Therapeutic innovation & regulatory science最新文献

During discussions at the Data Science Roundtable meeting in Japan, there were instances where the adoption of the BOIN design was declined, attributed to the extension of study duration and increased sample size in comparison to the 3 + 3 design. We introduce an accelerated BOIN design aimed at completing a clinical phase I trial at a pace comparable to the 3 + 3 design. Additionally, we introduce how we could have applied the BOIN design within our company, which predominantly utilized the 3 + 3 design for most of its clinical oncology dose escalation trials. The accelerated BOIN design is adaptable by using efficiently designated stopping criterion for the existing BOIN framework. Our approach is to terminate the dose escalation study if the number of evaluable patients treated at the current dose reaches 6 and the decision is to stay at the current dose for the next cohort of patients. In addition, for lower dosage levels, considering a cohort size smaller than 3 may be feasible when there are no safety concerns from non-clinical studies. We demonstrate the accelerated BOIN design using a case study and subsequently evaluate the performance of our proposed design through a simulation study. In the simulation study, the average difference in the percentage of correct MTD selection between the accelerated BOIN design and the standard BOIN design was - 2.43%, the average study duration and the average sample size of the accelerated BOIN design was reduced by 14.8 months and 9.22, respectively, compared with the standard BOIN design.

Post-approval changes (PACs) to the control and manufacturing processes of medicines and vaccines are routinely undertaken and critical to enable both innovation and secure sustained supply. In a world of global supply chains, the existence of divergent national PAC requirements (with additional countries introducing new requirements with potential differences) and other factors including document preparation and response timelines, can lead to long delays in approval (of up to 3-5 years) increasing the risk of disruption and shortages.We undertook an Industry survey in 2023 to assess implementation of ICH Q12, PAC procedures (change categorisation and review timelines) and use of reliance mechanisms across different countries (9 selected ICH Members and 19 Observers). Although this survey revealed limited implementation of Q12 in ICH Member countries, when comparing the data collected with those of a previous survey performed in 2020, we observed a broader adoption of risk-based approaches to variation categorisation (in all countries). This, however, was not reflected in improved timelines for approval.With regards to ICH Q12 adoption, the uptake of Post-Approval Change Management Protocols (PACMPs) was unchanged (with only one country reporting in-use) and implementation gaps were evident for Established Conditions (EC) and the Product Life Cycle Management document (PLCM). The survey found greater awareness of ICH Q12 and its tools compared to 2020, potentially illustrating the positive impact of training efforts. This illustrates the challenges being faced to broaden its implementation and use globally.In the same Industry survey, we also assessed PAC processes across different international countries. Long unpredictable timelines were the major concern across the countries surveyed together with limited capacity of the regulators. Four different CMC changes were selected and categorized by the respondents according to current knowledge of national classifications and timelines in the selected countries and compared with a reference classification and timeline from the European Medicines Agency and the World Health Organisation. This highlighted the lack of harmonisation of many countries with EU/WHO requirements, especially within the ICH Observer group.Last, this survey showed that some use of unilateral forms of reliance to Reference Authorities for PACs is starting. This is a mechanism all countries can employ, regardless of convergence of requirements and expertise, to enhance capacity building and reduce duplication of reviews, streamline variations approval, whilst accelerating patient access to innovation and securing supply.

Whereas AI/ML methods were considered experimental tools in clinical development for some time, nowadays they are widely available. However, stakeholders in the health care industry still need to answer the question which role these methods can realistically play and what standards should be adhered to. Clinical research in late-stage clinical development has particular requirements in terms of robustness, transparency and traceability. These standards should also be adhered to when applying AI/ML methods. Currently there is some formal regulatory guidance available, but this is more directed at settings where a device or medical software is investigated. Here we focus on the application of AI/ML methods in late-stage clinical drug development, i.e. in a setting where currently less guidance is available. This is done via first summarizing available regulatory guidance and work done by regulatory statisticians followed by the presentation of an industry application where the influence of extensive sets of baseline characteristics on the treatment effect can be investigated by applying ML-methods in a standardized manner with intuitive graphical displays leveraging explainable AI methods. The paper aims at stimulating discussions on the role such analyses can play in general rather than advocating for a particular AI/ML-method or indication where such methods could be meaningful.

The FDA published a final rule for Medical Devices; Laboratory Developed Tests in the Federal Register on May 6, 2024, which aims to ensure the safety and effectiveness of laboratory developed tests (LDTs) by amending current regulations. The rule also includes a policy to phase out the FDA's general enforcement discretion approach for LDTs, aligning them with other In Vitro Diagnostic Devices. Notably, direct-to-consumer (DTC) testing is exempt from this policy shift, as the FDA believes this category of tests has already met applicable requirements. This rule was first proposed in the Federal Register on October 3, 2023. The publication of this proposed rule sparked a considerable volume of public reactions during the comment period of the rule-making process, comprising general sentiment, key concerns, and suggestions. This commentary analyzes these concerns, particularly focusing on DTC tests, and offers recommendations, including reassessing the FDA's enforcement discretion for hybrid DTC tests, advocating for clear guidance on clinical oversight, and prioritizing a risk-based enforcement approach. Additionally, enhancing public education about the risks of DTC testing is crucial for safeguarding public health.

In multi-regional clinical trials, planning the sample size for participating regions is essential for the evaluation of the treatment effect consistency across regions. Based on the MRCT design and sample size allocation to regions, consistency probability is usually used to predict the consistent trend between regions and the overall population, while preserving a certain proportion of the overall treatment effect. Specific enrollment characteristics in a region of interest should also be considered during the time of the sample size planning. To facilitate efficient and harmonized regional sample size planning, we have developed RegionSizeR, a comprehensive and user-friendly interactive web-based R shiny application that can be obtained from https://github.com/rsr-ss/RegionSizeR . This simulation-based app can serve as an initial point for discussions on sample size allocation plans, following preservation of treatment effect method in ICH E17. The app accommodates various types of endpoints and designs, including continuous, binary, and time-to-event endpoints, for superiority, non-inferiority, and MCP-Mod designs. To ensure the validity of this app, independent testing is conducted allowing a discrepancy of no more than 1% across all results considering various scenarios.

Purpose: TransCelerate BioPharma surveyed its member biopharmaceutical companies to understand current practices and identify opportunities to complement safety signal assessment with rapid real-world data (RWD) analysis.

Methods: A voluntary 30-question questionnaire regarding the use of RWD in safety signal assessment was disseminated to subject matter experts at all TransCelerate member companies in July 2022. Responses were blinded, aggregated, summarized, and presented.

Results: Eighteen of 20 member companies provided responses to the questionnaire. Sixteen (89%) companies reported actively leveraging RWD in their signal assessment processes. Of 18 respondent companies, 8 (44%) routinely use rapid approaches to RWD analysis, 7 (39%) utilize rapid RWD analysis non-routinely or in a pilot setting, 2 (11%) are considering using rapid RWD analysis, and 1 (6%) has no plans to use rapid RWD analysis for their signal assessment. Most companies reported that RWD adds context to and improves quality of signal assessments. To conduct RWD analysis for signal assessment, 16 of 17 (94%) respondent companies utilize or plan to utilize internally available data, 8 (47%) utilize both internal and external data, and 3 (18%) utilize data networks. Respondents identified key challenges to rapidly performing RWD analyses, including data access/availability, time for analysis execution, and uncertainties regarding acceptance of minimal or non-protocolized approaches by health authorities.

Conclusion: Biopharmaceutical companies reported that they see value in the use of rapid RWD analyses for complementing signal assessments. Future work is recommended to offer a framework and process for use of rapid use of RWD analyses in signal assessment.

Background: Access to medical products of the required efficacy, quality and safety is essential for everyone's health and wellbeing. To achieve this milestone, every country needs a robust and strong performing National Regulatory Authority (NRA) that is independent and outcome oriented. With the help of the World Health Organization (WHO), the global benchmarking tool is the gold standard used to assess the regulatory capacity of NRAs.

Objectives: This study assessed the capacity of the National Medicines Regulatory Authority in Sierra Leone to perform its regulatory functions.

Methods: This descriptive cross-sectional study used both qualitative and quantitative approaches. A self-administered questionnaire was used for the quantitative approach, and the qualitative aspect consisted of a desk review looking at key regulatory documents such as laws, regulations, policies, guidelines, standard operating procedures and reports. The data collection tool used was the WHO global benchmarking tool (GBT) for "Evaluation of National Regulatory System of Medical Product Version VI.

Results: The majority of the participants had a postgraduate degree (60%), and 72% had over 10 years of experience working at the NRA. Out of 251 sub-indicators assessed, 85 (34%) sub-indicators were fully implemented. Of the eight (8) functions assessed, sub-indicators related to clinical trial oversight and vigilance were the most implemented, with 67% and 62%, respectively. Of the 9 indicators assessed, 79% of the sub-indicators that are related to quality and risk management were implemented. The results of this study showed that PBSL operates at maturity level 1. The absence of laws and regulations that give PBSL the mandate to perform its regulatory functions was a major challenge even though other indicators were met. The study reported other challenges toward effective functioning, including but not limited to a lack of sufficient staff, weak enforcement of the sale of medicines and a poorly equipped quality control laboratory.

Objectives: This manuscript presents a comprehensive framework for the assessment of the value of real-world evidence (RWE) in healthcare decision-making. While RWE has been proposed to overcome some limitations of traditional, one-off studies, no systematic framework exists to measure if RWE actually lowers the burden. This framework aims to fill that gap by providing conceptual approaches for evaluating the time and cost efficiencies of RWE, thus guiding strategic investments in RWE infrastructure.

Methods: The framework consists of four components: (114th Congress. 21st Century Cures Act.; 2015. https://www.congress.gov/114/plaws/publ255/PLAW-114publ255.pdf .) identification of stakeholders using and producing RWE, (National Health Council. Glossary of Patient Engagement Terms. Published 2019. Accessed May 18. 2021. https://nationalhealthcouncil.org/glossary-of-patient-engagement-terms/ .) understanding value propositions on how RWE can benefit stakeholders, (Center for Drug Evaluation and Research. CDER Patient-Focused Drug Development. U.S. Food & Drug Administration.) defining key performance indicators (KPIs), and (U.S. Department of Health and Human Services - Food and Drug Administration: Center for Devices and Radiological Health and Center for Biologics Evaluation and Research. Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices - Guidance for Industry and Food and Drug Administration Staff. 2017. http://www.fda.gov/BiologicsBloodVaccines/GuidanceComplianceRegulatoryInformation/Guida .) establishing metrics and case studies to assess value. KPIs are categorized as 'better, faster, or cheaper" as an indicator of value: better focusing on high-quality actionable evidence; 'faster,' denoting time-saving in evidence generation, and 'cheaper,' emphasizing cost-efficiency decision compared to methodologies that do not involve data routinely collected in clinical practice. Metrics and relevant case studies are tailored based on stakeholder value propositions and selected KPIs that can be used to assess what value has been created by using RWE compared to traditional evidence-generation approaches and comparing different RWE sources.

Results: Operationalized through metrics and case studies drawn from the literature, the value of RWE is documented as improving treatment effect heterogeneity evaluation, expanding medical product labels, and expediting post-market compliance. RWE is also shown to reduce the cost and time required to produce evidence compared to traditional one-off approaches. An original example of a metric that measures the time saved by RWE methods to detect a signal of a product failure was presented based on analysis of the National Cardiovascular Disease Registry.

Conclusions: The framework presented in this manuscript offers a comprehensive approach for evaluating the value of RWE, applicable to all sta

The increasing global drug shortage poses a substantial challenge to national healthcare systems and affects access to essential therapies. In Japan, this problem is exacerbated by a large-scale government campaign to switch from brand-name products to generic drugs and manufacturing/marketing authorization holders with poor development and manufacturing controls. Regulatory bodies, such as the U.S. Food and Drug Administration and the European Medicines Agency have developed guidelines aimed at ensuring continuous drug supply and mitigating manufacturing risks. However, Japan's efforts have primarily relied on voluntary industry guidelines, lacking the robust regulatory frameworks of other developed nations. Therefore, this study proposes a draft guideline for Japan's pharmaceutical industry to manage drug shortages effectively. The Japanese government needs to establish a framework system that will enable pharmaceutical companies to effectively maintain a stable supply based on the proposals developed in this study.