Current Computer - Aided Drug Design - Volume 9, Issue 1, 2013

One of the most promising methods of unveiling the pharmacology of marketed drugs is to screen them against new biological targets. In an attempt to find inhibitors for acetylcholinesterase (AChE), the Drug Bank Database and natural alkaloids with other known medicinal values were screened through a four-point pharmacophore built in this study. The development of the pharmacophore was based on a structurally diverse set of reported AChE inhibitors and was validated using a separate set of known inhibitors. The developed pharmacophore indicated that the presence of one H-acceptor motif, one H-donor motif, one positively charged group and one aromatic ring is needed for AChE inhibition. Selected hits were further investigated by molecular docking and in vitro testing. The assays revealed that the majority of these compounds showed reasonable inhibition, indicating that the developed pharmacophore can indeed reliably screen molecules for potential AChE inhibitors. It appears that several commercially available marketed drugs have further potential as AChE inhibitors. To extend our study the same compounds have been tested in the fibrillogenesis inhibition of amyloid β (Aβ) peptide to explore the possibility of their dual-function therapeutic activity in Alzheimer’s disease.

Molecular modeling techniques play a relevant role in drug design providing detailed information at atomistic level on the structural, dynamical, mechanistic and electronic properties of biological systems involved in diseases' onset, integrating and supporting commonly used experimental approaches. These information are often not accessible to the experimental techniques taken singularly, but are of crucial importance for drug design. Due to the enormous increase of the computer power in the last decades, quantum mechanical (QM) or first-principles-based methods have become often used to address biological issues of pharmaceutical relevance, providing relevant information for drug design. Due to their complexity and their size, biological systems are often investigated by means of a mixed quantum-classical (QM/MM) approach, which treats at an accurate QM level a limited chemically relevant portion of the system and at the molecular mechanics (MM) level the remaining of the biomolecule and its environment. This method provides a good compromise between computational cost and accuracy, allowing to characterize the properties of the biological system and the (free) energy landscape of the process in study with the accuracy of a QM description. In this review, after a brief introduction of QM and QM/MM methods, we will discuss few representative examples, taken from our work, of the application of these methods in the study of metallo-enzymes of pharmaceutical interest, of metal-containing anticancer drugs targeting the DNA as well as of neurodegenerative diseases. The information obtained from these studies may provide the basis for a rationale structure-based drug design of new and more efficient inhibitors or drugs.

Cycloxygenase-2 (COX-2) is well established for its role in inflammation, cancer and has also been reported to play a significant role in radiation induced inflammation and bystander effect. It has already been reported to have a role in protection against radiation induced damage, suggesting it to be an important target for identifying novel radiation countermeasure agents. Present study aims at identifying novel small molecules from pharmacopeia using COX-2 as target in silico. Systematic search of the molecules that are reported to exhibit radiation protection revealed that around 30% (40 in 130) of them have a role in inflammation and a small percentage of these molecules (20%; 8 in 40) are reported to act as non-steroidal anti-inflammatory drugs (NSAIDS). Docking studies further clarified that antiinflammatory compounds exhibited higher binding energy (BE). Out of 15 top hits, 14 molecules are reported to have anti-inflammatory property, suggesting the significant role of COX-2 in radiation protection. Further, Johns Hopkins Clinical Compound Library (JHCCL), a collection of small molecule clinical compounds, was screened virtually for COX-2 inhibition by docking approach. Docking of around 1400 small molecules against COX-2, leads to identification of a number of previously unreported molecules, which are likely to act as radioprotectors.

Indonesian herbal medicines made from mixtures of several plants are called “Jamu.” The efficacy of a particular Jamu is determined by its ingredients i.e. the composition of the plants. Thus, we modeled the ingredients of Jamu formulas using Partial Least Squares Discriminant Analysis (PLS-DA) in order to predict their efficacy. The plants used in each Jamu medicine served as the predictors, whereas the efficacy of each Jamu provided the responses. Utilizing response predictions obtained from PLS-DA, we predicted the efficacies of Jamu formulations using two methods: maximum response prediction and maximum probability. In predictions of Jamu efficacy, the maximum response prediction method produced a smaller error than that the maximum probability method. Furthermore, utilizing the PLSDA coefficient matrix, we determined the efficacy for which a plant is most useful, based on its largest coefficients.

The C-X-C chemokine receptor-4(CXCR4) is a G-protein coupled receptor (GPCR) which belongs to the family I GPCR or rhodopin-like GPCR family. CXCR4 plays a crucial role as a co-receptor with CCR5 for HIV-1 anchoring to mammalian cell membrane, and is implicated in cancer metastasis and inflammation. Recently, crystal structure of human CXCR4 receptor was reported, which facilitates the structure-based drug discovery of CXCR4 significantly. Here we summarize the structure feature of C-X-C chemokine and its difference from other rhodopsin-like GPCR family, the impact of recent crystal structure on CXCR4 drug development, the available active compounds for CXCR4 receptor, SAR studies of the available active compounds, the recognition mechanism of the inhibitors of CXCR4 receptor (molecular docking results and molecular dynamics results), which illustrates the interaction between the inhibitors and critical residues of CXCR4, and the outlook of drug development for CXCR4 receptor.

The present work aims at computational analysis of environmentally responsive hydrogels with enormous prospective in the formulation aspect of drug delivery systems. The drug delivery potential of hydrogels to the targets is owing to the specific stimuli responsive nature of the hydrogels. The environmental factors looked upon in the study are changes in pH, alteration of temperature and glucose concentration rise originated in the body as a result of various disease conditions. Polymers, synthetic polypeptides and dendrimers have been used in the present work to study the feasibility of drug delivery. The computational methods have been used to formulate polymer properties, pharmacokinetics and toxicity studies. Diverse interactions approximating electrostatic, hydrophobic and hydrogen bond interactions acquire place during incorporation of drugs within the polymer and dendrimers. The covalent and electrostatic interactions between a drug and the surface of polymer and dendrimer have been analyzed. The docking interaction studies have been performed and the best polymer and dendrimer complex have been selected based on the docking score, binding energy and interaction energy with the drugs. G5 generation of poly amidoamine dendrimers and poly N-Ndiethyl acrylamide (PDEAAM) have been identified as most suitable stimuli-responsive effective drug carriers for anti diabetic drugs and diuretics. Favorable results have been obtained while using poly acrylic acid (PAA) for corticosteroids and polylysine for diabetic drugs. ConA protein along with poly aspartic acid also showed good results.

Understanding protein-ligand interactions is a critical step in rational drug design/virtual ligand screening. In this work we applied the AMMOS_ProtLig software for post-docking optimization of estrogen receptor alpha complexes generated after virtual ligand screening protocol. Using MOE software we identified the ligand-receptor interactions in the optimized complexes at different levels of protein flexibility and compared them to the experimentally observed interactions. We analyzed in details the binding sites of three X-ray complexes of the same receptor and identified the key residues for the protein-ligand interactions. The complexes were further processed with AMMOS_ProtLig and the interactions in the predicted poses were compared to those observed in the X-ray structures. The effect of employing different levels of flexibility was analyzed. The results confirmed the AMMOS_ProtLig applicability as a helpful postdocking optimization tool for virtual ligand screening of estrogen receptors.

Malaria has been known as one of the major causes of morbidity and mortality on a large scale in tropical countries until now. In the past decades, many scientific groups have focused their attention on looking for ideal drugs to this disease. So far, this research area is still a hot topic. In the present study, the antimalarial activity of 1, 4- naphthoquinonyl derivatives was modeled by linear and nonlinear statistical methods, that is to say, by forward stepwise multilinear regression (MLR) and radial basis function neural networks (RBFNN). The derived QSAR models have been statistically validated both internally - by means of the Leave One Out (LOO) and Leave Many Out (LMO) crossvalidation, and Y-scrambling techniques, as well as externally (by means of an external test set). The statistical parameters provided by the MLR model were R2 =0.7876, LOOq2 =0.7068, RMS =0.3377, R0 2 =0.7876, k =1.0000 for the training set, and R2 =0.7648, q2 ext =0.7597, RMS=0.2556, R0 2=0.7598, k=1.0417 for the external test set. The RBFNN model gave the following statistical results, namely: R2=0.8338, LOOq2=0.5869, RMS=0.2781, R0 2 = 0.8335, k=1.0000 for the training set, and R2 =0.7586, q2 ext =0.7189, RMS=0.2788, R0 2=0.7129, k=1.0284 for the external test set. Overall, these results suggest that the QSAR MLR-based model is a simple, reliable, credible and fast tool for the prediction and virtual screening of 1, 4-naphoquinone derivatives with high antimalarial activity. In addition, the energies of the highest occupied molecular orbital were found to have high correlation with the activity.

In recent years, in the post genomic era, more and more data is being generated by biological high throughput technologies, such as proteomics and transcriptomics. This omics data can be very useful, but the real challenge is to analyze all this data, as a whole, after integrating it. Biomedical data integration enables making queries to different, heterogeneous and distributed biomedical data sources. Data integration solutions can be very useful not only in the context of drug design, but also in biomedical information retrieval, clinical diagnosis, system biology, etc. In this review, we analyze the most common approaches to biomedical data integration, such as federated databases, data warehousing, multi-agent systems and semantic technology, as well as the solutions developed using these approaches in the past few years.

A quantum mechanics (QM)-based scoring function has been applied to complexes of cyclin-dependent kinase 2 (CDK2) and thirty-one pyrazolo[1,5-a]pyrimidine-based inhibitors and their bioisosteres. A hybrid three-layer QM/MM setup (DFT-D/PM6-D3H4X/AMBER in generalized Born solvent) was used here for the first time as an extension of our previous full QM and SQM/MM (SQM means semiempirical QM) approaches. Two approaches to obtain the structures of the CDK2/inhibitor complexes were examined: i) building the modifications from one X-ray structure available coupled with a conformational search and ii) docking the compounds into CDK2. The QM-based scoring entailed a QM/SQM/MM optimization followed by calculations of the binding scores which were subsequently correlated with the experimental binding free energies. The correlation for the building protocol was good (r2 = 0.64, predictive index = 0.81), whereas the docking approach failed. A decomposition of the interaction energies to ligand fragments enabled us to rationalize the differences in the binding affinities. In conclusion, we have developed and refined a QM-based scoring protocol and successfully applied it to reproduce the binding affinities in congeneric series of CDK2 inhibitors and to rationalize their potency. We thus propose that such a tool can be used in computer-aided rational drug design.

A supervised artificial neural network model has been developed for the accurate prediction of the anti-T. cruzi activity of heterogeneous series of compounds. A representative set of 72 compounds of wide structural diversity was chosen in this study. The definition of the molecules was achieved from an unsupervised neural network using a new methodology, CODES program. This program codifies each molecule into a set of numerical parameters taking into account exclusively its chemical structure. The final model shows high average accuracy of 84% (training performance) and predictability of 77% (external validation performance) for the 4:4:1 architecture net with different training set and external prediction test. This approach using CODES methodology represents a useful tool for the prediction of pharmacological properties. CODES© is available free of charge for academic institutions.

A QSAR study was performed on curcumine derivatives as HIV-1 integrase inhibitors using multiple linear regression. The statistically significant model was developed with squared correlation coefficients (r2) 0.891 and cross validated r2 (r2 cv) 0.825. The developed model revealed that electronic, shape, size, geometry, substitution's information and hydrophilicity were important atomic properties for determining the inhibitory activity of these molecules. The model was also tested successfully for external validation (r2 pred = 0.849) as well as Tropsha's test for model predictability. Furthermore, the domain analysis was carried out to evaluate the prediction reliability of external set molecules. The model was statistically robust and had good predictive power which can be successfully utilized for screening of new molecules.