In silico toxicity profiling of natural product compound libraries from African flora with anti-malarial and anti-HIV properties. (February 2018)

• Record Type: Journal Article
• Authors: Onguéné, Pascal Amoa; Simoben, Conrad V.; Fotso, Ghislain W.; Andrae-Marobela, Kerstin; Khalid, Sami A.; Ngadjui, Bonaventure T.; Mbaze, Luc Meva'a; Ntie-Kang, Fidele
• Is Part Of: Computational biology and chemistry. Volume 72(2018)
• Page Start: 136
• Page End: 149
• Publication Date: 2018-02
• Subjects: 2D two dimensional -- 3D three dimensional -- Afro-HIV dataset of molecules from African flora with known activities against the human immunodeficiency virus -- AfroMalariaDb dataset of anti-malarial compounds from African flora -- DMPK drug metabolism and pharmacokinetics -- HBA hydrogen bond acceptors -- HBD hydrogen bond donors -- log P logarithm of the octan-1-ol/water partition coefficient -- MCSS most common substructures -- MW molecular weight -- NP natural product -- NRB number of rotatable bonds -- p-ANAPL Pan-African Natural Products Library -- PCA Principal Component Analysis -- pkCSM small-molecule pharmacokinetics prediction; Diversity -- Drug discovery -- Toxicity -- In silico -- Natural products -- Malaria -- HIV; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2017.12.002
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: The manuscript discusses toxicity predictions for three datasets of naturally occurring metabolites. One dataset includes about 500 metabolites (p-ANAPL) with diverse activities. Another contains 250 compounds (AntiMalariaDb) with anti-malarial activities and another contains 50 compounds with anti-HIV activities. One of the datasets (p-ANAPL) has available physical samples of the compounds, ready for testing. Two methods have been used, one based on human reasoning and one based on machine learning, discussing the relation to experimental findings. Abstract: This paper describes an analysis of the diversity and chemical toxicity assessment of three chemical libraries of compounds from African flora (the p-ANAPL, AfroMalariaDb, and Afro-HIV), respectively containing compounds exhibiting activities against diverse diseases, malaria and HIV. The diversity of the three data sets was done by comparison of the three most important principal components computed from standard molecular descriptors. This was also done by a study of the most common substructures (MCSS keys). Meanwhile, the in silico toxicity predictions were done through the identification of chemical structural alerts using Lhasa's knowledge based Derek system. The results show that the libraries occupy different chemical space and that only an insignificant part of the respective libraries could exhibit toxicities beyond acceptable limits. The predicted toxicities end points for compounds which were predicted to "plausible" were further discussed in the light of available experimental data in the literature. Toxicity predictions are in agreement when using a machine learning approach that employs graph-based structural signatures. The current study sheds further light towards the use of the studied chemical libraries for virtual screening purposes.