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Risedronate-loaded mPEG-coated hydroxyapatite, thiolated chitosan-based (coated) and non-coated nanoparticles were tested for their potential effects in the treatment of osteoporosis. The prepared nanoparticles were evaluated for their bone-targeting potential by inducing osteoporosis in female Wistar rats via oral administration of Dexona (dexamethasone sodium phosphate). In vivo pharmacokinetic and pharmacodynamic studies were performed on osteoporotic rat models treated with different formulations. The osteoporotic model treated with the prepared nanoparticles indicated a significant effect on bone. The relative bioavailability was enhanced for RIS-HA-TCS-mPEG nanoparticles given orally compared to RIS-HA-TCS, marketed, and API suspension. Biochemical investigations also showed a significant change in biomarker levels, ultimately leading to bone formation/resorption. Micro-CT analysis of bone samples also demonstrated that the RIS-HA-TCS-mPEG-treated group showed the best results compared to other treatment groups. Moreover, the histology of bone treated with RIS-HA-TCS-mPEG showed a marked restoration of the architecture of trabecular bone along with a well-connected bone matrix and narrow inter-trabecular spaces compared to the toxic group. A stability analysis was also carried out according to ICH guidelines (Q1AR2), and it was found that RIS-HA-TCS-mPEG was more stable than RIS-HA-TCS at 25 °C. Thus, the results of present study indicated that mPEG-RIS-HA-TCS has excellent potential for sustained delivery of RIS for the treatment and prevention of osteoporosis, and for minimizing the adverse effects of RIS typically induced via oral administration.

Machine learning (ML) algorithms have brought about a revolution in many industries where otherwise operation time, cost, and safety would have been compromised. Likewise, in lubrication research, ML has been utilized on many occasions. This review provides an in-depth understanding of seven ML algorithms from a tribological perspective. More specifically, it presents a comprehensive overview of recent advancements in ML applied to lubrication research, organized into four distinct categories. The first category, experimental parameter prediction, highlights the significant contributions of artificial neural networks (ANNs) in accurately forecasting operating conditions related to friction and wear. These predictions offer valuable insights that aid in forensic preparation. Discriminant analysis, Bayesian modeling, and transfer learning approaches have also been used to predict experimental parameters. Second, to predict the lubrication film thickness and identify the lubrication regime, algorithms such as logistic regression and ANN were useful. Such predictions provide up to 99.25% accuracy. Third, to predict the friction and wear for a given experimental condition, support vector machine (SVM), polynomial regression, and ANN offered an accuracy above 93%. Finally, for condition monitoring for bearings, gearboxes, gear trains, and similar critical situations where regular in-person inspection is difficult, Naïve Bayes, SVM, decision trees, and ANN were utilized to predict the safe life of lubricants. This review highlighted these four aspects with state-of-the-art examples and discussed the current situation and projected future possibilities of lubricant design facilitated by ML techniques.