The benefits of in silico modeling to identify possible small-molecule drugs and their off-target interactions

Accepted for publication in a future issue of Future Medicinal Chemistry.The research into the use of small molecules as drugs continues to be a key driver in the development of molecular databases, computer-aided drug design software and collaborative platforms. The evolution of computational approaches is driven by the essential criteria that a drug molecule has to fulfill, from the affinity to targets to minimal side effects while having adequate absorption, distribution, metabolism, and excretion (ADME) properties. A combination of ligand- and structure-based drug development approaches is already used to obtain consensus predictions of small molecule activities and their off-target interactions. Further integration of these methods into easy-to-use workflows informed by systems biology could realize the full potential of available data in the drug discovery and reduce the attrition of drug candidates.Peer reviewe

The advent of system toxicology: aims and aspect of toxicogenomics

Last fifty years a significant advancement has been established in biological science. It happened due to the discovery of gene, genome and genetic code, function of genes and mutation of genes. Through this, the scientists have discovered that genetic code is the building block and fundamental of all molecular activity in biological system. According to this, several molecular techniques have been established to prove molecular events, effects of chemical exposure within individuals and environment. For this evaluation, the necessary of toxicogenomics is crucial, that deals with the effects of chemical in changing the genetic pattern along with mutation into gene. Toxicogenomics also deals with transcription of proteins and metabolite profiling to investigate the interaction of genes and environment stress in disease. Toxicogenomics also described the altered expression of genes caused by mutation and chemical exposure that cause several disease and show toxicant functions in cell. The main objective of toxicogenomics is to remove this exposure and provide remedy of these toxical diseases. The use, application, correlation, combination and collaboration of different significant, major, modern biological fields like proteomics, transcriptomics, bioinformatics, microarray and several other molecular process is carried out by toxicogenomics that gradually evolving in systems toxicology. This review recovered the evolution and significant application of the different fields of toxicogenomics

UNDERSTANDING CONDITIONAL MODES OF ACTIONS IN CHEMICAL-INDUCED TOXICITY USING RULE MODELS

It is estimated that 115 million animals are used in experimental testing each year. Hence, shifting efforts toward alternative methods for toxicity assessment is essential. However, slow regulatory acceptance of new approaches is governed by knowledge gaps in toxicity modes of action. In this thesis, I describe these challenges and the use of in vitro screening as an alternative of animal testing. I also discuss common data-based methods to derive hypotheses about toxicity modes of actions, and the associated limitations in capturing multiple biological perturbations. I applied novel data-based workflows, using rule models, to prioritize in vitro assays predictive of toxicity as well as to detect significant polypharmacology profiles. I explain how constraints were applied to rule-based models to inform meaningful mechanistic interpretation for two toxicity endpoints: rat hepatotoxicity and acute toxicity. I compared assays selected, by rules, for predicting hepatotoxicity with endpoints used in in vitro models from commercial sources. An overlap was observed including cytochrome activity, mitochondrial toxicity and immunological responses. However, nuclear receptor activity, identified in rules, is not currently covered in commercial setups. I also demonstrate that endocrine disruption endpoints extrapolate better into in vivo toxicity when a set of specific conditions are met, such as physicochemical properties associated with good bioavailability. Next, I examined synergistic interactions between conditions in rules describing acute toxicity. I gained novel insights into how specific stressors potentiate the perturbation by known key events, such as acetylcholinesterase inhibition and neuro-signalling disruption. I show that examining polypharmacology profiles is particularly important at low bioactive potencies. Further, the overall predictive performance of rules describing acute toxicity was tested against a benchmark Random Forest model in a conformal prediction framework. Irrespective to the data type used in the training, the models were prone to bias over compounds promiscuity, by which high promiscuous compounds were more likely to be predicted as toxic. Overall, the studies conducted in this thesis provide novel insights into molecular mechanisms of toxicity, namely hepatotoxicity and acute toxicity, and with regards to chemical properties and polypharmacology. This knowledge can be used to improve the utility and design of alternative methods for toxicity, and hence, accelerate the regulatory acceptance.Islamic Development Bank Cambridge Trust Fun

Predicting drug metabolism: experiment and/or computation?

Drug metabolism can produce metabolites with physicochemical and pharmacological properties that differ substantially from those of the parent drug, and consequently has important implications for both drug safety and efficacy. To reduce the risk of costly clinical-stage attrition due to the metabolic characteristics of drug candidates, there is a need for efficient and reliable ways to predict drug metabolism in vitro, in silico and in vivo. In this Perspective, we provide an overview of the state of the art of experimental and computational approaches for investigating drug metabolism. We highlight the scope and limitations of these methods, and indicate strategies to harvest the synergies that result from combining measurement and prediction of drug metabolism.This is the accepted manuscript of a paper published in Nature Reviews Drug Discovery (Kirchmair J, Göller AH, Lang D, Kunze J, Testa B, Wilson ID, Glen RC, Schneider G, Nature Reviews Drug Discovery, 2015, 14, 387–404, doi:10.1038/nrd4581). The final version is available at http://dx.doi.org/10.1038/nrd458

Computationally Linking Chemical Exposure to Molecular Effects with Complex Data: Comparing Methods to Disentangle Chemical Drivers in Environmental Mixtures and Knowledge-based Deep Learning for Predictions in Environmental Toxicology

Chemical exposures affect the environment and may lead to adverse outcomes in its organisms. Omics-based approaches, like standardised microarray experiments, have expanded the toolbox to monitor the distribution of chemicals and assess the risk to organisms in the environment. The resulting complex data have extended the scope of toxicological knowledge bases and published literature. A plethora of computational approaches have been applied in environmental toxicology considering systems biology and data integration. Still, the complexity of environmental and biological systems given in data challenges investigations of exposure-related effects. This thesis aimed at computationally linking chemical exposure to biological effects on the molecular level considering sources of complex environmental data. The first study employed data of an omics-based exposure study considering mixture effects in a freshwater environment. We compared three data-driven analyses in their suitability to disentangle mixture effects of chemical exposures to biological effects and their reliability in attributing potentially adverse outcomes to chemical drivers with toxicological databases on gene and pathway levels. Differential gene expression analysis and a network inference approach resulted in toxicologically meaningful outcomes and uncovered individual chemical effects — stand-alone and in combination. We developed an integrative computational strategy to harvest exposure-related gene associations from environmental samples considering mixtures of lowly concentrated compounds. The applied approaches allowed assessing the hazard of chemicals more systematically with correlation-based compound groups. This dissertation presents another achievement toward a data-driven hypothesis generation for molecular exposure effects. The approach combined text-mining and deep learning. The study was entirely data-driven and involved state-of-the-art computational methods of artificial intelligence. We employed literature-based relational data and curated toxicological knowledge to predict chemical-biomolecule interactions. A word embedding neural network with a subsequent feed-forward network was implemented. Data augmentation and recurrent neural networks were beneficial for training with curated toxicological knowledge. The trained models reached accuracies of up to 94% for unseen test data of the employed knowledge base. However, we could not reliably confirm known chemical-gene interactions across selected data sources. Still, the predictive models might derive unknown information from toxicological knowledge sources, like literature, databases or omics-based exposure studies. Thus, the deep learning models might allow predicting hypotheses of exposure-related molecular effects. Both achievements of this dissertation might support the prioritisation of chemicals for testing and an intelligent selection of chemicals for monitoring in future exposure studies.:Table of Contents ... I Abstract ... V Acknowledgements ... VII Prelude ... IX 1 Introduction 1.1 An overview of environmental toxicology ... 2 1.1.1 Environmental toxicology ... 2 1.1.2 Chemicals in the environment ... 4 1.1.3 Systems biological perspectives in environmental toxicology ... 7 Computational toxicology ... 11 1.2.1 Omics-based approaches ... 12 1.2.2 Linking chemical exposure to transcriptional effects ... 14 1.2.3 Up-scaling from the gene level to higher biological organisation levels ... 19 1.2.4 Biomedical literature-based discovery ... 24 1.2.5 Deep learning with knowledge representation ... 27 1.3 Research question and approaches ... 29 2 Methods and Data ... 33 2.1 Linking environmental relevant mixture exposures to transcriptional effects ... 34 2.1.1 Exposure and microarray data ... 34 2.1.2 Preprocessing ... 35 2.1.3 Differential gene expression ... 37 2.1.4 Association rule mining ... 38 2.1.5 Weighted gene correlation network analysis ... 39 2.1.6 Method comparison ... 41 Predicting exposure-related effects on a molecular level ... 44 2.2.1 Input ... 44 2.2.2 Input preparation ... 47 2.2.3 Deep learning models ... 49 2.2.4 Toxicogenomic application ... 54 3 Method comparison to link complex stream water exposures to effects on the transcriptional level ... 57 3.1 Background and motivation ... 58 3.1.1 Workflow ... 61 3.2 Results ... 62 3.2.1 Data preprocessing ... 62 3.2.2 Differential gene expression analysis ... 67 3.2.3 Association rule mining ... 71 3.2.4 Network inference ... 78 3.2.5 Method comparison ... 84 3.2.6 Application case of method integration ... 87 3.3 Discussion ... 91 3.4 Conclusion ... 99 4 Deep learning prediction of chemical-biomolecule interactions ... 101 4.1 Motivation ... 102 4.1.1Workflow ...105 4.2 Results ... 107 4.2.1 Input preparation ... 107 4.2.2 Model selection ... 110 4.2.3 Model comparison ... 118 4.2.4 Toxicogenomic application ... 121 4.2.5 Horizontal augmentation without tail-padding ...123 4.2.6 Four-class problem formulation ... 124 4.2.7 Training with CTD data ... 125 4.3 Discussion ... 129 4.3.1 Transferring biomedical knowledge towards toxicology ... 129 4.3.2 Deep learning with biomedical knowledge representation ...133 4.3.3 Data integration ...136 4.4 Conclusion ... 141 5 Conclusion and Future perspectives ... 143 5.1 Conclusion ... 143 5.1.1 Investigating complex mixtures in the environment ... 144 5.1.2 Complex knowledge from literature and curated databases predict chemical- biomolecule interactions ... 145 5.1.3 Linking chemical exposure to biological effects by integrating CTD ... 146 5.2 Future perspectives ... 147 S1 Supplement Chapter 1 ... 153 S1.1 Example of an estrogen bioassay ... 154 S1.2 Types of mode of action ... 154 S1.3 The dogma of molecular biology ... 157 S1.4 Transcriptomics ... 159 S2 Supplement Chapter 3 ... 161 S3 Supplement Chapter 4 ... 175 S3.1 Hyperparameter tuning results ... 176 S3.2 Functional enrichment with predicted chemical-gene interactions and CTD reference pathway genesets ... 179 S3.3 Reduction of learning rate in a model with large word embedding vectors ... 183 S3.4 Horizontal augmentation without tail-padding ... 183 S3.5 Four-relationship classification ... 185 S3.6 Interpreting loss observations for SemMedDB trained models ... 187 List of Abbreviations ... i List of Figures ... vi List of Tables ... x Bibliography ... xii Curriculum scientiae ... xxxix Selbständigkeitserklärung ... xlii

Trace elements and C and N isotope composition in two mushroom species from a mine-spill contaminated site

Fungi play a key role in the functioning of soil in terrestrial ecosystems, and in particular in the remediation of degraded soils. The contribution of fungi to carbon and nutrient cycles, along with their capability to mobilise soil trace elements, is well-known. However, the importance of life history strategy for these functions has not yet been thoroughly studied. This study explored the soil-fungi relationship of two wild edible fungi, the ectomycorrhizal Laccaria laccata and the saprotroph Volvopluteus gloiocephalus. Fruiting bodies and surrounding soils in a mine-spill contaminated area were analysed. Isotope analyses revealed Laccaria laccata fruiting bodies were 15N-enriched when compared to Volvopluteus gloiocephalus, likely due to the transfer of 15N-depleted compounds to their host plant. Moreover, Laccaria laccata fruiting bodies δ13C values were closer to host plant values than surrounding soil, while Volvopluteus gloiocephalus matched the δ13C composition to that of the soil. Fungal species presented high bioaccumulation and concentrations of Cd and Cu in their fruiting bodies. Human consumption of these fruiting bodies may represent a toxicological risk due to their elevated Cd concentrations

Matrix and Tensor Factorization Methods for Toxicogenomic Modeling and Prediction

Peer reviewe