Pegylated Interferon Fractal Pharmacokinetics: Individualized Dosing for Hepatitis C Virus Infection

UT Southwestern Medical Center, Department of Internal Medicine, Dallas Texas.
Antimicrobial Agents and Chemotherapy (Impact Factor: 4.48). 11/2012; 57(3). DOI: 10.1128/AAC.02208-12
Source: PubMed


Background:Despite recent advances in hepatitis C virus (HCV) therapeutics, the combination of pegylated interferon (PEGIFN) and ribavirin (RBV) remains the cornerstone of treatment. Optimization and individualization of PEGIFN dosing could improve outcomes.Methods:Week one PEGIFN serum concentrations in 42 HCV genotype 1 infected patients treated with conventional PEGIFN/RBV were analyzed using multi-compartmental pharmacokinetic models. For each patient, pharmacokinetic parameter estimates, weight, age, IL-28B single nucleotide polymorphism, CD4 count, baseline HCV RNA, gender, race, and HIV status were examined using classification and regression tree analysis to identify factors predictive of sustained viral response (SVR). Survival analysis was performed to compare the time to undetectable viral load in patients with and without the highest scoring predictor.Results:PEGIFN concentrations varied at least 87-fold. Pharmacokinetics were best described by a two-compartment model with an 8.4hr absorption lag. Patient weight correlated with PEGIFN systemic clearance based on fractal geometry relationships. SVR was achieved in 36% of patients; PEGIFN cumulative one week area-under the curve (AUC) ≤0.79 mg*h/L scored highest in predicting poor response, followed by weight ≥93.7 kg. Patients with a PEGIFN AUC>0.79 mg*h/L achieved undetectable viral load more rapidly than those with a lower AUC (Hazard ratio=1.63; 95% confidence interval 1.21-2.04).Conclusions:PEGIFN exhibits wide pharmacokinetic variability, mainly driven by patient weight, so that the standard dose may not reach levels needed to achieve SVR. Optimizing dose to patient weight and PEGIFN AUC in the first week offers a solution to improve SVR, and to potentially shorten duration of therapy.

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Available from: Mamta K Jain, Jul 10, 2014
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    • "This would make it difficult to separate them out using standard multivariate logistic or linear regression models (Breiman et al., 1984; Steinberg and Colla, 1995; Shadish et al., 2001; Breiman, 2001). Our approach to using CART for variable selection, and threshold identification of diseases outcomes in patients has been well described in the past (Pasipanodya et al., 2013; Gumbo et al., 2014; Jain et al., 2013; Pasipanodya and Gumbo, 2011). The main outcome we examined was mortality on follow-up. "
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    Full-text · Article · Sep 2015 · EBioMedicine
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    • "Second, our study population was of 58 patients, which could also limit generalizability. However, CART analysis has been able to identify predictive concentration thresholds in even smaller populations on combination therapy with other anti-infective agents in the past.29 Third, several other parameters predict clinical outcomes in the treatment of tuberculosis, including drug concentrations, bacterial burden, chest X-ray findings of cavitation and HIV infection.2,4,12,20,30 "
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    Full-text · Article · May 2014 · Journal of Antimicrobial Chemotherapy
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    • "We employed machine learning models that have high predictive accuracy in identifying both high-order complexity non-linear and linear interactions to identify risk factors of poor long-term outcome in EPTB [21-27]. We did not assume linear interactions between risk factors and clinical outcomes since most biological processes are inherently non-linear and their interactions are obviously non-linear. "
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