A novel QSAR model for predicting the inhibition of CXCR3 receptor by 4-N-aryl-[1,4] diazepane ureas

Afantitis, Antreas; Melagraki, Georgia; Sarimveis, Haralambos; Igglessi‐Markopoulou, Olga; Kollias, George

doi:10.1016/j.ejmech.2008.05.028

Cited by 49 publications

(22 citation statements)

References 41 publications

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“…0.6 and pred_r 2 . 0.5 [33,34]. The F-test reflects the ratio of the variance explained by the model and the variance due to the error in the regression.…”

Section: Model Quality and Validationmentioning

confidence: 99%

QSAR studies on imidazopyridazine derivatives as PfPK7 inhibitors

Sahu

Shahi

Sharma

et al. 2011

Molecular Simulation

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Quantitative structure -activity relationship (QSAR) studies were carried out on a series of 35 recently synthesised imidazopyridazine derivatives to investigate the structural requirements of their inhibitory activity against malarial kinase, Plasmodium falciparum protein kinase 7 (PfPK7). Partial least square (PLS) methodology coupled with various feature selection methods, viz. stepwise (SW), genetic algorithm (GA) and simulated annealing, was applied to derive QSAR models which were further validated for statistical significance and predictive ability by internal and external validation. The statistically significant best 2D-QSAR model having correlation coefficient r 2 ¼ 0.9190 and cross-validated squared correlation coefficient q 2 ¼ 0.8575 with external predictive ability of pred_r 2 ¼ 0.7212 was developed by SW-PLS with the descriptors like StNE-index, T_N_O_2, T_N_O_7, YcompDipole and SaasCE-index. Molecular field analysis was used to construct the best 3D-QSAR model using GA-PLS method, showing good correlative and predictive capabilities in terms of q 2 ¼ 0.7494 and pred_r 2 ¼ 0.8841. The information rendered by 2D-and 3D-QSAR models may lead to a better understanding of structural requirements of PfPK7 inhibitors and also aid in designing novel potent antimalarial molecules.

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“…0.6 and pred_r 2 . 0.5 [33,34]. The F-test reflects the ratio of the variance explained by the model and the variance due to the error in the regression.…”

Section: Model Quality and Validationmentioning

confidence: 99%

QSAR studies on imidazopyridazine derivatives as PfPK7 inhibitors

Sahu

Shahi

Sharma

et al. 2011

Molecular Simulation

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“…The regression coefficient r 2 is a relative measure of fit by the regression equation. However, a QSAR model is considered to be predictive, if the following conditions are satisfied: r 2 > 0.6, q 2 > 0.6 and pred_r 2 > 0.5 [36].…”

Section: Cross-validationmentioning

confidence: 99%

A QSAR Analysis of 2-phenoxy-N-substituted Acetamide Analogues as Hypoxia-Inducible Factor-1(HIF-1) Inhibitors: A Rational Approach to Anticancer Drug Design

Noolvi

Patel²,

Kamboj³

2012

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A set of thirty one substituted 2-phenoxy-N-phenylacetamide derivatives with HIF-1 inhibitory activities was subjected to 2D and 3D Quantitative Structure Activity Relationship (QSAR) studies using various combinations of descriptors. 2D-QSAR was performed using Multiple Linear Regression (MLR), Principal Component Regression (PCR) and Partial Least Squares Regression (PLS) methods. Among these three methods Multiple Linear Regression (MLR) led to the statistically significant best 2D-QSAR Model-I having correlation coefficient r(2) = 0.9469 and cross validated squared correlation coefficient q(2) = 0.8933 with external predictive ability of pred_r(2) = 0.7128 with the descriptors like SssNHE-index, slogp, T_O_N_1 and T_2_Cl_1. 3D-QSAR study was performed using the simulated annealing variable selection procedures k-nearest neighbor molecular field analysis approach. 3D-QSAR shows interesting results in terms of internal and external predictability. Molecular field analysis was applied for the generation of steric, hydrophobic and electrostatic descriptors based on aligned structures which shows good correlative and predictive capabilities in terms of q(2) = 0.9672 and pred_r(2) = 0.8480. Hence the model proposed in this work provides important structural insight in designing novel derivatives with specific HIF-1 inhibitory activity.

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“…Compound 16 exhibited an IC 50 of 60 nM (CXCL11, Ca 2 þ ), inhibited chemotaxis (IC 50 $ 100 nM, CXCL11) and was selective over 14 other GPCRs. An independent QSAR study on this class of compounds suggests several key descriptors to explain the affinity of the compounds [79]. [80].…”

Section: 31mentioning

confidence: 99%

Therapeutic Targeting of the CXCR3 Receptor

Esch

Leurs

2010

Methods and Principles in Medicinal Chemistry

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A search for T lymphocyte-specific chemokine receptors led to the discovery of the CXCR3 receptor in 1996. A novel cDNA was isolated from a human CD4 þ T cell library and the encoded GPCR proved to have affinity for chemokines [1]. Further work revealed that CXCR3 consists of 368 amino acids which assemble in a typical seven-transmembrane a-helical architecture. Chapter 2 has described in detail the structural characteristics of the chemokine receptor family and CXCR3 fits this general picture. CXCR3 exhibits typical structural motifs for GPCRs, such as the conserved DRY motif, the NPxxYx 5,6 F motif and cysteine residues in the first and second extracellular loops [2]. Also, like most chemokine receptors, CXCR3 has additional cysteine residues in the N-terminus and third extracellular loop. The threonine and serine residues in the intracellular C-terminal tail are potential sites for phosphorylation by receptor-or second messenger-regulated kinases [1, 3].CXCR3 receptors are found on activated Th1 lymphocytes, blood T cells and on a small proportion of B cells and natural killer cells [1,4,5]. CXCR3 signaling occurs preferably through pertussis toxin-sensitive Ga i proteins and involves several downstream processes such as mediation of chemotaxis, induction of calcium flux and activation of kinases such as p44/p42 MAPK and Akt [1, 3,6]. The endogenous agonists for CXCR3 are CXC chemokines CXCL9, CXCL10 and CXCL11, which were traditionally called Mig, IP-10 and I-TAC/IP-9, respectively [1, 7-10]. Of these, CXCL11 has the highest potency and efficacy on CXCR3 [1,[7][8][9][10][11]. Several reports exist on the interaction of other CXCL chemokines with CXCR3, most notably CXCL13 [12] and CXCL4 [13]. Generally, a chemokine is considered to bind to the N-terminus and extracellular loops of a chemokine receptor. Activation is thought to occur through interaction of the N-terminus of the chemokine with the transmembrane domains of the receptor [14,15]. Indeed, manipulations at the N-terminal amino acids of CXCL10 or CXCL11 can change it from a CXCR3 agonist into a CXCR3 antagonist [16][17][18][19].

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A novel QSAR model for predicting the inhibition of CXCR3 receptor by 4-N-aryl-[1,4] diazepane ureas

Cited by 49 publications

References 41 publications

QSAR studies on imidazopyridazine derivatives as PfPK7 inhibitors

QSAR studies on imidazopyridazine derivatives as PfPK7 inhibitors

A QSAR Analysis of 2-phenoxy-N-substituted Acetamide Analogues as Hypoxia-Inducible Factor-1(HIF-1) Inhibitors: A Rational Approach to Anticancer Drug Design

Therapeutic Targeting of the CXCR3 Receptor

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