Sarath Chandran Chandrashekar Shenoy

The survey examined the understanding and utilization of AI among pharmacy faculties in India. A quantitative, cross-sectional study was conducted using a four-section questionnaire. The survey was conducted with 154 participants via social media and emails. Most participants were female, held a PhD, and had extensive academic experience. Most pharmacy colleges in India are in the private sector, and most participants are from urban areas. The study revealed a significant knowledge gap regarding AI among faculty members, with only 5.8% having an exceptional knowledge of AI. Over half of the respondents had not received any formal training or education on advanced technology. The most commonly used AI tools in India are ChatGPT, Grammarly, and QuillBot. No statistically significant associations were found between the use of AI and any of the demographic characteristics examined. The study's findings expressed concerns about relying less on traditional sources of information. Additionally, 73.4% of the participants were worried that AI incorporation might negatively impact their professional skills and individuality. The survey showed a strong consensus (94.8%) on the importance of AI in pharmacy education, believing that educating students about AI and its societal implications is essential. The study emphasized the importance of balancing AI's benefits of AI and preserving core professional competencies. The findings of this study can be used to equip teachers with the knowledge and skills necessary to effectively incorporate AI into pharmacy education.

Objective: The primary objective of this investigation was to develop mangiferin (MGF)-loaded bovine serum albumin (BSA) nanoparticles using a Quality by Design (QbD) approach. The study involved characterizing the optimized nanoparticles to confirm their successful formation, assessing their ability to inhibit the growth of MCF-7 breast cancer cell lines, and conducting a toxicity assay following OECD protocol 423. Methods: MGF-loaded BSA NP was prepared by the modified desolvation method with necessary modification. For the optimization, a QbD framework with a Central Composite Design (CCD) was employed. The optimization was done by evaluating the effect of critical factors such as BSA concentration, MGF loading, and stirring speed on selected responses such as particle size (Ps), polydispersibility index (PDI), zeta potential(Zp) and encapsulation efficiency (EE). The optimized formulation was further analyzed using SEM, FT-IR, and XRD and assessed for in vitro drug release, as well as using the MTT assay, it demonstrated anticancer efficacy against MCF-7 breast cancer cells. Finally, the safety profile of the formulation was established by acute toxicity study in Albino Wistar Rat as per OECD protocol 423. Results: Successful incorporation of MGF into BSA NP was achieved by modified desolvation technique. 20 formulations were developed as per CCD. The optimized nanoparticles exhibited a Ps of 96.83 nm, PDI of 0.25, Zp of-22.9 mV, and EE of 70.37%. SEM confirmed the spherical morphology, and FT-IR/XRD verified that there are no chemical reactions. The in vitro release study showed 90% drug release within 8 h. In vitro anticancer assays revealed dose-dependent efficacy (ID50:22.32 µg/ml) and comparable with that of doxorubicin. The statistical significance was observed between the optimized formulation and control groups. Acute toxicity studies confirmed its safety (no mortality/toxicity at 2000 mg/kg). Conclusion: The current investigation shows that MGF-loaded BSANP have a promising release profile for achieving better anti-breast cancer activity.