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Pharmacometric Models in Anesthesia and Analgesia
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Modeling is a valuable tool in drug development, to support decision making, improving study design, and aid in regulatory approval and labeling. This thesis describes the development of pharmacometric models for drugs used in anesthesia and analgesia.

Models describing the effects on anesthetic depth, measured by the bispectral index (BIS), for a commonly used anesthetic, propofol, and for a novel anesthetic, AZD3043, were developed. The propofol model consisted of two effect-site compartments, and could describe the effects of propofol when the rate of infusion is changed during treatment. AZD3043 had a high clearance and a low volume of distribution, leading to a short half-life. The distribution to the effect site was fast, and together with the short plasma half-life leading to a fast onset and offset of effects. It was also shown that BIS after AZD3043 treatment is related to the probability of unconsciousness similar to propofol.

In analgesia studies dropout due to lack of efficacy is common. This dropout is not at random and needs to be taken into consideration in order to avoid bias. A model was developed describing the PK, pain intensity and dropout hazard for placebo, naproxen and a novel analgesic compound, naproxcinod, after removal of a wisdom tooth. The model provides an opportunity to describe the effects of other doses or formulations. Visual predictive checks created by simultaneous simulations of PI and dropout provided a good way of assessing the goodness of fit when there is informative dropout.

The performance of non-linear mixed effects models in the presence of informative dropout, with and without including models that describe such informative dropout was investigated by simulations and re-estimations. When a dropout model was not included there was in general more bias. The bias increased with decreasing number of observations per subject, increasing placebo effect and increasing dropout rate. Bias was relatively unaffected by the number of subjects in the study. The bias had, in general, little effect on simulations of the underlying efficacy score, but a dropout model would still be needed in order to make realistic simulations.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2013. , 56 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 173
Keyword [en]
Pharmacometrics, Anesthesia, Analgesia, Dropout, NONMEM
National Category
Pharmaceutical Sciences
Research subject
Pharmacokinetics and Drug Therapy
Identifiers
URN: urn:nbn:se:uu:diva-205580ISBN: 978-91-554-8726-3 (print)OAI: oai:DiVA.org:uu-205580DiVA: diva2:642092
Public defence
2013-10-04, B22, Uppsala Biomedicinska Centrum (BMC), Husargatan 3, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2013-09-13 Created: 2013-08-20 Last updated: 2014-01-22
List of papers
1. A two-compartment effect site model describes the bispectral index after different rates of propofol infusion
Open this publication in new window or tab >>A two-compartment effect site model describes the bispectral index after different rates of propofol infusion
Show others...
2010 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 37, no 3, 243-255 p.Article in journal (Refereed) Published
Abstract [en]

Different estimates of the rate constant for the effect site distribution (k(e0)) of propofol, depending on the rate and duration of administration, have been reported. This analysis aimed at finding a more general pharmacodynamic model that could be used when the rate of administration is changed during the treatment. In a cross-over study, 21 healthy volunteers were randomised to receive a 1 min infusion of 2 mg/kg of propofol at one occasion, and a 1 min infusion of 2 mg/kg of propofol immediately followed by a 29 min infusion of 12 mg kg(-1) h(-1) of propofol at another occasion. Arterial plasma concentrations of propofol were collected up to 4 h after dosing, and BIS was collected before start of infusion and until the subjects were fully awake. The population pharmacokinetic-pharmacodynamic analysis was performed using NONMEM VI. A four-compartment PK model with time-dependent elimination and distribution described the arterial propofol concentrations, and was used as input to the pharmacodynamic model. A standard effect compartment model could not accurately describe the delay in the effects of propofol for both regimens, whereas a two-compartment effect site model significantly improved the predictions. The two-compartment effect site model included a central and a peripheral effect site compartment, possibly representing a distribution within the brain, where the decrease in BIS was linked to the central effect site compartment concentrations through a sigmoidal E-max model.

Keyword
Propofol, Pharmacokinetics, Pharmacodynamics, Bispectral index, NONMEM
National Category
Pharmaceutical Sciences Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-136145 (URN)10.1007/s10928-010-9157-1 (DOI)000279034400002 ()
Available from: 2010-12-10 Created: 2010-12-10 Last updated: 2017-12-11Bibliographically approved
2. Population model for pharmacokinetics and bispectral index after intravenous infusion of the sedative and anesthetic AZD3043 in healthy volunteers
Open this publication in new window or tab >>Population model for pharmacokinetics and bispectral index after intravenous infusion of the sedative and anesthetic AZD3043 in healthy volunteers
(English)Article in journal (Other academic) Submitted
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-202041 (URN)
Available from: 2013-06-19 Created: 2013-06-19 Last updated: 2014-01-23Bibliographically approved
3. Modelling of pain intensity and informative dropout in a dental pain model after naproxcinod, naproxen and placebo administration
Open this publication in new window or tab >>Modelling of pain intensity and informative dropout in a dental pain model after naproxcinod, naproxen and placebo administration
2011 (English)In: British Journal of Clinical Pharmacology, ISSN 0306-5251, E-ISSN 1365-2125, Vol. 71, no 6, 899-906 p.Article in journal (Refereed) Published
Abstract [en]

AIMS To describe pain intensity (PI) measured on a visual analogue scale (VAS) and dropout due to request for rescue medication after administration of naproxcinod, naproxen or placebo in 242 patients after wisdom tooth removal. METHODS Non-linear mixed effects modelling was used to describe the plasma concentrations of naproxen, either formed from naproxcinod or from naproxen itself, and their relationship to PI and dropout. Goodness of fit was assessed by simultaneous simulations of PI and dropout. RESULTS Baseline PI for the typical patient was 52.7 mm. The PI was influenced by placebo effects, using an exponential model, and by naproxen concentrations using a sigmoid E-max model. Typical maximal placebo effect was a decrease in PI by 20.2%, with an onset rate constant of 0.237 h-1. EC50 was 0.135 mu mol l-1. A Weibull time-to-event model was used for the dropout, where the hazard was dependent on the predicted PI and by the PI at baseline. Since the dropout was not at random, it was necessary to include the simulated dropout in visual predictive checks (VPC) of PI. CONCLUSIONS This model describes the relationship between drug effects, PI and the likelihood of dropout after naproxcinod, naproxen and placebo administration. The model provides an opportunity to describe the effects of other doses or formulations, after dental extraction. VPC created by simultaneous simulations of PI and dropout provides a good way of assessing the goodness of fit when there is informative dropout.

Keyword
dropout, naproxcinod, naproxen, NONMEM, time to event
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-154347 (URN)10.1111/j.1365-2125.2011.03924.x (DOI)000290449500012 ()21272053 (PubMedID)
Available from: 2011-05-31 Created: 2011-05-31 Last updated: 2017-12-11Bibliographically approved
4. Performance of Nonlinear Mixed Effects Models in the Presence of Informative Dropout
Open this publication in new window or tab >>Performance of Nonlinear Mixed Effects Models in the Presence of Informative Dropout
2015 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 17, no 1, 245-255 p.Article in journal (Refereed) Published
Abstract [en]

Informative dropout can lead to bias in statistical analyses if not handled appropriately. The objective of this simulation study was to investigate the performance of nonlinear mixed effects models with regard to bias and precision, with and without handling informative dropout. An efficacy variable and dropout depending on that efficacy variable were simulated and model parameters were reestimated, with or without including a dropout model. The Laplace and FOCE-I estimation methods in NONMEM 7, and the stochastic simulations and estimations (SSE) functionality in PsN, were used in the analysis. For the base scenario, bias was low, less than 5% for all fixed effects parameters, when a dropout model was used in the estimations. When a dropout model was not included, bias increased up to 8% for the Laplace method and up to 21% if the FOCE-I estimation method was applied. The bias increased with decreasing number of observations per subject, increasing placebo effect and increasing dropout rate, but was relatively unaffected by the number of subjects in the study. This study illustrates that ignoring informative dropout can lead to biased parameters in nonlinear mixed effects modeling, but even in cases with few observations or high dropout rate, the bias is relatively low and only translates into small effects on predictions of the underlying effect variable. A dropout model is, however, crucial in the presence of informative dropout in order to make realistic simulations of trial outcomes.

National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-202045 (URN)10.1208/s12248-014-9700-x (DOI)000347448900023 ()25421458 (PubMedID)
Available from: 2013-06-19 Created: 2013-06-19 Last updated: 2017-12-06

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