Regulatory Guidance Research Articles

Current Status and Future Directions: The Application of Artificial Intelligence/Machine Learning for Precision Medicine.

Technological innovations, such as artificial intelligence (AI) and machine learning (ML), have the potential to expedite the goal of precision medicine, especially when combined with increased capacity for voluminous data from multiple sources and expanded therapeutic modalities; however, they also present several challenges. In this communication, we first discuss the goals of precision medicine, and contextualize the use of AI in precision medicine by showcasing innovative applications (e.g., prediction of tumor growth and overall survival, biomarker identification using biomedical images, and identification of patient population for clinical practice) which were presented during the February 2023 virtual public workshop entitled "Application of Artificial Intelligence and Machine Learning for Precision Medicine," hosted by the US Food and Drug Administration (FDA) and University of Maryland Center of Excellence in Regulatory Science and Innovation (M-CERSI). Next, we put forward challenges brought about by the multidisciplinary nature of AI, particularly highlighting the need for AI to be trustworthy. To address such challenges, we subsequently note practical approaches, viz., differential privacy, synthetic data generation, and federated learning. The proposed strategies - some of which are highlighted presentations from the workshop - are for the protection of personal information and intellectual property. In addition, methods such as the risk-based management approach and the need for an agile regulatory ecosystem are discussed. Finally, we lay out a call for action that includes sharing of data and algorithms, development of regulatory guidance documents, and pooling of expertise from a broad-spectrum of stakeholders to enhance the application of AI in precision medicine.

The Power of the Regulator: Regulatory Guidance and the Firms that Didn't Follow It During the Emergence of U.S. COVID-19 Diagnostics

The Power of the Regulator: Regulatory Guidance and the Firms that Didn't Follow It During the Emergence of U.S. COVID-19 Diagnostics

Validity of Routine Health Data To Identify Safety Outcomes of Interest For Covid-19 Vaccines and Therapeutics in the Context of the Emerging Pandemic: A Comprehensive Literature Review.

Regulatory guidance encourages transparent reporting of information on the quality and validity of electronic health record data being used to generate real-world benefit-risk evidence for vaccines and therapeutics. We aimed to provide an overview of the availability of validated diagnostic algorithms for selected safety endpoints for Coronavirus disease 2019 (COVID-19) vaccines and therapeutics in the context of the emerging pandemic prior to December 2020. We reviewed the literature up to December 2020 to identify validation studies for various safety events of interest, including myocardial infarction, arrhythmia, myocarditis, acute cardiac injury, vasculitis/vasculopathy, venous thromboembolism, stroke, respiratory distress syndrome (RDS), pneumonitis, cytokine release syndrome (CRS), multiple organ dysfunction syndrome, and renal failure. We included studies published between 2015 and 2020 that were considered high quality assessed with QUADAS and that reported positive predictive values (PPVs). Out of 43 identified studies, we found that diagnostic algorithms for cardiovascular outcomes were supported by the highest number of validation studies (n=17). Accurate algorithms are available for myocardial infarction (median PPV 80%; IQR 22%), arrhythmia (PPV range >70%), venous thromboembolism (median PPV: 73%) and ischaemic stroke (PPV range ≥85%). We found a lack of validation studies for less common respiratory and cardiac safety outcomes of interest (eg, pneumonitis and myocarditis), as well as for COVID-specific complications (CRS, RDS). There is a need for better understanding of barriers to conducting validation studies, including data governance restrictions. Regulatory guidance should promote embedding validation within real-world EHR research used for decision-making.

The landscape of decentralized clinical trials (DCTs): focusing on the FDA and EMA guidance.

Decentralized clinical trials (DCTs) consist of off-site trial-related procedures referred to as decentralized elements. We aimed to provide an overview of the landscape of DCTs by comparing regulatory guidance reports and analyzing decentralized elements from clinical trial registries. Two guidance reports on DCTs published by the U.S. Food and Drug Administration and the European Medicines Agencies were summarized and analyzed. Both guidance publications commonly emphasized an assessment of the appropriateness of decentralized elements along 2 axes: patient safety and data integrity. DCT cases were identified from ClinicalTrials.gov by searching with 6 keywords: decentralized, remote, mobile, digital, virtual, and hybrid. Cases where the keyword was used in a non-DCT context, such as digital flexor tendon, were excluded by means of natural language processing. A total of 4,874 trials were identified as DCT cases, with annual increases, especially after 2020. The most common keywords were 'mobile' and 'digital' (36.2% and 24.8%, respectively). Interventions in the DCT cases were analyzed by means of a network analysis. Behavioral and technological tokens were frequently combined, such as 'rehabilitation' and 'app.' Drugs were used in only 1.8% of the DCT cases. Of these, most drugs had been approved previously (96.8%) and were in oral formulation (67.2%). Most of the DCT cases identified in this study involved simple interventions and low-risk drugs. These characteristics were in accordance with the common recommendations in the DCT guidance publications.

Assessment of Reader Technologies for Over-the-Counter Diagnostic Testing.

Background: Over-the-counter (OTC) diagnostic testing is on the rise with many in vitro diagnostic tests being lateral flow assays (LFAs). A growing number of these are adopting reader technologies, which provides an alternative to visual readouts for results interpretation, allowing for improved accessibility of OTC diagnostics. As the reader technology market develops, there are many technologies entering the market, but no clear, single solution has yet been identified. The purpose of this research is to identify and discuss important parameters for the assessment of LFA reader technologies for consideration by manufacturers or researchers. Methods: As part of The National Institute of Biomedical Imaging and Bioengineering's Rapid Acceleration of Diagnostics (RADx) Tech program, reader manufacturers were interviewed to investigate the current state of reader technology development through several parameters identified as important industry standards. Readers were categorized by technology type and parameters including cost, detection method, multiplex capabilities, assay type, maturity, and use case were all assessed. Results: Fifteen reader manufacturers were identified and interviewed, and information on a total of 19 technologies was assessed. Reader technology type was found to be predictive of other attributes, whether the reader is smart technology only, a standalone reader, a reader with smart technology required, or a reader with smart technology optional. Conclusions: Pairing reader technology with OTC diagnostic tests is important for improving existing COVID-19 tests and can be utilized in other diagnostics as the OTC use case grows in popularity. Reader technology type, which is predictive of core reader attributes, should be considered when selecting a reader technology for a specific LFA test within the context of regulatory guidance. As diagnostics increase in complexity, readers provide solutions to accessibility challenges, facilitate public health reporting, and ease the transition to multiplex testing, therefore increasing market availability.

Westinghouse fluence methodology uncertainty estimates for reactor vessel internals fast neutron exposure evaluation in subsequent license renewal period

The United States Nuclear Regulatory Commission (USNRC) has expressed concerns about the calculated fast neutron fluence exposure uncertainties for the reactor vessel internals (RVI) of light water reactors for extended operation [1, 2]. The current guidance for calculating neutron fluence, Regulatory Guide (RG) 1.190 [3], was developed with a focus on the reactor pressure vessel (RPV) region surrounding the active core, which may not be adequate for the RVI above and below the active core during the license renewal periods, especially the subsequent license renewal (SLR) period. An updated version of ANSI/ANS 19-10 [4] may fill the regulatory guidance gap on neutron fluence determination methodology for RVI but still faces the challenges of lacking RVI fluence measurement benchmark data and analytical uncertainty analysis, which are two cornerstones for qualifying a neutron fluence determination methodology. The Pressurized Water Reactor Owners Group (PWROG) has responded to this regulatory concern and have performed analytical uncertainty analysis and measurement benchmark work to address these two technical challenges for the nuclear power industry.

The Details Matter: SEC Regulatory Guidance and Informativeness in Non-GAAP Disclosure Decisions

The Details Matter: SEC Regulatory Guidance and Informativeness in Non-GAAP Disclosure Decisions

Developing a Robust In Vitro Release Method for a Polymeric Nanoparticle: Challenges and Learnings

Nanomedicines have been highlighted as a promising approach for targeted drug delivery, as they can improve drug efficacy and reduce systemic toxicity. Due to the wide range of formulation types and the complex nature of the drug release mechanism, the development of an in vitro release (IVR) method for nanomedicines is challenging and regulatory guidance is limited. In this work, we are outlining the IVR method development for a controlled-release polymeric nanoparticle. This included the optimisation of the release medium composition and the selection of an appropriate sampling technique for the isolation of the released drug from the formulation. In addition to the established ultracentrifugation, the NanoDis showed potential as an automated technique.

Engaging the Regulatory Community to Aid Environmental Consenting/Permitting Processes for Marine Renewable Energy

Regulators involved in consenting/permitting marine renewable energy (MRE) have faced multiple challenges due to relatively new, unfamiliar technologies and uncertainty surrounding potential environmental impacts. This has resulted in slow progress for the MRE industry, including long consenting timeframes and extensive and expensive monitoring requirements, which increase financial risk for investors. OES-Environmental has surveyed regulators internationally to understand their key knowledge gaps and perspectives to support the development of the MRE industry. From the results of these surveys a data transferability process and a risk retirement pathway have been developed to assess consenting and monitoring requirements in proportion to risk. A tool for discovering existing data sets by using an online matrix has been developed, along with training materials, regulatory guidance documents, and a strategic outreach plan to engage regulators and advisers. his engagement and the application of these products should lead to a better understanding of the environmental effects of marine energy, and more efficient consenting processes.

Measuring the Impact of AI in the Diagnosis of Hospitalized Patients

Artificial intelligence (AI) could support clinicians when diagnosing hospitalized patients; however, systematic bias in AI models could worsen clinician diagnostic accuracy. Recent regulatory guidance has called for AI models to include explanations to mitigate errors made by models, but the effectiveness of this strategy has not been established. To evaluate the impact of systematically biased AI on clinician diagnostic accuracy and to determine if image-based AI model explanations can mitigate model errors. Randomized clinical vignette survey study administered between April 2022 and January 2023 across 13 US states involving hospitalist physicians, nurse practitioners, and physician assistants. Clinicians were shown 9 clinical vignettes of patients hospitalized with acute respiratory failure, including their presenting symptoms, physical examination, laboratory results, and chest radiographs. Clinicians were then asked to determine the likelihood of pneumonia, heart failure, or chronic obstructive pulmonary disease as the underlying cause(s) of each patient's acute respiratory failure. To establish baseline diagnostic accuracy, clinicians were shown 2 vignettes without AI model input. Clinicians were then randomized to see 6 vignettes with AI model input with or without AI model explanations. Among these 6 vignettes, 3 vignettes included standard-model predictions, and 3 vignettes included systematically biased model predictions. Clinician diagnostic accuracy for pneumonia, heart failure, and chronic obstructive pulmonary disease. Median participant age was 34 years (IQR, 31-39) and 241 (57.7%) were female. Four hundred fifty-seven clinicians were randomized and completed at least 1 vignette, with 231 randomized to AI model predictions without explanations, and 226 randomized to AI model predictions with explanations. Clinicians' baseline diagnostic accuracy was 73.0% (95% CI, 68.3% to 77.8%) for the 3 diagnoses. When shown a standard AI model without explanations, clinician accuracy increased over baseline by 2.9 percentage points (95% CI, 0.5 to 5.2) and by 4.4 percentage points (95% CI, 2.0 to 6.9) when clinicians were also shown AI model explanations. Systematically biased AI model predictions decreased clinician accuracy by 11.3 percentage points (95% CI, 7.2 to 15.5) compared with baseline and providing biased AI model predictions with explanations decreased clinician accuracy by 9.1 percentage points (95% CI, 4.9 to 13.2) compared with baseline, representing a nonsignificant improvement of 2.3 percentage points (95% CI, -2.7 to 7.2) compared with the systematically biased AI model. Although standard AI models improve diagnostic accuracy, systematically biased AI models reduced diagnostic accuracy, and commonly used image-based AI model explanations did not mitigate this harmful effect. ClinicalTrials.gov Identifier: NCT06098950.

A full-document analysis of the semantic relation between European Public Assessment Reports and EMA guidelines using a BERT language model

In the European Union, the Committee for Medicinal Products for Human Use of the European Medicines Agency (EMA) develop guidelines to guide drug development, supporting development of efficacious and safe medicines. A European Public Assessment Report (EPAR) is published for every medicine application that has been granted or refused marketing authorisation within the EU. In this work, we study the use of text embeddings and similarity metrics to investigate the semantic similarity between EPARs and EMA guidelines. All 1024 EPARs for initial marketing authorisations from 2008 to 2022 was compared to the 669 current EMA scientific guidelines. Documents were converted to plain text and split into overlapping chunks, generating 265,757 EPAR and 27,649 guideline text chunks. Using a Sentence BERT language model, the chunks were transformed into embeddings and fed into an in-house piecewise matching algorithm to estimate the full-document semantic distance. In an analysis of the document distance scores and product characteristics using a linear regression model, EPARs of anti-virals for systemic use (ATC code J05) and antihemorrhagic medicines (B02) present with statistically significant lower overall semantic distance to guidelines compared to other therapeutic areas, also when adjusting for product age and EPAR length. In conclusion, we believe our approach provides meaningful insight into the interplay between EMA scientific guidelines and the assessment made during regulatory review, and could potentially be used to answer more specific questions such as which therapeutic areas could benefit from additional regulatory guidance.

PACKAGING–FOOD INTERACTION AND CHEMICAL MIGRATION

"Food packaging is intended to protect food and extend its shelf life, but it may affect food quality and safety because of chemical migration. Food quality and safety regarding packaging is a significant global concern. Legislations have an essential role in providing regulatory guidance on quality assurance systems and verifying their implementation as a means of regulatory compliance. The large number of various materials used in the manufacture of packages complicates the evaluation of food–packaging interactions. This review is an overview of literature data on the effects of printed food packaging on the migration of chemicals into foods, as well as on various migration sources of chemical compounds. Various aspects, such as the interaction between packaging and food starting with the production process of food packaging to food–packaging contact during storage, the effects of primary and secondary packaging on chemical migration, permeability of packaging materials, inkinduced migration in printed packaging, and types of transition from packaging to food, were examined in detail. Besides, studies on subjects such as the food contact materials analysis used to test the phenomenon of migration in foods and migration limits have been discussed. Moreover, studies on the use of recycled paper in packaging and its effect on migration, ink chemicals resulting from recycling and studies on this subject are included. Information is given on measures to reduce the effect of migration, low migration of printing inks, coatings and adhesives, and materials used in barrier applications. In line with this research study, suggestions were made for measures to reduce the harmful effects of chemical migration on human health and to prevent the risk of migration from packaging to food."

Addressing regulatory guidance for HEK293 cells and AAV-based therapeutics manufacturing

Addressing regulatory guidance for HEK293 cells and AAV-based therapeutics manufacturing

The current viral vector characterization landscape: from navigating evolving regulatory guidance to leveraging analytical toolsfor process development

The current viral vector characterization landscape: from navigating evolving regulatory guidance to leveraging analytical toolsfor process development

Analisis Daya Saing pada Industri Perbankan di Kota Medan: Digitalisasi Perbankan dengan Pendekatan SWOT

The banking industry in Medan City has experienced significant growth alongside technological advancements and digitalization. This transformation encompasses the utilization of Information and Communication Technology (ICT) to enhance operational efficiency and innovate banking services. While major banks have extensive branch networks and strong reputations, some may face challenges in swiftly adopting new technologies. Significant opportunities lie in expanding digital banking services through investments in mobile applications, cutting-edge technology, and collaborations with local fintech companies. However, competition from digital banks and cybersecurity risks pose noteworthy threats that require attention. SWOT analysis can provide crucial insights for banks in Medan to identify competitive advantages, address weaknesses, and capitalize on strategic opportunities. This research aims to understand the influence of digitalization on the competitiveness of the banking industry in Medan City, identifying strengths, weaknesses, opportunities, and threats, while offering strategic recommendations to sustain and improve positions in this rapidly evolving market. The benefits of this journal include profound comprehension of digitalization's impact, optimized competitive advantages, market adaptation, contributions to local economic growth, regulatory policy guidance, and added value to the community and local economy.

Analyzing the Sharing Economy from an Economic Model Perspective

The sharing economy is a new economic model that matches idle resources with the demand side through online platforms to achieve resource sharing and optimized utilization. This article analyzes the sharing economy from the perspective of economic models, mainly including platform economy model, co production economy model, and shared consumption economy model. At the same time, the advantages and challenges of the sharing economy were also explored, such as high resource utilization, creating employment opportunities and improving consumer welfare, as well as challenges such as incomplete laws and regulations, increased security risks, and increased inequality. Finally, the development trends and prospects of the sharing economy were envisioned, such as cross-border integration and innovative development, government regulation and regulatory guidance, user trust and brand building, as well as the importance of international cooperation and market expansion.

The comparability tales: A phase-appropriate roadmap for CGT drug product development

Cell and gene therapies (CGTs) have revolutionized patient outcomes and provided careoptions for previously untreatable conditions. The clinical and commercial progress of CGT therapies is hindered bychemistry, manufacturing, and control (CMC) challenges. This article summarizes recommendations from the 2023 Annual Meeting CMC sessions wherein speakers advocated for science-driven comparability strategies, proactive risk assessments, clearer regulatory guidance, and a shift from retrospective to prospective studies. Planning formanufacturing changes, statistical approaches, and consideration of multiple product versions also emerged as crucial elements to help sponsors navigate CMC hurdles for successful CGT clinical and commercial development.

Assessing ethics and sustainability standards in corporate practices

PurposeThis study aims to investigate how sustainability and ethics are being addressed both by the literature and companies. Furthermore, it seeks to identify the specific strategies that these companies use to foster ethical behaviour and promote sustainability in their business operations.Design/methodology/approachThe study entails a bibliometric analysis and a set of case studies from a sample of companies working in different industry sectors. Based on these tools, it analyses whether – and how – enterprises are placing an emphasis on sustainability and ethics as part of their businesses. In addition, the selected companies' unethical practices or socially irresponsible corporate activities were investigated and presented.FindingsThe findings suggest that using an ethics perspective can be a valuable tool in improving the accuracy and correctness of business decision-making. In addition, the paper has identified the fact that sustainability standards can be used to improve customer satisfaction as many important issues are addressed. Finally, the paper highlights the importance of ethical considerations when designing and implementing sustainability standards at enterprises and the need for regulatory guidance in this regard.Originality/valueThe paper addresses the need for studies on how sustainability and ethics are being discussed by both the literature and companies. The paper presents some elements that can be used as possible corporate indicators for a wider implementation of sustainability and ethics objectives in enterprises.

Research on the Synthesis of Hong Kong NFT Index Using Principal Component Analysis and Index Prediction Based on LSTM-Modified ARMA-GARCH Model

With the advent of the Web3.0 era, virtual assets have gained prominence in individuals asset portfolios, making Non-Fungible Tokens (NFTs) increasingly significant within the financial trading landscape. To address the issue of multicollinearity in regression analysis, this paper employs Principal Component Analysis (PCA) to perform dimensionality reduction on five correlated foundational sectors. Moreover, to enhance the accuracy and reliability of predictive outcomes, the study combines the Long Short-Term Memory (LSTM) model with the Autoregressive Moving Average-Generalized Autoregressive Conditional Heteroskedasticity (ARMA-GARCH) model. Through the application of these methods and practical implementation, the study forecasts the NFT index of the Hong Kong stock market for the next 30 days. This forecasting of return volatility contributes vital insights for investment decision-making. The research complements and offers application recommendations in financial innovation, deepening, and regulation. By devising novel products and tools to meet investor demands, providing risk management and investment opportunities, the models predictive outcomes can be utilized in regulatory and risk management strategies within the national financial trading market. This study provides regulatory guidance, policy formulation insights, and envisions further refinements of the research methodology by integrating information shock effects.

PP21 Are We Ready For It? Developing Criteria To Include Artificial Intelligence Medical Devices (AI-MDs) For Health Technology Assessment

IntroductionThe increasing pace at which artificial intelligence medical devices (AI-MDs) or digital health technologies (DHTs) have been introduced and integrated in healthcare has not been matched with appropriate selection criteria for health technology assessment (HTA) to inform funding decision-making. To align with international best practice and local regulatory guidance, the Agency for Care Effectiveness (ACE) developed criteria to include AI-MDs as part of its 2022 topic prioritization process for medical technologies. This abstract describes ACE’s approach to develop the inclusion criteria.MethodsTo develop key principles for including AI-MDs in ACE’s topic prioritization process, relevant information from overseas HTA agencies, local regulatory guidelines, and ACE’s existing topic selection criteria were reviewed. A search of international HTA agency websites was conducted in September 2022 to identify relevant information on inclusion of AI-MDs in healthcare for reimbursement recommendations.Additionally, local regulatory guidelines for AI-MDs in healthcare were also identified. The inclusion criteria were then piloted with AI-MDs identified from ACE’s horizon scanning workstream to examine their feasibility for HTA topic selection.ResultsOne overseas framework on DHTs from the National Institute for Health and Care Excellence (NICE) and two local regulatory guidelines were identified. Based on the key finding that the purpose of AI-MD use in guiding clinical management and its associated risks were important considerations, the following criteria were developed: (i) full registration with the regulatory body;(ii) device characteristics should be interventional, have direct impact on patient safety, or support accurate diagnosis or treatment which is critical to avoid death and serious health deterioration; and (iii) the AI algorithm should be fixed as opposed to adaptable as per regulatory requirements. Using this inclusion criteria, eight AI-MDs surfaced from horizon scanning were screened with the above criteria and deemed suitable for HTA topic selection.ConclusionsAs AI technologies are increasingly used to replace or supplement current clinical practice, continuous adaptation of HTA method is needed to ensure appropriate topic selection.