QSAR modelling of a large imbalanced aryl hydrocarbon activation dataset by rational and random sampling and screening of 80,086 REACH pre-registered and/or registered substances

The Aryl hydrocarbon receptor (AhR) plays important roles in many normal and pathological physiological processes, including endocrine homeostasis, foetal development, cell cycle regulation, cellular oxidation/antioxidation, immune regulation, metabolism of endogenous and exogenous substances, and carcinogenesis. An experimental data set for human in vitro AhR activation comprising 324,858 substances, of which 1,982 were confirmed actives, was used to test an in-house-developed approach to rationally select Quantitative Structure-Activity Relationship (QSAR) training set substances from an unbalanced data set. In the first iteration, active and inactive substances were selected by random to make QSAR models. Then, more inactive substances were added to the training set in two further iterations based on incorrect or out-of-domain predictions to produce larger models. The resulting 'rational' model, comprising 832 actives and four times as many inactives, i.e. 3,328, was compared to a model with a training set of same size and proportion of inactives chosen entirely by random. Both models underwent robust cross-validation and external validation showing good statistical performance, with the rational model having external validation sensitivity of 85.1% and specificity of 97.1%, compared to the random model with sensitivity 89.1% and specificity 91.3%. Furthermore, we integrated the training sets for both models with the 93 external validation test set actives and 372 randomly selected inactives to make two final models. They also underwent external validations for specificity and cross-validations, which confirmed that good predictivity was maintained. All developed models were applied to predict 80,086 EU REACH substances. The rational and random final models had 63.1% and 56.9% coverage of the REACH set, respectively, and predicted 1,256 and 3,214 substances as actives. The final models as well as predictions for AhR activation for 650,000 substances will be published in the Danish (Q)SAR Database and can, for example, be used for priority setting, in read-across predictions and in weight-of-evidence assessments of chemicals

High-Throughput Screening for Drug Discovery

The book focuses on various aspects and properties of high-throughput screening (HTS), which is of great importance in the development of novel drugs to treat communicable and non-communicable diseases. Chapters in this volume discuss HTS methodologies, resources, and technologies and highlight the significance of HTS in personalized and precision medicine

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

DEVELOPMENT OF AN INTEGRATED IN SILICO STRATEGY FOR THE RISK ASSESSMENT OF CHEMICALS AND THEIR MIXTURES ON DIFFERENT TOXICOLOGICAL OUTCOMES.

Daily, we are exposed to a mixture of multiple chemicals via food intake, inhalation and dermal contact. The risk for health that may result from this depends on how the effects of different chemicals in the mixture combine, and whether there is any synergism or antagonism between them. The number of different combinations of chemicals in mixtures is infinite and an efficient test strategy for mixtures is lacking. Furthermore, there is social pressure to reduce animal testing, which is the current practice in safety testing of chemicals. In this context, computational biochemistry and, more in general, bioinformatics meets all the requirements, and provides the foundation for further in vitro or in vivo studies. Aim of this PhD thesis is the development of an in silico workflow able to prioritize and discriminate chemicals that act as endocrine active substances (EAS), interfere with the retinoic acid pathway during embryo development and/or may cause liver toxicity. From the observation of the molecular initiating event to the description of the adverse outcome pathway, both ligand- and structure-based approaches were integrated with systems biology. Within this framework, (Q)SAR and molecular docking results were mixed into a majority consensus score to rank chemicals and low-mode molecular dynamic simulations were used to study their intrinsic activity, with respect to a specific nuclear receptor. Moreover, a computational approach based on both the transition state and the density functional theories was used to try discriminating a subset of chemicals as inhibitors or substrates of particular enzymes involved in the retinoic acid pathway, computing also their binding free energy values. This information was also included both in the pharmaco-dynamics (PD) and in the physiological based pharmaco-kinetics (PBPK) models. This in silico pipeline, besides being faster, has economic and ethical advantages, reducing both the research costs and the number of involved animals, in agreement with the \u201c3R\u201d principles (Reduction, Refinement and Replacement)

Toxicity pathways in zebrafish cell lines : an ecotoxicological perspective on ”toxicity testing in the 21st century”

Standard toxicological in vivo testing has been challenged as the procedures are time-consuming, expensive, and require a large number of animals; given the number of problematic chemicals. Novel toxicological frameworks, such as "toxicity testing in the 21st century", proposed the use of "new approach methods" (in vitro and in silico techniques), that can be applied in high-throughput setups and would allow for the testing of a large number of compounds. However, such new approach methods need to be designed and evaluated first. Especially within ecotoxicology, the coverage of species-specific bioanalytical tools, e.g. for fish, is rather scarce. Currently, mainly in vitro assays of mammalian and bacterial origin are used. This thesis outlines how to design and scrutinise fish transient reporter gene assays. We have established transient reporter gene assays in permanent zebrafish fibroblasts and hepatocytes of the oxidative stress response and the xenobiotic metabolism toxicity pathways. We identified non-specific effects caused by transient transfection itself and suggested preventive strategies. Further, we identified toxicity pathways' cross-talk as a significant driver of uncertainty in regards to the assessment of receptor-mediated toxicity. Additionally, we evaluated the correlation between cytotoxicity in cultured zebrafish cells and the acute toxicity observed in zebrafish embryos. When using chemical distribution models to derive bioavailable concentrations, we observed a good positive correlation between the two test systems. The results advocate an intensified use of fish in vitro assays in integrated testing strategies. Conclusively, new approach methods, as developed and applied in this thesis, show great potential in future toxicity testing and environmental monitoring

Synergy conformal prediction applied to large-scale bioactivity datasets and in federated learning

Confidence predictors can deliver predictions with the associated confidence required for decision making and can play an important role in drug discovery and toxicity predictions. In this work we investigate a recently introduced version of conformal prediction, synergy conformal prediction, focusing on the predictive performance when applied to bioactivity data. We compare the performance to other variants of conformal predictors for multiple partitioned datasets and demonstrate the utility of synergy conformal predictors for federated learning where data cannot be pooled in one location. Our results show that synergy conformal predictors based on training data randomly sampled with replacement can compete with other conformal setups, while using completely separate training sets often results in worse performance. However, in a federated setup where no method has access to all the data, synergy conformal prediction is shown to give promising results. Based on our study, we conclude that synergy conformal predictors are a valuable addition to the conformal prediction toolbox

Using high throughput screening for predictive modeling of reproductive toxicity

Traditional reproductive toxicity testing is inefficient, animal intensive and expensive with under a thousand chemicals ever tested among the tens of thousands of chemicals in our environment. Screening hundreds of chemicals through hundreds high-throughput biological assays generated a validated model predictive of rodent reproductive toxicity with potential application toward large-scale chemical testing prioritization and chemical testing decision-making. Chemical classification for model development began with the uniform capturing of the available animal reproductive toxicity test information utilizing an originally developed relational database and reproductive toxicity ontology. Similarly, quantitative high-throughput screening data were consistently processed, analyzed and stored in a relational database with gene and pathway mapping information. Chemicals with high quality in vivo and in vitro data comprised the training, test, external and forward validation chemical sets used to develop and assess the predictive model based on eight selected features generally targeting known modes of reproductive toxicity action. In three case studies, the forward validated predictive model reduced the overall costs of reproductive toxicity testing by roughly twenty percent. The model provides a starting point for the future of reproductive toxicity testing.Doctor of Philosoph