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The overall efficacy and safety profile of a new drug is partially evaluated by the therapeutic index in clinical studies and by the protective index (PI) in preclinical studies. In-silico predictive methods may facilitate the assessment of these indicators. Although QSAR and QSTR models can be used for predicting PI, their predictive capability has not been evaluated. To test this capability, we developed QSAR and QSTR models for predicting the activity and toxicity of anticonvulsants at accuracy levels above the literature-reported threshold (LT) of good QSAR models as tested by both the internal 5-fold cross validation and external validation method. These models showed significantly compromised PI predictive capability due to the cumulative errors of the QSAR and QSTR models. Therefore, in this investigation a new quantitative structure-index relationship (QSIR) model was devised and it showed improved PI predictive capability that superseded the LT of good QSAR models. The QSAR, QSTR and QSIR models were developed using support vector regression (SVR) method with the parameters optimized by using the greedy search method. The molecular descriptors relevant to the prediction of anticonvulsant activities, toxicities and PIs were analyzed by a recursive feature elimination method. The selected molecular descriptors are primarily associated with the drug-like, pharmacological and toxicological features and those used in the published anticonvulsant QSAR and QSTR models. This study suggested that QSIR is useful for estimating the therapeutic index of drug candidates.

Drug adverse effects (ADEs) and toxicities have been extensively studied from chemical structure, genetic, biological systems and clinical perspectives. The rapid accumulation of chemical, biological and clinical data are highly useful for characterizing and predicting ADEs, and have enabled increasing exploration of computational methods as low cost tools for predicting various ADEs and toxicities. This article reviews the strategies, current progresses, underlying difficulties and future prospects in using computational methods for predicting ADEs and toxicities.

As a successive work of our previous paper,1 the electron transfer matrix element (Vrp) in the oxidation of the simplified model molecule of α-amino carbon-centered radical by O2 has been investigated with ab initio calculation at the level of UHF/6-31 + + G*. Based on the optimized geometries of the reactant and the ion-pair complex obtained previously, the reaction heat and the inner reorganization energy have been obtained by constructing the potential energy curves of reactant and product states considering the solvent effect with the conductor-like screening model (COSMO). The solvent reorganization energy has been estimated using Lippert-Mataga relationship. The calculated results show that the value of Vrp, is several times larger than that of RT, which means that the model reaction is an adiabatic one. Theoretical investigation indicates that the solvent effect on the direct electron transfer (ET) process of oxidation of α-amino carbon-centered radical by oxygen is remarkable.

Pharmacophore approaches have become one of the major tools in drug discovery after the past century's development. Various ligand-based and structure-based methods have been developed for improved pharmacophore modeling and have been successfully and extensively applied in virtual screening, de novo design and lead optimization. Despite these successes, pharmacophore approaches have not reached their expected full capacity, particularly in facing the demand for reducing the current expensive overall cost associated with drug discovery and development. Here, the challenges of pharmacophore modeling and applications in drug discovery are discussed and recent advances and latest developments are described, which provide useful clues to the further development and application of pharmacophore approaches.

8-Chloro-cAMP and 8-chloro-adenosine (8-Cl-Ado) are known to inhibit proliferation of cancer cells by converting 8-Cl-Ado into an ATP analog, 8-chloro-ATP (8-Cl-ATP). Because type II topoisomerases (Topo II) are ATP-dependent, we infer that 8-Cl-Ado exposure might interfere with Topo II activities and DNA metabolism in cells. We found that 8-Cl-Ado exposure inhibited Topo II-catalytic activities in K562 cells, as revealed by decreased relaxation of the supercoiled pUC19 DNA and inhibited decatenation of the kinetoplast DNA (kDNA). In vitro assays showed that 8-Cl-ATP, but not 8-Cl-Ado, could directly inhibit Topo IIalpha-catalyzed relaxation and decatenation of substrate DNA. Furthermore, 8-Cl-ATP inhibited Topo II-catalyzed ATP hydrolysis and increased salt-stabilized closed clamp. In addition, 8-Cl-Ado exposure decreased bromo-deoxyuridine (BrdU) incorporation into DNA and led to enhanced DNA double-stranded breaks (DSBs) and to increased formation of gamma-H2AX nuclear foci in exposed K562 cells. Together, 8-Cl-Ado/8-Cl-ATP can inhibit Topo II activities in cells, thereby inhibiting DNA synthesis and inducing DNA DSBs, which may contribute to 8-Cl-Ado-inhibited proliferation of cancers.

A number of therapeutic targets have been explored for developing anticancer drugs. Continuous efforts have been directed at the discovery of new targets as well as the improvement of therapeutic efficacy of agents directed at explored targets. There are 84 and 488 targets of marketed and investigational drugs for the treatment of cancer or cancer related illness. Analysis of these targets, particularly those of drugs in clinical trials and US patents, provides useful information and perspectives about the trends, strategies and progresses in targeting key cancer-related processes and in overcoming the difficulties in developing efficacious drugs against these targets. The efficacy of anticancer drugs directed at these targets is frequently compromised by counteractive molecular interactions and network crosstalk, negative and adverse secondary effects of drugs, and undesired ADMET profiles. Multi-component therapies directed at multiple targets and improved drug targeting methods are being explored for alleviating these efficacy-reducing processes. Investigation of the modes of actions of these combinations and targeting methods offers clues to aid the development of more effective anticancer therapies.

A combined quantum mechanics and molecular mechanics (QM/MM) method has been employed to investigate binding energy contributions of the conserved bridging water molecules in CDK2-inhibitor complexes. Calculations to eight selected CDK2-inhibitor complexes with each one containing the same bridging water molecule WAT145 show that the energy contribution of WAT145 varies with ligands, indicating that the energy contribution depends on the characteristic of the corresponding ligand. Calculations to the CDK2-inhibitor complex containing two bridging water molecules show that different bridging water molecules in the same complex offer different binding energy contributions, which is due to the difference of their local environment.

Virtual screening performance of support vector machines (SVM) depends on the diversity of training active and inactive compounds. While diverse inactive compounds can be routinely generated, the number and diversity of known actives are typically low. We evaluated the performance of SVM trained by sparsely distributed actives in six MDDR biological target classes composed of a high number of known actives (983-1645) of high, intermediate, and low structural diversity (muscarinic M1 receptor agonists, NMDA receptor antagonists, thrombin inhibitors, HIV protease inhibitors, cephalosporins, and renin inhibitors). SVM trained by regularly sparse data sets of 100 actives show improved yields at substantially reduced false-hit rates compared to those of published studies and those of Tanimoto-based similarity searching method based on the same data sets and molecular descriptors. SVM trained by very sparse data sets of 40 actives (2.4%-4.1% of the known actives) predicted 17.5-39.5%, 23.0-48.1%, and 70.2-92.4% of the remaining 943-1605 actives in the high, intermediate, and low diversity classes, respectively, 13.8-68.7% of which are outside the training compound families. SVM predicted 99.97% and 97.1% of the 9.997 M PUBCHEM and 167K remaining MDDR compounds as inactive and 2.6%-8.3% of the 19,495-38,483 MDDR compounds similar to the known actives as active. These suggest that SVM has substantial capability in identifying novel active compounds from sparse active data sets at low false-hit rates.

The stem cell factor receptor (c-Kit) plays critical roles in initiating cell growth and proliferation. Its kinase functional abnormity has been thought to associate with several human cancers. The regulation of c-Kit kinase activity is achieved by phosphorylation on the residues Tyr568 and Tyr570 within juxtamembrane region (JMR) and subsequent structural transition of JMR and activation loop (A-loop). However, the detailed conformational dynamics of JMR and A-loop are far from clear, especially whether their conformational changes are coupled or not during the kinase activation transition. In this investigation, the complete conformational transition pathway was determined using a series of nanosecond conventional molecular dynamics (MD) and targeted molecular dynamics (TMD) simulations in explicit water systems. The results of the MD simulations show that the phosphorylation of residues Tyr568 and Tyr570 within JMR induces the detachment of JMR from the kinase C-lobe and increases the fluctuation in the structure of JMR, thus appearing to initiate the kinase activation process. During the course of the TMD simulation, which characterizes the conformational transition of c-Kit from autoinhibitory to activated state, the JMR undergoes a rapid departure from the allosteric binding site and drifts into solvent, followed by the conformational flip of A-loop from inactive (fold) state to active (extended) state. A change in the orientation of helix αC in response to the motion of JMR and A-loop has also been observed. The computational results presented here indicate that the dissociation of JMR from the kinase domain is prerequisite to c-Kit activation, which is consistent with previous experiments. Proteins 2008. © 2008 Wiley-Liss, Inc.

Combined Car−Parrinello and QM/MM dynamic simulations have been carried out in order to investigate the polymer chain propagation and termination processes in ethylene polymerization catalyzed by zirconocene with the counteranion [CH3B(C6F5)3]- included. A complete account is given not only of the potential energy profile (PEP) but also of the free energy profile (FEP) for the first and second steps of ethylene insertion, as well as of the chain termination. Solvation effects were included on the basis of a continuum model. Special attention was given to the counteranion mobility in the catalytic process and the possible differences between PEP and FEP. Two different modes of attack by ethylene, cis and trans to the counteranion [CH3B(C6F5)3]-, were considered. In general, the FEPs differ from the PEPs in the barrier height and the shape. The FEP gives a higher barrier over PEP due to entropic contributions. The transition state of ethylene uptake by the metal center, the ethylene π-complex, and the ethyl...

Calculations utilizing anionic substituted derivates of the cationic N(wedge)N--Ni(II) and Pd(II) diimine Brookhart complex have been carried out on the barriers of ethylene and acrylonitrile insertion into a M- methyl, propyl and CH(CN)Et bond for M = Ni, Pd. The possibility of side reactions such as chelate formation with the polar functionality and oligomerization of the active species after acrylonitrile insertion are explored. The diimine ring system N--N = -NR' 'CR(1)CR(2)NR' ' with R' ' = 2,6-C(6)H(3)(i-Pr)(2) and R(1),R(2) = Me was functionalized by adding one or two anionic groups (BF(3)(-), etc.) in place of i-Pr on the aryl rings or by replacing one Me diimine backbone group (R(1)) with BH(3)(-). The objective of this investigation is computationally to design catalysts for ethylene/acrylonitrile copolymerization that have activities that are comparable to that of the cationic Ni(II) diimine or at least the Pd(II) diimine Brookhart system for ethylene homopolymerization. Complexes that might meet this objective are discussed.

Calculations have been carried out on the complexation energy of acrylonitrile and ethylene using derivatives of the cationic N∧N Ni(II)−diimine Brookhart complex. For acrylonitrile (CH2CHCN) considerations were given to both π binding through the olefinic CC functionality and σ binding through the NC group. The diimine ring system N∧N = −NR‘ ‘CR1CR2NR‘ ‘ with R‘ ‘ = 2,6-C6H3(i-Pr)2 and R1, R2 = CH3 was functionalized by substituting a carbon in the diimine ring by B- and R1 or R2 (or both) by anionic groups (BF3-, OBF3-, etc.). Substitutions were also carried out at the aryl rings by replacing H or i-Pr with anionic groups. The objective has been to find substitutions that would reduce or eliminate the preference for σ complexation of acrylonitrile, a prerequisite for ethylene/acrylonitrile copolymerization.

Density functional theory (DFT) together with Car-Parrinello ab initio molecular dynamics (CP-AIMD) simulation has been used to investigate the free energy profiles of two representative SN2 reactions: (A) Cl- + CH3Cl → ClCH3 + Cl-; (B) NH3 + H3BNH3 → H3NBH3 + NH3. The free energy profiles along the reaction coordinates at 300 K and 600 K were determined directly by a pointwise thermodynamic integration (PTI) technique. Comparison between the well-known double-well potential energy profile (PEP) and the free energy profiles (FEP) has been made. The results show that, for reaction A, the double-well profile is maintained for the FEP at 300 K due to the stronger ion−dipole interaction between chloromethane and the chloride anion. In comparison with the PEP, the FEP has a higher central barrier and a more shallow well depth. However, at 600 K the double wells almost disappear on the FEP, whereas the central barrier increases further. For reaction B, the 300 K FEP also presents a higher central barrier peak ...

Conventional criteria and indices of aromaticity, including electronic, geometric, energetic and magnetic aspects have been applied to examine the aromaticity of five typical transition metal heterocyclic complexes, i.e. six-membered osmabezene 1 and iridabenzene 2, five-membered cobaltacyclopentadiene 3 and iridacyclopentadiene 4, and four-membered tungstacyclobutadiene 5. The results show that the cyclic, planar, conjugated and Huckel 4n+2 rule’s criteria in the transition-metal-containing heterocycles of the five complexes studied are all met. Five quantitative aromaticity indices, including Bird aromatic index (In), homodesmotic reaction aromatic stabilization energy (HASE), absolute hardness (η), diamagnetic susceptibility exaltation (Λ) and NMR chemical shift (δH), qualitatively lead to a consistent and affirmative conclusion that all of them are aromatic. However, they fail to draw a common conclusion for their relative magnitudes of aromaticity, which proves once again the multidimensional character of aromaticity.

The electronic reason for the stabilization of osmabenzyne Os[CC(SiMe 3 )C(CH 3 )C(SiMe 3 )C H]Cl2(PPh3)2 has been examined with the aid of orbital interaction analysis and density functional theory calculations. The results show that the six-membered metallacycle exhibits somewhat aromatic properties. The filled metal dx2−y2-orbital interacts with the equatorial p-orbital of the high reactive carbyne carbon (C) in a back-bonding fashion, which can deactivate the carbyne carbon. The chloride ligand trans to the carbyne carbon, which is a stronger π-donor, can enhance the d–p interaction. On the other hand, the π-donor ligand trans and cis to the carbyne carbon benefit the dxz–3π back-bonding interaction between the metal and carbon unit, hence stabilizing the osmabenzyne. And the silyl group on the carbon adjacent to the carbyne carbon can prevent the protonation back to the corresponding osmabenzene through its polarization effect in a way.

Protonation of [RuH(Cl)(PPh3)2(NBD)] (NBD = norbornadiene) in benzene with a limiting amount of HOTf gives [RuH(OTf)(PPh3)2(NBD)], norbornene, and [RuCl2(PPh3)2]2. In dichloromethane, [RuH(Cl)(PPh3...

The majority of six-coordinate d4 osmium/ruthenium(IV) porphyrin complexes [M(por)X2] have been found to be paramagnetic and have linear X−M−X structural units. However, an exception is found for [...

A strong promoting effect of water in the catalytic hydrogenation of CO2 to formic acid with the solvento metal hydride species TpRu(PPh3)(CH3CN)H is observed. High-pressure NMR monitoring of the c...

Rotational barriers of vinylidene ligands in the five-coordinate complexes M(X)Cl(CCHR)L2 (M = Os, Ru; L = phosphine) have been investigated by density functional theory calculations at the level of B3LYP. The effects of ligand X, transition metal M, and substituent R on the barriers have been examined. The results show that the rotational barriers increase with X from having π-acceptor, σ-donor to having π-donor properties. Ligands (X) with π-acceptor properties stabilize the transition state structures through interactions with the d orbital used for metal−vinylidene π bonding in the most stable conformations and, therefore, give smaller rotational barriers. Studies of the influence of different substituents R show that the rotational barriers also increase with the electron donation abilities of R. The rotational barriers for Os complexes are generally higher in comparison to those of the Ru analogues. This result is related to the stronger osmium−ligand interactions because of the more diffuse d orbit...

Treatment of OsCl2(PPh3)3 with HC⋮CCMe3produced a mixture of OsCl3(⋮CCH2CMe3)(PPh3)2 and OsCl(CCHCMe3)(C(C⋮CCMe3)CHCMe3)(PPh3)2. The structures of the new complexes have been confirmed by X-ray diffraction studies.

Hepatocellular carcinoma (HCC) is a serious life-threatening malignant disease of liver. Molecular targeted therapies are considered a promising strategy for the treatment of HCC. Sorafenib is the first, and so far the only targeted drug approved by the US Food and Drug Administration (FDA) for clinical therapy of HCC. Despite being effective in some HCC patients, some demerits of sorafenib in the treatment of HCC, such as modest survival benefits, and drug resistance, have also been reported, which highlights the unmet medical need among patients with HCC. Here, we report a novel multikinase inhibitor discovered by us, SKLB-329, which potently inhibits angiogenesis-related kinases including VEGFR1/2/3, and FGFR2, and the Src kinase. SKLB-329 significantly inhibited endothelial cell growth, migration, invasion and tube formation. It showed potent anti-angiogenic activity in a transgenic zebrafish model. Moreover, SKLB-329 could efficiently restrain the proliferation of HCC cells through down-regulation of Src-mediated FAK and Stat3 activity. In vivo, oral administration of SKLB-329 considerably suppressed the tumor growth in HCC xenograft models (HepG2 and SMMC7721) in a dose-dependent manner. In all of the in vitro and in vivo assays of this investigation, sorafenib was used as a positive control, and in most assays SKLB-329 exhibited a higher potency compared with the positive control. In addition, SKLB-329 also bears favorable pharmacokinetic properties. Collectively, the results of preclinical studies presented here demonstrate that SKLB-329 is a promising drug candidate for HCC treatment.

This paper describe the structural optimization of a hit compound, N2-(4-(4-methylpiperazin-1-yl)phenyl)-N8-phenyl-9H-purine-2,8-diamine (1), which is a reversible kinase inhibitor targeting both EGFR-activating and drug-resistance (T790M) mutations but has poor binding affinity. Structure-activity relationship studies led to the identification of 9-cyclopentyl-N2-(4-(4-methylpiperazin-1-yl)phenyl)-N8-phenyl-9H-purine-2,8-diamine (9e) that exhibits significant in vitro antitumor potency against the non-small-cell lung cancer (NSCLC) cell lines HCC827 and H1975, which harbor EGFR-activating and drug-resistance mutations, respectively. Compound 9e was further assessed for potency and selectivity in enzymatic assays and in vivo anti-NSCLC studies. The results indicated that compound 9e is a highly potent kinase inhibitor against both EGFR-activating and resistance mutations and has good kinase spectrum selectivity across the kinome. In vivo, oral administration of compound 9e at a dose of 5 mg/kg caused rapid and complete tumor regression in a HCC827 xenograft model, and an oral dose of 50 mg/kg initiated a considerable antitumor effect in an H1975 xenograft model.

Bruton's tyrosine kinase has emerged as a potential target for the treatment for B-cell malignancies and autoimmune diseases. Discovery of Bruton's tyrosine kinase inhibitors has thus attracted much attention recently. In this investigation, we introduced a hybrid protocol of virtual screening methods including support vector machine model-based virtual screening, pharmacophore model-based virtual screening and docking-based virtual screening for retrieving new Bruton's tyrosine kinase inhibitors from commercially available chemical databases. Performances of the hybrid virtual screening approach were evaluated against a test set, which results showed that the hybrid virtual screening approach significantly shortened the overall screening time, and considerably increased the hit rate and enrichment factor compared with the individual method (SB-VS, PB-VS and DB-VS) or their combinations by twos. This hybrid virtual screening approach was then applied to screen several chemical databases including Specs (202,408 compounds) and Enamine (980,000 compounds) databases. Thirty-nine compounds were selected from the final hits and have been shifted to experimental studies.

Virtual screening methods have been developed and explored as useful tools for searching drug lead compounds from chemical libraries, including large libraries that have become publically available. In this review, we discussed the new developments in exploring virtual screening methods for enhanced performance in searching large chemical libraries, their applications in screening libraries of ~ 1 million or more compounds in the last five years, the difficulties in their applications, and the strategies for further improving these methods.

The intuitive nature of the pharmacophore concept has made it widely accepted by the medicinal chemistry community, evidenced by the past 3 decades of development and application of computerized pharmacophore modeling tools. On the other hand, shape complementarity has been recognized as a critical factor in molecular recognition between drugs and their receptors. Recent development of fast and accurate shape comparison tools has facilitated the wide spread use of shape matching technologies in drug discovery. However, pharmacophore and shape technologies, if used separately, often lead to high false positive rate. Thus, integrating pharmacophore matching and shape matching technologies into one program has the potential to reduce the false positive rates in virtual screening. Other issues of current pharmacophore technologies include sometimes high false negative rate and non-quantitative prediction. In this article, we first focus on a recently implemented method (Shape4) that combines receptor based shape matching and pharmacophore comparison in a single algorithm to create shape pharmacophore models for virtual screening. We also examine a recent example that utilizes multi-complex information to develop receptor-based pharmacophore models that promises to reduce false negative rate. Finally, we review several methods that employ receptor-based pharmacophore map and pharmacophore key descriptors for QSAR modeling. We conclude by emphasizing the concept of receptor-based shape pharmacophore and its roles in future drug discovery.

We developed the quantative structure-property relationships (QSPRs) models to correlate the molecular structures of surfactant, cosurfactant, oil, and drug with the solubility of poorly water-soluble 2-aryl propionic acid nonsteroidal anti-inflammatory drugs (2-APA-NSAIDs) in self-emulsifying drug delivery systems (SEDDSs). The compositions were encoded with electronic, geometrical, topological, and quantum chemical descriptors. To obtain reliable predictions, we used multiple linear regression (MLR) and artificial neural network (ANN) methods for model development. The obtained equations were validated using a test set of 42 formulations and showed a great predictive power, and linear models were found to be better than nonlinear ones. The obtained QSPR models would greatly facilitate fast screening for the optimal formulations of SEDDS at the early stage of drug development and minimize experimental effort.

This investigation is to explore the feasibility of applying reverse docking method to the selectivity studies of protein kinase inhibitors. Firstly, a database that consists of 422 protein kinase structures was established through collecting the reported crystal structures or homology modeling. Then a reverse docking based method of protein kinase target screening was established, followed by the optimization of related parameters and scoring functions. Finally, seven typical selective kinase inhibitors were used to test the established method. The results show that the selective targets of these inhibitors have relatively high scoring function values (ranking in the first 35% of the tested kinase targets according to the scoring function values). This implies that the reverse docking method can be applied to the virtual screening of kinase targets and further to the selectivity studies of protein kinase inhibitors.

The regulation of p14ARF gene by E2F transcription factor, which differs from that of classical E2F targets, has recently been attributed to a variant E2F-response element. However, promoter assays suggest multiple elements present in the p14ARF promoter and argue against the idea that the ARF promoter has a unique ability to distinguish between aberrant and physiological levels of E2F1. Therefore, the functional characterization of the promoter still needs to be done. We demonstrate that at least two overlapping E2F1/Sp1 binding sites are present in the p14ARF promoter, and E2F1 activates the promoter through displacing constitutive Sp1 from the overlapping sites. We found that 8-chloro-adenosine (a metabolite of 8-Cl-cAMP) exposure induced the p14ARF gene in human lung cancer H1299 cells, followed by increased expression of E2F1 and constitutive expression of Sp1. The combination of cotransfection and electrophoretic mobility shift assay (EMSA) indicated that constitutive binding of Sp1 to the overlapping sites contributed to a constitutive expression of the ARF gene in unexposed H1299, whereas displacing Sp1 from the overlapping sites by E2F1 promoted the gene activation after exposure. EMSA and chromatin immunoprecipitation revealed increased association of E2F1 with the overlapping sites in the active promoter in 8-Cl-Ado-exposed cells. Together, these data suggest that the overlapping E2F1/Sp1 site, being present in multiple copies in the p14ARF promoter, may serve as the targets for both E2F1 and Sp1, thereby playing a crucial role in response to some oncogenic signals and stimulators, which activate the ARF gene through inducing E2F in the cell.

8-Chloro-cAMP (8-Cl-cAMP) and its metabolite 8-chloro-adenosine (8-Cl-Ado) inhibit cell growth by 8-Cl-Ado-converted 8-Cl-ATP that targets cell-cycle control and RNA metabolism. However, the cell-cycle checkpoint pathways remain to be identified. Recent studies have shown that 8-Cl-cAMP administration and 8-Cl-Ado exposure may damage chromosomal DNA in vivo and in vitro. In this study, we demonstrate that 8-Cl-Ado-induced DNA damage activates G2/M phase checkpoint, which is associated with ATM-activated CHK1-CDC25C-CDC2 pathway joined by BRCA1-CHK1 branch in apoptosis-resistant human myelocytic leukemia K562 (p53-null) cells. Inhibition of CHK1 kinase by Go6976, an inhibitor of CHK1 activity, can promote DNA damage and lead to the activation of CHK2, converting G2/M checkpoint into intra-S-phase checkpoint in which two parallel branches, the ATM-CHK2-CDC25A-CDK2 and the ATM-NBS1/SMC1 cascades, are involved. These observations may provide aid in better understanding of the mechanisms of 8-Cl-cAMP and 8-Cl-Ado actions and in potential design of the combined therapy.

Chloroquine is an antimalarial drug that has been used in the treatment and prophylaxis of malaria since the 1950s. The present study was undertaken to examine the effects of chloroquine on Bcap-37 human breast cancer cells' growth, cell cycle modulation, apoptosis induction, and associated molecular alterations in vitro. The chloroquine treatment decreased the viability of Bcap-37 cells in a concentration- and time-dependent manner, which correlated with G(2)/M phase cell cycle arrest. The chloroquine-mediated cell cycle arrest was associated with a decrease in protein levels/activity of polo-like kinase 1 (Plk1), phosphorylated cell division cycle 25C (Cdc25C), phosphorylated extracellular signal-regulated kinase 1/2 (ERK1/2), phosphorylated Akt. The chloroquine-treated Bcap-37 cells exhibited a marked decrease in the level of mitochondrial transmembrane potential (DeltaPsim), which was accompanied by the activation of caspase-3 and cleaved poly(ADP-ribose) polymerase (PARP). Exposure of Bcap-37 cells to chloroquine also resulted in the induction of spindle abnormalities. In conclusion, the findings in this study suggested that chloroquine might have potential anticancer efficacy, which could be attributed, in part, to its proliferation inhibition and apoptosis induction of cancer cells through modulation of apoptosis and cell cycle-related proteins expressions, down-regulation of mitochondrial transmembrane potential (DeltaPsim), and induction of spindle abnormalities.

Virtual screening performance of support vector machines (SVM) depends on the diversity of training active and inactive compounds. While diverse inactive compounds can be routinely generated, the number and diversity of known actives are typically low. We evaluated the performance of SVM trained by sparsely distributed actives in six MDDR biological target classes composed of a high number of known actives (983−1645) of high, intermediate, and low structural diversity (muscarinic M1 receptor agonists, NMDA receptor antagonists, thrombin inhibitors, HIV protease inhibitors, cephalosporins, and renin inhibitors). SVM trained by regularly sparse data sets of 100 actives show improved yields at substantially reduced false-hit rates compared to those of published studies and those of Tanimoto-based similarity searching method based on the same data sets and molecular descriptors. SVM trained by very sparse data sets of 40 actives (2.4%−4.1% of the known actives) predicted 17.5−39.5%, 23.0−48.1%, and 70.2−92.4%...

The purpose of this study was to investigate the effects of chloroquine diphosphate on the proliferation and apoptosis of human leukemic K562 cells, and to elucidate its possible mechanism of activity. The inhibitory effect of chloroquine diphosphate with different concentrations on K562 cell proliferation was detected by MTT method. Apoptosis was measured by flow cytometry (FCM); morphological analysis of apoptosis was performed after staining with propidium iodide (PI) under fluorescence microscope; cell apoptosis was assessed by the DNA ladder shown agarose gel electrophoresis. After treatment with chloroquine diphosphate, K562 cells were stained by Rhodamine 123 to detect changes in mitochondrial transmembrane potential (DeltaPsim) by FCM. The results showed that the cell viability decreased in dose-dependent manner, following chloroquine diphosphate treatment at different concentrations (1.5625, 3.125, 6.25, 12.5, 25, 50 and 100 micromol/L) for 24, 48 and 72 hours. By FCM analysis, the significant increases of sub-G(1) were observed. DNA ladder was detected and apoptotic nuclei were observed. DeltaPsim decreased in K562 cells after chloroquine diphosphate treatment. It is concluded that the chloroquine diphosphate can inhibit the proliferation of K562 cells and induce cell apoptosis, which may relate to down-regulation of mitochondrial transmembrane potential (DeltaPsim).

As part of the intensive efforts in facilitating drug discovery, computational methods have been explored as low-cost and efficient tools for predicting various toxicological properties and adverse drug reactions (ADR) of pharmaceutical agents. More recently, machine learning methods have been applied for developing tools capable of predicting diverse spectrum of compounds of different toxicological properties and ADR profiles. Based on the results of a number of studies, these methods have shown promising potential in predicting a variety of toxicological properties and ADR profiles. This article reviews the strategies, current progresses, underlying difficulties and future prospects in using machine learning methods for predicting compounds of specific toxicological property or ADR profile.

Kinases play crucial roles in the life of cell. Their functional abnormity usually leads to many human major diseases including tumors. The prospecting of ATP-competitive small-molecule kinase inhibitors targeting kinases of therapeutic interest has become the focus of researches. Due to the high conservation of the catalytic domain of kinases, the selectivity of kinase inhibitors is poor in general. However, along with the development of structural biology and computer-aided drug design, great progress in the research of selective, ATP-competitive kinase inhibitors has been achieved in recent years. In this account, the review has been made on the development of the design of selective kinase inhibitors.

Combined gradient-corrected density functional theory and molecular mechanics (QM/MM) has been used to investigate the copolymerization of ethylene with the CH2CHX α-olefins, where X = −H, −Me, −CN...