Ismail Ijjaali

In Silico Classification of hERG Channel Blockers: a Knowledge-Based Strategy

The blockage of the hERG potassium channel by a wide number of diverse compounds has become a major pharmacological safety concern as it can lead to sudden cardiac death. In silico models can be potent tools to screen out potential hERG blockers as early as possible during the drug‐discovery process. In this study, predictive models developed using the recursive partitioning method and created using diverse datasets from 203 molecules tested on the hERG channel are described. The first model was built with hERG compounds grouped into two classes, with a separation limit set at an IC50 value of 1 μm, and reaches an overall accuracy of 81 %. The misclassification of molecules having a range of activity between 1 and 10 μM led to the generation of a tri‐class model able to correctly classify high, moderate, and weak hERG blockers with an overall accuracy of 90 %. Another model, constructed with the high and weak hERG‐blocker categories, successfully increases the accuracy to 96 %. The results reported herein indicate that a combination of precise, knowledge management resources and powerful modeling tools are invaluable to assessing potential cardiotoxic side effects related to hERG blockage.

Assessing the chemical diversity of an hsp90 database

The 90-kDa heat shock protein (hsp90) has emerged as a new, promising target for cancer drug discovery. With the simultaneous disruption of a large range of oncogenic pathways, hsp90 inhibition results in either cytostasis or cell death. Diverse inhibitors of this molecular chaperone are currently under intensive study, and several have reached clinical trials. In the present work, patented and published structure-activity relationships on hsp90 inhibitors were organised in a database format that associates chemical structures with their biological activities. This hsp90 database contains 814 unique structures and, to our knowledge, is the most complete ever reported. With the aim to provide a general overview and evaluation of the chemical diversity of the ligands included in the dataset, a two-dimensional analysis was performed. A set of twenty-five topological molecular descriptors was calculated, allowing the emphasis of those that have higher importance for hsp90 active compounds, and for the three chemical scaffold families, geldanamycins, purines and pyrazole-isoxazoles. We have used a principal-component analysis (PCA) computational approach to analyse the 2D descriptor space of active and non-active hsp90 ligands. Furthermore, a fragment-based study highlighted the most frequently moieties represented in the active purine and pyrazole-isoxazole derivatives that are likely to be responsible for the observed biological activities.

Drug repositioning using in silico compound profiling

BACKGROUND Drug repositioning is a current strategy to find new uses for existing drugs, patented or not, and for late-stage candidates that failed for lack of efficacy. RESULTS In silico profiling of several marketed drugs (methadone, rapamycin, saquinavir and telmisartan) was performed, exploiting a vast amount of published information. Similar compounds were assessed in terms of target-activity profiles for major drug-target families. In silico profiles were visualized within an interactive heat map and detailed analysis was performed associated with the accessible current knowledge. CONCLUSION Based on a basic principle assuming that similar molecules share similar target activity, new potential targets and, therefore, opportunities of potential new indications have been identified and discussed.

Virtual screening for cytochromes p450: successes of machine learning filters.

Cytochromes P450 (CYPs) are crucial targets when predicting the ADME properties (absorption, distribution, metabolism, and excretion) of drugs in development. Particularly, CYPs mediated drug-drug interactions are responsible for major failures in the drug design process. Accurate and robust screening filters are thus needed to predict interactions of potent compounds with CYPs as early as possible in the process. In recent years, more and more 3D structures of various CYP isoforms have been solved, opening the gate of accurate structure-based studies of interactions. Nevertheless, the ligand-based approach still remains popular. This success can be explained by the growing number of available data and the satisfying performances of existing machine learning (ML) methods. The aim of this contribution is to give an overview of the recent achievements in ML applications to CYP datasets. Particularly, popular methods such as support vector machine, decision trees, artificial neural networks, k-nearest neighbors, and partial least squares will be compared as well as the quality of the datasets and the descriptors used. Consensus of different methods will also be discussed. Often reaching 90% of accuracy, the models will be analyzed to highlight the key descriptors permitting the good prediction of CYPs binding.

Ligand-based virtual screening to identify new T-type calcium channel blockers.

T-type calcium channels are involved in the generation of rhythmical firing patterns in the mammalian central nervous system and in various pathological alterations of neuronal excitability such as in epilepsy or neuropathic pain. In the search for new T-type calcium channel blockers that would help to treat these disorders, we have followed a bi-dimensional pharmacophore-based virtual screening approach to identify new inhibitors. Nineteen molecules extracted from AurSCOPE Ion Channels knowledgebase were used as query molecules to screen an external database. This in silico approach was then validated using electrophysiology. Interestingly, 16 compounds out of 38 distinct molecules tested showed more than 50% blockade of the CaV3.2 mediated T-type current. Two series of compounds show chemical originality compared with known T-type calcium channel blockers.

Development of an ADME and drug–drug interactions knowledge database for the acceleration of drug discovery and development

It is widely recognised that predicting or determining the absorption, distribution, metabolism and excretion (ADME) properties of a compound as early as possible in the drug discovery process helps to prevent costly late-stage failures. Although in recent years high-throughput in vitro absorption distribution metabolism excretion toxicity (ADMET) screens have been implemented, more efficient in silico filters are still highly needed to predict and model the most relevant metabolic and pharmacokinetic end points, and thereby accelerate drug discovery and development. The usefulness of the data generated and published for the chemist, biologist or project manager who ultimately wants to understand and optimise the ADME properties of lead compounds cannot be argued with. Collecting and comparing data is an overwhelming task for the time-pressed scientist. Aureus Pharma provides a uniquely specialised solution for knowledge generation in drug discovery. AurSCOPE® ADME/DDI (drug–drug interaction) is a fully annotated, structured knowledge database containing all the pertinent biological and chemical information on the metabolic properties of drugs. This Aureus knowledge database has proven to be highly useful in designing predictive models and identifying potential drug–drug interactions.

Knowledge-based analysis of multi-potent G-protein coupled receptors ligands

A large number of chemical structures that interact with G-protein coupled receptors (GPCRs) have been disclosed in patents or published papers. Most of these compounds are selective for a given protein target; however, it is well recognized that some GPCR-drugs interact with multiple targets. Using a literature database, we have identified compounds that act on different GPCRs. These protein targets are usually divided in three main classes, A, B and C, based on sequence similarity, but they can also be grouped pharmacologically based on endogenous ligand characteristics. In this paper, we specifically focus on compounds able to recognize two different classes or different pharmacological clusters within the same class. Despite the large number of GPCR ligands described in the literature, we identified a limited number of molecules acting on both classes A and B, only few acting on classes A and C and none acting on class B and C receptors. A search for bi- or multi-potent compounds exhibiting activities on different pharmacological clusters of class A receptors revealed cases of cross reactivity, the most frequent concerning amine and peptide receptor clusters.

Assessing the Chemical and Biological Diversity of an Ion Channels Knowledge Database

The aim of the present work is to assess the chemical and biological diversity of ligands reported in scientific articles or patents to be active against ion channels targets. A specific query of the AurSCOPE Ion Channel knowledge database was constructed to retrieve a set of the most active non-peptide ligands tested in binding or electrophysiology experiments against all ion channel families. A biological activity threshold cutoff expressed by Ki, IC50, or EC50 was set to 300 nM. This activity cutoff was selected such that we would retrieve a set of compounds which contain the most active ligands for all target families but is a reasonable number to analyze. To encode the chemical space for the entire active dataset (9897 molecules), ChemAxons chemical fingerprints were computed and optimized and then employed to cluster the dataset at a variety of different similarity thresholds. Concurrently, the exploration of the biological space was performed by associating with each chemical cluster the corresponding target or target family. Tri-dimensional visualization of different voltage- and ligand-gated ion channel families projected into the active chemical space was obtained after a principal components analysis performed using selected molecular descriptors. The findings presented herein give a global picture of the realm of ion channels active ligands and link the knowledge on chemical structures with their respective biological activities.