Prediction of human drug-induced liver injury (DILI) in relation to oral doses and blood concentrations.

Prediction of human drug-induced liver injury (DILI) in relation to oral doses and blood concentrations.

Albrecht, Wiebke;Kappenberg, Franziska;Brecklinghaus, Tim;Stoeber, Regina;Marchan, Rosemarie;Zhang, Mian;Ebbert, Kristina;Kirschner, Hendrik;Grinberg, Marianna;Leist, Marcel;Moritz, Wolfgang;Cadenas, Cristina;Ghallab, Ahmed;Reinders, Jörg;Vartak, Nachiket;van Thriel, Christoph;Golka, Klaus;Tolosa, Laia;Castell, José V;Damm, Georg;Seehofer, Daniel;Lampen, Alfonso;Braeuning, Albert;Buhrke, Thorsten;Behr, Anne-Cathrin;Oberemm, Axel;Gu, Xiaolong;Kittana, Naim;van de Water, Bob;Kreiling, Reinhard;Fayyaz, Susann;van Aerts, Leon;Smedsrød, Bård;Ellinger-Ziegelbauer, Heidrun;Steger-Hartmann, Thomas;Gundert-Remy, Ursula;Zeigerer, Anja;Ullrich, Anett;Runge, Dieter;Lee, Serene M L;Schiergens, Tobias S;Kuepfer, Lars;Aguayo-Orozco, Alejandro;Sachinidis, Agapios;Edlund, Karolina;Gardner, Iain;Rahnenführer, Jörg;Hengstler, Jan G;
archives of toxicology 2019 Vol. 93 pp. 1609-1637
160
albrecht2019predictionarchives

Abstract

Drug-induced liver injury (DILI) cannot be accurately predicted by animal models. In addition, currently available in vitro methods do not allow for the estimation of hepatotoxic doses or the determination of an acceptable daily intake (ADI). To overcome this limitation, an in vitro/in silico method was established that predicts the risk of human DILI in relation to oral doses and blood concentrations. This method can be used to estimate DILI risk if the maximal blood concentration (C) of the test compound is known. Moreover, an ADI can be estimated even for compounds without information on blood concentrations. To systematically optimize the in vitro system, two novel test performance metrics were introduced, the toxicity separation index (TSI) which quantifies how well a test differentiates between hepatotoxic and non-hepatotoxic compounds, and the toxicity estimation index (TEI) which measures how well hepatotoxic blood concentrations in vivo can be estimated. In vitro test performance was optimized for a training set of 28 compounds, based on TSI and TEI, demonstrating that (1) concentrations where cytotoxicity first becomes evident in vitro (EC) yielded better metrics than higher toxicity thresholds (EC); (2) compound incubation for 48 h was better than 24 h, with no further improvement of TSI after 7 days incubation; (3) metrics were moderately improved by adding gene expression to the test battery; (4) evaluation of pharmacokinetic parameters demonstrated that total blood compound concentrations and the 95%-population-based percentile of C were best suited to estimate human toxicity. With a support vector machine-based classifier, using EC and C as variables, the cross-validated sensitivity, specificity and accuracy for hepatotoxicity prediction were 100, 88 and 93%, respectively. Concentrations in the culture medium allowed extrapolation to blood concentrations in vivo that are associated with a specific probability of hepatotoxicity and the corresponding oral doses were obtained by reverse modeling. Application of this in vitro/in silico method to the rat hepatotoxicant pulegone resulted in an ADI that was similar to values previously established based on animal experiments. In conclusion, the proposed method links oral doses and blood concentrations of test compounds to the probability of hepatotoxicity.

Citation

ID: 34178
Ref Key: albrecht2019predictionarchives
Use this key to autocite in SciMatic or Thesis Manager

References

Blockchain Verification

Account:
NFT Contract Address:
0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
34178
Unique Identifier:
10.1007/s00204-019-02492-9
Network:
Scimatic Chain (ID: 481)
Loading...
Blockchain Readiness Checklist
Authors
Abstract
Journal Name
Year
Title
5/5
Creates 1,000,000 NFT tokens for this article
Token Features:
  • ERC-1155 Standard NFT
  • 1 Million Supply per Article
  • Transferable via MetaMask
  • Permanent Blockchain Record
Blockchain QR Code
Scan with Saymatik Web3.0 Wallet

Saymatik Web3.0 Wallet