scholarly journals 4-Aminoquinolines Active against Chloroquine-Resistant Plasmodium falciparum: Basis of Antiparasite Activity and Quantitative Structure-Activity Relationship Analyses

2011 ◽  
Vol 55 (5) ◽  
pp. 2233-2244 ◽  
Author(s):  
Simon J. Hocart ◽  
Huayin Liu ◽  
Haiyan Deng ◽  
Dibyendu De ◽  
Frances M. Krogstad ◽  
...  
Keyword(s):  
Plasmodium Falciparum ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Structural Features ◽  
Activity Relationship ◽  
Side Chain ◽  
Quantitative Structure ◽  
Content Type ◽  
Structure Activity ◽  
Contour Plots

ABSTRACTChloroquine (CQ) is a safe and economical 4-aminoquinoline (AQ) antimalarial. However, its value has been severely compromised by the increasing prevalence of CQ resistance. This study examined 108 AQs, including 68 newly synthesized compounds. Of these 108 AQs, 32 (30%) were active only against CQ-susceptiblePlasmodium falciparumstrains and 59 (55%) were active against both CQ-susceptible and CQ-resistantP. falciparumstrains (50% inhibitory concentrations [IC50s], ≤25 nM). All AQs active against both CQ-susceptible and CQ-resistantP. falciparumstrains shared four structural features: (i) an AQ ring without alkyl substitution, (ii) a halogen at position 7 (Cl, Br, or I but not F), (iii) a protonatable nitrogen at position 1, and (iv) a second protonatable nitrogen at the end of the side chain distal from the point of attachment to the AQ ring via the nitrogen at position 4. For activity against CQ-resistant parasites, side chain lengths of ≤3 or ≥10 carbons were necessary but not sufficient; they were identified as essential factors by visual comparison of 2-dimensional (2-D) structures in relation to the antiparasite activities of the AQs and were confirmed by computer-based 3-D comparisons and differential contour plots of activity againstP. falciparum. The advantage of the method reported here (refinement of quantitative structure-activity relationship [QSAR] descriptors by random assignment of compounds to multiple training and test sets) is that it retains QSAR descriptors according to their abilities to predict the activities of unknown test compounds rather than according to how well they fit the activities of the compounds in the training sets.

Quantitative Structure Activity Relationship studies of Antimicrobial Compounds:A Review

2021 ◽  
Vol 19 ◽  
Author(s):  
Sahaya Asirvatham ◽  
Jyoti Thakur ◽  
Savita Tauro ◽  
Bharat Dhokchawle
Keyword(s):  
Antimicrobial Activity ◽  
Antimicrobial Agents ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Structural Features ◽  
Activity Relationship ◽  
Antimicrobial Compounds ◽  
Quantitative Structure ◽  
Qsar Studies ◽  
Structure Activity

: With the current scenario of emerging drug-resistant microbial strains, there prevails a continuous threat to health and the development of new antimicrobial agents is a challenging task. Quantitative Structure Activity Relationship (QSAR) has proven to elevate the likelihood of finding a new pharmacophore. Intermolecular binding like hydrophobic bond, electrostatic and steric interactions helps to understand drug interaction with the receptors. Some common conclusions have been drawn after analyzing diverse case studies. Few descriptors were identified to be common in enhancing the antimicrobial activity. The structural features modifying the antimicrobial activity were analyzed using critically published results from significant QSAR studies on antimicrobial compounds. This commentary will assist the synthetic chemist to synthesize novel derivatives which could be potential antimicrobial compounds.

A Quantitative Structure-Activity Relationship Study on the Antimalarial Activities of 4-Aminoquinoline, Febrifugine and Artemisinin Compounds

2020 ◽  
Vol 5 (1) ◽  
pp. 63-79
Author(s):  
Yu Heng Ou ◽  
Chia Ming Chang
Keyword(s):  
Plasmodium Falciparum ◽  
Chemical Potential ◽  
Chemical Reactivity ◽  
Electron Flow ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Reactivity Descriptors ◽  
Structure Activity

Quantum chemical molecular descriptors representing different types of chemical reactivity were employed to investigate the antimalarial activities of 4-aminoquinoline, febrifugine, artemisinin and their derivatives. The quantitative structure-activity relationship results reveal that: (i) the antimalarial activities of 4-aminoquinoline compounds against the chloroquine-sensitive Plasmodium falciparum 3D7 strain are mainly affected by the electron flow and polarization interactions; (ii) The reactivity descriptors for the activities of febrifugine compounds against the chloroquine-resistant Plasmodium falciparum FCR-3 strain are the electron-acceptance chemical potential, the maximum nucleophilic and electrophilic local softness, the maximum positive charge of the hydrogen atom, etc.; (iii) The electron-donation chemical potential, the maximum negative charge, the inverse of the apolar surface area and the molar volume of artemisinin compounds are the most important descriptors for evaluating the activity against the chloroquine-resistant Plasmodium falciparum W-2 strain.

Prediction of Lipophilicity of some Quinolone Derivatives by using Quantitative Structure- Activity Relationship

2019 ◽  
Vol 16 ◽  
Author(s):  
Meysam Shirmohammadi ◽  
Zakiyeh Bayat ◽  
Esmat Mohammadinasab
Keyword(s):  
Partition Coefficient ◽  
Principal Component ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Selection Methods ◽  
Qsar Study ◽  
Structure Activity ◽  
Descriptor Selection

: Quantitative structure activity relationship (QSAR) was used to study the partition coefficient of some quinolones and their derivatives. These molecules are broad-spectrum antibiotic pharmaceutics. First, data were divided into two categories of train and test (validation) sets using random selection method. Second, three approaches including stepwise selection (STS) (forward), genetic algorithm (GA), and simulated annealing (SA) were used to select the descriptors, with the aim of examining the effect feature selection methods. To find the relation between descriptors and partition coefficient, multiple linear regression (MLR), principal component regression (PCR) and partial least squares (PLS) were used. QSAR study showed that the both regression and descriptor selection methods have vital role in the results. Different statistical metrics showed that the MLR-SA approach with (r2=0.96, q2=0.91, pred_r2=0.95) gives the best outcome. The proposed expression by MLR-SA approach can be used in the better design of novel quinolones and their derivatives.

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