In silico Strategy: A Promising Implement in the Development of Multitarget Drugs against Neurodegenerative Diseases

Multi-target drug development (MTDD) is the demand of the recent era, especially in the case of multi-factorial conditions such as cancer, depression, neurodegenerative diseases (NDs), etc. The MTDD approaches have many advantages; avoidance of drug-drug interactions, predictable pharmacokinetic profile, and less drug resistance. The wet lab practice in MTDD is very challenging for the researchers, and the chances of late-stage failure are obvious. Identification of an appropriate target (Target fishing) is another challenging task in the development of multi-target drugs. The in silico tools will be one of the promising tools in the MTDD for the NDs. Therefore the outlook of the review comprises a short description of NDs, target associated with different NDs, in silico studies so far done for MTDD for various NDs. The main thrust of this review is to explore the present and future aspects of in silico tools used in MTDD for different NDs in combating the challenge of drug development and the application of various in silico tools to solve the problem of target fishing.

Molecular Docking, QSAR and Microscopic Studies of Anti-trypanosomal Compounds from the Pathogen Box

BACKGROUND

Trypanosoma brucei (T. brucei) is the cause of the deadly human African trypanosomiasis (HAT) with a case fatality ratio of 10%.

OBJECTIVE

Targeting the essential Trypanosomal glucose metabolism pathway through the inhibition of phosphoglycerate kinase (PGK) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is a valid strategy for anti-T. brucei drug development.

METHODS

Here, quantitative structure activity relationship, molecular docking and microscopic studies were used to describe the mode of inhibition of selected compounds from the pathogen box PGK and GAPDH.

RESULTS

We identified 4 hit compounds from the pathogen box with optimal binding and chemical interactions. Notably, it was identified that interacting charge surface and atomic mass were key aspects of both PGK and GAPDH inhibition. Also, novel anti-trypanosomal compounds were identified from the pathogen box and their half maximal inhibitory concentrations were described.

CONCLUSION

Our study presents new anti-trypanosomal compounds with optimal pharmacological profiles and an optimization strategy for improving target specificity in the rational design of novel anti-trypanosomal compounds.

Applications of Machine Learning Methods in Drug Toxicity Prediction

Toxicity evaluation is an important part of the preclinical safety assessment of new drugs, which is directly related to human health and the fate of drugs. It is of importance to study how to evaluate drug toxicity accurately and economically. The traditional in vitro and in vivo toxicity tests are laborious, time-consuming, highly expensive, and even involve animal welfare issues. Computational methods developed for drug toxicity prediction can compensate for the shortcomings of traditional methods and have been considered useful in the early stages of drug development. Numerous drug toxicity prediction models have been developed using a variety of computational methods. With the advance of the theory of machine learning and molecular representation, more and more drug toxicity prediction models are developed using a variety of machine learning methods, such as support vector machine, random forest, naive Bayesian, back propagation neural network. And significant advances have been made in many toxicity endpoints, such as carcinogenicity, mutagenicity, and hepatotoxicity. In this review, we aimed to provide a comprehensive overview of the machine learning based drug toxicity prediction studies conducted in recent years. In addition, we compared the performance of the models proposed in these studies in terms of accuracy, sensitivity, and specificity, providing a view of the current state-of-the-art in this field and highlighting the issues in the current studies.

Review of Theoretical Models to Study Natural Products with Antiprotozoal Activity

In nature, pathogenic parasite species with different susceptibility patterns of antiparasitic drugs abound. In this sense, natural products derived from plants are a potency for drugs with potential antiparasitic activity. Unfortunately, there are many metabolites and studying all of them would be costly in terms of money and resources. To this end, theoretical studies such as QSAR models could be useful. These, for the most part, predict the biological activity of the drugs against a single species of parasite. Consequently, foretell the probability with which a drug is active against many different species with a single QSAR model is an important achievement. This review consists of three parts: the first part is a review of metabolites found in nature that have antiparasitic activity, in particular the antiprotozoal (Leishmania and Trypanosoma); the second part includes a review of theoretical studies looking for a model that predicts the antiprotozoal activity of natural products; the third and final part concerns the study of theoretical models focused on the interaction between drug and receptor, analyzing new metabolites with antiprotozoal activity.