Simon Wandel

MEK162 for patients with advanced melanoma harbouring NRAS or Val600 BRAF mutations: a non-randomised, open-label phase 2 study

BACKGROUND Patients with melanoma harbouring Val600 BRAF mutations benefit from treatment with BRAF inhibitors. However, no targeted treatments exist for patients with BRAF wild-type tumours, including those with NRAS mutations. We aimed to assess the use of MEK162, a small-molecule MEK1/2 inhibitor, in patients with NRAS-mutated or Val600 BRAF-mutated advanced melanoma. METHODS In our open-label, non-randomised, phase 2 study, we assigned patients with NRAS-mutated or BRAF-mutated advanced melanoma to one of three treatment arms on the basis of mutation status. Patients were enrolled at university hospitals or private cancer centres in Europe and the USA. The three arms were: twice-daily MEK162 45 mg for NRAS-mutated melanoma, twice-daily MEK162 45 mg for BRAF-mutated melanoma, and twice-daily MEK162 60 mg for BRAF-mutated melanoma. Previous treatment with BRAF inhibitors was permitted, but previous MEK inhibitor therapy was not allowed. The primary endpoint was the proportion of patients who had an objective response (ie, a complete response or confirmed partial response). We report data for the 45 mg groups. We assessed clinical activity in all patients who received at least one dose of MEK162 and in patients assessable for response (with two available CT scans). This study is registered with ClinicalTrials.gov, number NCT01320085, and is currently recruiting additional patients with NRAS mutations (based on a protocol amendment). FINDINGS Between March 31, 2011, and Jan 17, 2012, we enrolled 71 patients who received at least one dose of MEK162 45 mg. By Feb 29, 2012 (data cutoff), median follow-up was 3·3 months (range 0·6-8·7; IQR 2·2-5·0). No patients had a complete response. Six (20%) of 30 patients with NRAS-mutated melanoma had a partial response (three confirmed) as did eight (20%) of 41 patients with BRAF-mutated melanoma (two confirmed). The most frequent adverse events were acneiform dermatitis (18 [60%] patients with NRAS -mutated melanoma and 15 [37%] patients with the BRAF-mutated melanoma), rash (six [20%] and 16 [39%]), peripheral oedema (ten [33%] and 14 [34%]), facial oedema (nine [30%] and seven [17%]), diarrhoea (eight [27%] and 15 [37%]), and creatine phosphokinase increases (11 [37%] and nine [22%]). Increased creatine phosphokinase was the most common grade 3-4 adverse event (seven [23%] and seven [17%]). Four patients had serious adverse events (two per arm), which included diarrhoea, dehydration, acneiform dermatitis, general physical deterioration, irregular heart rate, malaise, and small intestinal perforation. No deaths occurred from treatment-related causes. INTERPRETATION To our knowledge, MEK162 is the first targeted therapy to show activity in patients with NRAS -mutated melanoma and might offer a new option for a cancer with few effective treatments. FUNDING Novartis Pharmaceuticals.

Meta-analysis of few small studies in orphan diseases

Meta‐analyses in orphan diseases and small populations generally face particular problems, including small numbers of studies, small study sizes and heterogeneity of results. However, the heterogeneity is difficult to estimate if only very few studies are included. Motivated by a systematic review in immunosuppression following liver transplantation in children, we investigate the properties of a range of commonly used frequentist and Bayesian procedures in simulation studies. Furthermore, the consequences for interval estimation of the common treatment effect in random‐effects meta‐analysis are assessed. The Bayesian credibility intervals using weakly informative priors for the between‐trial heterogeneity exhibited coverage probabilities in excess of the nominal level for a range of scenarios considered. However, they tended to be shorter than those obtained by the Knapp–Hartung method, which were also conservative. In contrast, methods based on normal quantiles exhibited coverages well below the nominal levels in many scenarios. With very few studies, the performance of the Bayesian credibility intervals is of course sensitive to the specification of the prior for the between‐trial heterogeneity. In conclusion, the use of weakly informative priors as exemplified by half‐normal priors (with a scale of 0.5 or 1.0) for log odds ratios is recommended for applications in rare diseases.

Meta-analysis of two studies in the presence of heterogeneity with applications in rare diseases.

Random-effects meta-analyses are used to combine evidence of treatment effects from multiple studies. Since treatment effects may vary across trials due to differences in study characteristics, heterogeneity in treatment effects between studies must be accounted for to achieve valid inference. The standard model for random-effects meta-analysis assumes approximately normal effect estimates and a normal random-effects model. However, standard methods based on this model ignore the uncertainty in estimating the between-trial heterogeneity. In the special setting of only two studies and in the presence of heterogeneity, we investigate here alternatives such as the Hartung-Knapp-Sidik-Jonkman method (HKSJ), the modified Knapp-Hartung method (mKH, a variation of the HKSJ method) and Bayesian random-effects meta-analyses with priors covering plausible heterogeneity values; R code to reproduce the examples is presented in an appendix. The properties of these methods are assessed by applying them to five examples from various rare diseases and by a simulation study. Whereas the standard method based on normal quantiles has poor coverage, the HKSJ and mKH generally lead to very long, and therefore inconclusive, confidence intervals. The Bayesian intervals on the whole show satisfying properties and offer a reasonable compromise between these two extremes.

The network meta-analytic-predictive approach to non-inferiority trials

In non-inferiority clinical trials, a test treatment is compared to an active-control rather than to placebo. Such designs are considered when placebo is unethical or not feasible. The critical question is whether the test treatment would have been superior to placebo, had placebo been used in the non-inferiority trial. This question can only be addressed indirectly, based on information from relevant historical trials with data on active-control and placebo. The network meta-analytic-predictive approach to non-inferiority trials is based on a network meta-analysis of the data from the historical trials and the non-inferiority trial, and the prediction of the putative test vs. placebo effect in the non-inferiority trial. The approach extends previous work by incorporating between-trial variability for all relevant parameters and focusing on the parameters in the non-inferiority trial rather than on population means. Two prominent examples with binary outcomes are used to illustrate the approach.

Robust exchangeability designs for early phase clinical trials with multiple strata.

Clinical trials with multiple strata are increasingly used in drug development. They may sometimes be the only option to study a new treatment, for example in small populations and rare diseases. In early phase trials, where data are often sparse, good statistical inference and subsequent decision-making can be challenging. Inferences from simple pooling or stratification are known to be inferior to hierarchical modeling methods, which build on exchangeable strata parameters and allow borrowing information across strata. However, the standard exchangeability (EX) assumption bears the risk of too much shrinkage and excessive borrowing for extreme strata. We propose the exchangeability-nonexchangeability (EXNEX) approach as a robust mixture extension of the standard EX approach. It allows each stratum-specific parameter to be exchangeable with other similar strata parameters or nonexchangeable with any of them. While EXNEX computations can be performed easily with standard Bayesian software, model specifications and prior distributions are more demanding and require a good understanding of the context. Two case studies from phases I and II (with three and four strata) show promising results for EXNEX. Data scenarios reveal tempered degrees of borrowing for extreme strata, and frequentist operating characteristics perform well for estimation (bias, mean-squared error) and testing (less type-I error inflation).

Renin angiotensin system inhibitors for patients with stable coronary artery disease without heart failure: systematic review and meta-analysis of randomized trials

Objective To critically evaluate the efficacy of renin angiotensin system inhibitors (RASi) in patients with coronary artery disease without heart failure, compared with active controls or placebo. Design Meta-analysis of randomized trials. Data sources PubMed, EMBASE, and CENTRAL databases until 1 May 2016. Eligibility criteria for selecting studies Randomized trials of RASi versus placebo or active controls in patients with stable coronary artery disease without heart failure (defined as left ventricular ejection fraction ≥40% or without clinical heart failure). Each trial had to enroll at least 100 patients with coronary artery disease without heart failure, with at least one year’s follow-up. Studies were excluded if they were redacted or compared use of angiotensin converting enzyme inhibitors with angiotensin receptor blockers. Outcomes were death, cardiovascular death, myocardial infarction, angina, stroke, heart failure, revascularization, incident diabetes, and drug withdrawal due to adverse effects. Results 24 trials with 198 275 patient years of follow-up were included. RASi reduced the risk of all cause mortality (rate ratio 0.84, 95% confidence interval 0.72 to 0.98), cardiovascular mortality (0.74, 0.59 to 0.94), myocardial infarction (0.82, 0.76 to 0.88), stroke (0.79, 0.70 to 0.89), angina, heart failure, and revascularization when compared with placebo but not when compared with active controls (all cause mortality, 1.05, 0.94 to 1.17; Pinteraction=0.006; cardiovascular mortality, 1.08, 0.93 to 1.25, Pinteraction<0.001; myocardial infarction, 0.99, 0.87 to 1.12, Pinteraction=0.01; stroke, 1.10, 0.93 to 1.31; Pinteraction=0.002). Bayesian meta-regression analysis showed that the effect of RASi when compared with placebo on all cause mortality and cardiovascular mortality was dependent on the control event rate, such that RASi was only beneficial in trials with high control event rates (>14.10 deaths and >7.65 cardiovascular deaths per 1000 patient years) but not in those with low control event rates. Conclusions In patients with stable coronary artery disease without heart failure, RASi reduced cardiovascular events and death only when compared with placebo but not when compared with active controls. Even among placebo controlled trials in this study, the benefit of RASi was mainly seen in trials with higher control event rates but not in those with lower control event rates. Evidence does not support a preferred status of RASi over other active controls.

Using phase II data for the analysis of phase III studies: An application in rare diseases.

Background: Clinical research and drug development in orphan diseases are challenging, since large-scale randomized studies are difficult to conduct. Formally synthesizing the evidence is therefore of great value, yet this is rarely done in the drug-approval process. Phase III designs that make better use of phase II data can facilitate drug development in orphan diseases. Methods: A Bayesian meta-analytic approach is used to inform the phase III study with phase II data. It is particularly attractive, since uncertainty of between-trial heterogeneity can be dealt with probabilistically, which is critical if the number of studies is small. Furthermore, it allows quantifying and discounting the phase II data through the predictive distribution relevant for phase III. A phase III design is proposed which uses the phase II data and considers approval based on a phase III interim analysis. The design is illustrated with a non-inferiority case study from a Food and Drug Administration approval in herpetic keratitis (an orphan disease). Design operating characteristics are compared to those of a traditional design, which ignores the phase II data. Results: An analysis of the phase II data reveals good but insufficient evidence for non-inferiority, highlighting the need for a phase III study. For the phase III study supported by phase II data, the interim analysis is based on half of the patients. For this design, the meta-analytic interim results are conclusive and would justify approval. In contrast, based on the phase III data only, interim results are inconclusive and require further evidence. Conclusion: To accelerate drug development for orphan diseases, innovative study designs and appropriate methodology are needed. Taking advantage of randomized phase II data when analyzing phase III studies looks promising because the evidence from phase II supports informed decision-making. The implementation of the Bayesian design is straightforward with public software such as R.

Designing and analysing clinical trials in mental health: an evidence synthesis approach

Objective When planning a clinical study, evidence on the treatment effect is often available from previous studies. However, this evidence is mostly ignored for the analysis of the new study. This is unfortunate, since using it could lead to a smaller study without compromising power. We describe a design that addresses this issue. Methods We use a Bayesian meta-analytic model to incorporate the available evidence in the analysis of the new study. The shrinkage estimate for the new study integrates the evidence from the other studies. At the planning phase of the study, it allows a statistically justified reduction of the sample size. Results The design is illustrated using data from an Food and Drug Administration (FDA) review of lurasidone for the treatment of schizophrenia. Three studies inform the meta-analysis before the new study is conducted. Results from an additional phase III study, which were not available at the time of the FDA review, are then used for the actual analysis. Conclusions In the presence of reliable and relevant evidence, the design offers a way to conduct a smaller study without compromising power. It therefore fills a gap between the assessment of evidence and its actual use in the design and analysis of studies.