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Population Pharmacodynamic Modeling and Methods for D2-receptor Antagonists
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Pharmacometrics)
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Early predictions of a potential drug candidate’s time-course of effect and side-effects, based on models describing drug concentrations, drug effects and disease progression, would be valuable to make drug development more efficient. Pharmacodynamic modeling can incorporate and propagate prior knowledge and be used for simulations of different scenarios.

In this thesis three population pharmacodynamic models were developed to describe the antipsychotic effects and the side-effects prolactin elevation and Extra Pyramidal Symptoms (EPS) following administration of D2-receptor antagonists, commonly used in the treatment of schizophrenia.

Model parameter estimates of prolactin elevating potencies of six compounds correlated with in vitro values of receptor affinities, and parameters related to diurnal prolactin variation and tolerance were similar for the different compounds. The developed prolactin model can thereby be used to predict the time-course of prolactin elevation in patients for a drug candidate using information on in vitro affinity to the D2-receptor. Furthermore, the clinical antipsychotic effect and the prolactin elevation was found to correlate on the individual level for the three antipsychotic compounds investigated and a quantitative relation between D2-receptor occupancy in the brain and prolactin elevation was established. These results support the use of prolactin concentrations as a biomarker in drug development or for individual dose adjustments in clinical care.

The developed model for spontaneously reported EPS adverse events, following treatment with one of five antipsychotics drugs, characterized both the duration and severity of EPS. The model successfully described both the proportions and number of transitions between severity grades and was shown to adequately simulate longitudinal categorical EPS data.

Complex pharmacodynamic models are often associated with long estimation times and non-normal distributions of individual parameters. A method for shortening computation times by substituting differential equations for difference equations was evaluated and shown to be valuable for some models. In addition, transformation of distributions allowed for non-normal distributions of between-subject variability to be better characterized and thereby simulation properties were improved.

In conclusion, population pharmacodynamic models for a range of D2-receptor antagonists were developed and together with the investigated methods the models can facilitate prediction of effects and side-effects in drug development.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2012. , 69 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 161
Keyword [en]
population modeling, schizophrenia, D2-antagonists, pharmacodynamics, drug development
National Category
Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-172540ISBN: 978-91-554-8346-3 (print)OAI: oai:DiVA.org:uu-172540DiVA: diva2:514914
Public defence
2012-05-25, B41, Biomedicinskt Centrum, Husargatan 3, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2012-05-03 Created: 2012-04-11 Last updated: 2012-08-01Bibliographically approved
List of papers
1. An agonist-antagonist interaction model for prolactin release following risperidone and paliperidone treatment
Open this publication in new window or tab >>An agonist-antagonist interaction model for prolactin release following risperidone and paliperidone treatment
2009 (English)In: Clinical Pharmacology and Therapeutics, ISSN 0009-9236, E-ISSN 1532-6535, Vol. 85, no 4, 409-417 p.Article in journal (Refereed) Published
Abstract [en]

A mechanistic pharmacokinetic/pharmacodynamic model is presented, characterizing the time course of prolactin in healthy as well as schizophrenic subjects following the administration of various doses and formulations of the antipsychotic drugs risperidone and paliperidone. Prolactin concentrations from nine studies (1,462 subjects) were analyzed in NONMEM. A competitive agonist-antagonist interaction model described the competition between these drugs and dopamine for the D(2) receptors that regulate prolactin release. Tolerance development was explained by a feedback loop with prolactin stimulating dopamine release, whereas models wherein tolerance is described in terms of depletion of a prolactin pool did not explain the data well. The diurnal prolactin rhythm was described by a two-period cosine function. Baseline prolactin was health-status dependent and higher in women than in men, although the drug-induced release was less than proportional to baseline. This quantitative mechanism-based model is the first to describe prolactin release in patients, and it confirms that paliperidone and risperidone have similar potencies for prolactin release.

National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-120661 (URN)10.1038/clpt.2008.234 (DOI)000264455300018 ()19109590 (PubMedID)
Available from: 2010-03-15 Created: 2010-03-15 Last updated: 2017-12-12Bibliographically approved
2. Predictions of In Vivo Prolactin Levels from In Vitro K (i) Values of D-2 Receptor Antagonists Using an Agonist-Antagonist Interaction Model
Open this publication in new window or tab >>Predictions of In Vivo Prolactin Levels from In Vitro K (i) Values of D-2 Receptor Antagonists Using an Agonist-Antagonist Interaction Model
2013 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 15, no 2, 533-541 p.Article in journal (Refereed) Published
Abstract [en]

Prolactin elevation is a side effect of all currently available D2 receptor antagonists used in the treatment of schizophrenia. Prolactin elevation is the result of a direct antagonistic D2 effect blocking the tonic inhibition of prolactin release by dopamine. The aims of this work were to assess the correlation between in vitro estimates of D2 receptor affinity and pharmacokinetic–pharmacodynamic model-based estimates obtained from analysis of clinical data using an agonist–antagonist interaction (AAI) model and to assess the value of such a correlation in early prediction of full prolactin time profiles. A population model describing longitudinal prolactin data was fitted to clinical data from 16 clinical phases 1 and 3 trials including five different compounds. Pharmacokinetic data were modeled for each compound and the prolactin model was both fitted in per-compound fits as well as simultaneously to all prolactin data. Estimates of prolactin elevating potency were compared to corresponding in vitro values and their predictability was evaluated through model-based simulations. The model successfully described the prolactin time course for all compounds. Estimates derived from experimental preclinical data and the model fit of the clinical data were strongly correlated (p  < 0.001), and simulations adequately predicted the prolactin elevation in five out of six compounds. The AAI model has the potential to be used in drug development to predict prolactin response for a given exposure of D2 antagonists using routinely produced preclinical data.

National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-172394 (URN)10.1208/s12248-012-9450-6 (DOI)000317136100025 ()
Available from: 2012-04-10 Created: 2012-04-10 Last updated: 2017-12-07Bibliographically approved
3. Investigation of the quantitative relationships between D2-receptor occupancy, prolactin elevation and efficacy in schizophrenia, using population modeling
Open this publication in new window or tab >>Investigation of the quantitative relationships between D2-receptor occupancy, prolactin elevation and efficacy in schizophrenia, using population modeling
(English)Manuscript (preprint) (Other academic)
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-172396 (URN)
Available from: 2012-04-10 Created: 2012-04-10 Last updated: 2012-08-01
4. Pharmacokinetic-Pharmacodynamic Modeling of Severity Levels of Extrapyramidal Side Effects with Markov Elements
Open this publication in new window or tab >>Pharmacokinetic-Pharmacodynamic Modeling of Severity Levels of Extrapyramidal Side Effects with Markov Elements
Show others...
(English)Manuscript (preprint) (Other academic)
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-172397 (URN)
Available from: 2012-04-10 Created: 2012-04-10 Last updated: 2012-08-01
5. Transforming parts of a differential equations system to difference equations as a method for run-time savings in NONMEM
Open this publication in new window or tab >>Transforming parts of a differential equations system to difference equations as a method for run-time savings in NONMEM
2010 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 37, no 5, 493-506 p.Article in journal (Refereed) Published
Abstract [en]

Computer models of biological systems grow more complex as computing power increase. Often these models are defined as differential equations and no analytical solutions exist. Numerical integration is used to approximate the solution; this can be computationally intensive, time consuming and be a large proportion of the total computer runtime. The performance of different integration methods depend on the mathematical properties of the differential equations system at hand. In this paper we investigate the possibility of runtime gains by calculating parts of or the whole differential equations system at given time intervals, outside of the differential equations solver. This approach was tested on nine models defined as differential equations with the goal to reduce runtime while maintaining model fit, based on the objective function value. The software used was NONMEM. In four models the computational runtime was successfully reduced (by 59-96%). The differences in parameter estimates, compared to using only the differential equations solver were less than 12% for all fixed effects parameters. For the variance parameters, estimates were within 10% for the majority of the parameters. Population and individual predictions were similar and the differences in OFV were between 1 and -14 units. When computational runtime seriously affects the usefulness of a model we suggest evaluating this approach for repetitive elements of model building and evaluation such as covariate inclusions or bootstraps.

Keyword
Population modeling, NONMEM, Differential equations solver, Run times
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-134141 (URN)10.1007/s10928-010-9169-x (DOI)000282873500003 ()
Available from: 2010-11-24 Created: 2010-11-22 Last updated: 2017-12-12Bibliographically approved
6. Semiparametric Distributions with Estimated Shape Parameters
Open this publication in new window or tab >>Semiparametric Distributions with Estimated Shape Parameters
2009 (English)In: Pharmaceutical research, ISSN 0724-8741, E-ISSN 1573-904X, Vol. 26, no 9, 2174-2185 p.Article in journal (Refereed) Published
Abstract [en]

PURPOSE: To investigate the use of adaptive transformations to assess the parameter distributions in population modeling. METHODS: The logit, box-cox, and heavy tailed transformations were investigated. Each one was used in conjunction with the standard (exponential) transformation for PK and PD parameters. The shape parameters of these transformations were estimated to allow the parameter distributions to more accurately resemble a wider range of parameter distributions. The transformations were tested both in simulated settings where the true distributions were known and in 30 models developed from real data. RESULTS: In the simulated setting the transformations were better than the standard lognormal distribution at characterizing the true distributions. Improvement could also be seen in objective function value (OFV) and in simulation based diagnostics. In the real datasets, significant model improvement based on OFV could be seen in 22, 18, and 22 out of the 30 models for the three transformations respectively. CONCLUSION: Transformations with estimated shape parameters are a promising approach to relax the often erroneous assumption of a known shape of the parameter distribution. They offer a simple and straightforward way of handling and characterizing parameter distributions.

Keyword
estimation, normality assumption, parameter distributions, population modeling, transformations
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-97515 (URN)10.1007/s11095-009-9931-1 (DOI)000268584700013 ()
Available from: 2008-09-12 Created: 2008-09-12 Last updated: 2017-12-14Bibliographically approved

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