Pharmaceutical removal by the activated carbon process.

International audienceThe adsorption of some major pharmaceutical products (sulfamethoxazole, caffeine, iopromide and carbamazepine) in water was evaluated using four types of activated carbon, three powdered activated carbon (PAC) and one fluidized, coagulated and flocculated activated carbon (FAC) extracted from a Carboplus®P pilot. These substances were the most frequently quantified (in 50% of samples at least) in surface waters of the Vilaine's basin (Brittany, France) during three sampling campaigns. Jar test experiments were carried out in order to assess the removal efficiency of the four activated carbons. Carbamazepine and caffeine were well removed with PAC with a maximum removal rate of 80% whereas it was more difficult for sulfamethoxazole and iopromide with a maximum of 39%. For each molecule, removal rates are clearly dependent on PAC nature. The overall results with FAC are clearly distinguishable from PAC tests with gains of performance on all target molecules (from 80 to >95%)

Insights into an Original Pocket-Ligand Pair Classification: A Promising Tool for Ligand Profile Prediction

<div><p>Pockets are today at the cornerstones of modern drug discovery projects and at the crossroad of several research fields, from structural biology to mathematical modeling. Being able to predict if a small molecule could bind to one or more protein targets or if a protein could bind to some given ligands is very useful for drug discovery endeavors, anticipation of binding to off- and anti-targets. To date, several studies explore such questions from chemogenomic approach to reverse docking methods. Most of these studies have been performed either from the viewpoint of ligands or targets. However it seems valuable to use information from both ligands and target binding pockets. Hence, we present a multivariate approach relating ligand properties with protein pocket properties from the analysis of known ligand-protein interactions. We explored and optimized the pocket-ligand pair space by combining pocket and ligand descriptors using Principal Component Analysis and developed a classification engine on this paired space, revealing five main clusters of pocket-ligand pairs sharing specific and similar structural or physico-chemical properties. These pocket-ligand pair clusters highlight correspondences between pocket and ligand topological and physico-chemical properties and capture relevant information with respect to protein-ligand interactions. Based on these pocket-ligand correspondences, a protocol of prediction of clusters sharing similarity in terms of recognition characteristics is developed for a given pocket-ligand complex and gives high performances. It is then extended to cluster prediction for a given pocket in order to acquire knowledge about its expected ligand profile or to cluster prediction for a given ligand in order to acquire knowledge about its expected pocket profile. This prediction approach shows promising results and could contribute to predict some ligand properties critical for binding to a given pocket, and conversely, some key pocket properties for ligand binding.</p></div

Pocket-ligand pair cluster prediction results (in percentage) using simple and double cross-validations.

<p>Ten-fold simple and double cross-validation results obtained on 100 simulations based on <i>pocket-ligand pair</i> models, <i>pocket-only</i> models and <i>ligand-</i>only models using k-nearest neighbors method with <i>k = 3, k = 3</i> and <i>k = 7</i> neighbors.</p><p>These rates are calculated over all pairs with no missing values, that is over 469 pairs instead of 483.</p

Principal Component Analysis (PCA) of pocket-ligand pair space.

<p>Ligand and pocket descriptors are indicated by respectively red and blue arrows in (A) and (B). Closer are the descriptors to the correlation circle, more they contribute to explain the variability captured by the corresponding principal components. (A) corresponds to the first principal component (PC1) that captures 22.3% of the variability <i>versus</i> second principal component PC2 (14.6%). (B) corresponds to the plot of the third principal component PC3 (11.4%) <i>versus</i> the fourth one PC4 (10.7%). These two planes capture a total of 59% of the variability of the data. (C) PCA of pocket-ligand pairs from the <i>training</i> set are depicted in black points with Astex dataset indicated by “+”. This illustrates Astex dataset is well sampled on the first plane with no specific characteristic in terms of the 24 considered descriptors.</p

Characterization of pocket-ligand pair clusters.

<p>One color is dedicated to each cluster for all the pictures. (A) The hierarchical classification tree of pocket-ligand pairs on the <i>optimal pocket-ligand space</i> (first 14 principal components of PCA model) presented five clusters: <b>a</b>, <b>b</b>, <b>c</b>, <b>d</b> and <b>e</b>. (B) The tree is first divided into two clusters whose main pocket (top stars) and ligand (bottom stars) properties are shown by grey star-plots. The solid surface of a star-plot is limited by the average value of the considered descriptors and the black lines correspond to the standard deviations of the considered descriptors. (C) The main pocket (top stars) and ligand (bottom stars) properties of each cluster are shown by colored star-plots. Pocket descriptors correspond to the first five star-plots and ligand descriptors correspond to the last five star-plots. (D) The main pocket-ligand pair cluster properties are summarized.</p

Representation of pocket and ligand properties for 9 pocket-ligand pairs seen in 3 different clusters.

<p>(A) Average pocket and ligand profiles of cluster a are represented on star plot. Dashed lines represent pocket and ligand properties of complexes beta-glucosidase bound to IMH ligand (pdb code 1B8O, magenta dashed line) and purine nucleoside phosphorylase bound to GI1 ligand (pdb code 2J7D, blue dashed line). (B) Average pocket and ligand profiles of cluster c are represented on star plot. Dashed lines represent the pocket and ligand properties of 4 cAMP-dependent protein kinases bound to M77 ligand (pdb code 1Q8W, blue dashed line), to 1QP ligand (pdb code 1YDR, dark green dashed line), to 1QS ligand (pdb code 1YDS, green dashed line) and to HFS ligand (pdb code 2ERZ, orange dashed line). (C) Average pocket and ligand profiles of cluster e are represented on star plot. Dashed lines represent the pocket and ligand properties of 3 cAMP-dependent protein kinases bound to BD2 ligand (pdb code 1RE8, magenta dashed line), to B1L ligand (pdb code 1REJ, orange dashed line) and to R69 ligand (pdb code 1XH9, blue dashed line).</p

PCA and representatives of five pocket-ligand pair illustrations.

<p>Five pocket-ligand pair clusters are illustrated by showing the pair closest from the pocket and ligand average values of each pair. Each pair is coloured according to the clustering of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063730#pone-0063730-g003" target="_blank">Figure 3</a>.</p

Averages and standard deviations of main pocket and ligand descriptors for the five clusters a, b, c, d and e.

<p>For the five clusters, if the corresponding analysis of variance and the Tukey’s HSD test are significant, a vertical line between two clusters is drawn. No vertical line means that the test is not significant <i>i.e.</i> the p-value is more than 0.05. One, two or three vertical lines mean that the test is significant and that: 0.01≤p-value≤0.05 for one vertical line, 0.001≤p-value≤0.01 for two vertical line or p-value≤0.001 for three vertical lines.</p

Pairwise percentage of sequence identity computed between all protein chains and Tanimoto coefficient similarity between all ligands.

<p>Proteins and ligands are ordered according to order of the five pocket-ligand clusters. The average protein pairwise percentage of sequence identity (values horizontally written) and the average Tanimoto coefficient similarity are also indicated (values vertically written) for the five pocket-ligand pair clusters.</p