How FDA currently makes decisions on clinical studies

Project rebuild the evidence base (REB): A method to interpret randomised clinical trials and their meta-analysis to present solid benefit-risk assessments to patients

Evidence-based medicine is the cornerstone of shared-decision making in healthcare today. The public deserves clear, transparent and trust-worthy information on drug efficacy. Yet today, many drugs are prescribed and used without solid evidence of efficacy. Clinical trials and randomised clinical trials (RCTs) are the best method to evaluate drug efficacy and side effects. In a shared medical decision-making approach, general practitioners need drug assessment based on patient-important outcomes. The aim of project rebuild the evidence base (REB) is to bridge the gap between the data needed in clinical practice and the data available from clinical research. The drugs will be assessed on clinical patient important outcomes and for a population. Using the Cochrane tools, we propose to analyse for each population and outcome: 1) a meta-analysis based on RCTs with a low risk of bias overall; 2) an evaluation of results of confirmatory RCTs; 3) a statistical analysis of heterrogeneity between RCTs and 4) an analysis of publication bias. Depending on the results of these analyses, the evidence will be categorized in 4 different levels: firm evidence, evidence (to be confirmed), signal or absence of evidence. Project REB proposes a method for reading and interpreting RCTs and their meta-analysis to produce quality data for general practitioners to focus on risk-benefit assessment in the interest of patients. If this data does not exist, it could enable clinical research to better its aim.

Facilitating treatment initiation and reproductive care postpartum to prevent substance-exposed pregnancies: A randomized bayesian pilot trial

BACKGROUND

For non-treatment-seeking women who use substances during pregnancy, immediately postpartum may be an optimum time for intervention. Our study tested a novel, brief, hospital-initiated, adaptive motivational interviewing plus acceptance and commitment therapy (MIACT) intervention to facilitate treatment initiation and reproductive planning postpartum among mothers who used substances during pregnancy.

METHODS

Mothers (N = 64) with an infant admitted to a neonatal intensive care unit were enrolled if they or their infant tested positive for an illicit substance at delivery or had a documented positive drug screen during pregnancy. A parallel group, randomized controlled design assigned participants to MIACT or conventional care (CC), with assessments at week 2 and 4 during treatment and follow-up at 2 and 6 months post treatment. Bayesian generalized linear modeling was used to evaluate outcomes as a function of treatment.

RESULTS

Results indicated that during treatment the MIACT group demonstrated an 84% probability of benefit relative to CC with regard to initiating treatment (RR=1.5), however the effect was not seen at follow-up. MIACT was also associated with an increased probability of attending a postpartum obstetrics visit (RR=1.4), and receiving contraception during treatment and at both follow-ups, with posterior probabilities of 96% or higher and relative risks ranging from 1.5 to 5.1 at varying timepoints. Substance use rates for the MIACT versus CC were higher at follow-up.

CONCLUSIONS

Brief, hospital-initiated interventions can assist postpartum mothers who use substances to enter treatment and obtain contraception in order to reduce future substance-exposed pregnancies.

Exenatide Adjunct to Nicotine Patch Facilitates Smoking Cessation and May Reduce Post-Cessation Weight Gain: A Pilot Randomized Controlled Trial

INTRODUCTION

Approved pharmacological treatments for smoking cessation are modestly effective, underscoring the need for improved pharmacotherapies. Glucagon-like peptide-1 receptor (GLP-1R) agonists attenuate the rewarding effects of nicotine in preclinical studies. We examined the efficacy of extended-release exenatide, a GLP-1R agonist, combined with nicotine replacement therapy (NRT, patch) for smoking cessation, craving, and withdrawal symptoms, with post-cessation body weight as a secondary outcome.

METHODS

Eighty-four prediabetic and/or overweight smokers were randomized (1 : 1) to once-weekly placebo or exenatide, 2 mg, subcutaneously. All participants received NRT (21 mg) and brief smoking cessation counseling. Seven-day point prevalence abstinence (expired CO level ≤5 ppm), craving, withdrawal, and post-cessation body weight were assessed following 6 weeks of treatment. A Bayesian approach for analyzing generalized linear models yielded posterior probabilities (PP) to quantify the evidence favoring hypothesized effects of treatment on the study outcomes.

RESULTS

Exenatide increased the risk for smoking abstinence compared to placebo (46.3% and 26.8%, respectively), (risk ratio [RR] = 1.70; 95% credible interval = [0.96, 3.27]; PP = 96.5%). Exenatide reduced end-of-treatment craving in the overall sample and withdrawal among abstainers. Post-cessation body weight was 5.6 pounds lower in the exenatide group compared to placebo (PP = 97.4%). Adverse events were reported in 9.5% and 2.3% of participants in the exenatide and placebo groups, respectively.

CONCLUSIONS

Exenatide, in combination with the NRT improved smoking abstinence, reduced craving and withdrawal symptoms, and decreased weight gain among abstainers. Findings suggest that the GLP-1R agonist strategy is worthy of further research in larger, longer duration studies.

IMPLICATIONS

Despite considerable progress in tobacco control, cigarette smoking remains the leading cause of preventable disease, disability, and death. In this pilot study, we showed that extended-release exenatide, a glucagon-like peptide-1 receptor agonist, added to the nicotine patch, improved abstinence and mitigated post-cessation body weight gain compared to patch alone. Further research is needed to confirm these initial positive results.

Single pivotal trials with few corroborating characteristics were used for FDA approval of cancer therapies

BACKGROUND AND OBJECTIVE

Novel cancer therapies are often approved with evidence from a single pivotal trial alone. There are concerns about the credibility of this evidence. Higher validity may be indicated by five methodological and statistical characteristics of pivotal trial evidence that were described by the U.S. Food and Drug Administration (FDA), which may corroborate the reliance on a single trial alone for approval decisions.

STUDY DESIGN

We did a metaepidemiologic evaluation of all single pivotal trials supporting FDA approval of novel drugs and therapeutic biologicals for cancers between 2000 and 2016. For each trial, we determined the presence of these five characteristics, which we operationalized as (1) large and multicenter trial (≥200 patients; more than one center); consistent treatment benefits across (2) multiple patient subgroups (in view of FDA reviewers), (3) multiple endpoints (including overall survival, progression-free survival, response rate, health related quality of life), and (4) multiple treatment comparisons (e.g., multi-arm studies); and (5) "statistically very persuasive" results (P-values <0.00125).

RESULTS

Thirty-five of 100 approvals were based on evidence from a single pivotal trial without any further supporting evidence on beneficial effects (20 randomized controlled trials and 15 single-arm trials). The number increased substantially from one approval before 2006 to 23 after 2011. Sixty-six percent (23/35) of the trials were large multicenter trials (median 301 patients and 63 centers). Consistent effects were demonstrated across subgroups in 66% (23/35), across endpoints in 43% (15/35), and across multiple comparisons in 3% (1/35). Very low P-values for the primary endpoint were seen in 34% (12/35). At least one of the corroborating characteristics was present in 94% (33/35) of all approvals, two or more were present in 54% (19/35), and none had all characteristics.

CONCLUSIONS

Single pivotal trials typically have some of the corroborating characteristics, but often only one or two. These characteristics need to be better operationalized, defined, and reported and whether single trials with such characteristics provide similar evidence about benefits and harms of novel treatments as multiple trials would do needs to be shown.

Integrated cognitive behavioral therapy for comorbid cannabis use and anxiety disorders: A pilot randomized controlled trial

Cannabis use disorder (CUD) is the most common illicit substance use disorder and individuals with CUD have high rates of comorbid anxiety disorders. Comorbidity between CUD and anxiety disorders is of public health relevance given that although motivation enhancement therapy (MET) combined with cognitive-behavioral therapy (CBT) is an efficacious intervention for CUD, outcomes are worse for patients with elevated anxiety. The current study tested the acceptability and efficacy of the integration of a transdiagnostic anxiety CBT (i.e., treatment of patients with any anxiety disorder) with MET-CBT (integrated cannabis and anxiety reduction treatment, or ICART) for CUD compared to MET-CBT alone. Treatment-seeking cannabis users (56.4% male, M_age = 23.2, 63.3% non-Hispanic White) with CUD and at least one comorbid anxiety disorder were randomly assigned to ICART (n = 27) or MET-CBT (n = 28). Patients in the ICART condition attended significantly more treatment sessions than those in the MET-CBT condition. Patients in the ICART condition were more likely to be abstinent post-treatment than those in MET-CBT. Further, treatment produced decreases in cannabis use and related problems. Notably, therapy type did not moderate the impact of treatment on frequency of use and related problems. Together, these data suggest that ICART may be at least as efficacious as a gold-standard psychosocial CUD treatment, MET-CBT, for a difficult-to-treat subpopulation of cannabis users.

A sequential multiple assignment randomized trial for cocaine cessation and relapse prevention: Tailoring treatment to the individual

Drug addiction is a chronic, devastating, but treatable disorder. A core principle of drug addiction treatment states that no single treatment is appropriate for everyone (NIDA, 2012); treatments need to adjust based on patient characteristics and response in order to be maximally effective. For cocaine use disorders (CUD), specifically, the most potent intervention currently available for initiating abstinence is behavior therapy using contingency management (CM) procedures, with early cessation being a robust predictor of future abstinence. This raises two key questions for treatment development research: First, can we significantly improve initial CM response rates with targeted adjunctive interventions? Second, for individuals who fail to achieve initial abstinence with CM, is pharmacotherapy an effective augmentation strategy? This paper describes how a sequential, multiple assignment, randomized trial (SMART) design has advantages over a fixed-intervention approach when it comes to collecting data needed to answer both questions. The first aim will examine whether Acceptance and Commitment Therapy (ACT) in combination with CM increases initial abstinence response rates (i.e., 2 consecutive weeks of cocaine-negative urine screens). The second aim will examine whether ACT+CM in combination with modafinil promotes abstinence achievement in initial non-responders. Results are expected to inform how we tailor treatment of CUD to maximize outcomes.

Exenatide once weekly for smoking cessation: study protocol for a randomized clinical trial

BACKGROUND

Cigarette smoking is the greatest preventable cause of morbidity and premature mortality in the United States. Approved pharmacological treatments for smoking cessation are marginally effective, underscoring the need for improved pharmacotherapies. A novel approach might use glucagon-like peptide-1 (GLP-1) agonists, which reduce alcohol and drug use in preclinical studies. GLP-1 is produced in the intestinal L-cells and in the hindbrain. The peptide maintains glucose homeostasis and reduces food intake. Several GLP-1 agonists are used clinically to treat type 2 diabetes and obesity, but none have been tested in humans to reduce smoking.

AIMS

We will examine whether extended-release exenatide reduces smoking, craving, and withdrawal symptoms, as well as cue-induced craving for cigarettes.

METHODS

We will enroll prediabetic and/or overweight treatment seeking smokers (n = 90) into a double-blind, placebo-controlled, randomized clinical trial. Participants will be randomized in a 1:1 ratio to receive exenatide or placebo. All participants will receive transdermal nicotine replacement therapy (NRT) and behavioral counseling. Abstinence from smoking (verified via expired CO level of ≤5 ppm), craving (Questionnaire of Smoking Urges score), and withdrawal symptoms (Wisconsin Scale of Withdrawal Symptoms score) will be assessed weekly during 6 weeks of treatment and at 1 and 4 weeks posttreatment. Cue-induced craving for cigarettes will be assessed at baseline and at 3 weeks of treatment following virtual reality exposure.

EXPECTED OUTCOMES

We hypothesize that exenatide will increase the number of participants able to achieve complete smoking abstinence above that achieved via standard NRT and that exenatide will reduce craving and withdrawal symptoms, as well as cue-induced craving for cigarettes.

Benefits of varenicline vs. bupropion for smoking cessation: a Bayesian analysis of the interaction of reward sensitivity and treatment

RATIONALE

We have shown that differences in the level of neural activation to stimuli associated with smoking vs. natural rewards, a biomarker related to reward sensitivity, predict treatment outcome.

OBJECTIVES

This paper examined whether this biomarker moderates the impact of bupropion or varenicline on smoking cessation.

METHODS

Prior to treatment randomization, smokers (N = 180) in a placebo-controlled trial using bupropion and varenicline completed event-related potential recording (late positive potential, LPP) while viewing pleasant (P), cigarette (C)-related, and other pictures. We used Bayesian models to estimate the probability of interaction between treatment and the LPP for both efficacy and comparative effectiveness analyses.

RESULTS

Efficacy analysis showed that smokers with more neural activation to pleasant vs. cigarette-related stimuli (P > C) had a 98-99% chance of achieving greater abstinence than placebo (OR >1.00), using either medication from the end of treatment (EOT, primary outcome) through the 3-month follow-up. Relative to placebo, smokers with higher activation to cigarette-related vs. pleasant stimuli (C > P) had a 99% chance of increased benefit from varenicline at both time points (OR >1), but only 67 and 43% with bupropion at the EOT and 3-month follow-up, respectively. Comparative effectiveness analysis found that smokers with the C > P activation pattern had a 95-98% chance of benefit from varenicline vs. bupropion, while P > C smokers had a 50-58% chance of similar improvement with varenicline at the EOT and 3 months.

CONCLUSIONS

Varenicline appears to be the treatment of choice for smokers with the C > P pattern of neural activation, while for those showing P > C, varenicline and bupropion have similar efficacy.

A framework for the analysis of heterogeneity of treatment effect in patient-centered outcomes research

Individuals vary in their response to a treatment. Understanding this heterogeneity of treatment effect is critical for evaluating how well a treatment can be expected to work for an individual or a subgroup of individuals. An overemphasis on hypothesis testing has resulted in a dichotomy of all heterogeneity of treatment effect analyses into confirmatory (hypothesis testing) and exploratory (hypothesis finding) analyses. This limited view of heterogeneity of treatment effect is inadequate for creating evidence that is useful for informing patient-centered decisions. An expanded framework for heterogeneity of treatment effect assessment is proposed. It recognizes four distinct goals of heterogeneity of treatment effect analyses: hypothesis testing, hypothesis finding, reporting subgroup effects for meta-analysis, and individual-level prediction. Accordingly, two new types of heterogeneity of treatment effect analyses are proposed: descriptive and predictive. Descriptive heterogeneity of treatment effect analyses report treatment effects for prespecified subgroups in accordance with prospectively specified analytic strategy. They need not be powered to detect heterogeneity of treatment effect. They emphasize estimation and reporting of subgroup effects rather than hypothesis testing. Sampling properties (e.g., standard error) of descriptive analysis can be characterized, thus facilitating meta-analysis of subgroup effects. Predictive heterogeneity of treatment effect analyses estimate probabilities of beneficial and adverse responses of individuals to treatments and facilitates optimal treatment decisions for different types of individuals. Procedures are also suggested to improve reliability of heterogeneity of treatment effect assessment from observational studies. Heterogeneity of treatment effect analysis should be identified as confirmatory, descriptive, exploratory, or predictive analysis. Evidence should be interpreted in a manner consistent with the analytic goal.

Drug development for rare mitochondrial disorders

Currently, all treatment of mitochondrial disorders is performed with dietary supplements or by off-label use of drugs approved for other indications. The present challenge is translation of our collective knowledge of the molecular details underlying the pathophysiology of mitochondrial disorders into safe and effective therapies that are approved by the regulatory authorities. Molecular details permit precise diagnoses, but homogeneity is gained at the expense of limiting numbers of subjects for clinical trials and of small markets from which to recoup the considerable expense of drug discovery and development. The Food and Drug Administration recognizes that trial designs suitable for common diseases are often not feasible for rare disorders. They have developed a number of programs to facilitate development of novel therapies for such rare diseases, without compromise of regulatory standards. With advances in technology, including the use of biomarkers, replacement therapies and sophisticated trial designs, both biotechnology firms and, increasingly, large integrated pharmaceutical companies, are taking advantage of the opportunities in rare disorders. Precise molecular delineation of pathophysiology and of responsive patients has led to success rates with rare diseases that are significantly greater than those for common disorders. It appears likely, but not yet proven, that this may now be the case for rare mitochondrial disorders as well.

Good decision making requires good communication

The methods used for regulatory decisions must facilitate three kinds of communication: (i) with individual experts who must translate their knowledge into usable form; (ii) among the experts whose pooled knowledge informs those choices; and (iii) between regulators and those affected by their choices. Decision-making methods vary in their reliance on expert judgement and computational methods and, hence, in their ability to meet the goals of sound decision making: breadth, depth, precision, neutrality, evaluability and transparency. An approach developed by the US FDA, the Benefit-Risk Framework, integrates judgement and computation, cognizant of their strengths and weaknesses. Its application both requires and facilitates good communication about risks and benefits.

Prosthetic heart valves: Objective Performance Criteria versus randomized clinical trial

The current Food and Drug Administration (FDA) heart valve guidance document uses an objective performance criteria (OPC) methodology to evaluate the clinical performance of prosthetic heart valves. OPC are essentially historical controls, but they have turned out to be an adequate, and perhaps optimal, study design in this situation. Heart valves have a simple open-and-close mechanism, device effectiveness is easy to document, and the common complications (thromboembolism, thrombosis, bleeding, leak, and infection) are well known and easily detected. Thus, randomized clinical trials (RCTs) have not been deemed necessary for the regulatory approval of prosthetic heart valves. The OPC are derived from the average complication rates of all approved heart valves. Studies based on OPC have been shown to work well; many different valve models have gained FDA market approval based on this methodology. Although heart valve RCTs are not required by the FDA, they have been done to compare valves or treatment regimens after approval. Recently, the Artificial Valve Endocarditis Reduction Trial (AVERT) was designed to compare a new Silzone sewing ring, designed to reduce infection, with the Standard sewing ring on a St. Jude Medical heart valve. This was the largest heart valve RCT ever proposed (4,400 valve patients, followed for as long as 4 years), but it was stopped prematurely because of a high leak rate associated with the Silzone valve. Examining the results showed that a much smaller, OPC-based study with 800 patient-years would have been sufficient to disclose this complication of the Silzone valve.