Anders N. Kristoffersson (1), Lena E. Friberg (1), Joakim Nyberg (1)
(1) Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
Objectives: IOV may adversely affect the precision of maximum a posteriori (MAP) estimated individual parameters and have long been recognized to impact the potential of feedback individualization of dosing regimens [1], yet the inclusion of IOV in OD for estimation of individual parameters has not been previously investigated. This work aims to explore methods for including IOV in individual OD.
Methods: The individual FIM maximum a posteriori (FIMMAP) was calculated as previously described [2] and three strategies were investigated to include IOV in the optimization: i) Inflate – The prior covariance matrix was inflated (re-estimated) to include IOV ii) POPocc – IOV was included in the individual FIM as a population occasion random effect . iii) MAPocc – IOV was added to the individual FIM as a fixed effect occasion deviation sampled per individual occasion from the prior IOV distribution. As reference the designs were optimized without inclusion of IOV, termed Ignore.
Two test models were used; a Colistin-PK model [3] as well as a constructed 1-compartment IV-bolus model (1-CIV). The individual deviation parameters, (ηCL, ηQ, ηRE) for Colistin-PK and (ηCL, ηV) for 1-CIV were set as interesting in the PopED [4] EDs criteria. The designs were evaluated by stochastic simulations and MAP re-estimations in NONMEM7 [5] with the expected standard deviation (SD) and the observed Root Mean Squared Error (RMSE) of the Empirical Bayes Estimates (EBE) compared with the expected SD from the PopED inverse FIMMAP.
Results: Inflate and ignore provided identical designs, sampling the first and last occasion for 1-CIV and all occasions for Colistin-PK. POPocc and MAPocc sampled all occasions for 1-CIV and the first occasion for Colistin-PK.
The mean PopED predicted SD of ηCL and ηV of model 1-CIV for Ignore, Inflate, MAPocc and POPocc were 13, 13, 55 and 52 % of the prior while the observed RMSE were 64, 64, 56 and 54 % of the prior. The same trend was found for the Colistin-PK model with MAPocc and POPocc providing in general better or equal RMSE compared to method Ignore.
The runtime for one PopED FIM calculation with 1-CIV for Inflate, MAPocc and POPocc were 1.0, 4.0 and 46 times slower relative to Ignore.
Conclusions: Not including IOV in the FIMMAP was detrimental to the design performance and provided overly optimistic PopED SD. Based on EBE RMSE as well as run times we would recommend method MAPocc for individual OD for models including IOV.
References:
[1] Karlsson MO, Sheiner LB. The importance of modeling interoccasion variability in population pharmacokinetic analyses. Journal of Pharmacokinetics and Biopharmaceutics. 1993;21(6):735-50.
[2] Hennig S, Nyberg J, Fanta S, Backman JT, Hoppu K, Hooker AC, et al. Application of the Optimal Design Approach to Improve a Pretransplant Drug Dose Finding Design for Ciclosporin. The Journal of Clinical Pharmacology. 2012;52(3):347-60.
[3] Mohamed AF, Karaiskos I, Plachouras D, Karvanen M, Pontikis K, Jansson B, et al. Application of a Loading Dose of Colistin Methanesulphonate (CMS) in Critically Ill Patients: Population Pharmacokinetics, Protein Binding and Prediction of Bacterial Kill. Antimicrobial Agents and Chemotherapy. 2012.
[4] Nyberg J, Ueckert S, Strömberg EA, Hennig S, Karlsson MO, Hooker AC. PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool. Computer Methods and Programs in Biomedicine. 2012;108(2):789-805.
[5] Beal S, Sheiner LB, Boeckmann A, Bauer RJ. NONMEM User’s Guides. Ellicott City, MD, USA: Icon Development Solutions; 1989-2011.
Reference: PAGE 22 () Abstr 2689 [www.page-meeting.org/?abstract=2689]
Poster: Study Design