Development and validation of a pharmacophore-based QSAR model for the prediction of CNS activity.

A QSAR model aimed at predicting central nervous system (CNS) activity was developed based on the structure-activity relationships of compounds from an in-house database of "drug-like" molecules. These compounds were initially identified as "CNS-active" or "CNS-inactive", and pharmacophore 3D descriptors were calculated from multiple conformations for each structure. A linear discriminant analysis (LDA) was performed on this structure-activity matrix, and this QSAR model was able to correctly classify approximately 80 % in both a learning set and a validation set. For validation purposes, the LDA model was applied to compounds for which the blood-brain barrier (BBB) penetration was known, and all of them were correctly predicted. The model was also applied to 960 other in-house compounds for which in vitro binding tests were performed on 20 receptors known to be present at the CNS level, and a high correlation was observed between compounds predicted as CNS-active and experimental hits. Finally, the model was also applied to a set of 700 structures with known CNS activity or inactivity randomly chosen from public sources, and more than 70 % of the compounds were correctly classified, including novel CNS chemotypes. These results demonstrate the applicability of the model to novel chemical structures and its usefulness for designing original CNS-focused compound libraries.

Fuzzy tricentric pharmacophore fingerprints. 1. Topological fuzzy pharmacophore triplets and adapted molecular similarity scoring schemes.

This paper introduces a novel molecular description--topological (2D) fuzzy pharmacophore triplets, 2D-FPT--using the number of interposed bonds as the measure of separation between the atoms representing pharmacophore types (hydrophobic, aromatic, hydrogen-bond donor and acceptor, cation, and anion). 2D-FPT features three key improvements with respect to the state-of-the-art pharmacophore fingerprints: (1) The first key novelty is fuzzy mapping of molecular triplets onto the basis set of pharmacophore triplets: unlike in the binary scheme where an atom triplet is set to highlight the bit of a single, best-matching basis triplet, the herein-defined fuzzy approach allows for gradual mapping of each atom triplet onto several related basis triplets, thus minimizing binary classification artifacts. (2) The second innovation is proteolytic equilibrium dependence, by explicitly considering all of the conjugated acids and bases (microspecies). 2D-FPTs are concentration-weighted (as predicted at pH=7.4) averages of microspecies fingerprints. Therefore, small structural modifications, not affecting the overall pharmacophore pattern (in the sense of classical rule-based assignment), but nevertheless triggering a pKa shift, will have a major impact on 2D-FPT. Pairs of almost identical compounds with significantly differing activities ("activity cliffs" in classical descriptor spaces) were in many cases predictable by 2D-FPT. (3) The third innovation is a new similarity scoring formula, acknowledging that the simultaneous absence of a triplet in two molecules is a less-constraining indicator of similarity than its simultaneous presence. It displays excellent neighborhood behavior, outperforming 2D or 3D two-point pharmacophore descriptors or chemical fingerprints. The 2D-FPT calculator was developed using the chemoinformatics toolkit of ChemAxon (www.chemaxon.com).

QSAR modeling of in vitro inhibition of cytochrome P450 3A4.

We report the QSAR modeling of cytochrome P450 3A4 (CYP3A4) enzyme inhibition using four large data sets of in vitro data. These data sets consist of marketed drugs and drug-like compounds all tested in four assays measuring the inhibition of the metabolism of four different substrates by the CYP3A4 enzyme. The four probe substrates are benzyloxycoumarin, testosterone, benzyloxyresorufin, and midazolam. We first show that using state-of-the-art QSAR modeling approaches applied to only one of these four data sets does not lead to predictive models that would be useful for in silico filtering of chemical libraries. We then present the development and the testing of a multiple pharmacophore hypothesis (MPH) that is formulated as a conceptual extension of the traditional QSAR approach to modeling the promiscuous binding of a large variety of drugs to CYP3A4. In the simplest form, the MPH approach takes advantage of the multiple substrate data sets and identifies the binding of test compounds as either proximal or distal relative to that of a given substrate. Application of the approach to the in silico filtering of test compounds for potential inhibitors of CYP3A4 is also presented. In addition to an improvement in the QSAR modeling for the inhibition of CYP3A4, the results from this modeling approach provide structural insights into the drug-enzyme interactions. The existence of multiple inhibition data sets in the BioPrint database motivates the original development of the concept of a multiple pharmacophore hypothesis and provides a unique opportunity for formulating alternative strategies of QSAR modeling of the inhibition of the in vitro metabolism of CYP3A4.

G-protein-coupled receptor affinity prediction based on the use of a profiling dataset: QSAR design, synthesis, and experimental validation.

A QSAR model accounting for "average" G-protein-coupled receptor (GPCR) binding was built from a large set of experimental standardized binding data (1939 compounds systematically tested over 40 different GPCRs) and applied to the design of a library of "GPCR-predicted" compounds. Three hundred and sixty of these compounds were randomly selected and tested in 21 GPCR binding assays. Positives were defined by their ability to inhibit by more than 70% the binding of reference compounds at 10 microM. A 5.5-fold enrichment in positives was observed when comparing the "GPCR-predicted" compounds with 600 randomly selected compounds predicted as "non-GPCR" from a general collection. The model was efficient in predicting strongest binders, since enrichment was greater for higher cutoffs. Significant enrichment was also observed for peptidic GPCRs and receptors not included to develop the QSAR model, suggesting the usefulness of the model to design ligands binding with newly identified GPCRs, including orphan ones.

Molecular similarity and property similarity.

This paper reviews the main efforts undertaken up to date in order to understand, rationalize and apply the similarity principle (similar compounds=>similar properties) as a computational tool in modern drug discovery. The best suited mathematical expression of this classical working hypothesis of medicinal chemistry needs to be carefully chosen (out of the virtually infinite possible implementations in terms of molecular descriptors and molecular similarity metrics), in order to achieve an optimal validation of the hypothesis that molecules that are neighbors in the Structural Space will also display similar properties. This overview will show why no single "absolute" measure of molecular similarity can be conceived, and why molecular similarity scores should be considered tunable tools that need to be adapted to each problem to solve.

Predicting ADME properties and side effects: the BioPrint approach.

Computational methods are increasingly used to streamline and enhance the lead discovery and optimization process. However, accurate prediction of absorption, distribution, metabolism and excretion (ADME) and adverse drug reactions (ADR) is often difficult, due to the complexity of underlying physiological mechanisms. Modeling approaches have been hampered by the lack of large, robust and standardized training datasets. In an extensive effort to build such a dataset, the BioPrint database was constructed by systematic profiling of nearly all drugs available on the market, as well as numerous reference compounds. The database is composed of several large datasets: compound structures and molecular descriptors, in vitro ADME and pharmacology profiles, and complementary clinical data including therapeutic use information, pharmacokinetics profiles and ADR profiles. These data have allowed the development of computational tools designed to integrate a program of computational chemistry into library design and lead development. Models based on chemical structure are strengthened by in vitro results that can be used as additional compound descriptors to predict complex in vivo endpoints. The BioPrint pharmacoinformatics platform represents a systematic effort to accelerate the process of drug discovery, improve quantitative structure-activity relationships and develop in vitro/in vivo associations. In this review, we will discuss the importance of training set size and diversity in model development, the implementation of linear and neighborhood modeling approaches, and the use of in silico methods to predict potential clinical liabilities.

Chemical and photochemical electron transfer of new helianthrone derivatives: aspects of their photodynamic activity.

Helianthrones 2-4 are a new class of synthetic photosensitizers, which have a molecular skeleton related to that of hypericin. We established that irradiation of heliantrones with visible light leads to the formation of semiquinone radicals and reactive oxygen species. The structures of the paramagnetic anion species produced by electron transfer were calculated on the density functional level and investigated by cyclovoltammetry, UV/vis, and EPR/ENDOR spectroscopy. As with hypericin, the pi system of the helianthrones was found to be considerably deviated from planarity, and, upon electron transfer, deprotonation in the bay region occurs. The structure of the semiquinone radicals was found to be identical in THF, DMF, and aqueous buffered solutions regardless of the means by which reduction was achieved. Semiquinone radicals can be formed via self-electron transfer between the excited state and the ground state or via electron transfer from an electron donor to the excited state of helianthrone. Therefore, the presence of an electron donor significantly enhanced the photogeneration of semiquinone and superoxide radical. The kinetic studies showed that no significant photochemical destruction of helianthrones occurred upon irradiation. Generation of superoxide and singlet oxygen upon irradiation of helianthrones was established by spin trapping techniques. This shows that both type I and type II mechanisms are of importance for the photodynamic action of these compounds.

Electron delocalization in the radical cation of 1,3,6,8-tetraazatricyclo[4.4.1.1(3,8)]dodecane, a 4-nitrogen-7-electron system.

The radical cation of 1,3,6,8-tetraazatricyclo [4.4.1.1(3,8)]dodecane (TTD) has been studied using magnetic resonance and optical spectroscopic methods and computational techniques. With the help of deuterated isotopomers, assignments of EPR and resonance Raman spectra could be unequivocally established. The results demonstrate that the radical cation has D(2d) symmetry, and instantaneous electron delocalization over the four equivalent nitrogen atoms occurs. This extensive delocalization in a completely saturated system is a unique feature of the TTD radical cation. The spectroscopy of TTD, in contrast to that of simpler diamines such as 1,4-diaza[2.2.2]bicyclooctane, simultaneously reveals the consequences of orbital interactions through space and through bonds. The relationship between nitrogen pyramidalization and hyperfine coupling constants in nitrogen-centered radical cations with a number of different bonding arrangements is reviewed.