Search

In the drug discovery path, most drug candidates failed at the early stages due to their pharmacokinetic behavior in the system. Early prediction of pharmacokinetic properties and screening methods can reduce the time and investment for lead discoveries. Plasma protein binding is one of these properties which has a vital role in drug discovery and development. The focus of the current study is to develop a computational model for the classification of Low Plasma Protein Binding (LPPB) and High Plasma Protein Binding (HPPB) drugs using machine learning methods for early screening of molecules through WEKA. Plasma protein binding drugs data was collated from the Drug Bank database where 617 drug candidates were found to interact with plasma proteins, out of which an equal proportion of high and low plasma protein binding drugs were extracted to build a training set of ~300 drugs. The machine learning algorithms were trained with a training set and evaluated by a test set. We also compared various machine learning-based classification algorithms i.e., the Naïve Bayes algorithm, Instance-Based Learner (IBK), multilayer perceptron, and random forest to determine the best model based on accuracy. It was observed that the random forest algorithm-based model outperforms with an accuracy of 99.67% and 0.9933 kappa value on training set and on test set as compared to other classification methods and can predict drug plasma binding capacity in the given data set using the WEKA tool.

With the interventions of globalization, the human race is lagging behind in its health management. Some of the major health risks involved are cardiac arrest, loss of sleep, lack of concentration, regular muscular pain, arthritis, etc. Arthritis and its various forms lead to regular joint pain and are hampering the daily lives of human beings. Garlic is reported to be one of the best natural medicines for arthritis. The pungency of garlic is due to its various sulfur compounds. Studies have been carried out in determining the effect of these sulfur compounds as antiinflammatory compounds and their role in the signaling pathways of arthritis. Also, there is a scope for advanced level research such as incorporation of nanotechnology and biotechnology for exploiting the potential of this wonder drug garlic for the treatment of arthritis.

The advent of new drugs has resulted in cures for many neural, cancerous, muscular, musculoskeletal, cardiac, and pulmonary diseases that were found incurable in earlier times. Still, from ancient times through today, natural products have been seen as the best way to cure diseases. On the same path, for many decades curcumin has been reported as an alternative cure for various diseases, the major one being arthritis. In this chapter, we have compiled and discussed the potential and prospect of curcumin in alleviating arthritis. The promising results of curcumin's effect on arthritis signaling pathways have been experimentally proved on various natural and induced animal models, paving the way for clinical trials and finally reaching patients. The incorporation of nanotechnology with curcumin administration has increased both the bioavailability and absorption into the patient's body. Curcumin's derivatives and analogs also seem to play a vital role in strengthening the backbone of arthritis treatment.

It’s challenging to understand the multifarious gene regulatory networks. Diverse biological processes, are mostly conferred by nature of cis- acting regulatory elements (CARE) located within the regulatory region which include abiotic stress responses, hormone responses and developmental processes. Thus, the identification of CARE and their organization is a crucial step for better understanding of spatiotemporal expression of the gene. The putative cis- acting element that are located within the 5' regulatory region of BADH isozymes of Hordeum vulgare , Oryza sativa , Sorghum bicolor , Leymus chinensis, Zoysia tenuifolia, Arabidopsis thaliana , Amaranthus hypochondriacus , Chrysanthemum lavandulifolium , Populus euphratica and Atriplex prostrate were identified using bioinformatics tools. Several probable cis-acting regulatory elements that are coupled with cellular development, hormonal and stress response were identified as: Me-JA, GARE, GT-1, MYB, W-box, I-box, Pyrimidine-box, ANAERO, ARF, CARE, ASF1, SORLIP, SURE, TAAAGSTKST1, GCC Box, CICADIAN, Sp1 and CGCG Box. These results shed light on probable cis- acting regulatory elements these BADH isoenzyme that confer their expression and regulation during cellular, hormonal and stress response.