Therapeutic innovation & regulatory science最新文献

{"title":"Insights into Support Systems for Orphan Drug Development: A Comparative Study.","authors":"Ye Leng, Yu Tang, Weijie Yu, Yiru Hou, Hanqing He, Yanjie Han, Huiyao Huang, Wenbao Zhang, Ning Li","doi":"10.1007/s43441-025-00833-8","DOIUrl":"https://doi.org/10.1007/s43441-025-00833-8","url":null,"abstract":"<p><p>Due to great unmet medical needs, orphan drug development is a common issue of high priority for policymakers, industry leaders, researchers and patients worldwide. The establishment of a harmonized support system is the way forward to address the dilemma facing rare diseases (RDs). This study pioneers the proposal of a framework and dimensions of support systems affecting orphan drug development, covering legal (political and legal basis), technical (regulatory guidance and acceleration), motivational (economic incentives and innovation returns) and logistical (fundamental infrastructure) factors. This study, based on the framework, shows that the conditions for orphan drug development above are basically developed in the US, the EU and China. Additionally, China lags behind in terms of economic incentives and fundamental infrastructure. Continuous improvements in pricing and reimbursement, as well as the acceleration of real-world data (RWD) database and biobank repository integration, are expected in China. Based on these findings from the three study regions, action plans with three strategies (national plans and strategies, a patient-centered health system, global governance and collaborations) and eleven actions are suggested for strengthening synergies between initiatives and stakeholders to satisfy the medical needs of RD patients and families. This study can provide a reference not only for orphan drug development in the three study regions but also for all other countries worldwide, especially for those with a late start in addressing RDs.</p>","PeriodicalId":23084,"journal":{"name":"Therapeutic innovation & regulatory science","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144627114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Clinical Studies and the FDA Adverse Event Reporting System (FAERS) Database for nirogacestat-related Adverse Events.","authors":"Yuanyuan Li, Lurong Yu, Yuxin Huang, Qing Zeng, Yao He, Limei Liu","doi":"10.1007/s43441-025-00834-7","DOIUrl":"https://doi.org/10.1007/s43441-025-00834-7","url":null,"abstract":"<p><p>Nirogacestat is the first drug specifically approved for desmoid tumours (DTs), rare, locally invasive tumours originating from connective tissue. This study aimed to explore potential safety concerns associated with nirogacestat, thereby providing references and research directions for subsequent clinical studies. We extracted data on nirogacestat from the FDA Adverse Event Reporting System (FAERS) database between Q4 2023 and Q4 2024 and mined adverse events (AEs) using the reporting odds ratio (ROR). For a separate retrospective analysis, we searched articles from PubMed, Cochrane, and EMBASE from the establishment of the databases until January 25, 2025.The study included 588 patients who were administered nirogacestat. Among all AEs, 89.78% were classified as non-serious. The most frequently reported preferred terms included diarrhoea [ROR (95% CI): 10.58 (9.22-12.13)], nausea [ROR (95% CI): 6.00 (5.06-7.12)], fatigue [ROR (95% CI): 3.77 (3.11-4.58)]. Disproportionality analysis revealed high signal strength for ovarian dysfunction [ROR (95% CI): 963.62 (306.61-3028.55)], ovarian failure [ROR (95% CI): 254.92 (101.73-638.79)], decreased blood phosphorus [ROR (95% CI): 107.66 (64.14-180.70)] and photopsia [ROR (95% CI): 91.19 (54.41-152.83)]. Notably, the risk ratios for alopecia, dermatitis acneiform, and ovarian toxicity were significantly higher in the nirogacestat group than in the placebo group. Despite the wide use of nirogacestat, most common AEs are not serious. Nevertheless, healthcare workers should proactively monitor patients for indicators of ovarian toxicity and hepatotoxicity. Additionally, photopsia and haematuria may be newly identified AEs. Further studies are required to validate our findings and explain the causal relationship between nirogacestat and AEs.</p>","PeriodicalId":23084,"journal":{"name":"Therapeutic innovation & regulatory science","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144601697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"\"Appropriateness\" of Clinical Data Under Regulation (EU) 2017/745- A Case Study and Survey.","authors":"Elisabeth Oltmanns, Michael D'Agosto, Folker Spitzenberger","doi":"10.1007/s43441-025-00827-6","DOIUrl":"https://doi.org/10.1007/s43441-025-00827-6","url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>Expert interviews with notified body reviewers and a survey were conducted to assess the regulatory landscape and the appropriateness of non-clinical data.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":23084,"journal":{"name":"Therapeutic innovation & regulatory science","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Has FDA's Drug Development Tools Qualification Program Improved Drug Development?","authors":"Felix Yang, Imein Bousnina, Anne Madej, Rasika Kalamegham","doi":"10.1007/s43441-025-00790-2","DOIUrl":"10.1007/s43441-025-00790-2","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The Drug Development Tools (DDTs) Qualification program creates a pathway to evaluate Clinical Outcome Assessments (COAs) that capture a specific concept of interest (COI) in a specified Context of Use (COU). If successfully qualified, a COA can be relied upon to measure a COI that has an application in drug development and regulatory decision-making. Thus, qualified COAs are important DDTs. This analysis aims to assess the Food and Drug Administration's (FDA's) performance reviewing applications in the COA Qualification Program, as well as the uptake of qualified COAs in drug development to date.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;In order to assess the use of qualified COAs in drug development, we analyzed the Summary Basis of Approvals (SBA) retrieved from Drugs@FDA and the COA compendium. The submission and review dates for the Letter of Intent (LOI), Qualification Plan (QP), and Full Qualification Package (FQP) steps were retrieved from Center for Drug Evaluation and Research (CDER) & Center for Biologics Evaluation and Research's (CBER) database, as well as the FDA COA Qualification Program website.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Our analysis showed that 86 COAs were listed on the FDA COA website, with a majority of them being Patient Reported Outcomes (PRO). Completeness Assessment (CA) for each portion of submission, as well as review times for the LOI, QP, and FQP steps vary widely, with 46.7% of submissions having a review time exceeding the published targets. To date, 7 COAs (8.1%) have achieved qualification, and one (1.1%) has been denied after undergoing all steps for qualification. On average, it takes 6 years for a COA to be qualified. Our analysis of FDA's approval documents shows that the Agency has relied on qualified COAs to support benefit-risk assessment of 11 medicines. Only three of the seven qualified COAs have been used to support benefit-risk assessment of medicines. The three qualified COAs that have been used are KCCQ, E-RS, and EXACT. Each of these has been used to support multiple indication claims. KCCQ - cardiomyopathy for 2 medicines, heart failure for 6 medicines; E-RS - chronic obstructive pulmonary disease (COPD) for 1 medicine; and EXACT - COPD for 3 medicines. Note: E-RS and EXACT were both used in aclidinium bromide/formoterol/fumarate. In each case they were used as secondary or exploratory endpoints, none as primary endpoints. Only 1 qualified COA was included in drug labels.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;The lengthy and unpredictable nature of the COA Qualification Program review timelines poses a risk for tool developers and sponsors intending to qualify a new COA, to use an existing COA or sponsors intending to qualify and use a new COA in the drug development process. Our findings show that, to date, the DDT Qualification Program has not significantly improved the inclusion of qualified COAs in clinical development plans to support regulatory decision-making and labe","PeriodicalId":23084,"journal":{"name":"Therapeutic innovation & regulatory science","volume":" ","pages":"871-881"},"PeriodicalIF":2.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12181102/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143987611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applying Aggregate Statistical Analyses to Safety Monitoring of Ongoing Clinical Studies, Issues, and Opportunities in a Test Case.","authors":"Ed Whalen, Steven Gilbert, James Buchanan","doi":"10.1007/s43441-025-00776-0","DOIUrl":"10.1007/s43441-025-00776-0","url":null,"abstract":"","PeriodicalId":23084,"journal":{"name":"Therapeutic innovation & regulatory science","volume":" ","pages":"643-649"},"PeriodicalIF":2.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143987502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Futility Interim Analyses - A Plea for Simplicity.","authors":"Gian-Andrea Thanei, Gaëlle Klingelschmitt, Claude Berge, Hans Ulrich Burger","doi":"10.1007/s43441-025-00779-x","DOIUrl":"10.1007/s43441-025-00779-x","url":null,"abstract":"<p><p>Futility interim analyses remain a topic of anxiety for most clinical drug development teams. While they can significantly reduce the risk involved in the development of innovative therapies and do so at a minimal cost, they are not used frequently enough. Development teams often shy away due to the fear of terminating a potentially successful therapy by mistake. We believe these risks are overestimated and hence necessitate a simple and effective communication to be better understood. The planning of futilities often evolves into complex statistical modeling exercises where various scenarios are simulated and multiple risk metrics are calculated. To assist teams in such discussions we propose a straightforward strategy for establishing a range for the futility threshold and an intuitive futility risk measure to evaluate the associated risks.</p>","PeriodicalId":23084,"journal":{"name":"Therapeutic innovation & regulatory science","volume":" ","pages":"650-658"},"PeriodicalIF":2.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144000555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Large Language Models for Advanced and Flexible Labelling of Protocol Deviations in Clinical Development.","authors":"Min Zou, Leszek Popko, Michelle Gaudio","doi":"10.1007/s43441-025-00785-z","DOIUrl":"10.1007/s43441-025-00785-z","url":null,"abstract":"<p><strong>Background: </strong>As described in ICH E3 Q&A R1 (International Council for Harmonisation. E3: Structure and content of clinical study reports-questions and answers (R1). 6 July 2012. Available from: https://database.ich.org/sites/default/files/E3_Q%26As_R1_Q%26As.pdf ): \"A protocol deviation (PD) is any change, divergence, or departure from the study design or procedures defined in the protocol\". A problematic area in human subject protection is the wide divergence among institutions, sponsors, investigators and IRBs regarding the definition of and the procedures for reviewing PDs. Despite industry initiatives like TransCelerate's holistic approach [Galuchie et al. in Ther Innov Regul Sci 55:733-742, 2021], systematic trending and identification of impactful PDs remains limited. Traditional Natural Language Processing (NLP) methods are often cumbersome to implement, requiring extensive feature engineering and model tuning. However, the rise of Large Language Models (LLMs) has revolutionised text classification, enabling more accurate, nuanced, and context-aware solutions [Nguyen P. Test classification in the age of LLMs. 2024. Available from: https://blog.redsift.com/author/phong/ ]. An automated classification solution that enables efficient, flexible, and targeted PD classification is currently lacking.</p><p><strong>Methods: </strong>We developed a novel approach using a large language model (LLM), Meta Llama2 [Meta. Llama 2: Open source, free for research and commercial use. 2023. Available from: https://www.llama.com/llama2/ ] with a tailored prompt to classify free-text PDs from Roches' PD management system. The model outputs were analysed to identify trends and assess risks across clinical programs, supporting human decision-making. This method offers a generalisable framework for developing prompts and integrating data to address similar challenges in clinical development.</p><p><strong>Result: </strong>This approach flagged over 80% of PDs potentially affecting disease progression assessment, enabling expert review. Compared to months of manual analysis, this automated method produced actionable insights in minutes. The solution also highlighted gaps in first-line controls, supporting process improvement and better accuracy in disease progression handling during trials.</p>","PeriodicalId":23084,"journal":{"name":"Therapeutic innovation & regulatory science","volume":" ","pages":"833-847"},"PeriodicalIF":2.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12181094/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144014765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Untangling the Biosimilars Interchangeability Puzzle: A Provocative Dive into Concepts and Terminology, and Developing a Strategy to Minimize Uncertainties from Interchangeability.","authors":"Dalia Mahmoud Ghorab, Kholoud Mamdouh Abdelfattah, Howida Kamal Ibrahim, Soha Aly Aly Elmorsy","doi":"10.1007/s43441-025-00765-3","DOIUrl":"10.1007/s43441-025-00765-3","url":null,"abstract":"<p><p>The interchangeability of bioimilars (off-patent biologics) is a topic of ongoing debate in the literature. In the US, the FDA requires a demonstration of interchangeability before biosimilars can be interchanged on the market. However, this requirement is not mandated in other regions. Conversely, a joint statement from the Head of Medicines Agency and the EMA declared that once a biosimilar is approved in the EU it is interchangeable without the need for additional systematic switch studies to support the interchangeability at the prescriber level. The term interchangeability has a different meanings, depending on the country. To ensure the best interchange practice, a global definition of interchangeability is needed. Should biosimilars demonstrate both similarity to and interchangeability with the reference product before substitution or it is a matter of confidence of the healthcare professional, which can be attained through self-practice and available data from already switched patients? Would the recent calls and existing guidelines for using real-world data to support regulatory and clinical decisions be adequate for such interchangeable designation? In this review, we'll draw insights from the interchangeability of off-patent medicines (generics), and highlight the unique characteristics of biosimilars that differentiate them from generics affecting their interchangeability. We will highlight some regulatory standpoints and discuss clinical practices related to this practice. We will shed light on the procedures covered by this umbrella term, like switching and substitution, discuss the potential risks associated with the interchangeability of biosimilars, and suggest strategies to handle uncertainties from interchangeability practice.</p>","PeriodicalId":23084,"journal":{"name":"Therapeutic innovation & regulatory science","volume":" ","pages":"680-692"},"PeriodicalIF":2.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144049579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Consideration for Assessing Data/Models/Tools Expiration Supporting Drug Development and Clinical Decision Making.","authors":"Jeffrey S Barrett, Mark A Turner","doi":"10.1007/s43441-025-00793-z","DOIUrl":"10.1007/s43441-025-00793-z","url":null,"abstract":"<p><p>Decision making of any kind is informed by data and often by models, tools or other solutions built from data. Data are evaluated for such purposes within a specific context of use (COU) but implicitly we often believe the data to be relevant, accurate and of high quality. In reality, this is not always the case. The status of data for various COUs must constantly be revisited for relevance and information value over time. Using drug development as an example, we postulate that there are indeed occasions where data value diminishes over time and consideration for data expiration with respect to its relevance for decision making should be entertained and at least identified with respect to a time-dependent change in status. Other situations exist which will also necessitate periodic review and condition reassessment. For example, considerations for patient privacy and consent along with compliance to regulatory standards must factor into future recommendations as well. Actions regarding data expiration are proposed as initial thoughts to be expanded upon but this assessment is primarily an attempt to explore factors which impact opinions about data information value for both drug development and clinical decision making.</p>","PeriodicalId":23084,"journal":{"name":"Therapeutic innovation & regulatory science","volume":" ","pages":"707-717"},"PeriodicalIF":2.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12181108/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144033332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quality of Randomized Controlled Trials in the Association of Southeast Asian Nations (ASEAN) - A Systematic Review.","authors":"Marlinang Diarta Siburian, Sifa Marie-Joelle Muchanga, Antonio Villanueva, Asuka Nagatani, Takuma Kato, Koji Wada, Masamitsu Eitoku, Takeshi Murakami, Narufumi Suganuma","doi":"10.1007/s43441-025-00789-9","DOIUrl":"10.1007/s43441-025-00789-9","url":null,"abstract":"<p><strong>Introduction: </strong>This study aimed to assess the quality and characteristics of RCTs in the ASEAN Member States (AMS) during a decade of harmonization efforts.</p><p><strong>Methods: </strong>This study is a systematic review to assess the quality of RCTs conducted in AMS. Studies were included if they were (1) reports of RCTs involving AMS from 2010 to 2021; (2) published in English; and (3) available in full text. Literature searches were conducted in MEDLINE and EMBASE. Study quality was assessed using the Cochrane's risk-of-bias tool and were classified as high- or low-quality. Multiple logistic regression analysis was used to identify characteristics associated with RCT quality.</p><p><strong>Results & discussions: </strong>A total of 51,177 articles were identified, among which 437 studies were eligible for quality assessment. Of the 437 studies, 41.6% were of high-quality. The use of blinding and a higher number of participants (100-1000 and > 1000) contributed positively to study quality (odds ratio (OR): 1.60 [95% confidence interval (CI): 1.05-2.41]; OR: 1.91 [95%CI: 1.24-2.94]; OR: 3.14 [95%CI: 1.22-8.10], respectively). Whereas the use of non-parallel assignment in the type of randomization contributed negatively to study quality (OR: 0.51 [95% CI: 0.26-1.03], P = 0.060). Among the six AMS with a high number of RCTs, two had high-quality studies > 50%, three had slightly more than 40% and one had less than 20%.</p><p><strong>Conclusions: </strong>In the decade of regulatory harmonization, less than half of RCTs in AMS were of high-quality. Large numbers of participants and the use of blinding were associated with high-quality RCTs, while the opposite was true for studies with non-parallel assignment. To improve the quality of RCTs, more training and capacity building of local researchers is needed, with particular attention to study design.</p>","PeriodicalId":23084,"journal":{"name":"Therapeutic innovation & regulatory science","volume":" ","pages":"718-727"},"PeriodicalIF":2.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144049269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}