Synthesis of tetramic and tetronic acids as beta-secretase inhibitors

The aspartic protease beta-secretase (BACE-1) is an attractive target for the therapy of Alzheimer's disease. The known inhibitors share a high analogy to the substrate peptide and, thus, display undesired pharmacological properties. Compact nonpeptidic lead structures are scarce. Here, we report the activities of tetronic and tetramic acids on BACE-1 inhibition. The compounds feature a low molecular weight and compact scaffold, which is accessible by solid-phase-supported diverse synthesis.     

In silico fragment-mapping method: a new tool for fragment-based/structure-based drug discovery

Here, we propose an in silico fragment-mapping method as a potential tool for fragment-based/structure-based drug discovery (FBDD/SBDD). For this method, we created a database named Canonical Subsite–Fragment DataBase (CSFDB) and developed a knowledge-based fragment-mapping program, Fsubsite. CSFDB consists of various pairs of subsite–fragments derived from X-ray crystal structures of known protein–ligand complexes. Using three-dimensional similarity-matching between subsites on one protein and another, Fsubsite compares the surface of a target protein with all subsites in CSFDB. When a local topography similar to the subsite is found on the surface, Fsubsite places a fragment combined with the subsite in CSFDB on the target protein. For validation purposes, we applied the method to the apo-structure of cyclin-dependent kinase 2 (CDK2) and identified four compounds containing three mapped fragments that existed in the list of known inhibitors of CDK2. Next, the utility of our fragment-mapping method for fragment-growing was examined on the complex structure of tRNA-guanine transglycosylase with a small ligand. Fsubsite mapped appropriate fragments on the same position as the binding ligand or in the vicinity of the ligand. Finally, a 3D-pharmacophore model was constructed from the fragments mapped on the apo-structure of heat shock protein 90-α (HSP90α). Then, 3D pharmacophore-based virtual screening was carried out using a commercially available compound database. The resultant hit compounds were very similar to a known ligand of HSP90α. As a result of these findings, this in silico fragment-mapping method seems to be a useful tool for computational FBDD and SBDD.     

In silico identification of noncompetitive inhibitors targeting an uncharacterized allosteric site of falcipain-2

Falcipain-2 (FP-2) is a Plasmodium falciparum hemoglobinase widely targeted in the search for antimalarials. FP-2 can be allosterically modulated by various noncompetitive inhibitors that have been serendipitously identified. Moreover, the crystal structures of two inhibitors bound to an allosteric site, termed site 6, of the homolog enzyme human cathepsin K (hCatK) suggest that the equivalent region in FP-2 might play a similar role. Here, we conduct the rational identification of FP-2 inhibitors through virtual screenings (VS) of compounds into several pocket-like conformations of site 6, sampled during molecular dynamics (MD) simulations of the free enzyme. Two noncompetitive inhibitors, ZINC03225317 and ZINC72290660, were confirmed using in vitro enzymatic assays and their poses into site 6 led to calculated binding free energies matching the experimental ones. Our results provide strong evidence about the allosteric inhibition of FP-2 through binding of small molecules to site 6, thus opening the way toward the discovery of new inhibitors against this enzyme.

     

In silico identification and in vitro antiviral validation of potential inhibitors against Chikungunya virus

Journal of Computer-Aided Molecular Design - The Chikungunya virus (CHIKV) has become endemic in the Africa, Asia and Indian subcontinent, with its continuous re-emergence causing a significant...     

In silico modeling of p450 substrates, inhibitors, activators, and inducers

Cytochrome P450 enzymes are the predominant mediators of phase I metabolism of exogenous small molecules. As a result of their extensive role in metabolism of xenobiotics, drug compounds, and endogenous compounds, as well as their wide tissue distribution, significant drug discovery resources are spent to avoid interacting with this class of enzymes. Here we review historical and recent in silico modeling of 7 cytochrome P450 enzymes of particular interest, specifically CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, and CYP3A4. For each we provide a brief biological background including known inhibitors, substrates, and inducers, as well as details of computational modeling efforts and advances in structural biology. We also provide similar details for 3 nuclear receptors known to regulate gene expression of these enzyme families.     

In silico drug repositioning of FDA-approved drugs to predict new inhibitors for alpha-synuclein aggregation

One of the hallmarks of Parkinson’s disease (PD), a long-term neurodegenerative syndrome, is the accumulation of alpha-synuclein (α-syn) fibrils. Despite numerous studies and efforts, inhibition of α-syn protein aggregation is still a challenge. To overcome this issue, we propose an in silico pharmacophore-based repositioning strategy, to find a pharmaceutical drug that, in addition to their defined role, can be used to prevent aggregation of the α-syn protein. Ligand-based pharmacophore modeling was developed and the best model was selected with validation parameters including 72 % sensitivity, 98 % specificity and goodness score about 0.7. The optimal model has three groups of hydrogen bond donor (HBD), three groups of hydrogen bond acceptor (HBA), and two aromatic rings (AR). The FDA-Approved reports in the ZINC15 database were screened with the pharmacophore model taken from inhibitor compounds. The model identified 22 hits, as promising candidate drugs for Parkinson's therapy. It is noteworthy that among these, 10 drugs have been reported to inhibition of α-syn aggregation or treat/reduce Parkinson's pathogenesis. This model was used to virtual screen ZINC, NCI databases, and natural products from the pomegranate. The results of this screen were filtered for their inability to cross the blood-brain barrier, poor oral bioavailability, etc. Finally, the selected compounds of two ZINC and NCI databases were combined and structurally clustered. Remained compounds were clustered in 28 different clusters, and the 17 compounds were introduced as final candidates.     

In silico structure-based design of enhanced peptide inhibitors targeting RNA polymerase PAN-PB1C interaction

Developing antivirals for influenza A virus (FluA) has become more challenging due to high range of antigenic mutation and increasing numbers of drug-resistant viruses. Finding a selective inhibitor to target highly conserved region of protein-protein interactions interface, thereby increasing its efficiency against drug resistant virus could be highly beneficial. In this study, we used in silico approach to derive FluAPep1 from highly conserved region, PA_N-PB1_C interface and generated 121 FluAPep1 analogues. Interestingly, we found that the FluAPep1 interaction region in the PA_N domain are highly conserved in many FluA subtypes. Especially, FluAPep1 targets two pandemic FluA strains, H1N1/avian/2009 and H3N2/Victoria/1975. All of these FluA subtypes PA_N domain (H1N1/H3N2CAN/H3N2VIC/H7N1/H7N2) were superimposed with PA_N domain from H17N10 and the calculated root mean standards deviations were less than 3 Å. FlexPepDock analysis revealed that FluAPep1 exhibited higher binding affinity (score -246.155) with the PA_N domain. In addition, around 86% of non-hot spot mutated peptides (FluAPep28-122) showed enhanced binding affinity with PA_N domain. ToxinPred analysis confirmed that designed peptides were non-toxic. Thus, FluAPep1 and its analogues has potential to be further developed into an antiviral treatment against FluA infection.     

Structural insight into exosite binding and discovery of novel exosite inhibitors of botulinum neurotoxin serotype A through in silico screening

Botulinum neurotoxin serotype A (BoNT/A) is the most lethal toxin among the Tier 1 Select Agents. Development of potent and selective small molecule inhibitors against BoNT/A zinc metalloprotease remains a challenging problem due to its exceptionally large substrate binding surface and conformational plasticity. The exosites of the catalytic domain of BoNT/A are intriguing alternative sites for small molecule intervention, but their suitability for inhibitor design remains largely unexplored. In this study, we employed two recently identified exosite inhibitors, D-chicoric acid and lomofungin, to probe the structural features of the exosites and molecular mechanisms of synergistic inhibition. The results showed that D-chicoric acid favors binding at the α-exosite, whereas lomofungin preferentially binds at the β-exosite by mimicking the substrate β-sheet binding interaction. Molecular dynamics simulations and binding interaction analysis of the exosite inhibitors with BoNT/A revealed key elements and hotspots that likely contribute to the inhibitor binding and synergistic inhibition. Finally, we performed database virtual screening for novel inhibitors of BoNT/A targeting the exosites. Hits C1 and C2 showed non-competitive inhibition and likely target the α- and β-exosites, respectively. The identified exosite inhibitors may provide novel candidates for structure-based development of therapeutics against BoNT/A intoxication.     

In silico prediction of drug toxicity

It is essential, in order to minimise expensive drug failures due to toxicity being found in late development or even in clinical trials, to determine potential toxicity problems as early as possible. In view of the large libraries of compounds now being handled by combinatorial chemistry and high-throughput screening, identification of putative toxicity is advisable even before synthesis. Thus the use of predictive toxicology is called for. A number of in silico approaches to toxicity prediction are discussed. Quantitative structure-activity relationships (QSARs), relating mostly to specific chemical classes, have long been used for this purpose, and exist for a wide range of toxicity endpoints. However, QSARs also exist for the prediction of toxicity of very diverse libraries, although often such QSARs are of the classification type; that is, they predict simply whether or not a compound is toxic, and do not give an indication of the level of toxicity. Examples are given of all of these. A number of expert systems are available for toxicity prediction, most of them covering a range of toxicity endpoints. Those discussed include TOPKAT, CASE, DEREK, HazardExpert, OncoLogic and COMPACT. Comparative tests of the ability of these systems to predict carcinogenicity show that improvement is still needed. The consensus approach is recommended, whereby the results from several prediction systems are pooled. It is simply amazing that we can formulate any kind of QSAR. The (desired activity) is only the starting point. The truly formidable problem is that of toxicity, especially the difficult long-term toxicities resulting from chronic usage'. (Hansch & Leo [1])     

In silico quest for stable phosphastannaallenes

A computational study at different levels of theory was performed for the not yet synthesized phosphastannaallenes >SnCP– in order to evaluate the strength of the SnC bond, the main postulated factor to stabilize such species, and the geometry in R₂SnCPR derivatives. The influence of the substituents with various electronic effects (H, Me, Ph, F, Cl, OMe, SiMe₃) at the Sn or P atoms of the SnCP unit on the SnC bond order was evaluated in the quest for a substituent that would stabilize the phosphastannaallenic unit. PC bond orders have also been calculated.     

In silico predictions of hERG channel blockers in drug discovery: from ligand-based and target-based approaches to systems chemical biology

The risk for cardiotoxic side effects represents a major problem in clinical studies of drug candidates and regulatory agencies have explicitly recommended that all new drug candidates should be tested for blockage of the human Ether-a-go-go Related-Gene (hERG) potassium channel. Indeed, several drugs with different therapeutic indications and recognized as hERG blockers were recently withdrawn due to the risk of QT prolongation, arrhythmia and Torsade de Pointes. In silico techniques can provide a priori knowledge of hERG blockers, thus reducing the costs associated with screening assays. Significant progress has been made in structure-based and ligand-based drug design and a number of models have been developed to predict hERG blockage. Although approaches such as homology modeling in combination with docking and molecular dynamics bring us closer to understand the drug-channel interactions whereas QSAR and classification models provide a faster assessment and detection of hERG-related drug toxicity, limitation on the applicability domain of the current models and integration of data from diverse in vitro approaches are still issues to challenge. Therefore, this review will emphasize on current methods to predict hERG blockers and the need of consistent data to improve the quality and performance of the in silico models. Finally, integration of network-based analysis on drugs inducing potentially long-QT syndrome and arrhythmia will be discussed as a new perspective for a better understanding of the drug responses in systems chemical biology.     

In silico drug design and molecular docking of novel amidophosphonates and sulfamidophosphonates as inhibitors of urokinase-type plasminogen activator

Cancer is one of the most serious health problems worldwide, affecting individuals from different sexes, ages, and races. However, the most frequent cancer types in the world are lung, prostate, stomach, colorectal, and esophagus in men; and breast, lung, stomach, colorectal and cervical in women. Currently, the search for new active substances used in oral targeted therapies are legitimate and opens up the possibility of an "ambulatory shift" in cancer treatment. In order to design anti-tumor drug candidates endowed with oral bioavailability, we studied trough an in silico approach the oral bioavailability of newly synthesized biomolecules; α-sulfamidophosphonates and α-amidophosphonates as well as their mechanism of action on the new target urokinase-type plasminogen activator (uPA). The studied compounds have been found to meet the five criteria of. Lipinski's rule. The Osiris, Molinspiration and SWISS/ADME calculations related to the compounds (1d, 2a) have shown that these compounds could be good candidates for interacting with the different targets, they have convincing characteristics in relation to the standard drug used. It can be concluded that these compounds are biologically important and possessing molecular properties desirable for being a drug candidate for oral use.The molecular docking results of the studied compounds revealed a good ligand-target interactions, the compounds (1d, 2a) presented a possibility of interacting as an inhibitor of the anticancer target: urokinase-type plasminogen activator (uPA).     

In silico design,synthesis and activity of potential drug-like chrysin scaffold-derived selective EGFR inhibitors as anticancer agents

Background & objectiveEpidermal growth factor receptor (EGFR) signaling pathway is one of the promising and well-established targets for anticancer therapy. The objective of the present study was to identify new EGFR inhibitors using ligand and structure-based drug designing methods, followed by a synthesis of selected inhibitors and evaluation of their activity.MethodsA series of C-7-hydroxyproton substituted chrysin derivatives were virtually drawn to generate a small compound library that was screened using 3D QSAR model created from forty-two known EGFR tyrosine kinase inhibitors. Next, the obtained hits with fitness score ≥ 1.0 were subjected to molecular docking analysis. Based on the predicted activity and XP glide score, three EGFR inhibitors were synthesized and characterized using ¹H-NMR, ¹³C-NMR and MS. Finally, comparative in vitro investigation of the biological activity of synthesized inhibitors was performed with that of the parent molecule, chrysin.ResultsThe data depicted a 3.2–fold enhanced cytotoxicity of chrysin derivative, CHM-04 against breast cancer cells as compared with chrysin as well as its binding with EGFR protein. Furthermore, the biological activity of CHM-04 was comparable to the standard EGFR inhibitor, AG1478 in increasing apoptosis and decreasing the migratory potential of triple-negative breast cancer cells as well as significantly lowering the mammosphere forming ability of breast cancer stem cells.ConclusionThe present study suggests CHM-04, an EGFR inhibitor possessing drug-like properties as a plausible therapeutic candidate against breast cancer.     

An in silico ensemble method for lead discovery: decision forest

Recent progress in combinatorial chemistry and parallel synthesis has radically changed the approach to drug discovery in the pharmaceutical industry. At present, thousands of compounds can be made in a short period, creating a need for fast and effective in silico methods to select the most promising lead candidates. Decision forest is a novel pattern recognition method, which combines the results of multiple distinct but comparable decision tree models to reach a consensus prediction. In this article, a decision forest model was developed using a structurally diverse training data set containing 232 compounds whose estrogen receptor binding activity was tested at the U.S. Food and Drug Administration (FDA)'s National Center for Toxicological Research (NCTR). The model was subsequently validated using a test data set of 463 compounds selected from the literature, and then applied to a large data set with 57,145 compounds as a screening example. The results show that the decision forest method is a fast, reliable and effective in silico approach, which could be useful in drug discovery.     

An in silico ensemble method for lead discovery: decision forest

Recent progress in combinatorial chemistry and parallel synthesis has radically changed the approach to drug discovery in the pharmaceutical industry. At present, thousands of compounds can be made in a short period, creating a need for fast and effective in silico methods to select the most promising lead candidates. Decision forest is a novel pattern recognition method, which combines the results of multiple distinct but comparable decision tree models to reach a consensus prediction. In this article, a decision forest model was developed using a structurally diverse training data set containing 232 compounds whose estrogen receptor binding activity was tested at the U.S. Food and Drug Administration (FDA)'s National Center for Toxicological Research (NCTR). The model was subsequently validated using a test data set of 463 compounds selected from the literature, and then applied to a large data set with 57,145 compounds as a screening example. The results show that the decision forest method is a fast, reliable and effective in silico approach, which could be useful in drug discovery.