Data-driven identification of structural alerts for mitigating the risk of drug-induced human liver injuries

El presente trabajo propone, tomando como eje los postulados de Iuri Lotman acerca de la memoria, una lectura de la novela Los pichiciegos (1984), del escritor argentino Rodolfo Enrique Fogwill (1941-2010). Escrito en paralelo a la guerra de las Malvinas, este relato pone en cuestión la narrativa oficial de la guerra y problematiza, en clave literaria, las tensiones entre memoria y ficción en la construcción de un acontecimiento relevante de la historia latinoamericana. De este modo, nuestro trabajo propone un abordaje, desde el campo de la semiótica de la cultura, de las complejas relaciones entre memoria y ficción.From the perspective of Iuri Lotman´s concept about memory, this paper propose a reading of Los pichiciegos, novel that wrote by the Argentinian author Rodolfo Enrique Fogwill (1941-2010). Written at the same moment of Falklans War happens, this story calls into question the official narrative of the war and discuss, in literary key, boundaries between memory and fiction on contemporary Latin-American history. Thus, from the field of Cultural Semiotics, our work proposes an approach of the complex relationship between memory and fiction in Los Pichiciegos.Aquest treball proposa, prenent com a eix els postulats de Iuri Lotman sobre la memòria, una lectura de la novel·la Los pichiciegos (1984), de l'escriptor argentí Rodolfo Enrique Fogwill (1941-2010). Escrit en paral·lel a la guerra de les Malvines, aquest relat posa en qüestió la narrativa oficial de la guerra i problematitza, en clau literària, les tensions entre memòria i ficció en construir un esdeveniment rellevant de la història llatinoamericana. D'aquesta manera, el nostre treball proposa un abordatge, des del camp de la semiòtica de la cultura, de les complexes relacions entre memòria i ficció

Computational Approaches for Drug-Induced Liver Injury (DILI) Prediction: State of the Art and Challenges

Drug-induced liver injury (DILI) is one of the prevailing causes of fulminant hepatic failure. It is estimated that three idiosyncratic drug reactions out of four result in liver transplantation or death. Additionally, DILI is the most common reason for withdrawal of an approved drug from the market. Therefore, the development of methods for the early identification of hepatotoxic drug candidates is of crucial importance. This review focuses on the current state of cheminformatics strategies being applied for the early in silico prediction of DILI. Herein, we discuss key issues associated with DILI modelling in terms of the data size, imbalance and quality, complexity of mechanisms, and the different levels of hepatotoxicity to model going from general hepatotoxicity to the molecular initiating events of DILI

KnowTox: pipeline and case study for confident prediction of potential toxic effects of compounds in early phases of development

Risk assessment of newly synthesised chemicals is a prerequisite for regulatory approval. In this context, in silico methods have great potential to reduce time, cost, and ultimately animal testing as they make use of the ever-growing amount of available toxicity data. Here, KnowTox is presented, a novel pipeline that combines three different in silico toxicology approaches to allow for confident prediction of potentially toxic effects of query compounds, i.e. machine learning models for 88 endpoints, alerts for 919 toxic substructures, and computational support for read-across. It is mainly based on the ToxCast dataset, containing after preprocessing a sparse matrix of 7912 compounds tested against 985 endpoints. When applying machine learning models, applicability and reliability of predictions for new chemicals are of utmost importance. Therefore, first, the conformal prediction technique was deployed, comprising an additional calibration step and per definition creating internally valid predictors at a given significance level. Second, to further improve validity and information efficiency, two adaptations are suggested, exemplified at the androgen receptor antagonism endpoint. An absolute increase in validity of 23% on the in-house dataset of 534 compounds could be achieved by introducing KNNRegressor normalisation. This increase in validity comes at the cost of efficiency, which could again be improved by 20% for the initial ToxCast model by balancing the dataset during model training. Finally, the value of the developed pipeline for risk assessment is discussed using two in-house triazole molecules. Compared to a single toxicity prediction method, complementing the outputs of different approaches can have a higher impact on guiding toxicity testing and de-selecting most likely harmful development-candidate compounds early in the development process

Systems and chemical biology approaches to study cell function and response to toxins

Toxicity is one of the main causes of failure during drug discovery, and of withdrawal once drugs reached the market. Prediction of potential toxicities in the early stage of drug development has thus become of great interest to reduce such costly failures. Since toxicity results from chemical perturbation of biological systems, we combined biological and chemical strategies to help understand and ultimately predict drug toxicities. First, we proposed a systematic strategy to predict and understand the mechanistic interpretation of drug toxicities based on chemical fragments. Fragments frequently found in chemicals with certain toxicities were defined as structural alerts for use in prediction. Some of the predictions were supported with mechanistic interpretation by integrating fragmentchemical, chemical-protein, protein-protein interactions and gene expression data. Next, we systematically deciphered the mechanisms of drug actions and toxicities by analyzing the associations of drugs’ chemical features, biological features and their gene expression profiles from the TG-GATEs database. We found that in vivo (rat liver) and in vitro (rat hepatocyte) gene expression patterns were poorly overlapped and gene expression responses in different species (rat and human) and different tissues (liver and kidney) varied widely. Eventually, for further understanding of individual differences in drug responses, we reviewed how genetic polymorphisms influence the individual's susceptibility to drug toxicity by deriving chemical-protein interactions and SNP variations from Mechismo database. Such a study is also essential for personalized medicine. Overall, this study showed that, integrating chemical and biological in addition to genetic data can help assess and predict drug toxicity at system and population levels

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

Alternative methods for regulatory toxicology – a state-of-the-art review

This state-of-the art review is based on the final report of a project carried out by the European Commission’s Joint Research Centre (JRC) for the European Chemicals Agency (ECHA). The aim of the project was to review the state of the science of non-standard methods that are available for assessing the toxicological and ecotoxicological properties of chemicals. Non-standard methods refer to alternatives to animal experiments, such as in vitro tests and computational models, as well as animal methods that are not covered by current regulatory guidelines. This report therefore reviews the current scientific status of non-standard methods for a range of human health and ecotoxicological endpoints, and provides a commentary on the mechanistic basis and regulatory applicability of these methods. For completeness, and to provide context, currently accepted (standard) methods are also summarised. In particular, the following human health endpoints are covered: a) skin irritation and corrosion; b) serious eye damage and eye irritation; c) skin sensitisation; d) acute systemic toxicity; e) repeat dose toxicity; f) genotoxicity and mutagenicity; g) carcinogenicity; h) reproductive toxicity (including effects on development and fertility); i) endocrine disruption relevant to human health; and j) toxicokinetics. In relation to ecotoxicological endpoints, the report focuses on non-standard methods for acute and chronic fish toxicity. While specific reference is made to the information needs of REACH, the Biocidal Products Regulation and the Classification, Labelling and Packaging Regulation, this review is also expected to be informative in relation to the possible use of alternative and non-standard methods in other sectors, such as cosmetics and plant protection products.JRC.I.5-Systems Toxicolog

Immune-Mediated Drug Induced Liver Injury: A Multidisciplinary Approach

This thesis presents an approach to expose relationships between immune mediated drug induced liver injury (IMDILI) and the three-dimensional structural features of toxic drug molecules and their metabolites. The series of analyses test the hypothesis that drugs which produce similar patterns of toxicity interact with targets within common toxicological pathways and that activation of the underlying mechanisms depends on structural similarity among toxic molecules. Spontaneous adverse drug reaction (ADR) reports were used to identify cases of IMDILI. Network map tools were used to compare the known and predicted protein interactions with each of the probe drugs to explore the interactions that are common between the drugs. The IMDILI probe set was then used to develop a pharmacophore model which became the starting point for identifying potential toxicity targets for IMDILI. Pharmacophore screening results demonstrated similarities between the probe IMDILI set of drugs and Toll-Like Receptor 7 (TLR7) agonists, suggesting TLR7 as a potential toxicity target. This thesis highlights the potential for multidisciplinary approaches in the study of complex diseases. Such approaches are particularly helpful for rare diseases where little knowledge is available, and may provide key insights into mechanisms of toxicity that cannot be gleaned from a single disciplinary study

Immune-Mediated Drug Induced Liver Injury: A Multidisciplinary Approach

This thesis presents an approach to expose relationships between immune mediated drug induced liver injury (IMDILI) and the three-dimensional structural features of toxic drug molecules and their metabolites. The series of analyses test the hypothesis that drugs which produce similar patterns of toxicity interact with targets within common toxicological pathways and that activation of the underlying mechanisms depends on structural similarity among toxic molecules. Spontaneous adverse drug reaction (ADR) reports were used to identify cases of IMDILI. Network map tools were used to compare the known and predicted protein interactions with each of the probe drugs to explore the interactions that are common between the drugs. The IMDILI probe set was then used to develop a pharmacophore model which became the starting point for identifying potential toxicity targets for IMDILI. Pharmacophore screening results demonstrated similarities between the probe IMDILI set of drugs and Toll-Like Receptor 7 (TLR7) agonists, suggesting TLR7 as a potential toxicity target. This thesis highlights the potential for multidisciplinary approaches in the study of complex diseases. Such approaches are particularly helpful for rare diseases where little knowledge is available, and may provide key insights into mechanisms of toxicity that cannot be gleaned from a single disciplinary study

Novel lipids to regulate obesity and brain function: comparing available evidence and insights from QSAR in silico models

Lipid molecules, such as policosanol, ergosterol, sphingomyelin, omega 3 rich phosphatidylcholine, α-tocopherol, and sodium butyrate, have emerged as novel additions to the portfolio of bioactive lipids. In this state-of-the-art review, we discuss these lipids, and their activity against obesity and mental or neurological disorders, with a focus on their proposed cellular targets and the ways in which they produce their beneficial effects. Furthermore, this available information is compared with that provided by in silico Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) models in order to understand the usefulness of these tools for the discovery of new bioactive compounds. Accordingly, it was possible to highlight how these lipids interact with various cellular targets related to the molecule transportation and absorption (e.g., α-tocopherol transfer protein for α-Tocopherol, ATP-binding cassette ABC transporters or Apolipoprotein E for sphingomyelins and phospholipids) or other processes, such as the regulation of gene expression (involving Sterol Regulatory Element-Binding Proteins for ergosterol or Peroxisome Proliferator-Activated Receptors in the case of policosanol) and inflammation (the regulation of interleukins by sodium butyrate). When comparing the literature with in silico Quantitative Structure–Activity Relationship (QSAR) models, it was observed that although they are useful for selecting bioactive molecules when compared in batch, the information they provide does not coincide when assessed individually. Our review highlights the importance of considering a broad range of lipids as potential bioactives and the need for accurate prediction of ADMET parameters in the discovery of new biomolecules. The information presented here provides a useful resource for researchers interested in developing new strategies for the treatment of obesity and mental or neurological disorders.info:eu-repo/semantics/publishedVersio

Preclinical models of idiosyncratic drug-induced liver injury (iDILI): Moving towards prediction

Idiosyncratic drug-induced liver injury (iDILI) encompasses the unexpected harms that pre- scription and non-prescription drugs, herbal and dietary supplements can cause to the liver. iDILI remains a major public health problem and a major cause of drug attrition. Given the lack of biomarkers for iDILI prediction, diagnosis and prognosis, searching new models to predict and study mechanisms of iDILI is necessary. One of the major limitations of iDILI preclinical assessment has been the lack of correlation between the markers of hepatotoxicity in animal toxicological studies and clinically significant iDILI. Thus, major advances in the understanding of iDILI susceptibility and pathogenesis have come from the study of well-phenotyped iDILI patients. However, there are many gaps for explaining all the complexity of iDILI susceptibility and mechanisms. Therefore, there is a need to optimize preclinical hu- man in vitro models to reduce the risk of iDILI during drug development. Here, the current experimental models and the future directions in iDILI modelling are thoroughly discussed, focusing on the human cellular models available to study the pathophysiological mechanisms of the disease and the most used in vivo animal iDILI models. We also comment about in silico approaches and the increasing relevance of patient-derived cellular models.This work was supported by grants of Instituto de Salud Carlos III cofounded by Fondo Europeo de Desarrollo Regional-FEDER (contract numbers: PI18/01804, PI19-00883, PT20/00127, UMA18-FEDERJA-194, PY18-3364, Spain) and grants of Consejería de Salud de Andalucía cofounded by FEDER (contract number: PEMP-0127-2020, Spain). M.V.P. holds a Sara Borrell (CD21/00198, Spain) research contract from ISCIII and Consejería de Salud de Andalucía. C.L.G. holds a Juan de la Cierva Incorporación (IJCI-2017-31466, Spain) research contract from Ministerio de Ciencia del Gobierno de España. SCReN and CIBERehd are funded by ISCIII (Spain). This publication is based upon work from COST Action “CA17112dProspective European Drug-Induced Liver Injury Network” supported by COST (European Cooperation in Science and Technology); www.cost.eu. The figures in this review were created with Biorender.com