- Email: [email protected]
How can mathematics help us understand HCV?
1546
EDITORIALS
5. Chan TO, Rittenhouse SE, Tsichlis PN. AKT/PKB and other 03 phosphoinositide-regulated kinases: kinase activation by phosphoinositide-dependent phosphorylation. Annu Rev Biochem 1999;68:965-1014. 6. Maehama T, Oixon JE. The tumor suppressor, PTEN/MMAC1, dephosphorylates the lipid second messenger, phosphatidylinositol 3,4,5-trisphosphate. J Bioi Chem 1998;273:13375-13378. 7. Maehama T, Oixon JE. PTEN: a tumour suppressor that functions as a phospholipid phosphatase. Trends Cell Bioi 1999;9:125128. 8. Jiang BH, Zheng JZ, Vogt PK. An essential role of phosphatidlyinositol 3-kinase in myogenic differentiation. Proc Natl Acad Sci USA 1998;95:14179-14183. 9. Lee JW, Juliano RL. u5~1 integrin protects intestinal epithelial cells from apoptosis through a phosphatidylinositol 3-kinase and protein kinase B-dependent pathway. Mol Bioi Cell 2000;11: 1973-1987. 10. Karam SM. Lineage commitment and maturation of epithelial cells in the gut. Front Biosci 1999;4:0286-0298. 11. Montrose MH, Keely SJ, Barrett KE. Secretion and absorption: small intestine and colon. In: Yamada T, Alpers OH, Laine L, Owyang C, Powell OW, eds. Textbook of gastroenterology. Philadelphia: Lippincott Williams & Wilkins, 1999:320-355.
GASTROENTEROLOGY Vol. 120, No. 6
12. Chow JYC, Uribe JM, Barrett KE. A role for protein kinase CE in the inhibitory effect of epidermal growth factor on calcium-stimulated chloride secretion in human colonic epithelial cells. J Bioi Chem 2000;275:21169-21176. 13. Khurana S, Nath SK, Levine SA, Bowser JM, Tse CM, Cohen ME, Oonowitz M. Brush border phosphatidylinositol 3-kinase mediates epidermal growth factor stimulation of intestinal NaCI absorption and Na+ /W exchange. J Bioi Chem 1996;271:99199927. 14. Podolsky OK. Regulation of intestinal epithelial proliferation: a few answers, many questions. Am J Physiol 1993;264:G179G186.
Address requests for reprints to: Kim E. Barrett, Ph.D., UCSD Medical Center 8414, 200 West Arbor Drive, San Diego, California 921038414. e-mail: [email protected]; fax: (619) 543-6969. Supported by grants from the National Institutes of Health (DK28305, DK53480, DK35108 [project 5]). The author thanks Glenda Wheeler for assistance with manuscript submission. © 2001 by the American Gastroenterological Association 0016-5085/01/$35.00 doi:10.1053/gast.2001.24395
How Can Mathematics Help Us Understand HCV? See article on page 1438.
A s many as 85% of patients who become infected with .ll.. hepatitis C virus (HCV) develop chronic infection, and once chronic infection is established, serum viral levels flucruate very little. 1 In this steady state, viral production equals viral clearance and, as such, measurements of viral levels provide little or no information regarding the production or clearance rate of virus, the turnover rate of virally infected cells, or the de novo infection rate of uninfected cells. To gain that information, the steady state has to be perturbed. One such way is by the initiation of therapeutic agents that reduce viral levels. The changing viral levels then need to be monitored frequently and mathematical models applied to interpret the data. This type of analysis has been used with great success in human immunodeficiency virus (HIV)-l infection, and a large body of information has emerged regarding viral dynamics and infected cell turnover rate that has enabled important classic insights into mechanism of drug action, emergence of resistance, and the time needed to eradicate all viral reservoirs if the drug(s) were 100% effective. 2- 5 These same principles are now being applied to understand the HCV life cycle and therapeutic response to interferon (IFN) and other antiviral agents. 6 - 11
So what have we learned regarding HCV dynamics using mathematics? The current mathematical models 2- 5 used to describe the life cycles of HIV, hepatitis B virus (HBV), and HCV are based on 3 differential equations that relate the dynamic relationship between uninfected target cells (T), infected cells (I), and serum viral levels (V): (equation 1) dT/dt = s-dT - (l-'Yj)I3VT; (equation 2) dIldt = (l-'Yj)I3VT- 81; and (equation 3) dV/dt = (l-E)pI - cV. In the case of HIV, target cells (T) are predominantly CD4lymphocytes, whereas in HBV and HCV, the target cells are hepatocytes. Target cells (equation 1) become infected by circulating virus (V) at a rate constant (13) and become virus-producing infected cells. Target cells (hepatocytes) turn over slowly with a rate constant (d), and new target cells are produced to take their place at a rate of "s." Because hepatocytes have a long half life, the death of uninfected cells is not considered when making calculations. Infected cells die (8) either by necrosis or apoptosis, depending on the starus of the immune system and the innate ability of the virus to escape cytotoxic T-lymphocyte recognition. Serum viral levels (equation 3) in chronic infection are determined by the balance between the production rate (p) of viruses by infected cells and by their clearance from blood (c). Antiviral drugs could lower viral serum levels by inhibiting the de novo infection of target cells with a certain degree of
May 2001
efficacy (I-"fJ) with viral levels declining as the result of loss of virus-producing infected cells. Alternatively, an agent could lower viral levels by inhibiting viral production with a varying degree of effectiveness (I-e.), and the subsequent decline in RNA levels would be dependent on free viral clearance. What happens when IFN-a is given to patients infected with HCV? Upon initiation of daily IFN therapy, there is an 8-10-hour delay during which viral levels remain unchanged. Thereafter, a dramatic 0.5-3.0 exponential log decline in HCV RNA levels occurs over the first 24-48 hours, after which the viral decline rate slows. 6 - 11 This initial decline is lO-fold greater than that seen in patients infected with HIV-1 treated with combination antiretroviral drugs. 2- 5 What mechanisms account for such a dramatic and rapid initial decline in viral load followed by a slower decline in viral levels? Fitting the data observed in IFN-treated patients with hepatitis C to the mathematical models has shown that the best explanation for this rapid first-phase decline is an IFN-dependent inhibition of viral production and/or release. 8 With IFN blocking viral production, viral RNA decline during this first phase is then based on its intrinsic viral clearance (free viral half-life), which was calculated to vary from 2.5 to 3.5 hours. 8 This estimated viral serum half-life is 2-3 times faster than that of HIV and 6-8 times faster than that ofHBV.12,13 Because viral production must equal its clearance, we can estimate that a trillion HCV virions are produced and cleared each day.8 Another means of studying viral kinetics is to measure virus levels during plasma apheresis, which temporarily increases clearance but has no effect on viral production. 14 When this was done in patients coinfected with HIV-l and HCV, calculated intrinsic viral clearance rates for HCV were similar to previous findings obtained through study ofIFN-treated patients. 8 The similar rates of intrinsic viral clearance obtained with these 2 different approaches altering the steady state provide additional support to the concept that the first-phase viral decline is dependent on the ability of IFN to inhibit viral production. Indeed, a number of IFN-dependent cellular pathways have been identified that could explain this dramatic inhibition of viral production. 15 - 17 If effectiveness in blocking production were 100%, then the serum viral decline would have been expected to be continuous and the time to serum viral disappearance entirely dependent on the half-life of the circulating virus. However, this was not what was observed. 6 - 11 Instead, after the first 24-48 hours, a slower second phase viral decline ensued. This bipha-
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sic viral decline is what mathematical modeling would predict if effectiveness in blocking viral production was less than 100%. 8 IFN effectiveness in blocking viral production was found to be dose-dependent, varying from 71 % for 3 million units (mU), to 81 % for 5 mU, and to 95%-97% for 10 mU IFN-a2b. 7,8 Interestingly, when first-phase viral decline and IFN effectiveness were compared between patients infected with genotype 1 and genotype 2,8,9 IFN effectiveness was found to be significantly better in genotype 2-treated patients (99.7% vs. 95%; equivalent to a 1.6 log decline difference). What follows this initial rapid response? After this first phase, the viral decline drastically slows. However, there is marked variation among patients and mathematical calculations indicate that this second phase is determined by two parameters: IFN effectiveness (e.) and infected cell death rate (8). It is also possible that the continued reduction in viral levels results from T cellmediated cytokine-induced interference with viral production, as has been described in HBV during IFN treatment. 18 Although initial studies suggested that the second phase decline was not IFN dose dependent,8 recent studies that compare second-phase slopes using doses ofIFN from 3 to 10 mU of IFN-a2b indicate that second-phase slope is IFN dose dependent, no doubt reflecting the very significant differences (71 % vs. 95 %) in drug effectiveness between these 2 doses of IFN.19 As well, within a given dose of IFN, significant variability occurs in second-phase decline rate among patients, which indicates that the death rate of infected cells has a profound impact on continued viral decline. Indeed, the calculated infected cell half-life was shown to vary from 1.7 days to >70 days.8 Patients with more rapid turnover of infected cells had a faster second-phase decline and higher baseline alanine aminotramferase values 8 and were more likely to clear virus early during therapy.8 Intriguingly, second-phase viral decline was significantly more rapid in genotype 2-infected patients compared with genotype I-infected patients,9 no doubt in part accounting for the greater rate of response seen in genotype 2-infected patients. Thus, from a mathematical point of view, eradication of virus with resultant sustained virologic response (SVR) is dependent on the effectiveness of IFN in blocking viral production in the setting of immunologic recognition and degradation of infected virus-producing hepatocytes. Preliminary findings suggest that the rate of the second-phase viral decay slope in the first month of therapy can be used as a reliable predictor of SVR during IFN monotherapy.2o Patients who have a decline slope of less than 0.1 per day
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over the first month of therapy (equivalent to a log decline of <0.3 per week) do not achieve SVR.20 What does mathematical modeling tell us regarding the better treatment response seen with ribavirin combination therapy and pegylated IFN? Zeuzem et al. reported that ribavirin did not alter the kinetics of HCV during IFN therapy, although the first- and second-phase declines were not studied in great detail. 21 In preliminary findings, Pawlotsky et al. 22 showed that ribavirin alone had a minimal «0.5 log copies/mL) and transient antiviral effect; when used in combination with IFN-cx2b (3 mD 3 times a week), ribavirin prevented the viral rebound seen on day 2 of IFN monotherapy dosing 3 times a week, thus leading to a stronger second slope decrease. Ribavirin, however, had no effect on either first- or second-phase kinetics when IFN was administered daily.22 Thus, from a mathematical point of view, the mechanism whereby ribavirin prevents posttreatment relapse is unknown and requires more detailed study. In this issue of GASTROENTEROLOGY, these same mathematical principles were used to assess the impact of covalent attachment of a 40-kilodalton branched polyethylene glycol (PEG) moiety to IFN-cx2a on viral kinetics in both genotype 1- and 2/3-infected patients. 23 The group of patients studied represents a subset of a larger group of patients who were treated in a multicenter study that compared PEG-IFN-cx2a at 180 I-1g once weekly for 48 weeks to IFN-cx2a at 6 mD 3 times a week for 3 months followed by 3 mD 3 times a week for an additional 9 months. 24 In the larger study, SVR in the PEG-IFN-treated group was 39% vs. 19% with standard IFN.24 The biphasic decline pattern in HCV RNA levels was again noted for patients treated with PEG-IFN. However, a first phase could not be detected in 24% of patients because of infrequent blood sampling. Thus, a true assessment of effectiveness could not be obtained from these studies, although no significant differences were apparent in blocking effectiveness between PEG- IFN and 6 mD of IFN-cx2a. As noted in previous studies comparing genotype I-infected patients to genotype 2-infected patients,9 first phase viral decline was significantly faster in genotype non-I-infected patients, reflecting a faster free virion turnover and greater IFN effectiveness. This observation was independent of pegylation. Also, second-phase decay rate was noted to differ even more significantly between genotype 1- and nonI-infected patients, with non-I-infected patients having an 8-fold faster decline slope. This observation shows, from a kinetic point of view, why genotype 2/3-infected patients can be treated with shorter courses of therapy. The second-phase viral decline slope was again predictive
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of SVR, supporting preliminary findings in other studies of IFN monotherapy.2o However, the second-phase viral decline slope was not enhanced for patients treated with PEG-IFN, regardless of genotype. How does pegylation enhance SVR if it has no significant effect on either first- or second-phase decline rates? To examine this paradox, the investigators divided patients into 3 groups based on the type of second-phase viral decline patterns seen in the first month of therapy: flat, slow, and fast responders. The end of treatment response in the flat, slow, and fast second-phase viral responders was improved in patients receiving PEG-IFN vs. standard IFN-cx2a, and SVR was better in the slow and rapid responder groups with PEG-IFN. Thus, why improved treatment response is seen with PEG-IFN in these different types of second-phase viral decline responders remains unclear. Still, an intriguing hypothesis is that sustained IFN levels allow for stabilization of the effect of IFN on inhibiting viral production and/or enhances the degradation of infected liver cells. More detailed kinetic studies could resolve this uncertainty. In summary, application of mathematical models of viral infection has unearthed detailed information on the dynamics of HCV infection and the impact of IFN on interfering with its life cycle. Continued application of mathematical model analysis to define the dynamic changes occurring during therapy could yield important information that would tell us how soon SVR and nonSVR can be predicted after institution of therapy. Mathematical analysis of viral response will help provide insight into the effects certain host factors such as age, sex, weight,25 race,26 and alcohol intake on treatment response. These answers will no doubt change our therapeutic approach to patients in whom chronic HCV infection proves resistant to therapy. JENNIFER E. LAYDEN THOMAS J. LAYDEN Section of Digestive and Liver Diseases Department of Medicine University of Illinois Chicago, Illinois
References 1. Nguyen n, Sedghi-Vaziri A, Wilkes LB, Mondala T, Pockros PJ, Lindsay KL, McHutchison JG. Fluctuations in viral load (HCV RNA) are relatively insignificant in untreated patients with chronic HCV infection. J Viral Hepat 1996;3:75-78. 2. Ho DD, Neuman AU, Perelson AS, Chen W, Leonard JM, Markowitz M. Rapid turnover of plasma virions and CD4 lymphocytes in HIV-1 infection. Nature 1995;373:123-126. 3. Wei X, Ghosh SK, Taylor ME, Johnson VA, Emini EA, Deutsch P, Lifson JD, Bonhoeffer S, Nowak MA, Hahn BH, et al. Viral dynamics in human immunodeficiency virus type 1 infection. Nature 1995;373:117-120.
May 2001
4. Perelson AS, Essunger P, Cao Y, Vesanen M, Hurley A, Saksala K, Markowitz M, Ho DO. Decay characteristics of HIV-1 infected compartments during combination therapy. Nature 1997;386: 188-191. 5. Perelson AS, Neumann AU, Markowitz M, Leonard JM, Ho DO. HIV-l dynamics in vivo: virion clearance rate, infected cell lifespan, and viral generation time. Science 1996;271:1582-1586. 6. Zeuzem S, Schmidt JM, Lee J-H, Ruster B, Roth WK. Effect of interferon-alpha on the dynamics of hepatits C virus turnover in vivo. Hepatology 1996;23:366-371. 7. Lam NP, Neumann AU, Gretch DR, Wiley TE, Perelson AS, Layden TJ. Dose-dependent acute clearance of hepatitis C genotype 1 virus with interferon alfa. Hepatology 1997;26:226-231. 8. Neumann AU, Lam NP, Dahari H, Gretch DR, WileyTE, Layden TJ, Perelson AS. Hepatitis C viral dynamics in vivo and the antiviral efficacy of interferon-a therapy. Science 1998;282:103-107. 9. Neumann AU, Lam NP, Dahari H, Davidian M, Wiley TE, Mika BP, Perelson AS, Layden TJ. Differences in viral dynamics between genotypes 1 and 2 of hepatitis C virus. J Infect Dis 2000;182: 28-35. 10. Bekkering FC, Brouwer JT, Schalm SW, Elewaut A. Hepatitis C: viral kinetics. Hepatology 1997;26:1691-1693. 11. Yasui K, Okanoue T, Murakami Y, Itoh Y, Minami M, Sakamoto S, Sakamoto M, Nishoioji K. Dynamics of hepatitis C viremia following interferon-a administration. J Infect Dis 1998;177:14751479. 12. Nowak MA, Bonhoeffer S, Hill AM, Boehme R, Thomas HC, McDade H. Viral dynamics in hepatitis B infection. Proc Natl Acad Sci USA 1996;93:4398-4402. 13. Tsiang M, Rooney JF, Toole JJ, Gibbs CS. Biphasic clearance kinetics of hepatitis V virus from patients during adefovir dipivoxil therapy. Hepatology 1999;6:1863-1869. 14. Ramratnam B, Bonhoeffer S, Binley J, Hurley A, Zhang L, Mittler JE, Markowitz M, Moore JP, Perelson AS, Ho DO. Rapid production and clearance of HIV-l and hepatitis C virus assessed by large volume plasma apheresis. Lancet 1999;354:1782-1785. 15. Gale MJ Jr, Korth MJ, Tan NL, Hopkins DA, Dever TE, Polyak SJ, Gretch DR, Katze MG. Evidence that hepatitis C virus resistance to interferon is mediated through repression of the PKR protein kinase by the nonstructural 5a protein. Virology 1997;230:217227. 16. Enomoto N, Sakuma I, Asahina Y, Kurosaki M, Murakami T, Yamamoto C, Izumi N, Marumo F, Sato C. Comparison of fulllength sequences of interferon-sensitive and resistant hepatitis C virus lb: sensitivity to interferon is conferred by amino acid substitutions in the NS5A gene. J Clin Invest 1995;96:224-230.
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17. Sen GC, Lengyel P. The interferon system: a bird's eye view of its biochemistry. J Bioi Chem 1992;267:5017-5020. 18. Guidotti LG, Rochford R, Chung J, Shapiro M, Purcell R, Chisari FV. Viral clearance without destruction of infected cells during acute HBV infection. Science 1999;284:825-829. 19. Bekkering FC, Stalgis C, McHutchison JG, Brouwer JT, Perelson AS. Estimation of early hepatitis C viral clearance in patients receiving daily interferon and ribaviron therapy using a mathematical model. Hepatology 2001;33:419-423. 20. Neumann AU, Layden TJ, Redyy KR, Levi-Drummer R, Poulakos J. The 2 nd phase slope of HCV decline is highly predictive of sustained virologic response (SVR) following consensus interferon (infergen) treatment for chronic hepatitis C and is determined by genotype but not by dose (abstr). Hepatology 2000;32:356A. 21. Zeuzem S, Schmidt JM, Lee JF, von Wagner M, Teuber G, Roth WK. Hepatitis C virus dynamics in vivo: effect of ribavirin and interferon alfa on viral turnover. Hepatology 1998;28:245-252. 22. Pawlotsky J-M, Neumann AU, Dahari H, Conrad A, Hezode C, Schmid P, Dhumeaux D. Hepatitis C virus (HCV) dynamics during induction therapy with interfon (IFN) alpha and/or ribavirin (abstr). Hepatology 2000;32:223A. 23. Zeuzem S, Herrmann E, Lee J-H, Fricke J, Neumann AU, Modi M, Colucci G, Roth WK. Viral kinetics in patients with chronic hepatitis C treated with standard or peginterferon OL2. Gastroenterology 2001;120:1438-1447. 24. Zeuzem S, Feinman SV, Rasenack JJ, Heathcote EJ, Lai M-Y, Gane E, O'Grady J, Reichen J, Diago M, Lin A, Hoffman J, Brunda MJ. Peginterferon alfa-2a in patients with chronic hepatitis C. N Engl J Med 2000;343:1666-1672. 25. Lam NP, Pitrak 0, Speralakis R, Lau AH, Wiley TE, Layden TJ. Effect of obesity on pharmacokinetics and biologic effect of interferon-a in hepatitis C. Dig Dis Sci 1997;42:178-185. 26. Reddy KR, Hoofnagle JH, Tong MJ, Lee WM, Pockros P, Heathcote EJ, Albert 0, John T. Racial differences in responses to therapy with interferon in chronic hepatitis C. Hepatology 1999; 30:787-793.
Address requests for reprints to: Thomas J. Layden, M.D., Department of Medicine, MC 787, 840 S. Wood, Chicago, Illinois 606012. e-mail: [email protected]; fax: (312) 996-5103. Dr. T. J. Layden is a lecturer for Schering and Amgen. © 2001 by the American Gastroenterological Association 0016-5085/01/$35.00 doi:10.1053/gast.2001.24401
MDR3 Mutations: A Glimpse Into Pandora's Box and the Future of Canalicular Pathophysiology See articles on pages 1448 and 1459.
dentification of MDR3 with a form of inheritable cholestasis has an interesting history. In 1986, MDR1 was the first bile canalicular adenosine triphosphate (ATP)-dependent transporter to be identified. l Because the C219 antibody that was used recognizes the product
I
of all MDR family members (mdr 1,2,3 in mice, MDR 1,3 in humans), the identity of individual MDR protein(s) in the canalicular membrane could not be ascertained. Studies using peptide-specific antibodies revealed that mdr2 (MDR3 in humans) constitutes more than 80% of canalicular MDR proteins. 2 What was MDR2 transporting in the canalicular membrane? For several years, many laboratories sought the answer, which
EDITORIALS
5. Chan TO, Rittenhouse SE, Tsichlis PN. AKT/PKB and other 03 phosphoinositide-regulated kinases: kinase activation by phosphoinositide-dependent phosphorylation. Annu Rev Biochem 1999;68:965-1014. 6. Maehama T, Oixon JE. The tumor suppressor, PTEN/MMAC1, dephosphorylates the lipid second messenger, phosphatidylinositol 3,4,5-trisphosphate. J Bioi Chem 1998;273:13375-13378. 7. Maehama T, Oixon JE. PTEN: a tumour suppressor that functions as a phospholipid phosphatase. Trends Cell Bioi 1999;9:125128. 8. Jiang BH, Zheng JZ, Vogt PK. An essential role of phosphatidlyinositol 3-kinase in myogenic differentiation. Proc Natl Acad Sci USA 1998;95:14179-14183. 9. Lee JW, Juliano RL. u5~1 integrin protects intestinal epithelial cells from apoptosis through a phosphatidylinositol 3-kinase and protein kinase B-dependent pathway. Mol Bioi Cell 2000;11: 1973-1987. 10. Karam SM. Lineage commitment and maturation of epithelial cells in the gut. Front Biosci 1999;4:0286-0298. 11. Montrose MH, Keely SJ, Barrett KE. Secretion and absorption: small intestine and colon. In: Yamada T, Alpers OH, Laine L, Owyang C, Powell OW, eds. Textbook of gastroenterology. Philadelphia: Lippincott Williams & Wilkins, 1999:320-355.
GASTROENTEROLOGY Vol. 120, No. 6
12. Chow JYC, Uribe JM, Barrett KE. A role for protein kinase CE in the inhibitory effect of epidermal growth factor on calcium-stimulated chloride secretion in human colonic epithelial cells. J Bioi Chem 2000;275:21169-21176. 13. Khurana S, Nath SK, Levine SA, Bowser JM, Tse CM, Cohen ME, Oonowitz M. Brush border phosphatidylinositol 3-kinase mediates epidermal growth factor stimulation of intestinal NaCI absorption and Na+ /W exchange. J Bioi Chem 1996;271:99199927. 14. Podolsky OK. Regulation of intestinal epithelial proliferation: a few answers, many questions. Am J Physiol 1993;264:G179G186.
Address requests for reprints to: Kim E. Barrett, Ph.D., UCSD Medical Center 8414, 200 West Arbor Drive, San Diego, California 921038414. e-mail: [email protected]; fax: (619) 543-6969. Supported by grants from the National Institutes of Health (DK28305, DK53480, DK35108 [project 5]). The author thanks Glenda Wheeler for assistance with manuscript submission. © 2001 by the American Gastroenterological Association 0016-5085/01/$35.00 doi:10.1053/gast.2001.24395
How Can Mathematics Help Us Understand HCV? See article on page 1438.
A s many as 85% of patients who become infected with .ll.. hepatitis C virus (HCV) develop chronic infection, and once chronic infection is established, serum viral levels flucruate very little. 1 In this steady state, viral production equals viral clearance and, as such, measurements of viral levels provide little or no information regarding the production or clearance rate of virus, the turnover rate of virally infected cells, or the de novo infection rate of uninfected cells. To gain that information, the steady state has to be perturbed. One such way is by the initiation of therapeutic agents that reduce viral levels. The changing viral levels then need to be monitored frequently and mathematical models applied to interpret the data. This type of analysis has been used with great success in human immunodeficiency virus (HIV)-l infection, and a large body of information has emerged regarding viral dynamics and infected cell turnover rate that has enabled important classic insights into mechanism of drug action, emergence of resistance, and the time needed to eradicate all viral reservoirs if the drug(s) were 100% effective. 2- 5 These same principles are now being applied to understand the HCV life cycle and therapeutic response to interferon (IFN) and other antiviral agents. 6 - 11
So what have we learned regarding HCV dynamics using mathematics? The current mathematical models 2- 5 used to describe the life cycles of HIV, hepatitis B virus (HBV), and HCV are based on 3 differential equations that relate the dynamic relationship between uninfected target cells (T), infected cells (I), and serum viral levels (V): (equation 1) dT/dt = s-dT - (l-'Yj)I3VT; (equation 2) dIldt = (l-'Yj)I3VT- 81; and (equation 3) dV/dt = (l-E)pI - cV. In the case of HIV, target cells (T) are predominantly CD4lymphocytes, whereas in HBV and HCV, the target cells are hepatocytes. Target cells (equation 1) become infected by circulating virus (V) at a rate constant (13) and become virus-producing infected cells. Target cells (hepatocytes) turn over slowly with a rate constant (d), and new target cells are produced to take their place at a rate of "s." Because hepatocytes have a long half life, the death of uninfected cells is not considered when making calculations. Infected cells die (8) either by necrosis or apoptosis, depending on the starus of the immune system and the innate ability of the virus to escape cytotoxic T-lymphocyte recognition. Serum viral levels (equation 3) in chronic infection are determined by the balance between the production rate (p) of viruses by infected cells and by their clearance from blood (c). Antiviral drugs could lower viral serum levels by inhibiting the de novo infection of target cells with a certain degree of
May 2001
efficacy (I-"fJ) with viral levels declining as the result of loss of virus-producing infected cells. Alternatively, an agent could lower viral levels by inhibiting viral production with a varying degree of effectiveness (I-e.), and the subsequent decline in RNA levels would be dependent on free viral clearance. What happens when IFN-a is given to patients infected with HCV? Upon initiation of daily IFN therapy, there is an 8-10-hour delay during which viral levels remain unchanged. Thereafter, a dramatic 0.5-3.0 exponential log decline in HCV RNA levels occurs over the first 24-48 hours, after which the viral decline rate slows. 6 - 11 This initial decline is lO-fold greater than that seen in patients infected with HIV-1 treated with combination antiretroviral drugs. 2- 5 What mechanisms account for such a dramatic and rapid initial decline in viral load followed by a slower decline in viral levels? Fitting the data observed in IFN-treated patients with hepatitis C to the mathematical models has shown that the best explanation for this rapid first-phase decline is an IFN-dependent inhibition of viral production and/or release. 8 With IFN blocking viral production, viral RNA decline during this first phase is then based on its intrinsic viral clearance (free viral half-life), which was calculated to vary from 2.5 to 3.5 hours. 8 This estimated viral serum half-life is 2-3 times faster than that of HIV and 6-8 times faster than that ofHBV.12,13 Because viral production must equal its clearance, we can estimate that a trillion HCV virions are produced and cleared each day.8 Another means of studying viral kinetics is to measure virus levels during plasma apheresis, which temporarily increases clearance but has no effect on viral production. 14 When this was done in patients coinfected with HIV-l and HCV, calculated intrinsic viral clearance rates for HCV were similar to previous findings obtained through study ofIFN-treated patients. 8 The similar rates of intrinsic viral clearance obtained with these 2 different approaches altering the steady state provide additional support to the concept that the first-phase viral decline is dependent on the ability of IFN to inhibit viral production. Indeed, a number of IFN-dependent cellular pathways have been identified that could explain this dramatic inhibition of viral production. 15 - 17 If effectiveness in blocking production were 100%, then the serum viral decline would have been expected to be continuous and the time to serum viral disappearance entirely dependent on the half-life of the circulating virus. However, this was not what was observed. 6 - 11 Instead, after the first 24-48 hours, a slower second phase viral decline ensued. This bipha-
EDITORIALS
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sic viral decline is what mathematical modeling would predict if effectiveness in blocking viral production was less than 100%. 8 IFN effectiveness in blocking viral production was found to be dose-dependent, varying from 71 % for 3 million units (mU), to 81 % for 5 mU, and to 95%-97% for 10 mU IFN-a2b. 7,8 Interestingly, when first-phase viral decline and IFN effectiveness were compared between patients infected with genotype 1 and genotype 2,8,9 IFN effectiveness was found to be significantly better in genotype 2-treated patients (99.7% vs. 95%; equivalent to a 1.6 log decline difference). What follows this initial rapid response? After this first phase, the viral decline drastically slows. However, there is marked variation among patients and mathematical calculations indicate that this second phase is determined by two parameters: IFN effectiveness (e.) and infected cell death rate (8). It is also possible that the continued reduction in viral levels results from T cellmediated cytokine-induced interference with viral production, as has been described in HBV during IFN treatment. 18 Although initial studies suggested that the second phase decline was not IFN dose dependent,8 recent studies that compare second-phase slopes using doses ofIFN from 3 to 10 mU of IFN-a2b indicate that second-phase slope is IFN dose dependent, no doubt reflecting the very significant differences (71 % vs. 95 %) in drug effectiveness between these 2 doses of IFN.19 As well, within a given dose of IFN, significant variability occurs in second-phase decline rate among patients, which indicates that the death rate of infected cells has a profound impact on continued viral decline. Indeed, the calculated infected cell half-life was shown to vary from 1.7 days to >70 days.8 Patients with more rapid turnover of infected cells had a faster second-phase decline and higher baseline alanine aminotramferase values 8 and were more likely to clear virus early during therapy.8 Intriguingly, second-phase viral decline was significantly more rapid in genotype 2-infected patients compared with genotype I-infected patients,9 no doubt in part accounting for the greater rate of response seen in genotype 2-infected patients. Thus, from a mathematical point of view, eradication of virus with resultant sustained virologic response (SVR) is dependent on the effectiveness of IFN in blocking viral production in the setting of immunologic recognition and degradation of infected virus-producing hepatocytes. Preliminary findings suggest that the rate of the second-phase viral decay slope in the first month of therapy can be used as a reliable predictor of SVR during IFN monotherapy.2o Patients who have a decline slope of less than 0.1 per day
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EDITORIALS
over the first month of therapy (equivalent to a log decline of <0.3 per week) do not achieve SVR.20 What does mathematical modeling tell us regarding the better treatment response seen with ribavirin combination therapy and pegylated IFN? Zeuzem et al. reported that ribavirin did not alter the kinetics of HCV during IFN therapy, although the first- and second-phase declines were not studied in great detail. 21 In preliminary findings, Pawlotsky et al. 22 showed that ribavirin alone had a minimal «0.5 log copies/mL) and transient antiviral effect; when used in combination with IFN-cx2b (3 mD 3 times a week), ribavirin prevented the viral rebound seen on day 2 of IFN monotherapy dosing 3 times a week, thus leading to a stronger second slope decrease. Ribavirin, however, had no effect on either first- or second-phase kinetics when IFN was administered daily.22 Thus, from a mathematical point of view, the mechanism whereby ribavirin prevents posttreatment relapse is unknown and requires more detailed study. In this issue of GASTROENTEROLOGY, these same mathematical principles were used to assess the impact of covalent attachment of a 40-kilodalton branched polyethylene glycol (PEG) moiety to IFN-cx2a on viral kinetics in both genotype 1- and 2/3-infected patients. 23 The group of patients studied represents a subset of a larger group of patients who were treated in a multicenter study that compared PEG-IFN-cx2a at 180 I-1g once weekly for 48 weeks to IFN-cx2a at 6 mD 3 times a week for 3 months followed by 3 mD 3 times a week for an additional 9 months. 24 In the larger study, SVR in the PEG-IFN-treated group was 39% vs. 19% with standard IFN.24 The biphasic decline pattern in HCV RNA levels was again noted for patients treated with PEG-IFN. However, a first phase could not be detected in 24% of patients because of infrequent blood sampling. Thus, a true assessment of effectiveness could not be obtained from these studies, although no significant differences were apparent in blocking effectiveness between PEG- IFN and 6 mD of IFN-cx2a. As noted in previous studies comparing genotype I-infected patients to genotype 2-infected patients,9 first phase viral decline was significantly faster in genotype non-I-infected patients, reflecting a faster free virion turnover and greater IFN effectiveness. This observation was independent of pegylation. Also, second-phase decay rate was noted to differ even more significantly between genotype 1- and nonI-infected patients, with non-I-infected patients having an 8-fold faster decline slope. This observation shows, from a kinetic point of view, why genotype 2/3-infected patients can be treated with shorter courses of therapy. The second-phase viral decline slope was again predictive
GASTROENTEROLOGY Vol. 120, No.6
of SVR, supporting preliminary findings in other studies of IFN monotherapy.2o However, the second-phase viral decline slope was not enhanced for patients treated with PEG-IFN, regardless of genotype. How does pegylation enhance SVR if it has no significant effect on either first- or second-phase decline rates? To examine this paradox, the investigators divided patients into 3 groups based on the type of second-phase viral decline patterns seen in the first month of therapy: flat, slow, and fast responders. The end of treatment response in the flat, slow, and fast second-phase viral responders was improved in patients receiving PEG-IFN vs. standard IFN-cx2a, and SVR was better in the slow and rapid responder groups with PEG-IFN. Thus, why improved treatment response is seen with PEG-IFN in these different types of second-phase viral decline responders remains unclear. Still, an intriguing hypothesis is that sustained IFN levels allow for stabilization of the effect of IFN on inhibiting viral production and/or enhances the degradation of infected liver cells. More detailed kinetic studies could resolve this uncertainty. In summary, application of mathematical models of viral infection has unearthed detailed information on the dynamics of HCV infection and the impact of IFN on interfering with its life cycle. Continued application of mathematical model analysis to define the dynamic changes occurring during therapy could yield important information that would tell us how soon SVR and nonSVR can be predicted after institution of therapy. Mathematical analysis of viral response will help provide insight into the effects certain host factors such as age, sex, weight,25 race,26 and alcohol intake on treatment response. These answers will no doubt change our therapeutic approach to patients in whom chronic HCV infection proves resistant to therapy. JENNIFER E. LAYDEN THOMAS J. LAYDEN Section of Digestive and Liver Diseases Department of Medicine University of Illinois Chicago, Illinois
References 1. Nguyen n, Sedghi-Vaziri A, Wilkes LB, Mondala T, Pockros PJ, Lindsay KL, McHutchison JG. Fluctuations in viral load (HCV RNA) are relatively insignificant in untreated patients with chronic HCV infection. J Viral Hepat 1996;3:75-78. 2. Ho DD, Neuman AU, Perelson AS, Chen W, Leonard JM, Markowitz M. Rapid turnover of plasma virions and CD4 lymphocytes in HIV-1 infection. Nature 1995;373:123-126. 3. Wei X, Ghosh SK, Taylor ME, Johnson VA, Emini EA, Deutsch P, Lifson JD, Bonhoeffer S, Nowak MA, Hahn BH, et al. Viral dynamics in human immunodeficiency virus type 1 infection. Nature 1995;373:117-120.
May 2001
4. Perelson AS, Essunger P, Cao Y, Vesanen M, Hurley A, Saksala K, Markowitz M, Ho DO. Decay characteristics of HIV-1 infected compartments during combination therapy. Nature 1997;386: 188-191. 5. Perelson AS, Neumann AU, Markowitz M, Leonard JM, Ho DO. HIV-l dynamics in vivo: virion clearance rate, infected cell lifespan, and viral generation time. Science 1996;271:1582-1586. 6. Zeuzem S, Schmidt JM, Lee J-H, Ruster B, Roth WK. Effect of interferon-alpha on the dynamics of hepatits C virus turnover in vivo. Hepatology 1996;23:366-371. 7. Lam NP, Neumann AU, Gretch DR, Wiley TE, Perelson AS, Layden TJ. Dose-dependent acute clearance of hepatitis C genotype 1 virus with interferon alfa. Hepatology 1997;26:226-231. 8. Neumann AU, Lam NP, Dahari H, Gretch DR, WileyTE, Layden TJ, Perelson AS. Hepatitis C viral dynamics in vivo and the antiviral efficacy of interferon-a therapy. Science 1998;282:103-107. 9. Neumann AU, Lam NP, Dahari H, Davidian M, Wiley TE, Mika BP, Perelson AS, Layden TJ. Differences in viral dynamics between genotypes 1 and 2 of hepatitis C virus. J Infect Dis 2000;182: 28-35. 10. Bekkering FC, Brouwer JT, Schalm SW, Elewaut A. Hepatitis C: viral kinetics. Hepatology 1997;26:1691-1693. 11. Yasui K, Okanoue T, Murakami Y, Itoh Y, Minami M, Sakamoto S, Sakamoto M, Nishoioji K. Dynamics of hepatitis C viremia following interferon-a administration. J Infect Dis 1998;177:14751479. 12. Nowak MA, Bonhoeffer S, Hill AM, Boehme R, Thomas HC, McDade H. Viral dynamics in hepatitis B infection. Proc Natl Acad Sci USA 1996;93:4398-4402. 13. Tsiang M, Rooney JF, Toole JJ, Gibbs CS. Biphasic clearance kinetics of hepatitis V virus from patients during adefovir dipivoxil therapy. Hepatology 1999;6:1863-1869. 14. Ramratnam B, Bonhoeffer S, Binley J, Hurley A, Zhang L, Mittler JE, Markowitz M, Moore JP, Perelson AS, Ho DO. Rapid production and clearance of HIV-l and hepatitis C virus assessed by large volume plasma apheresis. Lancet 1999;354:1782-1785. 15. Gale MJ Jr, Korth MJ, Tan NL, Hopkins DA, Dever TE, Polyak SJ, Gretch DR, Katze MG. Evidence that hepatitis C virus resistance to interferon is mediated through repression of the PKR protein kinase by the nonstructural 5a protein. Virology 1997;230:217227. 16. Enomoto N, Sakuma I, Asahina Y, Kurosaki M, Murakami T, Yamamoto C, Izumi N, Marumo F, Sato C. Comparison of fulllength sequences of interferon-sensitive and resistant hepatitis C virus lb: sensitivity to interferon is conferred by amino acid substitutions in the NS5A gene. J Clin Invest 1995;96:224-230.
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17. Sen GC, Lengyel P. The interferon system: a bird's eye view of its biochemistry. J Bioi Chem 1992;267:5017-5020. 18. Guidotti LG, Rochford R, Chung J, Shapiro M, Purcell R, Chisari FV. Viral clearance without destruction of infected cells during acute HBV infection. Science 1999;284:825-829. 19. Bekkering FC, Stalgis C, McHutchison JG, Brouwer JT, Perelson AS. Estimation of early hepatitis C viral clearance in patients receiving daily interferon and ribaviron therapy using a mathematical model. Hepatology 2001;33:419-423. 20. Neumann AU, Layden TJ, Redyy KR, Levi-Drummer R, Poulakos J. The 2 nd phase slope of HCV decline is highly predictive of sustained virologic response (SVR) following consensus interferon (infergen) treatment for chronic hepatitis C and is determined by genotype but not by dose (abstr). Hepatology 2000;32:356A. 21. Zeuzem S, Schmidt JM, Lee JF, von Wagner M, Teuber G, Roth WK. Hepatitis C virus dynamics in vivo: effect of ribavirin and interferon alfa on viral turnover. Hepatology 1998;28:245-252. 22. Pawlotsky J-M, Neumann AU, Dahari H, Conrad A, Hezode C, Schmid P, Dhumeaux D. Hepatitis C virus (HCV) dynamics during induction therapy with interfon (IFN) alpha and/or ribavirin (abstr). Hepatology 2000;32:223A. 23. Zeuzem S, Herrmann E, Lee J-H, Fricke J, Neumann AU, Modi M, Colucci G, Roth WK. Viral kinetics in patients with chronic hepatitis C treated with standard or peginterferon OL2. Gastroenterology 2001;120:1438-1447. 24. Zeuzem S, Feinman SV, Rasenack JJ, Heathcote EJ, Lai M-Y, Gane E, O'Grady J, Reichen J, Diago M, Lin A, Hoffman J, Brunda MJ. Peginterferon alfa-2a in patients with chronic hepatitis C. N Engl J Med 2000;343:1666-1672. 25. Lam NP, Pitrak 0, Speralakis R, Lau AH, Wiley TE, Layden TJ. Effect of obesity on pharmacokinetics and biologic effect of interferon-a in hepatitis C. Dig Dis Sci 1997;42:178-185. 26. Reddy KR, Hoofnagle JH, Tong MJ, Lee WM, Pockros P, Heathcote EJ, Albert 0, John T. Racial differences in responses to therapy with interferon in chronic hepatitis C. Hepatology 1999; 30:787-793.
Address requests for reprints to: Thomas J. Layden, M.D., Department of Medicine, MC 787, 840 S. Wood, Chicago, Illinois 606012. e-mail: [email protected]; fax: (312) 996-5103. Dr. T. J. Layden is a lecturer for Schering and Amgen. © 2001 by the American Gastroenterological Association 0016-5085/01/$35.00 doi:10.1053/gast.2001.24401
MDR3 Mutations: A Glimpse Into Pandora's Box and the Future of Canalicular Pathophysiology See articles on pages 1448 and 1459.
dentification of MDR3 with a form of inheritable cholestasis has an interesting history. In 1986, MDR1 was the first bile canalicular adenosine triphosphate (ATP)-dependent transporter to be identified. l Because the C219 antibody that was used recognizes the product
I
of all MDR family members (mdr 1,2,3 in mice, MDR 1,3 in humans), the identity of individual MDR protein(s) in the canalicular membrane could not be ascertained. Studies using peptide-specific antibodies revealed that mdr2 (MDR3 in humans) constitutes more than 80% of canalicular MDR proteins. 2 What was MDR2 transporting in the canalicular membrane? For several years, many laboratories sought the answer, which