Therapeutic innovation & regulatory science最新文献

External evidence from prior trials, registries, and fit-for-purpose real-world data can improve drug development efficiency. Hybrid-controlled designs are particularly appealing for reducing concurrent control enrollment while simultaneously providing internal validity with a randomized control arm. Yet regulatory adoption is limited due to major concerns around bias due to possible differences in characteristics and outcomes between the external data and the trial. To realize the benefits of the hybrid approach without compromising credibility, methodological guardrails are crucial for mitigating bias and enabling valid inference. We assessed eight statistical methods which proactively address differences between external data and trial data. We apply these methods to both a large clinical trial as a case study, as well as within a comprehensive simulation study with continuous outcomes that varied the amount of measured versus unmeasured confounding, the severity of the between-data-source heterogeneity, and the number of external data sources. Results show that two-step strategy, propensity score-based balancing followed by Bayesian dynamic borrowing, consistently delivered the most favorable trade-off between precision gain and bias control. This approach when used with fit-for-purpose external data can provide a robust implementation of the hybrid trial design beyond the narrow set of conditions where there is currently precedent.

Background: Current ICH guidelines, e.g. ICH E6 (R3), advocate a risk-based statistical review of clinical trial data to identify anomalies. The open-source R package, clinical trial anomaly spotter (CTAS) has been developed by Bayer and the Intercompany Quality Analytics (IMPALA) consortium, helps detect inconsistencies in subject time series data at both site and subject levels, facilitating timely intervention.

Methods: CTAS analyzes time series of equal length. Each subject-level time series is summarized as six optional scalars: mean, standard deviation, range, relative unique value count, autocorrelation and local outlier factor. To detect site-level anomalies, sites can be scored using 3 different scoring methods. The performance of the CTAS algorithm was tested using simulations, artificially introducing site anomalies of various types and degrees into clinical trial data sets.

Results: We found that CTAS can reliably detect site anomalies depending on the degree of the anomaly introduced. Less complex anomalies such as mean were easier to detect than complex outlier such as local outlier factor. The three scoring methods differed in their ability to detect anomalous sites with a small number of patients and their false positive rates.

Conclusions: CTAS is a valuable tool for timely detection of outliers in clinical data, suitable for integration into risk-based strategies. Choosing the appropriate site anomaly scoring method is crucial for handling sites with fewer subjects effectively.

Pharmaceutical impurities pose a significant challenge in the development and manufacturing of anti-cancer drugs due to their high potency, narrow therapeutic index, and prolonged administration in most treatment regimens. Even trace-level impurities can compromise drug safety, efficacy, and regulatory compliance. This review provides a comprehensive overview of various types of pharmaceutical impurities organic, inorganic, residual solvents, and genotoxic impurities with a focus on their origins, toxicological significance, and impact on oncology therapeutics. The paper discusses traditional and modern analytical methods used for impurity detection, including chromatographic techniques, spectroscopic tools, mass spectrometry, and capillary electrophoresis and advanced hyphenated systems. Regulatory frameworks from International Council for Harmonization, the U.S. Food and Drug Administration, the European Medicines Agency, Therapeutic Goods Administration, Medicines and Healthcare products Regulatory Agency and the World Health Organization are examined, particularly concerning acceptable limits for genotoxic and elemental impurities. In addition, this study explores recent advancements such as surface plasmon coupled emission technique, AI-assisted data analysis, portable sensors, and real-time monitoring technologies that enhance impurity profiling. The advantages and applications of the modern technologies are discussed, emphasizing their role in improving method efficiency, automation, and sustainability in connection with the impurity profiling.

Clinical research professionals are essential to the successful conduct of clinical trials yet training and retention of this workforce remain significant challenges, particularly with constrained budgets and declining indirect cost reimbursements. This study describes the implementation, and evaluation of a micro-credentialing program at an NCI-designated comprehensive cancer center. The CRC badge, developed through a collaboration between Rutgers School of Health Professions, Rutgers Cancer Institute, and the New Jersey Alliance for Clinical and Translational Science, offers self-paced, competency-based training aligned with the Joint Task Force for Clinical Trial Competency framework. Fifty-six clinical research staff were invited to complete the CRC Badge between May 2023 and May 2024. Survey data from the 38 completers (67%) demonstrated substantial self-reported learning gains across regulatory activities, research roles, and data management. Post-course results indicated that the CRC badge helped enhance onboarding efficiency and inspired interest in continued professional development. Administrative feedback confirmed improvements in staff readiness.

Background: Decentralized clinical trials (DCTs) leverage digital health technologies to conduct trials outside traditional settings, offering numerous benefits such as increased participant diversity and breaking down geographic and transportation barriers. However, they also present significant ethical challenges. Current regulatory and ethical frameworks are not fully equipped to address these issues, highlighting a critical gap in effective governance and oversight of DCTs. This scoping review aims to systematically identify and synthesise the ethical challenges reported in the literature and to outline recommendations that can inform future guidance and practice.

Methods: This scoping review followed Arksey and O'Malley's framework. We searched PubMed, Web of Science, and SCOPUS for peer-reviewed articles in English and applied predefined inclusion and exclusion criteria to guide study selection.

Results: The initial search yielded 757 documents. After applying inclusion and exclusion criteria and conducting a rigorous two-stage screening process, 32 articles were selected for detailed analysis. These articles identified six main areas of ethical challenges: electronic informed consent, equity and access, privacy and confidentiality, participant safety and welfare, scientific validity, and ethical and regulatory oversight.

Conclusions: This review underscores the necessity for clear guidelines, enhanced digital literacy, robust data protection measures, and comprehensive regulatory frameworks to address the ethical challenges of DCTs. By synthesizing existing literature, this paper provides actionable recommendations, such as simplifying consent processes and improving technical support, and identifies areas for future research to ensure DCTs are conducted ethically and effectively.

Quantitative benefit-risk assessment (qBRA) can reveal how patients balance benefits and risks of cancer treatments. To align with qBRA good practice guidelines, researchers must address challenges including attribute value dependence, double counting, attribute dominance, and uncertainty associated with immature clinical trial outcomes. We present a case study illustrating these challenges in a qBRA of treatment preferences among patients with Philadelphia chromosome-positive acute lymphoblastic leukemia. Preferences were elicited using a discrete choice experiment (DCE). First, we explain how we mitigated potential dominance of survival outcomes by narrowing the range of overall survival (OS) durations that each participant considered. Second, we describe how we acknowledged the conceptual interaction between OS and duration of remission (DOR) attributes and tested for a statistical interaction. Third, we detail how we conducted qBRA with uncertain efficacy data using bivariate sensitivity analysis. Bivariate sensitivity analysis based on DCE-elicited preferences and head-to-head clinical performance data showed that if the considered treatments - ponatinib + chemotherapy and imatinib + chemotherapy - had equivalent efficacy, 52.9% (95% CI: 52.5%-53.4%) of DCE participants would be expected to choose ponatinib over imatinib. If ponatinib offered 10-month longer DOR and 20-month longer OS vs. imatinib, 71.6% (95% CI: 67.2%-76.0%) would choose ponatinib. Probabilistic sensitivity analyses showed that the probability of  ≥ 70% of patients preferring ponatinib is 77.5% if ponatinib offers 15-month longer OS and DOR and 93.0% if it offers 45-month longer OS and DOR. Preference heterogeneity analyses identified that the overall choice probability results hold for all subgroups in nearly all scenarios.

Regulatory agencies have been promoting the incorporation of the patient perspective into benefit-risk assessment to better align regulatory decisions with patients' needs and priorities. Currently, benefit-risk assessments for regulatory submissions primarily capture the patient perspective through patient-reported outcome data from clinical trials. However, there is a push for a more systematic approach to capturing the patient perspective in benefit-risk assessment and decision-making throughout the drug development life cycle. Although different guidelines and frameworks have been developed, consensus on how to systematically incorporate the patient perspective into structured benefit-risk (sBR) assessment remains elusive. In 2023, Sullivan et al. published an sBR assessment framework that was developed to enhance systematic and collaborative decision-making throughout a drug life cycle. Here we propose how this sBR assessment framework could be expanded, committing to a patient-centered approach by considering the patient perspective at every step of drug development. These recommendations aim to put patients at the center of drug development, ultimately leading to better treatment outcomes and improved lives.

Despite recent advancements in oncology drug development, patient access to innovative cancer therapies remains inadequate. There is an urgent need for more patient-centric approaches, with meaningful patient input from trial design through to health technology assessment (HTA) consultation. Multi-stakeholder consensus calls for better representation of the diversity of the target population and integration of patients' preferences in clinical cancer research by systematically collecting patient-reported outcomes using standardized methods, and acknowledging trade-offs between survival and long-term wellbeing. Furthermore, the generation of insufficiently robust data for regulatory and HTA decision-making continue to delay patient access to innovation. This could be mitigated through smarter study designs, including smaller, fit-for-purpose randomized studies and prospectively designed trials. Finally, concerted efforts are required to develop and validate novel intermediate/surrogate endpoints that enable earlier assessment of treatment outcomes to facilitate timely, evidence-based decisions that improve the patient experience across the cancer care continuum.