Hépatotoxicité

Résumé

Hepatotoxicity (from hepatic toxicity) implies chemical-driven liver damage. Drug-induced liver injury is a cause of acute and chronic liver disease caused specifically by medications and the most common reason for a drug to be withdrawn from the market after approval. The liver plays a central role in transforming and clearing chemicals and is susceptible to the toxicity from these agents. Certain medicinal agents, when taken in overdoses (e.g. paracetamol) and sometimes even when introduced within therapeutic ranges (e.g. halothane), may injure the organ. Other chemical agents, such as those used in laboratories and industries, natural chemicals (e.g., microcystins), and herbal remedies (two prominent examples being kava, mechanism unknown, and comfrey, through its pyrrolizidine alkaloid content) can also induce hepatotoxicity. Chemicals that cause liver injury are called hepatotoxins. More than 900 drugs have been implicated in causing liver injury (see LiverTox, external link, below) and it is the most common reason for a drug to be withdrawn from the market. Hepatotoxicity and drug-induced liver injury also account for a substantial number of compound failures, highlighting the need for toxicity prediction models (e.g. DTI), and drug screening assays, such as stem cell-derived hepatocyte-like cells, that are capable of detecting toxicity early in the drug development process. Chemicals often cause subclinical injury to the liver, which manifests only as abnormal liver enzyme tests. Drug-induced liver injury is responsible for 5% of all hospital admissions and 50% of all acute liver failures. Adverse drug reactions are classified as type A (intrinsic or pharmacological) or type B (idiosyncratic). Type A drug reaction accounts for 80% of all toxicities. Drugs or toxins that have a pharmacological (type A) hepatotoxicity are those that have predictable dose-response curves (higher concentrations cause more liver damage) and well characterized mechanisms of toxicity, such as directly damaging liver tissue or blocking a metabolic process.

Source officielle

https://fr.wikipedia.org/wiki/Hépatotoxicité

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Séances de cours associées (5)

Targeted antibody and cytokine cancer immunotherapies through collagen affinity

Jeffrey Alan Hubbell, Melody Swartz, Seung Tae Lee, Lambert Charles François Potin, Jun Ishihara, Kazuto Fukunaga, Michal Mateusz Raczy

Cancer immunotherapy with immune checkpoint inhibitors (CPIs) and interleukin-2 (IL-2) has demonstrated clinical efficacy but is frequently accompanied with severe adverse events caused by excessive and systemic immune system activation. Here, we addressed this need by targeting both the CPI antibodies anti-cytotoxic T lymphocyte antigen 4 antibody (alpha CTLA4) + anti-programmed death ligand 1 antibody (alpha PD-L1) and the cytokine IL-2 to tumors via conjugation (for the antibodies) or recombinant fusion (for the cytokine) to a collagen-binding domain (CBD) derived from the blood protein von Willebrand factor (VWF) A3 domain, harnessing the exposure of tumor stroma collagen to blood components due to the leakiness of the tumor vasculature. We show that intravenously administered CBD protein accumulated mainly in tumors. CBD conjugation or fusion decreases the systemic toxicity of both alpha CTLA4+alpha PD-L1 combination therapy and IL-2, for example, eliminating hepatotoxicity with the CPI molecules and ameliorating pulmonary edema with IL-2. Both CBD-CPI and CBD-IL-2 suppressed tumor growth compared to their unmodified forms in multiple murine cancer models, and both CBD-CPI and CBD-IL-2 increased tumor-infiltrating CD8(+) T cells. In an orthotopic breast cancer model, combination treatment with CPI and IL-2 eradicated tumors in 9 of 13 animals with the CBD-modified drugs, whereas it did so in only 1 of 13 animals with the unmodified drugs. Thus, the A3 domain of VWF can be used to improve safety and efficacy of systemically administered tumor drugs with high translational promise.

AMER ASSOC ADVANCEMENT SCIENCE2019

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CD40 is an immune costimulatory receptor expressed by antigen-presenting cells. Agonistic anti-CD40 antibodies have demonstrated considerable antitumor effects yet can also elicit serious treatment-related adverse events, such as liver toxicity, including in man. We engineered a variant that binds extracellular matrix through a super-affinity peptide derived from placenta growth factor-2 (PIGF-2(123-144)) to enhance anti-CD40's effects when administered locally. Peritumoral injection of PIGF-2(123-144)-anti-CD40 antibody showed prolonged tissue retention at the injection site and substantially decreased systemic exposure, resulting in decreased liver toxicity. In four mouse tumor models, PICF-2(123-144)-anti-CD40 antibody demonstrated enhanced antitumor efficacy compared with its unmodified form and correlated with activated dendritic cells, B cells, and 'T' cells in the tumor and in the tumor-draining lymph node. Moreover, in a genetically engineered Braf(V600E) beta Cat(STA )melanoma model that does not respond to checkpoint inhibitors, PIGF-2(123-144)-anti-CD40 antibody treatment enhanced T-cell infiltration into the tumors and slowed tumor growth. Together, these results demonstrate the marked therapeutic advantages of engineering matrix-binding domains onto agonistic anti-CD40 antibody as a therapeutic given by tumori-regional injection for cancer immunotherapy. (C) 2018 AACR.

AMER ASSOC CANCER RESEARCH2018

The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

Darlene Goldstein, Chenxu Zhao, Yi Ren, Mauro Delorenzi, Li Zhang, Vlad Popovici, Jun Luo, Wen Luo, Wei Wang, Ying Liu, Jonathan Koh Boon Han, Li Guo, Rong Chen, Min Zhang, Li Li, Zhen Li, Rui Jiang, Jie Liu, Lei Xu, Lu Meng, Jianping Wu

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established In the MAQC II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans In total, >30,000 models were built using many combinations of analytical methods The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis

Nature Publishing Group2010