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.

Background: Options for patients to receive unauthorised medicines through compassionate use (CU) in Europe vary greatly. There are two CU pathways: cohort programmes, regulated uniformly by the Regulation across EU member states, and individual patient requests (IPRs) governed by the Directive. The latter allows member states to determine their own laws, resulting in heterogeneous regulatory requirements and challenges in operationalization. Consequently, patients may experience delays in accessing CU medicines depending on their country of residence. To compare CU availability across European countries and formulate recommendations for improvement, we analyzed 8,934 patient requests from 30 European countries.

Methods: An exploratory post-hoc analysis was conducted using pooled collaborative data from 8,934 patient requests provided by Merck KGaA, Novartis, Roche, and Sanofi, tracked from January 1, 2020 to April 30, 2023 across 30 countries. All requests with complete dates for submission, company approval, relevant Ethics Committees or Health Authorities, and shipment dates were included.

Results: While internal company CU approval steps were found to be similar with a median approval time of 5 days (median interquartile range (IQR) of 1 (0-6) for cohorts; median IQR 4 (1-8) for IPRs), the time from company approval until shipment varied between cohort requests (median IQR 5 (1-16) days) and IPRs (median IQR 8 (1-22) days). Challenges experienced included differences in the use of CU terminologies, scope, settings, regulatory, and operational requirements.

Conclusion: Our findings indicate that differing national requirements across Europe lead to operational challenges and inter-country variability in CU implementation posing operational challenges for stakeholders, highlighting the need for improved harmonization.

Background: In January 2024, China officially implemented the patent term extension (PTE) system, that is, began to implement PTE for innovative drugs approved after June 1, 2021. This study analyzed the PTE system's outcomes in China and its potential impact on drug accessibility.

Methods: We conducted a simulation analysis of the PTE duration and effective patent life after listing (EPLL) of innovative drugs approved by Chinese regulatory authorities from June 2021 to December 2024, using data collected from public databases such as China's marketed drug patent registration platform.

Results: A total of 148 innovative drugs were included in the study. Among them, 61% were able to obtain PTE, with a median PTE duration of 5 years (IQR: 2.3-5). The median EPLL before adding PTE was 9 years (IQR: 6.1-11.7), and the median EPLL after adding PTE was 14 years (IQR: 11.1-14), an increase of 55.6%. Furthermore, the study reveals variations across drug types and therapeutic areas.

Conclusions: Compared to findings from prior studies on the U.S. and the European Union, the median PTE duration for innovative drugs in China is longer, while the median EPLL is only marginally extended. Both Chinese domestic stakeholders and foreign enterprises should evaluate the potential impact by considering both the incremental PTE periods and the overall EPLL levels.

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.

In generic drug development, achieving bioequivalence followed by biowaivers for additional strengths are crucial for timely market entry. As per regulatory guidance, bioequivalence must be demonstrated for at least one strength-typically the highest, though sometimes a lower strength is chosen due to safety or tolerability concerns. Biowaivers for other strengths can be granted if specific criteria are met, that includes linear pharmacokinetics, same manufacturing process, proportional formulation composition, and similar dissolution profiles under defined conditions. The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) has introduced harmonized guidance, ICH M13B on biowaiver requirements for additional strengths. This guidance is currently under draft stage and shall be finalized soon. In this context, the present review article outlines the biowaiver criteria mentioned in the ICH M13B guidance and compares with that of requirements in European Medicines Agency (EMA) and the United States Food and Drug Administration (USFDA). This review highlights the advantages and challenges of ICH M13B at each stage: pharmacokinetic linearity, formulation proportionality, and dissolution similarity in comparison with existing requirements. Based on the assessment performed in the manuscript, there are challenges and advantages for additional strength biowaivers for USFDA and EMA. Because of the significant changes in dissolution similarity assessments, this review highlights the rationale, risk and benefit in comparison with previous criterion. Overall, this review article summarizes the current understanding and interpretation of ICH M13B against existing biowaiver criteria and provides a way forward for successful regulatory submissions for additional strength biowaivers.

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.

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 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.

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.