A systematic review and meta-analysis of the metal nano-particles loaded with herbal drugs moieties against breast cancer.

Background - Breast cancer is the most prevalent cancer among women. About 685K deaths were globally listed in 2020 by the World Health Organization. Nowadays, scientists prefer to use herbal medicines due to their low toxicity. Herbal medicines are used to overcome the toxicity effects of surgical removal, radio-chemo therapy and medication, which have a lot of risk of damaging the healthy tissues. To overcome this, enhance bioavailability and target specify, nano-formulation chemotherapy was introduced using herbal moiety for anticancer activity. The use of metallic nanoparticles (MNPs), particularly those made of silver, cobalt, zinc, and gold as contrast, antibacterial, anticancer, and drug delivery agents has revolutionised the medicinal field. Although MNPs can be made via exacting physical and chemical processes, a biological method utilising natural materials has been established recently. Objective - This review article will offer a succinct explanation of the use of MNPs and its potential impact on herbal medicines in the future. Methods - Using PRISMA principles, this review systematically examines studies that concentrate on metal nanoparticles loaded with herbal compounds for the treatment of breast cancer. Various Databases were studied: PubMed, Elsevier, ScienceDirect, SpringerLink, Taylor & Francis Online, ACS Publications, Publishing Royal Society of Chemistry, and Future Medicines. Studies were selected if they were peer-reviewed primary studies published in the past 10 years. Results - We found that many herbal nano-formulations are more effective in breast cancer treatment than other types of formulations. Efficacy, safety and drug stability are also enhanced using nano-formulations. Conclusion - Nano-formulation is found to be more effective in the treatment of breast cancer.

Phytochemicals in Anticancer Drug Development.

BACKGROUND: In spite of major technological advances in conventional therapies, cancer continues to remain the leading cause of mortality worldwide. Phytochemicals are gradually emerging as a rich source of effective but safer agents against many life-threatening diseases. METHODS: Various phytochemicals with reported anticancer activity have been simply categorized into major phytoconstituents- alkaloids, polyphenols, saponins, tannins and terpenoids. RESULTS: The adverse effects associated with currently available anticancer medications may be overcome by using plant-derived compounds either alone or in combination. Exploration of plant kingdom may provide new leads for the accelerated development of new anticancer agents. CONCLUSION: Although numerous potent synthetic drugs have been introduced for cancer chemotherapy, yet their serious toxicity concerns to normal cells apart from drug resistance have emerged as the major obstacles for their clinical utility over a prolonged duration of time. Current status and potential of phytochemicals and their derivatives in cancer therapy have been briefly reviewed in the present manuscript.

Diverse models for anti-HIV activity of purine nucleoside analogs.

BACKGROUND: Purine nucleoside analogs (PNAs) constitute an important group of cytotoxic drugs for the treatment of neoplastic and autoimmune diseases. In the present study, classification models have been developed for the prediction of the anti-HIV activity of purine nucleoside analogs. RESULTS: The topochemical version of superaugmented pendentic index-4 has been proposed and successfully utilized for the development of models. A total of 60 2D and 3D molecular descriptors (MDs) of diverse nature were selected for building the classification models using decision tree (DT), random forest (RF), support vector machine (SVM), and moving average analysis (MAA). The values of most of these descriptors for each of the analogs in the dataset were computed using the Dragon software (version 5.3). An in-house computer program was also employed to calculate additional MDs which were not included in the Dragon software. DT, RF, and SVM correctly classified the analogs into actives and inactives with an accuracy of 89 %, 83 %, and 78 %, respectively. MAA-based models predicted the anti-HIV activity of purine nucleoside analogs with a non-error rate up to 98 %. Therapeutic active spans of the suggested MAA-based models not only showed more potency but also exhibited enhanced safety as revealed by comparatively high values of selectivity index (SI). The statistical importance of the developed models was appraised via intercorrelation analysis, specificity, sensitivity, non-error rate, and Matthews correlation coefficient. CONCLUSIONS: High predictability of the proposed models clearly indicates an immense potential for developing lead molecules for potent but safe anti-HIV purine nucleoside analogs.

Models for H3 receptor antagonist activity of sulfonylurea derivatives.

The histamine H3 receptor has been perceived as an auspicious target for the treatment of various central and peripheral nervous system diseases. In present study, a wide variety of 60 2D and 3D molecular descriptors (MDs) were successfully utilized for the development of models for the prediction of antagonist activity of sulfonylurea derivatives for histamine H3 receptors. Models were developed through decision tree (DT), random forest (RF) and moving average analysis (MAA). Dragon software version 6.0.28 was employed for calculation of values of diverse MDs of each analogue involved in the data set. The DT classified and correctly predicted the input data with an impressive non-error rate of 94% in the training set and 82.5% during cross validation. RF correctly classified the analogues into active and inactive with a non-error rate of 79.3%. The MAA based models predicted the antagonist histamine H3 receptor activity with non-error rate up to 90%. Active ranges of the proposed MAA based models not only exhibited high potency but also showed improved safety as indicated by relatively high values of selectivity index. The statistical significance of the models was assessed through sensitivity, specificity, non-error rate, Matthew's correlation coefficient and intercorrelation analysis. Proposed models offer vast potential for providing lead structures for development of potent but safe H3 receptor antagonist sulfonylurea derivatives.