Cost effectiveness of caplacizumab in acquired thrombotic thrombocytopenic purpura.

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.

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.

Empowering phase II clinical trials to reduce phase III failures.

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.

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.

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.

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.

Conceptual clarifications regarding Chilean Act 20850 on public funding of high-cost diseases.

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.

En 2015 se publica en Chile la Ley 20850, cuyo objetivo declarado es el financiamiento público de enfermedades raras y de aquellas de alto costo diagnóstico y terapéutico. Inserto en la ley hay un articulado a introducir en el Código Sanitario, que exige de las investigaciones clínicas que mantengan los beneficios médicos determinados por el estudio, para los pacientes investigados, por todo el tiempo que sea médicamente necesario; amparado por extensos seguros para cubrir eventuales complicaciones y efectos indeseados. La redacción de la ley había motivado intensas polémicas, debido a su imprecisa redacción que permite interpretar que la protección exigida es extensible a todo estudio clínico; siendo que la lectura atenta y el contexto de este articulado claramente lo refieren a terapias experimentales. Este artículo distingue entre uso compasivo y terapias experimentales genuinas, que enlazan Fase I (delimita dosis máximas no tóxicas en individuos sanos) y Fase II (estudia efectividad en pequeños grupos de pacientes), investigando tanto farmacodinamia como efectos terapéuticos para enfermedades graves, en deterioro progresivo y huérfanas de tratamiento, con el objetivo ético y médico de la disponibilidad de efectos benéficos, más allá de terminado el estudio.

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.

Predicting Low Accrual in the National Cancer Institute's Cooperative Group Clinical Trials.

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.

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.

Bayesian probability of success for clinical trials using historical data.

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.

Quantifying the magnitude and cost of collecting extraneous protocol data.

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.

Treatment costs associated with interventional cancer clinical trials conducted at a single UK institution over 2 years (2009-2010).

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.

Proposal for a "phase II" multicenter trial model for preclinical new antiepilepsy therapy development.

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.

Fool's gold, lost treasures, and the randomized clinical trial.

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?"