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

ABSTRACT 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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    • "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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    ABSTRACT: To identify the pyrazinamide MIC above which standard combination therapy fails. MICs of pyrazinamide were determined for Mycobacterium tuberculosis isolates, cultured from 58 patients in a previous randomized clinical trial in Cape Town, South Africa. The MICs were determined using BACTEC MGIT 960 for isolates that were collected before standard treatment with isoniazid, rifampicin, pyrazinamide and ethambutol commenced. Weekly sputum collections were subsequently made for 8 weeks in order to culture M. tuberculosis in Middlebrook broth medium. Classification and regression tree (CART) analysis was utilized to identify the pyrazinamide MIC predictive of sputum culture results at the end of pyrazinamide therapy. The machine learning-derived susceptibility breakpoints were then confirmed using standard association statistics that took into account confounders of 2 month sputum conversion. The pyrazinamide MIC range was 12.5 to >100 mg/L for the isolates prior to therapy. The epidemiological 95% cut-off value was >100 mg/L. The 2 month sputum conversion rate in liquid cultures was 26% by stringent criteria and 48% by less stringent criteria. CART analysis identified an MIC breakpoint of 50 mg/L, above which patients had poor sputum conversion rates. The relative risk of poor sputum conversion was 1.5 (95% CI: 1.2-1.8) for an MIC >50 mg/L compared with an MIC ≤50 mg/L. We propose a pyrazinamide susceptibility breakpoint of 50 mg/L for clinical decision making and for development of rapid susceptibility assays. This breakpoint is identical to that identified using computer-aided simulations of hollow fibre system output.
    Journal of Antimicrobial Chemotherapy 05/2014; 69(9). DOI:10.1093/jac/dku136 · 5.31 Impact Factor
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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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    ABSTRACT: Tuberculosis is classified as either pulmonary or extra-pulmonary (EPTB). While much focus has been paid to pulmonary tuberculosis, EPTB has received scant attention. Moreover, EPTB is viewed as one wastebasket diagnosis, as "the other" which is not pulmonary. This is a retrospective cohort study of all patients treated for EPTB in the state of Texas between January 2000 and December 2005, who had no pulmonary disease. Clinical and epidemiological factors were abstracted from electronic records of the Report of Verified Case of Tuberculosis. The long-term outcome, which is death by December 2011, was established using the Social Security Administration Death Master File database. Survival in EPTB patients was compared to those with latent tuberculosis, as well as between different types of EPTB, using Cox proportional hazard models. A hybrid of the machine learning method of classification and regression tree analyses and standard regression models was used to identify high-order interactions and clinical factors predictive of long-term all-cause mortality. Four hundred and thirty eight patients met study criteria; the median study follow-up period for the cohort was 7.8 (inter-quartile range 6.0-10.1) years. The overall all-cause mortality rate was 0.025 (95% confidence interval [CI]: 0.021-0.030) per 100 person-year of follow-up. The significant predictors of poor long-term outcome were age (hazard ratio [HR] for each year of age-at-diagnosis was 1.05 [CI: 1.04-1.06], treatment duration, type of EPTB and HIV-infection (HR = 2.16; CI: 1.22, 3.83). Mortality in genitourinary tuberculosis was no different from latent tuberculosis, while meningitis had the poorest long-term outcome of 46.2%. Compared to meningitis the HR for death was 0.50 (CI: 0.27-0.91) for lymphatic disease, 0.42 (CI: 0.21-0.81) for bone/joint disease, and 0.59 (CI:0.27-1.31) for peritonitis. The relationship between mortality and therapy duration for each type of EPTB was a unique "V" shaped curve, with the lowest mortality observed at different therapy durations for each, beyond which mortality increased. EPTB is comprised of several different diseases with different outcomes and durations of therapy. The "V" shaped relationship between therapy duration and outcome leads to the hypothesis that longer duration of therapy may lead to higher patient mortality.
    BMC Infectious Diseases 03/2014; 14(1):115. DOI:10.1186/1471-2334-14-115 · 2.61 Impact Factor
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    ABSTRACT: Echinocandins such as caspofungin are commonly used to treat candidemia and aspergilllosis. Success rates for candidemia treatment are approximately 70%. Dose optimization may further help improve these success rates, given that microbial effect of these agents is concentration-dependent. There are conflicting data as regards the effect of weight and/or obesity on caspofungin drug concentrations. We designed a prospective study to evaluate the population pharmacokinetics of caspofungin in adult patients with a weight difference range of 100 kg. Caspofungin pharmacokinetics were best described using a two-compartment pharmacokinetic model. There were 18 subjects studied, of whom half were women. The typical central volume was 4.2 liters, but increased by a factor of (weight/53.6)¾. The peripheral compartment volume typical value was 2.53 liters, but increased by a factor of (weight/53.6)3/2, an unusual power law signature. Similarly, the ¾ power law best described the relationship between weight and systemic clearance for persons weighing >66.3 kilograms, whereas inter-compartmental clearance was best described by the 3/2 power signature. There are two implications of our findings. First, lower caspofungin area under the concentration-time curves are achieved in obese persons than thinner ones. This suggests that dose optimization in heavier patients may improve clinical success rates. Second, the 3/2 exponent is unusual in fractal geometry-based scaling, and warrants further study. Moreover, this suggests that use of a "floating" instead of fixed exponent may be more useful in studies where weight is under investigation as a potential cause of pharmacokinetic variability within adult patients.
    Antimicrobial Agents and Chemotherapy 03/2013; 57(5). DOI:10.1128/AAC.01490-12 · 4.48 Impact Factor
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