4-aminoquinolines active against chloroquine-resistant Plasmodium falciparum: basis of antiparasite activity and quantitative structure-activity relationship analyses

Abstract

Chloroquine (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-susceptible Plasmodium falciparum strains and 59 (55%) were active against both CQ-susceptible and CQ-resistant P. falciparum strains (50% inhibitory concentrations [IC50s], ≤25 nM). All AQs active against both CQ-susceptible and CQ-resistant P. falciparum strains 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 against P. 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.

Figures

Fig. 1.

Effects of descriptor optimization on q2 for training compounds, numbers of components, and descriptors from cross-validated, partial least squares (PLS) analyses against P. falciparum.

Fig. 2.

Effects of descriptor optimization on standard errors of prediction (SDEP) for unknown test compounds from PLS analyses against P. falciparum.

Fig. 3.

Effects of descriptor optimization on the predictive q2 (q2prediction) for unknown test compounds from PLS analyses against P. falciparum.

Fig. 4.

Effects of descriptor optimization on PLS contributions from QSAR analyses of activities against P. falciparum. HB, hydrogen bond.

Fig. 5.

Differential optimized CoMSIA QSAR contours for two AQ analogues active against CQ-resistant parasites and one analogue inactive against CQ-resistant parasites. The active long-side-chain and short-side-chain analogues (AQ-40 and AQ-13) are represented in black and red, respectively; the inactive intermediate-length side-chain analogue (CQ) is shown in blue. (A) Steric and electrostatic factors. Favored and disfavored steric interactions appear green and yellow, respectively. Favored and disfavored electrostatic interactions appear red and blue. (B) Hydrophobicity, hydrogen bond (HB) acceptors, and HB donors. For hydrophobicity, favored and disfavored interactions appear white (gray) and brown. For hydrogen bond acceptors, favored and disfavored interactions appear cyan and light blue. For hydrogen bond donors, favored and disfavored interactions appear purple and pink. Favored contributions are shown at a contour percentile of 80%; disfavored interactions are shown at a contour percentile of 20%.

Fig. 6.

Summary of SAR features for activity against CQ-resistant P. falciparum.