Development of a Generic Physiologically‐Based Pharmacokinetic Model for Lactation and Prediction of Maternal and Infant Exposure to Ondansetron via Breast Milk

Job, Kathleen M.; Dallmann, André; Parry, Samuel; Saade, George; Haas, David M.; Hughes, Brenna L.; Berens, Pamela D.; Chen, Jia‐Yu; Fu, Christina; Humphrey, Kelsey; Hornik, Christoph P.; Balevic, Stephen J.; Zimmerman, Kanecia O.; Watt, Kevin M.

doi:10.1002/cpt.2530

Cited by 14 publications

(26 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As an essential input of the model,

{fu}_{skim}

can either be measured using techniques, such as equilibrium dialysis, 1 or predicted as a function of

{fu}_{plasma}

7 :

{fu}_{skim} goodbreak= \frac{{fu}_{plasma}^{0.448}}{{(6.94 \times 10^{- 4})}^{0.448} + {fu}_{plasma}^{0.448}}

This function reasonably described the relationship between plasma and skim milk unbound fractions for several drugs with diverse physicochemical and binding properties and enables prediction of

{fu}_{skim}

from

{fu}_{plasma}

in lieu of measured

{fu}_{skim}

7,8 . Note that this equation has been misrepresented in a recent publication where the exponent (0.448) of the constant term 6.94 × 10 −4 has been omitted 3 …”

Section: Derivation Of Atkinson and Begg's Phase Distribution Modelmentioning

confidence: 96%

“…7,8 Note that this equation has been misrepresented in a recent publication where the exponent (0.448) of the constant term 6.94 × 10 −4 has been omitted. 3 Another essential input of the model, D milk , can be predicted using the following equation in lieu of experimental data 9 :…”

Section: Derivation Of Atkinson and Begg's Phase Distribution Modelmentioning

confidence: 99%

“…As another area for caution, in two recent papers, 3,5 the term

\frac{f_{aq, milk}}{{fu}_{skim}} + f_{fat, milk} \cdot D_{milk}

was misrepresented as

Log ()(, \frac{f_{aq, milk}}{{fu}_{skim}}) + f_{fat, milk} \cdot D_{milk}

and

()(, \frac{f_{aq, milk}}{{fu}_{skim}}) + f_{fat, milk} \cdot Log D_{milk}

, respectively, with slight differences in notations. Note that the latter mistake is identical with that in Equation , where

Log D_{milk}

Log P_{normalo : normalw}

was mistakenly used in the place of

D_{milk}

P_{normalo : normalw}

.…”

Section: Derivation Of Atkinson and Begg's Phase Distribution Modelmentioning

confidence: 99%

“…The phase distribution model, developed upon the same theoretical basis as Fleishaker's model, was coupled with additional equations to allow M/P ratio predictions using unbound drug fraction in plasma, pKa, and Log P or Log D of the drug 2 . Although the phase distribution model has been widely cited, the derivation of the equations has not been explicitly presented, and misrepresentations of the equations have been noted in recent publications 3–5 . Considering the increasing public health concerns around drug safety in lactation and renewed interest in the phase distribution model, we provide the theoretical background linking these important previous works within the context of recent applications and areas for caution that we have observed.…”

Section: Introductionmentioning

confidence: 99%

“… 2 Although the phase distribution model has been widely cited, the derivation of the equations has not been explicitly presented, and misrepresentations of the equations have been noted in recent publications. 3 , 4 , 5 Considering the increasing public health concerns around drug safety in lactation and renewed interest in the phase distribution model, we provide the theoretical background linking these important previous works within the context of recent applications and areas for caution that we have observed.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A perspective on the current use of the phase distribution model for predicting milk‐to‐plasma drug concentration ratio

Zhang

Sychterz

Chang

et al. 2022

CPT Pharmacom & Syst Pharma

View full text Add to dashboard Cite

The phase distribution model, proposed by Atkinson and Begg in 1990, has been widely used for predicting breastmilk‐to‐plasma drug concentration ratio. However, misrepresentations of the equations have been noted in recent publications. In this perspective, we revisit the derivation of the equations and provide an R/Shiny interface for the model with a view to helping scientists in this field acquire in‐depth understanding of the theoretical background and implementation of the model.

show abstract

“…As an essential input of the model,

{fu}_{skim}

can either be measured using techniques, such as equilibrium dialysis, 1 or predicted as a function of

{fu}_{plasma}

7 :

{fu}_{skim} goodbreak= \frac{{fu}_{plasma}^{0.448}}{{(6.94 \times 10^{- 4})}^{0.448} + {fu}_{plasma}^{0.448}}

This function reasonably described the relationship between plasma and skim milk unbound fractions for several drugs with diverse physicochemical and binding properties and enables prediction of

{fu}_{skim}

from

{fu}_{plasma}

in lieu of measured

{fu}_{skim}

7,8 . Note that this equation has been misrepresented in a recent publication where the exponent (0.448) of the constant term 6.94 × 10 −4 has been omitted 3 …”

Section: Derivation Of Atkinson and Begg's Phase Distribution Modelmentioning

confidence: 96%

Section: Derivation Of Atkinson and Begg's Phase Distribution Modelmentioning

confidence: 99%

“…As another area for caution, in two recent papers, 3,5 the term

\frac{f_{aq, milk}}{{fu}_{skim}} + f_{fat, milk} \cdot D_{milk}

was misrepresented as

Log ()(, \frac{f_{aq, milk}}{{fu}_{skim}}) + f_{fat, milk} \cdot D_{milk}

and

()(, \frac{f_{aq, milk}}{{fu}_{skim}}) + f_{fat, milk} \cdot Log D_{milk}

, respectively, with slight differences in notations. Note that the latter mistake is identical with that in Equation , where

Log D_{milk}

Log P_{normalo : normalw}

was mistakenly used in the place of

D_{milk}

P_{normalo : normalw}

.…”

Section: Derivation Of Atkinson and Begg's Phase Distribution Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A perspective on the current use of the phase distribution model for predicting milk‐to‐plasma drug concentration ratio

Zhang

Sychterz

Chang

et al. 2022

CPT Pharmacom & Syst Pharma

View full text Add to dashboard Cite

show abstract

Informing a Comprehensive Risk Assessment of Infant Drug Exposure From Human Milk: Application of a Physiologically Based Pharmacokinetic Lactation Model for Sotalol

Pressly

Schmidt

Guinn

et al. 2023

The Journal of Clinical Pharma

View full text Add to dashboard Cite

Characterization of infant drug exposure through human milk is important and underexplored. Because infant plasma concentrations are not frequently collected in clinical lactation studies, modeling and simulation approaches can integrate physiology, available milk concentrations, and pediatric data to inform exposure in breastfeeding infants.A physiologically based pharmacokinetic model was built for sotalol,a renally eliminated drug,to simulate infant drug exposure from human milk. Intravenous and oral adult models were built, optimized, and scaled to an oral pediatric model for a breastfeedingrelevant age group (<2 years). Model simulations captured the data that were put aside for verification. The resulting pediatric model was applied to predict the impacts of sex, infant body size, breastfeeding frequency, age, and maternal dose (240 and 433 mg) on drug exposure during breastfeeding. Simulations suggest a minimal effect of sex or frequency on total sotalol exposure. Infants in the 90th percentile in height and weight have predicted exposures ≈20% higher than infants of the same age in the 10th percentile due to increased milk intake. The simulated infant exposures increase throughout the first 2 weeks of life and are maintained at the highest concentrations in weeks 2-4, with a consistent decrease observed as infants age. Simulations suggest that breastfeeding infants will have plasma concentrations in the lower range observed in infants administered sotalol. With further validation on additional drugs, physiologically based pharmacokinetic modeling approaches could use lactation data to a greater extent and provide comprehensive information to support decisions regarding medication use during breastfeeding.

show abstract

Physiologically Based Pharmacokinetic Modeling of Nilotinib for Drug‐Drug Interactions, Pediatric Patients, and Pregnancy and Lactation

Liu,

Leong,

Burckart

et al. 2023

The Journal of Clinical Pharma

Self Cite

View full text Add to dashboard Cite

Nilotinib is a second generation BCR‐ABL tyrosine kinase inhibitor for the treatment of Philadelphia chromosome positive chronic myeloid leukemia in both adult and pediatric patients. The pharmacokinetics (PK) of nilotinib in specific populations such as pregnancy and lactation people remain poorly understood. Therefore, the objectives of the current study were to develop a physiologically‐based pharmacokinetic (PBPK) model to predict nilotinib PK in virtual drug‐drug interaction (DDI) studies, as well as in pediatric, pregnant, and lactating populations. The nilotinib PBPK model was built in PK‐Sim® which is part of the free and open‐source software Open Systems Pharmacology (OSP). The observed clinical data for the validation of the nilotinib models were obtained from the literature. The model reasonably predicted nilotinib concentrations in the adult population, the DDIs between nilotinib and rifampin or ketoconazole in the adult population, as well as the PK in the pediatric, pregnant, and lactating populations, although in the latter two populations plasma concentrations were slightly underestimated. The ratio of predicted vs observed PK parameters for the adult model ranged from 0.71‐1.11 for AUC and 0.55‐0.95 for Cmax. For the DDI, the predicted AUCR and CmaxR fell within the Guest criterion. The current study demonstrated the utility of using PBPK modeling to understand the mechanistic basis of PK differences between adults and special populations, such as pediatrics, and pregnant and lactating individuals, indicating that this technology can potentially inform or optimize dosing conditions in specific populations.This article is protected by copyright. All rights reserved

show abstract

Development of a Generic Physiologically‐Based Pharmacokinetic Model for Lactation and Prediction of Maternal and Infant Exposure to Ondansetron via Breast Milk

Cited by 14 publications

References 43 publications

A perspective on the current use of the phase distribution model for predicting milk‐to‐plasma drug concentration ratio

A perspective on the current use of the phase distribution model for predicting milk‐to‐plasma drug concentration ratio

Informing a Comprehensive Risk Assessment of Infant Drug Exposure From Human Milk: Application of a Physiologically Based Pharmacokinetic Lactation Model for Sotalol

Physiologically Based Pharmacokinetic Modeling of Nilotinib for Drug‐Drug Interactions, Pediatric Patients, and Pregnancy and Lactation

Contact Info

Product

Resources

About