Improving the prediction of organism-level toxicity through integration of chemical, protein target and cytotoxicity qHTS data.

Prediction of compound toxicity is essential because covering the vast chemical space requiring safety assessment using traditional experimentally-based, resource-intensive techniques is impossible. However, such prediction is nontrivial due to the complex causal relationship between compound structure and in vivo harm. Protein target annotations and in vitro experimental outcomes encode relevant bioactivity information complementary to chemicals' structures. This work tests the hypothesis that utilizing three complementary types of data will afford predictive models that outperform traditional models built using fewer data types. A tripartite, heterogeneous descriptor set for 367 compounds was comprised of (a) chemical descriptors, (b) protein target descriptors generated using an algorithm trained on 190 000 ligand-protein interactions from ChEMBL, and (c) descriptors derived from in vitro cell cytotoxicity dose-response data from a panel of human cell lines. 100 random forests classification models for predicting rat LD50 were built using every combination of descriptors. Successive integration of data types improved predictive performance; models built using the full dataset had an average external correct classification rate of 0.82, compared to 0.73-0.80 for models built using two data types and 0.67-0.78 for models built using one. Pairwise comparisons of models trained on the same data showed that including a third data domain on top of chemistry improved average correct classification rate by 1.4-2.4 points, with p-values <0.01. Additionally, the approach enhanced the models' applicability domains and proved useful for generating novel mechanism hypotheses. The use of tripartite heterogeneous bioactivity datasets is a useful technique for improving toxicity prediction. Both protein target descriptors - which have the practical value of being derived in silico - and cytotoxicity descriptors derived from experiment are suitable contributors to such datasets.

Chemically Aware Model Builder (camb): an R package for property and bioactivity modelling of small molecules.

BACKGROUND: In silico predictive models have proved to be valuable for the optimisation of compound potency, selectivity and safety profiles in the drug discovery process. RESULTS: camb is an R package that provides an environment for the rapid generation of quantitative Structure-Property and Structure-Activity models for small molecules (including QSAR, QSPR, QSAM, PCM) and is aimed at both advanced and beginner R users. camb's capabilities include the standardisation of chemical structure representation, computation of 905 one-dimensional and 14 fingerprint type descriptors for small molecules, 8 types of amino acid descriptors, 13 whole protein sequence descriptors, filtering methods for feature selection, generation of predictive models (using an interface to the R package caret), as well as techniques to create model ensembles using techniques from the R package caretEnsemble). Results can be visualised through high-quality, customisable plots (R package ggplot2). CONCLUSIONS: Overall, camb constitutes an open-source framework to perform the following steps: (1) compound standardisation, (2) molecular and protein descriptor calculation, (3) descriptor pre-processing and model training, visualisation and validation, and (4) bioactivity/property prediction for new molecules. camb aims to speed model generation, in order to provide reproducibility and tests of robustness. QSPR and proteochemometric case studies are included which demonstrate camb's application.Graphical abstractFrom compounds and data to models: a complete model building workflow in one package.

Prediction of the potency of mammalian cyclooxygenase inhibitors with ensemble proteochemometric modeling.

Cyclooxygenases (COX) are present in the body in two isoforms, namely: COX-1, constitutively expressed, and COX-2, induced in physiopathological conditions such as cancer or chronic inflammation. The inhibition of COX with non-steroideal anti-inflammatory drugs (NSAIDs) is the most widely used treatment for chronic inflammation despite the adverse effects associated to prolonged NSAIDs intake. Although selective COX-2 inhibition has been shown not to palliate all adverse effects (e.g. cardiotoxicity), there are still niche populations which can benefit from selective COX-2 inhibition. Thus, capitalizing on bioactivity data from both isoforms simultaneously would contribute to develop COX inhibitors with better safety profiles. We applied ensemble proteochemometric modeling (PCM) for the prediction of the potency of 3,228 distinct COX inhibitors on 11 mammalian cyclooxygenases. Ensemble PCM models ([Formula: see text], and RMSEtest = 0.71) outperformed models exclusively trained on compound ([Formula: see text], and RMSEtest = 1.09) or protein descriptors ([Formula: see text] and RMSEtest = 1.10) on the test set. Moreover, PCM predicted COX potency for 1,086 selective and non-selective COX inhibitors with [Formula: see text] and RMSEtest = 0.76. These values are in agreement with the maximum and minimum achievable [Formula: see text] and RMSEtest values of approximately 0.68 for both metrics. Confidence intervals for individual predictions were calculated from the standard deviation of the predictions from the individual models composing the ensembles. Finally, two substructure analysis pipelines singled out chemical substructures implicated in both potency and selectivity in agreement with the literature. Graphical AbstractPrediction of uncorrelated bioactivity profiles for mammalian COX inhibitors with Ensemble Proteochemometric Modeling.

Protective immunogenicity of group A streptococcal M-related proteins.

Many previous studies have focused on the surface M proteins of group A streptococci (GAS) as virulence determinants and protective antigens. However, the majority of GAS isolates express M-related protein (Mrp) in addition to M protein, and both have been shown to be required for optimal virulence. In the current study, we evaluated the protective immunogenicity of Mrp to determine its potential as a vaccine component that may broaden the coverage of M protein-based vaccines. Sequence analyses of 33 mrp genes indicated that there are three families of structurally related Mrps (MrpI, MrpII, and MrpIII). N-terminal peptides of Mrps were cloned, expressed, and purified from M type 2 (M2) (MrpI), M4 (MrpII), and M49 (MrpIII) GAS. Rabbit antisera against the Mrps reacted at high titers with the homologous Mrp, as determined by enzyme-linked immunosorbent assay, and promoted bactericidal activity against GAS emm types expressing Mrps within the same family. Mice passively immunized with rabbit antisera against MrpII were protected against challenge infections with M28 GAS. Assays for Mrp antibodies in serum samples from 281 pediatric subjects aged 2 to 16 indicated that the Mrp immune response correlated with increasing age of the subjects. Affinity-purified human Mrp antibodies promoted bactericidal activity against a number of GAS representing different emm types that expressed an Mrp within the same family but showed no activity against emm types expressing an Mrp from a different family. Our results indicate that Mrps have semiconserved N-terminal sequences that contain bactericidal epitopes which are immunogenic in humans. These findings may have direct implications for the development of GAS vaccines.

Proteochemometric modeling in a Bayesian framework.

Proteochemometrics (PCM) is an approach for bioactivity predictive modeling which models the relationship between protein and chemical information. Gaussian Processes (GP), based on Bayesian inference, provide the most objective estimation of the uncertainty of the predictions, thus permitting the evaluation of the applicability domain (AD) of the model. Furthermore, the experimental error on bioactivity measurements can be used as input for this probabilistic model. In this study, we apply GP implemented with a panel of kernels on three various (and multispecies) PCM datasets. The first dataset consisted of information from 8 human and rat adenosine receptors with 10,999 small molecule ligands and their binding affinity. The second consisted of the catalytic activity of four dengue virus NS3 proteases on 56 small peptides. Finally, we have gathered bioactivity information of small molecule ligands on 91 aminergic GPCRs from 9 different species, leading to a dataset of 24,593 datapoints with a matrix completeness of only 2.43%. GP models trained on these datasets are statistically sound, at the same level of statistical significance as Support Vector Machines (SVM), with [Formula: see text] values on the external dataset ranging from 0.68 to 0.92, and RMSEP values close to the experimental error. Furthermore, the best GP models obtained with the normalized polynomial and radial kernels provide intervals of confidence for the predictions in agreement with the cumulative Gaussian distribution. GP models were also interpreted on the basis of individual targets and of ligand descriptors. In the dengue dataset, the model interpretation in terms of the amino-acid positions in the tetra-peptide ligands gave biologically meaningful results.

How do metabolites differ from their parent molecules and how are they excreted?

Understanding which physicochemical properties, or property distributions, are favorable for successful design and development of drugs, nutritional supplements, cosmetics, and agrochemicals is of great importance. In this study we have analyzed molecules from three distinct chemical spaces (i) approved drugs, (ii) human metabolites, and (iii) traditional Chinese medicine (TCM) to investigate four aspects determining the disposition of small organic molecules. First, we examined the physicochemical properties of these three classes of molecules and identified characteristic features resulting from their distinctive biological functions. For example, human metabolites and TCM molecules can be larger and more hydrophobic than drugs, which makes them less likely to cross membranes. We then quantified the shifts in physicochemical property space induced by metabolism from a holistic perspective by analyzing a data set of several thousand experimentally observed metabolic trees. Results show how the metabolic system aims to retain nutrients/micronutrients while facilitating a rapid elimination of xenobiotics. In the third part we compared these global shifts with the contributions made by individual metabolic reactions. For better resolution, all reactions were classified into phase I and phase II biotransformations. Interestingly, not all metabolic reactions lead to more hydrophilic molecules. We were able to identify biotransformations leading to an increase of logP by more than one log unit, which could be used for the design of drugs with enhanced efficacy. The study closes with the analysis of the physicochemical properties of metabolites found in the bile, faeces, and urine. Metabolites in the bile can be large and are often negatively charged. Molecules with molecular weight >500 Da are rarely found in the urine, and most of these large molecules are charged phase II conjugates.