Screening Chemicals for Receptor‐Mediated Toxicological and Pharmacological Endpoints: Using Public Data to Build Screening Tools within a KNIME Workflow

Abstract

Assessing compounds for their pharmacological and toxicological properties is of great importance for industry and regulatory agencies. In this study an approach using open source software and open access databases to build screening tools for receptor-mediated effects is presented. The retinoic acid receptor (RAR), as a pharmacologically and toxicologically relevant target, was chosen for study. RAR agonists are used in the treatment of a number of dermal conditions and specific types of cancer, such as acute promyelocytic leukemia. However, when administered chronically, there is strong evidence that RAR agonists cause hepatosteatosis and liver injury. After compiling information on ligand-proteininteractions, common substructures and physico-chemical properties of ligands were identified manually and coded into SMARTS strings. Based on these SMARTS strings and calculated physico-chemical features, a rule-based screening workflow was built within the KNIME platform. The workflow was evaluated on two datasets: one with RAR agonists exclusively and another large, chemically diverse dataset containing only a few RAR agonists. Possible modifications and applications of screening workflows, dependent on their purpose, are presented.

Analysis of drug–endogenous human metabolite similarities in terms of their maximum common substructures

“…[66–72]) to write the appropriate workflows for these analyses. In particular, we used the RDKit [73] (http://rdkit.org/) MCS nodes for the MCS calculations.…”

Guidance for the identification of endocrine disruptors in the context of Regulations (EU) No 528/2012 and (EC) No 1107/2009

“…In vitro ER activity data from different sources including the Tox21 (~8,000 chemicals in four assays), EADB (~8,000 chemicals), METI (~2,000 chemicals), ChEMBL (~2,000 chemicals)In vitro ER activity data from EADB(Q)SAR and docking approaches were used with a common training set of 1,677 chemical structures from the US EPA, resulting in a total of 40 categorical and 8 continuous models developed for binding, agonist and antagonist ER activity Steinmetz et al (2015) NR binding: PPAR, AR, AhR, ER, GR, PR, FXR, LXR, PXR, TR, VDR, RXR Prediction of potential NR binding; freely available at https://knimewebportal.cosmostox.eu Developed by studying the physicochemical-chemical features of known nuclear receptor binders and elucidating the structural features needed for binding to the ligand-binding pocket using the Protein Data Bank and ChEMBL Al Sharif et al (2017), Tsakovska et al (2014) Potential for full PPARƴ agonism PPARƴ virtual screening. PPARc active full agonists share at least four common pharmacophoric features; the most active ones have additional interactions Developed taking into consideration structural elements (e.g.…”

In SilicoPrediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

“…Technically, these may need to extend the use of SMARTS strings into more sophisticated markup languages such as CSRML. A proposal has already been made for the incorporation of chemotypes, captured through CSRML to be integrated into KNIME Workflows for the prediction of chronic toxicity (57,65). …”

“…At this point, some of the large data compilations (e.g. ChEMBL, Pub-Chem) may be relevant to assist in the interpretation of models (61,65). …”