QSAR-guided pharmacophoric modeling reveals important structural requirements for Polo kinase 1 (Plk1) inhibitors.

Targeting Polo-like kinase 1 (Plk1) by molecular inhibitors is being a promising approach for tumor therapy. Nevertheless, insufficient methodical analyses have been done to characterize the interactions inside the Plk1 binding pocket. In this study, an extensive combined ligand and structure-based drug design workflow was conducted to data-mine the structural requirements for Plk1 inhibition. Consequently, the binding modes of 368 previously known Plk1 inhibitors were investigated by pharmacophore generation technique. The resulted pharmacophores were engaged in the context of Genetic function algorithm (GFA) and Multiple linear regression (MLR) analyses to search for a prognostic QSAR model. The most successful QSAR model was with statistical criteria of (r2277 = 0.76, r2adj = 0.76, r2pred = 0.75, Q2 = 0.73). Our QSAR-selected pharmacophores were validated by Receiver Operating Characteristic (ROC) curve analysis. Later on, the best QSAR model and its associated pharmacophoric hypotheses (HypoB-T4-5, HypoI-T2-7, HypoD-T4-3, and HypoC-T3-3) were used to identify new Plk1 inhibitory hits retrieved from the National Cancer Institute (NCI) database. The most potent hits exhibited experimental anti-Plk1 IC50 of 1.49, 3.79. 5.26 and 6.35 µM. Noticeably, our hits, were found to interact with the Plk1 kinase domain through some important amino acid residues namely, Cys67, Lys82, Cys133, Phe183, and Asp194.

Stable adaptive probabilistic Takagi-Sugeno-Kang fuzzy controller for dynamic systems with uncertainties.

In this study, an adaptive probabilistic Takagi-Sugeno-Kang fuzzy PID (APTSKF-PID) scheme is developed to control nonlinear systems. The proposed controller merges the features of the TSK fuzzy logic system, which possess a superior performance in system size and learning accuracy than the Mamdani-type fuzzy systems and the probabilistic processing method in nonlinear control, which handles the system uncertainties. To achieve controlled system stability, Lyapunov function is used for tuning the controller parameters. Tuning the probability parameters provides an extra degree of flexibility in controller design and improves the control performance. Furthermore, to ensure the effectiveness of the developed scheme for engineering applications, the proposed control technology is introduced to control nonlinear dynamical plants and its performance is compared with existing schemes. Simulation tasks indicate that the efficiency of APTSKF-PID scheme has high superiority over the other controller for external disturbances, random noise and a large scope of system uncertainties.

Ligand-based computer aided drug design reveals new tropomycin receptor kinase a (TrkA) inhibitors.

Targeting tropomycin kinase A (TrkA) by small molecule inhibitors is considered as a promising strategy for treating several human cancers. To achieve this goal, a ligand based QSAR model was applied using the Discovery studio 4.5 (DS 4.5). Hence, a total list of 161 TrkA inhibitors was investigated. The TrkA inhibitors were extensively explored to detect their optimal physicochemical properties and pharmacophoric binding modes, which were converted into numeric descriptors and allowed to compete within the context of the Genetic Function Algorithm (GFA) approximations to find the subset of terms that correlates best with the activity. The resulted successful QSAR equation had statistical criteria of (r2129=0.67, r2LOO=0.61 r2PRESS against 32 external test inhibitors=0.50). Afterwards, the most successful pharmacophore: HypoB-T5-3, was used to screen compounds within the National Cancer institute (NCI) database. Only 41 compounds were retrieved and 21 of them exhibited anti-TrkA activity. The most potent hit had an IC50 value of 2.4µM. Later, upon docking the active hits into the TrkA binding pocket, important interactions were revealed including hydrogen bonding with the amino acids Asp668 and Lys544 in addition to the cation-π interactions with the sidechain of Arg559.

Research advances in kinase enzymes and inhibitors for cardiovascular disease treatment.

The targeting of protein kinases has great future potential for the design of new drugs against cardiovascular diseases (CVDs). Enormous efforts have been made toward achieving this aim. Unfortunately, kinase inhibitors designed to treat CVDs have suffered from numerous limitations such as poor selectivity, bad permeability and toxicity. So, where are we now in terms of discovering effective kinase targeting drugs to treat CVDs? Various drug design techniques have been approached for this purpose since the discovery of the inhibitory activity of Staurosporine against protein kinase C in 1986. This review aims to provide context for the status of several emerging classes of direct kinase modulators to treat CVDs and discuss challenges that are preventing scientists from finding new kinase drugs to treat heart disease.

Structure based drug design of Pim-1 kinase followed by pharmacophore guided synthesis of quinolone-based inhibitors.

Over expression of Human phosphatidyl inositol mannoside kinases isoform 1 (Pim-1 kinase) has been reported in several leukemia and solid tumors. Our continuous interest to reveal the secrecies of the mysterious Pim-1 kinase binding pocket has led us to employ a structure based drug design procedure based on receptor-ligand pharmacophore generation protocol implemented in Discovery Studio 4.5 (DS 4.5). Subsequently, we collected 104 crystal structures of Pim-1 kinase from the Protein Data Bank (PDB) and used them to generate pharmacophores based on the anticipated co-crystallized ligand-Pim 1 kinase receptor interactions. All selected pharmacophoric features were enumerated and only those that had corresponding valuable receptor-ligand interactions were retained. This was followed by modeling all pharmacophore combinations and scoring them according to their Receiver Operating Characteristic (ROC) curve analysis parameters as well as a DS.4.5 built-in Genetic Function Algorithm (GFA) validating model. Accordingly, 111 pharmacophores resulted with acceptable ROC performances; 1XWS_2_04, 2BIK_2_06, and 1XWS_2_06 (ROC AUC value of: 0.770, 0.743 and 0.741 respectively) were the best pharmacophores. These pharmacophores were employed to guide the synthesis of new series of 7-[(2-Carboxyethyl)amino]-1-substituted-6-fluoro-8-nitro-4-oxo-1,4-dihydro-quinoline-3-carboxylic acid and their reduced 8-amino derivatives. The synthesized compounds were later evaluated for their Pim-1 kinase inhibitory potencies. Of which the most potent illustrated an IC50 value of 0.29µM against Pim-1 kinase.

Identification of novel inhibitors for Pim-1 kinase using pharmacophore modeling based on a novel method for selecting pharmacophore generation subsets.

Targeting Proviral integration-site of murine Moloney leukemia virus 1 kinase, hereafter called Pim-1 kinase, is a promising strategy for treating different kinds of human cancer. Headed for this a total list of 328 formerly reported Pim-1 kinase inhibitors has been explored and divided based on the pharmacophoric features of the most active molecules into 10 subsets projected to represent potential active binding manners accessible to ligands within the binding pocket of Pim-1 kinase. Discovery Studio 4.1 (DS 4.1) was employed to detect potential pharmacophoric active binding manners anticipated by Pim-1 Kinase inhibitors. The pharmacophoric models were then allowed to compete within Quantitative Structure Activity Relationship (QSAR) framework with other 2D descriptors. Accordingly Genetic algorithm and multiple linear regression investigation were engaged to find the finest QSAR equation that has the best predictive power r262(2) = 0.70, F = 119.14, rLOO(2) = 0.693, rPRESS(2) against 66 external test inhibitors = 0.71 q(2) = 0.55. Three different pharmacophores appeared in the successful QSAR equation this represents three different binding modes for inhibitors within the Pim-1 kinase binding pocket. Pharmacophoric models were later used to screen compounds within the National Cancer Institute database. Several low micromolar Pim-1 Kinase inhibitors were captured. The most potent hits show IC50 values of 0.77 and 1.03 µM. Also, upon analyzing the successful QSAR Equation we found that some polycyclic aromatic electron-rich structures namely 6-Chloro-2-methoxy-acridine can be considered as putative hits for Pim-1 kinase inhibition.