One of the largest spontaneous adverse events reporting databases in the world is the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). Unfortunately, researchers face many obstacles in analyzing data from the FAERS database. One of the major obstacles is the unstructured entry of drug names into the FAERS, as reporters might use generic names or trade names with different naming structures from all over the world and, in some cases, with typographical errors. Moreover, report duplication is a known problem in spontaneous adverse event-reporting systems, including the FAERS database. Hence, thorough text processing for database entries, especially drug name entries, coupled with a practical case-deduplication logic, is a prerequisite to analyze the database, which is a time- and resource-consuming procedure. In this study, we provide a clean, deduplicated, and ready-to-import dataset into any relational database management software of the FAERS database up to September 2021. Drug names are standardized to the RxNorm vocabulary and normalized to the single active ingredient level. Moreover, a pre-calculated disproportionate analysis is provided, which includes the reporting odds ratio (ROR), proportional reporting ratio (PRR), Chi-squared analysis with Yates correction ($x^2$), and information component (IC) for each drug-adverse event pair in the database.

Creating optimal logic mining is strongly dependent on how the learning data are structured. Without optimal data structure, intelligence systems integrated into logic mining, such as an artificial neural network, tend to converge to suboptimal solution. This paper proposed a novel logic mining that integrates supervised learning via association analysis to identify the most optimal arrangement with respect to the given logical rule. By utilizing Hopfield neural network as an associative memory to store information of the logical rule, the optimal logical rule from the correlation analysis will be learned and the corresponding optimal induced logical rule can be obtained. In other words, the optimal logical rule increases the chances for the logic mining to locate the optimal induced logic that generalize the datasets. The proposed work is extensively tested on a variety of benchmark datasets with various performance metrics. Based on the experimental results, the proposed supervised logic mining demonstrated superiority and the least competitiveness compared to the existing method.

Background and Objective. Clozapine is a second‐generation antipsychotic drug that is considered the most effective treatment for refractory schizophrenia. Several clozapine population pharmacokinetic models have been introduced in the last decades. Thus, a systematic review was performed (i) to compare published pharmacokinetics models and (ii) to summarize and explore identified covariates influencing the clozapine pharmacokinetics models. Methods. A search of publications for population pharmacokinetic analyses of clozapine either in healthy volunteers or patients from inception to April 2019 was conducted in PubMed and SCOPUS databases. Reviews, methodology articles, in vitro and animal studies, and noncompartmental analysis were excluded. Results. Twelve studies were included in this review. Clozapine pharmacokinetics was described as one‐compartment with first‐order absorption and elimination in most of the studies. Significant interindividual variations of clozapine pharmacokinetic parameters were found in most of the included studies. Age, sex, smoking status, and cytochrome P450 1A2 were found to be the most common identified covariates affecting these parameters. External validation was only performed in one study to determine the predictive performance of the models. Conclusions. Large pharmacokinetic variability remains despite the inclusion of several covariates. This can be improved by including other potential factors such as genetic polymorphisms, metabolic factors, and significant drug‐drug interactions in a well‐designed population pharmacokinetic model in the future, taking into account the incorporation of larger sample size and more stringent sampling strategy. External validation should also be performed to the previously published models to compare their predictive performances.

Background: Millions of individuals worldwide use statins, and their significant impact on cardiovascular disease (CVD) has been well-established. However, a lack of knowledge about the up-to-date guideline recommendations regarding statin therapy is a common barrier to implementation in clinical practice. Therefore, the present study aimed to assess the current clinical knowledge about statin therapy and its monitoring parameters. Also, we evaluated the barriers to cholesterol management guideline implementation in Yemen.Methods: This observational cross-sectional study was conducted over four months, from June/2021 to September/2021, in Sana’a, Yemen. A validated questionnaire was distributed face-to-face to 650 participants (350 physicians and 300 pharmacists). Physicians and pharmacists from governmental and private hospitals and those working in private clinics or community pharmacies were included in the study.Results: A total of 496 participants filled out the survey, with 22 being excluded due to incomplete data. So, the study has an overall response rate of 72.9% (474). The majority of pharmacists (81.8%) and physicians (78.7%) could not identify the patient group that needed ASCVD risk assessment before statin therapy initiation. Although a significant proportion of respondents knew of the fact that high-intensity statins are recommended for patients with ASCVD (65.4%) and primary hypercholesterolemia (58.4%), the majority of physicians and pharmacists could not identify the high (61.6% and 66.7.3%, respectively) and moderate statin-intensity doses (72.2% and 68.6%, respectively). Only 21.9% of all respondents knew that atorvastatin and rosuvastatin can be administered at any time of the day. Similarly, a low overall rate of respondents (19.6%) knew that atorvastatin does not need dose adjustment in chronic kidney diseases, with a statistically significant difference in knowledge between physicians and pharmacists (12.5% vs. 25.6%,p<0.001, respectively). Notably, only 39.2% of participants were aware that statins are not safe to use during breastfeeding. Around half of respondents (52.3%) correctly identify the duration (4 to 12 weeks) at which LD-C measuring is recommended after therapy initiation or dose change. The lowest knowledge scores for respondents were related to statin-drug interactions. Age, experience, degree, and previous guideline exposure were all significantly associated with the knowledge scores (p <0.05). The four most perceived barriers to implementing cholesterol management guidelines were no audit on adherence to the guidelines in the workplace (73.4%), insufficient resources to adequately implement and follow up on the guideline’s recommendations (73.6%), patient’s financial status (75.7%), and lack of familiarity about the guideline’s latest recommendations (63.3%).Conclusion: Physicians and pharmacists had suboptimal clinical knowledge regarding statin therapy, dose intensities, drug-drug interaction, contraindications, and monitoring parameters. Therefore, physicians’ and pharmacists’ educational interventions regarding the up-to-date recommendation about statins are recommended.

Pharmacy Practice is a free full-text journal with a scope on the practice of pharmacy. Pharmacy Practice covers pharmaceutical care, social pharmacy, pharmacy education, process and outcome research, health promotion and education, health informatics, pharmacoepidemiology

Background and Purpose. Diabetes mellitus has been reported as a strong independent risk factor for stroke recurrence. Data on the modifiable factors contributing to the recurrence of stroke in type 2 diabetic Malaysian population with a history of stroke stratified by genders are lacking, and this supports the importance of this study. Method. The data of 4622 patients with T2DM who had a history of stroke was obtained from the Malaysian National Stroke Registry. Univariate analysis was performed to differentiate between genders with and without stroke recurrence in terms of demographics, first stroke attack presentations, and other clinical characteristics. The significant factors determined from the univariate analysis were further investigated using logistic regression. Results. Ischemic heart diseases were found significantly associated with the stroke recurrence in males (OR=1.738; 95% CI: 1.071-2.818) as well as female (OR=5.859; 95% CI: 2.469-13.752) diabetic patients. The duration of hypertension, as well as the duration of diabetes, has been associated with the recurrence in both male and female subjects (p value < 0.05). Smoking status has an impact on the stroke recurrence in male subjects, while no significant association was observed among their peers. Conclusions. Most of the predictive factors contributing to the recurrence of stroke in type 2 diabetic Malaysian population with a history of stroke are modifiable, in which IHD was the most prominent risk factor in both genders. The impact of optimizing the management of IHD as well as blood glucose control on stroke recurrence may need to be elucidated. No major differences in recurrent stroke predictors were seen between genders among the Malaysian population with type 2 diabetes mellitus who had a previous history of stroke.