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A novel quantitative computer-assisted drug-induced liver injury causality assessment tool (DILI-CAT).

Publication ,  Journal Article
Tillmann, HL; Suzuki, A; Merz, M; Hermann, R; Rockey, DC
Published in: PLoS One
2022

BACKGROUND AND AIMS: We hypothesized that a drug's clinical signature (or phenotype) of liver injury can be assessed and used to quantitatively develop a computer-assisted DILI causality assessment-tool (DILI-CAT). Therefore, we evaluated drug-specific DILI-phenotypes for amoxicillin-clavulanate (AMX/CLA), cefazolin, cyproterone, and Polygonum multiflorum using data from published case series, to develop DILI-CAT scores for each drug. METHODS: Drug specific phenotypes were made up of the following three clinical features: (1) latency, (2) R-value, and (3) AST/ALT ratio. A point allocation system was developed with points allocated depending on the variance from the norm (or "core") for the 3 variables in published datasets. RESULTS: The four drugs had significantly different phenotypes based on latency, R-value, and AST/ALT ratio. The median cyproterone latency was 150 days versus < 43 days for the other three drugs (median: 26 for AMX/CLA, 20 for cefazolin, and 20 for Polygonum multiflorum; p<0.001). The R-value for the four drugs was also significantly different among drugs (cyproterone [median 12.4] and Polygonum multiflorum [median 10.9]) from AMX/CLA [median 1.44] and cefazolin [median 1.57; p<0.001]). DILI-CAT scores effectively separated cyproterone and Polygonum multiflorum from AMX/CLA and cefazolin, respectively (p<0.001). As expected, because of phenotypic overlap, AMX/CLA and cefazolin could not be well differentiated. CONCLUSIONS: DILI-CAT is a data-driven, diagnostic tool built to define drug-specific phenotypes for DILI adjudication. The data provide proof of principle that a drug-specific, data-driven causality assessment tool can be developed for different drugs and raise the possibility that such a process could enhance causality assessment methods.

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Published In

PLoS One

DOI

EISSN

1932-6203

Publication Date

2022

Volume

17

Issue

9

Start / End Page

e0271304

Location

United States

Related Subject Headings

  • Humans
  • General Science & Technology
  • Cyproterone
  • Computers
  • Chemical and Drug Induced Liver Injury
  • Cefazolin
  • Causality
  • Amoxicillin-Potassium Clavulanate Combination
 

Citation

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MLA
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Tillmann, H. L., Suzuki, A., Merz, M., Hermann, R., & Rockey, D. C. (2022). A novel quantitative computer-assisted drug-induced liver injury causality assessment tool (DILI-CAT). PLoS One, 17(9), e0271304. https://doi.org/10.1371/journal.pone.0271304
Tillmann, Hans L., Ayako Suzuki, Michael Merz, Richard Hermann, and Don C. Rockey. “A novel quantitative computer-assisted drug-induced liver injury causality assessment tool (DILI-CAT).PLoS One 17, no. 9 (2022): e0271304. https://doi.org/10.1371/journal.pone.0271304.
Tillmann HL, Suzuki A, Merz M, Hermann R, Rockey DC. A novel quantitative computer-assisted drug-induced liver injury causality assessment tool (DILI-CAT). PLoS One. 2022;17(9):e0271304.
Tillmann, Hans L., et al. “A novel quantitative computer-assisted drug-induced liver injury causality assessment tool (DILI-CAT).PLoS One, vol. 17, no. 9, 2022, p. e0271304. Pubmed, doi:10.1371/journal.pone.0271304.
Tillmann HL, Suzuki A, Merz M, Hermann R, Rockey DC. A novel quantitative computer-assisted drug-induced liver injury causality assessment tool (DILI-CAT). PLoS One. 2022;17(9):e0271304.

Published In

PLoS One

DOI

EISSN

1932-6203

Publication Date

2022

Volume

17

Issue

9

Start / End Page

e0271304

Location

United States

Related Subject Headings

  • Humans
  • General Science & Technology
  • Cyproterone
  • Computers
  • Chemical and Drug Induced Liver Injury
  • Cefazolin
  • Causality
  • Amoxicillin-Potassium Clavulanate Combination