Regulatory Decisions Research Articles

Artificial intelligence and machine learning in veterinary medicine: a regulatory perspective on current initiatives and future prospects

Abstract The US FDA's Center for Veterinary Medicine (CVM) is advancing its leadership in veterinary science by integrating AI and machine learning (ML) into its regulatory framework and scientific initiatives. This paper explores the CVM's strategic approach to harnessing these technologies to enhance human and animal health by supporting innovative products and methods. Key areas of focus include regulatory adaptation, genomic research, and information technology modernization. The Animal and Veterinary Innovation Agenda outlines the Center's commitment to fostering innovation in veterinary medicine while addressing emerging challenges. This includes developing AI/ML-driven tools for antimicrobial resistance research, genome editing safety, and postmarketing safety surveillance. The paper discusses the CVM's participation in the FDA's role in shaping guidance documents for AI in regulatory decision making. In genomic research, the CVM is utilizing AI/ML to study antimicrobial resistance and improve genomic editing techniques. These technologies enhance the understanding of resistance mechanisms and facilitate the precise identification of genetic alterations. Artificial intelligence is also pivotal in information technology modernization efforts, aimed at streamlining data management and enhancing operational efficiency. The paper highlights the efforts to integrate AI/ML in safety surveillance, including signal detection and case processing. It emphasizes the importance of human-led governance, data quality, and model validation in ensuring the ethical deployment of AI technologies. The CVM's initiatives represent a transformative shift toward more efficient and innovative regulatory approaches. The paper concludes with a call for continued collaboration among researchers, industry, and regulatory bodies to advance AI integration and achieve mutual goals in animal health.

Advancing Principled Pharmacoepidemiologic Research to Support Regulatory and Healthcare Decision Making: The Era of Real-World Evidence.

A compilation of factors over the past decade-including the availability of increasingly large and rich healthcare datasets, advanced technologies to extract unstructured information from health records and digital sources, advancement of principled study design and analytic methods to emulate clinical trials, and frameworks to support transparent study conduct-has ushered in a new era of real-world evidence (RWE). This review article describes the evolution of the RWE era, including pharmacoepidemiologic methods designed to support causal inferences regarding treatment effects, the role of regulators and other health authorities in establishing distributed real-world data networks enabling analytics at scale, and the many global guidance documents on principled methods of producing RWE. This article also highlights the growing opportunity for RWE to support decision making by regulators, health technology assessment groups, clinicians, patients, and other stakeholders and provides examples of influential RWE studies. RWE holds promise to address important questions that clinical trials typically do not answer about treatment benefits and risks, and to ultimately impact public health by helping to guide decision making across the healthcare ecosystem.

Unraveling Caco-2 cells through functional and transcriptomic assessments.

The static Caco-2 monolayer is an extensively utilized model for predicting the permeability of small molecules during the drug development process. While these cells can differentiate and develop key functional and morphological features that emulate human enterocytes, they do not fully replicate the complexity of human intestinal physiology. In this study, we investigated functional and morphological aspects of Caco-2cells, alongside their transcriptomic profiles, with a particular emphasis on genes encoding drug-metabolizing enzymes and drug transporters. We found that Caco-2cells not only established a robust and bio-relevant permeable intestinal barrier but also demonstrated functional maturity and differentiation in the intestinal epithelium, substantiated by the activities of important enzymes and an efflux transporter. However, our targeted gene expression analyses revealed that substantial disparities were found in mRNA transcript levels among Caco-2cells and human biopsy samples. These findings highlight that, although Caco-2cells are valuable for assessing the passive transport of drugs, their accuracy for predicting active transport or small intestinal drug metabolism is constrained by their transcriptomic divergence from human intestinal tissues. This study highlights the importance of understanding the Caco-2 model's inherent limitations and provides insights that could inform its appropriate application in drug development and regulatory decision-making.

Post-Market Evidence for Cancer Medicines in Regulatory and Clinical Decision-Making: A Scoping Review.

Cancer medicines usually have uncertain efficacy and safety profiles when they are first approved by medicines regulators because this evidence usually emerges post-market. Little is known about the extent to which post-market evidence is evaluated and integrated into evidence review processes in regulatory and clinical contexts. The objective of this scoping review is to examine the literature on how post-market evidence on benefits and harms is evaluated and integrated in regulatory decisions and guidance for clinical decision-making. This scoping review focussed on the organisations that review cancer medicines and post-market evidence for their benefits and harms. It examined all regulatory or clinical contexts in which this post-market evidence might be included in evidence review processes for evaluation then integration into regulatory or clinical contexts. Four electronic databases were searched. Titles and abstracts were screened for all retrieved references followed by full-text screening by two independent reviewers according to pre-specified inclusion criteria. In total, 28 studies met inclusion criteria. These included 31 assessments by medicines regulators, four by clinical practice guideline developers and two by health technology assessment agencies. Half of the studies evaluated clinical outcomes for benefit or harms (e.g., overall survival, serious adverse events). We found that more published literature evaluated and integrated post-market evidence for benefits and harms of cancer medicines in regulatory than in clinical situations, such as treatment guidelines and health technology assessments. In these studies, post-market evidence for harms seemed to be integrated more often than for benefits. And the studies showed a gap: only some of the evaluated post-market evidence was subsequently integrated in both regulatory and clinical situations. Overall, these findings raise important questions around the availability, accessibility, and assessment of post-market evidence for benefits and harms of cancer medicines so that it can be used by health professionals working in cancer services and by people with cancer.

INSIGHT: A Tool for Fit-for-Purpose Evaluation and Quality Assessment of Standardized Observational Data Sources for Real World Evidence on Medicine and Vaccine Safety.

To describe the development of INSIGHT, a real-world data quality tool to assess completeness, consistency, and fitness-for-purpose of observational health data sources. We designed a three-level pipeline with data quality assessments (DQAs) to be performed in ConcePTION Common Data Model (CDM) instances. The pipeline has been coded using R. INSIGHT is an open-source tool that identifies potential data quality issues in CDM-standardized instances through the systematic execution and summary of over 588 configurable DQAs. Level 1 focuses on conformance to the ConcePTION CDM specifications. Level 2 evaluates the temporal plausibility of events and uniqueness of records. Level 3 provides an overview of distributions, outliers, and trends over time to facilitate fit-for-purpose evaluation. Therefore, level 1 and 2 assure a proper data standardization, while level 3 provides information regarding the study population, and potential sub-populations. The DQAs are run locally and assessed centrally by a data quality revisor together with the data access provider's representatives. Data quality is the sum of several internal and external features of the data. While DQAs can provide reassurance about fitness-for-purpose for secondary-use data sources, improvements in data collection are essential to reduce errors and enhance overall data quality for Real World Evidence. INSIGHT aims to support clinical and regulatory decision-making for medicines and vaccines by evaluating the quality of observational health data sources to support fit for purpose assessment. Assessing and improving data quality will enhance the reliability and quality of the generated evidence. This research was registered in EU PAS registration with number EU50142.

The Growth of Alternative Investment Funds in India

Abstract: The Securities and Exchange Board of India (SEBI) launched alternative investment funds (AIFs) in India in 2012. This study investigates the organizational structure of AIFs and their progression since their establishment. The analysis considers several factors, such as the commitments raised, funds generated, and investments executed, employing both graphical and inferential analysis techniques. The results reveal that AIFs have made considerable strides as a viable investment option, primarily due to the numerous benefits and diverse opportunities they provide to investors, along with the creative freedom granted to fund managers. In the years ahead, there is significant potential for this emerging investment alternative to experience substantial growth in both the Indian and global markets, which will largely depend on SEBI's regulatory decisions regarding the AIF framework

Phytochemical, antibacterial and cytotoxic properties of piper betle L.

The phytochemical, antibacterial and cytotoxic characteristics of Piper betle L., leaf extract was examined. The phytochemical analysis revealed the presence of glycosides, alkaloids, terpenoids, flavonoids, tannins, and saponins. The presence of different phytochemicals will help in anticipating their pharmacological action in searching for bioactive agents. Antibacterial effects were evaluated against six bacterial strains. The tested extract exhibited no antibacterial action against any bacterial strains investigated. Cytotoxicity was assessed on HeLa and Vero cell lines employing the Cell Counting Kit-8 test. The methanolic extract exhibited cytotoxicity against HeLa and Vero cells with an IC50 value of 270 μg/ml and 240 μg/ml, respectively. This preliminary investigation highlights the promising anticancer activities of P. betle leaf extract in vitro, suggesting its potential for developing novel anticancer agents. However, cytotoxicity towards Vero cells raises concern for fundamental research in drug discovery and regulatory decision-making, thereby shaping the course of subsequent studies and applications. Bangladesh J. Bot. 53(4): 889-894, 2024 (December)

POP-REFINE: A Comprehensive Framework for Evaluating and Optimizing Representativeness in Clinical Trials.

Clinical research has historically failed to include representative levels of historically underrepresented populations and these inequities continue to persist. Ensuring representativeness in clinical trials is crucial for patients to receive clinically appropriate treatment and have equitable access to novel therapies; enhancing the generalizability of study results; and reducing the need for post-marketing commitments focused on underrepresented groups. As demonstrated by recent legislation and guidance documents, regulatory agencies have shown an increased interest in understanding how novel therapies will impact the patient population that will receive them. Despite these efforts, a systematic approach to measure and optimize representativeness remains underdeveloped. Here, we introduce the novel Population Optimization, Representativeness Evaluation, and Fine-tuning Framework, designed to quantify and enhance representativeness. Our framework includes methods for evaluating overall and subgroup representativeness, identifying drivers of non-representativeness, and optimizing eligibility criteria to achieve representative populations. We demonstrate our framework by selecting patients who met the eligibility criteria for nine oncology clinical trials from a nationwide electronic health record-derived de-identified database and quantifying the representativeness of each trial's eligible population. This framework addresses gaps in current literature by providing a comprehensive, data-driven approach to enhance the representativeness of clinical trials, thereby supporting regulatory and internal decision-making processes. This framework is adaptable to various disease indications and can be extended to evaluate enrolled study samples, ensuring that clinical trials are representative.

Assessing healthcare cost changes associated with transitioning away from cigarette smoking using healthcare claims data: an exploratory study among adult male patients with COPD

Abstract Background The assessment of potential health effects of switching from cigarette smoking to non-combustible tobacco products has important implications for public health and regulatory decisions. Robust epidemiological evidence requires long-term follow-up of a large number of individuals. Real-world evidence derived from health records has the potential to help fill the gap in the interim. To our knowledge, this is the first study using individual-level healthcare claims data to assess the potential impact of transitioning from cigarette smoking to smokeless tobacco on short-term direct healthcare costs. Methods We conducted a retrospective cohort study of adult male patients with COPD who smoked cigarettes at baseline using the MarketScan® Databases. We compared changes in direct healthcare costs between the 12-month periods before (baseline) and after the index date (follow-up) across three cohorts: continued smoking (CS), quit all tobacco (QT), or switched to smokeless tobacco (SW), using a non-linear difference-in-differences model with average marginal effects. Results A total of 23,427 COPD patients were included (CS: 11,167; QT: 12,013; SW: 247). At baseline, the QT cohort had the highest total average healthcare costs ($43,771), followed by SW ($38,419), and CS ($27,149). The unadjusted difference-in-differences model revealed no statistically significant differences in total healthcare cost changes when comparing the QT or SW cohorts to the CS cohort (-$1,532 [95% CI: -$3,671, $608] for the QT cohort, and -$452 [95% CI: -$15,415, $14,511] for the SW cohort). After adjusting for Deyo-Charlson Comorbidity Index and COPD exacerbation, assuming patients had two comorbidities and exacerbations, the QT cohort had greater reduction in total healthcare costs compared to the CS cohort (-$2,910 dollars [95% CI: -$4,485, $-1,335]). The same trend was observed for the SW cohort, although the estimate was not statistically significant (-$5,312 [95%CI: -$11,067, $442], p = 0.08). Conclusions This study demonstrated the feasibility of using administrative claims to conduct real-world evidence studies on the harm-reduction potential of non-combustible tobacco products and found evidence suggesting reductions in direct healthcare costs after quitting tobacco or switching to smokeless tobacco among patients with COPD. Based on the learnings and limitations identified during the study, we propose concrete recommendations to improve future observational studies by integrating additional real-world healthcare data from multiple data sources.

Spatiotemporal Analysis and Risk Prediction of Water Quality Using Copula Bayesian Networks: A Case in Qilu Lake, China

Lake water pollution under anthropogenic influences exhibits characteristics of high uncertainty, rapid evolution, and complex control challenges, presenting substantial threats to ecological systems and human health. Quantitative risk prediction provides crucial support for water quality deterioration prevention and management. This study employed the Copula Bayesian Network model to conduct a comprehensive risk assessment of water quality in Qilu Lake, China (2010–2020), incorporating inter-indicator correlations and multiple uncertainty sources. Analysis revealed generally “worse” water quality conditions (5.10 ± 1.35) according to established index classifications, with predicted probabilities of reaching “deteriorated” status ranging from 11.80% to 47.90%. Significant spatial and temporal variations in water quality and pollution risk were observed, primarily attributed to intensive agricultural non-point source loading and water resource deficiency. The study established early warning thresholds through key indicator concentration predictions, particularly for the southern region where “deteriorated” risk levels corresponded to specific ranges: TN (3.42–8.43 mg/L), TP (0.07–1.29 mg/L), and CODCr (27.75–67.19 mg/L). This methodology effectively characterizes lake water quality evolution while enabling risk prediction through key indicator monitoring. The findings provide substantial support for water pollution control strategies, risk management protocols, and regulatory decision-making for lake ecosystem administrators.

Cell-Based Assays to Detect Innate Immune Response Modulating Impurities: Application to Biosimilar Insulin

Characterizing and mitigating factors that impact product immunogenicity can aid in risk assessment and/or managing risk following manufacturing changes. For follow-on products that have the same indication, patient population, and active product ingredient, the residual immunogenicity risk resides predominantly on differences in product and process related impurities. Characterizing differences in innate immune modulating impurities (IIRMI), which could act as adjuvants by activating local antigen presenting cells (APCs), can inform the immunogenicity risk assessment potentially reducing the need for clinical trials. To date, assays to detect trace levels of IIRMI are being used to support regulatory decisions by FDA for selected synthetic peptide drug products that refer to reference listed drugs of rDNA origin but not recombinant protein or peptide products where more complex mixtures of trace impurities including host cell proteins are expected. Here we describe an exercise to explore whether or not there are differences in the innate immune response elicited by an insulin glargine (produced in E. coli) and its interchangeable biosimilar insulin (produced in P. pastoris) that could indicate differences in IIRMI. Our results suggest the two products elicit comparable innate immune responses as determined by the expression of 90 immune-related genes, including IL-1α, IL-1β, IL-6, CCL3, CCL2, and CXCL8. The data suggest that these assays can provide useful information when assessing recombinant proteins for the presence of IIRMI.Graphical

Important tool in our rare disease toolbox: hybrid retrospective-prospective natural history studies serve well as external comparators for rare disease studies

Natural history studies (NHS) can support regulatory decision-making at different stages of the drug product life cycle and are especially important in the context of rare diseases, which are associated with not only delayed or erroneous diagnoses but also a lack of approved treatments. Real-world evidence can fill knowledge gaps and support treatment decision-making, thereby benefiting affected patients. In this context, there are three important options for NHS design: retrospective, prospective, and cross-sectional. Each of these has been successfully used to support regulatory approval as external comparator arms (ECAs) for clinical studies, especially single-arm trials (SATs). While longitudinal data obtained from retrospective or prospective designs have been more commonly used and have been the focus of regulatory guidance documents, hybrid designs that combine retrospective and prospective data collection are particularly powerful for rare disease studies. This is due, in part, to the smaller number of patients impacted by each rare disease. In these settings, retrospective or prospective data collection alone may not be sufficient or fit-for-purpose for an external comparator. Rather, a strategic combination of all available data, regardless of timing, can deliver the right information of the desired quality and completeness to answer these important questions and support regulatory evidentiary needs. For instance, patients included in retrospective studies may differ from recently treated patients in terms of disease severity, disease variants, clinical management, or other important aspects of the disease that may impact patient outcomes. Further, retrospectively collected data may lack specific data elements required to achieve adequate comparison with the treated group in single-arm studies. In the context of prospective designs, the recruitment of sufficient new patients for prospective follow-up may not be feasible or may be prolonged due to the rarity of the disease. Further, the potential for premature truncation of patient follow-up may result in insufficient longitudinal data, or prospectively collected data alone may not provide insights into the disease course for specific groups of patients. In these situations, primary data collection in a prospective study may be supplemented with retrospectively collected data from chart reviews, registries, or electronic medical record databases, either for the same patients, in an ambispective design, or for a different set of patients. These hybrid designs allow for broader and more robust contextual information on the patient journey and the natural course of the disease to be obtained, which can improve the suitability of the data as an external comparator for SATs or studies that lack internal control in situations where a prospective design alone might not be sufficient. Because retrospective and prospective data, or any two data sources that are being combined, may differ in availability and quality, there are unique challenges alongside the strengths of these designs. In this paper, we discuss considerations for the design, analysis, and conduct of hybrid NHS intended as ECAs for single-arm studies in clinical development programs for rare diseases.

Informing the decision-making process for potential PMT/vPvM chemicals through the adoption of a risk-based prioritization framework: the ZeroPM approach

A risk-based strategy is presented aimed at prioritizing chemicals screened as potential persistent, mobile and toxic (PMT) or very persistent and very mobile (vPvM) substances. Prioritization is done to strengthen the decision-making process regarding actions that might be taken against chemicals screened as potential PMT/vPvM substances. Such actions can range from acquiring additional data aimed at reducing uncertainties in toxicological effect concentrations or internal exposure concentrations to—in case of acceptable uncertainty—suggesting compounds for prevention and/or removal measures in order to limit future exposure. The prioritization strategy is developed within the ZeroPM project and applies a variety of tools, including in silico and in vitro models for exposure and toxicity hazard assessment. These tools will be applied to chemicals identified as PMT/vPvM substances, with a preliminary emphasis on substances belonging to three chemical classes, i.e. perfluorinated compounds, triazines and triazoles. Here we describe the ZeroPM approach providing a proof-of-principle illustrative example, based on data-rich substances, results from which demonstrate how prioritization can be achieved using a risk-based approach that uses data obtained from new approach methodologies (NAMs) and environmental exposure concentrations, obtained either through modelling or monitoring studies. Results are communicated using a risk-based prioritization matrix, which can be used to help to communicate prioritization needs, such as identifying data gaps or for guiding actions aimed at mitigating exposure. The precision and accuracy of the prioritization matrix is evaluated using several data-rich chemicals, which identifies perfluorooctanoic acid and perfluorooctane sulfonic acid as high priority, due to a combination of toxicity and exposure estimates, whereas atrazine and melamine are observed at lower priority. The proposed risk-based prioritization framework thus represents a complementary source of information that should help support regulatory decision-making for PMT/vPvM substances.

Coordinating Research and evidence for Medical Devices (CORE MD) and beyond

Medical device regulations have its focal center on translating expert knowledge into advice for EU regulatory guidance, and building expertise in regulatory science in the clinical community, the Coordinating Research and Evidence for Medical Devices (CORE MD) project is here to review methodologies for the clinical evaluation of high-risk medical devices and recommend new designs to set an appropriate balance between innovation, safety, and clinical effectiveness. CORE-MD is a European Union Horizon 2020 project which began in April 2021 and was concluded in March 2024. For the first time ever CORE–MD consortium stands to bring together 33 medical associations, on one platform. The major objective of CORE MD is, developing guidance for evaluation of artificial intelligence. CORE MD includes cardiovascular, and diabetes medical devices, since these exemplify devices are used to reduce mortality and morbidity, the use of real-world evidence in regulatory decision making, evidences from clinical trials on High-risk medical devices, establishment of post approval evidence development schemes, tools to retrieve public information on medical devices and information on the performance of these High-Risk Medical Devices. As a result, the need for more sources to provide clinical evidences for medical devices is required, before they are approved for implantation in patients and to add on the focus, CORE MD project has brought brilliant potential in increasing the use of benefit measures and accelerating surrogate outcomes research which would optimize an implant’s benefit-risk ratio and making CORE-MD an ambitious project with great scope.

Biomarkers of exposure to nicotine and selected toxicants in individuals who use alternative tobacco products sold in Japan and Canada in 2018-2019.

Comparisons of nicotine and toxicant exposure between people who use different alternative tobacco products remains underexplored. This cross-sectional, multi-country study analyzed urinary metabolites of nicotine, NNK, and volatile organic compounds (acrolein, acrylamide, acrylonitrile) among established users (n=550) in Japan and Canada. Participants exclusively or concurrently used nicotine vaping products (NVPs; Canada only), heated tobacco products (HTPs; Japan only), and combustible cigarettes (CCs; Japan and Canada), or abstained (Japan and Canada). All product groups showed substantial nicotine exposure. Both HTPs and NVPs exposed exclusive users to lower toxicant levels than exclusive CC use. Canadian participants who exclusively used NVPs exhibited lower NNK and acrolein exposure but higher acrylamide exposure than Japanese participants who exclusively used HTPs. Concurrent use of CCs alongside alternative products exposed users to higher toxicant levels compared to exclusive use of either alternative product. Exclusive use of alternative tobacco products results in significant nicotine exposure but substantially lower toxicant exposure compared to exclusive CC use. People who use HTPs in Japan may experience higher exposure to nicotine and certain toxicants (NNK, acrolein) than people who use NVPs in Canada. Concurrent use results suggest that partially substituting CCs with alternative products may reduce toxicant exposure, but to a lesser extent than completely transitioning to alternative products. Exposure patterns between two popular alternative tobacco products differ. The overall toxicant exposure from these products is lower than CCs, providing critical data for regulatory decisions and public health considerations.

Shortwave Infrared Hyperspectral Imaging to Detect Contaminants in the U.S. Food Supply.

The U.S. Food and Drug Administration (FDA) ensures the safety of the nation's food supply using sampling and laboratory analysis of imported and domestic foods. Accurate detection and identification of extraneous filth elements in inspected food samples is critical in producing evidence for regulatory decision-making. As part of ongoing efforts to increase the efficiency and accuracy of data collection, to better inform regulatory decision-making, scientists at the FDA have been exploring the application of emerging imaging technologies. To this end, we tested the ability of shortwave infrared (SWIR) hyperspectral image analysis to simultaneously detect and identify filth elements from a variety of chemically digested single- and multiple-ingredient food matrices. We tested five stored-product beetle species on a background of four different food matrix types. Our analyses successfully detected whole beetles and fragments as small as 0.65 mm in 95% of samples. All beetle species tested were accurately detected from the background matrices, and initial classification results show identification to genus. Our results show that SWIR spectral image analysis is a very promising technology for application in the detection and identification of filth elements in food products in a regulatory context and further development has the potential to increase analytical efficiency at FDA regulatory labs.

EPH114 Facilitating Real-World Data Discoverability and Transparency of Real-World Evidence to Support Research and Regulatory Decision-Making: The HMA-EMA Real-World Data Catalogues of Data Sources and Studies

EPH114 Facilitating Real-World Data Discoverability and Transparency of Real-World Evidence to Support Research and Regulatory Decision-Making: The HMA-EMA Real-World Data Catalogues of Data Sources and Studies

From the Formation of Conceptual Framework to Regulatory Decision-Making: Considerations for the Developments of Patient-Reported Outcome Instruments.

In recent years, patient-focused drug development (PFDD) has received widespread attention as a new paradigm in clinical trials. The PFDD emphasizes patients are partners in research projects, where patients can participate in research design, implementation, and outcome measurement, rather than just providing data. PFDD has shown great value in the research and development of pharmaceutical products, such as in accelerating the process of patient enrollment and improving the success rate of drug approval. Many countries and regions, including the United States, China, and Europe, have issued relevant regulatory policies and guidelines related to PFDD, covering study design, implementation, and risk-benefit assessment. The core of PFDD implementation is clinical outcome assessment (COA), of which patient-reported outcome (PRO) is most common. As far as the US Food and Drug Administration is concerned, there are numerous COA tools waiting for qualification, but currently all qualified are PROs. This review focuses on PRO and explores the key elements of PRO instruments' development, application, and inclusion in regulatory decision-making.

Compendium of Information on the Use of Guideline‐Based Developmental Neurotoxicity Studies*

Abstract Recent regulatory use of in vitro assays for developmental neurotoxicity have promulgated the development of initial OECD guidance for use of data from the developmental neurotoxicity in vitro battery (DNT IVB). One limitation in use of data from NAMs like the DNT IVB are the uncertainties of in vitro to predict in vivo effects. The lack of any curated database of in vivo DNT studies hampers such a comparison. The goal of the current effort was construction of a repository of all DNT studies defined as publicly available studies that followed DNT and/or reproductive guidelines containing a DNT cohort. The first specific aim was construction of a dataset that identified published studies and regulatory documents that mention completed, underway or planned DNT studies. This included documents which use read across as well agency waivers for DNT studies. The second aim identified those documents from Aim 1 that report the results of completed DNT studies. The third aim identified DNT studies that provide regulatory reviews with summary data. A total of 1648 documents were found from the overall search that had evidence of, 1) a completed, ongoing, or planned DNT study; 2) a read‐across or planned read‐across; or 3) a waiver. The 1648 documents included 324 DNT study citations. Final filtering of these data yielded a list of 153 DNT studies conducted on 144 chemicals and one stressor (diet restriction) for which there were both extensive data summaries and regulatory reviews. These results likely undercount the number of conducted studies due to the limited release of information from many regulatory agencies. In addition, the amount of information provided publicly available reviews ranges widely, from a one sentence summary to an extensive review with summaries of the methods, results and data interpretations. It is hoped that this paper will persuade regulatory agencies to release more DNT studies that provide comprehensive information. This will foster transparency and ensure public confidence in regulatory decisions. In addition, these results should provide data that promotes comparisons between in vitro and in vivo DNT methods.

HTA276 The Difference Between Regulatory and Market Access Decisions on Treatment Availability for ATMPs Across Australia, EU4, and UK

HTA276 The Difference Between Regulatory and Market Access Decisions on Treatment Availability for ATMPs Across Australia, EU4, and UK