Therapeutic innovation & regulatory science最新文献

Medicine is increasingly supported by software, with digital health technologies offering innovative ways to capture insights and drive therapies. Globally, medical device software must follow regulatory processes based on risk classification. The introduction of MDR represents a significant shift in risk-based classification for Medical Devices in Europe, including classification Rule 11 for software, which has caused significant discussions among European regulators. Three years after implementation, we conducted a systematic impact assessment of MDR classification Rule 11 for MDSW through a qualitative and quantitative analysis of over 2000 software entries from the European Medical Device database, complemented by data from other public databases such as the German DiGA directory and mHealthBELGIUM. Our results indicate that classification Rule 11 of the MDR results in a narrow bandwidth for class I software, whereas this used to be the most frequent classification for software under the MDD: while most of legacy software in EUDAMED falls in the lowest risk category as MDD Class I (53%), the situation reverses after the implementation of MDR with the most entries in Class IIa (55%). Analyzing the legacy MDD patient apps in Germany implies that three quarters will have to re-classify as MDR Class IIa at the end of the transition period in 2028. A comparison of the European and US regulatory landscapes, along with a systematic review of software features for Class I vs. Class IIa products, explains our findings and enables us to recommend a regulatory strategy for developing MDSW compliant with MDR Class I rules, ensuring fast access to the European market.

The National Medical Products Administration of China has been implementing ICH E17, which describes the general principles for planning and designing of multi-regional clinical trials (MRCTs), yet there are several ambiguities in the execution and conduct remains in China or East Asia. In specific, pooling strategy, effect modifiers (EMs), statistical analysis, sample size allocation and their impact in alignment with global trial remains a challenge. In this paper, we explore on the criteria mentioned above under the context of China. EMs need to be determined and identified from intrinsic and extrinsic factors which might have the impact to the drug on specific populations. If no EMs are found, we use pooling by regions to understand whether differences across East Asian population exists, and whether pooling by East Asian is necessary. Statistical models used in the analysis are also listed to estimate the drug effect in pooled populations. In summary, this paper outlines the details of the MRCTs practices in China and provides better insights in practice both domestically and internationally for any future improvements.

Global harmonization of biosimilar developmental requirements will facilitate development leading to increased patient and societal benefits. However, there are several technical and regulatory hurdles that must be addressed to harmonize the regulatory requirements in different countries and regions. At times, there is a requirement for use of locally sourced reference product, forcing biosimilar developers to repeat analytical or clinical comparability studies against reference product batches sourced from within a given country. While most health authorities no longer require comparative animal toxicology studies of the proposed biosimilar and reference product, these are still required in several countries, forcing biosimilar companies to conduct such studies or risk non-approval of their product. At times, different health authorities request different clinical study designs. In some jurisdictions there is a requirement to generate clinical data in local ethnic populations. Some health authorities require a hybrid label that combines clinical data from the reference biologic and the biosimilar, in the patient leaflet. Recommendations are provided to address each of these hurdles to facilitate global regulatory harmonization of biosimilars. Overcoming these barriers will ultimately increase patient access to these medicines in all regions while providing financial relief to healthcare systems.

Background: Clinical outcome assessments (COAs) measure how patients feel or function and can be used to understand which patients experience benefits of treatment and which do not. Interpretation of COA data is influenced by how meaningful change is defined. We aimed to compare how different stakeholders define, assess, and use meaningful change for decisions that impact patients.

Methods: A targeted literature review was undertaken in July 2021 using Medline, Embase, online grey literature search engines, and stakeholder organization websites. Additionally, a stakeholder survey on meaningful change was fielded between March and June 2023. Both quantitative and qualitative methods were used to analyze responses and identify key themes.

Results: The literature review resulted in 86 references. These revealed different approaches to define, measure and validate meaningful change. There were 248 survey responses. Many respondents felt the terminology and methods for defining meaningful change are confusing. Respondents also emphasized the importance of distinguishing within-patient and between-group change, and defining meaningfulness from the patient perspective (most patients and caregivers do not share a similar definition of meaningfulness as their healthcare professionals).

Conclusion: Four key recommendations for defining, establishing, and interpreting meaningful change estimates for COAs are: (1) Be clear on the type of "meaningful change" that is discussed or needed for a COA, (2) Ensure the "patient voice" is informing meaningful change estimates/definitions, (3) Acknowledge that a meaningful change estimate for a COA may differ between populations, diseases, and disease states, and (4) Disseminate data in a way that reduces ambiguity.

Introduction: The sales patterns of original drugs after patent expiration in Korea show a relatively high market share and continuous sales growth differently from those in the U.S. and European countries. This study aims to investigates a five-year sales pattern of original drugs after patent expiration in Korea using empirical data.

Methods: Using data from the Ministry of Food and Drug Safety, original drugs whose patents expired in 2012-2018 were extracted. And we used IQVIA data to determine the market share and sales growth rate of 48 original drugs, whose generic drug launched for the first time in the same molecule market, and whose sales data over five years after first generic entry were available. We analyzed the differences by the attribute of variables.

Results: The sales volume of original drugs in the fifth year (Q 20) had an average growth rate of 150.6% compared with that before the first generic drug launched, indicating a continuous growth. The average market share of original drugs in the fifth year (Q 20) decreased to 70.6%, but it was higher than previously reported research results in Korea and other countries. Differences were observed across the category of attribute.

Conclusion: This study demonstrated that while market share of original drugs is decreasing, the sales volume increased continuously until the fifth year, differently from those of other countries. Variations in sales patterns by attributes reflect unique dynamics in Korea.

It has long been a basic principle of randomized clinical trials addressing serious outcomes and/or major public health issues that interim data should be inaccessible to investigators and to industry sponsors, with interim data reviewed on a regular basis by an independent data monitoring committee (DMC). Challenges to this principle may arise when sponsors and/or regulators perceive a need to review interim data while the trial remains ongoing-for example, when a trial is being considered for accelerated approval. In this paper we propose approaches that could minimize the extent of interim data that is made available to others while the trial continues.

The use of data monitoring committees (DMC) to safeguard patients' safety in clinical trials has evolved over the last decades and has become increasingly common. To ensure well-operating and high-performing DMCs, pharmaceutical companies need to establish clearly defined operational processes while continuously seeking to optimize these and adapt to the needs of drug development. Although there are health authority guidelines on establishing and managing a DMC, the perspectives and experiences of sponsors are often underrepresented. This publication shares insights on a sponsor, Novo Nordisk (NN), regarding principles and practices for DMC establishment and management across varying trial types and therapeutic areas, including challenges and solutions. Highlighting NN's structured and successful approach to DMCs, it details clearly defined roles and responsibilities that ensure productive DMC meetings and high-quality data for the DMC. Additionally, NN's practices for clear, transparent, and trustful communication between the sponsor, the DMC, and the independent external statistical vendor are described. Processes for quality control, internal audits, and learnings from inspections and how these are incorporated for continuous improvement of the DMC process are discussed. While the processes and practices described are primarily designed for medium and large pharmaceutical companies, certain aspects may also be relevant and beneficial for smaller companies.

In the high-stakes world of clinical trials, where a company's multimillion-dollar drug development investment is at risk, the increasing complexity of these trials only compounds the challenges. Therefore, the development of a robust risk mitigation strategy, as a crucial component of comprehensive risk planning, is not just important but essential for effective drug development, particularly in the RBQM (Risk-Based Quality Management) ecosystem and its component-RBM (Risk-Based Monitoring). This emphasis on the urgency and significance of risk mitigation strategy can help the audience understand the gravity of the topic. The paper introduces a novel modeling framework for deriving an efficient risk mitigation strategy at the planning stage of a clinical trial and establishing operational rules (thresholds) under the assumption that contingency resources are limited. The problem is solved in two steps: (1) Deriving a contingency budget and its efficient allocation across risks to be mitigated and (2) Deriving operational rules to be aligned with risk assessment and contingency resources. This approach is based on combining optimization and simulation models. The optimization model aims to derive an efficient contingency budget and allocate limited mitigation resources across mitigated risks. The simulation model aims to efficiently position each risk's QTL/KRI (Quality Tolerance Limits/Key Risk Indicators at a clinical trial level) and Secondary Limit thresholds. A case study illustrates the proposed technique's practical application and effectiveness. This example demonstrates the framework's potential and instills confidence in its successful implementation, reassuring the audience of its practicality and usefulness. The paper is structured as follows. (1) Introduction; (2) Methodology; (3) Models-Risk Optimizer and Risk Simulator; (4) Case study; (5) Discussion and (6) Conclusion.

Introduction: Recent research has raised questions about potential unintended consequences of the Inflation Reduction Act's Drug Price Negotiation Program (DPNP), suggesting that the timelines introduced by the law may reduce manufacturer incentives to invest in post-approval research towards additional indications. Given the role of multiple indications in expanding treatment options in patients with cancer, IRA-related changes to development incentives are especially relevant in oncology. This study aimed to describe heterogeneous drug-level trajectories and timelines of subsequent indications in a cohort of recently approved, multi-indication oncology drugs, including overall, across subgroups of drugs characterized by the timing and pace of additional indications, and by drug type (i.e., small molecule vs. biologic).

Methods: This cross-sectional study evaluated oncology drugs first approved by the FDA from 2008 to 2018 and later approved for one or more additional indications. Numbers, types, and approval timelines of subsequent indications were recorded at the drug level, with drugs grouped by quartile based on the pacing of post-approval development (i.e., "rapid pace" to "measured pace").

Results: Multi-indication oncology drugs (N = 56/86, 65.1%) had one or more subsequent indication approved in a new: cancer type (60.7%), line of treatment (50.0%), combination (41.1%), mutation (32.1%), or stage (28.6%). The median time between FDA approvals for indications increased from 0.6 years (IQR: 0.48, 0.74) in the "rapid pace" group to 1.6 years (IQR: 1.32, 1.66), 2.4 years (IQR: 2.29, 2.61), and 4.9 years (IQR: 3.43, 6.23) in the "moderate," "measured-moderate," and "measured" pace groups, respectively. Drugs in the "rapid pace" group often received their first subsequent indication approval within 9 months of initial approval (median: 0.7 years; IQR: 0.54, 1.59), whereas the "measured pace" group took a median of 5.7 years (IQR: 3.43, 6.98). Across all multi-indication drugs, the median time to the most recent approval for a subsequent indication was 5.5 years (IQR: 3.18, 7.95). One quarter (25%) of drugs were approved for their most recent subsequent indication after the time at which they would be DPNP-eligible.

Conclusion: Approval histories of new oncology drugs demonstrate the role of post-approval indications in expanding treatment options towards new cancer types, stages, lines, combinations, and mutations. Heterogeneous clinical development pathways provide insights into potential unintended consequences of IRA-related changes surrounding post-approval research and development.