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Introduction: Parkinson's Disease (PD) is a neurodegenerative central nervous system (CNS) disorder characterized by dopaminergic neuron degeneration with consequent reduction in striatal dopamine (DA) levels that leads to motor symptoms. Catechol-O-methyltransferase (COMT, E.C 2.1.1.6) inactivates dopamine and other substrates bearing catechol through the methylation of a hydroxyl group. COMT inhibition can block metabolism of catecholamines including DA. Since the increase in DA bioavailability is dependent on the inhibition of DA metabolism at the periphery, the development of COMT inhibitors as adjuvants to levodopa/aromatic amino acid decarboxylase (AADC) inhibitor treatment improves the clinical benefits of PD symptomatic treatment significantly.Areas covered: This review focuses on the contribution of computational studies to develop novel COMT inhibitors as therapeutics of Parkinson's disease with substantially improved efficacy.Expert opinion: The increasing use of in silico methods and the development of new chemoinformatic tools in combination with the knowledge gained from the development of different inhibitors studied both in silico, in vitro and in vivo, could help solve a number of issues related to the shortcomings of currently marketed treatments. They can also aid to open new avenues for centrally acting COMT inhibitors, and perhaps irreversible inhibitors, to be tested for PD and other neurological diseases.

Autotaxin (ATX), a glycoprotein (~125 kDa) isolated as an autocrine motility factor from melanoma cells, belongs to a seven-membered family of ectonucleotide pyrophosphatase/phosphodiesterase (ENPP), and exhibits lysophospholipase D activity. ATX is responsible for the hydrolysis of lysophosphatidylcholine (LPC) to produce the bioactive lipid lysophosphatidic acid (LPA), which is upregulated in a variety of pathological inflammatory conditions, including fibrosis, cancer, liver toxicity and thrombosis. Given its role in human disease, the ATX-LPA axis is an interesting target for therapy, and the development of novel potent ATX inhibitors is of great importance. In the present work a novel class of ATX inhibitors, optically active derivatives of 2-pyrrolidinone and pyrrolidine heterocycles were synthesized. Some of them exhibited interesting in vitro activity, namely the hydroxamic acid 16 (IC50 700 nM) and the carboxylic acid 40b (IC50 800 nM), while the boronic acid derivatives 3k (IC50 50 nM), 3l (IC50 120 nM), 3 m (IC50 180 nM) and 21 (IC50 35 nM) were found to be potent inhibitors of ATX.

The NanoCommons (https://www.nanocommons.eu/) research e-infrastructure and NanoSolveIT (https://nanosolveit.eu/) projects are developing the knowledge management and nanoinformatics tools to support in silico nanosafety assessment (published in Scitech Europa www.scitecheuropa.eu)

Autotaxin (ATX) has become an attractive target with a huge pharmacological and pharmacochemical interest in LPA-related diseases and to date many small organic molecules have been explored as potential ATX inhibitors. As a useful aid in the various efforts of identifying novel effective ATX inhibitors, in silico methods can serve as an important and valuable tool. Especially, Virtual Screening (VS) has recently received increased attention due to the large datasets made available, the development of advanced VS techniques and the encouraging fact that VS has contributed to the discovery of several compounds that have either reached the market or entered clinical trials. Different techniques and workflows have been reported in literature with the goal to prioritize possible potent hits. In this review article several deployed virtual screening strategies for the identification of novel potent ATX inhibitors are described.

Anin silicodrug discovery pipeline for the virtual screening of plant-origin natural products (NPs) was developed to explore new direct inhibitors of TNF and its close relative receptor activator of nuclear factor kappa-B ligand (RANKL), both representing attractive therapeutic targets for many chronic inflammatory conditions. Direct TNF inhibition through identification of potent small molecules is a highly desired goal; however, it is often hampered by severe limitations. Our approach yielded a priority list of 15 NPs as potential direct TNF inhibitors that were subsequently testedin vitroagainst TNF and RANKL. We thus identified two potent direct inhibitors of TNF function with low micromolar IC50values and minimal toxicity even at high concentrations. Most importantly, one of them (A11) was proved to be a dual inhibitor of both TNF and RANKL. Extended molecular dynamics simulations with the fully automated EnalosMD suite rationalized the mode of action of the compounds at the molecular level. To our knowledge, these compounds constitute the first NP TNF inhibitors, one of which being the first NP small-molecule dual inhibitor of TNF and RANKL, and could serve as lead compounds for the development of novel treatments for inflammatory and autoimmune diseases.

In this chapter we present and discuss, with the aid of several representative case studies from drug discovery and computational toxicology, a new cheminformatics platform, Enalos Suite, that was developed with open source and freely available software. Enalos Suite ( http://enalossuite.novamechanics.com/ ) was designed and developed as a useful tool to address a variety of cheminformatics problems, given that it expedites tasks performed in predictive modeling and allows access, data mining and manipulation for multiple chemical databases (PubChem, UniChem, etc.). Enalos Suite was carefully designed to permit its extension and adjustment to the special field of interest of each user, including, for instance, nanoinformatics, biomedical, and other applications. To demonstrate the functionalities of Enalos Suite that are useful in different cheminformatics applications, we present indicative case studies that include the exploitation of chemical databases within a drug discovery project, the calculation of molecular descriptors, and finally the development of a predictive QSAR model validated according to OECD principles. We aspire that at the end of this chapter, the reader will capture the effectiveness of different functionalities included in the Enalos Suite that could be of significant value in a multitude of cheminformatics applications.

In this chapter we present and discuss, with the aid of several representative case studies from drug discovery and computational toxicology, a new cheminformatics platform, Enalos Suite, that was developed with open source and freely available software. Enalos Suite ( http://enalossuite.novamechanics.com/ ) was designed and developed as a useful tool to address a variety of cheminformatics problems, given that it expedites tasks performed in predictive modeling and allows access, data mining and manipulation for multiple chemical databases (PubChem, UniChem, etc.). Enalos Suite was carefully designed to permit its extension and adjustment to the special field of interest of each user, including, for instance, nanoinformatics, biomedical, and other applications. To demonstrate the functionalities of Enalos Suite that are useful in different cheminformatics applications, we present indicative case studies that include the exploitation of chemical databases within a drug discovery project, the calculation of molecular descriptors, and finally the development of a predictive QSAR model validated according to OECD principles. We aspire that at the end of this chapter, the reader will capture the effectiveness of different functionalities included in the Enalos Suite that could be of significant value in a multitude of cheminformatics applications.

The overexpression of Tumor Necrosis Factor (TNF) is directly related to the development of several autoimmune diseases, such as rheumatoid and psoriatic arthritis, inflammatory bowel disease, Crohn's disease, refractory asthma, and multiple sclerosis. Receptor Activator of Nuclear Factor Kappa- B Ligand (RANKL) belongs to the TNF family and is the primary mediator of osteoclast-induced bone resorption through interaction with its receptor RANK. The function of RANKL is physiologically inhibited by the action of osteoprotegerin (OPG), which is a decoy receptor that binds to RANKL and prevents the process of osteoclastogenesis. Malfunction among RANK/RANKL/OPG can also result in bone loss diseases, including postmenopausal osteoporosis, rheumatoid arthritis, bone metastasis and multiple myeloma. To disrupt the unwanted functions of TNF and RANKL, current attempts focus on blocking TNF and RANKL binding to their receptors. In this review, we present the research efforts toward the development of low-molecular-weight pharmaceuticals that directly block the detrimental actions of TNF and RANKL.

An in silico drug discovery pipeline for the virtual screening of plant-origin natural products (NPs) was developed to explore new direct inhibitors of TNF and its close relative receptor activator of nuclear factor kappa-B ligand (RANKL), both representing attractive therapeutic targets for many chronic inflammatory conditions. Direct TNF inhibition through identification of potent small molecules is a highly desired goal; however, it is often hampered by severe limitations. Our approach yielded a priority list of 15 NPs as potential direct TNF inhibitors that were subsequently tested in vitro against TNF and RANKL. We thus identified two potent direct inhibitors of TNF function with low micromolar IC50 values and minimal toxicity even at high concentrations. Most importantly, one of them (A11) was proved to be a dual inhibitor of both TNF and RANKL. Extended molecular dynamics simulations with the fully automated EnalosMD suite rationalized the mode of action of the compounds at the molecular level. To our knowledge, these compounds constitute the first NP TNF inhibitors, one of which being the first NP small-molecule dual inhibitor of TNF and RANKL, and could serve as lead compounds for the development of novel treatments for inflammatory and autoimmune diseases. © 2018 Melagraki, Ntougkos, Papadopoulou, Rinotas, Leonis, Douni, Afantitis and Kollias.

Autotaxin (ATX) catalyzes the hydrolysis of lysophosphatidylcholine (LPC) generating the lipid mediator lysophosphatidic acid (LPA). Both ATX and LPA are involved in various pathological inflammatory conditions, including fibrosis and cancer, and have attracted great interest as medicinal targets over the past decade. Thus, the development of novel potent ATX inhibitors is of great importance. We have developed a novel class of ATX inhibitors containing the zinc binding functionality of hydroxamic acid. Such novel hydroxamic acids that incorporate a non-natural δ-amino acid residue exhibit high in vitro inhibitory potency over ATX (IC 50 values 50-60 nM). Inhibitor 32, based on δ-norleucine, was tested for its efficacy in a mouse model of pulmonary inflammation and fibrosis induced by bleomycin and exhibited promising efficacy. The novel hydroxamic ATX inhibitors provide excellent tools for the study of the role of the enzyme and could contribute to the development of novel therapeutic agents for the treatment of fibrosis and other chronic inflammatory diseases. © 2018 American Chemical Society.

The increasing use of nanoparticles (NPs) in a wide range of consumer and industrial applications has necessitated significant effort to address the challenge of characterizing and quantifying the underlying nanostructure – biological response relationships to ensure that these novel materials can be exploited responsibly and safely. Such efforts demand reliable experimental data not only in terms of the biological dose-response, but also regarding the physicochemical properties of the NPs and their interaction with the biological environment. The latter has not been extensively studied, as a large surface to bind biological macromolecules is a unique feature of NPs that is not relevant for chemicals or pharmaceuticals, and thus only limited data have been reported in the literature quantifying the protein corona formed when NPs interact with a biological medium and linking this with NP cellular association/uptake. In this work we report the development of a predictive model for the assessment of the biological response (cellular association, which can include both internalized NPs and those attached to the cell surface) of surface-modified gold NPs, based on their physicochemical properties and protein corona fingerprints, utilizing a dataset of 105 unique NPs. Cellular association was chosen as the end-point for the original experimental study due to its relevance to inflammatory responses, biodistribution, and toxicity in vivo. The validated predictive model is freely available online through the Enalos Cloud Platform (http://enalos.insilicotox.com/NanoProteinCorona/) to be used as part of a regulatory or NP safe-by-design decision support system. This online tool will allow the virtual screening of NPs, based on a list of the significant NP descriptors, identifying those NPs that would warrant further toxicity testing on the basis of predicted NP cellular association.

In this chapter we present and discuss Enalos+ nodes designed and developed by NovaMechanics Ltd. for the open-source KNIME platform, as a useful aid when dealing with cheminformatics and nanoinformatics problems or medicinal applications. Enalos+ nodes facilitate tasks performed in molecular modeling and allow access, data mining, and manipulation for multiple chemical databases through the KNIME interface. Enalos+ nodes automate common procedures that greatly facilitate the rapid workflow prototyping within KNIME. Μethods and techniques that are included in Enalos+ nodes are presented in order to offer a deeper understanding of the theoretical background of the incorporated functionalities. An emphasis is given to demonstrate the usefulness of Enalos+ nodes in different cheminformatics applications by presenting four indicative case studies. Specifically, we present case studies that underline the value and the effectiveness of the nodes for molecular descriptors calculation and QSAR predictive model development. In addition, case studies are also presented demonstrating the benefits of the use of Enalos+ nodes for database exploitation within a drug discovery project.

A combination of the following computational methods: (i) molecular docking, (ii) 3-D Quantitative Structure Activity Relationship Comparative Molecular Field Analysis (3D-QSAR CoMFA), (iii) similarity search and (iv) virtual screening using PubChem database was applied to identify new anthranilic acid-based inhibitors of hepatitis C virus (HCV) replication. A number of known inhibitors were initially docked into the “Thumb Pocket 2” allosteric site of the crystal structure of the enzyme HCV RNA-dependent RNA polymerase (NS5B GT1b). Then, the CoMFA fields were generated through a receptor-based alignment of docking poses to build a validated and stable 3D-QSAR CoMFA model. The proposed model can be first utilized to get insight into the molecular features that promote bioactivity, and then within a virtual screening procedure, it can be used to estimate the activity of novel potential bioactive compounds prior to their synthesis and biological tests.

The overexpression of Tumor Necrosis Factor (TNF) is directly related to the development of several autoimmune diseases, such as rheumatoid and psoriatic arthritis, inflammatory bowel disease, Crohn's disease, refractory asthma, and multiple sclerosis. Receptor Activator of Nuclear Factor Kappa- B Ligand (RANKL) belongs to the TNF family and is the primary mediator of osteoclast-induced bone resorption through interaction with its receptor RANK. The function of RANKL is physiologically inhibited by the action of osteoprotegerin (OPG), which is a decoy receptor that binds to RANKL and prevents the process of osteoclastogenesis. Malfunction among RANK/RANKL/OPG can also result in bone loss diseases, including postmenopausal osteoporosis, rheumatoid arthritis, bone metastasis and multiple myeloma. To disrupt the unwanted functions of TNF and RANKL, current attempts focus on blocking TNF and RANKL binding to their receptors. In this review, we present the research efforts toward the development of low-molecular-weight pharmaceuticals that directly block the detrimental actions of TNF and RANKL.

We present an in silico drug discovery pipeline developed and applied for the identification and virtual screening of small-molecule Protein-Protein Interaction (PPI) compounds that act as dual inhibitors of TNF and RANKL through the trimerization interface. The cheminformatics part of the pipeline was developed by combining structure–based with ligand–based modeling using the largest available set of known TNF inhibitors in the literature (2481 small molecules). To facilitate virtual screening, the consensus predictive model was made freely available at: http://enalos.insilicotox.com/TNFPubChem/. We thus generated a priority list of nine small molecules as candidates for direct TNF function inhibition. In vitro evaluation of these compounds led to the selection of two small molecules that act as potent direct inhibitors of TNF function, with IC50 values comparable to those of a previously-described direct inhibitor (SPD304), but with significantly reduced toxicity. These molecules were also identified as RANKL inhibitors and validated in vitro with respect to this second functionality. Direct binding of the two compounds was confirmed both for TNF and RANKL, as well as their ability to inhibit the biologically-active trimer forms. Molecular dynamics calculations were also carried out for the two small molecules in each protein to offer additional insight into the interactions that govern TNF and RANKL complex formation. To our knowledge, these compounds, namely T8 and T23, constitute the second and third published examples of dual small-molecule direct function inhibitors of TNF and RANKL, and could serve as lead compounds for the development of novel treatments for inflammatory and autoimmune diseases., Author summary Developing drugs that disrupt protein-protein interactions (PPIs) is a difficult task in pharmaceutical research. The interaction between protein Tumor Necrosis Factor (TNF) and its receptors is implicated in several physiological functions and diseases, such as rheumatoid and psoriatic arthritis, Crohn’s disease, and multiple sclerosis. Despite their potency, current medications that block the interaction between TNF and its receptors are also associated with many adverse functions. Here, we employ comprehensive computational and experimental methods to discover novel small molecules that are direct inhibitors of TNF function. Functionality for RANKL, a second, clinically-relevant member of the TNF protein family, was also examined. Using a combination of an in silico drug discovery pipeline, which includes structure- and ligand-based modeling, and in vitro experiments, we identified compounds T8 and T23 as dual inhibitors of TNF and RANKL. These compounds present low toxicity and may be further optimized in drug design targeting TNF and RANKL to develop improved treatments for a range of inflammatory and autoimmune diseases.

Background TNF deregulation plays a critical role in the pathogenesis of a range of immune-mediated diseases. On the other hand, RANKL, a member of the TNF family, regulates osteoclast formation and bone resorption. Inhibitors of these trimeric molecules have been used to treat patients suffering from chronic inflammatory diseases and osteoporosis, respectively. Our aim has been to create an integrated pipeline which through comprehensive computational and experimental methods would lead to the discovery of small molecule dual inhibitors of TNF and RANKL. Materials and methods Structure and ligand–based modelling as well as molecular dynamics were applied for the virtual screening and identification of small-molecules that act as dual inhibitors of TNF and RANKL via their shared trimerization interface. The highest ranking compounds were then tested in vitro in the L929 cell assay to assess their ability to inhibit the cytotoxic effect of TNF. Additionally, TNF-TNFRI binding inhibition was tested through ELISA. The successful compounds were also evaluated ex vivo in mouse bone marrow cells stimulated with RANKL and M-CSF to inhibit RANKL-dependent osteoclast differentiation. The cytotoxic effects of the compounds were also assessed. Results A consensus predictive model based on several known TNF inhibitors generated a priority list of 9 small molecules as candidates for TNF function inhibition. The model was made freely available through the Enalos Cloud platform. In vitro evaluation of the compounds led to the selection of two small molecules (T8 and T23) that inhibit TNF function, with IC50s similar to a previously-described inhibitor (SPD304), albeit displaying reduced toxicity. Both compounds were found to significantly inhibit RANKL in inducing the formation of osteoclasts. Further screening of compounds using the same approach led to identification of three additional nontoxic inhibitors of TNF that act at a nanomolar range. Conclusion Five new small molecule inhibitors of TNF have been identified. Two of them (T8 and T23) have been already confirmed as RANKL inhibitors. The compounds are currently under in vivo evaluation and their further optimisation may contribute to the development of novel dual inhibitor therapeutics.

Chronic myelogenous leukemia (CML) is a myeloproliferative disorder, characterized, in most cases, by the presence of the Bcr-Abl fusion oncogene. Bcr-Abl is a constitutively active tyrosine kinase that is responsible for the malignant transformation. Targeting the Bcr-Abl kinase is an attractive treatment strategy for CML. First and second generation Bcr-Abl inhibitors have focused on targeting the ATP-binding domain of the kinase. Mutations in that region are relatively resistant to drug manipulation. Therefore, non-ATP-competitive agents have been recently developed and tested. In the present study, in an attempt to aid the design of new chemotypes with enhanced cytotoxicity against K562 cells, 3D pharmacophore models were generated and 3D-QSAR CoMFA and CoMSIA studies were carried out on the 33 novel Abl kinase inhibitors (E)-#945;-benzylthio chalcones synthesized by Reddy et al. A five-point pharmacophore with a hydrogen bond acceptor, two hydrophobic groups and two aromatic rings as pharmacophore features, and a statistically significant 3D-QSAR model with excellent predictive power were developed. The pharmacophore model was also used for alignment of the 33 compounds in a CoMFA/CoMSIA analysis. The contour maps of the fields of CoMFA and CoMSIA models were utilized to provide structural insight into how these molecules promote their toxicity. The possibility of using this model for the design of drugs for the treatment of#946;-thalassemia and sickle cell disease (SCD), since several Bcr-Abl inhibitors are able to promote erythroid differentiation and#947;-globin expression in CML cell lines and primary erythroid cells is discussed.

Rheumatoid arthritis (RA), Crohn's disease and multiple sclerosis are common diseases, which are directly related to interactions developed between a protein called the Tumour Necrosis Factor (TNF) and its receptors. Current drugs that effectively disrupt TNF-receptor associations also cause serious side effects. In this feature, we present a computational scheme that resulted in the discovery of two non-toxic compounds, which directly inhibit TNF function. Moreover, the polypharmacology of these compounds was demonstrated as they were also found to inhibit the function of RANKL, a member of the TNF protein family. Optimization of the compounds may lead to the development of new medications for inflammatory and autoimmune treatments. © Biochemical Society.

In the last few decades, nanotechnology has been deeply established into human's everyday life with a great number of applications in cosmetics, textiles, electronics, optics, medicine, and many more. Although nanotechnology applications are rapidly increasing, the toxicity of some nanomaterials to living organisms and the environment still remains unknown and needs to be explored. The traditional toxicological evaluation of nanoparticles with the wide range of types, shapes, and sizes often involves expensive and time-consuming procedures. An efficient and cheap alternative is the development and application of predictive computational models using Quantitative Nanostructure-Activity Relationship (QNAR) methods. Towards this goal, researchers are mainly focused on the adverse effects of metal oxides and carbon nanotubes, but to date, QNAR studies are rare mainly because of the limited number of available organized datasets. In this chapter, recent studies for predictive QNAR models for the risk assessment of nanomaterials are reported and the perspectives of computational nanotoxicology that deeply relies on the intense collaboration between experimental and computational scientists are discussed.

Significant progress has been made over the last 10 years towards understanding those characteristics of nanoscale particles which correlate with enhanced biological activity and/or toxicity, as the basis for development of predictive tools for risk assessment and safer-by-design strategies. However, there are still a number of disconnects in the nanosafety workflow that hamper rapid progress towards full understanding of nano-specific mechanisms of action and nanomaterials (NMs)-induced adverse outcome pathways. One such disconnect is between physico-chemical characteristics determined experimentally as part of routine NMs characterisation, and the ability to predict a NM’s uptake and impacts on biological systems based on its pristine physico-chemical characteristics. Identification of critical properties (physico-chemical descriptors) that confer the ability to induce harm in biological systems under the relevant exposure conditions is central, in order to enable both prediction of impacts from related NMs [via quantitative property-activity or structure-activity relationships (QPARs/QSARs)] and development of strategies to ensure that these features are avoided in NM production in the future (“safety by design”). For this purpose, we have launched the Enalos InSilico platform, which is dedicated to the dissemination of our developed in silico workflows for NM risk assessment. So far, two predictive models have been made available online. The first tool is a Quantitative Nanostructure-Activity Relationship (QNAR) model for the prediction of the cellular uptake of NMs in pancreatic cancer cells and the second is an online tool for in silico screening of iron oxide NMs with a predictive classification model for their toxicological assessment.

We present an in silico drug discovery pipeline developed and applied for the identification and virtual screening of small-molecule Protein-Protein Interaction (PPI) compounds that act as dual inhibitors of TNF and RANKL through the trimerization interface. The cheminformatics part of the pipeline was developed by combining structure–based with ligand–based modeling using the largest available set of known TNF inhibitors in the literature (2481 small molecules). To facilitate virtual screening, the consensus predictive model was made freely available at: http://enalos.insilicotox.com/TNFPubChem/. We thus generated a priority list of nine small molecules as candidates for direct TNF function inhibition. In vitro evaluation of these compounds led to the selection of two small molecules that act as potent direct inhibitors of TNF function, with IC50 values comparable to those of a previously-described direct inhibitor (SPD304), but with significantly reduced toxicity. These molecules were also identified as RANKL inhibitors and validated in vitro with respect to this second functionality. Direct binding of the two compounds was confirmed both for TNF and RANKL, as well as their ability to inhibit the biologically-active trimer forms. Molecular dynamics calculations were also carried out for the two small molecules in each protein to offer additional insight into the interactions that govern TNF and RANKL complex formation. To our knowledge, these compounds, namely T8 and T23, constitute the second and third published examples of dual small-molecule direct function inhibitors of TNF and RANKL, and could serve as lead compounds for the development of novel treatments for inflammatory and autoimmune diseases

Chronic myelogenous leukemia (CML) is a myeloproliferative disorder, characterized, in most cases, by the presence of the Bcr-Abl fusion oncogene. Bcr-Abl is a constitutively active tyrosine kinase that is responsible for the malignant transformation. Targeting the Bcr-Abl kinase is an attractive treatment strategy for CML. First and second generation Bcr-Abl inhibitors have focused on targeting the ATP-binding domain of the kinase. Mutations in that region are relatively resistant to drug manipulation. Therefore, non-ATP-competitive agents have been recently developed and tested. In the present study, in an attempt to aid the design of new chemotypes with enhanced cytotoxicity against K562 cells, 3D pharmacophore models were generated and 3D-QSAR CoMFA and CoMSIA studies were carried out on the 33 novel Abl kinase inhibitors (E)-α-benzylthio chalcones synthesized by Reddy et al. A five-point pharmacophore with a hydrogen bond acceptor, two hydrophobic groups and two aromatic rings as pharmacophore features, and a statistically significant 3D-QSAR model with excellent predictive power were developed. The pharmacophore model was also used for alignment of the 33 compounds in a CoMFA/CoMSIA analysis. The contour maps of the fields of CoMFA and CoMSIA models were utilized to provide structural insight into how these molecules promote their toxicity. The possibility of using this model for the design of drugs for the treatment of β-thalassemia and sickle cell disease (SCD), since several Bcr-Abl inhibitors are able to promote erythroid differentiation and γ-globin expression in CML cell lines and primary erythroid cells is discussed.

The Janus kinases (JAKs) play a pivotal role in cytokine receptor signaling pathways via activation of downstream signal transducers and activators of transcription (STAT) pathway. Intracellular pathways that include JAKs are critical to immune cell activation and pro-inflammatory cytokine production. Selective inhibitors of JAKs are potentially disease-modifying anti-inflammatory drugs for the treatment of rheumatoid arthritis (RA). Each of the four members of the JAK family plays an individual role in the oncogenesis of the immune system, and therefore, the development of potent and specific inhibitors for each member is needed. Although there is a high sequence homology and structural identity of JAK1 and JAK2, such as a very similar binding mode of inhibitors at the ATPbinding site of enzymes, obvious differences surrounding the JAK1 and JAK2 ATP-binding sites provide a platform for the rational design of JAK2- and JAK1-specific inhibitors. In the present study, a dataset of 33 compounds characterized by a common scaffold of 2-amino-[1,2,4]triazolo[1,5-α]pyridine with well-defined in vitro activity values was computationally explored. Most of the compounds included in the dataset had higher ligand efficiency against JAK2 than JAK1. To improve further the selectivity of these triazolopyridines, Common Pharmacophore Hypotheses (CPHs) were generated and 3D-QSAR studies were carried out on them, in order to comprehend on the molecular features responsible for their selectivity. The proposed computational approach was applied in order to perform an in silico database virtual screening study with the aim to discover novel potent and selective JAK2 inhibitors.

A series of novel isoxazole derivatives were efficiently synthesized through the adaptation/modification of an in situ synthetic procedure for pyrazoles. All novel compounds were tested against four different cell lines to evaluate their antiproliferative activity. Based on the Hela cells results of this study and previous work, a classification model to predict the anti-proliferative activity of isoxazole and pyrazole derivatives was developed. Random Forest modeling was used in view of the development of an accurate and reliable model that was subsequently validated. A virtual screening study was then proposed for the design of novel active derivatives. Compounds 9 and 11 demonstrated significant cytostatic activity; the fused isoxazole derivative 18 and the virtually proposed compound 2v, were proved at least 10 times more potent as compared to compound 9, with IC50 values near and below 1 μM. In conclusion, a new series of isoxazoles was exploited with some of them exhibiting promising cytostatic activities. Further studies on the substitution pattern of the isoxazole core can potentially provide compounds with cytostatic action at the nM scale. In this direction the in silico approach described herein can also be used to screen existing databases to identify derivatives with anticipated activity.

Engineered nanoparticles (ENPs) are being extensively used in a great variety of applications with a pace that is increasingly growing. The evaluation of the biological effects of ENPs is of utmost importance and for that experimental and most recently computational methods have been suggested. In an effort to computationally explore available datasets that will lead to ready-to-use applications we have developed and validated a QNAR model for the prediction of the cellular uptake of nanoparticles in pancreatic cancer cells. Our insilico workflow was made available online through the Enalos InSilicoNano platform (http://enalos.insilicotox.com/QNAR_PaCa2/), a web service based solely on open source and freely available software that was developed with the purpose of making our model available to the interested user wishing to generate evidence on potential biological effects in the decision making framework. This web service will facilitate the computer aided nanoparticle design as it can serve as a source of activity prediction for novel nano-structures. To demonstrate the usefulness of the web service we have exploited the whole PubChem database within a virtual screening framework and then used the Enalos InSilicoNano platform to identify novel potent nanoparticles from a prioritized list of compounds.

A linear quantitative structure activity relationship model is obtained using Multiple Linear Regression (MLR) analysis as applied to a series of 49 dipeptidyl aspartyl fluoromethylketone derivatives with inhibitory activity of the caspase enzyme. For the selection of the best descriptors, the elimination selection stepwise regression method is utilized. The accuracy of the proposed MLR model is illustrated using the following evaluation techniques: cross validation, validation through an external test set, and Y-randomization. Furthermore, the domain of applicability which indicates the area of reliable predictions is defined. © 2006 Wiley-VCH Verlag GmbH & Co. KGaA. 25 10 928 935 Cited By :25

A series of symmetrically bis-substituted imidazole analogs bearing at the N-1 and N-3 two biphenyl moieties ortho substituted either with tetrazole or carboxylate functional groups was designed based on docking studies and utilizing for the first time an extra hydrophobic binding cleft of AT1 receptor. The synthesized analogs were evaluated for their in vitro antagonistic activities (pA 2 values) and binding affinities (–logIC 50 values) to the Angiotensin II AT1 receptor. Among them, the potassium (–logIC 50 = 9.04) and the sodium (–logIC 50 = 8.54) salts of 4-butyl- N , N ′-bis{[2′-(2 H -tetrazol-5-yl)biphenyl-4-yl]methyl}imidazolium bromide ( 12a and 12b , respectively) as well as its free acid 11 (–logIC 50 = 9.46) and the 4-butyl-2-hydroxymethyl- N , N ′-bis{[2′-(2 H -tetrazol-5-yl)biphenyl-4-yl]methyl}imidazolium bromide ( 14 ) (–logIC 50 = 8.37, pA 2 = 8.58) showed high binding affinity to the AT1 receptor and high antagonistic activity (potency). The potency was similar or even superior to that of Losartan (–logIC 50 = 8.25, pA 2 = 8.25). On the contrary, 2-butyl- N , N ′-bis{[2′-[2 H -tetrazol-5-yl)]biphenyl-4-yl]methyl}imidazolium bromide ( 27 ) (–logIC 50 = 5.77) and 2-butyl-4-chloro-5-hydroxymethyl- N , N ′-bis{[2′-[2 H -tetrazol-5-yl)]biphenyl-4-yl]methyl}imidazolium bromide ( 30 ) (–logIC 50 = 6.38) displayed very low binding affinity indicating that the orientation of the n -butyl group is of primary importance. Docking studies of the representative highly active 12b clearly showed that this molecule has an extra hydrophobic binding feature compared to prototype drug Losartan and it fits to the extra hydrophobic cavity. These results may contribute to the discovery and development of a new class of biologically active molecules through bis-alkylation of the imidazole ring by a convenient and cost effective synthetic strategy.

In this work we explore the corrosion inhibition of steel in acidic medium for a diverse set of organic compounds by developing a KNIME workflow including the newly introduced Enalos KNIME nodes. We have integrated in a single database 186 corrosion inhibition data of steel in acidic medium including 55 organic inhibitors in different concentrations and investigated the structural characteristics that influence the corrosion inhibition effect. We introduce the custom made Enalos KNIME nodes that are made publicly available by Novamechanics Ltd, as key – nodes to develop robust and validated quantitative structure–property models (QSPRs). Tasks such as assessing the structural characteristics of compounds, validating the model and defining the domain of its applicability are easily addressed using the Enalos family nodes. We have concluded in an accurate kNN model that can reliably predict the corrosion inhibition of a given compound.