Therapeutic innovation & regulatory science最新文献

Background: In 2019, the US Food and Drug Administration (FDA) finalized guidance for designating interchangeable biosimilars requiring pre-approval phase III clinical trials to evaluate safety when reference and biosimilar formulations are interchanged. In June 2024, Draft FDA Guidance on interchangeability relaxes FDA approval criteria focusing on analytic rather than clinical findings. This study examines US trends in interchangeable biosimilar approvals between 2019 and 2025, manufacturer strategies, and regulatory timelines.

Methods: We used the FDA Purple Book archive (September 2025) to identify interchangeable biosimilars, associated Biologic License Application (BLA) numbers, manufacturers, reference products, and FDA approval dates. FDA submission dates were obtained from FDA approval letters. We analyzed trends by year, manufacturer, product, and product class to assess patterns in approval timing and strategy.

Results: Since 2020, the FDA has approved 26 interchangeable biosimilars, with 19 approved between January 2024 and September 2025. Six are adalimumab biosimilars. Manufacturers employed two strategies: concurrent submission for interchangeability (17 cases) or delayed pursuit (9 cases). Average approval timelines decreased from 798 days for applications dated in 2020 to 364 days for applications dated in 2024. All interchangeable biosimilars completed pre-approval switching studies.

Conclusions: Concurrent interchangeable designation is now the predominant regulatory pathway. The FDA June 2024 Draft Guidance, removing switching studies requirement for interchangeability might cut biosimilar development costs and development timelines. These changes, alongside provider and patient education, could enhance biosimilar uptake and reduce biologic drug costs.

Background: The Kansas City Cardiomyopathy Questionnaire (KCCQ) is frequently used in heart failure (HF) clinical trials to evaluate treatment effects on function and symptoms. However, due to the 0-100 boundedness in KCCQ scores, conventional mean change from baseline analysis can underestimate effects for lower-and overestimate effects for higher-baselines. This study demonstrates key issues with conventional statistical methods for analyzing treatment effects on KCCQ in randomized trials and evaluates alternative statistical methods that appropriately account for 0-100 boundedness of scores.

Methods: We conducted clinical trial simulations and re-analyzed a real HF randomized trial-the PRIORITIZE-HF phase II trial of sodium zirconium cyclosilicate. KCCQ change from baseline was analyzed with conventional ANCOVA models, and compared to methods that account for the 0-100 score boundedness: ANCOVA interaction models, Tobit regression, and Beta regression. Mean treatment effects and extent of bias are summarized by method.

Results: There were clear baseline-dependencies in mean effects for KCCQ score change, both in simulated trials and in PRIORITIZE-HF. In the real trial, the conventional ANCOVA model mean effect on KCCQ-overall summary score was + 2.58 overall while for methods allowing effects to depend on baseline - ANCOVA interaction models and Beta regression - mean effects were over twice as large at baseline = 30 (+ 6.33 to + 6.75) and less than half at baseline = 70 (- 0.31 to + 0.89).

Conclusions: Conventional analyses of treatment effects on overall mean KCCQ score changes lead to misinterpretations in clinical trials, but this can be mitigated by using methods allowing for baseline-dependent effects.

Background: Physician awareness of evidence about off-label uses of prescription drugs can affect prescribing decisions. We tested approaches to disclosing unsupportive data about off-label uses and assessed disclosure impact on perceptions and intentions.

Design: Primary care providers (PCPs) and oncologists viewed a brief report describing an off-label use of trazodone for insomnia (PCPs) or imiquimod for lentigo maligna (oncologists) and provided their perceptions of its Usefulness and Importance for prescribing decisions, and Intentions to search for additional information about the off-label use. We tested four variations (plus control) of a disclosure about the existence of unsupportive data for the off-label use: complete brief report (specific information about the off-label use), summary plus citation (specific information about the off-label use), citation (general information about the off-label use), and statement that other data may exist (general information about the off-label use).

Results: Physicians who read any disclosure about the existence of unsupportive data for the off-label use rated the information as more Important for prescribing decisions than those receiving only information about the off-label use (control) (p = 0.01). PCPs reported specific data disclosures were more Useful for prescribing decisions (p < 0.001) than less specific disclosures. Oncologists found all disclosures about unsupportive data Useful but those who read disclosures stating unsupportive data may exist reported greater Intention to search for additional information about the off-label use of the drug (p = 0.03). Frequent off-label prescribing was associated with lower ratings for information Importance for prescribing decisions for both groups (p < 0.05). Prescribing the drug previously was associated with lower ratings of information Importance and Usefulness for prescribing decisions among PCPs (p < 0.001) and greater Intention to search for additional information about the drug (p < 0.001) among oncologists.

Conclusions: Disclosures about unsupportive data for off-label uses are important and useful for prescribing decisions. Physicians who prescribe drugs for off-label uses more frequently or have prescribed the drug previously may discount the importance of the information. Tailoring the specificity of the disclosure to the needs of the audience may increase its effectiveness.

Background: A continuous glucose monitoring system (CGM) is a digital health technology (DHT) device that measures interstitial glucose. CGMs are used in the management of diabetes. The purpose of this study was to retrospectively assess the quality of CGM data in a clinical trial involving patients with type 1 diabetes.

Methods: CGM data were collected from 461 patients with type 1 diabetes who participated in a 16-week, double-blind phase 3 trial designed to examine the efficacy of prandial (mealtime) insulins. CGM data were collected at three 2 week study time points. The patterns of missing CGM data (due to non-wear, user- and device-related causes) and the relationship between CGM metrics and hemoglobin A1C (HbA1c) were examined using data visualization approaches.

Results: Across the three observation periods, 4.7-6% of CGM observations were missing. Approximately 16% of daily values were missing on days when a new CGM sensor was inserted. For many patients, high glucose variability was associated with low time in range (TIR) and HbA1c >7%. Conversely, low glucose variability was associated with higher TIR.

Conclusions: Our analysis identified the need for adequate documentation indicating the presence of patient- and device-related events (e.g., sensor changes, non-wear time) to address causes of missing CGM data. These causes need to be considered prior to the statistical assessment of such data for research and regulatory purposes. Accurate documentation of post-baseline events is important for the development of research and regulatory standards for the collection and analysis of data acquired from DHTs.

Introduction: Hereditary hemochromatosis (HH) is a metabolic disorder characterized by excessive iron absorption, leading to damage of the liver, heart, and pancreas. The patient experience of living with HH has not been extensively described. We therefore aimed to understand the experiences of patients living with HH, including those who are intolerant to currently recommended treatment.

Methods: We consulted eight patients living with HH from North America, Europe, and Oceania, exploring their diagnostic journey and their lives and care management experiences. We also qualitatively assessed patient levels of satisfaction with their current treatment, evaluated their attitudes and views toward clinical research on HH, and identified potential drivers and barriers to clinical trial participation. A thematic approach was used to analyze the data.

Results: Patients were mostly of Northern or Western European ancestry (n = 7) and currently receiving phlebotomy (n = 6). Two patients were receiving iron chelation therapy. Patients ranged in age from their 20s to 70s. The median time since diagnosis was 2 (range 0‒33) years; 2 patients were diagnosed within the past year. Patients expressed varying levels of satisfaction with their treatment, based on experience of side effects (i.e., fainting, extreme fatigue), the emotional toll, needle phobia, treatment logistics, and time burden. Feelings of altruism, connections with the HH community, and the potential for new treatment options were among the motivators to join a clinical trial, while side effects were viewed as the primary barrier.

Conclusion: This research provides insights into the patient experience that can inform treatment management and clinical trial design.

Introduction: The amendments to the International Council for Harmonization (ICH) Good Clinical Practice (GCP) E8 guidelines were introduced to enhance clinical trial quality, patient safety, and efficiency through a more patient-centric, risk-based approach. This study investigates the impact of various study risk factors such as protocol amendments, informed consent changes, protocol complexity, and social determinants of health (SDOH) on protocol deviations and patient retention in clinical trials involving combination products.

Methods: A retrospective analysis of 14 clinical trials with 202 enrolled subjects was conducted. Key risk indicators (KRIs) such as protocol amendments, amendments triggering informed consent changes, study staff experience, and clinical trial phase were evaluated for their association with protocol deviations. The analysis also explored the influence of social factors, including age, gender, race, insurance type, and travel distance on protocol adherence.

Results: Study revealed that longer study participation was associated with an increased number of protocol deviations (p = 0.0003), while no significant associations were found between protocol deviations and demographic factors (p = 0.4039 for gender; p = 0.40650 for age), insurance type (p = 0.0640), or complexity scores (p = 0.7798). The findings highlight the importance of effective informed consent processes, study staff training, and risk management strategies to minimize protocol deviations and enhance data integrity in clinical trials.

Conclusion: while larger numbers of participant were associated with more deviations, site preparedness and patient compliance can mitigate these risks, underscoring the need for robust quality management systems in clinical trials.

Background: Cardiovascular and oncology trials increasingly require large sample sizes and long follow-up periods. Several approaches have been developed to optimize sample size including sample size re-estimation based on the promising zone approach. With time-to-event endpoints, methods traditionally used to test for treatment effects are based on proportional hazards assumptions, which may not always hold. We propose an adaptive design wherein using interim data, Bayesian computation of Predictive Power (PP) guides the increase in sample size and/or the minimum follow-up duration.

Methods: PROTECT IV is designed to evaluate mechanical circulatory support device vs. standard of care during high-risk percutaneous coronary intervention with the initial enrolment of 1252 patients and initial minimum follow-up of 12 months. The primary endpoint is the composite rate of all-cause death, stroke, durable left ventricular assist device implant or heart transplant, myocardial infarction or hospitalization for cardiovascular causes. The study will employ an adaptive increase in sample size and/or minimum follow-up at the Interim analysis. The adaptations utilize simulations to choose a new sample size up to 2500 and new minimal follow-up time up to 36 months that provides PP of at least 90%.

Results: Via extensive simulations, we have examined the utility of the proposed design for situations like delayed treatment effect, early benefit only and in general crossing of survival curves. Separate Piece-wise Constant Hazard Models with non-influential (weakly-informative) Gamma-priors are fitted to the interim data for the two treatment arms, free from the proportional hazards assumptions, thus yielding more robust interim decision making. The Bayesian modeling facilitates sampling of future observations from the posterior predictive distributions with the predictive probability of trial success is computed via Monte-Carlo simulations. Simulation results show that the fitting Bayesian Piecewise Exponential models to the interim data along with the use of the posterior predictive distributions lead to more "specific" adaptation rules compared to the frequentist Conditional Power while the overall operating characteristics, type-I error and power, are similar.

Conclusion: For clinical trials with time-to-event endpoints and where crossing of survival curves might be anticipated at the planning stage, flexible modeling along with wholesome use of patient-level data such as the calculation of predictive power as proposed here, may be more robust and efficient in making interim decisions such as sample size increase than the traditional use of the conditional power based on summary statistics and proportional hazards assumption.

Purpose: This article aims to explore the implications of Paragraph IV certification on generic drug market entry while innovators' patents are still active. It also examines the challenges posed by patent settlements, the BLOCKING Act, and the FDA's decision-making process regarding generic applications. The objective is to shed light on the complexities of pharmaceutical market dynamics, regulatory practices, and the balance between innovation and accessibility.

Methods: The study involves a detailed analysis of the USFDA's recently published Para IV certification list and application statuses from 2020 to 2024. It reviews litigation trends among top generic manufacturers and scrutinizes the impact of patent disputes on market entry. The study also evaluates the potential effects of the BLOCKING Act on the 180-day exclusivity period and generic drug market dynamics.

Results: The analysis reveals that nearly half of the ANDA applications submitted through Paragraph IV certification are deemed 'Eligible,' with significant portions either deferred or extinguished. A peak in eligible ANDA submissions was observed in 2023. Litigation trends show active patent challenges by leading generic companies like Teva, Apotex, and Actavis. The study highlights the FDA's role in facilitating first ANDA approvals, emphasizing the importance of providing safe and effective generic alternatives.

Conclusion: The study concludes that while the FDA supports generic companies in their application processes, patent litigation remains a significant hurdle, delaying the availability of cost-effective generic drugs. The BLOCKING Act, if enacted, is likely to disrupt the 180-day exclusivity incentive, reducing the predictability and value of generic drug market entry.

The use of composite endpoint is a common strategy often employed to enhance statistical power and address the low incidence of individual outcomes, particularly in cardiovascular and kidney outcome studies. By merging multiple clinically relevant events into a single variable, these endpoints negate the need for multiple testing adjustments and augment the event rate, thus enabling studies of reasonable size and duration. However, as underscored by the FDA's guidance, a thorough evaluation of each component's impact is equally important to ensure the clinical relevance of these endpoints. This article delves into controversies surrounding the interpretation of hazard ratios derived from analyzing the composite endpoint and its individual components, exemplified by an observation from the CLEAR outcome trials. It highlights a paradoxical scenario where the combined treatment effect for the composite endpoint appeared less favorable than when assessing individual components separately. Moreover, we did a re-evaluation of the suitability of using Cox proportional hazards model in this context through theoretical investigation and simulation studies.