Therapeutic innovation & regulatory science最新文献

Objectives: In compliance with legal requirements, medications are mandated to include a patient information leaflet (PIL) in a physical/paper format that serves as a vital source of information about the medication. While the use of an electronic PIL (ePIL) ensures the availability of the most current information for patients, the prevailing preference between the traditional PIL and ePIL remains uncertain. This observational study aimed to address this gap by reporting the findings of an online survey designed to compare patient preferences for an ePIL versus a paper PIL.

Methods: Conducted across a total of 15 pharmacies in Sweden, the survey enlisted patients via convenience sampling to rate their preference for an ePIL versus a PIL on a 7-point scale together with other questions on, for example, the ease of information retrieval after their visit to the pharmacy. Data collection commenced in December 2022 and ended in November 2023. The primary hypothesis posited that the ePIL format would emerge as the preferred choice. Most participants were female (66%), and the average age was 56 years.

Results: The findings highlighted an inclination towards the ePIL as the preferred choice (58% preferred ePIL, 11% preferred PIL, and 31% were indifferent), with respondents expressing that information retrieval was notably more convenient compared to the paper-based format. Older participants were generally indifferent towards the format. The majority of patients reported familiarity with receiving information in digital formats.

Conclusions: These results suggest the readiness of patients in Sweden to transition to ePILs from the traditional paper PIL.

Traditional Risk-Based Monitoring (RBM) strategies emphasise key risk indicators and site-level performance metrics but seldom address the heterogeneity of patient eligibility profiles. We present a data-driven framework that captures temporal and inter-site shifts in baseline inclusion characteristics. Central to this framework are two new metrics-Borderline Inclusion Index and Eligibility Distribution Divergence-that quantify departures from expected enrolment patterns. A Bayesian composite score synthesises these indicators to prioritise oversight actions. Through simulation experiments and a worked case study, we show that monitoring eligibility pattern shifts offers an early warning signal of operational or scientific risk and strengthens overall trial integrity. We operationalize the framework through an interactive Shiny web application that computes indicator-specific posteriors, generates composite site risk scores, and provides visual decision-support for centralized RBM implementation.

Following oral administration of a drug, we observe an absorption delay before the drug enters systemic circulation. This delay is attributed to the time taken for disintegration of the drug delivery system, drug dissolution from the delivery system, migration of the drug to the absorption site, and transfer of the drug through absorption site tissue. Additionally, pharmacokinetic profiles of such drugs frequently exhibit substantial variability among individuals from different sub-populations. In this article, we present a hierarchical transit compartment model that systematically describes absorption delay in orally administered drugs, and explicitly accounts for between sub-population variations. Such a transit compartment model can be regarded as an accurate representation of the underlying physiology, that also accounts for the influence of drug formulation and the physicochemical properties of the drug on the absorption process. Here, we specify non-informative priors on the pharmacokinetic parameters of interest, and develop an efficient computational scheme based on Hamiltonian Monte Carlo algorithm to conduct inference. The proposed joint inferential procedure ensures valid uncertainty quantification, and enables us to conduct simultaneous inference on sub-population specific as well as population parameters. The utility of the methodology is demonstrated through simulation studies, and two case studies.

There can be many challenges to conducting and completing clinical research trials, which may impact the successful and timely achievement of regulatory commitments. General difficulties can include a rare disease under study which limits the available study population, a relatively low number of interested study sites that have potential study participants, numerous required study procedures, invasive study procedures and, with regard to pediatric clinical trials, parents may be unwilling to provide consent for their children to participate. The utilization of externally sourced data as well as data collected and analyzed from internal previously completed clinical trials can be a useful and strategic method of supplementing clinical trial data that is needed to meet regulatory requirements. However, this strategy may not be widely known, understood or leveraged. The authors conducted a review of clinical trials and other types of regulatory commitments that successfully included external data and data that was collected and analyzed from previously completed studies, to meet regulatory requirements. It was concluded that the use of externally sourced data and data from other internally sponsored completed studies can offer a critical source of information, thereby adding robustness to regulatory submissions, particularly when there are gaps in clinical trials due to recruitment barriers and other challenges.

Introduction: There is widespread interest among patients, clinicians, regulators and other constituents in post-treatment patient-reported cancer data. Side effect bother is a patient-reported outcome (PRO) that can capture an important aspect of tolerability. In this study, we examined side effect bother at cancer treatment discontinuation and post-discontinuation in commercial cancer trials. We sought to understand completion rates, the extent of bother and its association with other PROs.

Materials and methods: Data were evaluated from three trials in patients with solid tumours (renal cell carcinoma and breast cancer). Side effect bother was measured with the Functional Assessment of Chronic Illness Therapy (FACIT) GP5 item. Symptom items were drawn from FACIT and function items were drawn from the EQ-5D-3L. FACIT items, including the GP5, are on a 0-4 scale (higher = worse symptoms/bother), and were dichotomised as 0-1 ("low") vs 2-4 ("moderate"). EQ-5D-3L items were characterised as no problems (1) and some problems (2-3). Descriptive and correlation analyses were conducted separately for each trial.

Results: Among patients who received treatment, completion rates at discontinuation for most items were at least 70%, and 52% to 78% at follow up. More than 20% of patients had high side effect bother at and after discontinuation, and similar percentages were seen for symptom items and functioning problems. GP5 and most items were at least somewhat correlated (≥ 0.2 in nearly all evaluations).

Discussion and conclusions: Persistent side effect bother and symptomatic and functional detriments at and after discontinuation suggest capturing this information post-treatment can inform understanding of tolerability, particularly with improved PRO completion.

In drug development, safety assessments must integrate data from heterogeneous sources including clinical trials, non-clinical toxicology, mechanistic evidence, and real-world evidence. At any given timepoint-such as protocol development, dose escalation, interim reviews, or regulatory safety reports-data may be incomplete, or contextually ambiguous. Teams often struggle to summarize the totality of evidence for a given safety topic in a way that is both scientifically rigorous and operationally consistent. There is a lack of a structured, reusable framework that enables critical appraisal and consistent communication of causality reasoning. Consequently, evaluations of safety information are frequently fragmented, logic trails get lost, and key conclusions may become difficult to defend or reproduce, particularly with changes in cross-functional teams or loss of institutional memory. Our framework recognizes and addresses these real-life operational challenges. We propose a structured application of the Bradford Hill Criteria (BHC) [1] that offers a "plug-and-play" architecture for organizing evidence from multiple sources consistently. The application of this framework standardizes the interpretation and also allows for the modular integration of new evidence over time. In order to achieve this, we offer a contemporary interpretation of the individual criteria in the context of safety assessments to facilitate their consistent application. Furthermore, teams are guided on mapping of data types-e.g., preclinical findings, spontaneous case reports, PK/PD data, or RWE-into a reproducible causality narrative that can be iteratively updated across the product lifecycle. Ultimately, this approach facilitates transparent communication of safety evaluations to stakeholders and supports informed decision-making in drug development and post-marketing surveillance. Practically, this structured approach can be used during safety planning [4] at critical milestones, including Investigational New Drug (IND) applications. In early clinical development, uncertainties are inevitable, and evolving safety signals must be evaluated rigorously and transparently in a comprehensive and consistent manner.

Obesity remains a critical global health challenge, with rising prevalence and a substantial cardiometabolic and psychosocial burden. Recent therapeutic advances, particularly in incretin-based strategies, underscore the need for a comprehensive characterization of the evolving pharmacological landscape. We conducted a systematic review of clinical trials registered on ClinicalTrials.gov between October 2019 and October 2024, focusing on pharmacological interventions for obesity from early exploratory stages to Phase 3. A total of 275 eligible trials were identified and analyzed. Incretin pathway modulators predominated (69.8%), especially GLP-1 receptor agonists and dual or triple agonists targeting GLP-1, GIP, and glucagon receptors. Most trials were in Phase 2 (40.7%) or Phase 3 (31.3%), indicating a maturing pipeline, while early-stage innovation remained limited (3.3% of trials). Drug repurposing was common (22.2%), notably involving semaglutide and liraglutide, originally approved for type 2 diabetes. Industry-sponsored trials constituted the majority (75.3%), with limited academic (22.2%) and governmental (2.5%) participation. Geographically, trials were concentrated in the United States, China, and Denmark, with relatively few international multicenter studies. While the obesity drug pipeline is expanding rapidly, it remains heavily centered on incretin-based therapies. This dominance, despite strong clinical efficacy, raises concerns regarding long-term safety, accessibility, and mechanistic diversity. Greater investment in early-phase innovation and alternative pharmacological targets will be essential to diversify treatment options and address the unmet need for equitable and sustainable obesity management.

Clinical trials for children with type II diabetes mellitus (T2DM) pose challenges often due to recruitment issues. The variability in the treatment effect for pediatrics with T2DM tends to be much larger than that for adults, therefore, a larger pediatric study is needed to independently detect a similar treatment effect. If leveraging adult information to a pediatric population can be appropriately justified, and scientific rational has been given for the relevancy of the adult information, then Bayesian borrowing methods can aid in reducing the number of patients needed for a pediatric study and therefore increase feasibility and efficiency. We introduce Bayesian borrowing methods to obtain scientifically sound and conclusive results with adequate study power in anti-diabetic products development for children with T2DM. To apply Bayesian borrowing methods, it is important to (1) identify the external data that can be leveraged, (2) pre-specify model parameters, (3) assess operating characteristics, and (4) pre-specify weights and the maximum amount of borrowing needed to achieve a study win. To protect against prior-data conflicts that may exist due to differences in T2DM between adults and pediatrics, we select a mixture prior to take an advantage that they adjust the degree of information borrowed based on the similarity between adults and pediatrics. We applied these methods to an anti-diabetic product. In conclusion, the outcomes of our step-by-step demonstration of the application of Bayesian borrowing methods provides a guide on how to pre-specify parameters and considerations that should be made when planning to implement said methods.