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In silico predictions consisting of unfiltered drug repurposing candidates for COVID-19.&nbsp

In this work, we describe the computational ("in silico") mode-of-action analysis of CNS-active drugs, which is taking both multiple simultaneous hypotheses as well as sets of protein targets for each mode-of-action into account, and which was followed by successful prospective in vitro and in vivo validation. Using sleep-related phenotypic readouts describing both efficacy and side effects for 491 compounds tested in rat, we defined an "optimal" (desirable) sleeping pattern. Compounds were subjected to in silico target prediction (which was experimentally confirmed for 21 out of 28 cases), followed by the utilization of decision trees for deriving polypharmacological bioactivity profiles. We demonstrated that predicted bioactivities improved classification performance compared to using only structural information. Moreover, DrugBank molecules were processed via the same pipeline, and compounds in many cases not annotated as sedative-hypnotic (alcaftadine, benzatropine, palonosetron, ecopipam, cyproheptadine, sertindole, and clopenthixol) were prospectively validated in vivo. Alcaftadine, ecopipam cyproheptadine, and clopenthixol were found to promote sleep as predicted, benzatropine showed only a small increase in NREM sleep, whereas sertindole promoted wakefulness. To our knowledge, the sedative-hypnotic effects of alcaftadine and ecopipam have not been previously discussed in the literature. The method described extends previous single-target, single-mode-of-action models and is applicable across disease areas.

Given the increasing utilization of phenotypic screens in drug discovery also the subsequent mechanism-of-action analysis gains increased attention. Such analyses frequently use in silico methods, which have become significantly more popular in recent years. However, identifying phenotype-specific mechanisms of action depends heavily on suitable phenotype identification in the first place, many of which rely on human input and are therefore inconsistent. In this work, we aimed at analysing the impact that human phenotype classification has on subsequent in silico mechanism-of-action analysis. To this end, an image analysis application was implemented for the rapid identification of seven high-level phenotypes in Xenopus laevis tadpoles treated with compounds from the National Cancer Institute Diversity Set II. It was found that manual and automated phenotype classifications were in agreement with some of the phenotypes (e.g. 73.9% agreement observed for general morphology abnormality), while this was not the case in others (e.g. melanophore migration with 37.6% agreement between both annotations). Based on both annotations, protein targets of active compounds were predicted in silico, and decision trees were generated to understand mechanisms-of-action behind every phenotype while also taking polypharmacology (combinations of targets) into account. It was found that the automated phenotype categorisation greatly increased the accuracy of the results of the mechanism-of-action model, where it improved the classification accuracy by 9.4%, as well as reducing the tree size by eight nodes and the number of leaves and the depth by three levels. Overall we conclude that consistent phenotype annotations seem to be generally crucial for successful subsequent mechanism-of-action analysis, and this is what we have shown here in Xenopus laevis screens in combination with in silico mechanism-of-action analysis.

Diversity selection is a frequently applied strategy for assembling high-throughput screening libraries, making the assumption that a diverse compound set increases chances of finding bioactive molecules. Based on previous work on experimental 'affinity fingerprints', in this study, a novel diversity selection method is benchmarked that utilizes predicted bioactivity profiles as descriptors. Compounds were selected based on their predicted activity against half of the targets (training set), and diversity was assessed based on coverage of the remaining (test set) targets. Simultaneously, fingerprint-based diversity selection was performed. An original version of the method exhibited on average 5% and an improved version on average 10% increase in target space coverage compared with the fingerprint-based methods. As a typical case, bioactivity-based selection of 231 compounds (2%) from a particular data set ('Cutoff-40') resulted in 47.0% and 50.1% coverage, while fingerprint-based selection only achieved 38.4% target coverage for the same subset size. In conclusion, the novel bioactivity-based selection method outperformed the fingerprint-based method in sampling bioactive chemical space on the data sets considered. The structures retrieved were structurally more acceptable to medicinal chemists while at the same time being more lipophilic, hence bioactivity-based diversity selection of compounds would best be combined with physicochemical property filters in practice.

In this chapter, we provide an overview of recent advancements related to the safety assessment of engineered nanomaterials (ENMs) using alternatives to animal testing strategies. Advanced risk assessment computational procedures include new methods for characterizing and describing the complex structures of ENMs, development of computational models predicting adverse effects, extension of “read-across” approaches taking into account different aspects of ENM similarity, integration of various testing strategies using a “weight-of-evidence” approach, and using omics data and pathways analysis technologies to provide insights into ENM mechanisms that potentially could induce toxicity.

Traditional Chinese medicine (TCM) still needs more scientific rationale to be proven for it to be accepted further in the West. We are now in the position to propose computational hypotheses for the mode-of-actions (MOAs) of 45 TCM therapeutic action (sub)classes fromin silicotarget prediction algorithms, whose target was later annotated with Kyoto Encyclopedia of Genes and Genomes pathway, and to discover the relationship between them by generating a hierarchical clustering. The results of 10,749 TCM compounds showed 183 enriched targets and 99 enriched pathways from Estimation Score ≤ 0 and ≥ 5% of compounds/targets in a (sub)class. The MOA of a (sub)class was established from supporting literature. Overall, the most frequent top three enriched targets/pathways were immune-related targets such as tyrosine-protein phosphatase nonreceptor type 2 (PTPN2) and digestive system such as mineral absorption. We found two major protein families, G-protein coupled receptor (GPCR), and protein kinase family contributed to the diversity of the bioactivity space, while digestive system was consistently annotated pathway motif, which agreed with the important treatment principle of TCM, “the foundation of acquired constitution” that includes spleen and stomach. In short, the TCM (sub)classes, in many cases share similar targets/pathways despite having different indications.

Decision trees are renowned in the computational chemistry and machine learning communities for their interpretability. Their capacity and usage are somewhat limited by the fact that they normally work on categorical data. Improvements to known decision tree algorithms are usually carried out by increasing and tweaking parameters, as well as the post-processing of the class assignment. In this work we attempted to tackle both these issues. Firstly, conditional mutual information was used as the criterion for selecting the attribute on which to split instances. The algorithm performance was compared with the results of C4.5 (WEKA's J48) using default parameters and no restrictions. Two datasets were used for this purpose, DrugBank compounds for HRH1 binding prediction and Traditional Chinese Medicine formulation predicted bioactivities for therapeutic class annotation. Secondly, an automated binning method for continuous data was evaluated, namely Scott's normal reference rule, in order to allow any decision tree to easily handle continuous data. This was applied to all approved drugs in DrugBank for predicting the RDKit SLogP property, using the remaining RDKit physicochemical attributes as input.

This deliverable focuses on extensions to the OpenTox descriptor calculation services, in order to account for the supra-molecular pattern (size, shape, porosity and irregularity of the surface area and coatings) governing the activities, effects and properties of ENMs. New descriptors have been derived by applying image analysis techniques on raw microscopy nanomaterial images. The quantummechanical semi-empirical calculation services using the MOPAC open-source software have been adapted and tested on the special requirements of ENM. Experimentally measured compositions of protein coronas (nano-bio interfaces) have been used as fingerprints of ENMs, addressing ENM exposure and life cycle assessment, as well as human and ecological hazard assessment. Further analysis of protein corona data, based on the integration of information form Gene Ontology (GO) resulted in the derivation of GO descriptors. Finally, the popular Java-based Chemistry Development Kit (CDK) has been extended to include nanomaterial descriptors.

eNanoMapper developed a modular infrastructure for data storage, sharing and searching, an ontologyfor the categorisation and characterisation of nanomaterials andcomputational models fornanomaterials safety assessment.

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.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.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.

Integrating gene expression profiles with certain proteins can improve our understanding of the fundamental mechanisms in protein-ligand binding. This paper spotlights the integration of gene expression data and target prediction scores, providing insight into mechanism of action (MoA). Compounds are clustered based upon the similarity of their predicted protein targets and each cluster is linked to gene sets using Linear Models for Microarray Data. MLP analysis is used to generate gene sets based upon their biological processes and a qualitative search is performed on the homogeneous target-based compound clusters to identify pathways. Genes and proteins were linked through pathways for 6 of the 8 MCF7 and 6 of the 11 PC3 clusters. Three compound clusters are studied; (i) the target-driven cluster involving HSP90 inhibitors, geldanamycin and tanespimycin induces differential expression for HSP90-related genes and overlap with pathway response to unfolded protein. Gene expression results are in agreement with target prediction and pathway annotations add information to enable understanding of MoA. (ii) The antipsychotic cluster shows differential expression for genes LDLR and INSIG-1 and is predicted to target CYP2D6. Pathway steroid metabolic process links the protein and respective genes, hypothesizing the MoA for antipsychotics. A sub-cluster (verepamil and dexverepamil), although sharing similar protein targets with the antipsychotic drug cluster, has a lower intensity of expression profile on related genes, indicating that this method distinguishes close sub-clusters and suggests differences in their MoA. Lastly, (iii) the thiazolidinediones drug cluster predicted peroxisome proliferator activated receptor (PPAR) PPAR-alpha, PPAR-gamma, acyl CoA desaturase and significant differential expression of genes ANGPTL4, FABP4 and PRKCD. The targets and genes are linked via PPAR signalling pathway and induction of apoptosis, generating a hypothesis for the MoA of thiazolidinediones. Our analysis show one or more underlying MoA for compounds and were well-substantiated with literature.

The increase of publicly available bioactivity data has led to the extensive development and usage of in silico bioactivity prediction algorithms. A particularly popular approach for such analyses is the multiclass Naive Bayes, whose output is commonly processed by applying empirically-derived likelihood score thresholds. In this work, we describe a systematic way for deriving score cut-offs on a per-protein target basis and compare their performance with global thresholds on a large scale using both 5-fold cross-validation (ChEMBL 14, 189k ligand-protein pairs over 477 protein targets) and external validation (WOMBAT, 63k pairs, 421 targets). The individual protein target cut-offs derived were compared to global cut-offs ranging from -10 to 40 in score bouts of 2.5. The results indicate that individual thresholds had equal or better performance in all comparisons with global thresholds, ranging from 95% of protein targets to 57.96%. It is shown that local thresholds behave differently for particular families of targets (CYPs, GPCRs, Kinases and TFs). Furthermore, we demonstrate the discrepancy in performance when we move away from the training dataset chemical space, using Tanimoto similarity as a metric (from 0 to 1 in steps of 0.2). Finally, the individual protein score cut-offs derived for the in silico bioactivity application used in this work are released, as well as the reproducible and transferable KNIME workflows used to carry out the analysis.

The simultaneous increase of computational power and the availability of chemical and biological data have contributed to the recent popularity of in silico bioactivity prediction algorithms. Such methods are commonly used to infer the ‘Mechanism of Action’ of small molecules and they can also be employed in cases where full bioactivity profiles have not been established experimentally. However, protein target predictions by themselves do not necessarily capture information about the effect of a compound on a biological system, and hence merging their output with a systems biology approach can help to better understand the complex network modulation which leads to a particular phenotype. In this work, we review approaches and applications of target prediction, as well as their shortcomings, and demonstrate two extensions of this concept which are exemplified using phenotypic readouts from a chemical genetic screen in Xenopus laevis. In particular, the experimental observations are linked to their predicted bioactivity profiles. Predicted targets are annotated with pathways, which lead to further biological insight. Moreover, we subject the prediction to further machine learning algorithms, namely decision trees, to capture the differential pharmacology of ligand-target interactions in biological systems. Both methodologies hence provide new insight into understanding the Mechanism of Action of compound activities from phenotypic screens.

G protein-coupled receptors (GPCRs) are a major family of membrane receptors in eukaryotic cells and play a crucial role in various biological processes. They represent a family of protein targets with significant therapeutic value, and accordingly more than 30% of prescription drugs are GPCR ligands [1]. Extending previous attempts to map the pharmacological space solely based on ligand chemical similarity,[2,3] we in this work relate GPCRs pharmacological space by combining structure-activity data from ChEMBL and WOMBAT that covers 167 human GPCRs and 67k ligands. By including more information from the ligand side in our analysis than previous studies, we hence attempted to construct a more detailed map of the pharmacological space. A statistical approach similar to the "Similarity Ensemble Approach" (SEA)[2] was implemented to relate proteins based on the chemical similarity of their ligands, and to rank the significance of the resulting similarity scores. A prospective external validation dataset was then employed to confirm new relationship between ligands and different GPCRs, providing mechanistic evidence for observed side effects of drugs in the dataset. The results of the study aim to contribute to a better understanding of the overlap of GPCRs in chemical space, and to the cross-reactivity observed even among distant biological targets, as defined by their sequence similarities[4] . Relevant applications range from understanding drug side effects to the design of drugs with a desired polypharmacological profile.

© 2019 John Wiley & Sons, Inc. The modes of action (MoAs) of drugs frequently are unknown, because many are small molecules initially identified from phenotypic screens, giving rise to the need to elucidate their MoAs. In addition, the high attrition rate for candidate drugs in preclinical studies due to intolerable toxicity has motivated the development of computational approaches to predict drug candidate (cyto)toxicity as early as possible in the drug-discovery process. Here, we provide detailed instructions for capitalizing on bioactivity predictions to elucidate the MoAs of small molecules and infer their underlying phenotypic effects. We illustrate how these predictions can be used to infer the underlying antidepressive effects of marketed drugs. We also provide the necessary functionalities to model cytotoxicity data using single and ensemble machine-learning algorithms. Finally, we give detailed instructions on how to calculate confidence intervals for individual predictions using the conformal prediction framework. © 2019 by John Wiley & Sons, Inc.