Therapeutic innovation & regulatory science最新文献

Background: Less participation by Japan in multi-regional clinical trials (MRCTs) is one of concerns that leads to drug loss in Japan, but the characteristics of Japan's participation in MRCTs have not been well studied.

Purpose: This study investigated Japan's situation in global drug development by characterizing its participation in MRCTs compared with East and South-East Asian countries/regions, with a focus on MRCTs sponsored by small-medium companies to discuss necessary measures in further promoting drug development in Japan.

Methods: Data from MRCTs conducted in East and South-East Asia during the period from January 1, 2013 to December 31, 2022 were analyzed.

Results: Japan's participation in MRCTs conducted in Asia (East Asia and South-East Asia) was limited. In particular, Japan participated in only 15-16% of MRCTs sponsored by Small/Medium-pharma (mainly US-based companies), with even less participation in Early Phase MRCTs. Japan's participation in MRCTs was markedly lower than other Asian countries/regions such as Singapore, South Korea, and Taiwan, although it was relatively higher in MRCTs that targeted neoplasms compared with other diseases.

Conclusion: Results of this study raise significant concern about future potential drug loss in Japan. It is urgent to increase the participation of Japan in MRCTs in order to continuously provide new globally developed drugs to patients in Japan. For that purpose, an integrated approach that includes continuous improvement in pharmaceutical regulations and the clinical trial environment, as well as market attractiveness, will be necessary in parallel with strengthening of collaborations between Japan and other Asian countries/regions.

The rapid advancement of cell and gene therapies (CGT) in the past ten years has inspired biopharmaceutical companies, biotechnologies, and nonprofits to tackle diseases that have traditionally been challenging to treat. Rare diseases, where roughly 80% have a genetic basis, have enjoyed this scrutiny, but the complexity of CGT trial design and implementation have proven challenging. This manuscript offers general guidance for CGT clinical development, current regulatory requirements and guidelines governed by FDA and EMA, considerations around preclinical development, safety monitoring and the need for long-term monitoring and follow up.

The ICH E9 (R1) Addendum provides a framework to define an estimand and perform sensitivity analysis. The clinical endpoint (i.e., variable, response, outcome) is one of the important estimand attributes. In our opinion, the selection of the endpoint in Alzheimer's disease requires more exploration beyond what is used currently. The change in a cognitive and functional assessment scale from baseline to a specific time point of interest is often used as a primary or key secondary endpoint in clinical trials. However, such a change from baseline to the time point of interest may not reflect the benefit of the treatment over the course of treatment duration and may be difficult to intuitively understand by patients and clinicians. For two patients with the same change from baseline, the patient with rapid disease progression in the beginning is considered to have overall worse quality of life compared to the other patient with slow disease progression in the beginning but rapid progression toward the end. We explore time-averaged measurement (TAM) as a new endpoint and propose using the relative change to quantify the treatment difference. Estimands under the ICH E9 (R1) Addendum were considered by using various strategies in handling intercurrent events and used corresponding methods for handling missing data. We illustrate the use of TAM and compare the results with other commonly used estimand endpoints (the change from baseline, the relative disease progression model, and the slope of disease progression) for different estimands and imputation methods from retrospective analyses of a historical study.

Purpose: Regulation (EU) 2017/745, the European Medical Device Regulation (MDR), raises clinical evidence requirements but lacks clarity on what constitutes "sufficient clinical evidence" for medium-risk, Class IIb non-implantable CE-marked devices. This research investigates whether a clinical evaluation of a newly developed Class IIb device can be conducted without a clinical investigation and explores the role of data from the same generic device group in clinical evaluations.

Methods: Expert interviews with notified body reviewers and a survey were conducted to assess the regulatory landscape and the appropriateness of non-clinical data.

Results: Findings reveal inconsistencies in the interpretation of MDR among notified bodies. While some reviewers accepted clinical evaluations based on non-clinical data, others required clinical or equivalent device data. The exclusion of data from the same generic device group under MDR complicates compliance and may impose unnecessary burdens on manufacturers, particularly for standard-of-care devices with well-documented safety profiles. Survey results indicate discrepancies in the role of non-clinical data, with notified bodies favouring standard-based bench testing while manufacturers and consultants advocate for advanced testing methodologies, such as in silico models. The study also highlights differing perspectives on the role of post-market clinical follow-up (PMCF) in clinical evaluations.

Conclusions: This research underscores the need for standardized guidance on clinical data requirements and the role of non-clinical evidence. Addressing these gaps is essential to balance patient safety with innovation and streamline the regulatory pathway for medium-risk medical devices, ensuring a more predictable and efficient approval process in the EU.

Major advances in biomedical science have transformed healthcare. However, system barriers to the appropriate, timely, and equitable use of biomedical innovations have led to slow and inconsistent adoption, limiting and delaying our ability to leverage their full potential to improve health. System barriers include inconsistent coverage, imperfect information systems for decision making and real word evidence, policy constraints, system capacity, social influences on health, and infrastructure gaps.We propose the development of an open access dynamic design "engine" to align biomedical and health system innovation. This engine will include coordinated collaborative design processes, frameworks, and tools, developed with input from all stakeholders, and centered around two critical, interdependent capabilities: (1) system design and (2) impact measurement. These capabilities will build capacity for efficient, model-driven design and implementation planning of sustainable, patient centered system innovations.The stakes are high for both the clinical promise of transformational products and their budget impact. Our current healthcare system is not ready to maximize benefit from transformational science and emerging biomedical innovations. We need to help the healthcare system catch up with the science.

The use of multi-national observational study in post-marketing safety assessment has been rising in recent years in parallel with the rapid development and adoption of electronic healthcare data (e.g., administrative claims, electronic health records) and novel statistical analysis methods to handle these data. Secondary use of routinely collected electronic health information has long been available to conduct pharmacoepidemiologic studies using data from millions of patients. Certain observational studies or surveillance activities, especially those investigating rare exposure or outcome or those designed to study specific patient subgroups (e.g., elderly, pediatric) or newly approved medical products, necessitate a multi-national approach. Other instances utilizing such study design include but not limited to (1) postmarketing study requested by multiple regulatory authorities; (2) multiple data systems chosen to complement each other (e.g., databases with long-term clinical outcome data combined with another that includes lab and radiology findings to allow case adjudication and/or algorithm validation); (3) multiple data sources needed to verify and replicate study findings. In this article, we share examples of multi-national postmarketing studies and discuss key pitfalls related to the design and analysis of such studies as well as strategies to mitigate biases.

Clinical development delays and failures do not serve public health. Reliance solely on expert opinion or historical patterns rather than evidence from representative, real-world point-of-care data about the indication results in suboptimal trial design, missed opportunities, and uninterpretable findings. Investment in real-world evidence (RWE) generation to build a deep, comprehensive, and current understanding of the characteristics, care, and outcomes of the indicated population is essential to improving clinical development decision making. Despite the recognized value of RWE, this evidence generation is not done systematically. Here we make integrated RWE generation more compelling and practicable by addressing concerns we have heard from biopharma leaders and, for emerging RWE leaders, providing a blueprint for designing real-world studies in a phased approach that aligns with clinical development investment. Our work is intended to facilitate more widespread adoption of integrated RWE generation, beginning early in development, so that robust RWE is in hand at the right time for evidence-based decision making by the sponsor, regulators, and payers.

Various initiatives and guidelines exist to support patient engagement (PE) throughout the lifecycle of a medicinal product. While the recent European Medicines Agency guideline on good pharmacovigilance practices (Module XVI; Revision 3) reinforces the importance of involving patients to create effective risk minimization strategies, frameworks supporting the systematic adoption of PE by Marketing Authorization Holders (MAHs) across pharmacovigilance, including the risk management system, are lacking. Furthermore, little is presented on the impact of patient review of additional risk minimization measures materials. We present a tested Pharmacovigilance Patient Centricity Framework describing key focus areas that can create the necessary infrastructure for systematic PE in effective risk minimization materials. Implementation of this framework highlighted the importance of collaboration to drive PE across the company at both local and global level, and externally, as relationships are established with patient organizations and best practices are shared with other MAHs. Therefore, this framework can be considered by other companies as a basis for developing a patient-centric approach to integrate the patient's voice into pharmacovigilance deliverables.

Non-randomized comparative studies are often used to compare treatment effects between an investigational product and a control when randomization is not feasible or difficult in practice. A typical situation is that the product is investigated in a single-arm study, and the control data are collected in an external data source. For such a situation, we propose an alternative approach to draw inference on the treatment effect difference. First, a potential outcome model (POM) for the outcome under control treatment is built based on the external control data source. Next, the POM is utilized to impute outcomes of subjects in the single-arm study as if they were treated with the control treatment. Then the inference on the treatment effect difference can be made by comparing imputed outcomes (for the control) and observed outcomes (for the investigational product). The main purpose of this paper is to provide a proof of concept regarding how to perform inference on the treatment effect between the investigational product and the control under this scenario. We illustrate our approach by assuming the endpoint to follow a normal distribution and the POM to be a linear regression model.