Early phase clinical trials in oncology: Realising the potential of seamless designs.

BACKGROUND: The pharmaceutical industry's productivity has been declining over the last two decades and high attrition rates and reduced regulatory approvals are being seen. The development of oncology drugs is particularly challenging with low rates of approval for novel treatments when compared with other therapeutic areas. Reliably establishing the potential of novel treatment and the corresponding optimal dosage is a key component to ensure efficient overall development. A growing interest lies in terminating developments of poor treatments quickly while enabling accelerated development for highly promising interventions. METHODS: One approach to reliably establish the optimal dosage and the potential of a novel treatment and thereby improve efficiency in the drug development pathway is the use of novel statistical designs that make efficient use of the data collected. RESULTS: In this paper, we discuss different (seamless) strategies for early oncology development and illustrate their strengths and weaknesses through real trial examples. We provide some directions for good practices in early oncology development, discuss frequently seen missed opportunities for improved efficiency and some future opportunities that have yet to fully develop their potential in early oncology treatment development. DISCUSSION: Modern methods for dose-finding have the potential to shorten and improve dose-finding and only small changes to current approaches are required to realise this potential.

Some performance considerations when using multi-armed bandit algorithms in the presence of missing data.

When comparing the performance of multi-armed bandit algorithms, the potential impact of missing data is often overlooked. In practice, it also affects their implementation where the simplest approach to overcome this is to continue to sample according to the original bandit algorithm, ignoring missing outcomes. We investigate the impact on performance of this approach to deal with missing data for several bandit algorithms through an extensive simulation study assuming the rewards are missing at random. We focus on two-armed bandit algorithms with binary outcomes in the context of patient allocation for clinical trials with relatively small sample sizes. However, our results apply to other applications of bandit algorithms where missing data is expected to occur. We assess the resulting operating characteristics, including the expected reward. Different probabilities of missingness in both arms are considered. The key finding of our work is that when using the simplest strategy of ignoring missing data, the impact on the expected performance of multi-armed bandit strategies varies according to the way these strategies balance the exploration-exploitation trade-off. Algorithms that are geared towards exploration continue to assign samples to the arm with more missing responses (which being perceived as the arm with less observed information is deemed more appealing by the algorithm than it would otherwise be). In contrast, algorithms that are geared towards exploitation would rapidly assign a high value to samples from the arms with a current high mean irrespective of the level observations per arm. Furthermore, for algorithms focusing more on exploration, we illustrate that the problem of missing responses can be alleviated using a simple mean imputation approach.

Predictors of long-term employment among patients with cystic fibrosis undergoing lung transplantation.

AIMS OF THE STUDY: Lung transplantation is an established therapy in selected patients with advanced cystic fibrosis lung disease. Resumption of employment after lung transplantation is generally supported. In Switzerland, there are no data on long-term employment in people with cystic fibrosis undergoing lung transplantation. METHODS: In a single-centre, cross-sectional study at a Swiss university hospital, clinical data from lung transplant recipients with cystic fibrosis, covering the transplantation period from January 1996 to December 2016, were analysed retrospectively. The potential influence of pre-lung transplantation factors (age, sex, lung function, body mass index, six-minute walk test distance, lung transplantation wait list time, paid employment on the wait list, education, relationship status, housing situation) and post-lung transplantation factors (chronic allograft dysfunction [CLAD], dialysis, cancer diagnosis [except skin cancer]) on paid employment and work percentage after lung transplantation were investigated using mixed logistic and linear regression models. Descriptive analyses of paid employment were performed for various periods after lung transplantation (<1, 1–3, 3–5, 5–10, >10 years). Data are reported as odds ratios (ORs) or coefficients (β) with their 95% confidence intervals (CIs). RESULTS: Eighty-four subjects (46.4% female) with a mean ± SD age of 29.9 ± 8.4 years were included in the study. Mean wait time for lung transplantation was 42.7 ± 40.2 weeks. The number (percentage) of subjects employed <1 year, 1–3 years, 3–5 years, 5–10 years and >10 years after lung transplantation was n = 23 (28%), n = 51 (65%), n = 44 (75%), n = 30 (68%) and n = 21 (75%), respectively. In mixed logistic regression models, pre-lung transplantation paid employment (OR 24.03, 95% CI 6.08 to 164.39, p <0.0001), academic education (OR 7.81, 95% CI 1.66 to 48.66, p = 0.01) and time post lung transplantation (on log scale, OR 5.81, 95% CI 3.15 to 12.78, p <0.0001) were the main factors influencing post-lung transplantation paid employment status. In mixed linear regression models, pre-lung transplantation paid employment (β = 21.40, 95% CI 10.98 to 31.81, p = 0.00014), academic education (β = 12.54, 95% CI 0.48 to 24.55, p = 0.05) and time post lung transplantation (on log scale, β = 8.96, 95% CI 6.17 to 11.82, p <0.0001) were the main factors influencing work percentage post lung transplantation. No evidence for an influence of clinical factors such as CLAD, cancer or dialysis on post-lung transplantation employment and work percentage was found. CONCLUSION: Pre-transplant employment is the dominant factor influencing lung transplantation employment in people with cystic fibrosis. People with cystic fibrosis undergoing lung transplantation should be encouraged to work for as long as their health status permits. Professional reintegration after successful lung transplantation should be supported by a multi-disciplinary lung transplant team.

Characterization of pharmacokinetic profiles and metabolic pathways of 20(S)-ginsenoside Rh1 in vivo and in vitro.

20(S)-Ginsenoside Rh1 is one of the important protopanaxatriol ginsenosides and has been reported to be the main hydrolysis product reaching the systemic circulation after oral ingestion of ginseng. However, its pharmacokinetic characteristics and metabolic fate have never been reported. The present study was therefore designed to elucidate its pharmacokinetic profiles and metabolic pathways both in vivo and in vitro. The absolute bioavailability of 20(S)-ginsenoside Rh1 in rats was only 1.01 %. Identification of metabolites showed that, after intragastrical administration of ginsenoside Rh1, two mono-oxygenated metabolites were detected from the urine, bile, liver tissue, and intestinal tract content, while the de-glucosylated product, 20(S)-protopanaxatriol, was only found in the contents of the intestinal tract. An in vitro incubation study confirmed that the CYP450-catalyzed mono-oxygenation, the intestinal bacteria mediated de-glucosylation, and the gastric acid mediated hydration reaction were the main metabolic pathways of 20(S)-ginsenoside Rh1. The presystemic metabolism as evidenced from this study may partially explain its poor bioavailability.