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  • 1
    ISSN: 1573-904X
    Keywords: temozolomide ; cancer ; population pharmacokinetics ; nonlinear mixed-effects models ; pharmacokinetics-toxicity relationship
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract Purpose. To evaluate covariate effects on the pharmacokinetics of temozolomide in cancer patients, and to explore the dose-pharmacokinetics-toxicity relationship of temozolomide. Methods. Non-linear mixed-effects modeling approach was used to analyze the data from 445 patients enrolled in eleven Phase I and Phase II clinical trials. All patients in the phase I trials had advanced cancer. Patients in the phase II trials had anaplastic astrocytoma (AA), glioblastoma multiforme (GBM) or malignant melanoma (MM). A sparse sampling scheme was prospectively developed using Phase I data and was successfully implemented in Phase II trials. Population factors included age, gender, height (HT), weight (WT), body surface area (BSA), serum creatinine (Sr.Cr.), estimated creatinine clearance, serum chemistry data as indices of hepatic function and disease, smoking status, and selected concomitant medications. Descriptive statistics were used to summarize the toxicity and temozolomide dose and exposure relationship. Results. The pharmacokinetics of temozolomide follows a one-compartment model with first order absorption and elimination. Temozolomide clearance (CL) increased with BSA for both genders. The population mean clearance for GBM or AA patients was 11.2 L/hr for male with BSA equal to 2.0 m2, and 8.8 L/hr for female with BSA equal to 1.7 m2. The mean clearance for MM patients was slightly higher. The inter-subject variability in clearance was 15%, and the residual variability was 26%. Other factors investigated in this analysis had little effect on clearance. The overall incidence of neutropenia and thrombocytopenia were 5-8%. Temozolomide dose and AUC did not predict nadir neutrophil and platelet counts due to large variability in counts. Conclusions. The current dose regimen is administered according to BSA which is the most important factor influencing temozolomide clearance. No further dose adjustment is required.
    Type of Medium: Electronic Resource
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  • 2
    ISSN: 1573-904X
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Type of Medium: Electronic Resource
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  • 3
    ISSN: 1573-904X
    Keywords: toxicokinetics ; sparse sampling ; pharmacokinetics ; toxicology
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract Purpose. The objective of this work was to develop and validate blood sampling schemes for accurate AUC determination from a few samples (sparse sampling). This will enable AUC determination directly in toxicology studies, without the need to utilize a large number of animals. Methods. Sparse sampling schemes were developed using plasma concentration-time (Cp-t) data in rats from toxicokinetic (TK) studies with the antiepileptic felbamate (F) and the antihistamine loratadine (L); Cp-t data at 13–16 time-points (N = 4 or 5 rats/time-point) were available for F, L and its active circulating metabolite descarboethoxyloratadine (DCL). AUCs were determined using the full profile and from 5 investigator designated time-points termed “critical” time-points. Using the bootstrap (re-sampling) technique, 1000 AUCs were computed by sampling (N = 2 rats/point, with replacement) from the 4 or 5 rats at each “critical” point. The data were subsequently modeled using PCNONLIN, and the parameters (ka, ke, and Vd) were perturbed by different degrees to simulate pharmacokinetic (PK) changes that may occur during a toxicology study due to enzyme induction/inhibition, etc. Finally, Monte Carlo simulations were performed with random noise (10 to 40%) applied to Cp-t and/or PK parameters to examine its impact on AUCs from sparse sampling. Results. The 5 time-points with 2 rats/point accurately and precisely estimated the AUC for F, L and DCL; the deviation from the full profile was ~10%, with a precision (%CV) of ~15%. Further, altered kinetics and random noise had minimal impact on AUCs from sparse sampling. Conclusions. Sparse sampling can accurately estimate AUCs and can be implemented in rodent toxicology studies to significantly reduce the number of animals for TK evaluations. The same principle is applicable to sparse sampling designs in other species used in safety assessments.
    Type of Medium: Electronic Resource
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