Clinical Translation in the Virtual Liver Network

Kuepfer, Lars; Kerb, Reinhold; Henney, AM

doi:10.1038/psp.2014.25

Cited by 19 publications

(17 citation statements)

References 9 publications

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“…To quantify drug clearance in vivo a specifically designed drug cocktail consisting of six marketed drugs was used 7 . For each of the six parent drugs and three of the corresponding metabolites PBPK models were developed.…”

Section: Discussionmentioning

confidence: 99%

Physiologically-based modelling in mice suggests an aggravated loss of clearance capacity after toxic liver damage

Schenk

Ghallab

Hofmann

et al. 2017

Sci Rep

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Diseases and toxins may lead to death of active liver tissue, resulting in a loss of total clearance capacity at the whole-body level. However, it remains difficult to study, whether the loss of metabolizing tissue is sufficient to explain loss of metabolic capacity of the liver or whether the surviving tissue undergoes an adaptive response to compensate the loss. To understand the cellular impact of toxic liver damage in an in vivo situation, we here used physiologically-based pharmacokinetic modelling to investigate pharmacokinetics of a specifically designed drug cocktail at three different sampling sites of the body in healthy mice and mice treated with carbon tetrachloride (CCl4). Liver zonation was explicitly quantified in the models through immunostaining of cytochrome P450s enzymes. Comparative analyses between the simulated decrease in clearance capacity and the experimentally measured loss in tissue volume indicated that CCl4-induced impairment of metabolic functions goes beyond the mere loss of metabolically active tissue. The here established integrative modelling strategy hence provides mechanistic insights into functional consequences of toxic liver damage in an in vivo situation, which would not have been accessible by conventional methods.

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Section: Discussionmentioning

confidence: 99%

Physiologically-based modelling in mice suggests an aggravated loss of clearance capacity after toxic liver damage

Schenk

Ghallab

Hofmann

et al. 2017

Sci Rep

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“… 79 The Virtual Liver Network is a German research initiative that bridges investigations from the subcellular level to patient and healthy volunteer studies in an integrated workflow to generate validated computer models of human liver physiology. 80 These in silico approaches rely on cumulative scores of known risk factors such as the administered dose or on potential liabilities such as mitochondrial toxicity, BSEP inhibition or the formation of reactive metabolites which can be measured in vitro. The major challenge when constructing predictive DILI models is to account for the broad range of chemotypes which have been associated with clinically relevant liver findings as well as the various mechanistic (pathway) considerations which translate into different clinical phenotypes of liver injury.…”

Section: In Vitro and In Silico Tools For The Assessment And Predictimentioning

confidence: 99%

Drug-induced liver injury: recent advances in diagnosis and risk assessment

Kullak‐Ublick

Andrade

Merz

et al. 2017

Gut

393

304

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Idiosyncratic drug-induced liver injury (IDILI) is a rare but potentially severe adverse drug reaction that should be considered in patients who develop laboratory criteria for liver injury secondary to the administration of a potentially hepatotoxic drug. Although currently used liver parameters are sensitive in detecting DILI, they are neither specific nor able to predict the patient's subsequent clinical course. Genetic risk assessment is useful mainly due to its high negative predictive value, with several human leucocyte antigen alleles being associated with DILI. New emerging biomarkers which could be useful in assessing DILI include total keratin18 (K18) and caspase-cleaved keratin18 (ccK18), macrophage colony-stimulating factor receptor 1, high mobility group box 1 and microRNA-122. From the numerous in vitro test systems that are available, monocyte-derived hepatocytes generated from patients with DILI show promise in identifying the DILI-causing agent from among a panel of coprescribed drugs. Several computer-based algorithms are available that rely on cumulative scores of known risk factors such as the administered dose or potential liabilities such as mitochondrial toxicity, inhibition of the bile salt export pump or the formation of reactive metabolites. A novel DILI cluster score is being developed which predicts DILI from multiple complimentary cluster and classification models using absorption–distribution–metabolism–elimination-related as well as physicochemical properties, diverse substructural descriptors and known structural liabilities. The provision of more advanced scientific and regulatory guidance for liver safety assessment will depend on validating the new diagnostic markers in the ongoing DILI registries, biobanks and public–private partnerships.

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“…These remain the challenges facing the VLN. With a focus on the study of inflammation, regeneration and metabolism in the liver, involving contributions from the molecular level through to clinical studies on volunteers and patients (Holzhutter et al, 2012;Küepfer et al, 2014), the VLN is in a position to provide evidence of genuine value, not only through providing tools to support approaches in predictive toxicology and systems pharmacology, where modeling approaches are showing signs of promise (Vicini and van der Graaf, 2013), but also by furthering our understanding of the mechanisms of disease progression.…”

Section: Vln: Addressing the Challenges And Future Perspectivementioning

confidence: 98%