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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., (© 2024 The Author(s). Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Acquired thrombotic thrombocytopenic purpura (TTP) is a life-threatening disease characterized by thrombotic microangiopathy leading to end-organ damage. The standard of care (SOC) treatment is therapeutic plasma exchange (TPE) alongside immunomodulation with steroids, with increasing use of rituximab ± other immunomodulatory agents. The addition of caplacizumab, a nanobody targeting von Willebrand factor, was shown to accelerate platelet count recovery and reduce TPE treatments and hospital length of stay in TTP patients treated in 2 major randomized clinical trials. The addition of caplacizumab to SOC also led to increased bleeding from transient reductions in von Willebrand factor and increased relapse rates. Using data from the 2 clinical trials of caplacizumab, we performed the first-ever cost-effectiveness analysis in TTP. Over a 5-year period, the projected incremental cost-effectiveness ratio (ICER) in our Markov model was $1 482 260, significantly above the accepted 2019 US willingness-to-pay threshold of $195 300. One-way sensitivity analyses showed the utility of the well state and the cost of caplacizumab to have the largest effects on ICER, with a reduction in caplacizumab cost demonstrating the single greatest impact on lowering the ICER. In a probabilistic sensitivity analysis, SOC was favored over caplacizumab in 100% of 10 000 iterations. Our data indicate that the addition of caplacizumab to SOC in treatment of acquired TTP is not cost effective because of the high cost of the medication and its failure to improve relapse rates. The potential impact of caplacizumab on health system cost using longer term follow-up data merits further study., (© 2021 by The American Society of Hematology.)

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 Initiative SM , or NIH HEAL Initiative SM , 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.

The large number of failures in phase III clinical trials, which occur at a rate of approximately 45%, is studied herein relative to possible countermeasures. First, the phenomenon of failures is numerically described. Second, the main reasons for failures are reported, together with some generic improvements suggested in the related literature. This study shows how statistics explain, but do not justify, the high failure rate observed. The rate of failures due to a lack of efficacy that are not expected, is considered to be at least 10%. Expanding phase II is the simplest and most intuitive way to reduce phase III failures since it can reduce phase III false negative findings and launches of phase III trials when the treatment is positive but suboptimal. Moreover, phase II enlargement is discussed using an economic profile. As resources for research are often limited, enlarging phase II should be evaluated on a case-by-case basis. Alternative strategies, such as biomarker-based enrichments and adaptive designs, may aid in reducing failures. However, these strategies also have very low application rates with little likelihood of rapid growth., (© 2019 John Wiley & Sons, Ltd.)

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., Competing Interests: The authors have declared that no competing interests exist.

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.

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., (Copyright © 2018 John Wiley & Sons, Ltd.)

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., (© 2018 The British Pharmacological Society.)

In 2015, Chile enacted the 20850 law, providing public funds for rare and costly diseases that demanded high diagnostic and therapeutic expenditures. The law modifies the Chilean Sanitary Code regulation of research with human beings, aiming at the protection of subjects by securing post-investigational medical benefits and insurance coverage for damage imputable to the research they participated in. Due to ambiguous phrasing, a polemic rose for fear that these protective measures applied to all clinical research, although a careful reading of the law in its context clearly suggests that it refers to phase I therapeutic trials. This paper stresses the distinction between compassionate use and genuine phase I/II therapeutic trials aimed at both pharmacodynamics and an intended therapeutic effect for severe and progressive diseases that are therapeutically orphaned, emphasizing the ethical and medical duty of providing post-trial beneficial medication.

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., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Background: The extent to which trial-level factors differentially influence accrual to trials has not been comprehensively studied. Our objective was to evaluate the empirical relationship and predictive properties of putative risk factors for low accrual in the National Cancer Institute's (NCI's) Cooperative Group Program, now the National Clinical Trials Network (NCTN)., Methods: Data from 787 phase II/III adult NCTN-sponsored trials launched between 2000 and 2011 were used to develop a logistic regression model to predict low accrual, defined as trials that closed with or were accruing at less than 50% of target; 46 trials opened between 2012 and 2013 were used for prospective validation. Candidate predictors were identified from a literature review and expert interviews; final predictors were selected using stepwise regression. Model performance was evaluated by calibration and discrimination via the area under the curve (AUC). All statistical tests were two-sided., Results: Eighteen percent (n = 145) of NCTN-sponsored trials closed with low accrual or were accruing at less than 50% of target three years or more after initiation. A multivariable model of twelve trial-level risk factors had good calibration and discrimination for predicting trials with low accrual (AUC in trials launched 2000-2011 = 0.739, 95% confidence interval [CI] = 0.696 to 0.783]; 2012-2013: AUC = 0.732, 95% CI = 0.547 to 0.917). Results were robust to different definitions of low accrual and predictor selection strategies., Conclusions: We identified multiple characteristics of NCTN-sponsored trials associated with low accrual, several of which have not been previously empirically described, and developed a prediction model that can provide a useful estimate of accrual risk based on these factors. Future work should assess the role of such prediction tools in trial design and prioritization decisions., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

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., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Although most research professionals believe that protocol designs contain a growing number of unnecessary and redundant procedures generating unused data, incurring high cost, and jeopardizing study success, there are no published studies systematically examining this issue. Between November 2011 and May 2012, Tufts Center for the Study of Drug Development conducted a study among a working group of 15 pharmaceutical companies in which a total of 25,103 individual protocol procedures were evaluated and classified using clinical study reports and analysis plans. The results show that the typical later-stage protocol had an average of 7 objectives and 13 end points of which 53.8% are supplementary. One (24.7%) of every 4 procedures performed per phase-III protocol and 17.7% of all phase-II procedures per protocol were classified as "Noncore" in that they supported supplemental secondary, tertiary, and exploratory end points. For phase-III protocols, 23.6% of all procedures supported regulatory compliance requirements and 15.9% supported those for phase-II protocols. The study also found that on average, $1.7 million (18.5% of the total) is spent in direct costs to administer Noncore procedures per phase-III protocol and $0.3 million (13.1% of the total) in direct costs are spent on Noncore procedures for each phase-II protocol. Based on the results of this study, the total direct cost to perform Noncore procedures for all active annual phase-II and phase-III protocols is conservatively estimated at $3.7 billion annually, not including the indirect costs associated with collecting and managing Noncore procedure data and the ethical costs of exposing study volunteers to unnecessary risks associated with conducting extraneous procedures.

Developing sophisticated statistical methods for go/no-go decisions is crucial for clinical trials, as planning phase III or phase IV trials is costly and time consuming. In this paper, we develop a novel Bayesian methodology for determining the probability of success of a treatment regimen on the basis of the current data of a given trial. We introduce a new criterion for calculating the probability of success that allows for inclusion of covariates as well as allowing for historical data based on the treatment regimen, and patient characteristics. A new class of prior distributions and covariate distributions is developed to achieve this goal. The methodology is quite general and can be used with univariate or multivariate continuous or discrete data, and it generalizes Chuang-Stein's work. This methodology will be invaluable for informing the scientist on the likelihood of success of the compound, while including the information of covariates for patient characteristics in the trial population for planning future pre-market or post-market trials., (Copyright © 2014 John Wiley & Sons, Ltd.)

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.

There is a pressing need to address the current major gaps in epilepsy treatment, in particular drug-resistant epilepsy, antiepileptogenic therapies, and comorbidities. A major concern in the development of new therapies is that current preclinical testing is not sufficiently predictive for clinical efficacy. Methodologic limitations of current preclinical paradigms may partly account for this discrepancy. Here we propose and discuss a strategy for implementing a "phase II" multicenter preclinical drug trial model based on clinical phase II/III studies designed to generate more rigorous preclinical data for efficacy. The goal is to improve the evidence resulting from preclinical studies for investigational new drugs that have shown strong promise in initial preclinical "phase I" studies. This should reduce the risk for expensive clinical studies in epilepsy and therefore increase the appeal for funders (industry and government) to invest in their clinical development., (Wiley Periodicals, Inc. © 2013 International League Against Epilepsy.)

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: 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., (© 2012 Cancer Research UK)

Pediatric oncology is an unrivaled success story in the recent history of medicine. This success is mostly based on a persistent refinement of evidence based therapeutic concepts. With that regard physicians and their staff are highly experience in the conduct of prospective evidence based trials and are therefore competent partners for the pharmaceutical industry. In times of personalized medicine the individual target population is diminishing and the borders of indications are not more disease based. A situation that requires new concepts from the industry. Therefore children with cancer could benefit early from the current developments as well as the pharmaceutical industry could benefit from the legislative incentives through highly recruiting and well conducted prospective trials. Pivotal is a functional platform of communication in order to maintain a close dialogue between academia and pharmaceutical companies., (© Georg Thieme Verlag KG Stuttgart · New York.)

In this note, we highlight the fact that the choice of type I and type II error rates should not simply be set at traditional levels in the phase II clinical trial setting when considering the relative success rate of previous trials in a given disease setting. For diseases in which it is rare that a new compound is active, we argue that more stringent type I error rates in the phase II setting may be more important relative to relaxing the type II error rates. The paper itself is more of a 'thought' experiment on this topic such that specific clinical trial settings will require specific applications of this approach. This is due in part to the fact that the real-world setting is more complex relative to overall decision process in terms of moving from phase II to phase III trials than our basic illustrative model., (Copyright © 2012 John Wiley & Sons, Ltd.)

Based on the observation that over the last 30 years the cost of development has risen regularly as the number of new chemical entities reaching the market has fallen, how can "savings" be made in terms of clinical development, the objective being more rapid access to a drug for medical needs that are not covered? Several instruments exist to enable innovative products to be made available more quickly: temporary use authorisations, which are not concerned by this work (ATUs), conditional marketing authorisations (MAs) and MAs under exceptional circumstances. These aspects have been taken up in the European medicines agency (EMA)'s "Road Map", which states "A key issue for Regulators will be if a more "staggered" approval should be envisaged, characterised by a better defined/more restricted population of good responders, followed by a broadening of the population post-authorisation when more "real life" data are available. In addition, maximising the value of information generated in the post-authorisation phase should be developed through the use of cohorts and other prospectively collected use data, especially in the case of conditional marketing authorisations." The rules of procedure of the Transparency Commission for their part provide for the notion of preliminary examination: in order to prepare as best as possible the examination of dossiers of products assumed to be innovative and to limit delays, the office can undertake a preliminary study as soon as the dossier has been filed at the Committee for medicinal products for human use (CHMP). It may, at this time, request the firm to provide further information and may call on external experts. The implementation of this preliminary study does not exonerate the firm of the obligation of filing a complete dossier. The post inscription studies requested by the Transparency Commission (ISPEP - public health benefit and post-marketing studies) are usually requested in the case of hesitations regarding the level of improvement of the medical benefit (ASMR) [level II/III or IV/V]. Such requests mainly concern uncertainties regarding the transposability, the patient profile or correct usage in real life. Among the studies whose results were provided, in 15 cases the results were in line with expectations, in 6 cases they resulted in downward re-evaluations and the final 3 cases were inconclusive. The final recommendations of the round table were: Defining the medical need that is not covered by working in consultation (Industry and Health Authorities); Providing a Complementary Investigations Plan (PIC) after the MA at a very early stage to reinforce the early MA, and/or HTA (health technology assessment) preparation and monitoring (possible constraining actions); Enhanced use of modelling techniques and their transposability; "Intussusception" of phases to optimise the development of a complete dossier; Early "scientific opinions" (EMA, French Health Products Safety Agency [Afssaps], French Health Authority [HAS]); Raising the awareness of the authorities, industry, doctors and patients with regard to controlled observational studies; Developing the use of public data bases., (© 2011 Société Française de Pharmacologie et de Thérapeutique.)

During the early stage of cancer clinical trials, when it is not convenient to construct an explicit hypothesis testing, a study on a new therapy often calls for a response rate (p) estimation concurrently with or right before a typical phase II study. We consider a two-stage process, where the acquired information from Stage I (with a small sample size (m)) would be utilized for sample size (n) recommendation for Stage II study aiming for a more accurate estimation. Once a sample size design and a parameter estimation protocol are applied, we study the overall utility (cost-effectiveness) in connection with the cost due to patient recruitment and treatment as well as the loss due to mean squared error from parameter estimation. Two approaches will be investigated including the posterior mixture method (a Bayesian approach) and the empirical variance method (a frequentist approach). We also discuss response rate estimation under truncated parameter space using maximum likelihood estimation with regard to sample size and mean squared error. The profiles of p-specific expected sample size, mean squared error and risk under different approaches motivate us to introduce the concept of "admissible sample size (design)"., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Drugs introduced over the past 25 years have benefitted many patients with acute myeloid leukemia (AML) and provided cure for some. Still, AML remains difficult to treat, and most patients will eventually die from their disease. Therefore, novel drugs and drug combinations are under intense investigation, and promising results eagerly awaited and embraced. However, drug development is lengthy and costs are staggering. While the phase 1-phase 2-phase 3 sequence of clinical drug testing has remained inviolate for decades, it appears intrinsically inefficient, and scientific flaws have been noted by many authors. Of major concern is the high frequency of false-positive results obtained in phase 2 studies. Here, we review features of phase 2 trials in AML that may contribute to this problem, particularly lack of control groups, patient heterogeneity, selection bias, and choice of end points. Recognizing these problems and challenges should provide us with opportunities to make drug development more efficient and less costly. We also suggest strategies for trial design improvement. Although our focus is on the treatment of AML, the principles that we highlight should be broadly applicable to the evaluation of new treatments for a variety of diseases.

Purpose: Activation of clinical trials is a lengthy process. We studied the procedures and time required to activate lung cancer clinical trials in a US academic center compared with a European center., Methods: A retrospective review was performed of all thoracic oncology therapeutic trials submitted for regulatory review between 2001 and 2008 at Washington University School of Medicine (WUSM; St Louis, MO) and the University of Torino (UT; Torino, Italy). A process map was drafted by both institutions to establish the order of required events., Results: We reviewed 137 therapeutic thoracic oncology trials from WUSM (n = 83) and UT (n = 54). The median times from submission to opening a trial were 163 days for WUSM and 112.5 days for UT (P = .048). The median times for regulatory approval were 75 days for WUSM and 31 days for UT (P < .001). The difference is more pronounced in a homogeneous subset of phase II, industrial-sponsored trials for the median calendar time from submission to opening a trial (239.5 days for WUSM v 112.5 days for UT; P < .001) and time for regulatory approval (99 days for WUSM v 13.5 days for UT; P < .001). The median number of patients accrued at WUSM was 7.4 patients per study compared with an average of 37 patients per study at UT. The proportion of trials that enrolled 20 patients or more represented 22.2% of trials at UT but only 1.1% of trials at WUSM., Conclusion: It takes additional steps and significantly longer time to activate a therapeutic thoracic clinical trial at a representative US site (WUSM) compared with a European site (UT).

Many phase I/II clinical trials in multiple sclerosis use gadolinium-enhanced lesions as the outcome measure. The best scanning interval and analysis for this outcome has not been determined. The objective of this study was to compare timing schemes and analysis techniques in terms of power for phase I/II clinical trials. Data were simulated under four scenarios assuming a negative binomial distribution for the number of new lesions and an exponential distribution for the duration of enhancement. The first scenario assumed an immediate treatment effect on the number of new lesions, while the second scenario assumed a delayed treatment effect. The third scenario assumed a higher proportion of patients had no new lesions, and the final scenario assumed an immediate treatment effect on the duration of enhancement. For each scenario, power for a six-month trial with 100 patients per arm was calculated using 10 analysis strategies. The scanning intervals tested were monthly scans, bimonthly scans and a single end-of-study scan. In addition, cost-effectiveness of each trial design and analysis was compared. Negative binomial regression models for the total number of new lesions were the most powerful analyses under an immediate treatment effect, and repeated measures models with a categorical time effect were the most powerful analyses under a delayed treatment effect. Although monthly scans generally provided most power, this design was also most costly. Designs with fewer scans per patient provide similar power and are more cost-effective. Negative binomial regression models are more powerful than non-parametric approaches.

The emergence of many newer, molecularly targeted anticancer drugs requires that we rethink the way that we conduct phase II trials in oncology. In particular, we can no longer afford to advance drugs (or combinations) to phase III trials with a high risk of failure to improve on outcomes. Drawing on phase II trials of therapeutics for other chronic and progressive diseases in medicine, we find that a randomized design is essential not only for selecting agents for further study but also for optimizing the design (dose, patient population, and endpoints) of the subsequent phase III trials. We use the example of advanced nonsmall cell lung cancer to demonstrate how randomized phase II trials have already made an impact in oncology, whereas single-arm phase II trials have led to negative phase III trials in the same disease. Finally, we make the case that randomized phase II trials are feasible, as long as reasonable statistical standards are applied.

This manuscript discusses optimal cost-effective designs for Phase II proof of concept (PoC) trials. Unlike a confirmatory registration trial, a PoC trial is exploratory in nature, and sponsors of such trials have the liberty to choose the type I error rate and the power. The decision is largely driven by the perceived probability of having a truly active treatment per patient exposure (a surrogate measure to development cost), which is naturally captured in an efficiency score to be defined in this manuscript. Optimization of the score function leads to type I error rate and power (and therefore sample size) for the trial that is most cost-effective. This in turn leads to cost-effective go-no go criteria for development decisions. The idea is applied to derive optimal trial-level, program-level, and franchise-level design strategies. The study is not meant to provide any general conclusion because the settings used are largely simplified for illustrative purposes. However, through the examples provided herein, a reader should be able to gain useful insight into these design problems and apply them to the design of their own PoC trials.

Background: Phase I/II placebo-controlled clinical trials of recombinant Factor VIIa (rFVIIa) suggested that administration of rFVIIa within 4 hours after onset of intracerebral hemorrhage (ICH) is safe, limits ICH growth, and improves outcomes. We sought to determine the cost-effectiveness of rFVIIa for acute ICH treatment, using published Phase II data. We hypothesized that rFVIIa would have a low marginal cost-effectiveness ratio (mCER) given the poor neurologic outcomes after ICH with conventional management., Methods: We performed an incremental cost-effectiveness analysis from the societal perspective, considering conventional management vs. 80 ug/kg rFVIIa treatment for acute ICH cases meeting Phase II inclusion criteria. The time frame for the analysis was 1. 25 years: data from the Phase II trial was used for 90 day outcomes and rFVIIa complications - arterial thromboembolic events (ATE). We assumed no substantial cost differences in care between the two strategies except: 1) cost of rFVIIa (for an 80 mcg/kg dose in an 80 kg patient, assumed cost of $6,408); 2) cost of ATE side effects from rFVIIa (which also decrease quality of life and increase the chance of death); and 3) differential monetary costs of outcomes and their impact on quality of life, including disposition (home vs. nursing home), and outpatient vs. inpatient rehabilitation. Sensitivity analyses were performed to explore uncertainty in parameter estimates, impact of rFVIIa cost, direct cost of neurologic outcomes, probability of ATE, and outcomes after ATE., Results: In the "base case", treating ICH with rFVIIa dominates the usual care strategy by being more effective and less costly. rFVIIa maintained a mCER < $50,000/QALY over a wide range of sensitivity analyses. Sensitivity analyses showed that the cost of rFVIIa must exceed $14,500, or the frequency of ATE exceed 29%, for the mCER to exceed $50,000/QALY. Varying the cost and/or reducing the utility of health states following ATE did not impact results., Conclusion: Based on data from preliminary trials, treating selected ICH patients with rFVIIa results in lower cost and improved clinical outcomes. This potential cost-effectiveness must be considered in light of the Phase III trial results.

Purpose: Prospectively planned collection and analysis of adverse event (AE) data are essential parts of well-conducted clinical trials. The AE data in a trial sponsor's database should be comparable with what is stipulated in the protocol and with the AE data published. We examined whether the published AE data differ from those in the sponsor's database and from the data collection requirements stated in study protocols., Methods: We searched the National Cancer Institute (NCI) Clinical Data Update System (CDUS) for studies that used the Common Toxicity Criteria version 2.0 and for which a final study publication was available. We extracted from the protocols information pertaining to AE collection and reporting methods and compared it with the methods cited in the article. We also compared the AE data in the trial publication with the AE data submitted by the investigators to CDUS., Results: We identified 22 studies meeting the criteria for this review. There was considerable inconsistency between AE collection and reporting methods cited in the protocols versus final publications. AE data in the article and CDUS were not identical. Twenty-seven percent of article high-grade AEs could not be matched to agent-attributable AEs in the CDUS. Twenty-eight percent of CDUS high-grade AEs could not be matched to AEs in the corresponding article. In 14 of 22 articles, the number of high-grade AEs in CDUS differed from the number in the articles by 20% or more., Conclusion: Lack of consistency in and reporting of AEs are associated with NCI database and trial publication AE data discrepancy.