Modeling Phospholipidosis Induction: Reliability and Warnings

Drug-induced phospholipidosis (PLD) is characterized by accumulation of phospholipids, the inducing drugs and lamellar inclusion bodies in the lysosomes of affected tissues. These side effects must be considered as early as possible during drug discovery, and, in fact, numerous in silico models designed to predict PLD have been published. However, the quality of any in silico model cannot be better than the quality of the experimental data set used to build it. The present paper reports an overview of the difficulties and errors encountered in the generation of databases used for the published PLD models. A new database of 466 compounds was constructed from seven literature sources, containing only publicly available compounds. A comparison of the PLD assignations in selected databases proved useful in revealing some inconsistencies and raised doubts about the previously assigned PLD+ and PLD– classifications for some chemicals. Finally, a Partial Least Squares Discriminant Analysis (PLS-DA) approach was also applied, revealing further anomalies and clearly showing that metabolism as well as data quality must be taken into account when generating accurate methods for predicting the likelihood that a compound will induce PLD. A new curated database of 331 compounds is proposed

BDDCS Class Prediction for New Molecular Entities

The Biopharmaceutics Drug Disposition Classification System (BDDCS) was successfully employed for predicting drug–drug interactions (DDIs) with respect to drug metabolizing enzymes (DMEs), drug transporters and their interplay. The major assumption of BDDCS is that the extent of metabolism (EoM) predicts high versus low intestinal permeability rate, and <i>vice versa</i>, at least when uptake transporters or paracellular transport is not involved. We recently published a collection of over 900 marketed drugs classified for BDDCS. We suggest that a reliable model for predicting BDDCS class, integrated with <i>in vitro</i> assays, could anticipate disposition and potential DDIs of new molecular entities (NMEs). Here we describe a computational procedure for predicting BDDCS class from molecular structures. The model was trained on a set of 300 oral drugs, and validated on an external set of 379 oral drugs, using 17 descriptors calculated or derived from the VolSurf+ software. For each molecule, a probability of BDDCS class membership was given, based on predicted EoM, FDA solubility (FDAS) and their confidence scores. The accuracy in predicting FDAS was 78% in training and 77% in validation, while for EoM prediction the accuracy was 82% in training and 79% in external validation. The actual BDDCS class corresponded to the highest ranked calculated class for 55% of the validation molecules, and it was within the top two ranked more than 92% of the time. The unbalanced stratification of the data set did not affect the prediction, which showed highest accuracy in predicting classes 2 and 3 with respect to the most populated class 1. For class 4 drugs a general lack of predictability was observed. A linear discriminant analysis (LDA) confirming the degree of accuracy for the prediction of the different BDDCS classes is tied to the structure of the data set. This model could routinely be used in early drug discovery to prioritize <i>in vitro</i> tests for NMEs (e.g., affinity to transporters, intestinal metabolism, intestinal absorption and plasma protein binding). We further applied the BDDCS prediction model on a large set of medicinal chemistry compounds (over 30,000 chemicals). Based on this application, we suggest that solubility, and not permeability, is the major difference between NMEs and drugs. We anticipate that the forecast of BDDCS categories in early drug discovery may lead to a significant R&D cost reduction

Detecting similar binding pockets to enable systems polypharmacology

<div><p>In the era of systems biology, multi-target pharmacological strategies hold promise for tackling disease-related networks. In this regard, drug promiscuity may be leveraged to interfere with multiple receptors: the so-called polypharmacology of drugs can be anticipated by analyzing the similarity of binding sites across the proteome. Here, we perform a pairwise comparison of 90,000 putative binding pockets detected in 3,700 proteins, and find that 23,000 pairs of proteins have at least one similar cavity that could, in principle, accommodate similar ligands. By inspecting these pairs, we demonstrate how the detection of similar binding sites expands the space of opportunities for the rational design of drug polypharmacology. Finally, we illustrate how to leverage these opportunities in protein-protein interaction networks related to several therapeutic classes and tumor types, and in a genome-scale metabolic model of leukemia.</p></div

Flavin Monooxygenase Metabolism: Why Medicinal Chemists Should Matter

FMO enzymes (FMOs) play a key role in the processes of detoxification and/or bioactivation of specific pharmaceuticals and xenobiotics bearing nucleophilic centers. The <i>N</i>-oxide and <i>S</i>-oxide metabolites produced by FMOs are often active metabolites. The FMOs are more active than cytochromes in the brain and work in tandem with CYP3A4 in the liver. FMOs might reduce the risk of phospholipidosis of CAD-like drugs, although some FMOs metabolites seem to be neurotoxic and hepatotoxic. However, in silico methods for FMO metabolism prediction are not yet available. This paper reports, for the first time, a substrate-specificity and catalytic-activity model for FMO3, the most relevant isoform of the FMOs in humans. The application of this model to a series of compounds with unknown FMO metabolism is also reported. The model has also been very useful to design compounds with optimal clearance and in finding erroneous literature data, particularly cases in which substances have been reported to be FMO3 substrates when, in reality, the experimentally validated in silico model correctly predicts that they are not

Distribution of multi-target opportunities in the binary interactome.

<p>(A) Network-based influence between proteins with similar cavities (red) and ligands (blue), compared to the background influence of random proteins in the interactome (gray). The relative influence between targets in drug combinations is plotted in purple. The distributions include groups with up to five comparisons, and the maximum influence among all of the pairs in the group was taken as the representative; in other words, closest pairs were picked in those drug combinations that involved more than one target per drug, and in cavity and ligand comparisons that brought together more than two proteins. To correct for the group size, we defined a Z-score by sampling 10,000 random groups in the size range. The orange shape spans the area between the quartiles of drug combinations and the unbiased sampling of nodes. (B) Characteristics of protein pairs that could be targeted simultaneously (polypharmacology opportunities) or that are used in successful drug combinations. In the upper plot, proportion of pairs of protein belonging to the same topological module in the network (as defined by the overlapping cluster generator [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005522#pcbi.1005522.ref042" target="_blank">42</a>]); in the middle plot, pairs of proteins with the same biological process (BP) broad (‘slim’) term [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005522#pcbi.1005522.ref070" target="_blank">70</a>]; similarly, in the bottom plot, pairs of proteins with the same molecular function (MF). For simplicity, in the case of drug combinations, only those with one target per drug are included here.</p

A selective target combination.

<p>(A) Structures of GPI (PDB ID: 1iri, chain C), DLD (PDB ID: 1zmc, chain C), PGD (PDB ID: 2jkv, chain D), SORD (PDB ID: 1pl8, chain C) and RPE (PDB ID: 3ovp, chain C) are displayed, together with cavity residues. Please note that these cavities are representative, as several structures exist for each of the proteins. For a deeper exploration, please refer to Supporting <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005522#pcbi.1005522.s001" target="_blank">S1 Data</a>. (B) Best similarity between cavities in GPI, DLD, PGD, SORD and RPE. Highest similarities represent, in principle, easier cases of polypharmacology design. In the upper triangle, the main metabolic chemotypes related to the enzymes are also displayed. (C) Reduction of biomass production upon the simultaneous inhibition in cancer (red) and normal (blue) cell lines. The effect of individual inhibitions in also showed. (D) Influence of each inhibition on metabolic cancer hallmarks. O₂ stands for oxygen consumption, Lac for lactate secretion, Glu for glucose uptake, and ROS for reactive oxygen species production. The assignment of ‘strong' and ‘mild’ reversals was based on visual inspection of the maximal flux of the corresponding reactions. When the maximal flux approached (>50% of the difference) the healthy cell line, it was classified as ‘strong reversal' ('strong worsening' if it otherwise diverged); `mild' effects were assigned to effects of less than 50%; ‘no effect' was assigned when one could observe essentially no change.</p

Unsupervised Pattern Cognition Analysis (UPCA) of BioGPS descriptors generated by DRY probe (hydrophobicity).

<p>The analyzed enzymes are labelled according to their PDB code and colored as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0109354#pone-0109354-g003" target="_blank">figure 3</a>. Improved mutants are highlighted in black triangles and poor mutants are in pink triangles.</p

Schematic illustration of the generation of BioGPS molecular descriptors.

<p>(a) Starting from the GRID mapping of enzyme active site the BioGPS algorithm identifies points used for generating quadruplets and a Common Reference Framework. (b) In order to compare two cavities (active sites), the algorithm searches for similar quadruplets and then overlaps the corresponding 3D structures (all against all approach). At the end a series of probe scores is generated.</p

Comparison of 1GVK (protease) and 2W22 (lipase) active site shape.

<p>1GVK and 2W22 are represented as green and magenta cartoon respectively. Active site shapes are represented as wireframes: 1GVK active site shape in green while the active site shape of 2W22 is in magenta.</p

BioGPS Descriptors for Rational Engineering of Enzyme Promiscuity and Structure Based Bioinformatic Analysis

<div><p>A new bioinformatic methodology was developed founded on the Unsupervised Pattern Cognition Analysis of GRID-based BioGPS descriptors (Global Positioning System in Biological Space). The procedure relies entirely on three-dimensional structure analysis of enzymes and does not stem from sequence or structure alignment. The BioGPS descriptors account for chemical, geometrical and physical-chemical features of enzymes and are able to describe comprehensively the active site of enzymes in terms of “pre-organized environment” able to stabilize the transition state of a given reaction. The efficiency of this new bioinformatic strategy was demonstrated by the consistent clustering of four different Ser hydrolases classes, which are characterized by the same active site organization but able to catalyze different reactions. The method was validated by considering, as a case study, the engineering of amidase activity into the scaffold of a lipase. The BioGPS tool predicted correctly the properties of lipase variants, as demonstrated by the projection of mutants inside the BioGPS “roadmap”.</p></div