Alan S Perelson

Los Alamos National Laboratory, Los Alamos, California, United States

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Publications (515)2762.54 Total impact

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    ABSTRACT: Background Fitness costs and slower disease progression are associated with a cytolytic T lymphocyte (CTL) escape mutation T242N in Gag in HIV-1-infected individuals carrying HLA-B*57/5801 alleles. However, the impact of different context in diverse HIV-1 strains on the fitness costs due to the T242N mutation has not been well characterized. To better understand the extent of fitness costs of the T242N mutation and the repair of fitness loss through compensatory amino acids, we investigated its fitness impact in different transmitted/founder (T/F) viruses.ResultsThe T242N mutation resulted in various levels of fitness loss in four different T/F viruses. However, the fitness costs were significantly compromised by preexisting compensatory amino acids in (Isoleucine at position 247) or outside (glutamine at position 219) the CTL epitope. Moreover, the transmitted T242N escape mutant in subject CH131 was as fit as the revertant N242T mutant and the elimination of the compensatory amino acid I247 in the T/F viral genome resulted in significant fitness cost, suggesting the fitness loss caused by the T242N mutation had been fully repaired in the donor at transmission. Analysis of the global circulating HIV-1 sequences in the Los Alamos HIV Sequence Database showed a high prevalence of compensatory amino acids for the T242N mutation and other T cell escape mutations.Conclusions Our results show that the preexisting compensatory amino acids in the majority of circulating HIV-1 strains could significantly compromise the fitness loss due to CTL escape mutations and thus increase challenges for T cell based vaccines.
    Retrovirology 11/2014; 11(1):101. · 5.66 Impact Factor
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    ABSTRACT: Chronic liver infection by hepatitis C virus (HCV) is a major public health concern. Despite partly successful treatment options, several aspects of intrahepatic HCV infection dynamics are still poorly understood, including the preferred mode of viral propagation, as well as the proportion of infected hepatocytes. Answers to these questions have important implications for the development of therapeutic interventions. In this study, we present methods to analyze the spatial distribution of infected hepatocytes obtained by single cell laser capture microdissection from liver biopsy samples of patients chronically infected with HCV. By characterizing the internal structure of clusters of infected cells, we are able to evaluate hypotheses about intrahepatic infection dynamics. We found that individual clusters on biopsy samples range in size from [Formula: see text] infected cells. In addition, the HCV RNA content in a cluster declines from the cell that presumably founded the cluster to cells at the maximal cluster extension. These observations support the idea that HCV infection in the liver is seeded randomly (e.g. from the blood) and then spreads locally. Assuming that the amount of intracellular HCV RNA is a proxy for how long a cell has been infected, we estimate based on models of intracellular HCV RNA replication and accumulation that cells in clusters have been infected on average for less than a week. Further, we do not find a relationship between the cluster size and the estimated cluster expansion time. Our method represents a novel approach to make inferences about infection dynamics in solid tissues from static spatial data.
    PLoS Computational Biology 11/2014; 10(11):e1003934. · 4.87 Impact Factor
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    ABSTRACT: Viral kinetic models have proven useful to characterize treatment effectiveness during HCV therapy with interferon (IFN) or with direct acting antivirals (DAAs).
    Antiviral therapy 10/2014; · 3.07 Impact Factor
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    ABSTRACT: HIV-1-infected cells in peripheral blood can be grouped into different transcriptional subclasses. Quantifying the turnover of these cellular subclasses can provide important insights into the viral life cycle and the generation and maintenance of latently infected cells. We used previously published data from five patients chronically infected with HIV-1 that initiated combination antiretroviral therapy (cART). Patient-matched PCR for unspliced and multiply spliced viral RNAs combined with limiting dilution analysis provided measurements of transcriptional profiles at the single cell level. Furthermore, measurement of intracellular transcripts and extracellular virion-enclosed HIV-1 RNA allowed us to distinguish productive from non-productive cells. We developed a mathematical model describing the dynamics of plasma virus and the transcriptional subclasses of HIV-1-infected cells. Fitting the model to the data allowed us to better understand the phenotype of different transcriptional subclasses and their contribution to the overall turnover of HIV-1 before and during cART. The average number of virus-producing cells in peripheral blood is small during chronic infection. We find that a substantial fraction of cells can become defectively infected. Assuming that the infection is homogenous throughout the body, we estimate an average in vivo viral burst size on the order of 104 virions per cell. Our study provides novel quantitative insights into the turnover and development of different subclasses of HIV-1-infected cells, and indicates that cells containing solely unspliced viral RNA are a good marker for viral latency. The model illustrates how the pool of latently infected cells becomes rapidly established during the first months of acute infection and continues to increase slowly during the first years of chronic infection. Having a detailed understanding of this process will be useful for the evaluation of viral eradication strategies that aim to deplete the latent reservoir of HIV-1.
    PLoS Computational Biology 10/2014; 10(10):e1003871. · 4.87 Impact Factor
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    Jessica M Conway, Alan S Perelson
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    ABSTRACT: Simple models of therapy for viral diseases such as hepatitis C virus (HCV) or human immunodeficiency virus assume that, once therapy is started, the drug has a constant effectiveness. More realistic models have assumed either that the drug effectiveness depends on the drug concentration or that the effectiveness varies over time. Here a previously introduced varying-effectiveness (VE) model is studied mathematically in the context of HCV infection. We show that while the model is linear, it has no closed-form solution due to the time-varying nature of the effectiveness. We then show that the model can be transformed into a Bessel equation and derive an analytic solution in terms of modified Bessel functions, which are defined as infinite series, with time-varying arguments. Fitting the solution to data from HCV infected patients under therapy has yielded values for the parameters in the model. We show that for biologically realistic parameters, the predicted viral decay on therapy is generally biphasic and resembles that predicted by constant-effectiveness (CE) models. We introduce a general method for determining the time at which the transition between decay phases occurs based on calculating the point of maximum curvature of the viral decay curve. For the parameter regimes of interest, we also find approximate solutions for the VE model and establish the asymptotic behavior of the system. We show that the rate of second phase decay is determined by the death rate of infected cells multiplied by the maximum effectiveness of therapy, whereas the rate of first phase decline depends on multiple parameters including the rate of increase of drug effectiveness with time.
    PLoS Computational Biology 08/2014; 10(8):e1003769. · 4.87 Impact Factor
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    ABSTRACT: scitranslmed.3008195 , 246ra98 (2014); 6 Sci Transl Med et al. Kenneth E. Sherman HCV/HIV co-infected patients Modulation of HCV replication after combination antiretroviral therapy in Editor's Summary infection. HIV suppression with antiretroviral medications plays an important role in the management of those with HCV and HIV This process is highly modulated by responses of the interferon-responsive gene family. The findings suggest that viral replication and evidence of liver injury. Over time, however, HIV suppression leads to reduced HCV replication. biological effects. They show that the initial response to effective HIV treatment results in a transient increase in HCV try and unravel some of these et al. virus (HCV). In a new study of patients co-infected with HCV and HIV, Sherman There is a complex interaction of biological effects when patients are infected with both HIV and the hepatitis C
    Science translational medicine 07/2014; 6(246):98. · 10.76 Impact Factor
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    ABSTRACT: The hepatitis C virus (HCV) is an important contributor to morbidity and mortality in patients co-infected with HIV. Co-infection results in increased HCV replication and more rapid rates of liver disease progression. The effect of HIV combination antiretroviral therapy (cART) on HCV replication has not been studied in depth. To address this issue, we enrolled a small cohort of HCV/HIV co-infected patients into a cART initiation trial and used dynamic modeling combined with evaluation of immune responses and microarray profiles to determine how effective treatment of HIV affects HCV. Treatment with cART resulted in increased HCV replication and increased alanine aminotransferase (ALT) in a subset of patients. Subjects with evidence of hepatic injury (increased ALT) were more likely to have HCV-specific immune responses directed against HCV epitopes. Over time, HCV viral loads declined. Reproducible and biologically important gene expression changes occurred in co-infected patients who underwent successful cART. The effective suppression of HIV by cART initiated a cascade of early and late events in treated patients. Early events involving down-regulation of interferon-stimulated genes may have led to transiently increased viral replication and hepatic injury. At later time points, HCV viral load declined to levels comparable to those seen in the setting of HCV monoinfection. These findings support early antiretroviral therapy in those with HCV/HIV co-infection.
    Science translational medicine 07/2014; 6(246):246ra98. · 10.76 Impact Factor
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    Stanca M Ciupe, Ruy M Ribeiro, Alan S Perelson
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    ABSTRACT: Hepatitis B is a DNA virus that infects liver cells and can cause both acute and chronic disease. It is believed that both viral and host factors are responsible for determining whether the infection is cleared or becomes chronic. Here we investigate the mechanism of protection by developing a mathematical model of the antibody response following hepatitis B virus (HBV) infection. We fitted the model to data from seven infected adults identified during acute infection and determined the ability of the virus to escape neutralization through overproduction of non-infectious subviral particles, which have HBs proteins on their surface, but do not contain nucleocapsid protein and viral nucleic acids. We showed that viral clearance can be achieved for high anti-HBV antibody levels, as in vaccinated individuals, when: (1) the rate of synthesis of hepatitis B subviral particles is slow; (2) the rate of synthesis of hepatitis B subviral particles is high but either anti-HBV antibody production is fast, the antibody affinity is high, or the levels of pre-existent HBV-specific antibody at the time of infection are high, as could be attained by vaccination. We further showed that viral clearance can be achieved for low equilibrium anti-HBV antibody levels, as in unvaccinated individuals, when a strong cellular immune response controls early infection.
    PLoS Computational Biology 07/2014; 10(7):e1003730. · 4.87 Impact Factor
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    Laetitia Canini, Alan S Perelson
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    ABSTRACT: Viral kinetic (VK) modeling has led to increased understanding of the within host dynamics of viral infections and the effects of therapy. Here we review recent developments in the modeling of viral infection kinetics with emphasis on two infectious diseases: hepatitis C and influenza. We review how VK modeling has evolved from simple models of viral infections treated with a drug or drug cocktail with an assumed constant effectiveness to models that incorporate drug pharmacokinetics and pharmacodynamics, as well as phenomenological models that simply assume drugs have time varying-effectiveness. We also discuss multiscale models that include intracellular events in viral replication, models of drug-resistance, models that include innate and adaptive immune responses and models that incorporate cell-to-cell spread of infection. Overall, VK modeling has provided new insights into the understanding of the disease progression and the modes of action of several drugs. We expect that VK modeling will be increasingly used in the coming years to optimize drug regimens in order to improve therapeutic outcomes and treatment tolerability for infectious diseases.
    Journal of Pharmacokinetics and Pharmacodynamics 06/2014; 41(5):431-443. · 1.81 Impact Factor
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    ABSTRACT: Several studies have proven oseltamivir to be efficient in reducing influenza viral titer and symptom intensity. However, the usefulness of oseltamivir can be compromised by the emergence and spread of drug-resistant virus. The selective pressure exerted by different oseltamivir therapy regimens have received little attention. Combining models of drug pharmacokinetics, pharmacodynamics, viral kinetics and symptom dynamics, we explored the efficacy of oseltamivir in reducing both symptoms (symptom efficacy) and viral load (virological efficacy). We simulated samples of 1000 subjects using previously estimated between-subject variability in viral and symptom dynamic parameters to describe the observed heterogeneity in a patient population. We simulated random mutations conferring resistance to oseltamivir. We explored the effect of therapy initiation time, dose, intake frequency and therapy duration on influenza infection, illness dynamics, and emergence of viral resistance. Symptom and virological efficacies were strongly associated with therapy initiation time. The proportion of subjects shedding resistant virus was 27-fold higher when prophylaxis was initiated during the incubation period compared with no treatment. It fell to below 1% when treatment was initiated after symptom onset for twice-a-day intakes. Lower doses and prophylaxis regimens led to lower efficacies and increased risk of resistance emergence. We conclude that prophylaxis initiated during the incubation period is the main factor leading to resistance emergence.
    PLoS Computational Biology 04/2014; 10(4):e1003568. · 4.87 Impact Factor
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    CROI 2014; 03/2014
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    ABSTRACT: Influenza virus infection is a public health problem that is generally more severe in individuals over 65 years of age (elderly). Immunosenescence enhances the susceptibility to viral infections and renders vaccination less effective. Understanding age-related changes in the immune system is crucial in order to design prophylactic and immunomodulatory strategies to reduce morbidity and mortality in the elderly.Here, we propose different mathematical models to provide a quantitative understanding of the immune strategies in the course of influenza virus infection using experimental data from young and aged mice. Simulation results suggest a central role of CD8(+) T cells for adequate viral clearance kinetics in young and aged mice. Adding the removal of infected cells by natural killer cells does not improve the fitting either in young or aged animals. We examine the infection resistant state of cells promoted by the cytokines IFN-α/β, IFN-γ and TNF-α separately. The combination of activated CD8(+) T cells with either of the cytokines provided the best fit in young and aged animals. During the first 3 days after infection the reproductive number for aged mice is 1.5 fold lower than in young mice (p-value<0.05).IMPORTANCE Fits of our models to experimental data suggest that the increased levels of IFN-α/β, IFN-γ and TNF-α ("inflammaging") promote a slower viral growth in aged mice, which consequently limits the stimulation of immune cells and contributes to the reported impaired responses in the elderly. A quantitative understanding of influenza pathogenesis and its shift in the elderly is the key contribution of this work.
    Journal of Virology 01/2014; · 5.08 Impact Factor
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    ABSTRACT: Antiretroviral therapy can reduce HIV-1 to undetectable levels in peripheral blood, but the effectiveness of treatment in suppressing replication in lymphoid tissue reservoirs has not been determined. Here we show in lymph node samples obtained before and during 6 mo of treatment that the tissue concentrations of five of the most frequently used antiretroviral drugs are much lower than in peripheral blood. These lower concentrations correlated with continued virus replication measured by the slower decay or increases in the follicular dendritic cell network pool of virions and with detection of viral RNA in productively infected cells. The evidence of persistent replication associated with apparently suboptimal drug concentrations argues for development and evaluation of novel therapeutic strategies that will fully suppress viral replication in lymphatic tissues. These strategies could avert the long-term clinical consequences of chronic immune activation driven directly or indirectly by low-level viral replication to thereby improve immune reconstitution.
    Proceedings of the National Academy of Sciences 01/2014; · 9.81 Impact Factor
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    ABSTRACT: Sofosbuvir (GS-7977) and GS-0938 are nucleotide analog hepatitis C virus (HCV) polymerase inhibitors, with sofosbuvir being a pyrimidine and GS-0938 being a purine. Mathematical modeling has provided important insights for characterizing HCV-RNA decline and for estimating the in vivo effectiveness of single direct-acting antiviral agents (DAAs); however it has not been used to characterize viral kinetics with combination DAA therapy. We evaluated the antiviral activity of sofosbuvir and GS-0938 given alone and in combination for 14 days in 32 HCV genotype 1 treatment naïve patients (P2938-0212; NUCLEAR study). Viral load declined rapidly in a biphasic manner in all subjects and could be well fitted by assuming that both drugs had a similar and additive level of effectiveness in reducing viral production equal to 99.96% on average. The model predicted that this level of effectiveness was not reached until 0.6 and 2 days for GS-0938 and sofosbuvir, respectively, and likely represents the time needed to accumulate intracellular triphosphates. Subsequently, both drugs led to a rapid second phase of viral decline with a mean rate of 0.35 d(-1). No effect of IL28B-polymorphism was found on viral kinetic parameters. Both sofosbuvir and GS-0938 are highly effective at blocking viral production from HCV-infected cells. Both drugs led to a rapid and consistent second phase viral decline and exhibited no breakthroughs or other signs of resistance. From a kinetics perspective, because both drugs were of the same class there was little benefit in combining them, suggesting that future DAA combinations should consider utilizing drugs with different modes of action.
    Antiviral therapy 01/2014; · 3.07 Impact Factor
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    ABSTRACT: The standard of care for hepatitis C virus (HCV) genotype 1 is a protease inhibitor (telaprevir or boceprevir) combined with pegylated interferon and ribavirin (P/R). A lead-in phase of P/R therapy before addition of the protease inhibitor has been used, with the aim of improving response rates by reducing the development of protease inhibitor resistance. However, whether such a strategy can bring benefit to patients is unclear. A viral dynamic model was used to compare in silico HCV dynamics in patients treated with a period of P/R lead-in therapy followed by the addition of a protease inhibitor versus immediate triple therapy without lead-in. The model predicts that both regimens result in a similar end of treatment viral load change (viral decline or breakthrough). Thus, the current lead-in strategy may not decrease the rate of viral breakthrough/relapse or increase the rate of sustained virologic response. This agrees with available data from clinical trials of several HCV protease inhibitors, such as telaprevir, boceprevir, and faldaprevir. These results suggest that current P/R lead-in strategies may not improve treatment outcomes. However, virus kinetics during a period of P/R therapy, combined with other factors such as the IL28B polymorphism and baseline viral load, can identify interferon-sensitive patients and help develop response-guided therapies.
    Antiviral therapy 01/2014; · 3.07 Impact Factor
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    ABSTRACT: BACKGROUND: HCV kinetic analysis and modeling during antiviral therapy have not been performed in decompensated cirrhotic patients awaiting liver transplantation. Here, viral and host parameters were compared in patients treated with daily intravenous silibinin (SIL) monotherapy for 7 days according to the severity of their liver disease. METHODS: Data were obtained from 25 patients, 12 non-cirrhotic, 8 with compensated cirrhosis and 5 with decompensated cirrhosis. The standard-biphasic model with time-varying SIL effectiveness (from 0 to εmax) was fit to viral kinetic data. RESULTS: Baseline viral load and age were significantly associated with the severity of liver disease (p<0.0001). A biphasic viral decline was observed in most patients with a higher first phase decline patients with less severe liver disease. The maximal effectiveness, εmax, was significantly (p≤0.032) associated with increasing severity of liver disease (εmax[s.e.]=0.86[0.05], εmax=0.69[0.06] and εmax=0.59[0.1]). . The 2(nd) phase decline slope was not significantly different among groups (mean 1.88±0.15 log10IU/ml/wk, p=0.75) as was the rate of change of SIL effectiveness (k=2.12/day[standard error, s.e.=0.18/day]). HCV-infected cell loss rate (δ[s.e.]=0.62/day[0.05/day]) was high and similar among groups. CONCLUSIONS: The high loss rate of HCV-infected cells suggests that sufficient dose and duration of SIL might achieve viral suppression in advanced liver disease.
    Antiviral therapy 01/2014; · 3.07 Impact Factor
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    ABSTRACT: The B cell response to influenza infection of the respiratory tract contributes to viral clearance and establishes profound resistance to reinfection by related viruses. Numerous studies have measured virus-specific antibody-secreting cell (ASC) frequencies in different anatomical compartments after influenza infection and provided a general picture of the kinetics of ASC formation and dispersion. However, the dynamics of ASC populations are difficult to determine experimentally and have received little attention. Here, we applied mathematical modeling to investigate the dynamics of ASC growth, death, and migration over the 2-week period following primary influenza infection in mice. Experimental data for model fitting came from high frequency measurements of virus-specific IgM, IgG, and IgA ASCs in the mediastinal lymph node (MLN), spleen, and lung. Model construction was based on a set of assumptions about ASC gain and loss from the sampled sites, and also on the directionality of ASC trafficking pathways. Most notably, modeling results suggest that differences in ASC fate and trafficking patterns reflect the site of formation and the expressed antibody class. Essentially all early IgA ASCs in the MLN migrated to spleen or lung, whereas cell death was likely the major reason for IgM and IgG ASC loss from the MLN. In contrast, the spleen contributed most of the IgM and IgG ASCs that migrated to the lung, but essentially none of the IgA ASCs. This finding points to a critical role for regional lymph nodes such as the MLN in the rapid generation of IgA ASCs that seed the lung. Results for the MLN also suggest that ASC death is a significant early feature of the B cell response. Overall, our analysis is consistent with accepted concepts in many regards, but it also indicates novel features of the B cell response to influenza that warrant further investigation.
    PLoS ONE 01/2014; 9(8):e104781. · 3.53 Impact Factor
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    ABSTRACT: Immune escape mutations that revert back to the consensus sequence frequently occur in newly HIV-1-infected individuals and have been thought to render the viruses more fit. However, their impact on viral fitness and their interaction with other immune escape mutations have not been evaluated in the background of their cognate transmitted/founder (T/F) viral genomes. To precisely determine the role of reversion mutations, we introduced reversion mutations alone or together with CD8+ T cell escape mutations in their unmodified cognate T/F viral genome and determined their impact on viral fitness in primary CD4+ T cells. Two reversion mutations, V247I and I64T, were identified in Gag and Tat, respectively, but neither had measurable effect on the fitness of their cognate T/F virus. The V247I and G248A mutations that were detected before and concurrently with the potent T cell escape mutation T242N, respectively, were selected by early T cell responses. The V247I or the G248A mutation alone partially restored the fitness loss caused by the T242N mutation. Together they could fully restore the fitness of the T242N mutant to the T/F level. These results demonstrate that the fitness loss caused by a T cell escape mutation could be compensated by preexisting or concurrent reversion and other T cell escape mutations. Our findings indicate that the overall viral fitness is modulated by the complex interplay among T cell escape, compensatory and reversion mutations to maintain the balance between immune escape and viral replication capacity.
    PLoS ONE 01/2014; 9(7):e102734. · 3.53 Impact Factor
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    ABSTRACT: Alisporivir (ALV) is a cyclophilin inhibitor with pan-genotypic activity against hepatitis C virus (HCV). Here we characterize the viral kinetics observed in 249 patients infected with HCV genotypes 2 or 3 and treated for six weeks with different doses of ALV with or without ribavirin (RBV). We use this model to predict the effects of treatment duration and different doses of ALV plus RBV on the sustained virologic response (SVR). Continuous viral decline was observed in 214 (86%) patients that could be well described by the model. All doses led to a high level of antiviral effectiveness equal to 0.98, 0.96 and 0.90 in patients treated with 1000, 800 and 600 mg ALV once-daily, respectively. Patients that received RBV had a significantly faster rate of viral decline, which was attributed to an enhanced loss rate of infected cells, δ (mean δ=0.35 d(-1) vs 0.21 d(-1) in patients +/- RBV, respectively, P=0.0001). The remaining 35 patients (14%) had a suboptimal response with flat or increasing levels of HCV RNA after one week of treatment, which was associated with ALV monotherapy, high body weight and low RBV levels in patients that received ALV+RBV. Assuming full compliance and the same proportion of suboptimal responders, the model predicted 71% and 79% SVR following ALV 400 mg with RBV 400 mg twice-daily for 24 and 36 weeks, respectively. The model predicted that response guided treatment could allow a reduction in the mean treatment duration to 25.3 weeks and attain a 78.6% SVR rate. Conclusion: Alisporivir plus ribavirin may represent an effective interferon-free treatment that is predicted to achieve high SVR rates in patients with HCV genotype 2 or 3 infection. (Hepatology 2013;).
    Hepatology 12/2013; · 12.00 Impact Factor
  • The Liver meeting, Washington DC; 11/2013

Publication Stats

28k Citations
2,762.54 Total Impact Points


  • 1982–2014
    • Los Alamos National Laboratory
      • • Theoretical Biology and Biophysics Group
      • • Theoretical Division
      Los Alamos, California, United States
  • 2013
    • Paris Diderot University
      Lutetia Parisorum, Île-de-France, France
    • St. Jude Children's Research Hospital
      • Department of Infectious Diseases
      Memphis, TN, United States
  • 2012–2013
    • Oakland University
      • Department of Mathematics and Statistics
      Rochester, Michigan, United States
    • Max Planck Institute for Developmental Biology
      Tübingen, Baden-Württemberg, Germany
    • National Renewable Energy Laboratory
      Golden, Colorado, United States
    • University of Pennsylvania
      • Perelman School of Medicine
      Philadelphia, PA, United States
  • 1993–2013
    • Universiteit Utrecht
      • Division of Theoretical Biology and Bioinformatics
      Utrecht, Provincie Utrecht, Netherlands
  • 1989–2013
    • Santa Fe Institute
      Santa Fe, New Mexico, United States
  • 2008–2012
    • University of North Carolina at Chapel Hill
      • Department of Medicine
      Chapel Hill, NC, United States
    • Massachusetts General Hospital
      Boston, Massachusetts, United States
    • Ryerson University
      • Department of Physics
      Toronto, Ontario, Canada
    • Max Planck Institute for Mathematics in the Sciences
      Leipzig, Saxony, Germany
  • 2003–2012
    • University of Illinois at Chicago
      • Department of Medicine (Chicago)
      Chicago, IL, United States
    • University of California, San Diego
      • Division of Infectious Diseases
      San Diego, CA, United States
  • 2011
    • Duke University Medical Center
      • Duke Human Vaccine Institute
      Durham, NC, United States
  • 2008–2011
    • University of Rochester
      • Department of Biostatistics and Computational Biology
      Rochester, NY, United States
  • 1973–2011
    • University of California, Berkeley
      • Biophysics Graduate Group
      Berkeley, MO, United States
  • 2010
    • Wisconsin National Primate Research Center
      Madison, Wisconsin, United States
  • 2008–2010
    • University of Alabama at Birmingham
      • Department of Medicine
      Birmingham, AL, United States
  • 2009
    • University of Utah
      • Department of Mathematics
      Salt Lake City, UT, United States
  • 1996–2009
    • University of Amsterdam
      • • Institute for Theoretical Physics
      • • Faculty of Medicine AMC
      Amsterdam, North Holland, Netherlands
    • University of Minnesota Twin Cities
      • Department of Microbiology
      Minneapolis, MN, United States
  • 2007
    • Purdue University
      • Department of Mathematics
      West Lafayette, IN, United States
    • Texas Tech University
      • Department of Mathematics and Statistics
      Lubbock, TX, United States
  • 2004–2007
    • University of New South Wales
      • Centre for Vascular Research (CVR)
      Kensington, New South Wales, Australia
  • 2006
    • Weill Cornell Medical College
      • Center for the Study of Hepatitis C
      New York City, New York, United States
    • Fred Hutchinson Cancer Research Center
      Seattle, Washington, United States
    • University Medical Center Utrecht
      • Department of Immunology
      Utrecht, Provincie Utrecht, Netherlands
  • 2002–2005
    • Cornell University
      • • Department of Ecology and Evolutionary Biology
      • • Department of Medicine
      Ithaca, NY, United States
    • Friedrich-Alexander Universität Erlangen-Nürnberg
      Erlangen, Bavaria, Germany
    • U.S. Department of Veterans Affairs
      Washington, Washington, D.C., United States
    • Royal Melbourne Hospital
      Melbourne, Victoria, Australia
  • 1998–2004
    • The Rockefeller University
      New York City, New York, United States
    • Bar Ilan University
      • Faculty of Life Sciences
      Ramat Gan, Tel Aviv, Israel
  • 1997–2004
    • University of New Mexico
      • Department of Computer Science
      Albuquerque, NM, United States
    • Massachusetts Institute of Technology
      • Department of Physics
      Cambridge, MA, United States
  • 1989–2004
    • University of Michigan
      • • Department of Mathematics
      • • Department of Microbiology and Immunology
      • • Division of Computer Science and Engineering
      Ann Arbor, MI, United States
  • 1993–2003
    • CUNY Graduate Center
      New York City, New York, United States
  • 2001
    • University of Melbourne
      • Department of Microbiology and Immunology
      Melbourne, Victoria, Australia
  • 2000
    • Duke University
      • Department of Mathematics
      Durham, NC, United States
    • Friedrich Miescher Institute for Biomedical Research
      Bâle, Basel-City, Switzerland
    • Texas A&M University - Corpus Christi
      Corpus Christi, Texas, United States
  • 1999
    • Northern Arizona University
      Flagstaff, Arizona, United States
  • 1997–1999
    • Princeton University
      • Department of Molecular Biology
      Princeton, New Jersey, United States
  • 1995–1998
    • North Carolina State University
      • Department of Statistics
      Raleigh, NC, United States
  • 1989–1996
    • Weizmann Institute of Science
      • • Department of Immunology
      • • Department of Computer Science and Applied Mathematics
  • 1994
    • Russian Academy of Sciences
      Moskva, Moscow, Russia
    • University of Vienna
      • Department of Theoretical Chemistry
      Vienna, Vienna, Austria
  • 1990
    • Ecole Normale Supérieure de Paris
      • Laboratoire de Physique Statistique
      Paris, Ile-de-France, France
  • 1986
    • University of Auckland
      • Department of Mathematics
      Auckland, Auckland, New Zealand
  • 1985
    • Lincoln University California
      Oakland, California, United States
  • 1980–1981
    • Brown University
      Providence, Rhode Island, United States