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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.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.

Orloff and colleagues describe how moving from the traditional approach to clinical trials based on sequential, distinct phases towards a more integrated strategy that increases flexibility and maximizes the use of accumulated knowledge could have a key role in improving the efficiency and cost-effectiveness of drug development. Using examples in which novel trial designs have been successfully applied, they also illustrate the use of the tools involved, such as Bayesian methodologies, and discuss the advantages and challenges for their more widespread implementation. Declining pharmaceutical industry productivity is well recognized by drug developers, regulatory authorities and patient groups. A key part of the problem is that clinical studies are increasingly expensive, driven by the rising costs of conducting Phase II and III trials. It is therefore crucial to ensure that these phases of drug development are conducted more efficiently and cost-effectively, and that attrition rates are reduced. In this article, we argue that moving from the traditional clinical development approach based on sequential, distinct phases towards a more integrated view that uses adaptive design tools to increase flexibility and maximize the use of accumulated knowledge could have an important role in achieving these goals. Applications and examples of the use of these tools — such as Bayesian methodologies — in early- and late-stage drug development are discussed, as well as the advantages, challenges and barriers to their more widespread implementation.

As R&D costs spiral for drug developers, disruptive approaches to clinical trial design and management are gaining traction. Get ready for electronic data capture, precompetitive data sharing, virtual trials and a variety of bold new paradigms.

In the last few decades, developers of new drugs, biologics, and devices have increasingly leveraged digital health technologies (DHTs) to assess clinical trial digital endpoints. To our knowledge, a comprehensive assessment of the financial net benefits of digital endpoints in clinical trials has not been conducted. We obtained data from the Digital Medicine Society (DiMe) Library of Digital Endpoints and the US clinical trials registry, ClinicalTrials.gov. The benefit metrics are changes in trial phase duration and enrollment associated with the use of digital endpoints. The cost metric was obtained from an industry survey of the costs of including digital endpoints in clinical trials. We developed an expected net present value (eNPV) model of the cash flows for new drug development and commercialization to assess financial value. The value measure is the increment in eNPV that occurs when digital endpoints are employed. We also calculated a return on investment (ROI) as the ratio of the estimated increment in eNPV to the mean digital endpoint implementation cost. For phase II trials, the increase in eNPV varied from $2.2 million to $3.3 million, with ROIs between 32% and 48% per indication. The net benefits were substantially higher for phase III trials, with the increase in eNPV varying from $27 million to $40 million, with ROIs that were four to six times the investment. The use of digital endpoints in clinical trials can provide substantial extra value to sponsors developing new drugs, with high ROIs.

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.

Background Randomized controlled trials with a survival endpoint are the gold standard for clinical research, but have failed to achieve cures for most advanced malignancies. The high costs of randomized clinical trials slow progress (thereby causing avoidable loss of life) and increase health care costs. Discussion A malignancy may be caused by several different mutations. Therapies effective vs one mutation may be discarded due to lack of statistical significance across the entire population. Conversely, expensive large randomized trials may have sufficient statistical power to demonstrate benefit despite the therapy only working in subgroups. Non-cost-effective therapy is then applied to all patients (including subgroups it cannot help). Randomized trials comparing therapies with different mechanisms of action are misleading since they may conclude the therapies are “equivalent” despite benefitting different subpopulations, or may erroneously conclude that one therapy is superior simply because it targets a larger subpopulation. Furthermore, minor variances in patient selection may determine study outcome, a therapy may be discarded as ineffective despite substantial benefit in one subpopulation if harmful in another, randomized trials may more effectively detect therapies with minor benefit in most patients vs marked benefit in subpopulations, and randomized trials in unselected patients may erroneously conclude that “shot-gun” combinations are superior to single agents when sequential administration of personalized single agents might work better and spare patients treatment with drugs that cannot help them. We must identify predictive biomarkers early by comparing responding to progressing patients in phase I-II trials. Enriching randomized trials for biomarker-positive patients can markedly reduce required patient numbers and costs despite expensive screening for biomarker-positive patients. Available data support approval of new drugs without randomized trials if they yield single-agent sustained responses in patients refractory to standard therapies. Conversely, new approaches are needed to guide development of drug combinations since both standard phase II approaches and phase II-III randomized trials have a high risk of misleading. Summary Traditional randomized clinical trials approaches are often inefficient, wasteful, and unreliable. New clinical research paradigms are needed. The primary outcome of clinical research should be “Who (if anyone) benefits?” rather than “Does the overall group benefit?”

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.

Background: Many clinical trials show no overall benefit. We examined futility analyses applied to trials with different effect sizes. Methods: Ten randomised cancer trials were retrospectively analysed; target sample size reached in all. The hazard ratio indicated no overall benefit ( n =5), or moderate ( n =4) or large ( n =1) treatment effects. Futility analyses were applied after 25, 50 and 75% of events were observed, or patients were recruited. Outcomes were conditional power (CP), and time and cost savings. Results: Futility analyses could stop some trials with no benefit, but not all. After observing 50% of the target number of events, 3 out of 5 trials with no benefit could be stopped early (low CP⩽15%). Trial duration for two studies could be reduced by 4–24 months, saving £44 000–231 000, but the third had already stopped recruiting, hence no savings were made. However, of concern was that 2 of the 4 trials with moderate treatment effects could be stopped early at some point, although they eventually showed worthwhile benefits. Conclusions: Careful application of futility can lead to future patients in a trial not being given an ineffective treatment, and should therefore be used more often. A secondary consideration is that it could shorten trial duration and reduce costs. However, studies with modest treatment effects could be inappropriately stopped early. Unless there is very good evidence for futility, it is often best to continue to the planned end.

Background: The conduct of clinical trials should be an integral part of routine patient care. Treating patients in trials incurs additional costs over and above standard of care (SOC), but the extent of the cost burden is not known. We undertook a retrospective cost attribution analysis to quantitate the treatment costs associated with cancer clinical trial protocols conducted over a 2 year period. Methods: All patients entered into oncology (non-haematology) clinical trials involving investigational medicinal products in 2009 and 2010 in a single UK institution were identified. The trial protocols on which they were treated were analysed to identify the treatment costs for the experimental arm(s) of the trial and the equivalent SOC had the patient not been entered in the trial. The treatment cost difference was calculated by subtracting the experimental treatment cost from SOC cost. For randomised trials, an average treatment cost was estimated by taking into account the number of arms and randomisation ratio. An estimate of the annual treatment costs was calculated. Results: A total of 357 adult oncology patients were treated on 53 different trial protocols: 40 phase III, 2 randomised II/III and 11 phase II design. A total of 27 trials were academic, non-commercial sponsored trials and 26 were commercial sponsored trials. When compared with SOC, the average treatment cost per patient was an excess of £431 for a non-commercial trial (range £6393 excess to £6005 saving) and a saving of £9294 for a commercial trial (range £0 to £71 480). There was an overall treatment cost saving of £388 719 in 2009 and £496 556 in 2010, largely attributable to pharmaceutical company provision of free drug supplies. Conclusion: On an average, non-commercial trial protocols were associated with a small per patient excess treatment cost, whereas commercial trials were associated with a substantially higher cost saving. Taking into account the total number of patients recruited annually, treatment of patients on clinical trial protocols was associated with considerable cost savings across both the non-commercial and commercial portfolio.

Across the drug industry, around 20% of clinical trials now involve some element of adaptive design, in which researchers make preplanned adjustments to protocols at interim checkpoints. The idea is to save both time and money, helping drug developers collect data and modify parameters midstudy. But while adaptive trials have commonly involved simple changesmostly futility analyses or sample size reestimationsa new effort is underway to facilitate more sophisticated adjustments to drug dosages in these types of clinical studies. On 18 February, three large pharmaceutical companies and a contract research organization (CRO) specializing in adaptive trial design announced a new coalition with this aim. The ADDPLAN DF Consortium so far includes Novartis, Janssen Pharmaceuticals and Eli Lilly. Four other major drugmakers are also in discussions about joining.