In vitro to in vivo extrapolation for drug-induced liver injury using a pair ranking method

Liu, Zhichao

doi:10.14573/altex.1610201

Cited by 12 publications

(37 citation statements)

References 22 publications

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“…In more detail, we describe three indicators to show the improved consistencies achieved by using "drugresponding component." 1) We first compared the similarities [PRank score (Liu et al, 2017)] between the in vivo (original and "drug-responding component") and in vitro data for each pair of compounds by using the PRank method (Figures 2A, B). 2) Then, we counted the number of Dice's coefficients of each compoundpair, revealing different tendencies in in vivo (original and "drugresponding component") and in vivo data among 7021 compound-pairs.…”

Section: Methodsmentioning

confidence: 99%

“…The Pair Ranking (PRank) method was used to investigate the consistency among the three rat TGx assay systems (Liu et al, 2017;Liu et al, 2018b). The PRank score was utilized to measure the similar extent between every system in the validation part as well as in the application.…”

Section: Pair Ranking (Prank) Methodsmentioning

confidence: 99%

“…In vivo and in vitro experimental systems are two essential ways to discover functional performance during drug-discovery, vital processes occurring in living organisms and toxicological research (Nuwaysir et al, 1999). For animal trials, in vivo experiments still can be launched properly for toxicological research (Hussain et al, 1987;Liu et al, 2017). The only problem is that in vivo costs are higher than those for in vitro assays, so it is more difficult to utilize in vivo systems in large-scale projects.…”

Section: Introductionmentioning

confidence: 99%

“…After assessing the capacity of in vitro screening studies to predict the in vivo pulmonary toxicity of several fine or nanoscale particles in rats, a poor correlation between in vivo and in vitro studies was observed (Sayes et al, 2007). The pair ranking (PRank) method (Liu et al, 2017) was proposed by the National Center for Toxicological Research of the U.S. FDA to assess IVIVE and to quantitatively measure similarity (Chen et al, 2012a;Chen et al, 2014). The PRank method has made it possible to properly compare gene expression data between in vivo and in vitro assays.…”

Section: Introductionmentioning

confidence: 99%

“…It has been indicated that the inner environment of a living subject has different patterns of physiological function and mechanism. Additionally, therapy-pharmacokinetics (Harada-Shiba et al, 2002), pharmacokinetics and pharmacodynamics (PK/PD) (Liu et al, 2017) are usually not considered within the proposed IVIVE assessment of toxicogenomic data. Eventually, with the comprehensive effects and variables that are determined by the complicated inner-environment of in vivo data, an inconsistent gap has emerged between in vivo data and in vitro data, and this inconsistency is further reflected at the level of gene expression.…”

Section: Introductionmentioning

confidence: 99%

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Narrowing the Gap Between In Vitro and In Vivo Genetic Profiles by Deconvoluting Toxicogenomic Data In Silico

et al. 2020

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Toxicogenomics (TGx) is a powerful method to evaluate toxicity and is widely used in both in vivo and in vitro assays. For in vivo TGx, reduction, refinement, and replacement represent the unremitting pursuit of live-animal tests, but in vitro assays, as alternatives, usually demonstrate poor correlation with real in vivo assays. In living subjects, in addition to drug effects, inner-environmental reactions also affect genetic variation, and these two factors are further jointly reflected in gene abundance. Thus, finding a strategy to factorize inner-environmental factor from in vivo assays based on gene expression levels and to further utilize in vitro data to better simulate in vivo data is needed. We proposed a strategy based on post-modified non-negative matrix factorization, which can estimate the gene expression profiles and contents of major factors in samples. The applicability of the strategy was first verified, and the strategy was then utilized to simulate in vivo data by correcting in vitro data. The similarities between real in vivo data and simulated data (single-dose 0.72, repeat-doses 0.75) were higher than those observed when directly comparing real in vivo data with in vitro data (single-dose 0.56, repeat-doses 0.70). Moreover, by keeping environment-related factor, a simulation can always be generated by using in vitro data to provide potential substitutions for in vivo TGx and to reduce the launch of live-animal tests.

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Section: Methodsmentioning

confidence: 99%

Section: Pair Ranking (Prank) Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Narrowing the Gap Between In Vitro and In Vivo Genetic Profiles by Deconvoluting Toxicogenomic Data In Silico

et al. 2020

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AI-based language models powering drug discovery and development

Liu

Roberts

Chen

et al. 2021

Drug Discovery Today

Self Cite

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The discovery and development of new medicines is expensive, time-consuming, and often inefficient, with many failures along the way. Powered by artificial intelligence (AI), language models (LMs) have changed the landscape of natural language processing (NLP), offering possibilities to transform treatment development more effectively. Here, we summarize advances in AI-powered LMs and their potential to aid drug discovery and development. We highlight opportunities for AI-powered LMs in target identification, clinical design, regulatory decision-making, and pharmacovigilance. We specifically emphasize the potential role of AI-powered LMs for developing new treatments for Coronavirus 2019 (COVID-19) strategies, including drug repurposing, which can be extrapolated to other infectious diseases that have the potential to cause pandemics. Finally, we set out the remaining challenges and propose possible solutions for improvement.

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Transcriptomics in toxicology

Joseph

2017

Food and Chemical Toxicology

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Xenobiotics, of which many are toxic, may enter the human body through multiple routes. Excessive human exposure to xenobiotics may exceed the body's capacity to defend against the xenobiotic-induced toxicity and result in potentially fatal adverse health effects. Prevention of the adverse health effects, potentially associated with human exposure to the xenobiotics, may be achieved by detecting the toxic effects at an early, reversible and, therefore, preventable stage. Additionally, an understanding of the molecular mechanisms underlying the toxicity may be helpful in preventing and/or managing the ensuing adverse health effects. Human exposures to a large number of xenobiotics are associated with hepatotoxicity or pulmonary toxicity. Global gene expression changes taking place in biological systems, in response to exposure to xenobiotics, may represent the early and mechanistically relevant cellular events contributing to the onset and progression of xenobiotic-induced adverse health outcomes. Hepatotoxicity and pulmonary toxicity resulting from exposure to xenobiotics are discussed as specific examples to demonstrate the potential application of transcriptomics or global gene expression analysis in the prevention of adverse health effects associated with exposure to xenobiotics.

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In vitro to in vivo extrapolation for drug-induced liver injury using a pair ranking method

Cited by 12 publications

References 22 publications

Narrowing the Gap Between In Vitro and In Vivo Genetic Profiles by Deconvoluting Toxicogenomic Data In Silico

Narrowing the Gap Between In Vitro and In Vivo Genetic Profiles by Deconvoluting Toxicogenomic Data In Silico

AI-based language models powering drug discovery and development

Transcriptomics in toxicology

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