Current Computer - Aided Drug Design - Volume 16, Issue 4, 2020

Background: Drug repurposing has grown significantly in recent years. Research and innovation in drug repurposing are extremely popular due to its practical and explicit advantages. However, its adoption into practice is slow because researchers and industries have to face various challenges. Objective: As this field, there is a lack of a comprehensive platform for systematic identification for removing development limitations. This paper deals with a comprehensive classification of challenges in drug repurposing. Methods: Initially, a classification of various existing repurposing models is propounded. Next, the benefits of drug repurposing are summarized. Further, a categorization for computational drug repurposing shortcomings is presented. Finally, the methods are evaluated based on their strength to addressing the drawbacks. Results: This work can offer a desirable platform for comparing the computational repurposing methods by measuring the methods in light of these challenges. Conclusion: A proper comparison could prepare guidance for a genuine understanding of methods. Accordingly, this comprehension of the methods will help researchers eliminate the barriers thereby developing and improving methods. Furthermore, in this study, we conclude why despite all the benefits of drug repurposing, it is not being done anymore.

Background: Identification and development of new drug candidates to be used singly or in combination therapy is critical in anticancer research. In recent years, accumulating evidence encouraged us to investigate the anti-proliferative effects of a small and emerging phytochemical Wedelolactone (WDL) in estrogen-dependent and independent multiple gynecological tumor models. Objective: The aim of this study was to investigate the growth inhibitory effect of WDL on estrogen- dependent and independent gynecological cell lines and to explore its inhibitory potential towards key targets through in silico study. Methods: Cytotoxicity of WDL was investigated in human breast and ovarian cancer cell lines (MCF-7 and SKOV3) through 3-(4,5-Dimethyl-2-thiazolyl)-2, 5-diphenyl-2H-tetrazolium bromide (MTT) reduction assay. Epigallocatechingallate (EGCG) was used as reference natural compound while cisplatin was taken as a standard clinical agent. Both WDL and EGCG in combination with cisplatin were also evaluated for their combined growth inhibitory potential in MCF-7 cells. WDL was also evaluated in silico against key factors including braf kinases, CDPK, ERα, aromatase, topoisomerase II and dihydrofolate reductase (DHFR) playing pivotal roles in driving multiple tumors. Results and Discussion: The IC50 value of WDL was 25.77 ± 4.82 μM and 33.64 ± 1.45 μM in MCF-7 and SKOV-3 respectively. The binding energy order was as follows; WDL: DHFR >Braf kinases > CDPK; aromatase > topoisomerase II> ERα > NFkB > alkaline phosphatase; EGCG dihydrofolatereductase (DHFR) > aromatase >CDPK > topoisomerase II > braf kinases > alkaline phosphatase > CDPK > ERα > NFkB. Conclusion: We identified WDL as a cytotoxic agent in breast and ovarian tumor models with the potential to inhibit multiple targets in the oncogenic pathway including estrogen receptor ERα, as depicted through its in silico study. Based on our own research findings and from literature evidence, we conclude that further research should be encouraged to investigate different aspects of wedelolactone as an additional agent to be combined with antiestrogen/endocrine therapy.

Background: Ixora coccinea is a tropical ornamental shrub employed in ethnomedicine for the treatment of a number of diseases none of which include the Human Immunodeficiency Virus (HIV) infection. Ixoratannin A-2, one of the constituents, was previously identified via virtual-screening and experimentally confirmed to possess significant anti-HIV-1 activity in an in vitro CD4+ replication assay. This activity was observed to be significantly reduced in degree in viruses lacking the protein Vpu. This suggests the involvement of Vpu as well as other extra-Vpu macromolecules in its antiviral activity. Methods: In the present computational search for the identity of the other macromolecules that could possibly explain the observed activity, a panel of fourteen established HIV-1 macromolecular targets was assembled against which ixoratannin A-2 and other major phytoconstituents of I. coccinea were virtually screened. Results: Structural analyses of the computed ligand-bound complexes, as well as the careful investigation of the thermodynamic attributes of the predicted binding, revealed subtle selectivity patterns at the atomistic level that suggest the likely involvement of multiple macromolecular processes. Some of the binding interactions were found to be thermodynamically favourable, including the multidrug-resistant HIV protease enzyme, CXCR4 and the human elongin C protein all of which formed reasonably strong interactions with ixoratannin A-2 and other constituents of I. coccinea. Conclusion: Ixoratannin A-2’s ability to favourably interact with multiple HIV-1 and human targets could explain its observed extra-Vpu antiviral activity. This, however, does not imply uncontrolled binding with all available targets; on the other hand, molecular size of ixoratannin A-2 and combination of functional groups confer on it a decent level of selectivity against many of the investigated HIV/AIDS targets.

Background: The great emergence of multi-resistant bacterial strains and the low renewal of antibiotics molecules are leading human and veterinary medicine to certain therapeutic impasses. Therefore, there is an urgent need to find new therapeutic alternatives including new molecules in the current treatments of infectious diseases. Methionine aminopeptidase (MetAP) is a promising target for developing new antibiotics because it is essential for bacterial survival. Objective: To screen for potential MetAP inhibitors by in silico virtual screening of the ZINC database and evaluate the best potential lead molecules by in vitro studies. Methods: We have considered 200,000 compounds from the ZINC database for virtual screening with FlexX software to identify potential inhibitors against bacterial MetAP. Nine chemical compounds of the top hits predicted were purchased and evaluated in vitro. The antimicrobial activity of each inhibitor of MetAP was tested by the disc-diffusion assay against one Gram-positive (Staphylococcus aureus) and two Gram-negative (Escherichia coli & Pseudomonas aeruginosa) bacteria. Among the studied compounds, compounds ZINC04785369 and ZINC03307916 showed promising antibacterial activity. To further characterize their efficacy, the minimum inhibitory concentration was determined for each compound by the microdilution method which showed significant results. Results: These results suggest compounds ZINC04785369 and ZINC03307916 as promising molecules for developing MetAP inhibitors. Conclusion: Furthermore, they could therefore serve as lead molecules for further chemical modifications to obtain clinically useful antibacterial agents.

Background: In this present investigation, some 2, 3 disubstituted-quinazolin-4-one derivatives are designed and docked against chain A and chain B of (3WDF) receptor. Methods: The heterocyclic fused rings quinazolinone have drawn a great attention owing to their expanded applications in the field of pharmaceutical chemistry. The diverse range of molecules with quinazoline/quinazolinone moieties have been reported to exhibit a broad spectrum of biological activities. Results: The results designate that the quinazolinone ring forms hydrophobic and hydrogen bond contacts with ASN 127 A, ALA 126 A, and SER 83 B, SER 183 B amino acid residue. Conclusion: Molecular docking is safe and straightforward to use tool which facilitates in investigating, interpreting, enplaning and identification of molecular properties using 3D structures.

Background: Virtual screening of candidate drug molecules using machine learning techniques plays a key role in pharmaceutical industry to design and discovery of new drugs. Computational classification methods can determine drug types according to the disease groups and distinguish approved drugs from withdrawn ones. Introduction: Classification models developed in this study can be used as a simple filter in drug modelling to eliminate potentially inappropriate molecules in the early stages. In this work, we developed a Drug Decision Support System (DDSS) to classify each drug candidate molecule as potentially drug or non-drug and to predict its disease group. Methods: Molecular descriptors were identified for the determination of a number of rules in drug molecules. They were derived using ADRIANA.Code program and Lipinski's rule of five. We used Artificial Neural Network (ANN) to classify drug molecules correctly according to the types of diseases. Closed frequent molecular structures in the form of subgraph fragments were also obtained with Gaston algorithm included in ParMol Package to find common molecular fragments for withdrawn drugs. Results: We observed that TPSA, XlogP Natoms, HDon_O and TPSA are the most distinctive features in the pool of the molecular descriptors and evaluated the performances of classifiers on all datasets and found that classification accuracies are very high on all the datasets. Neural network models achieved 84.6% and 83.3% accuracies on test sets including cardiac therapy, anti-epileptics and anti-parkinson drugs with approved and withdrawn drugs for drug classification problems. Conclusion: The experimental evaluation shows that the system is promising at determination of potential drug molecules to classify drug molecules correctly according to the types of diseases.

Background: Parkinson’s disease is characterized by decreased level of dopaminergic neurotransmitters and this decrease is due to the degradation of dopamine by protein Monoamine Oxidase B (MAO-B). In order to treat Parkinson’s disease, MAO-B should be inhibited. Objective: To find out the novel phytochemicals from plant Ocimum basilicum that can inhibit MAO-B by using the in silico methods. Methods: The data of chemical constituents from plant Ocimum basilicum was collected and inhibitory activity of these phytochemicals was then predicted by using the Structure-Based (SB) and Ligand-Based Virtual Screening (LBVS) methods. Molecular docking, one of the common Structure-Based Virtual Screening method, has been used during this search. Traditionally, molecular docking is used to predict the orientation and binding affinity of the ligand within the active site of the protein. Molegro Virtual Docker (MVD) software has been used for this purpose. On the other hand, Random Forest Model, one of the LBVS method, has also been used to predict the activity of these chemical constituents of Ocimum basilicum against the MAO-B. Results: During the docking studies, all the 108 compounds found in Ocimum basilicum were docked within the active site of MAO-B (PDB code: 4A79) out of which, 57 compounds successfully formed the hydrogen bond with tyr 435, a crucial amino acid for the biological activity of the enzyme. Rutin (-182.976 Kcal/mol), Luteolin (-163.171 Kcal/mol), Eriodictyol-7-O-glucoside (- 160.13 Kcal/mol), Rosmarinic acid (-133.484 Kcal/mol) and Isoquercitrin (-131.493 Kcal/mol) are among the top hits with the highest MolDock score along with hydrogen interaction with tyr 435. Using the RF model, ten compounds out of 108 chemical constituent of Ocimum basilicum were predicted to be active, Apigenin (1.0), Eriodictyol (1.0), Orientin (0.876), Kaempferol (0.8536), Luteolin (0.813953) and Rosmarinic-Acid (0.7738095) are predicted to be most active with the highest RF score. Conclusion: The comparison of the two screening methods show that the ten compounds that were predicted to be active by the RF model, are also found in top hits of docking studies with the highest score. The top hits obtained during this study are predicted to be the inhibitor of MAO-B, thus, could be used further for the development of drugs for the treatment of Parkinson’s disease (PD).

Background: Balaguluchyadi kashayam, a polyherbal Ayurvedic decoction prepared from Sidacordifolia L., Tinospora cordifolia (Willd.) Miers, and Cedrusdeodara (Roxb. ex D.Don) G.Don, is used in Ayurveda for the treatment of chronic inflammatory conditions. Although this herbal decoction has been used for a long period for treating chronic inflammatory conditions, the mechanism of action of the decoction in reducing inflammatory conditions associated with chronic inflammation has not been clearly understood. Mass spectroscopy-based identification of bioactive molecules present in the decoction and its interaction with enzymes/proteins involved in the pathogenesis of chronic inflammation has been carried and reported in this study. Introduction: Polyherbalism is one of the major principles of Ayurveda. Various phytoconstituents with different activities in the polyherbal decoction act on multi targets of a wide range of diseases. Balaguluchyadi kashayam is a polyherbal decoction prescribed for chronic inflammatory etiologies and the present study aims to evaluate the binding potential of the compounds, identified from Balaguluchyadi kashayam to enzymes/proteins involved in the development and progression of chronic inflammation. Methods: The bioactive compounds present in the Balaguluchyadi Kashayam fractions were extracted by preparative HPLC and identified using UPLC MS Q-TOF. The physicochemical characteristics and ADMET properties of the compounds were calculated using Mol soft, Swiss ADME and OSIRIS data warrior software. Then the binding interactions between the molecules and the proinflammatory mediators such as 5 Lipoxygenase, Cyclooxygenase 2, Tumor necrosis factoralpha convertase enzyme (TACE) and Caspase 1 were determined using molecular docking software Auto Dock 4.0 (http://autodock.scripps.edu/downloads). Results: The identified bioactive molecules in the decoction showed a good binding affinity towards the enzymes/proteins involved in the development and progression of chronic inflammation compared to the binding affinity of known inhibitors/drugs to the respective enzymes/proteins. Conclusion: The bioactive molecules identified in Balaguluchyadi Kashayam could be developed as potential therapeutic molecules against enzymes/proteins involved in the development and progression of chronic inflammation.

Background: Sulfotransferase family comprises key enzymes involved in drug metabolism. Oxamniquine is a pro-drug converted into its active form by schistosomal sulfotransferase. The conformational dynamics of side-chain amino acid residues at the binding site of schistosomal sulfotransferase towards activation of oxamniquine has not received attention. Objective: The study investigated the conformational dynamics of binding site residues in free and oxamniquine bound schistosomal sulfotransferase systems and their contribution to the mechanism of oxamniquine activation by schistosomal sulfotransferase using molecular dynamics simulations and binding energy calculations. Methods: Schistosomal sulfotransferase was obtained from Protein Data Bank and both the free and oxamniquine bound forms were subjected to molecular dynamics simulations using GROMACS-4.5.5 after modeling it’s missing amino acid residues with SWISS-MODEL. Amino acid residues at its binding site for oxamniquine was determined and used for Principal Component Analysis and calculations of side-chain dihedrals. In addition, binding energy of the oxamniquine bound system was calculated using g_MMPBSA. Results: The results showed that binding site amino acid residues in free and oxamniquine bound sulfotransferase sampled different conformational space involving several rotameric states. Importantly, Phe45, Ile145 and Leu241 generated newly induced conformations, whereas Phe41 exhibited shift in equilibrium of its conformational distribution. In addition, the result showed binding energy of -130.091 ± 8.800 KJ/mol and Phe45 contributed -9.8576 KJ/mol. Conclusion: The results showed that schistosomal sulfotransferase binds oxamniquine by relying on hybrid mechanism of induced fit and conformational selection models. The findings offer new insight into sulfotransferase engineering and design of new drugs that target sulfotransferase.

Background: Bioisosteric replacement is widely used in drug design for lead optimization. However, the identification of a suitable bioisosteric group is not an easy task. Methods: In this work, we present MolOpt, a web server for in silico drug design using bioisosteric transformation. Potential bioisosteric transformation rules were derived from data mining, deep generative machine learning and similarity comparison. MolOpt tries to assist the medicinal chemist in his/her search for what to make next. Results and Discussion: By replacing molecular substructures with similar chemical groups, MolOpt automatically generates lists of analogues. MolOpt also evaluates forty important pharmacokinetic and toxic properties for each newly designed molecule. The transformed analogues can be assessed for possible future study. Conclusion: MolOpt is useful for the identification of suitable lead optimization ideas. The MolOpt Server is freely available for use on the web at http://xundrug.cn/molopt.

Background: Vitamin E is comprised of α, β, γ and δ-tocopherols (Ts) and α, β, γ and δ- tocotrienols (T3s). Vitamin E has neuroprotective antioxidant, anti-cancer, and cholesterol-lowering effects. Intracellular trafficking of these isomers remains largely unknown, except for αT which is selectively transported by αT transfer protein (αTTP). Objective: This study aimed to determine the binding of vitamin E isomers on transport proteins using in silico docking. Methods: Transport proteins were selected using AmiGo Gene Ontology tool based on the same molecular function annotation as αTTP. Protein structures were obtained from the Protein Data Bank. Ligands structures were obtained from ZINC database. In silico docking was performed using SwissDock. Results and Discussion: A total of 6 transport proteins were found: SEC14-like protein 2, glycolipid transfer protein (GLTP), pleckstrin homology domain-containing family A member 8, collagen type IV alpha-3-binding protein, ceramide-1-phosphate transfer protein and afamin. Compared with other transport proteins, αTTP had the highest affinities for all isomers except βT3. Binding order of vitamin E isomers toward αTTP was γT > βT > αT > δT > αT3 > γT3 > δT3 > βT3. GLTP had a higher affinity for tocotrienols than tocopherols. βT3 bound stronger to GLTP than αTTP. Conclusion: αTTP remained as the most preferred transport protein for most of the isomers. The binding affinity of αT toward αTTP was not the highest than other isomers suggested that other intracellular trafficking mechanisms of these isomers may exist. GLTP may mediate the intracellular transport of tocotrienols, especially βT3. Improving the bioavailability of these isomers may enhance their beneficial effects to human.

Background: Coronary heart disease generally occurs due to cholesterol accumulation in the walls of the heart arteries. Statins are the most widely used drugs which work by inhibiting the active site of 3-Hydroxy-3-methylglutaryl-CoA reductase (HMGCR) enzyme that is responsible for cholesterol synthesis. A series of atorvastatin analogs with HMGCR inhibition activity have been synthesized experimentally which would be expensive and time-consuming. Methods: In the present study, we employed both the QSAR model and chemical similarity search for identifying novel HMGCR inhibitors for heart-related diseases. To implement this, a 2D QSAR model was developed by correlating the structural properties to their biological activity of a series of atorvastatin analogs reported as HMGCR inhibitors. Then, the chemical similarity search of atorvastatin analogs was performed by using PubChem database search. Results and Discussion: The three-descriptor model of charge (GATS1p), connectivity (SCH-7) and distance (VE1_D) of the molecules is obtained for HMGCR inhibition with the statistical values of R2= 0.67, RMSEtr= 0.33, R2 ext= 0.64 and CCCext= 0.76. The 109 novel compounds were obtained by chemical similarity search and the inhibition activities of the compounds were predicted using QSAR model, which were close in the range of experimentally observed threshold. Conclusion: The present study suggests that the QSAR model and chemical similarity search could be used in combination for identification of novel compounds with activity by in silico with less computation and effort.

Background: In this investigation, 2-cyano-N-(2,4-dioxo-1,2,3,4-tetrahydropyrimidin-5-yl) acetamide (3) reacts with dimethylformamide dimethyl acetal (DMF-DMA) to afford the corresponding (E)- 2-cyano-3-(dimethylamino)-N-(2,4-dioxo-1,2,3,4-tetrahydropyrimidin-5-yl)acrylam-ide (4) utilizing microwave irradiation. The condensation reactions of acrylamide derivative 4 with hydrazine derivatives obtain pyrazole derivatives 6a and 6b; respectively. The synthesized compounds demonstrate in vitro antitumor activity against liver tumor cell line HepG2. Furthermore, additional studies were carried out on the most effective compound 6b to evaluate the potential interaction against 4hdq synthase complex with ΔE= -4.5Kcal/mol and with short distance = 1.727Å and 2.027Å, respectively. The comprehensive theoretical studies of compounds 6a and 6b is based on bond length, bond angles and energy gap HOMO-LUMO. In addition, the vibrational frequencies of optimized compounds 6a and 6b were examined through DFT/B3LYP/6+31G(d) basis set. Methods: In this research, synthesis of novel pyrimidiopyrazole derivatives calculated the computational studies to find suitable drug-receptor interactions and biological activity. Results and Discussion: The synthesized pyrimidiopyrazole derivative 6b exhibited high antitumor activity IC50 =12.6 μg/ml and interacted it with 4hdq synthase complex with ΔE=-4.5Kcal/mol and with short distance = 1.727Å and 2.027Å. Furthermore, the optimized compounds utilize Gaussian 09W. Conclusion: In the optimized pyrimidiopyrazole derivatives, 6b showed better antitumor activity HeG-2 against 5-flurouracil due to its energy and confirmed more potent of hydrogen bond interaction with protein pocket.