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Karaboga A.S.,Harmonic Pharma | Planesas J.M.,Ramon Llull University | Petronin F.,Harmonic Pharma | Teixido J.,Ramon Llull University | And 3 more authors.
Journal of Chemical Information and Modeling | Year: 2013

HIV infection is initiated by fusion of the virus with the target cell through binding of the viral gp120 protein with the CD4 cell surface receptor protein and the CXCR4 or CCR5 coreceptors. There is currently considerable interest in developing novel ligands that can modulate the conformations of these coreceptors and, hence, ultimately block virus-cell fusion. Herein, we present a highly specific and sensitive pharmacophore model for identifying CXCR4 antagonists that could potentially serve as HIV entry inhibitors. Its performance was compared with docking and shape-matching virtual screening approaches using 3OE6 CXCR4 crystal structure and high-affinity ligands as query molecules, respectively. The performance of these methods was compared by virtually screening a library assembled by us, consisting of 228 high affinity known CXCR4 inhibitors from 20 different chemotype families and 4696 similar presumed inactive molecules. The area under the ROC plot (AUC), enrichment factors, and diversity of the resulting virtual hit lists was analyzed. Results show that our pharmacophore model achieves the highest VS performance among all the docking and shape-based scoring functions used. Its high selectivity and sensitivity makes our pharmacophore a very good filter for identifying CXCR4 antagonists. © 2013 American Chemical Society.


Perez-Nueno V.I.,Harmonic Pharma | Souchet M.,Harmonic Pharma | Karaboga A.S.,Harmonic Pharma | Ritchie D.W.,French Institute for Research in Computer Science and Automation
Journal of Chemical Information and Modeling | Year: 2015

The in silico prediction of unwanted side effects (SEs) caused by the promiscuous behavior of drugs and their targets is highly relevant to the pharmaceutical industry. Considerable effort is now being put into computational and experimental screening of several suspected off-target proteins in the hope that SEs might be identified early, before the cost associated with developing a drug candidate rises steeply. Following this need, we present a new method called GESSE to predict potential SEs of drugs from their physicochemical properties (three-dimensional shape plus chemistry) and to target protein data extracted from predicted drug-target relationships. The GESSE approach uses a canonical correlation analysis of the full drug-target and drug-SE matrices, and it then calculates a probability that each drug in the resulting drug-target matrix will have a given SE using a Bayesian discriminant analysis (DA) technique. The performance of GESSE is quantified using retrospective (external database) analysis and literature examples by means of area under the ROC curve analysis, "top hit rates", misclassification rates, and a χ2 independence test. Overall, the robust and very promising retrospective statistics obtained and the many SE predictions that have experimental corroboration demonstrate that GESSE can successfully predict potential drug-SE profiles of candidate drug compounds from their predicted drug-target relationships. © 2015 American Chemical Society.


Perez-Nueno V.I.,Harmonic Pharma
Expert Opinion on Drug Discovery | Year: 2015

Over the past three decades, the predominant paradigm in drug discovery was designing selective ligands for a specific target to avoid unwanted side effects. However, in the last 5 years, the aim has shifted to take into account the biological network in which they interact. Quantitative and Systems Pharmacology (QSP) is a new paradigm that aims to understand how drugs modulate cellular networks in space and time, in order to predict drug targets and their role in human pathophysiology.Areas covered: This review discusses existing computational and experimental QSP approaches such as polypharmacology techniques combined with systems biology information and considers the use of new tools and ideas in a wider systems-level context in order to design new drugs with improved efficacy and fewer unwanted off-target effects.Expert opinion: The use of network biology produces valuable information such as new indications for approved drugs, drug-drug interactions, proteins-drug side effects and pathways-gene associations. However, we are still far from the aim of QSP, both because of the huge effort needed to model precisely biological network models and the limited accuracy that we are able to reach with those. Hence, moving from one molecule for one target to give one therapeutic effect to the big systems-based picture seems obvious moving forward although whether our current tools are sufficient for such a step is still under debate. © 2015 Taylor and Francis.


Karaboga A.S.,CNRS Lorraine Research Laboratory in Informatics and its Applications | Petronin F.,CNRS Lorraine Research Laboratory in Informatics and its Applications | Marchetti G.,CNRS Lorraine Research Laboratory in Informatics and its Applications | Souchet M.,Harmonic Pharma | Maigret B.,CNRS Lorraine Research Laboratory in Informatics and its Applications
Journal of Molecular Graphics and Modelling | Year: 2013

Since 3D molecular shape is an important determinant of biological activity, designing accurate 3D molecular representations is still of high interest. Several chemoinformatic approaches have been developed to try to describe accurate molecular shapes. Here, we present a novel 3D molecular description, namely harmonic pharma chemistry coefficient (HPCC), combining a ligand-centric pharmacophoric description projected onto a spherical harmonic based shape of a ligand. The performance of HPCC was evaluated by comparison to the standard ROCS software in a ligand-based virtual screening (VS) approach using the publicly available directory of useful decoys (DUD) data set comprising over 100,000 compounds distributed across 40 protein targets. Our results were analyzed using commonly reported statistics such as the area under the curve (AUC) and normalized sum of logarithms of ranks (NSLR) metrics. Overall, our HPCC 3D method is globally as efficient as the state-of-the-art ROCS software in terms of enrichment and slightly better for more than half of the DUD targets. Since it is largely admitted that VS results depend strongly on the nature of the protein families, we believe that the present HPCC solution is of interest over the current ligand-based VS methods. © 2013 Elsevier Inc. All rights reserved.


Genet C.,CNRS Laboratory of Design and Application of Bioactive Molecules | Genet C.,Novalix Inc. | Strehle A.,University of Strasbourg | Schmidt C.,PhytoDia | And 7 more authors.
Journal of Medicinal Chemistry | Year: 2010

We describe here the biological screening of a collection of natural occurring triterpenoids against the G protein-coupled receptor TGR5, known to be activated by bile acids and which mediates some important cell functions. This work revealed that betulinic (1), oleanolic (2), and ursolic acid (3) exhibited TGR5 agonist activity in a selective manner compared to bile acids, which also activated FXR, the nuclear bile acid receptor. The most potent natural triterpenoid betulinic acid was chosen as a reference compound for an SAR study. Hemisyntheses were performed on the betulinic acid scaffold, and we focused on structural modifications of the C-3 alcohol, the C-17 carboxylic acid, and the C-20 alkene. In particular, structural variations around the C-3 position gave rise to major improvements of potency exemplified with derivatives 18 dia 2 (RG-239) and 19 dia 2. The best derivative was tested in vitro and in vivo, and its biological profile is discussed. © 2009 American Chemical Society.


Perez-Nueno V.I.,Harmonic Pharma | Karaboga A.S.,Harmonic Pharma | Souchet M.,Harmonic Pharma | Ritchie D.W.,French Institute for Research in Computer Science and Automation
Journal of Chemical Information and Modeling | Year: 2014

Polypharmacology is now recognized as an increasingly important aspect of drug design. We previously introduced the Gaussian ensemble screening (GES) approach to predict relationships between drug classes rapidly without requiring thousands of bootstrap comparisons as in current promiscuity prediction approaches. Here we present the GES "computational polypharmacology fingerprint" (CPF), the first target fingerprint to encode drug promiscuity information. The similarity between the 3D shapes and chemical properties of ligands is calculated using PARAFIT and our HPCC programs to give a consensus shape-plus-chemistry ligand similarity score, and ligand promiscuity for a given set of targets is quantified using the GES fingerprints. To demonstrate our approach, we calculated the CPFs for a set of ligands from DrugBank that are related to some 800 targets. The performance of the approach was measured by comparing our CPF with an in-house "experimental polypharmacology fingerprint" (EPF) built using publicly available experimental data for the targets that comprise the fingerprint. Overall, the GES CPF gives very low fall-out while still giving high precision. We present examples of polypharmacology relationships predicted by our approach that have been experimentally validated. This demonstrates that our CPF approach can successfully describe drug-target relationships and can serve as a novel drug repurposing method for proposing new targets for preclinical compounds and clinical drug candidates. © 2014 American Chemical Society.


Patent
Institute Curie and Harmonic Pharma | Date: 2015-10-07

The present invention relates to a new class of cephalosporin derivatives of formula (I), notably having CXCR4 receptor antagonist effect, useful as a therapeutic agent for treating cancer, in particular for treating breast cancer, lung cancer and uveal melanoma. The invention further relates to a pharmaceutical composition comprising a compound of formula (I) and an additional antitumor drug for treating cancer.


PubMed | Harmonic Pharma and French Institute for Research in Computer Science and Automation
Type: Journal Article | Journal: Journal of chemical information and modeling | Year: 2015

The in silico prediction of unwanted side effects (SEs) caused by the promiscuous behavior of drugs and their targets is highly relevant to the pharmaceutical industry. Considerable effort is now being put into computational and experimental screening of several suspected off-target proteins in the hope that SEs might be identified early, before the cost associated with developing a drug candidate rises steeply. Following this need, we present a new method called GESSE to predict potential SEs of drugs from their physicochemical properties (three-dimensional shape plus chemistry) and to target protein data extracted from predicted drug-target relationships. The GESSE approach uses a canonical correlation analysis of the full drug-target and drug-SE matrices, and it then calculates a probability that each drug in the resulting drug-target matrix will have a given SE using a Bayesian discriminant analysis (DA) technique. The performance of GESSE is quantified using retrospective (external database) analysis and literature examples by means of area under the ROC curve analysis, top hit rates, misclassification rates, and a (2) independence test. Overall, the robust and very promising retrospective statistics obtained and the many SE predictions that have experimental corroboration demonstrate that GESSE can successfully predict potential drug-SE profiles of candidate drug compounds from their predicted drug-target relationships.


PubMed | Harmonic Pharma
Type: Journal Article | Journal: Journal of chemical information and modeling | Year: 2014

Polypharmacology is now recognized as an increasingly important aspect of drug design. We previously introduced the Gaussian ensemble screening (GES) approach to predict relationships between drug classes rapidly without requiring thousands of bootstrap comparisons as in current promiscuity prediction approaches. Here we present the GES computational polypharmacology fingerprint (CPF), the first target fingerprint to encode drug promiscuity information. The similarity between the 3D shapes and chemical properties of ligands is calculated using PARAFIT and our HPCC programs to give a consensus shape-plus-chemistry ligand similarity score, and ligand promiscuity for a given set of targets is quantified using the GES fingerprints. To demonstrate our approach, we calculated the CPFs for a set of ligands from DrugBank that are related to some 800 targets. The performance of the approach was measured by comparing our CPF with an in-house experimental polypharmacology fingerprint (EPF) built using publicly available experimental data for the targets that comprise the fingerprint. Overall, the GES CPF gives very low fall-out while still giving high precision. We present examples of polypharmacology relationships predicted by our approach that have been experimentally validated. This demonstrates that our CPF approach can successfully describe drug-target relationships and can serve as a novel drug repurposing method for proposing new targets for preclinical compounds and clinical drug candidates.


The present invention relates to a compound for use in the prevention and/or treatment of a disease selected from a neurodegenerative disease, especially Alzheimer disease, and a disease involving an activation of phosphodiesterase-4 (PDE4), wherein said compound is of general formula (I) or a pharmaceutically acceptable salt or solvate thereof:Y represents O or S;R1, R2 and R3 may be the same or different and represent a hydrogen atom, a halogen atom, hydroxyl, nitro, cyano, amino, amido, carbamate, alkyl, alkenyl, alkynyl, alkoxy, heterocyclic, carbocyclic, alkylthio, carboxy, or alkoxycarbonyl; R4 represent a hydrogen atom; R5, R6, R7 and R8 may be the same or different and represent a hydrogen atom, halogen atom, hydroxyl, nitro, amino, alkyl, alkoxy, alkylthio, or trifluoromethyl; R9 and R10 may be the same or different and represent a hydrogen atom, alkyl, alkylcarbonyl, heterocyclic, carbocyclic, ONO_(2), or OSO_(2)R wherein R is hydroxyl, alkyl, carbocyclic; and R11 represents an alkyl, alkenyl, alkynyl, alkoxy, akoxycarbonyl, amino, amido, heterocyclic, carbocyclic, or optionally substituted azolyl.

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