A study of new antimalarial artemisinins through molecular modeling and multivariate analysis

Discovery of potential Q-marker of traditional Chinese medicine based on chemical profiling, chemometrics, network pharmacology, and molecular docking: Centipeda minima as an example

INTRODUCTION

The characteristics of chemical components or groups of chemical components in traditional Chinese medicines (TCMs) determine their clinical efficacy. Quality markers (Q-markers) is of great significance for standardizing the quality control system of TCM.

OBJECTIVES

We aimed to develop a new strategy to discover potential Q-markers of TCM by integrating chemometrics, network pharmacology, and molecular docking, using Centipeda minima (also known as ebushicao [EBSC]) as an example.

MATERIALS AND METHODS

First, fingerprints of different batches of EBSC and its counterfeit Arenaria oreophila (also known as zaozhui [ZZ]) were established. Second, chemometric analysis was conducted to determine the influence of varying authenticity/batches of herbs on quality and the chemical markers were screened out. Third, network pharmacology and molecular docking simulations were used to verify the relationship between active ingredients and targets. Lastly, potential Q-markers were selected based on TCM theory.

RESULTS

The chemical profiles of EBSC and ZZ were investigated. It was found that different batches of EBSC have differences in chemical composition. Based on our chemometric analysis, chlorogenic acid, rutin, isochlorogenic acid A, quercetin, arnicolide D, and brevilin A were selected as candidate active ingredients. ATIL6, EGFR, CASP3, MYC, HIF1A, and VEGFA were the main targets. Molecular docking was used to verify the binding ability. Based on the concept of Q-marker, arnicolide D and brevilin A were identified as potential Q-markers for EBSC.

CONCLUSIONS

Our strategy could be used as a practical approach to discover Q-markers of TCM to evaluate overall chemical consistency.

Chemometric methods in antimalarial drug design from 1,2,4,5-tetraoxanes analogues

A set of 23 steroidal 1,2,4,5-tetraoxane analogues were studied using quantum-chemical method (B3LYP/6-31 G*) and multivariate analyses (PCA, HCA, KNN and SIMCA) in order to calculate the properties and correlate them with antimalarial activity (log RA) against Plasmodium falciparum clone D-6 from Sierra Leone. PCA results indicated 99.94% of the total variance and it was possible to divide the compounds into two classes: less and more active. Descriptors responsible for separating were: highest occupied molecular orbital energy (HOMO), bond length (O1-O2), Mulliken electronegativity (χ) and Bond information content (BIC0). We use HCA, KNN and SIMCA to explain relationships between molecular properties and biological activity of a training set and to predict antimalarial activity (log RA) of 13 compounds (#24-36) with unknown biological activity. We apply molecular docking simulations to identify intermolecular interactions with a selected biological target. The results obtained in multivariate analysis aided in the understanding of the activity of the new compound's design (#24-36). Thus, through chemometric analyses and docking molecular study, we propose theoretical synthetic routes for the most promising compounds 28, 30, 32 and 36 that can proceed to synthesis steps and in vitro and in vivo assays.

A SAR and QSAR study of new artemisinin compounds with antimalarial activity

The Hartree-Fock method and the 6-31G** basis set were employed to calculate the molecular properties of artemisinin and 20 derivatives with antimalarial activity. Maps of molecular electrostatic potential (MEPs) and molecular docking were used to investigate the interaction between ligands and the receptor (heme). Principal component analysis and hierarchical cluster analysis were employed to select the most important descriptors related to activity. The correlation between biological activity and molecular properties was obtained using the partial least squares and principal component regression methods. The regression PLS and PCR models built in this study were also used to predict the antimalarial activity of 30 new artemisinin compounds with unknown activity. The models obtained showed not only statistical significance but also predictive ability. The significant molecular descriptors related to the compounds with antimalarial activity were the hydration energy (HE), the charge on the O11 oxygen atom (QO11), the torsion angle O1-O2-Fe-N2 (D2) and the maximum rate of R/Sanderson Electronegativity (RTe⁺). These variables led to a physical and structural explanation of the molecular properties that should be selected for when designing new ligands to be used as antimalarial agents.