Ghemtio L.,University of Lorraine |
Ghemtio L.,University of Helsinki |
Perez-Nueno V.I.,University of Lorraine |
Perez-Nueno V.I.,French Institute for Research in Computer Science and Automation |
And 10 more authors.
Combinatorial Chemistry and High Throughput Screening | Year: 2012
Virtual screening (VS) is becoming an increasingly important approach for identifying and selecting biologically active molecules against specific pharmaceutically relevant targets. Compared to conventional high throughput screening techniques, in silico screening is fast and inexpensive, and is increasing in popularity in early-stage drug discovery endeavours. This paper reviews and discusses recent trends and developments in three-dimensional (3D) receptor-based and ligand-based VS methodologies. First, we describe the concept of accessible chemical space and its exploration. We then describe 3D structural ligand-based VS techniques, hybrid approaches, and new approaches to exploit additional knowledge that can now be found in large chemogenomic databases. We also briefly discuss some potential issues relating to pharmacokinetics, toxicity profiling, target identification and validation, inverse docking, scaffold-hopping and drug re-purposing. We propose that the best way to advance the state of the art in 3D VS is to integrate complementary strategies in a single drug discovery pipeline, rather than to focus only on theoretical or computational improvements of individual techniques. Two recent 3D VS case studies concerning the LXR-.. receptor and the CCR5/CXCR4 HIV co-receptors are presented as examples which implement some of the complementary methods and strategies that are reviewed here. © 2012 Bentham Science Publishers.
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.
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.
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.