Therapeutic innovation & regulatory science最新文献

Accurate and timely reporting of adverse events (AEs) in clinical trials is crucial to ensuring data integrity and patient safety. However, AE under-reporting remains a challenge, often highlighted in Good Clinical Practice (GCP) audits and inspections. Traditional detection methods, such as on-site investigator audits via manual source data verification (SDV), have limitations. Addressing this, the open-source R package {simaerep} was developed to facilitate rapid, comprehensive, and near-real-time detection of AE under-reporting at each clinical trial site. This package leverages patient-level AE and visit data for its analyses. To validate its efficacy, three member companies from the Inter coMPany quALity Analytics (IMPALA) consortium independently assessed the package. Results showed that {simaerep} consistently and effectively identified AE under-reporting across all three companies, particularly when there were significant differences in AE rates between compliant and non-compliant sites. Furthermore, {simaerep}'s detection rates surpassed heuristic methods, and it identified 50% of all detectable sites as early as 25% into the designated study duration. The open-source package can be embedded into audits to enable fast, holistic, and repeatable quality oversight of clinical trials.

Background: Appropriate exploratory efficacy data from Phase I trials are vital for subsequent phases. Owing to the uniqueness of brain tumors (BTs), use of different strategies to evaluate efficacy is warranted. We studied exploratory efficacy evaluation in Phase I trials involving BTs.

Methods: Using Clarivate's Cortellis^™, 42 Phase I trials of BT interventions conducted from 2020 to 2022 were analyzed for efficacy endpoints, which were set as primary endpoints (PEs) or secondary endpoints (SEs). Additionally, these metrics were compared in two subgroups: trials including only BTs (Group-A) and those including BTs among mixed solid tumors (Group-B).

Results: Selected studies included a median of 1.5 PEs (range, 1-6) and 5 SEs (range, 0-19). Efficacy endpoints were included as PEs and SEs in 2 (5%) and 31 (78%) trials, respectively. Among the latter 31 trials that included 94 efficacy endpoints, 24, 22, 20, 9, and 8 reflected overall response rate (ORR), progression-free survival (PFS), overall survival (OS), duration of response (DOR), and disease control rate (DCR), respectively. ORR for BT was determined using various methods; however, the Response Evaluation Criteria in Solid Tumors (RECIST) was used less frequently in Group-A than in Group-B (p = 0.0039).

Conclusions: Recent Phase I trials included efficacy endpoints as SEs, with ORR, PFS, or OS included in ~ 50% trials and DOR or DCR in ~ 25%. No established criteria exist for imaging evaluation of BTs. Phase I trials involving mixed solid tumor cohorts revealed challenges in designing methods to assess the exploratory efficacy of BTs.

In June 2021, FDA released a Draft Guidance on Sponsor Responsibilities for IND Safety Reporting and cited components of a recommended Safety Surveillance Plan (SSP). To meet the expectations of the 2021 FDA guidance, sponsors should document their plan for aggregate safety assessment. The Drug Information Association-American Statistical Association Interdisciplinary Safety Evaluation scientific working group has proposed an Aggregate Safety Assessment Plan (ASAP) that addresses this recommendation. The 2021 FDA guidance also discusses potential strategies for unblinded review of safety data from ongoing studies by an independent Assessment Entity, which could occur via planned periodic evaluations or "triggered" reviews based on blinded data assessments. The Assessment Entity reviewing unblinded data makes recommendations as to whether the threshold has been met for submission of an aggregate IND safety report. In this paper, we discuss how the ASAP supports IND aggregate safety reporting decisions, including elements to be included in a proposed SSP appendix to the ASAP. In addition, the authors advocate for the benefits of developing a charter (or specific section of the Data Monitoring Committee charter, if applicable) that describes the responsibilities and conduct of the Assessment Entity. With these components in place, study sponsors will meet the objective of having clearly defined processes for the monitoring of clinical trial safety data in aggregate and making IND safety reporting decisions.

Introduction: Pharmaceutical regulation on a global scale is a complex process, with regulatory bodies overseeing various aspects, including licensing, registration, manufacturing, marketing, and labeling. Among these, the USFDA plays a crucial role in upholding public health. The pharmaceutical industry contributes significantly to well-being by developing and distributing therapeutic agents. The journey of evaluating new pharmaceuticals involves meticulous examination through several phases, from safety and efficacy assessments to toxicity evaluation. Drug approval involves submitting New Drug Applications (NDAs) to regulatory agencies like the USFDA and EMA. However, disparities in durations contribute to the phenomenon known as "drug lag." This lag refers to delays in a pharmaceutical product's availability in one market compared to another. Addressing this issue is crucial, given its impact on patient access to treatments.

Method: This study aims to analyze the extent of drug lag, focusing on newly approved oncology targeted therapies in Saudi Arabia, the United States, and the European Union. Data for cancer treatments authorized by the USFDA, EMA, and SFDA from January 1, 1997, to December 31, 2022, were collected from regulatory agency websites. The data sources included authorization letters, prescription information, and evaluation documents. We conducted a comparative assessment of drug lag for approved oncology targeted therapies between Saudi Arabia, the US, and the EU.

Result: Our analysis identified 135 newly approved oncology-targeted drugs within the specified timeframe. Of these, 71 received approval in all three regions, while disparities were evident in others. The USFDA consistently had the highest number of approved drugs, with 98.5% of drugs initially approved there. In contrast, Saudi Arabia had the lowest number of approved drugs and a significantly longer median drug lag, indicating substantial delays in drug availability.

Conclusion: This study highlights the significance of mitigating drug lag to enhance global healthcare outcomes and patient access to innovative therapies. Further research and collaborative efforts are essential to bridging these disparities and promoting equitable healthcare worldwide.

Background: Diabetes, a chronic disease worldwide, may be associated with a poorer prognosis in patients with coronavirus disease 2019 (COVID-19). While some antihyperglycemic medications may be beneficial, others may increase the risk of adverse clinical outcomes of COVID-19. We aimed to analyze the effect of antihyperglycemic medications on COVID-19.

Methods: We searched the Web of Science, Cochrane Library, EMBASE, PubMed, and Scopus databases from December 2019 to June 2022 to identify literature related to patients with COVID-19 and type 2 diabetes mellitus (T2DM) treated with antihyperglycemic medications.

Results: 56 studies were included in the analysis. Metformin (OR 0.66; 95% CI 0.58-0.74; p < 0.05), Glucagon-like peptide-1 receptor agonist (GLP-1ra) (OR 0.73; 95% CI 0.59-0.91; p < 0.05), and sodium-dependent glucose transporters 2 inhibitor (SGLT 2i) (OR 0.77; 95% CI 0.69-0.87; p < 0.05) were associated with lower mortality risk, while insulin was associated with increased mortality risk (OR 1.40; 95% CI 1.26-1.55; p < 0.05). Meanwhile, metformin (OR 0.65; 95% CI 0.50-0.85; p < 0.05) and GLP-1ra (OR 0.84; 95% CI 0.76-0.94; p < 0.05) were significantly associated with decreased severe manifestation risk. What's more, metformin (OR 0.77; 95% CI 0.62-0.96; p < 0.05), GLP-1ra (OR 0.86; 95% CI 0.81-0.92; p < 0.05), and SGLT 2i (OR 0.87; 95% CI 0.79-0.97; p < 0.05) were also associated with a decreased risk of hospitalization, but insulin were associated with an increased risk of hospitalization (OR 1.31; 95% CI 1.12-1.52; p < 0.05). Nevertheless, the results of the subgroup analyses showed that the effects of different glucose-lowering agents on COVID-19 may be related to in-hospital use or out-hospital use, elderly or non-elderly patients use, and different geography.

Conclusion: Metformin, GLP-1ra, and SGLT 2i have shown a positive effect on clinical outcomes in COVID-19, particularly in non-elderly individuals. However, insulin use may pose a higher risk, especially in elderly patients, so need with caution. Meanwhile, DPP-4i, TZD, α-GLUi, and sulfonylureas appeared to have a neutral effect. These results need to be validated in future clinical studies.

Background: Whereas anecdotal reports suggest that the use of decentralized clinical trial (DCT) solutions can improve participant diversity in clinical trials there is no quantitative evidence to support such reports.

Methods: Tufts CSDD conducted this initial study based on data collected from prior research and publicly available data drawn from Clinicaltrials.gov to compare and contrast participant diversity in trials which included various DCT solutions - virtual visits or televisits, home visits, devices or wearables, and the use of local labs.

Results: The results of this analysis indicate that the use of local labs is associated with a lower percentage of white participants; the use of virtual visits or televisits is associated with a lower percentage of Black participants; and the use of devices or wearables was not associated with any significant change in participant demographics. The use of home visits could not be tested for significant differences.

Real-World Evidence (RWE), which has historically been used to support post-approval safety studies, has recently gained acceptance for new drug applications as supportive evidence or as new clinical evidence for medicinal products with orphan designation and/or in disease areas with high unmet need. Here, we present a case study for the use of RWE in the approval of abaloparatide in the European Union (EU) under the tradename Eladynos. In addition to data from the pivotal Phase 3 study, the marketing authorization application (MAA) included clinical data from additional interventional and observational studies, as well as post-marketing data obtained from the United States (US) market since approval of abaloparatide by the Food and Drug Administration (FDA) in 2017. The new interventional studies were not designed to assess fracture efficacy and cardiovascular safety which were topics of concern raised by the Committee for Medicinal Products for Human Use (CHMP) during their review of the initial MAA submitted in 2015. However, these studies taken together with the RWE formed the basis for a new MAA. Prior to the planned resubmission in the EU, national Scientific Advice (SA) was sought on the proposed clinical program, specifically on the relevance of Real-World Data (RWD) derived from an observational study to support and complement the efficacy and safety data already available from prospective randomized clinical trials. This case study demonstrates successful use of RWE to address a previously identified gap raised by the CHMP during the review of an earlier MAA, which led to the approval of Eladynos for the treatment of osteoporosis in the EU.

A clinical trial is any research on human subjects that involves an investigational medicinal product or device. Investigational medicinal products include unlicensed drugs or drugs used outside the product license (e.g. for a new indication) (ICH-GCP). As per the internationally accepted ICH-GCP guidelines, clinical trials should be conducted strictly per the approved protocol. However, during the lifecycle of a trial, protocol deviations may occur. Under ICH efficacy guidelines, protocol deviations are divided into non-important (minor) or important (major), and the latter can jeopardise the participant's rights, safety or the quality of data generated by the study. Existing guidelines on protocol deviation management do not detail or standardise actions to be taken for participants, investigational products, data or samples as part of a holistic management of important protocol deviations. Herein, we propose guidelines to address the current literature gap and promote the standardisation of actions to address important protocol deviations in clinical trials. The advised actions should complement the existing local institutional review board and national regulatory authority requirements.

Introduction: Competency-based education has been commonly used to enhance the healthcare workforce for some time. A translational discipline that is integral to drug development and impactful on healthcare and public health is clinical pharmacology. With such contribution, it is essential that the clinical pharmacology workforce is adequately equipped to address the demands of emerging trends of drug development.

Objectives: The primary objective of this study was to determine the most significant competencies needed for a clinical pharmacologist in the regulatory environment.

Methods: A two round modified Delphi technique was administered to 29 clinical pharmacologists within the Office of Clinical Pharmacology (OCP) between November 2021-January 2022. A questionnaire consisting of core and technical competencies was administered electronically using SurveyMonkey ® to gain consensus about essential clinical pharmacology competencies. Participants used a Likert scale to rank importance of competencies from strongly agree (1), agree (2), neutral (3), disagree (4), strongly disagree (5). Participants also suggested topics to be included in the next round. Consensus was set at 60%. The competencies receiving the most consensus at 60% in round one and the new topics proceeded to the second round. In the second and final round, participants ranked the suggested competencies. Descriptive statistics and a McNemar change test were utilized to analyze data. Only data from the participants who completed both rounds was used in the study.

Results: In round one participants ranked all fifty-six core and technical competencies as essential with consensus of at least 60%. In round two, participants ranked sixty-two competencies as essential with consensus of at least 60%. A McNemar change test demonstrated stability of ranking between rounds.

Conclusion: Essential core and technical competencies can build education programs to sustain the emerging clinical pharmacology workforce in the Office of Clinical Pharmacology. The Delphi technique is a suitable approach to determine essential competencies because it cultivates consensus and gains insight from experts in the forefront of drug development.

Background: Multiple criteria decision analysis (MCDA) and stochastic multi-criteria acceptability analysis (SMAA) in their current implementation cannot incorporate prior or external information on benefits and risks. We demonstrate how to incorporate prior data using a Bayesian mixture model approach while conducting quantitative benefit-risk assessments (qBRA) for medical products.

Methods: We implemented MCDA and SMAA in a Bayesian framework. To incorporate information from a prior study, we use mixture priors on each benefit and risk attribute that mixes information from a previous study with a vague prior distribution. The degree of borrowing is varied using a mixing proportion parameter.

Results: A demonstration case study for qBRA using the supplementary New Drug Application (sNDA) filing for Rivaroxaban for the indication of reduction in the risk of major thrombotic vascular events in patients with peripheral artery disease (PAD) was used to illustrate the method. Net utility scores, obtained from the randomized controlled trial data to support the sNDA, from the MCDA for Rivaraxoban and comparator were 0.48 and 0.56, respectively, with Rivaroxaban being the preferred alternative only 33% of the time. We show that with only 30% borrowing from a previous RCT, the MCDA and SMAA results are favorable for Rivaroxaban, accounting for the seemingly aberrant results on all-cause death in the trial data used to support the sNDA.

Conclusion: Our method to formally incorporate prior data in MCDA and SMAA is easy to use and interpret. Software in the form of an RShiny App is available here: https://sai-dharmarajan.shinyapps.io/BayesianMCDA_SMAA/ .