Explore the vast range of titles available.

Objective This study examined the impact of a short-term study abroad program, focusing on program evaluation, attendee satisfaction, and acquired knowledge and skills. A questionnaire survey was conducted covering various aspects including demographics, program evaluation, and feedback. Results Results indicated higher female participation due to gender imbalances in pharmacy students in Egypt, with senior students recognizing the value of international experience. Attendee satisfaction was high, with positive feedback on accommodation, tours, and workshop materials. Field visits and workshops provided valuable experiential learning, with attendees suggesting extending the program’s duration. The program equipped attendees with knowledge and skills relevant to pharmaceutical products and services, leading to improved competences and perceptions. The study concludes that such study abroad experiences profoundly impact personal growth and recommends integrating them into educational curricula for valuable experiences.

In response to the increasing application of machine learning (ML) across many facets of pharmaceutical development, this pilot study investigated if ML, using artificial neural networks (ANNs), could predict the apparent degree of supersaturation (aDS) from two supersaturated LBFs (sLBFs). Accuracy was compared to partial least squares (PLS) regression models. Equilibrium solubility in Capmul MCM and Maisine CC was obtained for 21 poorly water-soluble drugs at ambient temperature and 60 °C to calculate the aDS ratio. These aDS ratios and drug descriptors were used to train the ML models. When compared, the ANNs outperformed PLS for both sLBFCapmulMC (r2 0.90 vs. 0.56) and sLBFMaisineLC (r2 0.83 vs. 0.62), displaying smaller root mean square errors (RMSEs) and residuals upon training and testing. Across all the models, the descriptors involving reactivity and electron density were most important for prediction. This pilot study showed that ML can be employed to predict the propensity for supersaturation in LBFs, but even larger datasets need to be evaluated to draw final conclusions.