Probability Of Trial Success Research Articles

Does the precision oncology medicine development pathway deliver value for health systems?

e18818 Background: While there is increasing evidence that precision cancer medicines (PCMs) can be more effective than “one-size-fits-all” (OSFA) oncology medicines, questions persist as to their cost, availability, and overall patient benefit. We determined whether using a PCM approach to medicines discovery/development delivers medicines within cost constraints of health systems. R&D costs of developing a medicine are a subject of debate, with average estimates ranging from $314 million (M) - $2.8 billion (B). Methods: Data on oncology medicines approved (2011-2020) by the US Food and Drug Administration (FDA) were analyzed, employing two approaches to estimate R&D costs to bring the medicine to market: (i) Securities and Exchange Commission (SEC) data were compiled from 10-K and 10-Q financial performance filings on the medicine’s development cost through its R&D lifetime. (ii) Employing a base figure of $100,271 (the estimated cost per patient in a phase III clinical trial), costs were calculated, based on data from clinicaltrials.gov. Trial data were split into clinical trial phases and probability of trial success calculated, along with preclinical costs. Cost-of-capital (CoC) approach was applied and if appropriate an orphan drug tax credit (ODTC) was subtracted from the total. Results: 19 PCMs and 11 OSFA oncology drugs listed by the National Cancer Institute which had complete data were analyzed from 22 companies (Table). Initial costs from SEC data suggest a 50% reduced development spend for PCM, ($605M; range = $181M-$1.1B) (Clinical Trial Modelling Analysis: $604M ($63M-$2.29B)), compared to an OSFA oncology drug development spend ($877M; $333M-$3.34B) (Modelling Analysis: $597M ($41M-$1.64B)). Once preclinical costs were added total R&D costs capitalized and ODTC subtracted, there was an 83% increase in costs, from $2.44B ($614M-$5.23B) (Modelling Analysis: $3.1B ($209M-$11.52 B)) for PCMs to $4.46B ($1.59-$14.9B) (Modelling Analysis: $3.92B ($282-$12.74B) for OSFA oncology drugs. SEC data are more accurate, as they capture real data, rather than modelling assumptions. Conclusions: Our results provide an estimate of the R&D spend required to bring an oncology medicine to market. R&D cost of a PCM is below the top range of prior estimates published, while R&D cost of an OSFA oncology medicine is almost twice the cost of a PCM, emphasizing the potential for PCM treatments to deliver care at greater value to patients and health systems.[Table: see text]

Biomarker-Defined Subgroup Selection Adaptive Design for Phase III Confirmatory Trial with Time-to-Event Data: Comparing Group Sequential and Various Adaptive Enrichment Designs

Predictive and prognostic biomarkers play an important role in personalized medicine to determine strategies for drug evaluation and treatment selection. In the context of continuous biomarkers, identification of an optimal cutoff for patient selection can be challenging due to limited information on biomarker predictive value, the biomarker’s distribution in the intended use population, and the complexity of the biomarker relationship to clinical outcomes. As a result, prespecified candidate cutoffs may be rationalized based on biological and practical considerations. In this context, adaptive enrichment designs have been proposed with interim decision rules to select a biomarker-defined subpopulation to optimize study performance. With a group sequential design as a reference, the performance of several proposed adaptive designs are evaluated and compared under various scenarios (e.g., sample size, study power, enrichment effects) where type I error rates are well controlled through closed testing procedures and where subpopulation selections are based upon the predictive probability of trial success. It is found that when the treatment is more effective in a subpopulation, these adaptive designs can improve study power substantially. Furthermore, we identified one adaptive design to have generally higher study power than the other designs under various scenarios.

Ignorance is not bliss: Statistical power is not probability of trial success.

The purpose of this commentary is to place probability of trial success, or assurance, in the context of decision making in drug development, and to illustrate its properties in an intuitive manner for the readers of Clinical Pharmacology and Therapeutics. The hope is that this will stimulate a dialog on how assurance should be incorporated into a quantitative decision approach for clinical development and trial design that uses all available information.

Vessel occlusion, penumbra, and reperfusion - translating theory to practice.

The management of ischemic stroke is at a critical juncture. Administration of intravenous tPA is currently restricted to within 4.5 h from stroke onset with several trials in longer time windows proving neutral (1, 2). Revascularization success with tPA in major vessel occlusion is widely recognized as suboptimal (3). Alternative thrombolytic agents with theoretical efficacy advantages such as tenecteplase and desmoteplase are yet to show benefit in phase 3 trials. The promise of endovascular therapy has also yet to translate into positive randomized trials (4–6), although a new generation of devices is currently being studied. While it is possible that these therapeutic approaches are simply ineffective, the heterogeneity of stroke pathophysiology is likely to be contributing to the neutral results we often observe. Imaging selection has been proposed as a means of reducing heterogeneity by identifying patients with the potential to benefit from revascularization and therefore enhancing the probability of success in trials of new therapies. However, whether it is sufficient to demonstrate an occluded artery as the target or to also require evidence of salvageable downstream tissue has been debated. The recent announcement of neutral results in DIAS 3 (7), a trial that compared desmoteplase versus placebo 3–9 h after stroke onset in patients with vessel occlusion, without reference to downstream tissue status other than what was visible on non-contrast CT, will no doubt further stimulate this discussion. It is, therefore, salient to consider the current methods to identify salvageable ischemic penumbra and the potential value of commonly used surrogates for clinical outcome, chiefly reperfusion, recanalization, and infarct growth.

Interrelationships among enrichment with diagnostics, probability of success and demonstration of effectiveness in clinical trials

Background: Prevalence of disease phenotype in clinical practice is often not given adequate importance during formulation, validation, and implementation of diagnostic tests in clinical research and development. After promising biomarkers have been identified as potential screening diagnostics, an important strategic question for optimal decision-making in clinical development of a therapeutic is when to choose an enrichment study design over the traditional all-comer randomized control trial design. Methods: A hypothetical example of a cholesterol lowering treatment is used to illustrate influences of key statistical criteria and clinical considerations for choosing study designs. Computer simulations demonstrate how results of such analyses can aid in deciding whether or not to choose enrichment study designs. Results: This study shows how understanding of disease prevalence in practice, predictive values of diagnostic test, and prespecified establishment of a clinically meaningful minimum effectiveness all need to be integrated to insure clinical trial success and appropriate benefit to targeted patient subgroups. The most important statistical and clinical considerations were the anticipated effect size, phenotype prevalence, predictive values of diagnostics test, study power, and desired clinically meaningful difference. Conclusions: This study illustrates how successful clinical studies can be designed with careful planning and utilization of computer simulations to increase not only the probability of trial success but also to demonstrate to payers convincing evidence of clinical effectiveness. A six-step checklist is recommended as an evidence-based guideline to assist in decision-making on whether or not to adopt a diagnostics enriched clinical study design.

Doubly Randomized Delayed-Start Design for Enrichment Studies with Responders or Nonresponders

High placebo response has been a major source of bias and is difficult to deal with in many central nervous system (CNS) clinical trials. This bias has led to a high failure rate in mood disorder trials even with known effective drugs. For cancer trials, the traditional parallel group design biases the inference on the maintenance effect of the new drug with the traditional time-to-treatment failure analysis. To minimize bias, we propose a doubly randomized delayed-start design for clinical trials with enrichment. The design consists of two periods. In the first period, patients can be randomized to receive several doses of a new drug or a control. In the second period, control patients of the first period of an enriched population can be rerandomized to receive the same or fewer doses of the new drug or to continue on the control. Depending on the clinical needs, different randomization ratios can be applied to the two periods. The essential feature is that the design is naturally adaptive because of the randomization for the second period. As a result, other aspects of the second period, such as the sample size, can be modified adaptively when an interim analysis is set up for the first period. At the end of the trial, response data from both randomizations are combined in an integrated analysis. Because of the enrichment in the second period, the design increases the probability of trial success and, in addition, reduces the required sample size. Thus, for clinical development, the design offers greater efficiency.

The use of group sequential, information-based sample size re-estimation in the design of the PRIMO study of chronic kidney disease

Chronic kidney disease is associated with a marked increase in risk for left ventricular hypertrophy and cardiovascular mortality compared with the general population. Therapy with vitamin D receptor activators has been linked with reduced mortality in chronic kidney disease and an improvement in left ventricular hypertrophy in animal studies. PRIMO (Paricalcitol capsules benefits in Renal failure Induced cardia MOrbidity) is a multinational, multicenter randomized controlled trial to assess the effects of paricalcitol (a selective vitamin D receptor activator) on mild to moderate left ventricular hypertrophy in patients with chronic kidney disease. Subjects with mild-moderate chronic kidney disease are randomized to paricalcitol or placebo after confirming left ventricular hypertrophy using a cardiac echocardiogram. Cardiac magnetic resonance imaging is then used to assess left ventricular mass index at baseline, 24 and 48 weeks, which is the primary efficacy endpoint of the study. Because of limited prior data to estimate sample size, a maximum information group sequential design with sample size re-estimation is implemented to allow sample size adjustment based on the nuisance parameter estimated using the interim data. An interim efficacy analysis is planned at a pre-specified time point conditioned on the status of enrollment. The decision to increase sample size depends on the observed treatment effect. A repeated measures analysis model, using available data at Week 24 and 48 with a backup model of an ANCOVA analyzing change from baseline to the final nonmissing observation, are pre-specified to evaluate the treatment effect. Gamma-family of spending function is employed to control family-wise Type I error rate as stopping for success is planned in the interim efficacy analysis. If enrollment is slower than anticipated, the smaller sample size used in the interim efficacy analysis and the greater percent of missing week 48 data might decrease the parameter estimation accuracy, either for the nuisance parameter or for the treatment effect, which might in turn affect the interim decision-making. The application of combining a group sequential design with a sample-size re-estimation in clinical trial design has the potential to improve efficiency and to increase the probability of trial success while ensuring integrity of the study.