Pralsetinib in patients with RET fusion-positive non-small-cell lung cancer: A plain language summary of the ARROW study.

WHAT IS THIS SUMMARY ABOUT?: This is a summary of a research study called ARROW, which tested a medicine called pralsetinib in patients with non-small cell lung cancer (NSCLC), thyroid cancer, and other advanced solid tumours caused by a change in a gene called RET. For the purposes of this summary, only patients with NSCLC with a change in RET called fusion (RET fusion+) are highlighted. WHAT WERE THE RESULTS?: In total, 281 patients with RET fusion+ NSCLC had taken part in this study across the USA, Europe, and Asia. Patients were asked to take four pills (adding up to 400 mg) of pralsetinib each day and were checked for any changes in their tumours, as well as for any side effects. After an average of 8 months of treatment with pralsetinib, 72% of previously untreated patients and 59% of patients who had previously received chemotherapy had considerable shrinkage of their tumours. Among 10 patients with tumours which had spread to the brain (all of whom had received previous treatments), 70% had their tumours shrink greatly in the brain after treatment with pralsetinib. On average, patients lived with little to no tumour growth for 16 months. In previously untreated patients, the most common severe side effects that were considered related to pralsetinib treatment were decreased white blood cells (neutrophils and lymphocytes), increased blood pressure, and an increase in a blood protein called creatine phosphokinase. In previously treated patients, the severe side effects were decreased white blood cells (neutrophils, lymphocytes, and leukocytes), increased blood pressure, and low levels of red blood cells. In both untreated and previously treated patients, the most common severe side effects that required hospital attention were lung inflammation/swelling causing shortness of breath (pneumonitis) and lung infection (pneumonia). WHAT DO THE RESULTS MEAN?: Overall, the ARROW study showed that pralsetinib was effective in shrinking tumours in patients with RET fusion+ NSCLC regardless of previous treatment history. The recorded side effects were expected in patients receiving this type of medicine. Clinical Trial Registration: NCT03037385 (ARROW) (ClinicalTrials.gov).

Exposure-Response Relationships for Pralsetinib in Patients with RET-Altered Thyroid Cancer or RET Fusion-Positive Nonsmall Cell Lung Cancer.

Pralsetinib is a highly potent oral kinase inhibitor of oncogenic RET (rearranged during transfection) fusions and mutations. Pralsetinib received approval from the United States Food and Drug Administration for the treatment of patients with metastatic RET fusion-positive non-small cell lung cancer (NSCLC), and received accelerated approval for the treatment of patients with RET fusion-positive thyroid cancer. Exposure-response (ER) analyses of efficacy were performed separately in patients with thyroid cancer and in patients with NSCLC, but data for all patients were pooled for the safety analysis. ER models were developed with time-varying exposure; the effect of covariates was also examined. For patients with NSCLC, a higher starting dose was associated with improved progression-free survival (PFS), but this improvement did not correlate with a higher exposure overall. Significant covariates included sex and baseline Eastern Cooperative Oncology Group (ECOG) score. For patients with thyroid cancer, a higher exposure was associated with improved PFS. Significant covariates included prior systemic cancer therapy and ECOG score. For safety, higher exposure was associated with a greater risk of grade ≥3 anemia, pneumonia, and lymphopenia. Patients with an ECOG score of ≥1 had an increased risk of grade ≥3 pneumonia. Non-White patients had a lower risk of grade ≥3 lymphopenia. ER analysis revealed that higher pralsetinib exposure was associated with improved PFS in thyroid cancer, but not in NSCLC. However, a higher starting dose (ie, 400 vs ≤300 mg daily) was correlated with better PFS for all indications. Higher exposure was also associated with an increased risk of grade ≥3 adverse events (AEs); however, the overall incidence of these events was acceptably low (≤20%). This analysis supports the use of a 400 mg starting dose of pralsetinib, allowing for dose reduction in the event of AEs.

Statistical inferences under step stress partially accelerated life testing based on multiple censoring approaches using simulated and real-life engineering data.

Evaluating the lifespan distribution of highly reliable commodities under regular use is exceedingly difficult, time consuming, and extremely expensive. As a result of its ability to provide more failure data faster and at a lower experimental cost, accelerated life testing has become increasingly important in life testing studies. In this article, we concentrate on parametric inference for step stress partially life testing utilizing multiple censored data based on the Tampered Random Variable model. Under normal stress circumstances, the lifespan of the experimental units is assumed to follow the Nadarajah-Haghighi distribution, with and being the shape and scale parameters, respectively. Maximum likelihood estimates for model parameters and acceleration factor are developed using multiple censored data. We build asymptotic confidence intervals for the unknown parameters using the observed Fisher information matrix. To demonstrate the applicability of the different methodologies, an actual data set based on the timings of subsequent failures of consecutive air conditioning system failures for each member of a Boeing 720 jet aircraft fleet is investigated. Finally, thorough simulation studies utilizing various censoring strategies are performed to evaluate the estimate procedure performance. Several sample sizes were studied in order to investigate the finite sample features of the considered estimators. According to our numerical findings, the values of mean squared errors and average asymptotic confidence intervals lengths drop as sample size increases. Furthermore, when the censoring level is reduced, the considered estimates of the parameters approach their genuine values.

Parameter Estimation in Step Stress Partially Accelerated Life Testing under Different Types of Censored Data.

A long testing period is usually required for the life testing of high-reliability products or materials. It is possible to shorten the testing process by using ALTs (accelerated life tests). Due to the fact that ALTs test products in harsher settings than are typical use conditions, the life expectancy of the objects they evaluate is reduced. Censored data in which the specific failure timings of all units assigned to test are not known, or all units assigned to test have not failed, may arise in ALTs for a variety of reasons, including operational failure, device malfunction, expense, and time restrictions. In this paper, we have considered the step stress partially accelerated life test (SSPALT) under two different censoring schemes, namely the type-I progressive hybrid censoring scheme (type-I PHCS) and the type-II progressive censorship scheme (type-II PCS). The failure times of the items are assumed to follow NH distribution, while the tampered random variable (TRV) model is used to explain the effect of stress change. In order to obtain the estimates of the unknown parameters, the maximum likelihood estimation (MLE) approach is adopted. Furthermore, based on the asymptotic theory of MLEs, the approximate confidence intervals (ACIs) are also constructed. The point estimates under two censoring schemes are compared in terms of root mean squared errors (RMSEs) and relative absolute biases (RABs), while ACIs are compared in terms of their lengths and coverage probabilities (CPs). The performance of the estimators has been evaluated and compared under two censoring schemes with various sample sizes through a simulation study. Simulation results show that estimates with type-I PHCS outperform estimates with type-II PCS in terms of RMSEs, RABs, lengths, and CPs. Finally, a real-world numerical example of insulating fluid failure times is presented to show how the approaches will work in reality.