Re-discover the value of protein binding assessments in hepatic and renal impairment studies and its contributions in drug labels and dose decisions.

One of the key pharmacokinetic properties of most small molecule drugs is their ability to bind to serum proteins. Unbound or free drug is responsible for pharmacological activity while the balance between free and bound drug can impact drug distribution, elimination, and other safety parameters. In the hepatic impairment (HI) and renal impairment (RI) clinical studies, unbound drug concentration is often assessed; however, the relevance and impact of the protein binding (PB) results is largely limited. We analyzed published clinical safety and pharmacokinetic studies in subjects with HI or RI with PB assessment up to October 2022 and summarized the contribution of PB results on their label dose recommendations. Among drugs with HI publication, 32% (17/53) associated product labels include PB results in HI section. Of these, the majority (9/17, 53%) recommend dose adjustments consistent with observed PB change. Among drugs with RI publication, 27% (12/44) of associated product labels include PB results in RI section with the majority (7/12, 58%) recommending no dose adjustment, consistent with the reported absence of PB change. PB results were found to be consistent with a tailored dose recommendation in 53% and 58% of the approved labels for HI and RI section, respectively. We further discussed the interpretation challenges of PB results, explored treatment decision factors including total drug concentration, exposure-response relationships, and safety considerations in these case examples. Collectively, comprehending the alterations in free drug levels in HI and RI informs treatment decision through a risk-based approach.

Learning with an evolving medicine label: how artificial intelligence-based medication recommendation systems must adapt to changing medication labels.

INTRODUCTION: Artificial intelligence or machine learning (AI/ML) based systems can help personalize prescribing decisions for individual patients. The recommendations of these clinical decision support systems must relate to the "label" of the medicines involved. The label of a medicine is an approved guide that indicates how to prescribe the drug in a safe and effective manner. AREAS COVERED: The label for a medicine may evolve as new information on drug safety and effectiveness emerges, leading to the addition or removal of warnings, drug-drug interactions, or to permit new indications. However, the speed at which these updates are made to these AI/ML recommendation systems may be delayed and could influence the safety of prescribing decisions. This article explores the need to keep AI/ML tools 'in sync' with any label changes. Additionally, challenges relating to medicine availability and geographical suitability are discussed. EXPERT OPINION: These considerations highlight the important role that pharmacoepidemiologists and drug safety professionals must play within the monitoring and use of these tools. Furthermore, these issues highlight the guiding role that regulators need to have in planning and oversight of these tools.

Artificial intelligence or machine learning (AI/ML) based systems that guide the prescription of medications have the potential to vastly improve patient care, but these tools should only provide recommendations that are in line with the label of a medicine. With a constantly evolving medication label, this is likely to be a challenge, and this also has implications for the off-label use of medicines.

Postmarket safety communications on drugs approved in Japan: A 25-year analysis.

Drug safety communications (DSCs) are essential tools for communicating important postmarket serious drug safety information to healthcare professionals and patients. Previous studies characterized DSCs issued by the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA); however, knowledge about the activities of the Pharmaceuticals and Medical Devices Agency (PMDA)/the Ministry of Health, Labor and Welfare (MHLW) is limited. This study characterized DSCs by the PMDA/MHLW in comparison with previously reported DSCs by the FDA and the EMA. We retrospectively analyzed 37 DSCs of 41 adverse drug reactions (ADRs) for 33 drugs in Japan from 1997 to 2022. Most DSCs were related to non-oncology drugs (30/37, 81.1%), and the median (interquartile range) time from approval to DSC issuance was 19 (10-51) months. Notably, the regulatory review reports and the latest labels before DSC issuance did not describe 16/28 (57.1%) and 12/37 (32.4%) of the ADRs related to DSCs, respectively. Most DSCs resulted in label revisions (36/37, 97.3%) and seven drugs were eventually withdrawn. Some DSC characteristics are similar among the PMDA/MHLW, the FDA, and the EMA; however, the number, contents, and range of new safety issues addressed by DSCs differ among the three jurisdictions. Our study emphasized the importance of continuous efforts to gather postmarket drug safety information because substantial ADRs that led to DSCs were recognized after approval and were associated with critical label revisions and withdrawals. Future studies are required to address global challenges for regulatory harmonization of safety-related regulatory actions.

FDA Issues Warning About 'Gas Station Heroin'.

The highly addictive dietary supplement mimics opioid effects.

A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document.

Regulatory agencies consistently deal with extensive document reviews, ranging from product submissions to both internal and external communications. Large Language Models (LLMs) like ChatGPT can be invaluable tools for these tasks, however present several challenges, particularly the proprietary information, combining customized function with specific review needs, and transparency and explainability of the model's output. Hence, a localized and customized solution is imperative. To tackle these challenges, we formulated a framework named askFDALabel on FDA drug labeling documents that is a crucial resource in the FDA drug review process. AskFDALabel operates within a secure IT environment and comprises two key modules: a semantic search and a Q&A/text-generation module. The Module S built on word embeddings to enable comprehensive semantic queries within labeling documents. The Module T utilizes a tuned LLM to generate responses based on references from Module S. As the result, our framework enabled small LLMs to perform comparably to ChatGPT with as a computationally inexpensive solution for regulatory application. To conclude, through AskFDALabel, we have showcased a pathway that harnesses LLMs to support agency operations within a secure environment, offering tailored functions for the needs of regulatory research.

Streamlining Considerations for Safety Measures: A Predictive Model for Addition of Clinically Significant Adverse Reactions to Japanese Drug Package Inserts.

The addition of clinically significant adverse reactions (CSARs) to Japanese package inserts (PIs) is an important safety measure that can be used to inform medical personnel of potential health risks; however, determining the necessity of their addition can be lengthy and complex. Therefore, we aimed to construct a machine learning-based model that can predict the addition of CSARs at an early stage due to the accumulation of both Japanese and overseas adverse drug reaction (ADR) cases. The target comprised CSARs added to PIs from August 2011 to March 2022. The control group consisted of drugs without the same CSARs in their PIs by March 2022. Features were generated using ADR case accumulation data obtained from the Japanese Adverse Drug Event Report and the U.S. Food and Drug Administration Adverse Event Reporting System databases. The model was constructed using DataRobot, and its performance evaluated using the Matthews correlation coefficient. The target for the addition of CSARs included 414 cases, comprising 302 due to domestic case accumulation, 22 due to both domestic and overseas case accumulation, 12 due to overseas case accumulation, and 78 due to revisions of the company core data sheet. The best model was a generalized linear model with informative features, achieving a cross-validation of 0.8754 and a holdout of 0.8995. In conclusion, the proposed model effectively predicted CSAR additions to PIs resulting from the accumulation of ADR cases using data from both Japan and the United States.

Patient information leaflets and package inserts of ibuprofen provided in the UK and Thailand: a comparative assessment.

OBJECTIVES: Written medicine information (WMI) is important for ensuring patients understand and use their medicines optimally, but relatively little research has assessed the quality of available WMI. This study assessed the quality of WMI using a sample of leaflets for ibuprofen in the UK and Thailand. METHODS: Leaflets were obtained by purchasing a product from retail outlets or community pharmacies, 18 from each country. In the UK, these were patient information leaflets (PILs); in Thailand, they were package inserts PIs not specifically designed for patients. Leaflets were assessed for content, layout, and readability using standard methods and compared to relevant guidelines. KEY FINDINGS: The UK PILs were uniform and conformed to EU regulatory requirements for content, whereas Thai PIs varied considerably, many failing to include important information required by Thai regulations. Several forms of Thai PIs were found, including some very short leaflets, containing minimal information. The readability of both was rated as poor, all used small font size and had less than desirable white space. Fewer Thai PIs than UK PILs met the Keystone Criteria for ibuprofen. CONCLUSIONS: The extent of variation in format and content of Thai WMI could potentially cause confusion and reduce willingness to read it. PILs, conforming to Thai regulatory guidelines, should be provided with medicines instead. Leaflets in both countries would benefit from improved readability and layout.

Penicillin allergy reassessment for treatment improvement: A dental office tool to support appropriate penicillin allergy labeling.

BACKGROUND: Dental appointments offer an opportunity to evaluate a documented penicillin (PCN) allergy and determine whether the patient might be a candidate for medical reassessment of their allergy. The authors gathered feedback on the Penicillin Allergy Reassessment for Treatment Improvement (PARTI) tool, designed to enhance dentist-patient communications regarding PCN allergies. METHODS: From January 2022 through May 2023, the authors conducted a mixed-methods study, collecting focus group data from patients with PCN allergies and surveying health care workers (HCWs) regarding the PARTI tool. Feedback focused on reassessment procedures, patient-centered allergy information, and medical records updates. Thematic analysis was used for focus group data. RESULTS: The study included 15 patients in focus groups and 50 HCW survey respondents representing diverse US regions. Patient demographic characteristics included varied races, the mean age was 52 years, and most of the patients were female (53.3%). Most patients had health care interactions within the preceding year, at which 86.6% of patients were asked about drug allergies. HCW respondents primarily consisted of pharmacists (30%) and dentists, dental hygienists, and dental assistants (28%). Feedback on the PARTI tool was constructive, with both patients and HCWs recognizing its potential benefits and providing insights for improvement. Many HCWs (68%) highlighted the importance of step 3 of the PARTI tool, that is, the section on PCN allergy testing. Feedback from participants was incorporated into the final PARTI tool. CONCLUSIONS: Patient and HCW feedback on the PARTI tool was used to finalize a tool for the dental office to provide to patients who are candidates for PCN allergy reassessment. The feedback will also be used to inform an upcoming pilot study in US dental offices, focused on the process for PCN allergy reassessment and health record documentation. PRACTICAL IMPLICATIONS: Deploying the PARTI tool in dental offices is pivotal, as mislabeling patients with PCN allergies could have severe consequences, such as hindering the prescription of lifesaving antibiotics for conditions like endocarditis, in the future. This implementation not only enhances communication between dentists and patients, but it is also crucial for ensuring improved patient safety and maintaining accurate medical records among health care settings.

Accuracy of Labeling of Galantamine Generic Drugs and Dietary Supplements.

This study examines the accuracy of labeling for galantamine products formulated as both generic drugs and dietary supplements, as well as tests for contamination with microorganisms.

FDA Adds Boxed Warning to CAR T-Cell Therapies, but Says Benefits Outweigh Risks of Secondary Cancers.

This Medical News article discusses the US Food and Drug Administration's new boxed warning requirement and investigation of secondary malignancies after CAR T-cell therapy.

Prescriber perceptions of boxed warnings: A qualitative study.

PURPOSE: To explore how boxed warning (BW) information fits within the context of prescribers' overall treatment decision-making and communication with patients. METHODS: In-depth interviews (N = 52) were conducted with primary care providers and specialists. Participants were presented with one of two prescribing scenarios: (1) estrogen vaginal inserts to treat vulvovaginal atrophy (VVA) associated with menopause; or (2) direct-acting antivirals (DAA) to treat chronic hepatitis C virus infection (HCV). The semi-structured interviews explored participants' treatment decision-making within the scenario, reactions to current prescribing information for a product within the FDA-approved drug class, as well as their perceptions of BWs generally. RESULTS: Across scenarios, providers described that the BW is only one of several factors that influence treatment decision-making. In the VVA scenario, symptom severity, family history, and experience with nonprescription drugs were raised as common factors that influence prescribing considerations; compared to comorbid infections, viral load, and HCV genotype in the HCV scenario. Perceptions of the DAA BW were generally positive or neutral, as many participants found the information important and appropriate. The VVA BW was viewed less favorably, with many participants stating the BW overstates the risk for this drug. CONCLUSIONS: Findings suggest that BWs are one of several factors that influence providers' treatment decisions, and BW influence largely depends on context. Providers across scenarios expressed notable differences in their perceptions of the risk information provided in the presented BWs; however, across scenarios participants expressed consideration of how patients may perceive the BW.

Health warning labels on cannabis products. What is the best design?

BACKGROUND: Health warning label on cannabis products has been recently studied, and with the latest trends of regulation around the world, there is a need to determine the most effective ways to apply this strategy. The current study aimed to examine the effects of different health warning label designs (pictorial vs text-only, background color, warning themes) on cannabis products. METHODS: An online experiment study (N=533) was carried out in Colombia with a between-subject design. Participants were randomly assigned to five package conditions: without warning, text-only white warning, text-only yellow warning, pictorial white warning, and pictorial yellow warning. Participants performed an attention task and rated each of the stimuli based on product appeal, perceived addictiveness, harm perception, and interest in trying cannabis products. RESULTS: Pictorial health warnings were generally the most effective. Especially, pictorial health warnings with a yellow background were found to decrease product appeal and interest in trying cannabis products, as well as increase harm perception compared to other designs. The most effective warning themes were mental health, smoke toxicity, aesthetic implications, and traffic accidents. CONCLUSION: The current study provides empirical evidence on the effectiveness of different designs of cannabis health warnings. Our results suggest that graphic yellow warnings are the most effective in communicating the risks of cannabis use.