Generation of ligand-based pharmacophore model and virtual screening for identification of novel tubulin inhibitors with potent anticancer activity

Network-Based Methods and Their Applications in Drug Discovery

Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.

Nanoinformatics and Personalized Medicine: An Advanced Cumulative Approach for Cancer Management

BACKGROUND

Even though the battle against one of the deadliest diseases, cancer, has advanced remarkably in the last few decades and the survival rate has improved significantly; the search for an ultimate cure remains a utopia. Nanoinformatics, which is bioinformatics coupled with nanotechnology, endows many novel research opportunities in the preclinical and clinical development of personalized nanosized drug carriers in cancer therapy. Personalized nanomedicines serve as a promising treatment option for cancer owing to their noninvasiveness and their novel approach. Explicitly, the field of personalized medicine is expected to have an enormous impact soon because of its many advantages, namely its versatility to adapt a drug to a cohort of patients.

OBJECTIVE

The current review explains the application of this newly emerging field called nanoinformatics to the field of precision medicine. This review also recapitulates how nanoinformatics could hasten the development of personalized nanomedicine for cancer, which is undoubtedly the need of the hour.

CONCLUSION

This approach has been facilitated by a humongous impending field named Nanoinformatics. These breakthroughs and advances have provided insight into the future of personalized medicine. Imperatively, they have been enabling landmark research to merge all advances, creating nanosized particles that contain drugs targeting cell surface receptors and other potent molecules designed to kill cancerous cells. Nanoparticle- based medicine has been developing and has become a center of attention in recent years, focusing primely on proficient delivery systems for various chemotherapy drugs.

Structure-based molecular modeling in SAR analysis and lead optimization

In silico methods like molecular docking and pharmacophore modeling are established strategies in lead identification. Their successful application for finding new active molecules for a target is reported by a plethora of studies. However, once a potential lead is identified, lead optimization, with the focus on improving potency, selectivity, or pharmacokinetic parameters of a parent compound, is a much more complex task. Even though in silico molecular modeling methods could contribute a lot of time and cost-saving by rationally filtering synthetic optimization options, they are employed less widely in this stage of research. In this review, we highlight studies that have successfully used computer-aided SAR analysis in lead optimization and want to showcase sound methodology and easily accessible in silico tools for this purpose.

Discovering Anti-Cancer Drugs via Computational Methods

New drug discovery has been acknowledged as a complicated, expensive, time-consuming, and challenging project. It has been estimated that around 12 years and 2.7 billion USD, on average, are demanded for a new drug discovery via traditional drug development pipeline. How to reduce the research cost and speed up the development process of new drug discovery has become a challenging, urgent question for the pharmaceutical industry. Computer-aided drug discovery (CADD) has emerged as a powerful, and promising technology for faster, cheaper, and more effective drug design. Recently, the rapid growth of computational tools for drug discovery, including anticancer therapies, has exhibited a significant and outstanding impact on anticancer drug design, and has also provided fruitful insights into the area of cancer therapy. In this work, we discussed the different subareas of the computer-aided drug discovery process with a focus on anticancer drugs.

Computational Studies in Drug Design Against Cancer

Background:

The application of in silico tools in the development of anti cancer drugs.

Objective:

The summing of different computer aided drug design approaches that have been applied in the development of anti cancer drugs.

Methods:

Structure based, ligand based, hybrid protein-ligand pharmacophore methods, Homology modeling, molecular docking aids in different steps of drug discovery pipeline with considerable saving in time and expenditure. In silico tools also find applications in the domain of cancer drug development.

Results:

Structure-based pharmacophore modeling aided in the identification of PUMA inhibitors, structure based approach with high throughput screening for the development of Bcl-2 inhibitors, to derive the most relevant protein-protein interactions, anti mitotic agents; I-Kappa-B Kinase β (IKK- β) inhibitor, screening of new class of aromatase inhibitors that can be important targets in cancer therapy.

Conclusion:

Application of computational methods in the design of anti cancer drugs was found to be effective.

Theaflavin and epigallocatechin-3-gallate synergistically induce apoptosis through inhibition of PI3K/Akt signaling upon depolymerizing microtubules in HeLa cells

Theaflavin (TF) and epigallocatechin-3-gallate (EGCG) both have been reported previously as microtubule depolymerizing agents that also have anticancer effects on various cancer cell lines and in animal models. Here, we have applied TF and EGCG in combination on HeLa cells to investigate if they can potentiate each other to improve their anticancer effect in lower doses and the underlying mechanism. We found that TF and EGCG acted synergistically, in lower doses, to inhibit the growth of HeLa cells. We found the combination of 50 µg/mL TF and 20 µg/mL EGCG to be the most effective combination with a combination index of 0.28. The same combination caused larger accumulation of cells in the G ₂ /M phase of the cell cycle, potent mitochondrial membrane potential loss, and synergistic augmentation of apoptosis. We have shown that synergistic activity might be due to stronger microtubule depolymerization by simultaneous binding of TF and EGCG at different sites on tubulin: TF binds at vinblastine binding site on tubulin, and EGCG binds near colchicines binding site on tubulin. A detailed mechanistic analysis revealed that stronger microtubule depolymerization caused effective downregulation of PI3K/Akt signaling and potently induced mitochondrial apoptotic signals, which ultimately resulted in the apoptotic death of HeLa cells in a synergistic manner.

Design, synthesis, and biological evaluation of novel combretastatin A-4 thio derivatives as microtubule targeting agents

A series of novel combretastatin A-4 (CA-4) thio derivatives containing different molecular cores, namely α-phenylcinnamic acids (core 1), (Z)-stilbenes (core 2), 4,5-disubstituted oxazoles (core 3), and 4,5-disubstituted N-methylimidazoles (core 4), as cis-restricted analogues were designed and synthesized. They were selected with the use of a parallel virtual screening protocol including the generation of a virtual combinatorial library based on an elaborated synthesis protocol of CA-4 analogues. The selected compounds were evaluated for antiproliferative activity against a panel of six human cancer cell lines (A431, HeLa, MCF7, MDA-MB-231, A549 and SKOV) and two human non-cancer cell lines (HaCaT and CCD39Lu). Moreover, the effect of the test compounds on the inhibition of tubulin polymerization in vitro was estimated. In the series studied here, oxazole-bridged analogues exhibited the most potent antiproliferative activity. Compounds 23a, 23e, and 23i efficiently inhibited tubulin polymerization with IC₅₀ values of 0.86, 1.05, and 0.85 μM, respectively. Thio derivative 23i, when compared to its oxygen analogue 23j, showed a 5-fold higher inhibitory impact on tubulin polymerization. Compounds 23e and 23i, which showed both best cytotoxic and antitubulin activity, were further studied in terms of their effect on cell cycle distribution and proapoptotic activity. Compound 23e induced a statistically significant block of the cell cycle at the G2/M phase in A431, HaCaT, HeLa, MCF-7, MDA-MB-231, and SKOV-3 cells to an extent comparable to that observed in CA-4. In HeLa and SKOV-3 cells incubated with 23i, a concentration-dependent block of the G2/M phase was observed. The proapoptotic effect of 23e and 23i in A431, HaCaT, MCF-7, MDA-MB-231, and SKOV-3 was demonstrated with ELISA assay and double staining with Annexin V-FITC/PI. The results indicated that compound 23e and 23i may serve as novel lead compounds in research on more effective anticancer agents.

Application of computational methods for anticancer drug discovery, design, and optimization

Developing a novel drug is a complex, risky, expensive and time-consuming venture. It is estimated that the conventional drug discovery process ending with a new medicine ready for the market can take up to 15 years and more than a billion USD. Fortunately, this scenario has recently changed with the arrival of new approaches. Many novel technologies and methodologies have been developed to increase the efficiency of the drug discovery process, and computational methodologies have become a crucial component of many drug discovery programs. From hit identification to lead optimization, techniques such as ligand- or structure-based virtual screening are widely used in many discovery efforts. It is the case for designing potential anticancer drugs and drug candidates, where these computational approaches have had a major impact over the years and have provided fruitful insights into the field of cancer. In this paper, we review the concept of rational design presenting some of the most representative examples of molecules identified by means of it. Key principles are illustrated through case studies including specifically successful achievements in the field of anticancer drug design to demonstrate that research advances, with the aid of in silico drug design, have the potential to create novel anticancer drugs.

Application of computational methods for anticancer drug discovery, design, and optimization

Developing a novel drug is a complex, risky, expensive and time-consuming venture. It is estimated that the conventional drug discovery process ending with a new medicine ready for the market can take up to 15 years and more than a billion USD. Fortunately, this scenario has recently changed with the arrival of new approaches. Many novel technologies and methodologies have been developed to increase the efficiency of the drug discovery process, and computational methodologies have become a crucial component of many drug discovery programs. From hit identification to lead optimization, techniques such as ligand- or structure-based virtual screening are widely used in many discovery efforts. It is the case for designing potential anticancer drugs and drug candidates, where these computational approaches have had a major impact over the years and have provided fruitful insights into the field of cancer. In this paper, we review the concept of rational design presenting some of the most representative examples of molecules identified by means of it. Key principles are illustrated through case studies including specifically successful achievements in the field of anticancer drug design to demonstrate that research advances, with the aid of in silico drug design, have the potential to create novel anticancer drugs.

Discovery, design and synthesis of 6H-anthra[1,9-cd]isoxazol-6-one scaffold as G9a inhibitor through a combination of shape-based virtual screening and structure-based molecular modification

Protein lysine methyltransferase G9a is widely considered as an appealing antineoplastic target. Herein we present an integrated workflow combining shape-based virtual screening and structure-based molecular modification for the identification of novel G9a inhibitors. The shape-based similarity screening through ROCS overlay on the basis of the structure of UNC0638 was performed to identify CPUY074001 contained a 6H-anthra[1,9-cd]isoxazol-6-one scaffold as a hit. Analysis of the binding mode of CPUY074001 with G9a and 3D-QSAR results, two series compounds were designed and synthesized. The derivatives were confirmed to be active by in vitro assay and the SAR was explored by docking stimulations. Besides, several analogues showed acceptable anti-proliferative effects against several cancer cell lines. Among them, CPUY074020 displayed potent dual G9a inhibitory activity and anti-proliferative activity. Furthermore, CPUY074020 induced cell apoptosis in a dose-dependent manner and displayed a significant decrease in dimethylation of H3K9. Simultaneously, CPUY074020 showed reasonable in vivo PK properties. Altogether, our workflow supplied a high efficient strategy in the identification of novel G9a inhibitors. Compounds reported here can serve as promising leads for further study.

Pharmacophore modeling and in silico toxicity assessment of potential anticancer agents from African medicinal plants

Molecular modeling has been employed in the search for lead compounds of chemotherapy to fight cancer. In this study, pharmacophore models have been generated and validated for use in virtual screening protocols for eight known anticancer drug targets, including tyrosine kinase, protein kinase B β, cyclin-dependent kinase, protein farnesyltransferase, human protein kinase, glycogen synthase kinase, and indoleamine 2,3-dioxygenase 1. Pharmacophore models were validated through receiver operating characteristic and Güner–Henry scoring methods, indicating that several of the models generated could be useful for the identification of potential anticancer agents from natural product databases. The validated pharmacophore models were used as three-dimensional search queries for virtual screening of the newly developed AfroCancer database (~400 compounds from African medicinal plants), along with the Naturally Occurring Plant-based Anticancer Compound-Activity-Target dataset (comprising ~1,500 published naturally occurring plant-based compounds from around the world). Additionally, an in silico assessment of toxicity of the two datasets was carried out by the use of 88 toxicity end points predicted by the Lhasa’s expert knowledge-based system (Derek), showing that only an insignificant proportion of the promising anticancer agents would be likely showing high toxicity profiles. A diversity study of the two datasets, carried out using the analysis of principal components from the most important physicochemical properties often used to access drug-likeness of compound datasets, showed that the two datasets do not occupy the same chemical space.

Computationally Guided Identification of Novel Mycobacterium tuberculosis GlmU Inhibitory Leads, Their Optimization, and in Vitro Validation

Mycobacterium tuberculosis (Mtb) infections are causing serious health concerns worldwide. Antituberculosis drug resistance threatens the current therapies and causes further need to develop effective antituberculosis therapy. GlmU represents an interesting target for developing novel Mtb drug candidates. It is a bifunctional acetyltransferase/uridyltransferase enzyme that catalyzes the biosynthesis of UDP-N-acetyl-glucosamine (UDP-GlcNAc) from glucosamine-1-phosphate (GlcN-1-P). UDP-GlcNAc is a substrate for the biosynthesis of lipopolysaccharide and peptidoglycan that are constituents of the bacterial cell wall. In the current study, structure and ligand based computational models were developed and rationally applied to screen a drug-like compound repository of 20,000 compounds procured from ChemBridge DIVERSet database for the identification of probable inhibitors of Mtb GlmU. The in vitro evaluation of the in silico identified inhibitor candidates resulted in the identification of 15 inhibitory leads of this target. Literature search of these leads through SciFinder and their similarity analysis with the PubChem training data set (AID 1376) revealed the structural novelty of these hits with respect to Mtb GlmU. IC50 of the most potent identified inhibitory lead (5810599) was found to be 9.018 ± 0.04 μM. Molecular dynamics (MD) simulation of this inhibitory lead (5810599) in complex with protein affirms the stability of the lead within the binding pocket and also emphasizes on the key interactive residues for further designing. Binding site analysis of the acetyltransferase pocket with respect to the identified structural moieties provides a thorough analysis for carrying out the lead optimization studies.

Discovery of new Mycobacterium tuberculosis proteasome inhibitors using a knowledge-based computational screening approach

Mycobacterium tuberculosis bacteria cause deadly infections in patients [Corrected]. The rise of multidrug resistance associated with tuberculosis further makes the situation worse in treating the disease. M. tuberculosis proteasome is necessary for the pathogenesis of the bacterium validated as an anti-tubercular target, thus making it an attractive enzyme for designing Mtb inhibitors. In this study, a computational screening approach was applied to identify new proteasome inhibitor candidates from a library of 50,000 compounds. This chemical library was procured from the ChemBridge (20,000 compounds) and the ChemDiv (30,000 compounds) databases. After a detailed analysis of the computational screening results, 50 in silico hits were retrieved and tested in vitro finding 15 compounds with IC₅₀ values ranging from 35.32 to 64.15 μM on lysate. A structural analysis of these hits revealed that 14 of these compounds probably have non-covalent mode of binding to the target and have not reported for anti-tubercular or anti-proteasome activity. The binding interactions of all the 14 protein-inhibitor complexes were analyzed using molecular docking studies. Further, molecular dynamics simulations of the protein in complex with the two most promising hits were carried out so as to identify the key interactions and validate the structural stability.

Homology modeling of DFG-in FMS-like tyrosine kinase 3 (FLT3) and structure-based virtual screening for inhibitor identification

The inhibition of FMS-like tyrosine kinase 3 (FLT3) activity using small-molecule inhibitors has emerged as a target-based alternative to traditional chemotherapy for the treatment of acute myeloid leukemia (AML). In this study, we report the use of structure-based virtual screening (SBVS), a computer-aided drug design technique for the identification of new chemotypes for FLT3 inhibition. For this purpose, homology modeling (HM) of the DFG-in FLT3 structure was carried using two template structures, including PDB ID: 1RJB (DFG-out FLT3 kinase domain) and PDB ID: 3LCD (DFG-in CSF-1 kinase domain). The modeled structure was able to correctly identify known DFG-in (SU11248, CEP-701, and PKC-412) and DFG-out (sorafenib, ABT-869 and AC220) FLT3 inhibitors, in docking studies. The modeled structure was then used to carry out SBVS of an HTS library of 125,000 compounds. The top scoring 97 compounds were tested for FLT3 kinase inhibition, and two hits (BPR056, IC₅₀ = 2.3 and BPR080, IC₅₀ = 10.7 μM) were identified. Molecular dynamics simulation and density functional theory calculation suggest that BPR056 (MW: 325.32; cLogP: 2.48) interacted with FLT3 in a stable manner and could be chemically optimized to realize a drug-like lead in the future.

Dual binding site and selective acetylcholinesterase inhibitors derived from integrated pharmacophore models and sequential virtual screening

In this study, we have employed in silico methodology combining double pharmacophore based screening, molecular docking, and ADME/T filtering to identify dual binding site acetylcholinesterase inhibitors that can preferentially inhibit acetylcholinesterase and simultaneously inhibit the butyrylcholinesterase also but in the lesser extent than acetylcholinesterase. 3D-pharmacophore models of AChE and BuChE enzyme inhibitors have been developed from xanthostigmine derivatives through HypoGen and validated using test set, Fischer's randomization technique. The best acetylcholinesterase and butyrylcholinesterase inhibitors pharmacophore hypotheses Hypo1_A and Hypo1_B, with high correlation coefficient of 0.96 and 0.94, respectively, were used as 3D query for screening the Zinc database. The screened hits were then subjected to the ADME/T and molecular docking study to prioritise the compounds. Finally, 18 compounds were identified as potential leads against AChE enzyme, showing good predicted activities and promising ADME/T properties.

Discovery and Bioevaluation of Novel Pyrazolopyrimidine Analogs as Competitive Hsp90 Inhibitors Through Shape-Based Similarity Screening

Hsp90 as a promising therapeutic target for the treatment of cancer has received great attention. Many Hsp90 inhibitors such as BIIB021 and CUDC-305 have been in clinical. In this paper shape-based similarity screening through ROCS overlays on the basis of CUDC-305, BIIB021, PU-H71 and PU-3 were performed to discover HSP90 inhibitors. A set of 19 novel pyrazolopyrimidine analogues was identified and evaluated on enzyme level and cell-based level as Hsp90 inhibitors. The compound HDI4-04 with IC50 0.35 µM in the Hsp90 ATP hydrolysis assay exhibited potent cytotoxicity against five human cancer cell lines. Western blot analysis and Hsp70 luciferase reporter assay further confirmed that HDI4-04 targeted the Hsp90 protein folding machinery. And according to the biological assay, the SAR was discussed and summarized, which will guide us for further optimization of these compounds.

Oncogenic protein interfaces: small molecules, big challenges

Historically, targeting protein-protein interactions with small molecules was not thought possible because the corresponding interfaces were considered mostly flat and featureless and therefore 'undruggable'. Instead, such interactions were targeted with larger molecules, such as peptides and antibodies. However, the past decade has seen encouraging breakthroughs through the refinement of existing techniques and the development of new ones, together with the identification and exploitation of unexpected aspects of protein-protein interaction surfaces. In this Review, we describe some of the latest techniques to discover modulators of protein-protein interactions and how current drug discovery approaches have been adapted to successfully target these interfaces.

Computational methods in drug discovery

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.

Discovery and design of tricyclic scaffolds as protein kinase CK2 (CK2) inhibitors through a combination of shape-based virtual screening and structure-based molecular modification

Protein kinase CK2 (CK2), a ubiquitous serine/threonine protein kinase for hundreds of endogenous substrates, serves as an attractive anticancer target. One of its most potent inhibitors, CX-4945, has entered a phase I clinical trial. Herein we present an integrated workflow combining shape-based virtual screening for the identification of novel CK2 inhibitors. A shape-based model derived from CX-4945 was built, and the subsequent virtual screening led to the identification of several novel scaffolds with high shape similarity to that of CX-4945. Among them two tricyclic scaffolds named [1,2,4]triazolo[4,3-c]quinazolin and [1,2,4]triazolo[4,3-a]quinoxalin attracted us the most. Combining strictly chemical similarity analysis, a second-round shape-based screening was performed based on the two tricyclic scaffolds, leading to 28 derivatives. These compounds not only targeted CK2 with potent and dose-dependent activities but also showed acceptable antiproliferative effects against a series of cancer cell lines. Our workflow supplies a high efficient strategy in the identification of novel CK2 inhibitors. Compounds reported here can serve as ideal leads for further modifications.

Tubulin-interactive stilbene derivatives as anticancer agents

Microtubules are dynamic polymers that occur in eukaryotic cells and play important roles in cell division, motility, transport and signaling. They form during the process of polymerization of α- and β-tubulin dimers. Tubulin is a significant and heavily researched molecular target for anticancer drugs. Combretastatins are natural cis-stilbenes that exhibit cytotoxic properties in cultured cancer cells in vitro. Combretastatin A-4 (3′-hydroxy-3,4,4′, 5-tetramethoxy-cis-stilbene; CA-4) is a potent cytotoxic cis-stilbene that binds to β-tubulin at the colchicine-binding site and inhibits tubulin polymerization. The prodrug CA-4 phosphate is currently in clinical trials as a chemotherapeutic agent for cancer treatment. Numerous series of stilbene analogs have been studied in search of potent cytotoxic agents with the requisite tubulin-interactive properties. Microtubule-interfering agents include numerous CA-4 and transresveratrol analogs and other synthetic stilbene derivatives. Importantly, these agents are active in both tumor cells and immature endothelial cells of tumor blood vessels, where they inhibit the process of angiogenesis. Recently, computer-aided virtual screening was used to select potent tubulin-interactive compounds. This review covers the role of stilbene derivatives as a class of antitumor agents that act by targeting microtubule assembly dynamics. Additionally, we present the results of molecular modeling of their binding to specific sites on the α- and β-tubulin heterodimer. This has enabled the elucidation of the mechanism of stilbene cytotoxicity and is useful in the design of novel agents with improved anti-mitotic activity. Tubulin-interactive agents are believed to have the potential to play a significant role in the fight against cancer.

5-HT1A receptor pharmacophores to screen for off-target activity of α1-adrenoceptor antagonists

The α1-adrenoceptors (α1-ARs), in particular the α1A-AR subtype, are current therapeutic targets of choice for the treatment of urogenital conditions, such as benign prostatic hyperplasia (BPH). Due to the similarity between the transmembrane domains of the α1-AR subtypes, and the serotonin receptor subtype 1A (5-HT1A-R), currently used α1-AR subtype-selective drugs to treat BPH display considerable off-target affinity for the 5-HT1A-R, leading to side effects. We describe the construction and validation of pharmacophores for 5-HT1A-R agonists and antagonists. Through the structural diversity of the training sets used in their development, these pharmacophores define the properties of a compound needed to bind to 5-HT1A receptors. Using these and previously published pharmacophores in virtual screening and profiling, we have identified unique chemical compounds (hits) that fit the requirements to bind to our target, the α1A-AR, selectively over the off-target, the 5-HT1A-R. Selected hits have been obtained and their affinities for α1A-AR, α1B-AR and 5-HT1A-R determined in radioligand binding assays, using membrane preparations which contain human receptors expressed individually. Three of the tested hits demonstrate statistically significant selectivity for α1A-AR over 5-HT1A-R. All seven tested hits bind to α1A-AR, with two compounds displaying K i values below 1 μM, and a further two K i values of around 10 μM. The insights and knowledge gained through the development of the new 5-HT1A-R pharmacophores will greatly aid in the design and synthesis of derivatives of our lead compound, and allow the generation of more efficacious and selective ligands.

Discovery of novel tubulin inhibitors via structure-based hierarchical virtual screening

To discover novel tubulin inhibitors, we performed structure-based virtual screening against the colchicine binding pocket. In combination with a hierarchical docking and scoring procedure, the structural information of an additional subpocket in colchicine site was applied to filter out the undesired docking hits. This strategy automatically resulted in 63 candidates meeting the structural and energetic criteria from a screening library containing approximately 100,000 diverse druglike compounds. Among them, nine molecules were chosen for experimental validation, which all share the similar binding pose and contain an enriched scaffold bearing thiophene core. Encouragingly, five compounds are active in tubulin polymerization assay. The most potent inhibitor, 2-(2-fluorobenzamido)-3-carboxamide-4,5-dimethylthiophene, is structurally distinct to any known colchicine site binders and has higher ligand efficiency than colchicine. On the basis of its predicted binding pose, we systematically probed its binding characteristics by testing series of structural modifications. The obtained structure-activity relationship results are consistent with our binding model, and the inhibition activities of two analogues are improved by 2-fold. We expect that the novel structure discovered in the present study may serve as a starting point for developing tubulin inhibitors with improved efficacy and fewer side effects. We also expect that our hierarchical strategy may be generally applicable in structure-based virtual screening campaigns.

Molecular simulations of drug–receptor complexes in anticancer research

Molecular modeling and computer simulation techniques have matured significantly in recent years and proved their value in the study of drug–DNA, drug–DNA–protein, drug–protein and protein–protein interactions. Evolution in this area has gone hand-in-hand with an increased availability of structural data on biological macromolecules, major advances in molecular mechanics force fields and considerable improvements in computer technologies, most significantly processing speeds, multiprocessor programming and data-storage capacity. The information derived from molecular simulations of drug–receptor complexes can be used to extract structural and energetic information that is usually beyond current experimental possibilities, provide independent accounts of experimentally observed behavior, help in the interpretation of biochemical or pharmacological results, and open new avenues for research by posing novel relevant questions that can guide the design of new experiments. As drug-screening tools, ligand- and fragment-docking platforms stand out as powerful techniques that can provide candidate molecules for hit and lead development. This review provides an overall perspective of the main methods and focuses on some selected applications to both classical and novel anticancer targets.

Discovery of novel histamine H4 and serotonin transporter ligands using the topological feature tree descriptor

Ligand-based approaches are particularly important in the hit identification process of drug discovery when no structural information on the target is available. Pharmacophore descriptors that use a topological representation of the ligands are usually fast enough to screen large compound libraries effectively when seeking novel lead candidates. One example of this kind is the Feature Tree descriptor, a reduced graph representation implemented in the FTrees software. In this study, we tested the screening efficiency of FTrees by both retrospective and prospective screens using known histamine H4 antagonists and serotonin transporter (SERT) inhibitors as query molecules. Our results demonstrate that FTrees can effectively find actives. Particularly when combined with a subsequent 2D fingerprint-based diversity selection, FTrees was found to be extremely effective at discovering a diverse set of scaffolds. Prospective screening of our in-house compound deck provided several novel H4 and SERT ligands that could serve as suitable starting points for further optimization.

Software and resources for computational medicinal chemistry

Computer-aided drug design plays a vital role in drug discovery and development and has become an indispensable tool in the pharmaceutical industry. Computational medicinal chemists can take advantage of all kinds of software and resources in the computer-aided drug design field for the purposes of discovering and optimizing biologically active compounds. This article reviews software and other resources related to computer-aided drug design approaches, putting particular emphasis on structure-based drug design, ligand-based drug design, chemical databases and chemoinformatics tools.

Discovery of 7-hydroxy-6-methoxy-2-methyl-3-(3,4,5-trimethoxybenzoyl)benzo[b]furan (BNC105), a tubulin polymerization inhibitor with potent antiproliferative and tumor vascular disrupting properties

A structure-activity relationship (SAR) guided design of novel tubulin polymerization inhibitors has resulted in a series of benzo[b]furans with exceptional potency toward cancer cells and activated endothelial cells. The potency of early lead compounds has been substantially improved through the synergistic effect of introducing a conformational bias and additional hydrogen bond donor to the pharmacophore. Screening of a focused library of potent tubulin polymerization inhibitors for selectivity against cancer cells and activated endothelial cells over quiescent endothelial cells has afforded 7-hydroxy-6-methoxy-2-methyl-3-(3,4,5-trimethoxybenzoyl)benzo[b]furan (BNC105, 8) as a potent and selective antiproliferative. Because of poor solubility, 8 is administered as its disodium phosphate ester prodrug 9 (BNC105P), which is rapidly cleaved in vivo to return the active 8. 9 exhibits both superior vascular disrupting and tumor growth inhibitory properties compared with the benchmark agent combretastatin A-4 disodium phosphate 5 (CA4P).

3-Aryl-2-[1H-benzotriazol-1-yl]acrylonitriles: a novel class of potent tubulin inhibitors

During a screening for compounds that could act against Mycobacterium tuberculosis, a series of new cellular antiproliferative agents was identified. The most cytotoxic molecules were evaluated against a panel of human cell lines derived from hematological and solid human tumors. In particular, (E)-2-(1H-benzo[d] [1,2,3]triazol-1-yl)-3-(4-methoxyphenyl)acrylonitrile (1) was found to be of a potency comparable to etoposide and greater than 6-mercaptopurine in all cell lines tested. Accordingly, a synthesis of a new series of (E)-2-(5,6-dichloro-1H-benzo[d] [1,2,3]triazol-1-yl)-3-(4-R-phenyl)acrylonitriles was conducted in order to extend the studies of structure-activity relationship (SAR) for this class of molecules. With the aim to evaluate if 3-aryl-2-[1H-benzotriazol-1-yl]acrylonitriles were able to act like tubulin binding agents, the effects on cell cycle distribution of the most active compounds (1, 2a, 3 and 4) were analyzed in K562 cells. A detailed molecular modeling study of the putative binding mode of this series of compounds on tubulin is also reported.

Structure-based virtual screening of novel tubulin inhibitors and their characterization as anti-mitotic agents

Microtubule cytoskeletons are involved in many essential functions throughout the life cycle of cells, including transport of materials into cells, cell movement, and proper progression of cell division. Small compounds that can bind at the colchicine site of tubulin have drawn great attention because these agents can suppress or inhibit microtubule dynamics and tubulin polymerization. To find novel tubulin polymerization inhibitors as anti-mitotic agents, we performed a virtual screening study of the colchicine binding site on tubulin. Novel tubulin inhibitors were identified and characterized by their inhibitory activities on tubulin polymerization in vitro. The structural basis for the interaction of novel inhibitors with tubulin was investigated by molecular modeling, and we have proposed binding models for these hit compounds with tubulin. The proposed docking models were very similar to the binding pattern of colchicine or podophyllotoxin with tubulin. These new hit compound derivatives exerted growth inhibitory effects on the HL60 cell lines tested and exhibited strong cell cycle arrest at G2/M phase. Furthermore, these compounds induced apoptosis after cell cycle arrest. In this study, we show that the validated derivatives of compound 11 could serve as potent lead compounds for designing novel anti-cancer agents that target microtubules.