The use of minimal topological differences to inspire the design of novel tetrahydroisoquinoline analogues with antimalarial activity

Joelle Ngo Hanna,Vincent De Paul N Nziko,Fidele Ntie-Kang,James A Mbah,Flavien A.A Toze

doi:10.1016/j.heliyon.2021.e07032

Abstract

A quantitative structure-activity relationship (QSAR) study was conducted using nineteen previously synthesized, and tested 1-aryl-6-hydroxy-1,2,3,4-tetrahydroisoquinolines with proven in vitro activities against Plasmodium falciparum. In order to computationally design and screen potent antimalarial agents, these compounds with known biological activity ranging from 0.697 to 35.978 μM were geometry optimized at the B3LYP/6-311 + G(d,p) level of theory, using the Gaussian 09W software. To calculate the topological differences, the series of the nineteen compounds was superimposed and a hypermolecule obtained with s¯ = 17 and 20 vertices. Other molecular descriptors were considered in order to build a highly predictive QSAR model. These include the minimal topological differences (MTD), LogP, two dimensional polarity surface area (TDPSA), dipole moment (μ), chemical hardness (η), electrophilicity (ω), potential energy (Ep), electrostatic energy (Eele) and number of rotatable bonds (NRB). By using a training set composed of 15 randomly selected compounds from this series, several QSAR equations were derived. The QSAR equations obtained were then used to attempt to predict the IC50 values of 4 remaining compounds in a test (or validation) set. Ten analogues were proposed by a fragment search of a fragment library containing the pharmacophore model of the active compounds contained in the training set. The most active proposed analogue showed a predicted activity within the lower micromolar range.

Highlights

Malaria is a parasitic disease that causes death and economic loss in about half the population of the world (Bloland, 2001)
We report the computer-assisted design of a virtual library of tetrahydroisoquinoline analogues, followed by the in silico screening by use of quantitative structure-activity relationship (QSAR) methods
Sequential multiple linear regression (MLR) analysis was conducted on the training set compounds with the goal of establishing a correlation between physicochemical properties and experimental activities

Summary

Introduction

Malaria is a parasitic disease that causes death and economic loss in about half the population of the world (Bloland, 2001). Malaria caused by Plasmodium falciparum is transmitted by female anopheline mosquitoes (Bloland, 2001). The most effective current method of controlling malaria is by the administration of antimalarial drugs to sick patients (Patel et al, 2003; Vangapandu et al, 2007). New strains of Plasmodium falciparum, the most dangerous malaria parasite to humans have emerged that do not respond to known antimalarials (Ridney, 2002), requiring the need for new antimalarial drugs, including those from natural-product-like scaffolds (Onguene et al, 2013; Ntie-Kang et al, 2014; Bekono et al, 2020)

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Results

Conclusion