptive allocation, and dose-response estimation (for both efficacy and tolerability), among other features. We present various metrics from our simulation study, and conclude that this is an extremely efficient way of characterizing the risk-benefit profile of a drug during clinical development.

How Adaptive Trial Designs can Increase Efficiency in Psychiatric Drug Development: A Case Study.

This paper uses a recently completed study to illustrate how adaptive trial designs can increase efficiency of psychiatric drug development. The design employed allowed a continuous reassessment of the estimated dose-response such that patients were randomized in a double-blind fashion to one of seven doses of the investigational drug, placebo, or active comparator. The study design also permitted early detection of futility allowing for early study termination. By using the adaptive trial design approach, only 202 patients were needed to make the determination of futility. In contrast, a conventional design would have required enrollment of 450 patients and considerably more time and expense to reach the same conclusion. Adaptive trial designs are important at this time when many pharmaceutical companies are abandoning the development of psychiatric medications because of the inefficiency of conventional approaches.

Phase I trials: from traditional to newer approaches part II.

In the first part of this series, the authors discussed strengths and weaknesses of traditional phase I drug development involving single ascending dose studies followed by multiple ascending dose studies in healthy volunteers. They then discussed how these traditional designs are being challenged by the development of truly novel molecular compounds that are not derived from earlier drugs and how the extent and design of phase I studies will need to be expanded and altered to investigate these novel compounds. In this column, the authors focus in more detail on limitations of traditional phase I studies for investigating truly novel compounds and propose solutions to address these problems. Adaptive trial designs and biomarker endpoints are discussed.

Phase I trials: from traditional to newer approaches. Part I.

Phase I clinical trials have traditionally been focused on populations of normal healthy volunteers with the goal of determining the safety, tolerability, and pharmacokinetic profile of new investigational agents. As CNS drug development shifts its focus to the development of novel molecular entities, this approach will undergo an evolution. In this first part of a two-part series, the authors describe the traditional Phase I approach as well as challenges facing CNS drug development. The second half of the series will propose modifications to the traditional phase I design, including the incorporation of different populations, bio-marker surrogates, and adaptive designs.

Viewpoints on the FDA draft adaptive designs guidance from the PhRMA working group.

The US Food and Drug Administration has recently released a draft guidance document on adaptive clinical trials. We comment on the document from the particular perspective of the authors as members of a PhRMA working group on this topic, which has interacted with FDA personnel on adaptive trial issue during recent years. We describe the activities and prior work of our working group, and use this as a basis to discuss the content of the guidance document as it relates to several issues of current relevance, such as data monitoring processes, adaptive dose finding, so-called seamless trial designs, and sample size reestimation.

Barriers and opportunities for implementation of adaptive designs in pharmaceutical product development.

BACKGROUND: This review discusses barriers to implementing adaptive designs in a pharmaceutical R&D environment and provides recommendations on how to overcome challenges. A summary of findings from a survey conducted through PhRMA's working group on adaptive designs is followed by a report based on our experience as statistical and clinical consultants to project teams charged with establishing the clinical development strategy for investigational compounds and interested in applying innovative approaches. FINDINGS AND RECOMMENDATIONS: Adaptive designs require additional work in that clinical trial simulations are needed to develop the design. Some project teams, due to time and resource constraints, are unable to invest the additional effort required to conduct necessary scenario analyses of options through simulation. We recommend formally integrating the planning time for scenario analyses and to incentivize optimal designs (e.g., designs offering the highest information value per resource unit invested). Regardless of the trial design ultimately chosen, quantitatively comparing alternative trial design options through simulation will enable earlier and better decision making in the context of the overall clinical development plan. Adhering to 'Good Adaptive Practices' will be key to achieving this goal. OUTLOOK: Implementing adaptive designs efficiently requires top-down and bottom- up support and the willingness to invest into integrated process and information technology infrastructures. Success is conditional on the willingness of the R&D environment to embrace the implementation of adaptive designs as a Change Management Initiative in the spirit of the Critical Path of the Food and Drug Administration.

A Bayesian dose-finding trial with adaptive dose expansion to flexibly assess efficacy and safety of an investigational drug.

BACKGROUND: Adaptive dose-ranging trials are more efficient than traditional approaches and may be designed to explicitly address the goals and decisions inherent in learn-phase drug development. We report the design, implementation, and outcome of an innovative Bayesian, response-adaptive, dose-ranging trial of an investigational drug in patients with diabetes, incorporating a dose expansion approach to flexibly address both efficacy and safety. PURPOSE: The design was developed to assess whether one or more doses of an investigational drug demonstrated superior efficacy to an active control while maintaining an acceptable safety profile. METHODS: The trial used a two-stage design, in which patients were initially allocated equally to placebo, investigational drug at a low and a medium dose, and an active control. Movement to the second stage was contingent upon evidence of efficacy (measured by change in fasting blood glucose) to add a very low dose of the investigational drug and of safety (measured by weight gain) to add a high dose of the investigational drug. The design incorporated a longitudinal model to maximize use of incomplete data, predictive probabilities to guide the decisions to terminate the trial for futility or move on to Stage 2, and a dose-response model in Stage 2 to borrow information across adjacent doses. Extensive simulations were used to fine tune trial parameters, to define operating characteristics, and to determine the required sample sizes. A data monitoring committee was provided with frequent reports to aid in trial oversight. RESULTS: In Stage 1, as trial data accrued, the predictive probability that either the low or medium dose of the investigational drug was superior to the active control fell to low values. Stage 1 termination was recommended after 199 subjects were randomized, out of a maximum trial size of 500 subjects, and the final sample size was 218. Thus the trial did not progress to Stage 2. LIMITATIONS: Because of the relatively narrow dose range to be assessed, and the inability to utilize the highest dose at the beginning of the trial, a fully responsive-adaptive design incorporating dose-response modeling was not considered a viable option. This limited the efficiency gains possible with a full set of adaptive design elements. CONCLUSIONS: The two-stage dose-expansion design functioned as designed, recommending early termination based on a low probability that the tested doses had efficacy greater than the active control.

Evaluation of structural models to describe the effect of placebo upon the time course of major depressive disorder.

Major depressive disorder (MDD) is the leading cause of disability in many countries. Designing and evaluating clinical trials of antidepressants is difficult due to the pronounced and variable placebo response which is poorly defined and may be affected by trial design. Approximately half of recent clinical trials of commonly used antidepressants failed to show statistical superiority for the drug over placebo, which is partly attributable to a marked placebo response. These failures suggest the need for new tools to evaluate placebo response and drug effect in depression, as well as to help design more informative clinical trials. Disease progression modeling is a tool that has been employed for such evaluations and several models have been proposed to describe MDD. Placebo data from three clinical depression trials were used to evaluate three published models: the inverse Bateman (IBM), indirect response (IDR) and transit (TM) models. Each model was used to describe Hamilton Rating Scale for major depression (HAMD) data and results were evaluated. The IBM model had several deficiencies, making it unsuitable. The IDR and TM models performed well on most evaluations and appear suitable. Comparing the IDR and TM models showed less clear distinctions, although overall the TM was found to be somewhat better than the IDR model. Model based evaluation can provide a useful tool for evaluating the time course of MDD and detecting drug effect. However, the models used should be robust, with well estimated parameters.

An innovative design to establish proof of concept of the antidepressant effects of the NR2B subunit selective N-methyl-D-aspartate antagonist, CP-101,606, in patients with treatment-refractory major depressive disorder.

This randomized, placebo-controlled, double-blind study was the first to evaluate the antidepressant efficacy, safety, and tolerability of an NR2B subunit-selective N-methyl-D-aspartate receptor antagonist, CP-101,606. Subjects had major depression, according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria and a history of treatment refractoriness to least 1 adequate trial of a selective serotonin reuptake inhibitor. The study had 2 treatment periods. In period 1, subjects first received a 6-week open-label trial of paroxetine and a single-blind, intravenous placebo infusion. Period 1 nonresponders (n = 30) then received a randomized double-blind single infusion of CP-101,606 or placebo plus continued treatment with paroxetine for up to an additional 4 weeks (period 2). Depression severity was assessed using the Montgomery-Asberg Depression Rating Scale and 17-item Hamilton Depression Rating Scale. On the prespecified main outcome measure (change from baseline in the Montgomery-Asberg Depression Rating Scale total score at day 5 of period 2), CP-101,606 produced a greater decrease than did placebo (mean difference, 8.6; 80% confidence interval, -12.3 to -4.5) (P < 0.10). Hamilton Depression Rating Scale response rate was 60% for CP-101,606 versus 20% for placebo. Seventy-eight percent of CP-101,606-treated responders maintained response status for at least 1 week after the infusion. CP-101,606 was safe, generally well tolerated, and capable of producing an antidepressant response without also producing a dissociative reaction. Antagonism of the NR2B subtype of the N-methyl-D-aspartate receptor may be a fruitful target for the development of a new antidepressant with more robust effects and a faster onset compared with those currently available and capable of working when existing antidepressants do not.

Effects of a NR2B selective NMDA glutamate antagonist, CP-101,606, on dyskinesia and Parkinsonism.

Glutamate antagonists decrease dyskinesia and augment the antiparkinsonian effects of levodopa in animal models of Parkinson's disease (PD). In a randomized, double-blind, placebo-controlled clinical trial, we investigated the acute effects of placebo and two doses of a NR2B subunit selective NMDA glutamate antagonist, CP-101,606, on the response to 2-hour levodopa infusions in 12 PD subjects with motor fluctuations and dyskinesia. Both doses of CP-101,606 reduced the maximum severity of levodopa-induced dyskinesia approximately 30% but neither dose improved Parkinsonism. CP-101,606 was associated with a dose-related dissociation and amnesia. These results support the hypothesis that glutamate antagonists may be useful antidyskinetic agents. However, future studies will have to determine if the benefits of dyskinesia suppression can be achieved without adverse cognitive effects.