| 1. |
- Bonate, Peter L., et al.
(författare)
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Methods and strategies for assessing uncontrolled drug-drug interactions in population pharmacokinetic analyses : results from the International Society of Pharmacometrics (ISOP) Working Group
- 2016
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 43:2, s. 123-135
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- The purpose of this work was to present a consolidated set of guidelines for the analysis of uncontrolled concomitant medications (ConMed) as a covariate and potential perpetrator in population pharmacokinetic (PopPK) analyses. This white paper is the result of an industry-academia-regulatory collaboration. It is the recommendation of the working group that greater focus be given to the analysis of uncontrolled ConMeds as part of a PopPK analysis of Phase 2/3 data to ensure that the resulting outcome in the PopPK analysis can be viewed as reliable. Other recommendations include: (1) collection of start and stop date and clock time, as well as dose and frequency, in Case Report Forms regarding ConMed administration schedule; (2) prespecification of goals and the methods of analysis, (3) consideration of alternate models, other than the binary covariate model, that might more fully characterize the interaction between perpetrator and victim drug, (4) analysts should consider whether the sample size, not the percent of subjects taking a ConMed, is sufficient to detect a ConMed effect if one is present and to consider the correlation with other covariates when the analysis is conducted, (5) grouping of ConMeds should be based on mechanism (e.g., PGP-inhibitor) and not drug class (e.g., beta-blocker), and (6) when reporting the results in a publication, all details related to the ConMed analysis should be presented allowing the reader to understand the methods and be able to appropriately interpret the results.
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| 2. |
- Friberg, Lena E., et al.
(författare)
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Integrated Population Pharmacokinetic Analysis of Voriconazole in Children, Adolescents, and Adults
- 2012
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Ingår i: Antimicrobial Agents and Chemotherapy. - 0066-4804 .- 1098-6596. ; 56:6, s. 3032-3042
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Tidskriftsartikel (refereegranskat)abstract
- To further optimize the voriconazole dosing in the pediatric population, a population pharmacokinetic analysis was conducted on pooled data from 112 immunocompromised children (2 to <12 years), 26 immunocompromised adolescents (12 to <17 years), and 35 healthy adults. Different maintenance doses (i.e., 3, 4, 6, 7, and 8 mg/kg of body weight intravenously [i.v.] every 12 h [q12h]; 4 mg/kg, 6 mg/kg, and 200 mg orally q12h) were evaluated in these children. The adult dosing regimens (6 mg/kg i.v. q12h on day 1, followed by 4 mg/kg i.v. q12h, and 300 mg orally q12h) were evaluated in the adolescents. A two-compartment model with first-order absorption and mixed linear and nonlinear (Michaelis-Menten) elimination adequately described the voriconazole data. Larger interindividual variability was observed in pediatric subjects than in adults. Deterministic simulations based on individual parameter estimates from the final model revealed the following. The predicted total exposure (area under the concentration-time curve from 0 to 12 h [AUC(0-12)]) in children following a 9-mg/kg i.v. loading dose was comparable to that in adults following a 6-mg/kg i.v. loading dose. The predicted AUC(0-12)s in children following 4 and 8 mg/kg i.v. q12h were comparable to those in adults following 3 and 4 mg/kg i.v. q12h, respectively. The predicted AUC(0-12) in children following 9 mg/kg (maximum, 350 mg) orally q12h was comparable to that in adults following 200 mg orally q12h. To achieve voriconazole exposures comparable to those of adults, dosing in 12- to 14-year-old adolescents depends on their weight: they should be dosed like children if their weight is <50 kg and dosed like adults if their weight is >= 50 kg. Other adolescents should be dosed like adults.
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| 3. |
- Ravva, Patanjali, et al.
(författare)
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A linearization approach for the model-based analysis of combined aggregate and individual patient data
- 2014
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Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 33:9, s. 1460-1476
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Tidskriftsartikel (refereegranskat)abstract
- The application of model-based meta-analysis in drug development has gained prominence recently, particularly for characterizing dose-response relationships and quantifying treatment effect sizes of competitor drugs. The models are typically nonlinear in nature and involve covariates to explain the heterogeneity in summary-level literature (or aggregate data (AD)). Inferring individual patient-level relationships from these nonlinear meta-analysis models leads to aggregation bias. Individual patient-level data (IPD) are indeed required to characterize patient-level relationships but too often this information is limited. Since combined analyses of AD and IPD allow advantage of the information they share to be taken, the models developed for AD must be derived from IPD models; in the case of linear models, the solution is a closed form, while for nonlinear models, closed form solutions do not exist. Here, we propose a linearization method based on a second order Taylor series approximation for fitting models to AD alone or combined AD and IPD. The application of this method is illustrated by an analysis of a continuous landmark endpoint, i.e., change from baseline in HbA1c at week 12, from 18 clinical trials evaluating the effects of DPP-4 inhibitors on hyperglycemia in diabetic patients. The performance of this method is demonstrated by a simulation study where the effects of varying the degree of nonlinearity and of heterogeneity in covariates (as assessed by the ratio of between-trial to within-trial variability) were studied. A dose-response relationship using an Emax model with linear and nonlinear effects of covariates on the emax parameter was used to simulate data. The simulation results showed that when an IPD model is simply used for modeling AD, the bias in the emax parameter estimate increased noticeably with an increasing degree of nonlinearity in the model, with respect to covariates. When using an appropriately derived AD model, the linearization method adequately corrected for bias. It was also noted that the bias in the model parameter estimates decreased as the ratio of between-trial to within-trial variability in covariate distribution increased. Taken together, the proposed linearization approach allows addressing the issue of aggregation bias in the particular case of nonlinear models of aggregate data.
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