Emerging technologies and their impact on regulatory science

Abstract

There is an evolution and increasing need for the utilization of emerging cellular, molecular and in silico technologies and novel approaches for safety assessment of food, drugs, and personal care products. Convergence of these emerging technologies is also enabling rapid advances and approaches that may impact regulatory decisions and approvals. Although the development of emerging technologies may allow rapid advances in regulatory decision making, there is concern that these new technologies have not been thoroughly evaluated to determine if they are ready for regulatory application, singularly or in combinations. The magnitude of these combined technical advances may outpace the ability to assess fit for purpose and to allow routine application of these new methods for regulatory purposes. There is a need to develop strategies to evaluate the new technologies to determine which ones are ready for regulatory use. The opportunity to apply these potentially faster, more accurate, and cost-effective approaches remains an important goal to facilitate their incorporation into regulatory use. However, without a clear strategy to evaluate emerging technologies rapidly and appropriately, the value of these efforts may go unrecognized or may take longer. It is important for the regulatory science field to keep up with the research in these technically advanced areas and to understand the science behind these new approaches. The regulatory field must understand the critical quality attributes of these novel approaches and learn from each other's experience so that workforces can be trained to prepare for emerging global regulatory challenges. Moreover, it is essential that the regulatory community must work with the technology developers to harness collective capabilities towards developing a strategy for evaluation of these new and novel assessment tools.

Society for birth defects research and prevention's multidisciplinary research needs workshop 2022: A call to action

“…Application of AI/ML to the prediction of mechanisms has been utilized (Davenport & Kalakota, 2019; Vamathevan et al, 2019) although applications to regulatory decision making are not straightforward due to the challenges integrating data across different biological scales (e.g., molecular, cellular, tissue, organismal). Several examples of clinical diagnosis were discussed. However, it was evident that the use of AI/ML was most readily demonstrated for data mining and diagnostic imaging (Anklam et al, 2022; Mohsen et al, 2022). The translatable application for of AI/ML for risk factor analysis for prognosis and pattern recognition were less frequently noted. As a collection of structured and unstructured data from many different sources, mobilizing big data for identifying data streams that offer the characteristics of volume, value, velocity, variety, and veracity has an important role for AI/ML.…”

“…Several examples of clinical diagnosis were discussed. However, it was evident that the use of AI/ML was most readily demonstrated for data mining and diagnostic imaging (Anklam et al, 2022;Mohsen et al, 2022). The translatable application for of AI/ML for risk factor analysis for prognosis and pattern recognition were less frequently noted.…”

Adaptability of AI for safety evaluation in regulatory science: A case study of drug-induced liver injury

“…As two important aspects of regulatory significance, especially for the application of AI, the applicability domain and context of use play a significant role in enhancing AI solutions for risk assessments within the regulatory arena. On every occasion, the context of use should clearly convey to users where the model is best utilized as well as whether the model is intended to complement or replace current technologies (Anklam et al, 2022 ), while the applicability domain outlines how the model is used through defining best practices (Anklam et al, 2022 ).…”

“…AI and DL tools have begun to play a crucial role in the advancement of computer-aided drug discovery, design, and development (Gupta et al, 2021 ), specifically for the study of drug safety and efficacy. DL is arguably the most advanced ML approach that frequently outperforms conventional ML approaches (Slikker et al, 2012 ; Gupta et al, 2021 ; Anklam et al, 2022 ). DL usually consists of multiple layers of neural networks which can be constructed and connected in diverse ways, giving rise to a broad range of methodologies.…”

Big Question to Developing Solutions: A Decade of Progress in the Development of Aquatic New Approach Methodologies from 2012 to 2022

“…Different multistakeholder groups, including the OECD and specific advisory groups (OECD, n.d.b), have collaborated to improve the adoption of these approaches in ERA through the development of guidance documents and frameworks (Harrill et al, 2021; Viant et al, 2019). But without a clear strategy to evaluate emerging technologies which are both rapid and appropriate, their full potential will remain largely unrecognized and unused (Anklam et al, 2022). Yet, notable change is emerging.…”