perspec tives in J–Tpeakc may substantially reassure sponsors and regulators about the safety of a prospective drug, thereby preventing premature elimination of promising molecules. Additionally, the potential for modifying the risk for TdP presented by pure hERG blockers by combining them with mixed channel-blocking drugs (e.g., ranolazine) should be prospectively evaluated. ACKNOWLEDGMENTS The views expressed in this paper reflect the opinions of the author only and not the official policy of the United States Army, the Uniformed Services University, or the Department of Defense. CONFLICT OF INTEREST The author declared no conflict of interest. © 2014 ASCPT
1. January, C.T. & Riddle, J.M. Early after depolarizations: mechanism of induction and block: a role for L-type Ca++ current. Circ. Res. 64, 977–990 (1989). 2. Antzelevitch, C. Role of transmural dispersion of repolarization in the genesis of druginduced torsades de pointes. Heart Rhythm 2(2 suppl.), S9–S15 (2005). 3. Roden, D.M. Cellular basis of drug-induced torsades de pointes. Br. J. Pharmacol. 154, 1502–1507 (2008). 4. Hondeghem, L.M. Thorough QT/QTc not so thorough: removes torsadogenic predictors from the T-wave, incriminates safe drugs, and misses profibrillatory drugs. J. Cardiovasc. Electrophysiol. 17, 337–340 (2006). 5. Johannesen, L. et al. Improving the assessment of heart toxicity for all new drugs through translational regulatory science. Clin. Pharmacol. Ther. 95, 501–508 (2014). 6. Johannesen, L. et al. Differentiating drug-induced multichannel block on the electrocardiogram: randomized study of dofetilide, quinidine, ranolazine, and verapamil. Clin. Pharmacol. Ther. 96, 549–558 (2014). 7. Yang, T. et al. Screening for acute IKr block is insufficient to detect torsades de pointes liability: role of late sodium current. Circulation 130, 224–234 (2014). 8. Wedam, E.F., Bigelow, G.E., Johnson, R.E., Nuzzo, P.A. & Haigney, M.C. QT-interval effects of methadone, levomethadyl, and buprenorphine in a randomized trial. Arch. Intern. Med. 167, 2469–2475 (2007). 9. Demolis, J.L., Funck-Brentano, C., Ropers, J., Ghadanfar, M., Nichols, D.J. & Jaillon, P. Influence of dofetilide on QT-interval duration and dispersion at various heart rates during exercise in humans. Circulation 94, 1592–1599 (1996). 10. Kirchhof, P., Franz, M.R., Bardai, A. & Wilde, A.M. Giant T-U waves precede torsades de pointes in long-QT syndrome: a systematic electrocardiographic analysis in patients with acquired and congenital QT prolongation. J. Am. Coll. Cardiol. 54, 143–149 (2009). 536
See article page 589
Application of Systems Pharmacology to Explore Mechanisms of Hepatotoxicity J Shon1 and DR Abernethy1 Advances in systems biology have allowed the development of a highly characterized systems pharmacology model to study mechanisms of drug-induced hepatotoxicity. In this issue of CPT, Yang et al. describe a model, DILIsym, used to characterize mechanisms of hepatotoxicity of troglitazone. Their modeling approach has provided new insight into troglitazone-induced hepatotoxicity in humans but is not associated with hepatotoxicity in rats, consistent with preclinical data for this drug. Drug-induced hepatotoxicity that occurs only in a small number of individuals among the entire population exposed to the drug has often been called idiosyncratic toxicity, implying that the individual patient characteristics that predispose to the toxicity are poorly characterized. Similarly, drug-induced idiosyncratic hepatotoxicity not found in animal models has been observed in humans, reflecting cross-species differences that are often not well characterized. Both circumstances present major challenges for drug development and predictive toxicology. Yang et al. report the use of a highly characterized hepatic systems pharmacology model to address both of these challenges in order to elucidate molecular mechanisms of troglitazone-induced hepatotoxicity.1 The systems model used in their work has been in development for some time, with each version released incorporating additional elements of hepatic pathophysiology and physiology that are potentially relevant to hepatic drug toxicity.2,3
Previous work by this group and others has demonstrated that troglitazoneinduced hepatotoxicity in vivo is due to both the parent drug and its major metabolite, troglitazone sulfate. These studies suggested that the hepatotoxicity of troglitazone and its metabolite was at least in part attributable to inhibition of bile salt transporters and subsequent hepatocyte injury due to intrahepatocyte bile salt accumulation.4,5 However, why hepatotoxicity occurred in only a small fraction of all patients exposed to troglitazone could not be adequately addressed. In addition, the interspecies difference—in humans with hepatotoxicity including liver failure and rats with no evidence of hepatic injury—could not be determined. To address these issues, the current study represents an important advance adapted from other areas of physiological modeling. In a sister paper to this one, the same group identifies relevant factors in bile acid homeostasis in humans and rats, key components determined from physiologically based pharmacokinetic
1Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA. Correspondence: DR Abernethy (Darrell. [email protected])
doi:10.1038/clpt.2014.167 VOLUME 96 NUMBER 5 | NOVEMBER 2014 | www.nature.com/cpt
perspec tives modeling of troglitazone and troglitazone sulfate, and what are characterized as system-specific factors (body weight and specific bile acid impairment of adenosine triphosphate synthesis), a population was simulated to establish the bounds of variability in humans and rats.6 By varying parameters in a systematic way, the authors demonstrated a reasonable correlation to observed clinical trial data for troglitazone with respect to frequency of alanine transferase elevations in the simulated populations (see Table 1 in ref. 1). In addition, the time delay of onset of hepatotoxicity was reasonably modeled. The mechanism for this time delay was postulated on the basis of simulations by this group to establish temporal relationships of bile acid flux and mechanisms for bile acid accumulation as described in ref. 6. The authors attribute the differences between rats and humans to species-specific differences in bile acid composition; rats have more hydrophilic bile acid composition whereas humans have more chenodeoxycholic acid, a less hydrophilic bile acid previously implicated in bile acid–mediated hepatotoxicity. The results of an effort to use pioglitazone—a structurally and pharmacologically similar analog of troglitazone—as a nonhepatotoxic control suggested that the mechanisms put forth for trog litazone hepatotoxicity were plausible, as pioglitazone might have similar direct toxic effects on the hepatocyte, whereas physiological pharmacokinetic modeling predicts very low intrahepatocyte concentration of pioglitazone due to rapid biotransformation. These data are incompletely developed; no information regarding pioglitazone metabolite effects was available or incorporated into the modeling. Although this model reasonably simulated the clinical trial data that describes troglitazone-induced hepatotoxicity
based on alterations in bile acid transport and its accumulation and the attendant effects on adenosine triphosphate synthesis, it did not incorporate other known mechanisms for troglitazone hepatotoxicity such as direct mitochondrial impairment effects.7 It is encouraging that the model simulated the clinical trial data quite well; however, it is not clear that alterations in bile acid transport were the only source of toxicity in patients exposed to troglitazone who experienced frank hepatic failure. This effort is a nice demonstration of the application of a highly specified mechanistic hepatic model that utilizes extensive kinetic information among interrelated biological processes. This allows the development of solvable differential equations to establish dynamic relationships between processes. The simulated populations critical to the analysis are dependent on this level of characterization of the individual biological processes, and they certainly represent a strength of this approach. At the same time, this level of detail is not currently available for many other biological systems that are relevant to the study of drug toxicity (e.g., neurotoxicity, cardiotoxicity), and if the use of systems pharmacological analyses to explore mechanisms of toxicity in these less well-specified systems is to advance, different analytical approaches must be developed.8 In conclusion, Yang et al.1 are to be congratulated for the continued development of the highly specified DILIsym hepatic model. Its application in efforts to elucidate mechanisms of troglitazone-induced hepatotoxicity is a good illustration of the strength and promise of the systems pharmacology approach. This approach to better understanding mechanisms of drug toxicity and its prediction is also consistent with the regulatory-science research program in mechanism-based predictive toxicology being developed by
the US Food and Drug Administration.9 We look forward to future efforts to apply this model for prediction of hepatotoxicity that has not been clinically observed. ACKNOWLEDGMENTS J.S. was a recipient of an ORISE stipend from the US Food and Drug Administration. The views described are those of the authors and do not necessarily represent the position of the US Food and Drug Administration or the US government. CONFLICT OF INTEREST The authors declared no conflict of interest. © 2014 ASCPT
1. Yang, K., Woodhead, J.L., Watkins, P.B., Howell, B.A. & Brouwer, K.L.R. Systems pharmacology modeling predicts delayed presentation and species differences in bile acid–mediated troglitazone hepatotoxicity. Clin. Pharmacol. Ther. 96, 589–598 (2014). 2. Shoda, L.K., Woodhead, J.L., Siler, S.Q., Watkins, P.B. & Howell, B.A. Linking physiology to toxicity using DILIsym®, a mechanistic mathematical model of drug-induced liver injury. Biopharm. Drug Dispos. 35, 33–49 (2014). 3. Howell, B.A. et al. In vitro to in vivo extrapolation and species response comparisons for druginduced liver injury (DILI) using DILIsymTM: a mechanistic, mathematical model of DILI. J. Pharmacokinet. Pharmacodyn. 39, 527–541 (2012). 4. Funk, C., Ponelle, C., Scheuermann, G. & Pantze, M. Cholestatic potential of troglitazone as a possible factor contributing to troglitazone-induced hepatotoxicity: in vivo and in vitro interaction at the canalicular bile salt export pump (Bsep) in the rat. Mol. Pharmacol. 59, 627–635 (2001). 5. Marion, T.L., Leslie, E.M. & Brouwer, K.L. Use of sandwich-cultured hepatocytes to evaluate impaired bile acid transport as a mechanism of drug-induced hepatotoxicity. Mol. Pharm. 4, 911–918 (2007). 6. Woodhead, J.L. et al. Mechanistic modeling reveals the critical knowledge gaps in bile acid–mediated DILI. CPT Pharmacometrics Syst. Pharmacol. 3, e123 (2014). 7. Pessayre, D. et al. Central role of mitochondria in drug-induced liver injury. Drug Metab. Rev. 44, 34–87 (2012). 8. Birtwistle, M.R., Mager, D.E. & Gallo, J.M. Mechanistic vs. empirical network models of drug action. CPT Pharmacometrics Syst. Pharmacol. 2, e72 (2013). 9. Bai, J.P.F. & Abernethy, D.R . Systems pharmacology to predict drug toxicity: integration across levels of biological organization. Annu. Rev. Pharmacol. Toxicol. 53, 451–473 (2013).
Clinical pharmacology & Therapeutics | VOLUME 96 NUMBER 5 | NOVEMBER 2014
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1. January, C.T. & Riddle, J.M. Early after depolarizations: mechanism of induction and block: a role for L-type Ca++ current. Circ. Res. 64, 977–990 (1989). 2. Antzelevitch, C. Role of transmural dispersion of repolarization in the genesis of druginduced torsades de pointes. Heart Rhythm 2(2 suppl.), S9–S15 (2005). 3. Roden, D.M. Cellular basis of drug-induced torsades de pointes. Br. J. Pharmacol. 154, 1502–1507 (2008). 4. Hondeghem, L.M. Thorough QT/QTc not so thorough: removes torsadogenic predictors from the T-wave, incriminates safe drugs, and misses profibrillatory drugs. J. Cardiovasc. Electrophysiol. 17, 337–340 (2006). 5. Johannesen, L. et al. Improving the assessment of heart toxicity for all new drugs through translational regulatory science. Clin. Pharmacol. Ther. 95, 501–508 (2014). 6. Johannesen, L. et al. Differentiating drug-induced multichannel block on the electrocardiogram: randomized study of dofetilide, quinidine, ranolazine, and verapamil. Clin. Pharmacol. Ther. 96, 549–558 (2014). 7. Yang, T. et al. Screening for acute IKr block is insufficient to detect torsades de pointes liability: role of late sodium current. Circulation 130, 224–234 (2014). 8. Wedam, E.F., Bigelow, G.E., Johnson, R.E., Nuzzo, P.A. & Haigney, M.C. QT-interval effects of methadone, levomethadyl, and buprenorphine in a randomized trial. Arch. Intern. Med. 167, 2469–2475 (2007). 9. Demolis, J.L., Funck-Brentano, C., Ropers, J., Ghadanfar, M., Nichols, D.J. & Jaillon, P. Influence of dofetilide on QT-interval duration and dispersion at various heart rates during exercise in humans. Circulation 94, 1592–1599 (1996). 10. Kirchhof, P., Franz, M.R., Bardai, A. & Wilde, A.M. Giant T-U waves precede torsades de pointes in long-QT syndrome: a systematic electrocardiographic analysis in patients with acquired and congenital QT prolongation. J. Am. Coll. Cardiol. 54, 143–149 (2009). 536
See article page 589
Application of Systems Pharmacology to Explore Mechanisms of Hepatotoxicity J Shon1 and DR Abernethy1 Advances in systems biology have allowed the development of a highly characterized systems pharmacology model to study mechanisms of drug-induced hepatotoxicity. In this issue of CPT, Yang et al. describe a model, DILIsym, used to characterize mechanisms of hepatotoxicity of troglitazone. Their modeling approach has provided new insight into troglitazone-induced hepatotoxicity in humans but is not associated with hepatotoxicity in rats, consistent with preclinical data for this drug. Drug-induced hepatotoxicity that occurs only in a small number of individuals among the entire population exposed to the drug has often been called idiosyncratic toxicity, implying that the individual patient characteristics that predispose to the toxicity are poorly characterized. Similarly, drug-induced idiosyncratic hepatotoxicity not found in animal models has been observed in humans, reflecting cross-species differences that are often not well characterized. Both circumstances present major challenges for drug development and predictive toxicology. Yang et al. report the use of a highly characterized hepatic systems pharmacology model to address both of these challenges in order to elucidate molecular mechanisms of troglitazone-induced hepatotoxicity.1 The systems model used in their work has been in development for some time, with each version released incorporating additional elements of hepatic pathophysiology and physiology that are potentially relevant to hepatic drug toxicity.2,3
Previous work by this group and others has demonstrated that troglitazoneinduced hepatotoxicity in vivo is due to both the parent drug and its major metabolite, troglitazone sulfate. These studies suggested that the hepatotoxicity of troglitazone and its metabolite was at least in part attributable to inhibition of bile salt transporters and subsequent hepatocyte injury due to intrahepatocyte bile salt accumulation.4,5 However, why hepatotoxicity occurred in only a small fraction of all patients exposed to troglitazone could not be adequately addressed. In addition, the interspecies difference—in humans with hepatotoxicity including liver failure and rats with no evidence of hepatic injury—could not be determined. To address these issues, the current study represents an important advance adapted from other areas of physiological modeling. In a sister paper to this one, the same group identifies relevant factors in bile acid homeostasis in humans and rats, key components determined from physiologically based pharmacokinetic
1Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA. Correspondence: DR Abernethy (Darrell. [email protected])
doi:10.1038/clpt.2014.167 VOLUME 96 NUMBER 5 | NOVEMBER 2014 | www.nature.com/cpt
perspec tives modeling of troglitazone and troglitazone sulfate, and what are characterized as system-specific factors (body weight and specific bile acid impairment of adenosine triphosphate synthesis), a population was simulated to establish the bounds of variability in humans and rats.6 By varying parameters in a systematic way, the authors demonstrated a reasonable correlation to observed clinical trial data for troglitazone with respect to frequency of alanine transferase elevations in the simulated populations (see Table 1 in ref. 1). In addition, the time delay of onset of hepatotoxicity was reasonably modeled. The mechanism for this time delay was postulated on the basis of simulations by this group to establish temporal relationships of bile acid flux and mechanisms for bile acid accumulation as described in ref. 6. The authors attribute the differences between rats and humans to species-specific differences in bile acid composition; rats have more hydrophilic bile acid composition whereas humans have more chenodeoxycholic acid, a less hydrophilic bile acid previously implicated in bile acid–mediated hepatotoxicity. The results of an effort to use pioglitazone—a structurally and pharmacologically similar analog of troglitazone—as a nonhepatotoxic control suggested that the mechanisms put forth for trog litazone hepatotoxicity were plausible, as pioglitazone might have similar direct toxic effects on the hepatocyte, whereas physiological pharmacokinetic modeling predicts very low intrahepatocyte concentration of pioglitazone due to rapid biotransformation. These data are incompletely developed; no information regarding pioglitazone metabolite effects was available or incorporated into the modeling. Although this model reasonably simulated the clinical trial data that describes troglitazone-induced hepatotoxicity
based on alterations in bile acid transport and its accumulation and the attendant effects on adenosine triphosphate synthesis, it did not incorporate other known mechanisms for troglitazone hepatotoxicity such as direct mitochondrial impairment effects.7 It is encouraging that the model simulated the clinical trial data quite well; however, it is not clear that alterations in bile acid transport were the only source of toxicity in patients exposed to troglitazone who experienced frank hepatic failure. This effort is a nice demonstration of the application of a highly specified mechanistic hepatic model that utilizes extensive kinetic information among interrelated biological processes. This allows the development of solvable differential equations to establish dynamic relationships between processes. The simulated populations critical to the analysis are dependent on this level of characterization of the individual biological processes, and they certainly represent a strength of this approach. At the same time, this level of detail is not currently available for many other biological systems that are relevant to the study of drug toxicity (e.g., neurotoxicity, cardiotoxicity), and if the use of systems pharmacological analyses to explore mechanisms of toxicity in these less well-specified systems is to advance, different analytical approaches must be developed.8 In conclusion, Yang et al.1 are to be congratulated for the continued development of the highly specified DILIsym hepatic model. Its application in efforts to elucidate mechanisms of troglitazone-induced hepatotoxicity is a good illustration of the strength and promise of the systems pharmacology approach. This approach to better understanding mechanisms of drug toxicity and its prediction is also consistent with the regulatory-science research program in mechanism-based predictive toxicology being developed by
the US Food and Drug Administration.9 We look forward to future efforts to apply this model for prediction of hepatotoxicity that has not been clinically observed. ACKNOWLEDGMENTS J.S. was a recipient of an ORISE stipend from the US Food and Drug Administration. The views described are those of the authors and do not necessarily represent the position of the US Food and Drug Administration or the US government. CONFLICT OF INTEREST The authors declared no conflict of interest. © 2014 ASCPT
1. Yang, K., Woodhead, J.L., Watkins, P.B., Howell, B.A. & Brouwer, K.L.R. Systems pharmacology modeling predicts delayed presentation and species differences in bile acid–mediated troglitazone hepatotoxicity. Clin. Pharmacol. Ther. 96, 589–598 (2014). 2. Shoda, L.K., Woodhead, J.L., Siler, S.Q., Watkins, P.B. & Howell, B.A. Linking physiology to toxicity using DILIsym®, a mechanistic mathematical model of drug-induced liver injury. Biopharm. Drug Dispos. 35, 33–49 (2014). 3. Howell, B.A. et al. In vitro to in vivo extrapolation and species response comparisons for druginduced liver injury (DILI) using DILIsymTM: a mechanistic, mathematical model of DILI. J. Pharmacokinet. Pharmacodyn. 39, 527–541 (2012). 4. Funk, C., Ponelle, C., Scheuermann, G. & Pantze, M. Cholestatic potential of troglitazone as a possible factor contributing to troglitazone-induced hepatotoxicity: in vivo and in vitro interaction at the canalicular bile salt export pump (Bsep) in the rat. Mol. Pharmacol. 59, 627–635 (2001). 5. Marion, T.L., Leslie, E.M. & Brouwer, K.L. Use of sandwich-cultured hepatocytes to evaluate impaired bile acid transport as a mechanism of drug-induced hepatotoxicity. Mol. Pharm. 4, 911–918 (2007). 6. Woodhead, J.L. et al. Mechanistic modeling reveals the critical knowledge gaps in bile acid–mediated DILI. CPT Pharmacometrics Syst. Pharmacol. 3, e123 (2014). 7. Pessayre, D. et al. Central role of mitochondria in drug-induced liver injury. Drug Metab. Rev. 44, 34–87 (2012). 8. Birtwistle, M.R., Mager, D.E. & Gallo, J.M. Mechanistic vs. empirical network models of drug action. CPT Pharmacometrics Syst. Pharmacol. 2, e72 (2013). 9. Bai, J.P.F. & Abernethy, D.R . Systems pharmacology to predict drug toxicity: integration across levels of biological organization. Annu. Rev. Pharmacol. Toxicol. 53, 451–473 (2013).
Clinical pharmacology & Therapeutics | VOLUME 96 NUMBER 5 | NOVEMBER 2014
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