CoMFA, HQSAR and molecular docking studies of butitaxel analogues with ?-tubulin

Theoretically exploring selective-binding mechanisms of BRD4 through integrative computational approaches

The origin of cancer is related to the dysregulation of multiple signal pathways and of physiological processes. Bromodomain-containing protein 4 (BRD4) has become an attractive target for the development of anticancer and anti-inflammatory agents since it can epigenetically regulate the transcription of growth-promoting genes. The synthesized BRD4 inhibitors with new chemical structures can reduce the drug resistance, but their binding modes and the inhibitory mechanism remain unclear. Here, we initially constructed robust QSAR models based on 68 reported tetrahydropteridin analogues using topomer CoMFA and HQSAR. On the basis of QSAR results, we designed 16 novel tetrahydropteridin analogues with modified structures and carried out docking studies. Instead of significant hydrogen bondings with amino acid residue Asn140 as reported in previous research, the molecular docking modelling suggested a novel docking pose that involves the amino acid residues (Trp81, Pro82, Val87, Leu92, Leu94, Cys136, Asp144, and Ile146) at the active site of BRD4. The MD simulations, free energy calculations, and residual energy contributions all indicate that hydrophobic interactions are decisive factors affecting bindings between inhibitors and BRD4. The current study provides new insights that can aid the discovery of BRD4 inhibitors with enhanced anti-cancer ability.

Structural modification of 4, 5-dihydro-[1, 2, 4] triazolo [4, 3-f] pteridine derivatives as BRD4 inhibitors using 2D/3D-QSAR and molecular docking analysis

Cancer treatment continues to be one of the most serious public health issues in the world. The overexpression of BRD4 protein has led to a series of malignant tumors, hence the development of small molecule BRD4 protease inhibitors has always been a hot spot in the field of medical research. In this study, a series of 4,5-dihydro-[1, 2, 4] triazolo [4, 3-f] pteridine derivatives were used to establish 3D/2D-QSAR models and to discuss the relationship between inhibitor structure and activity. Four ideal models were established, including the comparative molecular field analysis (CoMFA: [Formula: see text] = 0.574, [Formula: see text] = 0.947) model, comparative molecular similarity index analysis (CoMSIA: [Formula: see text]= 0.622, [Formula: see text] = 0.916) model, topomer CoMFA ([Formula: see text] = 0.691, [Formula: see text]= 0.912) model and hologram quantitative structure-activity relationship (HQSAR: [Formula: see text]= 0.759, [Formula: see text] = 0.963) model. They show quite good external predictive power for the test set, with [Formula: see text] values of 0.602, 0.624, 0.671 and 0.750, respectively. In addition, the contour and color code map given by the 2D/3D-QSAR model with the results of molecular docking analyzed to chalk up modification methods for improving inhibitory activity, which was verified by designing novel compounds. The analysis results are helpful to promote the modification of the inhibitor framework and to provide a reference for the construction of new and promising BRD4 inhibitor compounds.

Exploration of the binding mode between (-)-zampanolide and tubulin using docking and molecular dynamics simulation

The binding mode of (-)-zampanolide (ZMP) to tubulin was investigated using docking, molecular dynamics (MD) simulation, and binding free-energy calculations. The docking studies validated the experimental results indicating that the paclitaxel site is the binding site for (-)-ZMP. The 18 ns MD simulation shows the docking mode has changed a lot, whereas it offers more reliable binding data. MM-PBSA binding free-energy calculations further confirmed the results of the MD simulation. The study revealed that hydrophobic interactions play an important role in stabilizing the binding, and the strong hydrogen bond formed with Asp224 enhances the affinity for tubulin. Meanwhile, the results support the assumption that (-)-ZMP can be attacked by His227, leading to a nucleophilic reaction and covalent binding. These theoretical results lead to a greater understanding of the mechanism of action of binding to tubulin, and will therefore aid the design of new compounds with higher affinities for tubulin.

Model-based drug development applied to oncology

Model-based drug development (MBDD) is an approach that is used to organize the vast and complex data streams that feed the drug development pipelines of small molecule and biotechnology sponsors. Such data streams are ultimately reviewed by the global regulatory community as evidence of a drug's potential to treat and/or harm patients. Some of this information is captured in the scientific literature and prescribing compendiums forming the basis of how new and existing agents will ultimately be administered and further evaluated in the broader patient community. As this data stream evolves, the details of data qualification, the assumptions and/or critical decisions based on these data are lost under conventional drug development paradigms. MBDD relies on the construction of quantitative relationships to connect data from discrete experiments conducted along the drug development pathway. These relationships are then used to ask questions relevant at critical development stages, hopefully, with the understanding that the various scenarios explored represent a path to optimal decision making. Oncology, as a therapeutic area, presents a unique set of challenges and perhaps a different development paradigm as opposed to other disease targets. The poor attrition of development compounds in the recent past attests to these difficulties and provides an incentive for a different approach. In addition, given the reliance on multimodal therapy, oncological disease targets are often treated with both new and older agents spanning several drug classes. As MBDD becomes more integrated into the pharmaceutical research community, a more rational explanation for decisions regarding the development of new oncology agents as well as the proposed treatment regimens that incorporate both new and existing agents can be expected. Hopefully, the end result is a more focussed clinical development programme, which ultimately provides a means to optimize individual patient care.

Immunoassay for phenylurea herbicides: application of molecular modeling and quantitative structure-activity relationship analysis on an antigen-antibody interaction study

An indirect competitive enzyme-linked immunosorbent assay (icELISA) for 12 phenylurea herbicides (PUHs) was established with the half-maximum inhibition concentration (IC(50)) of 1.7-920.7 μg L(-1). A method of computer-aided molecular modeling was established in quantitative structure-activity relationship (QSAR) studies to obtain a deeper insight into the PUHs' antibody interactions on how and which molecular properties of the analytes quantitatively affect the antibody recognition. A two-dimensional (2D)-QSAR model based on the Hansch equation and a hologram QSAR (HQSAR) model were constructed, and both showed highly predictive abilities with cross-validation q(2) values of 0.820 and 0.752, respectively. It was revealed that the most important impact factor of the antibody recognition was the PUHs' hydrophobicity (log P), which provided a quadratic correlation to the antibody recognition. Hapten-carrier linking groups were less exposed to antibodies during immunization; thus, groups of the analytes in the same position were generally considered to be less contributive to antibody recognition during immunoassay. But the results of substructure-level analysis showed that these groups played an important role in the antigen-antibody interaction. In addition, the frontier-orbital energy parameter E(LUMO) was also demonstrated as a related determinant for this reaction. In short, the result demonstrated that the hydrophobicity and the lowest unoccupied molecular orbital energy (E(LUMO)) of PUH molecules were mainly responsible for antibody recognition.

Fragment-based QSAR: perspectives in drug design

Drug design is a process driven by innovation and technological breakthroughs involving a combination of advanced experimental and computational methods. A broad variety of medicinal chemistry approaches can be used for the identification of hits, generation of leads, as well as to accelerate the optimization of leads into drug candidates. Quantitative structure-activity relationship (QSAR) methods are among the most important strategies that can be applied for the successful design of small molecule modulators having clinical utility. Hologram QSAR (HQSAR) is a modern 2D fragment-based QSAR method that employs specialized molecular fingerprints. HQSAR can be applied to large data sets of compounds, as well as traditional-size sets, being a versatile tool in drug design. The HQSAR approach has evolved from a classical use in the generation of standard QSAR models for data correlation and prediction into advanced drug design tools for virtual screening and pharmacokinetic property prediction. This paper provides a brief perspective on the evolution and current status of HQSAR, highlighting present challenges and new opportunities in drug design.

Modeling resistance index of taxoids to MCF-7 cell lines using ANN together with electrotopological state descriptors

AIM

To develop an artificial neural network model for predicting the resistance index (RI) of taxoids.

METHODS

A dataset of 63 experimental data points were compiled from published studies and randomly subdivided into training and external test sets. Electrotopological state (E-state) indices were calculated to characterize molecular structure together with a principle component analysis to reduce the variable space and analyze the relative importance of E-state indices. Back propagation neural network technique was used to build the models. Five-fold cross-validation was performed and 5 models with different compound composition in training and validation sets were built. The independent external test set was used to evaluate the predictive ability of models.

RESULTS

The final model proved to be good with the cross-validation Q2cv0.62, external testing R2 0.84, and the slope of the regression line through the origin for the testing set at 0.9933.

CONCLUSION

The quantitative structure-activity relationship model can predict the RI to a relative nicety, which will aid in the development of new anti-multidrug resistance taxoids.

Molecular modeling of the cytoskeleton

Molecular modeling techniques have truly come of age in recent decades, and here we cover several of the most commonly used techniques, namely molecular dynamics, Brownian dynamics, and molecular docking. In each case, we explain the physical basis and limitations of the various techniques and then illustrate their application to various problems related to the cytoskeleton. This set of studies covers a relatively wide range of examples and is comprehensive enough to clearly see how these techniques could be applied to other systems. Finally, we cover several related methodologies that expand on these basic techniques to allow for more detailed and specific simulation and analysis.

Effect of steric molecular field settings on CoMFA predictivity

Steric molecular field can be represented in a number of ways in comparative molecular field analysis (CoMFA). This study aimed to investigate whether the choice of steric molecular field settings significantly influences the predictive performance of CoMFA and, if so, which is the best. The three-dimensional quantitative structure activity relationship (3D-QSAR) models based on Lennard-Jones, indicator, parabolic and Gaussian steric fields were compared using 28 datasets taken from the literature. The analysis of the predictive ability of these models (cross validated R(2)) indicates that steric fields in which the value drops off quickly with distance (i.e. Lennard-Jones and indicator fields) tend to perform better than the Gaussian version, which has a slower and smoother decrease. Furthermore, depending on the steric field type used, the field sampling density (i.e. grid spacing) has a variable influence on the predictive ability of the models generated.

BK virus nephropathy and kidney transplantation

Nephropathy from BK virus (BKV) infection is an evolving challenge in kidney transplant recipients. It is the consequence of modern potent immunosuppression aimed at reducing acute rejection and improving allograft survival. Untreated BKV infections lead to kidney allograft dysfunction or loss. Decreased immunosuppression is the principle treatment but predisposes to acute and chronic rejection. Screening protocols for early detection and prevention of symptomatic BKV nephropathy have improved outcomes. Although no approved antiviral drug is available, leflunomide, cidofovir, quinolones, and intravenous Ig have been used. Retransplantation after BKV nephropathy has been successful.

Simian virus 40 in humans

Simian virus 40 (SV40) is a monkey virus that was administered to human populations by contaminated vaccines which were produced in SV40 naturally infected monkey cells. Recent molecular biology and epidemiological studies suggest that SV40 may be contagiously transmitted in humans by horizontal infection, independently from the earlier administration of SV40-contaminated vaccines.SV40 footprints in humans have been found associated at high prevalence with specific tumor types such as brain and bone tumors, mesotheliomas and lymphomas and with kidney diseases, and at lower prevalence in blood samples from healthy donors. Contrasting reports appeared in the literature on the circulation of SV40 in humans by contagious transmission and its association, as a possible etiologic cofactor, with specific human tumors. As a consequence of the conflicting results, a considerable debate has developed in the scientific community. In the present review we consider the main results obtained by different groups investigating SV40 sequences in human tumors and in blood specimens, the putative role of SV40 in the onset/progression of specific human tumors, and comment on the hypotheses arising from these data.

Mesothelioma mortality in Europe: impact of asbestos consumption and simian virus 40

Background

It is well established that asbestos is the most important cause of mesothelioma. The role of simian virus 40 (SV40) in mesothelioma development, on the other hand, remains controversial. This potential human oncogene has been introduced into various populations through contaminated polio vaccines. The aim of this study was to investigate whether the possible presence of SV40 in various European countries, as indicated either by molecular genetic evidence or previous exposure to SV40-contaminated vaccines, had any effect on pleural cancer rates in the respective countries.

Methods

We conducted a Medline search that covered the period from January 1969 to August 2005 for reports on the detection of SV40 DNA in human tissue samples. In addition, we collected all available information about the types of polio vaccines that had been used in these European countries and their SV40 contamination status.

Results

Our ecological analysis confirms that pleural cancer mortality in males, but not in females, correlates with the extent of asbestos exposure 25 – 30 years earlier. In contrast, neither the presence of SV40 DNA in tumor samples nor a previous vaccination exposure had any detectable influence on the cancer mortality rate in neither in males (asbestos-corrected rates) nor in females.

Conclusion

Using the currently existing data on SV40 prevalence, no association between SV40 prevalence and asbestos-corrected male pleural cancer can be demonstrated.

Is there a role for SV40 in human cancer?

The question of whether Simian Virus 40 (SV40) can cause human tumors has been one of the most highly controversial topics in cancer research during the last 50 years. The longstanding debate began with the discovery of SV40 as a contaminant in poliovirus vaccine stocks that were used to inoculate approximately 100 million children and adults in the United States between 1955 and 1963, and countless more throughout the world. Concerns regarding the potential health risk of SV40 exposure were reinforced by studies demonstrating SV40's potential to transform human cells and promote tumor growth in animal models. Many studies have attempted to assess the relationship between the potential exposure of humans to SV40 and cancer incidence. Reports of the detection of SV40 DNA in a variety of cancers have raised serious concerns as to whether the inadvertent inoculation with SV40 has led to the development of cancer in humans. However, inconsistent reports linking SV40 with various tumor types has led to conflicting views regarding the potential of SV40 as a human cancer virus. Several recent studies suggest that older detection methodologies were flawed, and the limitations of these methods could account for most, if not all, of the positive correlations of SV40 in human tumors to date. Although many people may have been exposed to SV40 by polio vaccination, there is inadequate evidence to support widespread SV40 infection in the population, increased tumor incidence in those individuals who received contaminated vaccine, or a direct role for SV40 in human cancer.