Browse
Submit Data
Databases
API
Help

Dataset Information

9 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Population PK modelling and simulation based on fluoxetine and norfluoxetine concentrations in milk: a milk concentration-based prediction model.

ABSTRACT:

Aims

Population pharmacokinetic (pop PK) modelling can be used for PK assessment of drugs in breast milk. However, complex mechanistic modelling of a parent and an active metabolite using both blood and milk samples is challenging. We aimed to develop a simple predictive pop PK model for milk concentration-time profiles of a parent and a metabolite, using data on fluoxetine (FX) and its active metabolite, norfluoxetine (NFX), in milk.

Methods

Using a previously published data set of drug concentrations in milk from 25 women treated with FX, a pop PK model predictive of milk concentration-time profiles of FX and NFX was developed. Simulation was performed with the model to generate FX and NFX concentration-time profiles in milk of 1000 mothers. This milk concentration-based pop PK model was compared with the previously validated plasma/milk concentration-based pop PK model of FX.

Results

Milk FX and NFX concentration-time profiles were described reasonably well by a one compartment model with a FX-to-NFX conversion coefficient. Median values of the simulated relative infant dose on a weight basis (sRID: weight-adjusted daily doses of FX and NFX through breastmilk to the infant, expressed as a fraction of therapeutic FX daily dose per body weight) were 0.028 for FX and 0.029 for NFX. The FX sRID estimates were consistent with those of the plasma/milk-based pop PK model.

Conclusions

A predictive pop PK model based on only milk concentrations can be developed for simultaneous estimation of milk concentration-time profiles of a parent (FX) and an active metabolite (NFX).

SUBMITTER: Tanoshima R

PROVIDER: S-EPMC4239985 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Disposition of chiral and racemic fluoxetine and norfluoxetine across childbearing.

Project description:ObjectiveTo add to the limited data on the clinical pharmacology of antidepressants during pregnancy, we examined the dose-corrected chiral and racemic levels (level/dose) of fluoxetine (FLX) and norfluoxetine (NorFLX) during pregnancy and early postpartum.MethodsThe authors evaluated 17 pregnant women who received fluoxetine therapy. Doses were recorded weekly across gestation and postpartum. At 20, 30, and 36 weeks of gestation, during delivery, and 12 weeks after delivery, the depression level was assessed with the Hamilton Rating Scale for Depression (HRS-D), and plasma samples were analyzed for levels of S- and R-FLX and S- and R-NorFLX.ResultsThe mean ratios of the chiral parent drug (S-FLX + R-FLX) to metabolite levels (S-NorFLX + R-NorFLX) decreased across pregnancy. The differences were significant between 20-36 weeks and 30-36 weeks. After delivery, the mean dose-corrected level of the active moiety S-FLX and the mean ratio of the chiral parent drug (S-FLX + R-FLX) to metabolite level (S-NorFLX + R-NorFLX) significantly increased between delivery and 12 weeks postpartum. Most of the fluoxetine-treated subjects experienced remitted depressive episodes and euthymic mood levels during pregnancy and postpartum.ConclusionsThe findings extend earlier reports of increased antidepressant metabolism during pregnancy and refractory metabolism after delivery. These data may inform treatment decisions related to dosing in patients who receive fluoxetine during pregnancy.

| S-EPMC3109731 | biostudies-literature

Physiologically Based Pharmacokinetic Modelling and Simulation to Predict the Plasma Concentration Profile of Doxorubicin.

Project description:Doxorubicin (DOX) is still an important anticancer agent despite its tricky pharmacokinetics (PK) and toxicity potential. The advent of systems pharmacology enables the construction of PK models able to predict the concentration profiles of drugs and shed light on the underlying mechanisms involved in PK and pharmacodynamics (PD). By utilizing existing published data and by analysing two clinical case studies we attempt to create physiologically based pharmacokinetic (PBPK) models for DOX using widely accepted methodologies. Based on two different approaches on three different key points we derived eight plausible models. The validation of the models provides evidence that is all performing as designed and opens the way for further exploitation by integrating metabolites and pharmacogenomic information.

| S-EPMC8949582 | biostudies-literature

Improved prediction of tacrolimus concentrations early after kidney transplantation using theory-based pharmacokinetic modelling.

Project description:AIMS:The aim was to develop a theory-based population pharmacokinetic model of tacrolimus in adult kidney transplant recipients and to externally evaluate this model and two previous empirical models. METHODS:Data were obtained from 242 patients with 3100 tacrolimus whole blood concentrations. External evaluation was performed by examining model predictive performance using Bayesian forecasting. RESULTS:Pharmacokinetic disposition parameters were estimated based on tacrolimus plasma concentrations, predicted from whole blood concentrations, haematocrit and literature values for tacrolimus binding to red blood cells. Disposition parameters were allometrically scaled to fat free mass. Tacrolimus whole blood clearance/bioavailability standardized to haematocrit of 45% and fat free mass of 60?kg was estimated to be 16.1?l?h?1 [95% CI 12.6, 18.0?l?h?1]. Tacrolimus clearance was 30% higher (95% CI 13, 46%) and bioavailability 18% lower (95% CI 2, 29%) in CYP3A5 expressers compared with non-expressers. An Emax model described decreasing tacrolimus bioavailability with increasing prednisolone dose. The theory-based model was superior to the empirical models during external evaluation displaying a median prediction error of ?1.2% (95% CI ?3.0, 0.1%). Based on simulation, Bayesian forecasting led to 65% (95% CI 62, 68%) of patients achieving a tacrolimus average steady-state concentration within a suggested acceptable range. CONCLUSION:A theory-based population pharmacokinetic model was superior to two empirical models for prediction of tacrolimus concentrations and seemed suitable for Bayesian prediction of tacrolimus doses early after kidney transplantation.

| S-EPMC4243902 | biostudies-literature

Physiologically Based Modelling Framework for Prediction of Pulmonary Pharmacokinetics of Antimicrobial Target Site Concentrations.

Project description:Background and objectivesPrediction of antimicrobial target-site pharmacokinetics is of relevance to optimize treatment with antimicrobial agents. A physiologically based pharmacokinetic (PBPK) model framework was developed for prediction of pulmonary pharmacokinetics, including key pulmonary infection sites (i.e. the alveolar macrophages and the epithelial lining fluid).MethodsThe modelling framework incorporated three lung PBPK models: a general passive permeability-limited model, a drug-specific permeability-limited model and a quantitative structure-property relationship (QSPR)-informed perfusion-limited model. We applied the modelling framework to three fluoroquinolone antibiotics. Incorporation of experimental drug-specific permeability data was found essential for accurate prediction.ResultsIn the absence of drug-specific transport data, our QSPR-based model has generic applicability. Furthermore, we evaluated the impact of drug properties and pathophysiologically related changes on pulmonary pharmacokinetics. Pulmonary pharmacokinetics were highly affected by physiological changes, causing a shift in the main route of diffusion (i.e. paracellular or transcellular). Finally, we show that lysosomal trapping can cause an overestimation of cytosolic concentrations for basic compounds when measuring drug concentrations in cell homogenate.ConclusionThe developed lung PBPK model framework constitutes a promising tool for characterization of pulmonary exposure of systemically administrated antimicrobials.

| S-EPMC9676785 | biostudies-literature

Toxicokinetics of the Antidepressant Fluoxetine and Its Active Metabolite Norfluoxetine in <i>Caenorhabditis elegans</i> and Their Comparative Potency.

Project description:The nematode Caenorhabditis elegans is a valuable model for ecotoxicological research, yet limited attention has been given to understanding how it absorbs, distributes, metabolizes, and excretes chemicals. This is crucial for C. elegans because the organism is known to have strong uptake barriers that are known to be susceptible to potential confounding effects of the presence of Escherichia coli as a food source. One frequently studied compound in C. elegans is the antidepressant fluoxetine, which has an active metabolite norfluoxetine. In this study, we evaluated the toxicokinetics and relative potency of norfluoxetine and fluoxetine in chemotaxis and activity tests. Toxicokinetics experiments were conducted with varying times, concentrations of fluoxetine, and in the absence or presence of E. coli, simulated with a one-compartment model. Our findings demonstrate that C. elegans can take up fluoxetine and convert it into norfluoxetine. Norfluoxetine proved slightly more potent and had a longer elimination half-life. The bioconcentration factor, uptake, and elimination rate constants depended on exposure levels, duration, and the presence of E. coli in the exposure medium. These findings expand our understanding of toxicokinetic modeling in C. elegans for different exposure scenarios, underlining the importance of considering norfluoxetine formation in exposure and bioactivity assessments of fluoxetine.

| S-EPMC10882974 | biostudies-literature

Prediction of drug concentrations in milk during breastfeeding, integrating predictive algorithms within a physiologically-based pharmacokinetic model.

Project description:There is a risk of exposure to drugs in neonates during the lactation period due to maternal drug intake. The ability to predict drugs of potential hazards to the neonates would be useful in a clinical setting. This work aimed to evaluate the possibility of integrating milk-to-plasma (M/P) ratio predictive algorithms within the physiologically-based pharmacokinetic (PBPK) approach and to predict milk exposure for compounds with different physicochemical properties. Drug and physiological milk properties were integrated to develop a lactation PBPK model that takes into account the drug ionization, partitioning between the maternal plasma and milk matrices, and drug partitioning between the milk constituents. Infant dose calculations that take into account maternal and milk physiological variability were incorporated in the model. Predicted M/P ratio for acetaminophen, alprazolam, caffeine, and digoxin were 0.83 ± 0.01, 0.45 ± 0.05, 0.70 ± 0.04, and 0.76 ± 0.02, respectively. These ratios were within 1.26-fold of the observed ratios. Assuming a daily milk intake of 150 ml, the predicted relative infant dose (%) for these compounds were 4.0, 6.7, 9.9, and 86, respectively, which correspond to a daily ingestion of 2.0 ± 0.5 mg, 3.7 ± 1.2 µg, 2.1 ± 1.0 mg, and 32 ± 4.0 µg by an infant of 5 kg bodyweight. Integration of the lactation model within the PBPK approach will facilitate and extend the application of PBPK models during drug development in high-throughput screening and in different clinical settings. The model can also be used in designing lactation trials and in the risk assessment of both environmental chemicals and maternally administered drugs.

| S-EPMC8376129 | biostudies-literature

Benchmarking pK(a) prediction.

Project description:BackgroundpKa values are a measure of the protonation of ionizable groups in proteins. Ionizable groups are involved in intra-protein, protein-solvent and protein-ligand interactions as well as solubility, protein folding and catalytic activity. The pKa shift of a group from its intrinsic value is determined by the perturbation of the residue by the environment and can be calculated from three-dimensional structural data.ResultsHere we use a large dataset of experimentally-determined pKas to analyse the performance of different prediction techniques. Our work provides a benchmark of available software implementations: MCCE, MEAD, PROPKA and UHBD. Combinatorial and regression analysis is also used in an attempt to find a consensus approach towards pKa prediction. The tendency of individual programs to over- or underpredict the pKa value is related to the underlying methodology of the individual programs.ConclusionOverall, PROPKA is more accurate than the other three programs. Key to developing accurate predictive software will be a complete sampling of conformations accessible to protein structures.

| S-EPMC1513386 | biostudies-literature

Computational modelling and simulation for immunotoxicity prediction induced by skin sensitisers.

Project description:In many domains regulating chemicals and chemical products, there is a legal requirement to determine skin sensitivity to allergens. While many in vitro assays to detect contact hypersensitivity have been developed as alternatives to animal testing over the past ten years and significant progress has been made in this area, there is still a need for continued investment in the creation of techniques and strategies that will allow accurate identification of potential contact allergens and their potency in vitro. In silico models are promising tools in this regard. However, none of the state-of-the-art systems seems to function well enough to serve as a stand-alone hazard identification tool, especially in evaluating the possible allergenicity effects in humans. The Universal Immune System Simulator, a mechanistic computational platform that simulates the human immune system response to a specific insult, provides a means of predicting the immunotoxicity induced by skin sensitisers, enriching the collection of computational models for the assessment of skin sensitization. Here, we present a specific disease layer implementation of the Universal Immune System Simulator for the prediction of allergic contact dermatitis induced by specific skin sensitizers.

| S-EPMC9674872 | biostudies-literature

Fluoxetine- and norfluoxetine-mediated complex drug-drug interactions: in vitro to in vivo correlation of effects on CYP2D6, CYP2C19, and CYP3A4.

Project description:Fluoxetine and its circulating metabolite norfluoxetine comprise a complex multiple-inhibitor system that causes reversible or time-dependent inhibition of the cytochrome P450 (CYP) family members CYP2D6, CYP3A4, and CYP2C19 in vitro. Although significant inhibition of all three enzymes in vivo was predicted, the areas under the concentration-time curve (AUCs) for midazolam and lovastatin were unaffected by 2-week dosing of fluoxetine, whereas the AUCs of dextromethorphan and omeprazole were increased by 27- and 7.1-fold, respectively. This observed discrepancy between in vitro risk assessment and in vivo drug-drug interaction (DDI) profile was rationalized by time-varying dynamic pharmacokinetic models that incorporated circulating concentrations of fluoxetine and norfluoxetine enantiomers, mutual inhibitor-inhibitor interactions, and CYP3A4 induction. The dynamic models predicted all DDIs with less than twofold error. This study demonstrates that complex DDIs that involve multiple mechanisms, pathways, and inhibitors with their metabolites can be predicted and rationalized via characterization of all the inhibitory species in vitro.

| S-EPMC4029899 | biostudies-literature

Prediction of clinical irrelevance of PK differences in atorvastatin using PK/PD models derived from literature-based meta-analyses.

Project description:To support the development of a fixed-dose combination (FDC) of ezetimibe and atorvastatin for the treatment of dyslipidemia, bioequivalence (BE) studies were conducted across a combined dose range (10/10, 10/20, 10/40, and 10/80?mg of ezetimibe/atorvastatin). In the BE trials, all parameters met traditional BE bounds except for atorvastatin peak plasma concentration (Cmax) at two intermediate doses. Literature-based metadata analysis predicted that the observed difference in Cmax between an ezetimibe+atorvastatin FDC and coadministration of these agents translates directly into a non-clinically significant change of <1.2% absolute difference in the percentage lowering of low-density-lipoprotein cholesterol . Both FDC doses were confirmed to be clinically equivalent to coadministration in the subsequent clinical equivalence trials. These data suggest that modeling of dose-response relationships may be useful in predicting clinical equivalence, lowering cost/timelines through effective powering of studies, and predicting the effectiveness of new dosage formulations without the need for additional clinical efficacy trials in regulatory settings.

| S-EPMC4062834 | biostudies-other

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data