阿斯利康

The adoption of Continuous Manufacturing (CM) for Oral Solid Dosages (OSD) is often challenged by the limited sensitivity of traditional Process Analytical Technology (PAT), such as Near-infrared (NIR) and Raman spectroscopy, to provide sufficient accuracy for process monitoring and control of low-dose or fixed-dose formulations. This manuscript explores solutions by highlighting advanced control strategies and alternative manufacturing technologies. These strategies include enhanced spectroscopic methods (e.g., Spatially resolved-NIRS, Light-induced fluorescence) to provide improved accuracy/precision, the use of process data and process models (Residence Time Distribution, Multivariate Statistical Process Control) as soft sensors, hybrid PAT and process models and more traditional at-line/off-line monitoring using NIR, Raman or high-sensitivity liquid chromatography with stratified sampling and bracketing. Alternatively, several technologies inherently ensure high content uniformity, such as semi-Continuous Manufacturing (sCM) with accurate mini-batch dispensing and Dry Coating Technology. For Twin-Screw Hot Melt Extrusion (HME) molecular-level mixing delivers more uniform blends, but current low-dose applications still require pre-blending of the drug substance with suitable excipients. When fed with a uniform powder blend, twin screw wet granulation also ensures compliant content uniformity without the need for PAT monitoring. In conclusion, a successful CM of low dose products may be possible when strategically combining advanced spectral and data approaches, modelling, and innovative platforms to build robust and validated process controls. This has been demonstrated across multiple peer reviewed studies and is now gradually being incorporated into control strategies for the commercial manufacture of pharmaceutical products.

In meta-analysis, the conventional random-effects model (REM) is commonly used when heterogeneity is thought probable a priori. However, there are no standard tests of the adequacy of this model. To address this issue, we propose a new third-order Cochran statistic. Our new J index, and associated test, measures departures from the REM, analogous to the way that Cochran's heterogeneity statistic and the I2 index are used to quantify departures from the fixed effect model. We also introduce a conceptually simple plot that is useful in showing the presence of unusual features of outcome data, that may cast doubt on conventional modelling approaches. A measure of the diversity of study sizes is also introduced. The performance of the proposed methodology is explored in a simulation study and illustrated by analysing some well-travelled datasets. Our proposals can be used as descriptive statistics, to convey the adequacy of conventional statistical methodologies, or help determine model choice.

Individuals who are immunocompromised (IC) with COVID-19 have consistently been associated with worse clinical outcomes than individuals who are non-immunocompromised (non-IC). The aim of this study was to describe and compare in-hospital outcomes between individuals who are IC and non-IC hospitalised with COVID-19, with a particular focus on mortality across periods defined by the predominant circulating SARS-CoV-2 variants. This is an observational, retrospective, descriptive study based on a secondary database from Brazil's Unified Health System. Patients hospitalised with laboratory-confirmed COVID-19 were included. Descriptive statistics were used to summarize the cohort; categorical variables were expressed as frequencies and percentages, and continuous variables as mean ± standard deviation. Univariate logistic regression analyses were conducted to estimate the association between immunocompromised status and two key outcomes: in-hospital mortality and ICU admission, stratified by dominant SARS-CoV-2 variant. Results were reported as odds ratios (OR) with 95

PURPOSE:Immunotherapy targeting PD-L1 improves outcomes in patients with unresectable stage III non-small cell lung cancer (NSCLC) and no progression after definitive, concurrent chemoradiotherapy (cCRT). Earlier administration of immunotherapy, simultaneously with cCRT, may improve outcomes further. METHODS:Eligible patients were randomly assigned (2:1) to receive either durvalumab or placebo administered from the start of cCRT. Patients without progression after completing cCRT received consolidation durvalumab or placebo (per initial random assignment) until progression. The primary end point was progression-free survival (PFS) by blinded independent central review. Key secondary end points included objective response rate (ORR), overall survival (OS), the proportion of patients alive at 24 months (OS24), and safety. RESULTS:In total, 328 patients were randomly assigned to receive durvalumab (n = 219) or placebo (n = 109). There was no statistically significant difference with durvalumab versus placebo in PFS (hazard ratio [HR], 0.85 [95% CI, 0.65 to 1.12]; P = .247) or OS (HR, 1.03 [95% CI, 0.78 to 1.39]; P = .823); OS24 was 58.4% versus 59.5%, respectively. Confirmed ORR was 60.7% with durvalumab versus 60.6% with placebo (difference, 0.2% [95% CI, -15.2 to 16.3%]; P = .976). With durvalumab versus placebo, respectively, maximum grade 3 or 4 adverse events (AEs) occurred in 53.4% versus 59.3% of patients, pneumonitis or radiation pneumonitis (group term) in 28.8% (grade ≥3: 4.6%) versus 28.7% (grade ≥3: 5.6%), AEs leading to discontinuation of durvalumab or placebo in 25.6% versus 12.0%, and fatal AEs in 13.7% versus 10.2%. CONCLUSION:Among patients with unresectable stage III NSCLC, durvalumab administered from the start of cCRT failed to demonstrate additional benefit compared with cCRT plus placebo. Consolidation durvalumab following definitive cCRT remains the standard of care in this setting.

BACKGROUND:The evaluation of innovative oncology medicines presents significant challenges related to the selection of appropriate clinical endpoints and the sufficiency of evidence, particularly in therapeutic areas, such as immunotherapies, targeted treatments, single-arm trials, and tumor-agnostic therapies. In these contexts, the added value of new treatments is often not adequately captured by traditional clinical trial endpoints, such as overall survival (OS), which remains the gold standard in oncology assessment. This article aims to analyze the main sources of uncertainty in the value assessment of oncology therapies in Spain, with a focus on the limitations of current endpoints and evidence-generation processes, and to provide recommendations for enhancing the recognition and assessment of additional clinical benefit. METHODS:A multidisciplinary expert panel composed of twelve professionals, including medical oncologists, hospital pharmacists, health economists, and patient representatives, was convened to identify and discuss key sources of uncertainty in the value assessment of oncology treatments, particularly those related to clinical endpoint selection and evidence generation. The panel participated in three structured plenary sessions. Additional external experts were engaged to provide complementary input in areas, such as tumor-specific characteristics and statistical methodology. Consensus statements were developed through an iterative process of discussion, critical appraisal, and refinement across and between sessions. RESULTS:The expert panel issued twelve recommendations to improve value assessment in oncology. These include tailoring clinical endpoints to treatment type, tumor characteristics, and stage; complementing overall survival with milestone analysis and quality-of-life measures; and standardizing real-world evidence collection across the healthcare system. The panel advocated for a national portfolio of prioritized endpoints, appropriate statistical methods by context, and the conditional use of early-phase data for decision-making. Additional recommendations addressed the use of synthetic control arms, flexible reimbursement models, advanced analytics (e.g., AI and Big Data), evaluator expertise, and the promotion of stakeholder training and transparency. CONCLUSIONS:Addressing the challenges of clinical endpoint selection and evidence generation is essential to reduce uncertainty in the value assessment of innovative oncology treatments. The twelve expert recommendations outlined in this study provide a structured roadmap to improve methodological consistency, enhance the relevance and robustness of clinical and real-world data, and promote a more adaptive and transparent evaluation framework. These proposals aim to support more evidence-based, equitable, and sustainable decision-making within the Spanish healthcare system, while aligning with broader European initiatives in oncology drug assessment.