TeachOpenCADD-KNIME: A Teaching Platform for Computer-Aided Drug Design Using KNIME Workflows.

Open-source workflows have become more and more an integral part of computer-aided drug design (CADD) projects since they allow reproducible and shareable research that can be easily transferred to other projects. Setting up, understanding, and applying such workflows involves either coding or using workflow managers that offer a graphical user interface. We previously reported the TeachOpenCADD teaching platform that provides interactive Jupyter Notebooks (talktorials) on central CADD topics using open-source data and Python packages. Here we present the conversion of these talktorials to KNIME workflows that allow users to explore our teaching material without any line of code. TeachOpenCADD KNIME workflows are freely available on the KNIME Hub: https://hub.knime.com/volkamerlab/space/TeachOpenCADD .

Empowering pharmacoinformatics by linked life science data.

With the public availability of large data sources such as ChEMBLdb and the Open PHACTS Discovery Platform, retrieval of data sets for certain protein targets of interest with consistent assay conditions is no longer a time consuming process. Especially the use of workflow engines such as KNIME or Pipeline Pilot allows complex queries and enables to simultaneously search for several targets. Data can then directly be used as input to various ligand- and structure-based studies. In this contribution, using in-house projects on P-gp inhibition, transporter selectivity, and TRPV1 modulation we outline how the incorporation of linked life science data in the daily execution of projects allowed to expand our approaches from conventional Hansch analysis to complex, integrated multilayer models.

Integrate mechanistic evidence from new approach methodologies (NAMs) into a read-across assessment to characterise trends in shared mode of action.

Read-across approaches often remain inconclusive as they do not provide sufficient evidence on a common mode of action across the category members. This read-across case study on thirteen, structurally similar, branched aliphatic carboxylic acids investigates the concept of using human-based new approach methods, such as in vitro and in silico models, to demonstrate biological similarity. Five out of the thirteen analogues have preclinical in vivo studies. Three out of them induced lipid accumulation or hypertrophy in preclinical studies with repeated exposure, which leads to the read-across hypothesis that the analogues can potentially induce hepatic steatosis. To confirm the selection of analogues, the expression patterns of the induced differentially expressed genes (DEGs) were analysed in a human liver model. With increasing dose, the expression pattern within the tested analogues got more similar, which serves as a first indication of a common mode of action and suggests differences in the potency of the analogues. Hepatic steatosis is a well-known adverse outcome, for which over 55 adverse outcome pathways have been identified. The resulting adverse outcome pathway (AOP) network, comprised a total 43 MIEs/KEs and enabled the design of an in vitro testing battery. From the AOP network, ten MIEs, early and late KEs were tested to systematically investigate a common mode of action among the grouped compounds. The targeted testing of AOP specific MIE/KEs shows that biological activity in the category decreases with side chain length. A similar trend was evident in measuring liver alterations in zebra fish embryos. However, activation of single MIEs or early KEs at in vivo relevant doses did not necessarily progress to the late KE "lipid accumulation". KEs not related to the read-across hypothesis, testing for example general mitochondrial stress responses in liver cells, showed no trend or biological similarity. Testing scope is a key issue in the design of in vitro test batteries. The Dempster-Shafer decision theory predicted those analogues with in vivo reference data correctly using one human liver model or the CALUX reporter assays. The case study shows that the read-across hypothesis is the key element to designing the testing strategy. In the case of a good mechanistic understanding, an AOP facilitates the selection of reliable human in vitro models to demonstrate a common mode of action. Testing DEGs, MIEs and early KEs served to show biological similarity, whereas the late KEs become important for confirmation, as progression from MIEs to AO is not always guaranteed.

MetScore: Site of Metabolism Prediction Beyond Cytochrome P450 Enzymes.

The metabolism of xenobiotics by humans and other organisms is a complex process involving numerous enzymes that catalyze phase I (functionalization) and phase II (conjugation) reactions. Herein we introduce MetScore, a machine learning model that can predict both phase I and phase II reaction sites of drugs in a single prediction run. We developed cheminformatics workflows to filter and process reactions to obtain suitable phase I and phase II data sets for model training. Employing a recently developed molecular representation based on quantum chemical partial charges, we constructed random forest machine learning models for phase I and phase II reactions. After combining these models with our previous cytochrome P450 model and calibrating the combination against Bayer in-house data, we obtained the MetScore model that shows good performance, with Matthews correlation coefficients of 0.61 and 0.76 for diverse phase I and phase II reaction types, respectively. We validated its potential applicability to lead optimization campaigns for a new and independent data set compiled from recent publications. The results of this study demonstrate the usefulness of quantum-chemistry-derived molecular representations for reactivity prediction.

Novel scaffolds for modulation of TRPV1 identified with pharmacophore modeling and virtual screening.

AIM: The transient receptor potential vanilloid type 1 (TRPV1) is responsible for pain perception in the peripheral nervous system (PNS). TRPV1 is thus considered a versatile target for development of non-opioid analgesics. RESULTS: Pharmacophore-based clustering of a publicly available data set of TRPV1 antagonists revealed a set of models, which were validated with data sets of inactive compounds, decoys and known drug candidates. The top ranked pharmacophore models were subsequently used for virtual screening. Based on a unique in-house protocol, a set of compounds was selected and biologically tested for modulation of TRPV1 in a voltage-clamp model. CONCLUSION: Pharmacophore models extracted from large public data sets are a valuable source for identification of novel scaffolds for TRPV1 receptor modulation.

Exploiting open data: a new era in pharmacoinformatics.

Within the last decade open data concepts has been gaining increasing interest in the area of drug discovery. With the launch of ChEMBL and PubChem, an enormous amount of bioactivity data was made easily accessible to the public domain. In addition, platforms that semantically integrate those data, such as the Open PHACTS Discovery Platform, permit querying across different domains of open life science data beyond the concept of ligand-target-pharmacology. However, most public databases are compiled from literature sources and are thus heterogeneous in their coverage. In addition, assay descriptions are not uniform and most often lack relevant information in the primary literature and, consequently, in databases. This raises the question how useful large public data sources are for deriving computational models. In this perspective, we highlight selected open-source initiatives and outline the possibilities and also the limitations when exploiting this huge amount of bioactivity data.

Efficient modulation of γ-aminobutyric acid type A receptors by piperine derivatives.

Piperine activates TRPV1 (transient receptor potential vanilloid type 1 receptor) receptors and modulates γ-aminobutyric acid type A receptors (GABAAR). We have synthesized a library of 76 piperine analogues and analyzed their effects on GABAAR by means of a two-microelectrode voltage-clamp technique. GABAAR were expressed in Xenopus laevis oocytes. Structure-activity relationships (SARs) were established to identify structural elements essential for efficiency and potency. Efficiency of piperine derivatives was significantly increased by exchanging the piperidine moiety with either N,N-dipropyl, N,N-diisopropyl, N,N-dibutyl, p-methylpiperidine, or N,N-bis(trifluoroethyl) groups. Potency was enhanced by replacing the piperidine moiety by N,N-dibutyl, N,N-diisobutyl, or N,N-bistrifluoroethyl groups. Linker modifications did not substantially enhance the effect on GABAAR. Compound 23 [(2E,4E)-5-(1,3-benzodioxol-5-yl)-N,N-dipropyl-2,4-pentadienamide] induced the strongest modulation of GABAA (maximal GABA-induced chloride current modulation (IGABA-max = 1673% ± 146%, EC50 = 51.7 ± 9.5 µM), while 25 [(2E,4E)-5-(1,3-benzodioxol-5-yl)-N,N-dibutyl-2,4-pentadienamide] displayed the highest potency (EC50 = 13.8 ± 1.8 µM, IGABA-max = 760% ± 47%). Compound 23 induced significantly stronger anxiolysis in mice than piperine and thus may serve as a starting point for developing novel GABAAR modulators.