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.

Fine-tuning BERT for automatic ADME semantic labeling in FDA drug labeling to enhance product-specific guidance assessment.

Product-specific guidances (PSGs) recommended by the United States Food and Drug Administration (FDA) are instrumental to promote and guide generic drug product development. To assess a PSG, the FDA assessor needs to take extensive time and effort to manually retrieve supportive drug information of absorption, distribution, metabolism, and excretion (ADME) from the reference listed drug labeling. In this work, we leveraged the state-of-the-art pre-trained language models to automatically label the ADME paragraphs in the pharmacokinetics section from the FDA-approved drug labeling to facilitate PSG assessment. We applied a transfer learning approach by fine-tuning the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model to develop a novel application of ADME semantic labeling, which can automatically retrieve ADME paragraphs from drug labeling instead of manual work. We demonstrate that fine-tuning the pre-trained BERT model can outperform conventional machine learning techniques, achieving up to 12.5% absolute F1 improvement. To our knowledge, we were the first to successfully apply BERT to solve the ADME semantic labeling task. We further assessed the relative contribution of pre-training and fine-tuning to the overall performance of the BERT model in the ADME semantic labeling task using a series of analysis methods, such as attention similarity and layer-based ablations. Our analysis revealed that the information learned via fine-tuning is focused on task-specific knowledge in the top layers of the BERT, whereas the benefit from the pre-trained BERT model is from the bottom layers.

Leveraging GPT-4 for food effect summarization to enhance product-specific guidance development via iterative prompting.

Food effect summarization from New Drug Application (NDA) is an essential component of product-specific guidance (PSG) development and assessment, which provides the basis of recommendations for fasting and fed bioequivalence studies to guide the pharmaceutical industry for developing generic drug products. However, manual summarization of food effect from extensive drug application review documents is time-consuming. Therefore, there is a need to develop automated methods to generate food effect summary. Recent advances in natural language processing (NLP), particularly large language models (LLMs) such as ChatGPT and GPT-4, have demonstrated great potential in improving the effectiveness of automated text summarization, but its ability with regard to the accuracy in summarizing food effect for PSG assessment remains unclear. In this study, we introduce a simple yet effective approach,iterative prompting, which allows one to interact with ChatGPT or GPT-4 more effectively and efficiently through multi-turn interaction. Specifically, we propose a three-turn iterative prompting approach to food effect summarization in which the keyword-focused and length-controlled prompts are respectively provided in consecutive turns to refine the quality of the generated summary. We conduct a series of extensive evaluations, ranging from automated metrics to FDA professionals and even evaluation by GPT-4, on 100 NDA review documents selected over the past five years. We observe that the summary quality is progressively improved throughout the iterative prompting process. Moreover, we find that GPT-4 performs better than ChatGPT, as evaluated by FDA professionals (43% vs. 12%) and GPT-4 (64% vs. 35%). Importantly, all the FDA professionals unanimously rated that 85% of the summaries generated by GPT-4 are factually consistent with the golden reference summary, a finding further supported by GPT-4 rating of 72% consistency. Taken together, these results strongly suggest a great potential for GPT-4 to draft food effect summaries that could be reviewed by FDA professionals, thereby improving the efficiency of the PSG assessment cycle and promoting generic drug product development.

Utility of patient information leaflet and perceived impact of its use on medication adherence.

BACKGROUND: Although patients frequently use patient information leaflets (PILs) to obtain information about medicine, their confidence in using it may be diminished after reading it. This study aimed to assess the public perception of PIL's quality and the perceived impact of its use on medication adherence. METHODS: A community-based cross-sectional study of 1,138 adult individuals in Saudi Arabia, April-May 2020, was conducted via Survey Monkey using an anonymous validated e-questionnaire. Data were collected on personal characteristics, PIL readership and preferences, perception towards PIL quality and impact of its use on taking medication, and reasons for not reading PIL. In addition, logistic regression analysis was performed to identify the significant predictors of reading PIL. Significance was considered at p < 0.05. RESULTS: Nearly all participants (91.1%) reported reading PIL. The more read PIL's sections were directions of use (52.7%) and side effects (30.3%). Female gender (OR = 5.64, 95%CI: 3.53,9.02), age over 40 years (OR = 2.80, 95%CI: 1.69,4.64), and secondary education or more (OR = 1.74, 95%CI: 1.06,2.85) were the significant predictors of reading PIL. The majority of PIL readers reported their preference for verbal information (65.8%), hard copy presentation (77%), adding graphics (71.1%), and concise content of PIL (68.8%). In addition, most participants reported PIL always/usually adds to their knowledge of medicines (70.6%) and said that PIL reading positively impacted their medication adherence (64.9%). For only 8.8%, PIL reading negatively impacted their adherence, primarily because of reading information on medicine's side effects and complications (74.4%). More than one-half of participants perceived the PIL quality as good/excellent in terms of; font size (51.3%), language comprehensiveness (64.9%), paper quality (68.0%), and general appearance (64.9%). Getting sufficient information from doctors and pharmacists was the main reason for not reading the PIL (59.2%). Most participants (92.5%) agreed on standardizing how information is displayed in the PIL among all PILs of all companies. CONCLUSION: PIL is read by nearly all the study sample, especially females, older, and educated subjects. It was perceived as beneficial in upgrading medication adherence. Effective designing of PILs should focus on patients' literacy level and age. Standardization of the PIL structure in all pharmaceutical companies is recommended.

Evaluation of off-label medication use and drug safety in a pediatric intensive care unit.

Safety and efficacy are essential in the process of disease treatment. However, off-label medication use is inevitable because various medications do not contain regulatory labels for pediatric use. We aimed to examine off-label medication use and analyze the risk factors correlated with adverse drug reactions (ADRs). This study was performed retrospectively using electronic medical data from a pediatric intensive care unit (PICU) of a tertiary hospital in Korea from July 2019 to June 2020. A total 6,183 prescribed medications from 502 PICU patients were examined in the present study. A total of 80% were infants or children, and 96.0% of them were treated with off-label medications. It was discovered that 4,778 off-label cases (77.2%) of the top 100 drugs had prescriptions with dosage (67.8%). Drugs prescribed to patients admitted to the cardiothoracic department (odds ratio [OR], 3.248; p = 0.019), total number of medications (OR, 1.116; p = 0.001), and length of PICU stay of ≥ 7 days (OR, 4.981; p = 0.008) were significantly associated with ADRs. ADRs were noted to be more severe in off-label use (p = 0.0426). For appropriate medication use, evidence regarding the safety of off-label medications is required and ultimately reflected in the official regulation.

Prenatal exposure to teratogenic medications in the era of Risk Evaluation and Mitigation Strategies.

BACKGROUND: Prevention of prenatal exposures to teratogenic drugs is a significant clinical and public health concern. With the enactment of the US Food and Drug Administration Amendments Act in 2007, the US Food and Drug Administration has begun to require manufacturers to implement Risk Evaluation and Mitigation Strategies to prevent prenatal exposures. Among 12 risk evaluation and mitigation strategy drugs, several had predecessor risk mitigation plans (eg, isotretinoin) and some were newly required (eg, mycophenolate). Only a small proportion of teratogenic drugs are currently subject to Risk Evaluation and Mitigation Strategies, and the extent of prenatal exposure to the universe of teratogenic drugs compared with drugs subject to Risk Evaluation and Mitigation Strategies is unknown. Moreover, the effectiveness of such advanced risk mitigation programs in preventing prenatal exposure is not clear. OBJECTIVE: This study aimed to characterize the epidemiology of prenatal exposures to definite and potential teratogens during the risk evaluation and mitigation strategy era. STUDY DESIGN: We constructed a time-series of pregnancies identified from a national private insurance claims database (IBM MarketScan) to estimate prenatal exposures to teratogenic drugs (2006-2017). Pregnancy outcomes, gestational age, and the onset of pregnancy were determined with previously validated algorithms. The Teratology Information Service and Clinical Pharmacology databases were used to identify drugs with definite (n=141) or potential (n=65) teratogenic effects, and drugs with debatable risks such as benzodiazepines, statins, tetracyclines, sex hormones, infertility treatments, and gonadotropin-releasing hormone analogs were excluded. We defined prenatal exposure as ≥1 prescription fill or medical encounter involving administration of drugs with a definite teratogenic risk (including 12 for which there is a "current or discontinued" risk evaluation and mitigation strategy) or a potential teratogenic risk. We evaluated secular trends and modeled the effects of age, preconception exposure, and state healthcare quality rankings on prenatal exposure, adjusting for demographic factors and clinical conditions. RESULTS: The cohort included 3,445,612 pregnancies (2,532,444 live deliveries). Prenatal exposures to definite teratogens decreased slightly during the study years from 1.86 to 1.24 per 100 pregnancies between 2006 and 2017, whereas exposure increased for potential teratogens from 3.40% to 5.33%. Prenatal exposure prevalences were higher during the first trimester and for pregnancies that ended in nonlive outcomes. Drugs subject to Risk Evaluation and Mitigation Strategies had low background utilization and contributed to a small proportion of prenatal exposures (15.1 per 100,000 pregnancies). We also observed fewer prenatal exposures to risk evaluation and mitigation strategy drugs among women of childbearing age who used these treatments (0.14% vs 0.36% for any definite teratogen). Age extremes and low state-level healthcare quality rankings were independent predictors of prenatal exposure. CONCLUSION: Fetuses in more than 1 in 16 pregnancies continued to be exposed to teratogenic drugs during the past decade. Drugs with Risk Evaluation and Mitigation Strategies imposed a small burden of prenatal exposure because of the low background utilization rates and lower pregnancy prevalence among women of childbearing age who used these drugs. Although the declining exposure rates to teratogenic drugs with definite risk are encouraging, the rising prenatal exposure to drugs with potential risk calls for more assessments. Future research is needed to elucidate the health outcomes of fetuses exposed to potential risk drugs, understand the effectiveness of risk evaluation and mitigation strategy programs, and prioritize teratogenic drugs for advanced risk mitigation.

A Review of Patient-Reported Outcome Labeling of FDA-Approved New Drugs (2016-2020): Counts, Categories, and Comprehensibility.

OBJECTIVES: A review of new drug approvals (NDAs) by the US Food and Drug Administration (FDA) for 2006 to 2015 showed that approximately 20% of new drugs had labeling based on patient-reported outcomes (PROs). The purpose of this study was to review labeling text based on PRO endpoints for NDAs from 2016 to 2020, with a special focus on the comprehensibility of such statements when included. METHODS: We reviewed drug approval reports on the Drugs@FDA web page of the FDA website to determine the number of NDAs from 2016 to 2020. For all identified NDAs, drug approval package and product labels were reviewed. NDAs from 2016 to 2020 were grouped by disease category as per International Classification of Diseases 10th Revision. Data were summarized for diseases that traditionally rely on PROs for evaluating treatment benefit (PRO dependent) and for diseases that traditionally do not rely on PROs (non-PRO dependent). Results were compared with NDAs from 2006 to 2010. RESULTS: NDAs amounting to 228 were identified from 2016 to 2020, 26.3% of which had labeling statements based on PRO endpoints. From 2006 to 2015 and from 2016 to 2020, PRO labeling statements were included in 46.5% (46 of 99) and 50.0% (47 of 94), respectively, of NDAs for PRO-dependent new molecular entities and in 6.0% (12 of 199) and 9.7% (13 of 199), respectively, of NDAs for non-PRO-dependent new molecular entities. Comprehensibility of labeling statements based on PRO endpoints was judged to be complex in 56.7% of product labels. CONCLUSIONS: The increase in labeling text based on PRO endpoints in product labels is encouraging. However, there is room for improvement on the comprehensibility of labeling statements based on PRO endpoints.

Use of Patient and Investigator Global Impression Scales: A Review of Food and Drug Administration-Approved Labeling, 2009 to 2019.

OBJECTIVE: Identify disease categories in which single-item global impression (GI) scales were included in product labeling of new drugs approved by the US Food and Drug Administration (FDA) in January 2009-December 2019 and review the characteristics of GIs included in product labeling of new FDA-approved drugs (January 2017-December 2019). METHODS: FDA Clinical Outcome Assessment (COA) Compendium was reviewed for drug labels that included GIs for drugs approved in 2009-2016. The indication, year of approval, ICD-10 code, and GI respondent were noted. A manual review of labels of FDA-approved drugs (2017-2019) was undertaken to identify GIs included in the labels. Corresponding drug approval packages were reviewed to identify details of any regulatory reviewer comments related to GIs. GI characteristics were noted from the drug label or the review documents, including the respondent, type of measure (static or dynamic), item wording, concept assessed, and response options. RESULTS: Product labeling containing GIs was most common in diseases related to the skin, nervous system, behavioral disorders, and the musculoskeletal system. GIs were included in 30/77 (39.0%) drug labels in the four disease categories. CONCLUSION: In the past 10 years, GIs have been included as endpoint measures in confirmatory clinical trials and have generated evidence of treatment benefit in diseases related to the skin, nervous system, behavioral disorders, and the musculoskeletal system. GIs frequently provide important insights into the patient experience. Before GIs are included in clinical trials to assess treatment benefit, it is important to ensure that they are valid, reliable, and responsive.

The Best Pharmaceuticals for Children Act and Pediatric Research Equity Act reach the age of majority-An oncology perspective.

The scarcity of adequate pediatric drug labeling information has long been problematic in the pediatric population, which may place children at risk for adverse drug effects. The ontogeny of infants, children, and adolescents over the course of the first two decades of life pose complex pharmacokinetic, dosing, administration, effectiveness, and toxicity-related questions that require specific investigation. Here, we review the history that led to the passage of the Best Pharmaceuticals for Children Act (BPCA) and Pediatric Research Equity Act (PREA), and provide commentary on issues relevant to pediatric oncology now and in the future.

Characterization of Pharmacogenetic Information in Food and Drug Administration Drug Labeling and the Table of Pharmacogenetic Associations.

BACKGROUND: The US Food and Drug Administration (FDA) recommends using only FDA-reviewed pharmacogenetic information to make prescribing decisions based on genetic test results. Such information is available in drug labeling and in the Table of Pharmacogenetic Associations ("Associations table"). OBJECTIVE: To compile a list of drug-gene pairs from drug labeling and the Associations table and categorize the pharmacogenetic information and clinical outcome associated with each drug-gene pair. METHODS: This was a cross-sectional analysis of pharmacogenetic information in the Associations table and individual drug labeling in March 2020. We used the Table of Pharmacogenomic Biomarkers in Drug Labeling to identify drug labels to review. We categorized the pharmacogenetic information for each drug-gene pair according to whether the purpose was to describe (1) polymorphisms affecting drug disposition (metabolism or transport), (2) polymorphisms affecting a direct drug target, (3) variants associated with adverse drug reaction (ADR) susceptibility, (4) variants associated with therapeutic failure, (5) a biomarker-defined indication, or (6) a biomarker-defined ADR. We also categorized the clinical outcome-efficacy, safety, or unknown-associated with each drug-gene pair. We reported counts and proportions of drug-gene pairs in each pharmacogenetic information and clinical outcome category. RESULTS: We identified 308 drug-gene pairs, of which 36% were associated with a biomarker-defined drug indication, 33% with polymorphic drug metabolism, and 28% with ADR susceptibility. Most drug-gene pairs (n = 267, 87%) were associated with an efficacy or safety-related outcome. CONCLUSION AND RELEVANCE: FDA-reviewed pharmacogenetic information is available for more than 300 drug-gene pairs and can help guide prescribing decisions.

Pharmacokinetic investigation of branched oligohexamethyleneguanidine hydrochloride in a liquid composition for the treatment of stomatitis and gingivitis.

Branched oligohexamethyleneguanidine hydrochloride (branched OHMG-HC) possesses high biocidal activity. This study aimed at evaluating the pharmacokinetics of branched OHMG-HC and is mandatory for obtaining permission to conduct clinical studies. The thermal activation method was used to obtain radioactive-labeled drugs for the investigation of substance distribution in various tissues of experimental animals (rats and rabbits). Substance administration was carried out both orally (a dose was split into two equal volumes that were applied to buccal zones of the oral cavities of the animals) and intravenously to get a clear pharmacokinetic profile. In this research, the drug was applied in the concentration of 0.77 mg/kg with the addition of 0.037 mg/kg 3H-OHMG for rats, and 0.42 mg/kg with the addition of 0.015 mg/kg 3H-OHMG for Chinchilla rabbits. The selected samples of blood, organs, and urine underwent alkaline mineralization. A quantitative determination of 3H-OHMG was carried out using a liquid ß-, γ-counter according to the level of scintillation in the sample. Branched OHMG-HC displayed uniform distribution within all main organs and tissues upon oral administration. The highest concentrations were found in liver and kidney tissues, whereas the lowest in blood, cardiac muscle tissue, and brain. The closeness of the fabs values obtained from different animals (24.5% for rats and 29.0% for rabbits) demonstrated the absence of the species specificity in response to the pharmaceutical substance. The main parameters of excretion were established, and the half-life time was estimated to be 15 h.

A Rational Use of Clozapine Based on Adverse Drug Reactions, Pharmacokinetics, and Clinical Pharmacopsychology.

Using Richardson and Davidson's model and the sciences of pharmacokinetics and clinical pharmacopsychology, this article reviewed the: (1) poor life expectancy associated with treatment-resistant schizophrenia (TRS), which may be improved in patients who adhere to clozapine; (2) findings that clozapine is the best treatment for TRS (according to efficacy, effectiveness and well-being); and (3) potential for clozapine to cause vulnerabilities, including potentially lethal adverse drug reactions such as agranulocytosis, pneumonia, and myocarditis. Rational use requires: (1) modification of the clozapine package insert worldwide to include lower doses for Asians and to avoid the lethality associated with pneumonia, (2) the use of clozapine levels for personalizing dosing, and (3) the use of slow and personalized titration. This may make clozapine as safe as possible and contribute to increased life expectancy and well-being. In the absence of data on COVID-19 in clozapine patients, clozapine possibly impairs immunological mechanisms and may increase pneumonia risk in infected patients. Psychiatrists should call their clozapine patients and families and explain to them that if the patient develops fever or flu-like symptoms, the psychiatrist should be called and should consider halving the clozapine dose. If the patient is hospitalized with pneumonia, the treating physician needs to assess for symptoms of clozapine intoxication since halving the dose may not be enough for all patients; consider decreasing it to one-third or even stopping it. Once the signs of inflammation and fever have disappeared, the clozapine dose can be slowly increased to the prior dosage level.

Prevention strategies to identify LASA errors: building and sustaining a culture of patient safety.

BACKGROUND: Potential look-alike, sound-alike (LASA) errors in outpatient and inpatient prescriptions have been widely described worldwide. However, most strategies of reducing drug name confusion have been only focused on the processes of prescribing and dispensing, often following local rules. MAIN TEXT: An illustrative recent example about this topic is given: the antidepressant Brintellix® (vortioxetine) (Takeda Pharmaceuticals USA, Inc.) and the antiplatelet medication Brilinta® (ticagrelor) (AstraZeneca LP). Revision of the initiatives that are currently applied to prevent potential LASA errors in different countries around the world and debate about the emerging strategies that could be implemented in short and mid-term. At present, a common policy worldwide on the authorization of unique names for innovative medicines does not exist. The implication of authorities in topdown strategies and the importance of developing an international health policy on the authorization of unique names for innovative medicines are highlighted in the following piece of opinion. CONCLUSIONS: Building and sustaining a culture of patient safety should be considered as a global top-down strategy which involved all the elements in the system (regulatory bodies, manufacturers and suppliers). The precedent established by the FDA in prevention strategies to identify and avoid LASA errors has been extremely important and should lead to international discussion. Coordinated international efforts are urgently needed in this area for the sake of patients' safety.

DLI-IT: a deep learning approach to drug label identification through image and text embedding.

BACKGROUND: Drug label, or packaging insert play a significant role in all the operations from production through drug distribution channels to the end consumer. Image of the label also called Display Panel or label could be used to identify illegal, illicit, unapproved and potentially dangerous drugs. Due to the time-consuming process and high labor cost of investigation, an artificial intelligence-based deep learning model is necessary for fast and accurate identification of the drugs. METHODS: In addition to image-based identification technology, we take advantages of rich text information on the pharmaceutical package insert of drug label images. In this study, we developed the Drug Label Identification through Image and Text embedding model (DLI-IT) to model text-based patterns of historical data for detection of suspicious drugs. In DLI-IT, we first trained a Connectionist Text Proposal Network (CTPN) to crop the raw image into sub-images based on the text. The texts from the cropped sub-images are recognized independently through the Tesseract OCR Engine and combined as one document for each raw image. Finally, we applied universal sentence embedding to transform these documents into vectors and find the most similar reference images to the test image through the cosine similarity. RESULTS: We trained the DLI-IT model on 1749 opioid and 2365 non-opioid drug label images. The model was then tested on 300 external opioid drug label images, the result demonstrated our model achieves up-to 88% of the precision in drug label identification, which outperforms previous image-based or text-based identification method by up-to 35% improvement. CONCLUSION: To conclude, by combining Image and Text embedding analysis under deep learning framework, our DLI-IT approach achieved a competitive performance in advancing drug label identification.

Study of serious adverse drug reactions using FDA-approved drug labeling and MedDRA.

BACKGROUND: Adverse Drug Reactions (ADRs) are of great public health concern. FDA-approved drug labeling summarizes ADRs of a drug product mainly in three sections, i.e., Boxed Warning (BW), Warnings and Precautions (WP), and Adverse Reactions (AR), where the severity of ADRs are intended to decrease in the order of BW > WP > AR. Several reported studies have extracted ADRs from labeling documents, but most, if not all, did not discriminate the severity of the ADRs by the different labeling sections. Such a practice could overstate or underestimate the impact of certain ADRs to the public health. In this study, we applied the Medical Dictionary for Regulatory Activities (MedDRA) to drug labeling and systematically analyzed and compared the ADRs from the three labeling sections with a specific emphasis on analyzing serious ADRs presented in BW, which is of most drug safety concern. RESULTS: This study investigated New Drug Application (NDA) labeling documents for 1164 single-ingredient drugs using Oracle Text search to extract MedDRA terms. We found that only a small portion of MedDRA Preferred Terms (PTs), 3819 out of 21,920 or 17.42%, were observed in a whole set of documents. In detail, 466/3819 (12.0%) PTs were in BW, 2023/3819 (53.0%) were in WP, and 2961/3819 (77.5%) were in AR sections. We also found a higher overlap of top 20 occurring BW PTs with WP sections compared to AR sections. Within the MedDRA System Organ Class levels, serious ADRs (sADRs) from BW were prevalent in Nervous System disorders and Vascular disorders. A Hierarchical Cluster Analysis (HCA) revealed that drugs within the same therapeutic category shared the same ADR patterns in BW (e.g., nervous system drug class is highly associated with drug abuse terms such as dependence, substance abuse, and respiratory depression). CONCLUSIONS: This study demonstrated that combining MedDRA standard terminologies with data mining techniques facilitated computer-aided ADR analysis of drug labeling. We also highlighted the importance of labeling sections that differ in seriousness and application in drug safety. Using sADRs primarily related to BW sections, we illustrated a prototype approach for computer-aided ADR monitoring and studies which can be applied to other public health documents.

Implementation fidelity of patient-centered prescription label to promote opioid safe use.

PURPOSE: Patient-centered labels may improve safe medication use, but implementation challenges limit use. We assessed implementation of a patient-centered "PRN" (as needed) label entitled "Take-Wait-Stop" (TWS) with three deconstructed steps replacing traditional wording. METHODS: As part of a larger investigation, patients received TWS prescriptions (eg, Take: 1 pill if you have pain; Wait: at least 4 h before taking again; Stop: do not take more than 6 pills in 24 h). Prescriptions labels recorded at follow-up were classified into three categories: (1) one-step wording (Take 1 pill every 4 h [without daily limits]), (2) two-step wording (Take 1 pill every 4 h; do not exceed 6 pills/day), and (3) three-step wording. There were three subtypes of three-step wording: (3a) three-step, not TWS (three deconstructed steps, not necessarily TWS wording), (3b) TWS format, employing three steps with leading verbs, but "with additions or replacements" (eg, replaced "do not take" with "do not exceed"), and (3c) verbatim TWS. RESULTS: Two hundred eleven participants completed follow-up. Mean age was 44.3 years (SD 14.3); 44% were male. One-step bottles represented 12% (n = 25) of the sample, whereas 26% (n = 55) had two-step wording. The majority (44%, n = 93) had three-deconstructed steps, not TWS (3a); 16% (n = 34) retained TWS structure, but not verbatim (3b). Only 2% (n = 4) displayed verbatim TWS wording (3c). All category three labels (utilizing deconstructed instructions) were considered adequate implementation (62%). CONCLUSIONS: Exact intervention adherence was not achieved in the majority of cases, limiting impact. Nonetheless, community pharmacies were responsive to new instructions, but higher implementation reliability requires additional supports.

A comparison of new drugs approved by the FDA, the EMA, and Swissmedic: an assessment of the international harmonization of drugs.

INTRODUCTION: This study compared the characteristics of new human drugs approved by the Food and Drug Administration (FDA), the European Medicine Agency (EMA), and Swissmedic (SMC) in the period 2007 to 2016. METHODS: The list of new drugs and therapeutic biologics approved by the FDA, the EMA, and SMC in the period 2007 to 2016 was collected from websites of those agencies. The study included regulatory information, approval date, and indication for each drug. Descriptive statistical t tests and x2-tests were performed for the analysis. RESULTS: From 2007 to 2016, 134 new drugs were approved by all three regulatory agencies. Overall, 66.4% of the drugs were first approved by the FDA, 30.6% by the EMA, and 3.0% by SMC. The difference in approval dates between SMC and the EMA, SMC and the FDA, and the FDA and the EMA were statistically significant. The indications approved by the FDA, the EMA, and SMC for the same drugs were similar in content for 23.1% drugs and different in 76.9% of the drugs. Significant differences in indications existed between the FDA and SMC and the FDA and the EMA, but not between the EMA and SMC. CONCLUSION: There were differences in the characteristics of new drugs approved by the EMA, the FDA, and SMC in the period 2007-2016. Overall, two thirds of the new drugs were first approved by the FDA. Differences in indications were found in three out of four new drugs approved by the three regulatory agencies. Despite international drug regulation harmonization efforts, significant differences in the characteristics of new drugs approved by different agencies persist.

Characteristics of drugs safety signals that predict safety related product information update.

PURPOSE: Investigation of drug safety signals is one of the major tasks in pharmacovigilance. Among many potential signals identified, only a few reflect adverse drug reactions requiring regulatory actions, such as product information (PI) update. Limited information is available regarding the signal characteristics that might predict PI update following signal evaluation. The objective of this study was to identify signal characteristics associated with PI updates following signal evaluation by the European Medicines Agency Pharmacovigilance Risk Assessment Committee during 2012 to 2016. METHODS: A comparative study was performed based on data from 172 safety signals. Characteristics of signals were extracted from the European Pharmacovigilance Issues Tracking Tool database. Multivariable logistic regression analysis was used to assess the relationship between signal characteristics and the decision to update the PI. RESULTS: Multivariable logistic regression analysis showed that the presence of evidence in multiple types of data sources (adjusted odds ratio [OR] 7.8 95% CI [1.5, 40.1]); mechanistic plausibility of the drug-event association (adjusted OR 3.9 95% CI [1.9, 8.0]); seriousness of the event (adjusted OR 4.2 95% CI [1.3, 13.9]); and age of drugs ≤5 years (adjusted OR 3.9 95% CI [1.2, 12.7]) were associated with the decision to change the PI (P < 0.05). CONCLUSIONS: This study identified 4 characteristics of drug safety signals that have shown to be associated with PI changes as outcome of signal evaluation. These characteristics may be used as criteria for selection and prioritization of potential signals that are more likely to necessitate product information updates.

Risk perception regarding drug use in pregnancy.

Pregnant women, but also physicians, have unrealistically high perceptions of teratogenic drug effects. This may result in suboptimal treatment of disease and even influence decisions of whether to continue pregnancy. To attain more realistic teratogenic risk perceptions, several factors that influence this issue should be considered, and these are further discussed in this Clinical Opinion. Importantly, drug use may have several benefits, both for the pregnant woman's health and to avoid negative fetal effects of untreated maternal disease. A greater focus on this aspect may act to balance risk perceptions. Furthermore, both pregnant women and physicians need access to drug information sources that provide realistic risk estimates to increase confidence in appropriate drug use and prescribing. We suggest that access to decision support and individually tailored information provided by drug information centers may contribute to this goal.