Developing a Bayesian adaptive design for a phase I clinical trial: a case study for a novel HIV treatment

Lessons Learned From Implementing a Novel Bayesian Adaptive Dose-Finding Design in Advanced Pancreatic Cancer

PURPOSE

Novel Bayesian adaptive designs provide an effective way to improve clinical trial efficiency. These designs are superior to conventional methods, but implementing them can be challenging. The aim of this article was to describe what we learned while applying a novel Bayesian phase I-II design in a recent trial.

METHODS

The primary goal of the trial was to optimize radiation therapy (RT) dose among three levels (low, standard, and high), given either with placebo (P) or an investigational agent (A), for treating locally advanced, radiation-naive pancreatic cancer, deemed appropriate for RT rather than surgery. Up to 48 patients were randomly assigned fairly between RT plus P and RT plus A, with RT dose-finding done within each arm using the late-onset efficacy-toxicity design on the basis of two coprimary end points, tumor response and dose-limiting toxicity, both evaluated at up to 90 days. The random assignment was blinded, but within each arm, unblinded RT doses were chosen adaptively using software developed within the institution.

RESULTS

Implementing the design involved double-blind balance-restricted random assignment, real-time assessment of patient outcomes to evaluate the efficacy-toxicity trade-off for each RT dose in each arm to optimize each patient's RT dose adaptively, and transition from a single-center trial to a multicenter trial. We present lessons learned and illustrative documentation.

CONCLUSION

Implementing novel Bayesian adaptive trial designs requires close collaborations between physicians, pharmacists, statisticians, data managers, and sponsors. The process is difficult but manageable and essential for efficient trial conduct. Close collaboration during trial conduct is a key component of any trial that includes real-time adaptive decision rules.

The Bayesian Design of Adaptive Clinical Trials

This paper presents a brief overview of the recent literature on adaptive design of clinical trials from a Bayesian perspective for statistically not so sophisticated readers. Adaptive designs are attracting a keen interest in several disciplines, from a theoretical viewpoint and also—potentially—from a practical one, and Bayesian adaptive designs, in particular, have raised high expectations in clinical trials. The main conceptual tools are highlighted here, with a mention of several trial designs proposed in the literature that use these methods, including some of the registered Bayesian adaptive trials to this date. This review aims at complementing the existing ones on this topic, pointing at further interesting reading material.

Early phase dose-finding trials in virology

Little has been published in terms of dose-finding methodology in virology. Aside from a few papers focusing on HIV, the considerable progress in dose-finding methodology of the last 25 years has focused almost entirely on oncology. While adverse reactions to cytotoxic drugs may be life threatening, for anti-viral agents we anticipate something different: side effects that provoke the cessation of treatment. This would correspond to treatment failure. On the other hand, success would not be yes/no but would correspond to a range of responses, from small, no more than say 20% reduction in viral load to the complete elimination of the virus. Less than total success matters since this may allow the patient to achieve immune-mediated clearance. The motivation for this article is an upcoming dose-finding trial in chronic norovirus infection. We propose a novel methodology whose goal is twofold: first, to identify the dose that provides the most favorable distribution of treatment outcomes, and, second, to do this in a way that maximizes the treatment benefit for the patients included in the study.

Evaluation of toxicities related to novel therapy in clinical trials for women with gynecologic cancer

BACKGROUND

Women with gynecologic cancer may be at increased risk for adverse events (AEs) due to peritoneal disease burden and prior treatment (surgery, chemotherapy, and pelvic radiotherapy). This study compared the toxicity profiles of patients with and without gynecologic cancer enrolled in phase 1 trials.

METHODS

This was a retrospective analysis of the National Cancer Institute phase 1 database for all trials enrolling 1 or more patients with gynecologic cancer over 2 decades (1995-2015). Clinical parameters collected included demographics, cancer history, trial information, AEs, and responses. AEs (according to the Common Terminology Criteria for Adverse Events) were documented for each patient during treatment, and they were counted once and analyzed on the basis of the highest grade and drug attribution. Multiple regression models were used to compare AEs at the baseline and during treatment.

RESULTS

A total of 4269 patients enrolled in 150 trials were divided into 3 groups: 1) women with gynecologic cancer (n = 685), 2) women with nongynecologic cancer (n = 1698), and 3) men with cancer (n = 1886). The median age was 58 years. The mean number of total AEs reported during treatment was highest for women with gynecologic cancer (17.1 vs 14.7 vs 13.5; P < .001), even though they were similar at the baseline (7.0 vs 7.4 vs 7.0; P = .09). The mean number of drug-related AEs was also highest for women with gynecologic cancer (8.3 vs 6.9 vs 6.2; P < .001). Grade 3 to 5 AEs were similar (2.3 vs 2.3 vs 2.1); however, grade 2 AEs were more frequent in women with gynecologic cancer (4.6 vs 3.9 vs 3.5). Treatment discontinuations due to AEs were similar (9% vs 9% vs 10%).

CONCLUSIONS

Women with gynecologic cancer experienced more frequent low-grade AEs during treatment, and this warrants attention to support their symptom burden. Study dose management should be considered for recurrent grade 2 AEs, particularly during continuous therapy.

The Case for an Unblinded Modeler in Early Clinical Development

The current trend for clinical pharmacology is toward more complex studies (eg, umbrella protocols covering single and multiple ascending doses, food effect, metabolism pathways), requiring many decisions to be made during their conduct. This article discusses guidance of such early clinical studies by modeling and simulation. The ability to make use of all available information each time new data become available during the study requires the modeling scientist to be unblinded. This must of course not jeopardize the blinding of the clinical team, and this article discusses how unblinding can be prevented. Although modeling and simulation are established for guidance of the drug development process overall, they are not frequently used for guidance on a small scale, that is, during studies with the largest uncertainty, the first-in-human studies. Application of a quantitative model backbone makes early clinical drug development a more efficient process and provides additional safety for healthy subjects and patients. Real clinical impact is illustrated by 3 case studies that show different contributions from unblinded modeling: dose escalation based on safety data, modeling and predicting with explicit incorporation of in vitro data, and dose escalation supported by unblinded analysis of adverse event data, which resulted in new insights of the clinical team without being unblinded and made it possible to proceed with dose escalation and to extend the study with an up-titration group.

Adaptive designs in clinical trials: why use them, and how to run and report them

Adaptive designs can make clinical trials more flexible by utilising results accumulating in the trial to modify the trial's course in accordance with pre-specified rules. Trials with an adaptive design are often more efficient, informative and ethical than trials with a traditional fixed design since they often make better use of resources such as time and money, and might require fewer participants. Adaptive designs can be applied across all phases of clinical research, from early-phase dose escalation to confirmatory trials. The pace of the uptake of adaptive designs in clinical research, however, has remained well behind that of the statistical literature introducing new methods and highlighting their potential advantages. We speculate that one factor contributing to this is that the full range of adaptations available to trial designs, as well as their goals, advantages and limitations, remains unfamiliar to many parts of the clinical community. Additionally, the term adaptive design has been misleadingly used as an all-encompassing label to refer to certain methods that could be deemed controversial or that have been inadequately implemented.We believe that even if the planning and analysis of a trial is undertaken by an expert statistician, it is essential that the investigators understand the implications of using an adaptive design, for example, what the practical challenges are, what can (and cannot) be inferred from the results of such a trial, and how to report and communicate the results. This tutorial paper provides guidance on key aspects of adaptive designs that are relevant to clinical triallists. We explain the basic rationale behind adaptive designs, clarify ambiguous terminology and summarise the utility and pitfalls of adaptive designs. We discuss practical aspects around funding, ethical approval, treatment supply and communication with stakeholders and trial participants. Our focus, however, is on the interpretation and reporting of results from adaptive design trials, which we consider vital for anyone involved in medical research. We emphasise the general principles of transparency and reproducibility and suggest how best to put them into practice.

A first-in-human study of the novel HIV-fusion inhibitor C34-PEG4-Chol

Long-acting injectable antiretroviral (LA-ARV) drugs with low toxicity profiles and propensity for drug-drug interactions are a goal for future ARV regimens. C34-PEG₄-Chol is a novel cholesterol tagged LA HIV-fusion-inhibitor (FI). We assessed pre-clinical toxicology and first-in-human administration of C34-PEG₄-Chol. Pre-clinical toxicology was conducted in 2 species. HIV-positive men were randomised to a single subcutaneous dose of C34-PEG₄-Chol at incrementing doses or placebo. Detailed clinical (including injection site reaction (ISR) grading), plasma pharmacokinetic (time-to-minimum-effective-concentration (MEC, 25 ng/mL) and pharmacodynamic (plasma HIV RNA) parameters were assessed. In both mice and dogs, no-observed-adverse effect level (NOAEL) was observed at a 12 mg/kg/dose after two weeks. Of 5 men enrolled, 3 received active drug (10 mg, 10 mg and 20 mg). In 2 individuals grade 3 ISR occurred and the study was halted. Both ISR emerged within 12 hours of active drug dosing. No systemic toxicities were observed. The time-to-MEC was >72 and >96 hours after 10 and 20 mg dose, respectively, and mean change in HIV RNA was −0.9 log10 copies/mL. These human pharmacodynamic and pharmacokinetic data, although limited to 3 subjects, of C34-PEG-4-Chol suggest continuing evaluation of this agent as a LA-ARV. However, alternative administration routes must be explored.

Developing a Bayesian adaptive design for a phase I clinical trial: a case study for a novel HIV treatment

The design of phase I studies is often challenging, because of limited evidence to inform study protocols. Adaptive designs are now well established in cancer but much less so in other clinical areas. A phase I study to assess the safety, pharmacokinetic profile and antiretroviral efficacy of C34-PEG₄ -Chol, a novel peptide fusion inhibitor for the treatment of HIV infection, has been set up with Medical Research Council funding. During the study workup, Bayesian adaptive designs based on the continual reassessment method were compared with a more standard rule-based design, with the aim of choosing a design that would maximise the scientific information gained from the study. The process of specifying and evaluating the design options was time consuming and required the active involvement of all members of the trial's protocol development team. However, the effort was worthwhile as the originally proposed rule-based design has been replaced by a more efficient Bayesian adaptive design. While the outcome to be modelled, design details and evaluation criteria are trial specific, the principles behind their selection are general. This case study illustrates the steps required to establish a design in a novel context. Copyright © 2016 John Wiley & Sons, Ltd.