Therapeutic innovation & regulatory science最新文献

The placebo lead-in design is commonly utilized in psychiatric clinical trials to mitigate high placebo response rates; conventional methods for estimating treatment effects typically rely solely on data from the second period, disregarding the enrichment process integral to this design. This limitation can result in an incomplete assessment of the true treatment effect in the intended target population, as desired in real-world clinical settings. To overcome this limitation, a novel adjusted estimator is proposed, leveraging the probability structure of the placebo lead-in design to provide a more accurate estimation of the treatment effect for the target population. When a treatment effect exists, the traditional estimator often tends to overestimate the effect. In contrast, the adjusted estimator delivers a more reliable estimate, particularly when the proportion of non-responders during the placebo lead-in period is sufficiently high (e.g., above 70%). A case study is included to illustrate the analysis approach and result interpretation. Furthermore, actionable recommendations are provided to support the effective implementation of the placebo lead-in design.

Medical devices used in health care should fulfill the requirements of the technical regulations to protect patient health. Difficulties in enforcing stricter rules in the new medical device regulations may negatively affect the continuity of care. This study examines the status of manufacturers' compliance with medical device regulations, based on predefined criteria, and proposes a collaborative action plan and an approach to verify regulatory compliance. We conducted a nationwide survey comprising questions grouped by criteria to understand the status of the manufacturers in terms of compliance with the Medical Device Regulation. Four hundred sixty-seven manufacturers participated in the survey. We achieved a Cronbach's alpha of 0.77, which indicates that the survey is statistically reliable. We applied the independent samples t-test to the responses to determine significant features per question and employed factor analysis to investigate the relationships of the questions. The results of independent samples t-tests showed statistically significant differences across groups in replies to several survey items (p < 0.05), indicating that participants' opinions varied based on their demographic characteristics. We applied Exploratory Factor Analysis to introduce the relationships between the questions. The analysis revealed that manufacturers continue to face substantial challenges in acquiring sufficient knowledge and operational capability to meet MDR requirements. In light of these findings, we focused on the person responsible for regulatory compliance, who plays a central role in maintaining regulatory compliance within manufacturing organizations. We proposed an action plan at the macro level to introduce more effective action plans in cooperation with other stakeholders, including healthcare providers, and a verification approach for regulatory compliance to enhance the Person Responsible for Regulatory Compliance's competence. Manufacturers should implement effective postmarketing clinical follow-up plans involving device-oriented parameters for monitoring in the healthcare system, especially in collaboration with health professionals.

Kidney diseases have been a highly challenging area for new drug development because of traditional requirements for reaching doubling of serum creatinine, dialysis, or transplantation endpoints for regulatory approval, which translates into clinical trials needing several years of follow up and large numbers of study participants to achieve adequate power. In recent years, however, progress in surrogate endpoints (specifically proteinuria reduction and slowing of estimated glomerular filtration rate decline in rare glomerular diseases) has resulted in greatly increased interest by biotechnology and pharmaceutical sponsors in investing in these indications.

Lord-Bessen et al. demonstrated that both single-item-per-screen and multiple-items-per-screen ePRO formats are acceptable, with minimal differences in usability and completion time. While format preference may be individual-specific, broader considerations-including patient cognitive load, device type, language complexity, and trial phase-are crucial for context-aware ePRO design. Future research should explore adaptive systems that dynamically adjust format in real time, subgroup analyses for older adults and low digital literacy participants, and language-specific validation. Moving beyond fixed formats toward adaptive, patient-tailored delivery can better align with regulatory priorities for patient-focused drug development, enhancing both participant experience and data integrity.

Background: Bristol Myers Squibb (BMS), in collaboration with ZS and Tufts CSDD, recently conducted a detailed evaluation of participation burden associated with clinical outcome assessments, including patient reported outcomes.

Methods: A mixed-methods approach was used including an online global survey followed by in-depth interviews with patients and investigative sites to understand underlying causes of participation burden.

Results: 258 patients completed the online survey, 12 interviews were conducted among patients, and 12 interviews were conducted among investigative site personnel. The results show significant differences in patient perceptions of participation burden depending on the modalities and types of clinical outcome assessments administered in clinical trials. Specific modalities associated with elevated burden included those longer than 30 min and those completed several times per month. Perceived burden of clinical outcome assessments varied significantly by patient age and ethnicity. Investigative sites also reported the burden associated with administering electronic clinical outcome assessments - most notably the technical challenges and additional patient assistance required during initial setup, first patient visit, and technology management across different sponsors.

Conclusion: The results of this study raise clinical team and protocol author awareness of the patient participation burden associated with distinct types and modalities of clinical outcome assessments and informs decisions to selectively reduce this burden. The results of this study build on the Tufts CSDD-ZS Patient Burden Algorithm.

Background: European legislation requires Marketing Authorization Holders (MAHs) to continuously monitor Eudravigilance (EV) data and inform the European Medicines Agency and national competent authorities of validated safety signals. The process follows the Good Pharmacovigilance Practice Module IX and is based on the review of individual case safety reports (ICSR) both from the MAH's internal database and from EV. The data is reviewed and evaluated by a medically trained person, who should, based on the information provided in the case, determine the causal relationship between the suspect drug and the reported event. In order to do this with a certain degree of confidence, the case needs to report enough high-quality information on the drug, patient and adverse event, without significant confounders.

Methods: For this purpose, we performed an evaluation of data quality of ICSRs within the Eudravigilance database, focusing on drug toxicity cases for five commonly implicated substances: paracetamol, diazepam, fentanyl, quetiapine, and fluoxetine. A medically driven review was conducted on 500 randomly selected ICSRs from 2015 to 2024. A detailed quality assessment framework was developed and applied, scoring cases across several criteria (including data on suspect drugs, patient demographics, adverse event description, time to event, and case narratives), resulting in a maximum quality score of 25.

Results: Main study findings revealed a generally low data quality, with an average score of 11.57 out of 25. Key quality deficiencies included improper classification of drugs as suspects (e.g., reporting concomitant medications or treatments as suspects), reporting of underlying diseases and indications as adverse events, lack of information on patients' medical history and missing time-to-event information. Cases from non-European Economic Area (non-EEA) countries and consumer-reported cases exhibited the lowest quality, while regulatory agency-reported cases were of higher quality. The study also identified a frequent misclassification of non-prescription or illicit substances (e.g., fentanyl) as prescription products, complicating signal detection and causality assessments. The analysis highlights a very important gap in pharmacovigilance signal detection and evaluation processes, underscoring risks for misleading results, increased workload, and potential misinterpretation of product safety profiles. The results highlight the need for enhanced case reporting trainings, improved quality control, better follow-up processes, and a collective mindset shift across stakeholders to prioritize data quality.

Conclusion: In conclusion, significant improvements in the completeness, accuracy, and clinical relevance of ICSRs are essential to support effective safety signal detection and benefit-risk assessment in the post-marketing surveillance of medicinal products.

Purpose: To identify and review scientific evidence from experimental studies utilizing unmanned aerial vehicles (UAVs) to transport samples for the diagnosis of COVID-19 and tuberculosis (TB). This exploratory study aims to support the future development of UAVs for transporting biological samples within the Brazilian Unified Health System (SUS).

Methods: This scoping review defined its eligibility criteria using the PECO acronym, focusing on: Population: biological samples for diagnosing COVID-19 or TB; Exposure: UAV transportation; Comparator: land transportation; Outcomes: Cost, effectiveness, methods for sample preservation, flight parameters (time, altitude, speed, distance), and quality of transported samples. Eligible studies were identified through searches in Medline via PubMed, Scopus, Embase, and Web of Science. Grey literature was explored via Google Scholar.

Results: Of the 2,052 articles initially found, 797 were duplicates, 1,247 were screened by title and abstract and excluded, eight were retrieved (and fully read) of which five met the eligibility criteria and were included in the review. These studies provided diverse evidence regarding cost, operational performance, safety, and sample integrity.

Conclusion: The reviewed studies demonstrate promising applications of UAVs in healthcare logistics. However, regulatory and legal frameworks require adaptation to ensure operational safety. Further experimental studies are necessary, particularly involving beyond visual line of sight (BVLOS) operations, to evaluate scalability and potential cost reductions.

Objective: This study explores the revolutionary potential of in-silico clinical trials (ISCTs) in medical device development, emphasizing the integration of computational modeling and simulation (CM&S), artificial intelligence (AI), and machine learning (ML). It evaluates regulatory advancements by the FDA, EMA, and PMDA, identifies barriers to global ISCTs adoption, and proposes strategies to enhance credibility, standardization, and ethical alignment.

Methods: A systematic review following PRISMA 2020 guidelines reviewed 72 studies (2014-2025) from Scopus, PubMed, Web of Science, and regulatory reports. Excluding non-regulatory or non-medical device research, inclusion criteria emphasized ISCTs technologies and regulatory frameworks.

Result: ISCTs employ CM&S techniques, including finite element analysis, computational fluid dynamics, and agent-based modeling, to simulate medical device performance and generate synthetic patient cohorts, thereby reducing costs and addressing ethical concerns. AI/ML further enhances predictive accuracy and optimizes trial design. Regulatory agencies have developed advanced frameworks like the FDA's model credibility and AI guidelines, the EMA promotes its 3R Guidelines, and the PMDA supports computational validation through dedicated subcommittees. Key challenges include regulatory fragmentation, limited data accessibility, computational complexity, and ethical risks such as algorithmic bias. Proposed solutions include global harmonization of regulatory guidelines, explainable AI implementation, federated learning adoption for secure data collaboration, and hybrid trial designs that integrate ISCTs with traditional methodologies.

Conclusion: ISCTs can revolutionize the development and assessment of medical devices. Standardized validation frameworks, regulatory standards, and interdisciplinary cooperation are required to address these issues. Clear guidelines must ensure ISCTs legitimacy and acceptance and promote safer and ethical medical innovations.

Internationally, medical regulators are seeking to make better use of real-world data (RWD) to support their decision making. While the UK National Health Service collects population-wide cradle-to-grave data, challenges remain around siloing, interoperability and access to data across different care settings. In 2023, a `Study-A-Thon' was held to explore how mobilisation of a UK distributed data network might be used to generate real-world evidence (RWE) for regulatory purposes by increasing availability of RWD in a timely manner. Two research questions focusing on high-priority data gaps (medical devices and secondary care prescribing) were selected as case studies to support this work. This paper summarises details of the Study-A-Thon and discusses key learnings for the UK's Medicines and Healthcare products Regulatory Agency (MHRA), UK stakeholders and international partners to reflect on when developing and implementing RWD strategies. Shortcomings of the data are discussed, such as a lack of follow-up for patients across care settings and the need to develop common data models to capture relevant information on medical product utilisation. The importance of local data and clinical expertise for success is highlighted, from encouraging better data collection at point of care through to appropriate interpretation of results. Successful delivery of results for both studies supports the view that, with further development, a UK federated data model could enhance national regulatory decision-making across the product lifecycle.

Background: Personalized cancer treatment using combination therapies offers substantial therapeutic benefits over single-agent treatments in most cancers. However, unmet clinical needs and increasing market competition pressure drug developers to quickly optimize combination doses and clearly demonstrate the contribution of each component when developing and evaluating new combination treatments.

Methods: We propose a Bayesian optimal phase II drug-combination (BOP2-Comb) design that optimizes the combination dose and evaluates the proof-of-concept as well as the contribution of each component in two seamless stages. Our optimal calibration scheme minimizes the total trial sample size while controlling incorrect decision rates at nominal levels. This calibration procedure is Monte Carlo simulation-free and provides a theoretical guarantee of false-positive control.

Results: We demonstrate the superior finite-sample operating characteristics of the proposed design through extensive simulations, achieving reduced sample sizes and improved control of both correct and incorrect decision rates compared to existing approaches. To illustrate its utility, we apply the BOP2-Comb design to redesign a real phase II trial evaluating the combination therapy of bevacizumab and lomustine.

Conclusions: The BOP2-Comb design provides a valuable framework for designing future randomized phase II trials of combination therapies, particularly when both dose optimization and assessment of component contributions are required.