Predictive modeling of skin permeability for molecules: Investigating FDA-approved drug permeability with various AI algorithms.

The transdermal route of drug administration has gained popularity for its convenience and bypassing the first-pass metabolism. Accurate skin permeability prediction is crucial for successful transdermal drug delivery (TDD). In this study, we address this critical need to enhance TDD. A dataset comprising 441 records for 140 molecules with diverse LogKp values was characterized. The descriptor calculation yielded 145 relevant descriptors. Machine learning models, including MLR, RF, XGBoost, CatBoost, LGBM, and ANN, were employed for regression analysis. Notably, LGBM, XGBoost, and gradient boosting models outperformed others, demonstrating superior predictive accuracy. Key descriptors influencing skin permeability, such as hydrophobicity, hydrogen bond donors, hydrogen bond acceptors, and topological polar surface area, were identified and visualized. Cluster analysis applied to the FDA-approved drug dataset (2326 compounds) revealed four distinct clusters with significant differences in molecular characteristics. Predicted LogKp values for these clusters offered insights into the permeability variations among FDA-approved drugs. Furthermore, an investigation into skin permeability patterns across 83 classes of FDA-approved drugs based on the ATC code showcased significant differences, providing valuable information for drug development strategies. The study underscores the importance of accurate skin permeability prediction for TDD, emphasizing the superior performance of nonlinear machine learning models. The identified key descriptors and clusters contribute to a nuanced understanding of permeability characteristics among FDA-approved drugs. These findings offer actionable insights for drug design, formulation, and prioritization of molecules with optimum properties, potentially reducing reliance on costly experimental testing. Future research directions include offering promising applications in pharmaceutical research and formulation within the burgeoning field of computer-aided drug design.

Knowledge, attitude and practice among pharmacy students and faculty members towards artificial intelligence in pharmacy practice: A multinational cross-sectional study.

BACKGROUND: Modern patient care depends on the continuous improvement of community and clinical pharmacy services, and artificial intelligence (AI) has the potential to play a key role in this evolution. Although AI has been increasingly implemented in various fields of pharmacy, little is known about the knowledge, attitudes, and practices (KAP) of pharmacy students and faculty members towards this technology. OBJECTIVES: The primary objective of this study was to investigate the KAP of pharmacy students and faculty members regarding AI in six countries in the Middle East as well as to identify the predictive factors behind the understanding of the principles and practical applications of AI in healthcare processes. MATERIAL AND METHODS: This study was a descriptive cross-sectional survey. A total of 875 pharmacy students and faculty members in the faculty of pharmacy in Jordan, Palestine, Lebanon, Egypt, Saudi Arabia, and Libya participated in the study. Data was collected through an online electronic questionnaire. The data collected included information about socio-demographics, understanding of AI basic principles, participants' attitudes toward AI, the participants' AI practices. RESULTS: Most participants (92.6%) reported having heard of AI technology in their practice, but only a small proportion (39.5%) had a good understanding of its concepts. The overall level of knowledge about AI among the study participants was moderate, with the mean knowledge score being 42.3 ± 21.8 out of 100 and students having a significantly higher knowledge score than faculty members. The attitude towards AI among pharmacy students and faculty members was positive, but there were still concerns about the impact of AI on job security and patient safety. Pharmacy students and faculty members had limited experience using AI tools in their practice. The majority of respondents (96.2%) believed that AI could improve patient care and pharmacy services. However, only a minority (18.6%) reported having received education or training on AI technology. High income, a strong educational level and background, and previous experience with technologies were predictors of KAP toward using AI in pharmacy practice. Finally, there was a positive correlation between knowledge about AI and attitudes towards AI as well as a significant positive correlation between AI knowledge and overall KAP scores. CONCLUSION: The findings suggest that while there is a growing awareness of AI technology among pharmacy professionals in the Middle East and North Africa (MENA) region, there are still significant gaps in understanding and adopting AI in pharmacy Practice.

Financial indicators utilization among community pharmacists: A comprehensive study for pharmacy management.

BACKGROUND: The financial management of community pharmacies is a critical aspect of healthcare delivery, as pharmacists often operate as healthcare providers and business managers. Understanding pharmacists' awareness, perceptions, and practices related to financial indicators is essential for effective pharmacy management. There is a paucity of research addressing this issue regionally and locally. OBJECTIVES: This study aimed to investigate the perceptions and utilization of financial indicators among community pharmacists in Jordan and identify demographic and contextual factors influencing their financial practices. METHODS: A cross-sectional study was conducted, surveying 353 community pharmacists from various regions of Jordan. The developed and validated survey assessed demographic characteristics, utilizations of financial indicators, and perceptions of their significance. Pharmacists were queried about their financial practices, including the use of various financial indicators. Descriptive and analytical statistics were used to portray the study's findings. RESULTS: The study included a diverse group of community pharmacists in terms of demographic characteristics. Most pharmacists exhibited awareness of financial indicators, with a higher awareness of profitability and liquidity indicators. Pharmacists generally had positive perceptions of the importance of these indicators in daily practice. High agreement was observed in financial practices, including following up on payables and receivables, monitoring changes in monthly revenue, and preparing income statements. There was significant variation in the utilization and perception of financial indicators based on factors such as pharmacy ownership, province, foundation age, and practical experience. CONCLUSION: The findings indicate a positive correlation between utilization and perception, emphasizing the importance of raising awareness of financial indicators among pharmacists. The study also highlights the significance of tailored financial training programs for pharmacists at different stages of their careers and the importance of regional context in financial practices. Understanding these variations can lead to more effective financial management and improved healthcare services in community pharmacies.