Biju Patnaik University of Technology

The biodegradable and eco-friendly nature of vegetable oils make them promising replacement for the depleting mineral oils as industrial lubricants.However, the poor tribol. properties of vegetable oils remain a challenge in their industrial applications.In this study, environmentally friendly halloysite clay nanotube (HNT) additives have been incorporated (0.25, 0.5, 0.75, and 1 weight%) in the pyrolysis date seed oil and castor oil blends for enhancement of tribol. properties.HNTs are easily available, low priced, and non-toxic nanomaterials, making them potential green lubricant additives.The date seed oil was extracted employing a 20-L pyrolysis reactor and blended with castor oil in different ratios.The addition of HNTs into castor oil and date seed oil blends (bio-lubricant) increased the viscosities by up to 13.4% with 1 weight% HNTs added.Wear scar diameter (WSD) was also reduced by 7.72, 8.08, 15.22, and 21.32% for formulated bio-lubricants containing 0.25, 0.5, 0.75, and 1 weight% HNTs, resp.Furthermore, coefficient of friction (COF) of bio-lubricants was reduced by 12.6, 16.4, 16.7, and 28.2% with 0.25, 0.5, 0.75, and 1 weight% HNTs.SEM images further confirmed the level of abrasive wear was reduced and smooth surfaces were observed in bio-lubricants with HNT additives.The formulated bio-lubricants showed good stability with different concentrations of HNTs as tested for a period of up to 72 h.The environment friendly bio-lubricants formulated by utilizing date seeds waste source and non-toxic biocompatible HNTs with improved tribol. properties will lead to develop advanced bio-lubricants for multiple energy conservation and environmental benefits.

Capecitabine is an anticancer antimetabolite of fluorouracil that inhibits cell proliferation and impairs DNA repair in cancer cells. It is given specifically to treat metastatic breast, gastric and colorectal cancers.

To design, formulate, optimize and evaluate Capecitabine Nanosuspension using the QbD Box-Behnken model.

Deploying the Box-Behnken Design (BBD) model, Capecitabine nanosuspension was for-mulated with seventeen runs; the independent variables selected are Pullulan concentration, Poloxamer F407 concentration, and sonication time. The developed nanosuspension was evaluated for particle size, Zeta potential, entrapment efficiency, in vitro drug release, and anti-cancer effectiveness. Drug-excipient compatibility was assessed using the Fourier transform infrared spectroscopy method. X-ray Diffraction studies and Differential Scanning Calorimetry were used to perform and assess the crys-talline nature. Additionally, characteristics of the developed nanosuspension were assessed by per-forming a scanning electron microscopy study.

The effect of various factors on key responses, namely particle size, entrapment efficiency, and drug release up to 8 hours, was assessed. A polynomial equation was employed through the anal-ysis to optimize the formulation, considering the significance levels indicated by the p-values. Notably, these variables demonstrated a substantial influence on the responses. The comparison between ob-served and predicted values revealed a relatively minor variance (85.90, 69.57, and 76.34 for particle size, entrapment efficiency, and percentage drug release at 8 hours, respectively), indicating the mod-el';s suitability. The prepared nanosuspension exhibited compatibility with the used excipients, with capecitabine-entrapped nanoparticles uniformly dispersed within the suspending medium.

Developed capecitabine nanosuspension demonstrated better efficacy and effectiveness against gastric cancer, with independent variables like pullulan and Poloxamer F207 concentration, along with sonication time, influencing particle size, entrapment efficiency, and drug release.

The applications of artificial intelligence (AI) in pharmaceutical sectors have advanced drug discovery and development methods. AI has been applied in virtual drug design, molecule synthesis, advanced research, various screening methods, and decision-making processes. In the fourth industrial revolution, when medical discoveries are happening swiftly, AI technology is essential to reduce the costs, effort, and time in the pharmaceutical industry. Further, it will aid “genome-based medicine” and “drug discovery.” AI may prepare proactive databases according to diseases, disorders, and appropriate usage of drugs which will facilitate the required data for the process of drug development. The application of AI has improved clinical trials on patient selection in a population, stratification, and sample assessment such as biomarkers, effectiveness measures, dosage selection, and trial length. Various studies suggest AI could be perform better compared to conventional techniques in drug discovery. The present review focused on the positive impact of AI in drug discovery and development processes in the pharmaceutical industry and beneficial usage in health sectors as well.