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

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

(Q)SAR Modelling of Nanomaterial Toxicity - A Critical Review

There is an increasing recognition that nanomaterials pose a risk to human health, and that the novel engineered nanomaterials (ENMs) in the nanotechnology industry and their increasing industrial usage poses the most immediate problem for hazard assessment, as many of them remain untested. The large number of materials and their variants (different sizes and coatings for instance) that require testing and ethical pressure towards non-animal testing means that expensive animal bioassay is precluded, and the use of (quantitative) structure activity relationships ((Q)SAR) models as an alternative source of hazard information should be explored. (Q)SAR modelling can be applied to fill the critical knowledge gaps by making the best use of existing data, prioritize physicochemical parameters driving toxicity, and provide practical solutions to the risk assessment problems caused by the diversity of ENMs. This paper covers the core components required for successful application of (Q)SAR technologies to ENMs toxicity prediction, and summarizes the published nano-(Q)SAR studies and outlines the challenges ahead for nano-(Q)SAR modelling. It provides a critical review of (1) the present status of the availability of ENMs characterization/toxicity data, (2) the characterization of nanostructures that meets the need of (Q)SAR analysis, (3) the summary of published nano-(Q)SAR studies and their limitations, (4) the in silico tools for (Q)SAR screening of nanotoxicity and (5) the prospective directions for the development of nano-(Q)SAR models

Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome

Objective Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substructures associated with Stevens-Johnson Syndrome (SJS) to be identified and utilized for ADR prediction by QSAR models

Profiling Chemicals Based on Chronic Toxicity Results from the U.S. EPA ToxRef Database

Attribute filters allow enhancement and extraction of features without distorting their borders, and never introduce new image features. These are highly desirable properties in biomedical imaging, where accurate shape analysis is paramount. However, setting the attribute-threshold parameters has to date only been done manually. This paper explores simple, fast and automated methods of computing attribute threshold parameters based on image segmentation, thresholding and data clustering techniques. Though several techniques perform well on blood-vessel filtering, the choice of technique appears to depend on the imaging mode.

CATMoS: Collaborative Acute Toxicity Modeling Suite.

BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495

Classification of Chemicals Based on Structured Toxicity Information

Thirty years and millions of dollars worth of pesticide registration toxicity studies, historically stored as hardcopy and scanned documents, have been digitized into highly standardized and structured toxicity data within the Toxicity Reference Database (ToxRefDB). Toxicity-based classifications of chemicals were performed as a model application of ToxRefDB. These endpoints will ultimately provide the anchoring toxicity information for the development of predictive models and biological signatures utilizing in vitro assay data. Utilizing query and structured data mining approaches, toxicity profiles were uniformly generated for greater than 300 chemicals. Based on observation rate, species concordance and regulatory relevance, individual and aggregated effects have been selected to classify the chemicals providing a set of predictable endpoints. ToxRefDB exhibits the utility of transforming unstructured toxicity data into structured data and, furthermore, into computable outputs, and serves as a model for applying such data to address modern toxicological problems

Language modelling for clinical natural language understanding and generation

One of the long-standing objectives of Artificial Intelligence (AI) is to design and develop algorithms for social good including tackling public health challenges. In the era of digitisation, with an unprecedented amount of healthcare data being captured in digital form, the analysis of the healthcare data at scale can lead to better research of diseases, better monitoring patient conditions and more importantly improving patient outcomes. However, many AI-based analytic algorithms rely solely on structured healthcare data such as bedside measurements and test results which only account for 20% of all healthcare data, whereas the remaining 80% of healthcare data is unstructured including textual data such as clinical notes and discharge summaries which is still underexplored. Conventional Natural Language Processing (NLP) algorithms that are designed for clinical applications rely on the shallow matching, templates and non-contextualised word embeddings which lead to limited understanding of contextual semantics. Though recent advances in NLP algorithms have demonstrated promising performance on a variety of NLP tasks in the general domain with contextualised language models, most of these generic NLP algorithms struggle at specific clinical NLP tasks which require biomedical knowledge and reasoning. Besides, there is limited research to study generative NLP algorithms to generate clinical reports and summaries automatically by considering salient clinical information. This thesis aims to design and develop novel NLP algorithms especially clinical-driven contextualised language models to understand textual healthcare data and generate clinical narratives which can potentially support clinicians, medical scientists and patients. The first contribution of this thesis focuses on capturing phenotypic information of patients from clinical notes which is important to profile patient situation and improve patient outcomes. The thesis proposes a novel self-supervised language model, named Phenotypic Intelligence Extraction (PIE), to annotate phenotypes from clinical notes with the detection of contextual synonyms and the enhancement to reason with numerical values. The second contribution is to demonstrate the utility and benefits of using phenotypic features of patients in clinical use cases by predicting patient outcomes in Intensive Care Units (ICU) and identifying patients at risk of specific diseases with better accuracy and model interpretability. The third contribution is to propose generative models to generate clinical narratives to automate and accelerate the process of report writing and summarisation by clinicians. This thesis first proposes a novel summarisation language model named PEGASUS which surpasses or is on par with the state-of-the-art performance on 12 downstream datasets including biomedical literature from PubMed. PEGASUS is further extended to generate medical scientific documents from input tabular data.Open Acces