scholarly journals Investigating the Mechanism of Trimethoprim-Induced Skin Rash and Liver Injury

2021 ◽  
Author(s):  
Yanshan Cao ◽  
Ahsan Bairam ◽  
Alison Jee ◽  
Ming Liu ◽  
Jack Uetrecht
Keyword(s):  
Liver Injury ◽  
Skin Rash ◽  
Covalent Binding ◽  
Reactive Metabolites ◽  
Important Data ◽  
Drug Induced ◽  
Interindividual Differences ◽  
Immune Mediated ◽  
Sulfate Conjugate

Abstract Trimethoprim (TMP)-induced skin rash and liver injury are likely to involve the formation of reactive metabolites. Analogous to nevirapine-induced skin rash, one possible reactive metabolite is the sulfate conjugate of α-hydroxyTMP, a metabolite of TMP. We synthesized this sulfate and found that it reacts with proteins in vitro. We produced a TMP-antiserum and found covalent binding of TMP in the liver of TMP-treated rats. However, we found that α-hydroxyTMP is not a substrate for human sulfotransferases, and we did not detect covalent binding in the skin of TMP-treated rats. Although less reactive than the sulfate, α-hydroxyTMP was found to covalently bind to liver and skin proteins in vitro. Even though there was covalent binding to liver proteins, TMP did not cause liver injury in rats or in our impaired immune tolerance mouse model that has been able to unmask the ability of other drugs to cause immune-mediated liver injury. This is likely because there was much less covalent binding of TMP in the livers of TMP-treated mice than TMP-treated rats. It is possible that some patients have a sulfotransferase that can produce the reactive benzylic sulfate; however, α-hydroxyTMP, itself, has sufficient reactivity to covalently bind to proteins in the skin and may be responsible for TMP-induced skin rash. Interspecies and interindividual differences in TMP metabolism may be one factor that determines the risk of TMP-induced skin rash. This study provides important data required to understand the mechanism of TMP-induced skin rash and drug-induced skin rash in general.

Development and Application of a Transcriptomic Signature of Bioactivation in an Advanced In Vitro Liver Model to Reduce Drug-induced Liver Injury Risk Early in the Pharmaceutical Pipeline

2020 ◽  
Vol 177 (1) ◽  
pp. 121-139 ◽  
Author(s):  
Wen Kang ◽  
Alexei A Podtelezhnikov ◽  
Keith Q Tanis ◽  
Stephen Pacchione ◽  
Ming Su ◽  
...  
Keyword(s):  
Liver Injury ◽  
Human Model ◽  
Reactive Metabolites ◽  
In Vitro Test ◽  
Drug Induced ◽  
Vitro Assay ◽  
Drug Induced Liver Injury ◽  
Drug Candidates ◽  
Transcriptomic Signature

Abstract Early risk assessment of drug-induced liver injury (DILI) potential for drug candidates remains a major challenge for pharmaceutical development. We have previously developed a set of rat liver transcriptional biomarkers in short-term toxicity studies to inform the potential of drug candidates to generate a high burden of chemically reactive metabolites that presents higher risk for human DILI. Here, we describe translation of those NRF1-/NRF2-mediated liver tissue biomarkers to an in vitro assay using an advanced micropatterned coculture system (HEPATOPAC) with primary hepatocytes from male Wistar Han rats. A 9-day, resource-sparing and higher throughput approach designed to identify new chemical entities with lower reactive metabolite-forming potential was qualified for internal decision making using 93 DILI-positive and -negative drugs. This assay provides 81% sensitivity and 90% specificity in detecting hepatotoxicants when a positive test outcome is defined as the bioactivation signature score of a test drug exceeding the threshold value at an in vitro test concentration that falls within 3-fold of the estimated maximum drug concentration at the human liver inlet following highest recommended clinical dose administrations. Using paired examples of compounds from distinct chemical series and close structural analogs, we demonstrate that this assay can differentiate drugs with lower DILI risk. The utility of this in vitro transcriptomic approach was also examined using human HEPATOPAC from a single donor, yielding 68% sensitivity and 86% specificity when the aforementioned criteria are applied to the same 93-drug test set. Routine use of the rat model has been adopted with deployment of the human model as warranted on a case-by-case basis. This in vitro transcriptomic signature-based strategy can be used early in drug discovery to derisk DILI potential from chemically reactive metabolites by guiding structure-activity relationship hypotheses and candidate selection.

Evaluation of the Potential for Drug-Induced Liver Injury Based on in Vitro Covalent Binding to Human Liver Proteins

2009 ◽  
Vol 37 (12) ◽  
pp. 2383-2392 ◽  
Author(s):  
Toru Usui ◽  
Masashi Mise ◽  
Takanori Hashizume ◽  
Masashi Yabuki ◽  
Setsuko Komuro
Keyword(s):  
Liver Injury ◽  
Human Liver ◽  
Covalent Binding ◽  
Drug Induced ◽  
Drug Induced Liver Injury

scholarly journals Recent Advances in Models of Immune-Mediated Drug-Induced Liver Injury

2021 ◽  
Vol 3 ◽  
Author(s):  
Farah Tasnim ◽  
Xiaozhong Huang ◽  
Christopher Zhe Wei Lee ◽  
Florent Ginhoux ◽  
Hanry Yu
Keyword(s):  
T Cells ◽  
Liver Injury ◽  
Complex Disease ◽  
Cell Contact ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Immune Mediated ◽  
Cell Cell

Hepatic inflammation is a key feature of a variety of liver diseases including drug-induced liver injury (DILI), orchestrated by the innate immune response (Kupffer cells, monocytes, neutrophils, dendritic cells) and the adaptive immune system (T cells and natural killer T cells). In contrast to acute DILI, prediction of immune-mediated DILI (im-DILI) has been more challenging due to complex disease pathogenesis, lack of reliable models and limited knowledge of underlying mechanisms. This review summarizes in vivo and in vitro systems that have been used to model im-DILI. In particular, the review focuses on state-of-the-art in vitro human-based multicellular models which have been developed to supplement the use of in vivo models due to interspecies variation and increasing ethical concerns regarding animal use. Advantages of the co-cultures in maintaining hepatocyte functions and importantly, introducing heterotypic cell-cell interactions to mimic inflammatory hepatic microenvironment are discussed. Challenges regarding cell source and incorporation of different cells with physical cell-cell contact are outlined and potential solutions are proposed. It is likely that better understanding of the interplay of immune cells in liver models will allow for the development of more accurate systems to better predict hepatotoxicity and stratification of drugs that can cause immune-mediated effects.

scholarly journals Immune-Mediated Drug-Induced Liver Injury: Immunogenetics and Experimental Models

2021 ◽  
Vol 22 (9) ◽  
pp. 4557
Author(s):  
Alessio Gerussi ◽  
Ambra Natalini ◽  
Fabrizio Antonangeli ◽  
Clara Mancuso ◽  
Elisa Agostinetto ◽  
...  
Keyword(s):  
Liver Injury ◽  
Experimental Models ◽  
Clinical Event ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Immune Mediated ◽  
Liver Injuries ◽  
Immunological Mechanisms ◽  
Molecular Aspects ◽  
Therapeutic Tools

Drug-induced liver injury (DILI) is a challenging clinical event in medicine, particularly because of its ability to present with a variety of phenotypes including that of autoimmune hepatitis or other immune mediated liver injuries. Limited diagnostic and therapeutic tools are available, mostly because its pathogenesis has remained poorly understood for decades. The recent scientific and technological advancements in genomics and immunology are paving the way for a better understanding of the molecular aspects of DILI. This review provides an updated overview of the genetic predisposition and immunological mechanisms behind the pathogenesis of DILI and presents the state-of-the-art experimental models to study DILI at the pre-clinical level.

Drug-induced liver injury classification model based on in vitro human transcriptomics and in vivo rat clinical chemistry data

2014 ◽  
Vol 2 (4) ◽  
pp. 63-70 ◽  
Author(s):  
Danyel Jennen ◽  
Jan Polman ◽  
Mark Bessem ◽  
Maarten Coonen ◽  
Joost van Delft ◽  
...  
Keyword(s):  
Liver Injury ◽  
Clinical Chemistry ◽  
Classification Model ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Chemistry Data ◽  
Model Based ◽  
Injury Classification

scholarly journals Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

2020 ◽  
Author(s):  
Robert Ancuceanu ◽  
Marilena Viorica Hovanet ◽  
Adriana Iuliana Anghel ◽  
Florentina Furtunescu ◽  
Monica Neagu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Injury ◽  
Computational Models ◽  
Liver Toxicity ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Drug Induced ◽  
Reference Drug ◽  
Drug Induced Liver Injury

Drug induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized drugs and candidate drugs and predicting hepatotoxicity from the chemical structure of a substance remains a challenge worth pursuing, being also coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016 a group of researchers from FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans”, DILIrank. This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A number of 78 models with reasonable performance have been selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

scholarly journals A case report of a drug‐induced liver injury (DILI) caused by multiple antidepressants with causality established by the updated Roussel Uclaf causality assessment method (RUCAM) and in vitro testing

2020 ◽  
Vol 8 (12) ◽  
pp. 3105-3109
Author(s):  
Miguel González‐Muñoz ◽  
Jaime Monserrat Villatoro ◽  
Eva Marín‐Serrano ◽  
Stefan Stewart ◽  
Belén Bardón Rivera ◽  
...  
Keyword(s):  
Case Report ◽  
Liver Injury ◽  
Causality Assessment ◽  
Assessment Method ◽  
In Vitro Testing ◽  
Drug Induced ◽  
Drug Induced Liver Injury

Biopsy Pathology and Immunohistochemistry of a Case of Immune‐Mediated Drug‐Induced Liver Injury With Atabecestat

Hepatology ◽  
2020 ◽  
Author(s):  
Sandra De Jonghe ◽  
Daniel Weinstock ◽  
Jason Aligo ◽  
Kay Washington ◽  
Dean Naisbitt
Keyword(s):  
Liver Injury ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Immune Mediated

scholarly journals Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

2020 ◽  
Vol 21 (6) ◽  
pp. 2114
Author(s):  
Robert Ancuceanu ◽  
Marilena Viorica Hovanet ◽  
Adriana Iuliana Anghel ◽  
Florentina Furtunescu ◽  
Monica Neagu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Injury ◽  
Computational Models ◽  
Liver Toxicity ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Drug Induced ◽  
Reference Drug ◽  
Drug Induced Liver Injury

Drug-induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized and candidate drugs, and predicting hepatotoxicity from the chemical structure of a substance remains a task worth pursuing. Such an approach is coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016, a group of researchers from the FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans” (DILIrank). This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A total of 78 models with reasonable performance were selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

The Role of Reactive Drug Metabolites in Immune-Mediated Adverse Drug Reactions

1997 ◽  
Vol 31 (11) ◽  
pp. 1378-1387 ◽  
Author(s):  
David A Hess ◽  
Michael J Rieder
Keyword(s):  
Immune Response ◽  
Drug Therapy ◽  
Adverse Drug Reactions ◽  
Covalent Binding ◽  
Cellular Damage ◽  
Hypersensitivity Reactions ◽  
Drug Reactions ◽  
Drug Metabolites ◽  
Immune Mediated

OBJECTIVE: To highlight recent advances in the understanding of adverse drug reactions (ADRs), with a focus on models outlining interactions between drug metabolism, disease processes, and immunity. Specific mechanisms that identify the metabolic pathways responsible for drug bioactivation to reactive drug metabolites (RDMs) involved in the initiation and propagation of specific immune-mediated hypersensitivity reactions are discussed. Drug classes well known to be associated with immune-mediated ADRs are reviewed and the clinical implications of current research are discussed. DATA SOURCES: Original experimental research and immunologic review articles relevant to ADR diagnosis and etiology. DATA EXTRACTION: Results of relevant in vitro experiments and clinical reactions to drug therapy were compiled and reviewed. Critical discoveries concerning the identification of RDMs involved in ADRs were highlighted, with respect to RDM involvement in the production of an immune response to drug haptens. DATA SYNTHESIS: Drug adverse effects are classified according to clinical characteristics, immune interactions, and mechanistic similarities. Cytochrome P450 bioactivation of drug molecules to RDMs is a prerequisite to many ADRs. An electrophilic metabolite may react with cellular macromolecules (i.e., lipids, proteins, nucleic acids), resulting in direct cellular damage and organ toxicity. Covalent binding of an RDM to cellular macromolecules may also result in the formation of a hapten that is capable of eliciting a cellular or humoral immune response against drug or protein epitopes, culminating in the characteristic symptoms of hypersensitivity reactions. Mechanistic details concerning the identification of stable protein-metabolite conjugates and their interaction with the immune system remain unclear. Genetic imbalance between bioactivation and detoxification pathways, as well as reduced cellular defense against RDMs due to disease or concomitant drug therapy, act as risk factors to the onset and severity of ADRs. CONCLUSIONS: Adverse reactions to drug therapy cause significant morbidity and mortality. Identification of the pathways involved in drug bioactivation and detoxification may elucidate the potential of chemical agents to induce immune-mediated ADRs. Understanding the mechanisms of ADRs to current xenobiotics is helpful in the prevention and management of ADRs, and may prove useful in the design of novel therapeutic agents with reduced incidence of severe adverse events.

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