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High lipophilicity and high daily dose of oral medications are associated with significant risk for drug-induced liver injury
Abstract
Drug-induced liver injury (DILI) is a leading cause of drug failure in clinical trials and a major reason for drug withdrawals from the market. Although there is evidence that dosages of ≥100 mg/day are associated with increased risk for hepatotoxicity, many drugs are safe at such dosages. There is an unmet need to predict risk for DILI more reliably, and lipophilicity might be a contributing factor. We analyzed the combined factors of daily dose and lipophilicity for 164 US Food and Drug Administration–approved oral medications and observed high risk for hepatotoxicity (odds ratio [OR], 14.05; P < 0.001) for drugs given at dosages ≥100 mg/day and octanol-water partition coefficient (logP) ≥3. This defined the “rule-of-two.” Similar results were obtained for an independent set of 179 oral medications with 85% of the rule-of-two positives being associated with hepatotoxicity (OR, 3.89; P < 0.01). Using the World Health Organization's Anatomical Therapeutic Chemical classification system, the rule-of-two performed best in predicting DILI in seven therapeutic categories. Among 15 rule-of-two positives, 14 were withdrawn from hepatotoxic drugs, and one was over-the-counter medication labeled for liver injury. We additionally examined drug pairs that have similar chemical structures and act on the same molecular target but differ in their potential for DILI. Again, the rule-of-two predicted hepatotoxicity reliably. Finally, the rule-of-two was applied to clinical case studies to identify hepatotoxic drugs in complex comedication regimes to further demonstrate its use. Conclusion: Apart from dose, lipophilicity contributes significantly to risk for hepatotoxicity. Applying the rule-of-two is an appropriate means of estimating risk for DILI compared with dose alone. (HEPATOLOGY 2013)
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In vivo formation of reactive metabolites plays an important role in iDILI pathophysiology.
“The in vivo formation of RM plays an important role in iDILI pathophysiology, and integration of RM into the new “rule-of-two” model can precisely predict the risk and severity of iDILI [31].”
From Chinese guideline for the diagnosis and treatment of drug-induced liver injury: an update (2024) · cites this paper as [31]
Integration of reactive metabolites into the new "rule-of-two" model can precisely predict the risk and severity of iDILI.
“The in vivo formation of RM plays an important role in iDILI pathophysiology, and integration of RM into the new “rule-of-two” model can precisely predict the risk and severity of iDILI [31].”
From Chinese guideline for the diagnosis and treatment of drug-induced liver injury: an update (2024) · cites this paper as [31]
A drug with high lipophilicity (LogP > 3) and daily dose > 100 mg (the "rule-of-two") may be associated with an increased risk of iDILI.
“A drug with high lipophilicity (log of octanol-water partition coefficient, LogP > 3) and daily dose > 100 mg, referred to as the “ruleof-two,” may be associated with an increased risk of iDILI [31].”
From Chinese guideline for the diagnosis and treatment of drug-induced liver injury: an update (2024) · cites this paper as [31]
Citations
Cites
The influence of drug-like concepts on decision-making in medicinal chemistry
Leeson, P. D., & Springthorpe, B. (2007) · Nature Reviews Drug Discovery · doi:10.1038/nrd2445 · Cited by 2,403
Concordance of the toxicity of pharmaceuticals in humans and in animals
Olson, H., Betton, G., Robinson, D., Thomas, K., Monro, A., Kolaja, G., Lilly, P., Sanders, J., Sipes, G., Bracken, W., et al. (2000) · Regulatory Toxicology and Pharmacology · doi:10.1006/rtph.2000.1399 · Cited by 2,050
Risk Factors for Idiosyncratic Drug-Induced Liver Injury
Chalasani, N., & Björnsson, E. (2010) · Gastroenterology · doi:10.1053/j.gastro.2010.04.001 · Cited by 347
Flagged first, then by citations
Research works printed but not linked: 25
- Senior, 2007, Drug hepatotoxicity from a regulatory perspective., Clin Liver Dis, 11, 507524, 10.1016/j.cld.2007.06.002 · DOI
- Zimmerman, 2000, Druginduced liver disease., Clin Liver Dis, 4, 7396, 10.1016/S1089-3261(05)70097-0 · DOI · Paper page
- Ostapowicz, 2002, Results of a prospective study of acute liver failure at 17 tertiary care centers in the United States., Ann Intern Med, 137, 947954, 10.7326/0003-4819-137-12-200212170-00007 · DOI · Paper page
- Fielden, 2008, The role of early in vivo toxicity testing in drug discovery toxicology., Expert Opin Drug Saf, 7, 107110, 10.1517/14740338.7.2.107 · DOI
- Zhang, 2012, Is toxicogenomics a more reliable and sensitive biomarker than conventional indicators from rats to predict druginduced liver injury in humans?, Chem Res Toxicol, 25, 122129, 10.1021/tx200320e · DOI
- Ballet, 2010, Back to basics for idiosyncratic druginduced liver injury: dose and metabolism make the poison., Gastroenterol Clin Biol, 34, 348350, 10.1016/j.gcb.2010.04.015 · DOI
- Walgren, 2005, Role of metabolism in druginduced idiosyncratic hepatotoxicity., Crit Rev Toxicol, 35, 325361, 10.1080/10408440590935620 · DOI
- Lammert, 2008, Relationship between daily dose of oral medications and idiosyncratic druginduced liver injury: search for signals., HEPATOLOGY, 47, 20032009, 10.1002/hep.22272 · DOI
- Lammert, 2010, Oral medications with significant hepatic metabolism at higher risk for hepatic adverse events., HEPATOLOGY, 51, 615620, 10.1002/hep.23317 · DOI
- Uetrecht, 2001, Prediction of a new drugs potential to cause idiosyncratic reactions., Curr Opin Drug Discov Devel, 4, 5559
- Waring, 2010, Lipophilicity in drug discovery., Expert Opin Drug Discovery, 5, 235248, 10.1517/17460441003605098 · DOI
- Hughes, 2008, Physiochemical drug properties associated with in vivo toxicological outcomes., Bioorg Med Chem Lett, 18, 48724875, 10.1016/j.bmcl.2008.07.071 · DOI · Paper page
- Peters, 2009, Pharmacological promiscuity: dependence on compound properties and target specificity in a set of recent Roche compounds., ChemMedChem, 4, 680686, 10.1002/cmdc.200800411 · DOI
- Waring, 2009, Defining optimum lipophilicity and molecular weight ranges for drug candidatesMolecular weight dependent lower logD limits based on permeability., Bioorg Med Chem Lett, 19, 28442851, 10.1016/j.bmcl.2009.03.109 · DOI
- Chen, 2011, FDAapproved drug labeling for the study of druginduced liver injury., Drug Discov Today, 16, 697703, 10.1016/j.drudis.2011.05.007 · DOI · Paper page
- Greene, 2010, Developing structureactivity relationships for the prediction of hepatotoxicity., Chem Res Toxicol, 23, 12151222, 10.1021/tx1000865 · DOI
- de Cerqueira Lima, 2006, Combinatorial QSAR modeling of Pglycoprotein substrates., J Chem Inf Model, 46, 12451254, 10.1021/ci0504317 · DOI
- Chen, 2012, A decade of toxicogenomic research and its contribution to toxicological science., Toxicol Sci, 130, 217228, 10.1093/toxsci/kfs223 · DOI
- McBurney, 2009, The liver toxicity biomarker study: phase I design and preliminary results., Toxicol Pathol, 37, 5264, 10.1177/0192623308329287 · DOI
- Price, 2009, Physicochemical drug properties associated with in vivo toxicological outcomes: a review., Expert Opin Drug Metab Toxicol, 5, 921931, 10.1517/17425250903042318 · DOI
- Schnackenberg, 2009, Evaluations of the transsulfuration pathway in multiple liver toxicity studies., Toxicol Appl Pharmacol, 235, 2532, 10.1016/j.taap.2008.11.015 · DOI
- Padwal, 2007, Drug treatments for obesity: orlistat, sibutramine, and rimonabant., Lancet, 369, 7177, 10.1016/S0140-6736(07)60033-6 · DOI · Paper page
- Banks, 1995, Diclofenacassociated hepatotoxicity: analysis of 180 cases reported to the Food and Drug Administration as adverse reactions., HEPATOLOGY, 22, 820827, 10.1002/hep.1840220320 · DOI
- Borlak, 2009, 3.3 Trovafloxacin: a case study of idiosyncratic or iatrogenic liver toxicitymolecular mechanisms and lessons for pharmacotoxicity., Hum Exp Toxicol, 28, 119121, 10.1177/0960327109105767 · DOI
- Sun, 2007, Mechanisms of trovafloxacin hepatotoxicity: studies of a model cyclopropylaminecontaining system., Bioorg Med Chem Lett, 17, 66826686, 10.1016/j.bmcl.2007.10.070 · DOI
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Andrade, R. J., Aithal, G. P., Björnsson, E. S., Kaplowitz, N., Kullak-Ublick, G. A., Larrey, D., & Karlsen, T. H. (2019) · Journal of Hepatology · doi:10.1016/j.jhep.2019.02.014 · Cited by 1,276
Andrade, R. J., Chalasani, N., Björnsson, E. S., Suzuki, A., Kullak-Ublick, G. A., Watkins, P. B., Devarbhavi, H., Merz, M., Lucena, M. I., Kaplowitz, N., et al. (2019) · Nature Reviews Disease Primers · doi:10.1038/s41572-019-0105-0 · Cited by 924
Carvalho, A. F., Sharma, M. S., Brunoni, A. R., Vieta, E., & Fava, G. A. (2016) · Psychotherapy and Psychosomatics · doi:10.1159/000447034 · Cited by 650
Targeted Covalent Inhibitors for Drug Design
Baillie, T. A. (2016) · Angewandte Chemie International Edition · doi:10.1002/anie.201601091 · Cited by 578
Chen, M., Suzuki, A., Thakkar, S., Yu, K., Hu, C., & Tong, W. (2016) · Drug Discovery Today · doi:10.1016/j.drudis.2016.02.015 · Cited by 398
Self-citationAASLD practice guidance on drug, herbal, and dietary supplement–induced liver injury
Fontana, R. J., Liou, I., Reuben, A., Suzuki, A., Fiel, M. I., Lee, W., & Navarro, V. (2023) · Hepatology · doi:10.1002/hep.32689 · Cited by 340
Utility of spherical human liver microtissues for prediction of clinical drug-induced liver injury
Proctor, W. R., Foster, A. J., Vogt, J., Summers, C., Middleton, B., Pilling, M. A., Shienson, D., Kijanska, M., Ströbel, S., Kelm, J. M., et al. (2017) · Archives of Toxicology · doi:10.1007/s00204-017-2002-1 · Cited by 328
“Statistical analysis employing ROC assessments Dose and drug exposure, as measured by the total plasma Cmax levels, has been shown to be associated with DILI (Chen et al. 2013; Shah et al. 2015).”
BackgroundMethodInfluentialAleo, M. D., Luo, Y., Swiss, R., Bonin, P. D., Potter, D. M., & Will, Y. (2014) · Hepatology · doi:10.1002/hep.27206 · Cited by 215
Baudy, A. R., Otieno, M. A., Hewitt, P., Gan, J., Roth, A., Keller, D., Sura, R., Van Vleet, T. R., & Proctor, W. R. (2020) · Lab on a Chip · doi:10.1039/c9lc00768g · Cited by 150
Shah, F., Leung, L., Barton, H. A., Will, Y., Rodrigues, A. D., Greene, N., & Aleo, M. D. (2015) · Toxicological Sciences · doi:10.1093/toxsci/kfv152 · Cited by 148
Sundaram, V., & Björnsson, E. S. (2017) · Hepatology Communications · doi:10.1002/hep4.1088 · Cited by 138
Predicting Drug-Induced Liver Injury with Bayesian Machine Learning
Williams, D. P., Lazic, S. E., Foster, A. J., Semenova, E., & Morgan, P. (2020) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.9b00264 · Cited by 124
Minimizing DILI risk in drug discovery — A screening tool for drug candidates
Schadt, S., Simon, S., Kustermann, S., Boess, F., McGinnis, C., Brink, A., Lieven, R., Fowler, S., Youdim, K., Ullah, M., et al. (2015) · Toxicology in Vitro · doi:10.1016/j.tiv.2015.09.019 · Cited by 89
Immune-mediated drug-induced liver injury: immunogenetics and experimental models
Gerussi, A., Natalini, A., Antonangeli, F., Mancuso, C., Agostinetto, E., Barisani, D., Di Rosa, F., Andrade, R., & Invernizzi, P. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22094557 · Cited by 87
Recent advances in AI-based toxicity prediction for drug discovery
Lee, H., Kim, J., Kim, J.-W., & Lee, Y. (2025) · Frontiers in Chemistry · doi:10.3389/fchem.2025.1632046 · Cited by 76
Chinese guideline for the diagnosis and treatment of drug-induced liver injury: an update
Mao, Y., Ma, S., Liu, C., Liu, X., Su, M., Li, D., Li, Y., Chen, G., Chen, J., Chen, J., et al. (2024) · Hepatology International · doi:10.1007/s12072-023-10633-7 · Cited by 62
Controlling Drug Delivery Using Nanosheet-Embedded Electrospun Fibers for Efficient Tumor Treatment
Kumar, S., Singh, A. P., Senapati, S., & Maiti, P. (2019) · ACS Applied Bio Materials · doi:10.1021/acsabm.8b00735 · Cited by 36
Connor, S., Li, T., Qu, Y., Roberts, R. A., & Tong, W. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.103938 · Cited by 21
Self-citation
Metadata
- Authors
- Chen, M., Borlak, J., & Tong, W.
- Venue
- Hepatology
- Published
- 2013-07
- Volume
- 58
- Issue
- 1
- Pages
- 388-396
- ISSN
- 0270-9139
- Cited by
- 348
- References
- 3
- Language
- en
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