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.

Advantages and Challenges of Phenotypic Screens

Phenotypic screens are effective starting points to identify compounds with desirable activities. To find novel antifungals, we conducted a phenotypic screen in Saccharomyces cerevisiae and identified two discrete scaffolds with good growth inhibitory characteristics. Lack of broad-spectrum activity against pathogenic fungi called for directed chemical compound optimization requiring knowledge of the molecular target. Chemogenomic profiling identified effects on geranylgeranyltransferase I (GGTase I), an essential enzyme that prenylates proteins involved in cell signaling, such as Cdc42p and Rho1p. Selection of resistant mutants against both compounds confirmed the target hypothesis and enabled mapping of the compound binding site to the substrate binding pocket. Differential resistance-conferring mutations and selective substrate competition demonstrate distinct binding modes for the two chemotypes. Exchange of the S. cerevisiae GGTase I subunits with those of Candida albicans resulted in an absence of growth inhibition for both compounds, thus confirming the identified target as well as the narrow antifungal spectrum of activity. This prenylation pathway is reported to be nonessential in pathogenic species and challenges the therapeutic value of these leads while demonstrating the importance of an integrated target identification platform following a phenotypic screen.

Proteochemometric modelling coupled to in silico target prediction: an integrated approach for the simultaneous prediction of polypharmacology and binding affinity/potency of small molecules

The rampant increase of public bioactivity databases has fostered the development of computational chemogenomics methodologies to evaluate potential ligand-target interactions (polypharmacology) both in a qualitative and quantitative way. Bayesian target prediction algorithms predict the probability of an interaction between a compound and a panel of targets, thus assessing compound polypharmacology qualitatively, whereas structure-activity relationship techniques are able to provide quantitative bioactivity predictions. We propose an integrated drug discovery pipeline combining in silico target prediction and proteochemometric modelling (PCM) for the respective prediction of compound polypharmacology and potency/affinity. The proposed pipeline was evaluated on the retrospective discovery of Plasmodium falciparum DHFR inhibitors. The qualitative in silico target prediction model comprised 553,084 ligand-target associations (a total of 262,174 compounds), covering 3,481 protein targets and used protein domain annotations to extrapolate predictions across species. The prediction of bioactivities for plasmodial DHFR led to a recall value of 79% and a precision of 100%, where the latter high value arises from the structural similarity of plasmodial DHFR inhibitors and T. gondii DHFR inhibitors in the training set. Quantitative PCM models were then trained on a dataset comprising 20 eukaryotic, protozoan and bacterial DHFR sequences, and 1,505 distinct compounds (in total 3,099 data points). The most predictive PCM model exhibited R²_0test and RMSE_test values of 0.79 and 0.59 pIC₅₀ units respectively, which was shown to outperform models based exclusively on compound (R²_0test/RMSE_test = 0.63/0.78) and target information (R²_0test/RMSE_test = 0.09/1.22), as well as inductive transfer knowledge between targets, with respective R²_0test and RMSE_test values of 0.76 and 0.63 pIC₅₀ units. Finally, both methods were integrated to predict the protein targets and the potency on plasmodial DHFR for the GSK TCAMS dataset, which comprises 13,533 compounds displaying strong anti-malarial activity. 534 of those compounds were identified as DHFR inhibitors by the target prediction algorithm, while the PCM algorithm identified 25 compounds, and 23 compounds (predicted pIC₅₀ > 7) were identified by both methods. Overall, this integrated approach simultaneously provides target and potency/affinity predictions for small molecules.

Graphical abstract

Extending in silico mechanism-of-action analysis by annotating targets with pathways: application to cellular cytotoxicity readouts

Background: An in silico mechanism-of-action analysis protocol was developed, comprising molecule bioactivity profiling, annotation of predicted targets with pathways and calculation of enrichment factors to highlight targets and pathways more likely to be implicated in the studied phenotype. Results: The method was applied to a cytotoxicity phenotypic endpoint, with enriched targets/pathways found to be statistically significant when compared with 100 random datasets. Application on a smaller apoptotic set (10 molecules) did not allowed to obtain statistically relevant results, suggesting that the protocol requires modification such as analysis of the most frequently predicted targets/annotated pathways. Conclusion: Pathway annotations improved the mechanism-of-action information gained by target prediction alone, allowing a better interpretation of the predictions and providing better mapping of targets onto pathways.

Comparative mode-of-action analysis following manual and automated phenotype detection in Xenopus laevis

Given the increasing utilization of phenotypic screens in drug discovery also the subsequent mechanism-of-action analysis gains increased attention.