Therapeutic innovation & regulatory science最新文献

Background: Post-marketing risk minimization measures for medicines, e.g. package leaflets and patient cards, aim to ensure that patients and health care professionals receive timely and relevant medicines safety information. The use of digital technologies for risk minimization is emerging but still limited, despite being central in patients' and healthcare professionals' information access. There is currently limited high-level, strategic discussion about the digitalization of additional risk minimization measures.

Objective: To elicit key expert stakeholders' future visions of digital risk minimization measures in the European Union and to explore possible policy and healthcare implications of future digital risk minimization technologies.

Methods: Co-design workshops were conducted in three EU member states with clinicians, industry representatives, regulators, health information technology developers, and legal experts. Workshop transcripts and visual products were analyzed thematically in two coding cycles.

Results: Digitalizing risk minimization measures will transform roles, responsibilities, and collaborations, as stakeholders revise their remits with digital strategies and forge new collaborations to ensure the effectiveness and sustainability of new digital solutions. Success depends on revised implementation pathways that reflect patients' and healthcare professionals' digital access to medicines information and the diverse information ecosystems across the EU. Patients may gain new roles as active data subjects through remote monitoring, raising new ethical and legal considerations.

Conclusion: Digital risk minimization in the EU offers opportunities for timely, personalized interventions but presents challenges in implementation complexity, healthcare professionals' workload, and patient ethics, warranting more focused policy deliberation and research on digital health.

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 the context of clinical research, computational models have received increasing attention over the past decades. In this systematic review, we aimed to provide an overview of the role of so-called in silico clinical trials (ISCTs) in medical applications. Exemplary for the broad field of clinical medicine, we focused on in silico (IS) methods applied in drug development, sometimes also referred to as model informed drug development (MIDD). We searched PubMed and ClinicalTrials.gov for published articles and registered clinical trials related to ISCTs. We identified 202 articles and 48 trials, and of these, 76 articles and 19 trials were directly linked to drug development. We extracted information from all 202 articles and 48 clinical trials and conducted a more detailed review of the methods used in the 76 articles that are connected to drug development. Regarding application, most articles and trials focused on cancer and imaging-related research while rare and pediatric diseases were only addressed in 14 articles and 5 trials, respectively. While some models were informed combining mechanistic knowledge with clinical or preclinical (in-vivo or in-vitro) data, the majority of models were fully data-driven, illustrating that clinical data is a crucial part in the process of generating synthetic data in ISCTs. Regarding reproducibility, a more detailed analysis revealed that only 24% (18 out of 76) of the articles provided an open-source implementation of the applied models, and in only 20% of the articles the generated synthetic data were publicly available. Despite the widely raised interest, we also found that it is still uncommon for ISCTs to be part of a registered clinical trial and their application is restricted to specific diseases leaving potential benefits of ISCTs not fully exploited.

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.

Background: Product recalls play a critical role in pharmacovigilance, safeguarding public health under modern regulatory frameworks. Since 2018, the detection of carcinogenic nitrosamine impurities especially those exceeding acceptable intake (AI) limits has become a significant driver of drug recalls. These incidents have raised serious concerns about prolonged patient exposure, revealing systemic weaknesses in current Good Manufacturing Practices (cGMP), such as ineffective impurity control, poor risk assessment, and inadequate supplier oversight. Regulatory authorities now classify these as major cGMP violations, necessitating stronger quality systems and proactive compliance measures.

Methods: A retrospective analysis was conducted on recalls issued by the United States Food and Drug Administration (USFDA) from July 2018 to March 2025. Data were obtained from the USFDA Drug Recall Database, Enforcement Reports, and nitrosamine-specific resources. Recalls linked to nitrosamines and Nitrosamine Drug Substance-Related Impurities (NDSRIs) were identified, categorized, and evaluated using descriptive statistical methods to determine frequency, root causes, and mitigation approaches.

Results: The analysis revealed that Angiotensin II Receptor Blockers (ARBs) and Metformin accounted for the highest number of recalls. Recalls were particularly associated with older drug formulations and structurally complex Active Pharmaceutical Ingredients (APIs). Although risk mitigation strategies such as reformulation, enhanced analytical methods, and real-time monitoring have been introduced, their application remains inconsistent across the industry.

Conclusions: This study highlights the urgent need for enhanced regulatory-industry collaboration, adaptive compliance frameworks, and the adoption of risk-based strategies. Strengthening quality systems and aligning industry practices with regulatory expectations are essential for minimizing future nitrosamine-related recalls and ensuring continuous patient safety.

Pharmacopoeia reference standards play a vital role in the pharmaceutical industry by setting benchmarks for the quality, purity and consistency of drugs. Established by official pharmacopeia bodies like British Pharmacopoeia Commission, European Pharmacopoeia Commission, Indian Pharmacopoeia Commission and United States Pharmacopeia; these standards provide the authoritative guidelines for analytical and quality control methods and ensure that pharmaceutical products worldwide meet regulatory requirements and remain safe for consumers. Despite their critical importance, the comprehensive scientific and regulatory processes involved in developing pharmacopoeial reference standards remain insufficiently documented in accessible literature, creating knowledge gaps for researchers, regulators and industry stakeholders. The current article addresses these gaps by detailing the key stages of pharmacopoeia reference standard development, drawing on the principles outlined in ISO 17034 (general requirements for the competence of reference material producers), ISO Guide 31 (covering the contents of certificates and labels for reference materials), ISO Guide 34 (general requirements for the competence of reference material producers), ISO/Guide 35:2017 (guidance on characterization and assessment of homogeneity and stability of reference materials) and WHO TRS 943 (general guidelines for the establishment, maintenance and distribution of chemical reference substances). Particular emphasis is placed on the development of Indian Pharmacopoeia Reference Standards, underscoring their pivotal role in analytical testing, method validation, quality control, and the broader framework of global pharmaceutical harmonization. Moreover, it elucidates how rigorous adherence to pharmacopoeial reference standards enables the pharmaceutical industry to ensure the safety and efficacy of medicinal products, thereby safeguarding public health and fostering sustained confidence in therapeutic interventions.

The growing number of cancer cases and deaths highlights the urgent need for innovative treatment approaches. One technique that has lately been recognized as having potential to improve the existing cancer immunotherapy treatment is drug repurposing. By utilizing previously studied molecules that have safety data yet provide superior solutions beyond those of the conventional treatments, it will essentially speed up medication discovery. By influencing the tumor microenvironment, encouraging T cell activation, and thwarting resistance mechanisms, repurposed medications may increase the efficacy of immune checkpoint inhibitors (ICIs). Notable medications include RANKL inhibitors, which boost immune cell responses; TGF-β inhibitors, which improve T cell infiltration; and metformin, which activates cytotoxic T lymphocytes. Other substances, such as GM-CSF and thymosin α1, have also been demonstrated to enhance antitumor immunity in a number of ways by encouraging T-cell activity and dendritic cell maturation. On the other hand, aspirin may enhance immune checkpoint inhibitors by blocking the immunological milieu that tumors produce. Even with the encouraging outcomes of preclinical research and continuing clinical trials, there are still a number of obstacles to overcome in order to optimize dosage strategies, reduce the possibility of off-target effects, and navigate the intricate regulatory environment. Future developments in personalized medicine and artificial intelligence may increase the efficacy of drug repurposing in cancer treatment. To fully realize the potential of repurposed medications in enhancing the results for cancer patients, interdisciplinary cooperation between the academic community, business leaders, and regulatory bodies will be required.

The integration of artificial intelligence (AI) and machine learning (ML) into medical devices has revolutionized healthcare, enhancing diagnostic accuracy and clinical decision-making. However, their rapid evolution poses challenges to traditional regulatory frameworks, particularly in ensuring safety and effectiveness. This review examines current Food and Drug Administration (FDA) regulatory pathways for AI/ML-enabled cardiovascular medical devices. It assesses the pre- and postmarket criteria evaluated by the FDA and discusses strengths and gaps in the regulatory pathway. We conducted a systematic review of FDA-authorized AI/ML-enabled cardiovascular devices from November 1995 to May 2025. Inclusion criteria required devices to utilize AI/ML technology, be FDA-authorized via 510(k) pathway, and be intended for cardiovascular applications. Data extracted included device purpose, primary users, indications, pre- and postmarket evaluations, and identified limitations. Out of 1,247 FDA-authorized AI/ML devices, we selected 96 cardiovascular devices. All were cleared through 510(k) pathway. Common premarket evaluations included clinical validation, bench testing, and algorithm performance assessments. However, postmarket surveillance requirements beyond baseline FDA regulations, Medical Device Reporting (MDR) and the Quality System Regulation (QSR), were rarely identified, especially before recent FDA guidelines. Key limitations included restricted patient demographics and lack of transparency in AI capabilities. Public summaries showed no evidence of autonomous, in-field model retraining without human oversight; changes were managed through existing mechanisms or proposed Predetermined Change Control Plans (PCCPs). FDA's framework has enabled broad authorization, but stronger transparency and device-appropriate postmarket monitoring tailored to AI/ML-specific risks (e.g., dataset shift, bias, evolving performance) would enhance long-term safety and effectiveness.

The standard logrank test may lose statistical power substantially when the underlying proportional hazards (PH) assumption is violated. Among non-PH patterns, delayed treatment effects are very commonly anticipated and actually observed not only in cancer immunotherapies and their related combinations with other therapeutic agents, but also in numerous situations across multiple therapeutic areas. A frequently considered scenario is that the PH pattern does not emerge until a certain period of time has elapsed. Based on the generalized piecewise weighted (GPW) logrank test, which is the asymptotically most powerful weighted logrank test detecting random delayed effects, we developed and evaluated a group sequential framework for maximum duration trials based on this test. A variance based procedure for the determination of the sequence of information fractions is proposed. Simulation studies are performed comparing various types of design and handling of information fractions. The procedure can control type I error rate in this group sequential design framework. Power gain of the GPW logrank test is demonstrated for the non-PH scenarios with delayed treatment effects. These results underscore the importance of considering GPW type of logrank test with appropriate design strategies when such delayed effect patterns are expected. Moreover, we have highlighted the economic advantage of sequential monitoring, providing early decision opportunities to accelerate drug development.