Critical NIH Resources to Advance Therapies for Pain: Preclinical Screening Program and Phase II Human Clinical Trial Network.

Opioid-related death and overdose have now reached epidemic proportions. In response to this public health crisis, the National Institutes of Health (NIH) launched the Helping to End Addiction Long-term InitiativeSM, or NIH HEAL InitiativeSM, an aggressive, trans-agency effort to speed scientific solutions to stem the national opioid public health crisis. Herein, we describe two NIH HEAL Initiative programs to accelerate development of non-opioid, non-addictive pain treatments: The Preclinical Screening Platform for Pain (PSPP) and Early Phase Pain Investigation Clinical Network (EPPIC-Net). These resources are provided at no cost to investigators, whether in academia or industry and whether within the USA or internationally. Both programs consider small molecules, biologics, devices, and natural products for acute and chronic pain, including repurposed and combination drugs. Importantly, confidentiality and intellectual property are protected. The PSPP provides a rigorous platform to identify and profile non-opioid, non-addictive therapeutics for pain. Accepted assets are evaluated in in vitro functional assays to rule out opioid receptor activity and to assess abuse liability. In vivo pharmacokinetic studies measure plasma and brain exposure to guide the dose range and pretreatment times for the side effect profile, efficacy, and abuse liability. Studies are conducted in accordance with published rigor criteria. EPPIC-Net provides academic and industry investigators with expert infrastructure for phase II testing of pain therapeutics across populations and the lifespan. For assets accepted after a rigorous, objective scientific review process, EPPIC-Net provides clinical trial design, management, implementation, and analysis.

Advances in clinical trial design: Weaving tomorrow's TB treatments.

Christian Lienhardt and co-authors discuss the conclusions of the PLOS Medicine Collection on advances in clinical trial design for development of new tuberculosis treatments.

Influence of Modeling Choices on Value of Information Analysis: An Empirical Analysis from a Real-World Experiment.

BACKGROUND: Value of information (VOI) analysis often requires modeling to characterize and propagate uncertainty. In collaboration with a cancer clinical trial group, we integrated a VOI approach to assessing trial proposals. OBJECTIVE: This paper aims to explore the impact of modeling choices on VOI results and to share lessons learned from the experience. METHODS: After selecting two proposals (A: phase III, breast cancer; B: phase II, pancreatic cancer) for in-depth evaluations, we categorized key modeling choices relevant to trial decision makers (characterizing uncertainty of efficacy, evidence thresholds to change clinical practice, and sample size) and modelers (cycle length, survival distribution, simulation runs, and other choices). Using a $150,000 per quality-adjusted life-year (QALY) threshold, we calculated the patient-level expected value of sample information (EVSI) for each proposal and examined whether each modeling choice led to relative change of more than 10% from the averaged base-case estimate. We separately analyzed the impact of the effective time horizon. RESULTS: The base-case EVSI was $118,300 for Proposal A and $22,200 for Proposal B per patient. Characterizing uncertainty of efficacy was the most important choice in both proposals (e.g. Proposal A: $118,300 using historical data vs. $348,300 using expert survey), followed by the sample size and the choice of survival distribution. The assumed effective time horizon also had a substantial impact on the population-level EVSI. CONCLUSIONS: Modeling choices can have a substantial impact on VOI. Therefore, it is important for groups working to incorporate VOI into research prioritization to adhere to best practices, be clear in their reporting and justification for modeling choices, and to work closely with the relevant decision makers, with particular attention to modeling choices.

Development and First Use of the Patient's Qualitative Assessment of Treatment (PQAT) Questionnaire in Type 2 Diabetes Mellitus to Explore Individualised Benefit-Harm of Drugs Received During Clinical Studies.

INTRODUCTION: Individualised benefit-harm assessments can help identify patient-perceived benefits and harms of a treatment, and associated trade-offs that may influence patients' willingness to use a treatment. This research presents the first use of a patient-reported outcome measure designed to assess patient-perceived benefits and disadvantages of drugs received during clinical studies. METHODS: The Patient's Qualitative Assessment of Treatment (PQAT) was developed in English and cognitively tested with US (n = 4) and Canadian (n = 3) patients with type 1 and type 2 diabetes mellitus (T2DM). The revised version of the PQAT comprises three qualitative open-ended questions focused on the benefits and disadvantages of treatment and reasons why patients would choose to continue/discontinue treatment. A final quantitative question asks patients to evaluate the balance between benefits and disadvantages using a 7-point scale. The revised version of the questionnaire was administered as an exploratory endpoint in a phase II clinical trial for a new injectable treatment for T2DM. Qualitative data were analysed using thematic analysis, and relationships between qualitative and quantitative data were identified. RESULTS: Patient-reported benefits of treatment administered during the clinical trial included clinical markers of efficacy and subjective markers. Disadvantages reported by patients were mainly related to drug adverse effects or to the mode of administration. Of the 57 patients completing the PQAT, 70.2% reported being willing to continue treatment, with 59.6% reporting that the benefits outweighed the disadvantages. The reported benefits of feeling better and improved energy levels were more likely to be associated with a more positive ratio (70% and 71.4%, respectively), while the disadvantages of fatigue, headaches, and stomach pain were associated with a negative ratio and patients not being willing to continue the treatment. CONCLUSIONS: The PQAT is a unique patient-reported outcome tool designed to aid understanding patients' real experience of benefits and disadvantages of a treatment. It combines the richness of qualitative data with quantitative data-information valuable for various stakeholders to make well-informed treatment decisions. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02973321.

Evaluation of the Implementation of the Response Assessment in Neuro-Oncology Criteria in the HERBY Trial of Pediatric Patients with Newly Diagnosed High-Grade Gliomas.

BACKGROUND AND PURPOSE: HERBY was a Phase II multicenter trial setup to establish the efficacy and safety of adding bevacizumab to radiation therapy and temozolomide in pediatric patients with newly diagnosed non-brain stem high-grade gliomas. This study evaluates the implementation of the radiologic aspects of HERBY. MATERIALS AND METHODS: We analyzed multimodal imaging compliance rates and scan quality for participating sites, adjudication rates and reading times for the central review process, the influence of different Response Assessment in Neuro-Oncology criteria in the final response, the incidence of pseudoprogression, and the benefit of incorporating multimodal imaging into the decision process. RESULTS: Multimodal imaging compliance rates were the following: diffusion, 82%; perfusion, 60%; and spectroscopy, 48%. Neuroradiologists' responses differed for 50% of scans, requiring adjudication, with a total average reading time per patient of approximately 3 hours. Pseudoprogression occurred in 10/116 (9%) cases, 8 in the radiation therapy/temozolomide arm and 2 in the bevacizumab arm (P < .01). Increased target enhancing lesion diameter was a reason for progression in 8/86 cases (9.3%) but never the only radiologic or clinical reason. Event-free survival was predicted earlier in 5/86 (5.8%) patients by multimodal imaging (diffusion, n = 4; perfusion, n = 1). CONCLUSIONS: The addition of multimodal imaging to the response criteria modified the assessment in a small number of cases, determining progression earlier than structural imaging alone. Increased target lesion diameter, accounting for a large proportion of reading time, was never the only reason to designate disease progression.

Applications of Bayesian statistical methodology to clinical trial design: A case study of a phase 2 trial with an interim futility assessment in patients with knee osteoarthritis.

Development of new pharmacological treatments for osteoarthritis that address unmet medical needs in a competitive market place is challenging. Bayesian approaches to trial design offer advantages in defining treatment benefits by addressing clinically relevant magnitude of effects relative to comparators and in optimizing efficiency in analysis. Such advantages are illustrated by a motivating case study, a proof of concept, and dose finding study in patients with osteoarthritis. Patients with osteoarthritis were randomized to receive placebo, celecoxib, or 1 of 4 doses of galcanezumab. Primary outcome measure was change from baseline WOMAC pain after 8 weeks of treatment. Literature review of clinical trials with targeted comparator therapies quantified treatment effects versus placebo. Two success criteria were defined: one to address superiority to placebo with adequate precision and another to ensure a clinically relevant treatment effect. Trial simulations used a Bayesian dose response and longitudinal model. An interim analysis for futility was incorporated. Simulations indicated the study had ≥85% power to detect a 14-mm improvement and ≤1% risk for a placebo-like drug to pass. The addition of the second success criterion substantially reduced the risk of an inadequate, weakly efficacious drug proceeding to future development. The study was terminated at the interim analysis due to inadequate analgesic efficacy. A Bayesian approach using probabilistic statements enables clear understanding of success criteria, leading to informed decisions for study conduct. Incorporating an interim analysis can effectively reduce sample size, save resources, and minimize exposure of patients to an inadequate treatment.

Comparisons of Analysis Methods for Assessment of Pharmacodynamic Interactions Including Design Recommendations.

Quantitative evaluation of potential pharmacodynamic (PD) interactions is important in tuberculosis drug development in order to optimize Phase 2b drug selection and ultimately to define clinical combination regimens. In this work, we used simulations to (1) evaluate different analysis methods for detecting PD interactions between two hypothetical anti-tubercular drugs in in vitro time-kill experiments, and (2) provide design recommendations for evaluation of PD interactions. The model used for all simulations was the Multistate Tuberculosis Pharmacometric (MTP) model linked to the General Pharmacodynamic Interaction (GPDI) model. Simulated data were re-estimated using the MTP-GPDI model implemented in Bliss Independence or Loewe Additivity, or using a conventional model such as an Empirical Bliss Independence-based model or the Greco model based on Loewe Additivity. The GPDI model correctly characterized different PD interactions (antagonism, synergism, or asymmetric interaction), regardless of the underlying additivity criterion. The commonly used conventional models were not able to characterize asymmetric PD interactions, i.e., concentration-dependent synergism and antagonism. An optimized experimental design was developed that correctly identified interactions in ≥ 94% of the evaluated scenarios using the MTP-GPDI model approach. The MTP-GPDI model approach was proved to provide advantages to other conventional models for assessing PD interactions of anti-tubercular drugs and provides key information for selection of drug combinations for Phase 2b evaluation.

Sample size determination and treatment screening in two-stage phase II clinical trials via ROC curve.

In pharmaceutical-related research, we usually use clinical trials methods to identify valuable treatments and compare their efficacy with that of a standard control therapy. Although clinical trials are essential for ensuring the efficacy and postmarketing safety of a drug, conducting clinical trials is usually costly and time-consuming. Moreover, to allocate patients to the little therapeutic effect treatments is inappropriate due to the ethical and cost imperative. Hence, there are several 2-stage designs in the literature where, for reducing cost and shortening duration of trials, they use the conditional power obtained from interim analysis results to appraise whether we should continue the lower efficacious treatments in the next stage. However, there is a lack of discussion about the influential impacts on the conditional power of a trial at the design stage in the literature. In this article, we calculate the optimal conditional power via the receiver operating characteristic curve method to assess the impacts on the quality of a 2-stage design with multiple treatments and propose an optimal design using the minimum expected sample size for choosing the best or promising treatment(s) among several treatments under an optimal conditional power constraint. In this paper, we provide tables of the 2-stage design subject to optimal conditional power for various combinations of design parameters and use an example to illustrate our methods.

A Programme for Risk Assessment and Minimisation of Progressive Multifocal Leukoencephalopathy Developed for Vedolizumab Clinical Trials.

INTRODUCTION: Over the past decade, the potential for drug-associated progressive multifocal leukoencephalopathy (PML) has become an increasingly important consideration in certain drug development programmes, particularly those of immunomodulatory biologics. Whether the risk of PML with an investigational agent is proven (e.g. extrapolated from relevant experience, such as a class effect) or merely theoretical, the serious consequences of acquiring PML require careful risk minimisation and assessment. No single standard for such risk minimisation exists. Vedolizumab is a recently developed monoclonal antibody to α4ß7 integrin. Its clinical development necessitated a dedicated PML risk minimisation assessment as part of a global preapproval regulatory requirement. OBJECTIVE: The aim of this study was to describe the multiple risk minimisation elements that were incorporated in vedolizumab clinical trials in inflammatory bowel disease patients as part of the risk assessment and minimisation of PML programme for vedolizumab. METHODS: A case evaluation algorithm was developed for sequential screening and diagnostic evaluation of subjects who met criteria that indicated a clinical suspicion of PML. An Independent Adjudication Committee provided an independent, unbiased opinion regarding the likelihood of PML. RESULTS: Although no cases were detected, all suspected PML events were thoroughly reviewed and successfully adjudicated, making it unlikely that cases were missed. CONCLUSION: We suggest that this programme could serve as a model for pragmatic screening for PML during the clinical development of new drugs.

Non-commercial vs. commercial clinical trials: a retrospective study of the applications submitted to a research ethics committee.

There are many difficulties in undertaking independent clinical research without support from the pharmaceutical industry. In this retrospective observational study, some design characteristics, the clinical trial public register and the publication rate of noncommercial clinical trials were compared to those of commercial clinical trials. A total of 809 applications of drug-evaluation clinical trials were submitted from May 2004 to May 2009 to the research ethics committee of a tertiary hospital, and 16.3% of trials were noncommercial. They were mainly phase IV, multicentre national, and unmasked controlled trials, compared to the commercial trials that were mainly phase II or III, multicentre international, and double-blind masked trials. The commercial trials were registered and published more often than noncommercial trials. More funding for noncommercial research is still needed. The results of the research, commercial or noncommercial, should be disseminated in order not to compromise either its scientific or its social value.

Integrating dose estimation into a decision-making framework for model-based drug development.

Model-informed drug discovery and development offers the promise of more efficient clinical development, with increased productivity and reduced cost through scientific decision making and risk management. Go/no-go development decisions in the pharmaceutical industry are often driven by effect size estimates, with the goal of meeting commercially generated target profiles. Sufficient efficacy is critical for eventual success, but the decision to advance development phase is also dependent on adequate knowledge of appropriate dose and dose-response. Doses which are too high or low pose risk of clinical or commercial failure. This paper addresses this issue and continues the evolution of formal decision frameworks in drug development. Here, we consider the integration of both efficacy and dose-response estimation accuracy into the go/no-go decision process, using a model-based approach. Using prespecified target and lower reference values associated with both efficacy and dose accuracy, we build a decision framework to more completely characterize development risk. Given the limited knowledge of dose response in early development, our approach incorporates a set of dose-response models and uses model averaging. The approach and its operating characteristics are illustrated through simulation. Finally, we demonstrate the decision approach on a post hoc analysis of the phase 2 data for naloxegol (a drug approved for opioid-induced constipation).

A Proposal for Progression-Free Survival Assessment in Patients with Early Progressive Cancer.

BACKGROUND/AIM: Progression-free survival (PFS), which is evaluated in oncology clinical trials, is determined based on tumor progression evaluated according to an assessment schedule. There is possibly a bias in median PFS and hazard ratio (HR) for PFS depending on the assessment schedule referring to randomized controlled trials (RCTs) in patients with metastatic colorectal cancer. MATERIALS AND METHODS: We re-analyzed the PFS in the FTD/TPI phase 2 trial by changing the assessment schedule. To assess biases in median PFS and HR for PFS resulting from different assessment schedules, we performed a computational simulation. RESULTS: The reanalysis of FTD/TPI phase 2 trial and the simulation results showed that there were biases in median PFS and HR for PFS. CONCLUSION: In RCTs for early progressive cancer, median PFS is dependent on the assessment schedule; however, HR for PFS can be assessed without clinically-meaningful differences between assessment schedules, regardless of biomarker assumptions.

Adaptive phase I/II clinical trials for drug combination assessment in oncology using the outcomes of each cycle.

Many new anticancer agents can be combined with existing drugs, as combining a number of drugs may be expected to have a better therapeutic effect than monotherapy owing to synergistic effects. Furthermore, to drive drug development and to reduce the associated cost, there has been a growing tendency to combine these as phase I/II trials. With respect to phase I/II oncology trials for the assessment of dose combinations, in the existing methodologies in which efficacy based on tumor response and safety based on toxicity are modeled as binary outcomes, it is not possible to enroll and treat the next cohort of patients unless the best overall response has been determined in the current cohort. Thus, the trial duration might be potentially extended to an unacceptable degree. In this study, we proposed a method that randomizes the next cohort of patients in the phase II part to the dose combination based on the estimated response rate using all the available observed data upon determination of the overall response in the current cohort. We compared the proposed method to the existing method using simulation studies. These demonstrated that the percentage of optimal dose combinations selected in the proposed method is not less than that in the existing method and that the trial duration in the proposed method is shortened compared to that in the existing method. The proposed method meets both ethical and financial requirements, and we believe it has the potential to contribute to expedite drug development.

Optimizing adaptive design for Phase 2 dose-finding trials incorporating long-term success and financial considerations: A case study for neuropathic pain.

In this paper, we propose an adaptive randomization design for Phase 2 dose-finding trials to optimize Net Present Value (NPV) for an experimental drug. We replace the traditional fixed sample size design (Patel, et al., 2012) by this new design to see if NPV from the original paper can be improved. Comparison of the proposed design to the previous design is made via simulations using a hypothetical example based on a Diabetic Neuropathic Pain Study.

Assessment of objective responses in thymic epithelial tumors using ITMIG modified criteria.

OBJECTIVES: Pleural metastases of thymic epithelial tumors (TETs) are relatively common, and this unique growth pattern makes the use of RECIST (response evaluation criteria in solid tumors) response criteria difficult. To standardize tumor measurement in TETs, the International Thymic Malignancy Interest Group (ITMIG) has proposed newly developed criteria. We compared evaluation of objective response between ITMIG modified criteria and RECIST 1.1 in patients with TET treated with systemic chemotherapy. PATIENTS AND METHODS: We retrospectively evaluated the tumor response of 40 patients with unresectable TET who were enrolled in a phase II clinical trial using ITMIG modified criteria, and compared the findings with prospectively evaluated tumor response assessed by RECIST 1.1. Agreement analyses for the response at each time point, including overall response and declaring progression, were performed and the time to progression (TTP) was also assessed using the two different measurements. RESULTS: The overall response rate assessed by the two methods did not differ significantly, with kappa value of 0.897. Agreement analysis for declaring progression of disease (PD) at the date of RECIST 1.1-designated PD showed 95% concordance rate with ITMIG modified criteria (p=1.000, kappa index=0.875). The median TTP according to RECIST 1.1 and ITMIG modified criteria was 8.4 and 7.9 months (p=0.983), respectively. Validation with another cohort of 27 TET patients treated with neoadjuvant chemotherapy also showed a 96% concordance rate in overall response between the two different criteria. CONCLUSIONS: ITMIG modified criteria showed a high concordance rate with RECIST 1.1 criteria in response assessment of TETs. Given the rarity of TETs, further evaluation of ITMIG modified criteria in a larger number of patients will be required before their implementation in clinical trials.

Statistical methods for the analysis of clinical trials data containing many zeros: An application in vaccine development.

In recent years, many vaccines have been developed for the prevention of a variety of diseases. Many of these vaccines, like the one for herpes zoster, are supposed to act in a multilevel way. Ideally, they completely prevent expression of the virus, but failing that they help to reduce the severity of the disease. A simple approach to analyze these data is the so-called burden-of-illness test. The method assigns a score, say W, equal to 0 for the uninfected and a post-infection outcome X > 0 for the infected individuals. One of the limitations of this test is the potential low power when the vaccine efficacy is close to 0. To overcome this limitation, we propose a Fisher adjusted test where we combine a statistic for infection with a statistic for post-infection outcome adjusted for selection bias. The advantages and disadvantages of different methods proposed in the literature are discussed. We compared the methods via simulations in herpes zoster, HIV, and malaria vaccine trial settings. In addition, we applied these methods to published data on HIV vaccine. The paper ends with some recommendations and conclusions.

Decision-theoretic designs for small trials and pilot studies: A review.

Pilot studies and other small clinical trials are often conducted but serve a variety of purposes and there is little consensus on their design. One paradigm that has been suggested for the design of such studies is Bayesian decision theory. In this article, we review the literature with the aim of summarizing current methodological developments in this area. We find that decision-theoretic methods have been applied to the design of small clinical trials in a number of areas. We divide our discussion of published methods into those for trials conducted in a single stage, those for multi-stage trials in which decisions are made through the course of the trial at a number of interim analyses, and those that attempt to design a series of clinical trials or a drug development programme. In all three cases, a number of methods have been proposed, depending on the decision maker's perspective being considered and the details of utility functions that are used to construct the optimal design.

Cost-benefit assessment of using electronic health records data for clinical research versus current practices: Contribution of the Electronic Health Records for Clinical Research (EHR4CR) European Project.

INTRODUCTION: The widespread adoption of electronic health records (EHR) provides a new opportunity to improve the efficiency of clinical research. The European EHR4CR (Electronic Health Records for Clinical Research) 4-year project has developed an innovative technological platform to enable the re-use of EHR data for clinical research. The objective of this cost-benefit assessment (CBA) is to assess the value of EHR4CR solutions compared to current practices, from the perspective of sponsors of clinical trials. MATERIALS AND METHODS: A CBA model was developed using an advanced modeling approach. The costs of performing three clinical research scenarios (S) applied to a hypothetical Phase II or III oncology clinical trial workflow (reference case) were estimated under current and EHR4CR conditions, namely protocol feasibility assessment (S1), patient identification for recruitment (S2), and clinical study execution (S3). The potential benefits were calculated considering that the estimated reduction in actual person-time and costs for performing EHR4CR S1, S2, and S3 would accelerate time to market (TTM). Probabilistic sensitivity analyses using Monte Carlo simulations were conducted to manage uncertainty. RESULTS: Should the estimated efficiency gains achieved with the EHR4CR platform translate into faster TTM, the expected benefits for the global pharmaceutical oncology sector were estimated at €161.5m (S1), €45.7m (S2), €204.5m (S1+S2), €1906m (S3), and up to €2121.8m (S1+S2+S3) when the scenarios were used sequentially. CONCLUSIONS: The results suggest that optimizing clinical trial design and execution with the EHR4CR platform would generate substantial added value for pharmaceutical industry, as main sponsors of clinical trials in Europe, and beyond.

Recent innovations in the USA National Cancer Institute-sponsored investigator initiated Phase I and II anticancer drug development.

Exciting recent advancements in deep-sequencing technology have enabled a rapid and cost-effective molecular characterization of patient-derived tumor samples. Incorporating these innovative diagnostic technologies into early clinical trials could significantly propel implementation of precision medicine by identifying genetic markers predictive of sensitivity to agents. It may also markedly accelerate drug development and subsequent regulatory approval of novel agents. Particularly noteworthy, a high-response rate in a Phase II trial involving a biomarker-enriched patient cohort could result in a regulatory treatment approval in rare histologies, which otherwise would not be a candidate for a large randomized clinical trial. Furthermore, even if a trial does not meet its statistical endpoint, tumors from a few responders should be molecularly characterized as part of the new biomarker-mining processes. In order to accommodate patient screening and accelerate the accrual process, institutions conducting early clinical trials need to be a part of a multi-institution clinical trials network. Future clinical trial design will incorporate new biomarkers discovered by a 'phenotype-to-genotype' effort with an appropriate statistical design. To help advance such changes, the National Cancer Institute has recently reformed the existing early phase clinical trials network. A new clinical trial network, the Experimental Therapeutics Clinical Trials Network (ET-CTN), was begun and, in addition to its pre-existing infrastructure, an up-to-date clinical trial registration system, clinical trial monitoring system including electronic database and a central Institutional Review Board were formed. Ultimately, these reforms support identifying the most appropriate therapy for each tumor type by incorporating state-of-the-art molecular diagnostic tools into early clinical trials.