Current trends in quantitative structure activity relationships on FXa inhibitors: Evaluation and comparative analysis

Theoretical Study of Molecular Structure and Physicochemical Properties of Novel Factor Xa Inhibitors and Dual Factor Xa and Factor IIa Inhibitors

The geometries and energies of factor Xa inhibitors edoxaban, eribaxaban, fidexaban, darexaban, letaxaban, and the dual factor Xa and thrombin inhibitors tanogitran and SAR107375 in both the gas-phase and aqueous solution were studied using the Becke3LYP/6-31++G(d,p) or Grimme’s B97D/6-31++G(d,p) method. The fully optimized conformers of these anticoagulants show a characteristic l-shape structure, and the water had a remarkable effect on the equilibrium geometry. According to the calculated pKa values eribaxaban and letaxaban are in neutral undissociated form at pH 7.4, while fidexaban and tanogitran exist as zwitterionic structures. The lipophilicity of the inhibitors studied lies within a large range of log P between 1 and 4. The dual inhibitor SAR107375 represents an improvement in structural, physicochemical and pharmacokinetic characteristics over tanogitran. At blood pH, SAR107375 predominantly exists in neutral form. In contrast with tanogitran, it is better absorbed and more lipophilic and active after oral application.

Synthesis, evaluation of anticancer activity and COMPARE analysis of N-bis(trifluoromethyl)alkyl-N'-substituted ureas with pharmacophoric moieties

A series of new synthesized N-bis(trifluoromethyl)alkyl-N'-substituted ureas have been tested in the National Cancer Institute (NCI, Bethesda, USA) by Program NCI-60 DTP Human Tumor Cell Line Screen at a single high dose (10(-5) M). COMPARE analysis has been carried out for all tested compounds. The tested compounds showed antitumor activity against individual cell lines. The most sensitive cell lines relative to the tested compounds are: 5 g Leukemia RPMI-8226 (GI% 52.7), Non-Small Cell Lung cancer HOP-92 (GI % 88.53), NCI-H522 (GI % 64.41), Melanoma UACC-62 (GI% 53.08), SK-MEL-5 (GI % 74.63), Breast cancer MDA-MB-468 (GI% 51.29), T-47D (GI % 65.1), 5b Leukemia K-562 (GI % 55.55), 7 m Leukemia HL-60(TB) (GI % 51.76).

Factor Xa active site substrate specificity with substrate phage display and computational molecular modeling

Structural origin of substrate-enzyme recognition remains incompletely understood. In the model enzyme system of serine protease, canonical anti-parallel beta-structure substrate-enzyme complex is the predominant hypothesis for the substrate-enzyme interaction at the atomic level. We used factor Xa (fXa), a key serine protease of the coagulation system, as a model enzyme to test the canonical conformation hypothesis. More than 160 fXa-cleavable substrate phage variants were experimentally selected from three designed substrate phage display libraries. These substrate phage variants were sequenced and their specificities to the model enzyme were quantified with quantitative enzyme-linked immunosorbent assay for substrate phage-enzyme reaction kinetics. At least three substrate-enzyme recognition modes emerged from the experimental data as necessary to account for the sequence-dependent specificity of the model enzyme. Computational molecular models were constructed, with both energetics and pharmacophore criteria, for the substrate-enzyme complexes of several of the representative substrate peptide sequences. In contrast to the canonical conformation hypothesis, the binding modes of the substrates to the model enzyme varied according to the substrate peptide sequence, indicating that an ensemble of binding modes underlay the observed specificity of the model serine protease.

Prediction of factor Xa inhibitors by machine learning methods

Factor Xa (FXa) inhibitors have been explored as anticoagulants for treatment and prevention of thrombotic diseases. Molecular docking, pharmacophore, quantitative structure-activity relationships, and support vector machines (SVM) have been used for computer prediction of FXa inhibitors. These methods achieve promising prediction accuracies of 69-80% for FXa inhibitors and 85-99% for non-inhibitors. Prediction performance, particularly for inhibitors, may be further improved by exploring methods applicable to more diverse range of compounds and by using more appropriate set of molecular descriptors. We tested the capability of several machine learning methods (C4.5 decision tree, k-nearest neighbor, probabilistic neural network, and support vector machine) by using a much more diverse set of 1098 compounds (360 inhibitors and 738 non-inhibitors) than those in other studies. A feature selection method was used for selecting molecular descriptors appropriate for distinguishing FXa inhibitors and non-inhibitors. The prediction accuracies of these methods are 89.1-97.5% for FXa inhibitors and 92.3-98.1% for non-inhibitors. In particular, compared to other studies, support vector machine gives a substantially improved accuracy of 94.6% for FXa non-inhibitors and maintains a comparable accuracy of 98.1% for inhibitors, based-on a more rigorous test with more diverse range of compounds. Our study suggests that machine learning methods such as SVM are useful for facilitating the prediction of FXa inhibitors.

Anthranilamide inhibitors of factor Xa

SAR about the B-ring of a series of N(2)-aroyl anthranilamide factor Xa (fXa) inhibitors is described. B-ring o-aminoalkylether and B-ring p-amine probes of the S1' and S4 sites, respectively, afforded picomolar fXa inhibitors that performed well in in vitro anticoagulation assays.