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Berezowska M, Sharma P, Pilla Reddy V, Coppola P. Physiologically Based Pharmacokinetic modelling of drugs in pregnancy: A mini-review on availability and limitations. Fundam Clin Pharmacol 2024; 38:402-409. [PMID: 37968879 DOI: 10.1111/fcp.12967] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 09/14/2023] [Accepted: 10/17/2023] [Indexed: 11/17/2023]
Abstract
Physiologically based pharmacokinetic (PBPK) modelling in pregnancy is a relatively new approach that is increasingly being used to assess drug systemic exposure in pregnant women to potentially inform dosing adjustments. Physiological changes throughout pregnancy are incorporated into mathematical models to simulate drug disposition in the maternal and fetal compartments as well as the transfer of drugs across the placenta. This mini-review gathers currently available pregnancy PBPK models for drugs commonly used during pregnancy. In addition, information about the main PBPK modelling platforms used, metabolism pathways, drug transporters, data availability and drug labels were collected. The aim of this mini-review is to provide a concise overview, demonstrate trends in the field, highlight understudied areas and identify current gaps of PBPK modelling in pregnancy. Possible future applications of this PBPK approach are discussed from a clinical, regulatory and industry perspective.
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Affiliation(s)
- Monika Berezowska
- Clinical Pharmacology and Quantitative Pharmacology, Biopharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Pradeep Sharma
- Clinical Pharmacology and Quantitative Pharmacology, Biopharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Venkatesh Pilla Reddy
- Clinical Pharmacology and Quantitative Pharmacology, Biopharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Paola Coppola
- Clinical Pharmacology and Quantitative Pharmacology, Biopharmaceuticals R&D, AstraZeneca, Cambridge, UK
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Werdan Romão MA, Pinto L, Cavalli RC, Duarte G, de Moraes NV, Abduljalil K, Moreira FDL. Mechanistic Framework to Predict Maternal-Placental-Fetal Pharmacokinetics of Nifedipine Employing Physiologically Based Pharmacokinetic Modeling Approach. J Clin Pharmacol 2024; 64:568-577. [PMID: 38305718 DOI: 10.1002/jcph.2404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 12/29/2023] [Indexed: 02/03/2024]
Abstract
Nifedipine is used for treating mild to severe hypertension and preventing preterm labor in pregnant women. Nevertheless, concerns about nifedipine fetal exposure and safety are always raised. The aim of this study was to develop and validate a maternal-placental-fetal nifedipine physiologically based pharmacokinetic (PBPK) model and apply the model to predict maternal, placental, and fetal exposure to nifedipine at different pregnancy stages. A nifedipine PBPK model was verified with nonpregnant data and extended to the pregnant population after the inclusion of the fetoplacental multicompartment model that accounts for the placental tissue and different fetal organs within the Simcyp Simulator version 22. Model parametrization involved scaling nifedipine transplacental clearance based on Caco-2 permeability, and fetal hepatic clearance was obtained from in vitro to in vivo extrapolation encompassing cytochrome P450 3A7 and 3A4 activities. Predicted concentration profiles were compared with in vivo observations and the transplacental transfer results were evaluated using 2-fold criteria. The PBPK model predicted a mean cord-to-maternal plasma ratio of 0.98 (range, 0.86-1.06) at term, which agrees with experimental observations of 0.78 (range, 0.59-0.93). Predicted nifedipine exposure was 1.4-, 2.0-, and 3.0-fold lower at 15, 27, and 39 weeks of gestation when compared with nonpregnant exposure, respectively. This innovative PBPK model can be applied to support maternal and fetal safety assessment for nifedipine at various stages of pregnancy.
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Affiliation(s)
- Marya Antônya Werdan Romão
- Laboratório de Farmacometria (LabFarma), Faculdade de Farmácia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - Leonardo Pinto
- Universidade Federal de Ouro Preto, Ouro Preto, MG, Brazil
| | - Ricardo Carvalho Cavalli
- Departamento de Obstetrícia e Ginecologia, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, Brazil
| | - Geraldo Duarte
- Departamento de Obstetrícia e Ginecologia, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, Brazil
| | - Natália Valadares de Moraes
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, FL, USA
| | | | - Fernanda de Lima Moreira
- Laboratório de Farmacometria (LabFarma), Faculdade de Farmácia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil
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Abduljalil K, Gardner I, Jamei M. An Application of a Physiologically Based Pharmacokinetic Approach to Predict Ceftazidime Pharmacokinetics in a Pregnant Population. Pharmaceutics 2024; 16:474. [PMID: 38675135 PMCID: PMC11054561 DOI: 10.3390/pharmaceutics16040474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 03/19/2024] [Accepted: 03/25/2024] [Indexed: 04/28/2024] Open
Abstract
Physiological changes during pregnancy can alter maternal and fetal drug exposure. The objective of this work was to predict maternal and umbilical ceftazidime pharmacokinetics during pregnancy. Ceftazidime transplacental permeability was predicted from its physicochemical properties and incorporated into the model. Predicted concentrations and parameters from the PBPK model were compared to the observed data. PBPK predicted ceftazidime concentrations in non-pregnant and pregnant subjects of different gestational weeks were within 2-fold of the observations, and the observed concentrations fell within the 5th-95th prediction interval from the PBPK simulations. The calculated transplacental clearance (0.00137 L/h/mL of placenta volume) predicted an average umbilical cord-to-maternal plasma ratio of 0.7 after the first dose, increasing to about 1.0 at a steady state, which also agrees well with clinical observations. The developed maternal PBPK model adequately predicted the observed exposure and kinetics of ceftazidime in the pregnant population. Using a verified population-based PBPK model provides valuable insights into the disposition of drug concentrations in special individuals that are otherwise difficult to study and, in addition, offers the possibility of supplementing sparse samples obtained in vulnerable populations with additional knowledge, informing the dosing adjustment and study design, and improving the efficacy and safety of drugs in target populations.
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Affiliation(s)
- Khaled Abduljalil
- Certara Predictive Technologies, Level 2-Acero, 1 Concourse Way, Sheffield S1 2BJ, UK
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Pan X, Abduljalil K, Almond LM, Pansari A, Yeo KR. Supplementing clinical lactation studies with PBPK modeling to inform drug therapy in lactating mothers: Prediction of primaquine exposure as a case example. CPT Pharmacometrics Syst Pharmacol 2024; 13:386-395. [PMID: 38084656 PMCID: PMC10941563 DOI: 10.1002/psp4.13090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 10/28/2023] [Accepted: 11/20/2023] [Indexed: 03/16/2024] Open
Abstract
Evaluating the safety of primaquine (PQ) during breastfeeding requires an understanding of its pharmacokinetics (PKs) in breast milk and its exposure in the breastfed infant. Physiologically-based PK (PBPK) modeling is primed to assess the complex interplay of factors affecting the exposure of PQ in both the mother and the nursing infant. A published PBPK model for PQ describing the metabolism by monoamine oxidase A (MAO-A; 90% contribution) and cytochrome P450 2D6 (CYP2D6; 10%) in adults was applied to predict the exposure of PQ in mothers and their breastfeeding infants. Plasma exposures following oral daily dosing of 0.5 mg/kg in the nursing mothers in a clinical lactation study were accurately captured, including the observed ranges. Reported infant daily doses based on milk data from the clinical study were used to predict the exposure of PQ in breastfeeding infants greater than or equal to 28 days. On average, the predicted exposures were less than or equal to 0.13% of the mothers. Furthermore, in simulations involving neonates less than 28 days, PQ exposures remain less than 0.16% of the mothers. Assuming that MAO-A increases slowly with age, the predicted relative exposure of PQ remains low in neonates (<0.46%). Thus, the findings of our study support the recommendation made by the authors who reported the results of the clinical lactation study, that is, that when put into context of safety data currently available in children, PQ should not be withheld in lactating women as it is unlikely to cause adverse events in breastfeeding infants greater than or equal to 28 days old.
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Affiliation(s)
- Xian Pan
- Certara UK Limited (Simcyp Division)SheffieldUK
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Cuquerella-Gilabert M, Reig-López J, Serna J, Rueda-Ferreiro A, Merino-Sanjuan M, Mangas-Sanjuan V, Sánchez-Herrero S. Phys-DAT: A physiologically-based pharmacokinetic model for unraveling the dissolution, transit and absorption processes using PhysPK®. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107929. [PMID: 38006685 DOI: 10.1016/j.cmpb.2023.107929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 11/11/2023] [Accepted: 11/13/2023] [Indexed: 11/27/2023]
Abstract
BACKGROUND AND OBJECTIVE In silico methods have become the key for efficiently testing and qualifying drug properties. Due to the complexity of the LADME processes and drug characteristics associated to oral drug absorption, there is a growing demand in the development of Physiologically-based Pharmacokinetic (PBPK) software with greater flexibility. Thus, the aims of this work are (i) to develop a mechanistic-based modeling framework of dissolution, transit and absorption (Phys-DAT) processes in the PhysPK platform and (ii) to assess the predictive power of the acausal MOOM methodology embedded in Phys-DAT versus reference ODE-based PBPK software. METHODS A PBPK model was developed including unreleased, undissolved and dissolved thermodynamic states of the drug. The gastrointestinal tract (GI) was represented by nine compartments and first-order transit kinetics was assumed for the drug fractions. Dissolution processes were described using solubility-independent or solubility-dependent mechanisms and pH effects. Linear transit and linear absorption mechanisms including gradual decrease absorption rate were considered to represent the passive diffusion process. Internal validation of the Phys-DAT model was performed through simulation-based analysis, considering different theoretical scenarios. External validation was carried out using in silico and in vivo data of GI segments and plasma concentrations. Both BCS I and II class drugs were included. RESULTS The model predicts plasma-concentration profiles of each compartment for undissolved, dissolved, and absorbed fractions using PhysPK® v.2.4.1. Internal and external validations demonstrate that the model aligned with the theoretical assumptions and accurately predicted Cmax, Tmax, and AUC 0-t for both BCS I and II drugs. Average Fold Error (AFE), Absolute Average Fold Error (AAFE), and Percent Prediction Error (PPE) calculations indicate good predictive performance, with predicted/observed ratios falling within the acceptable range. CONCLUSIONS Phys-DAT represents a mechanistic model for predicting oral absorption, including the dissolution, pH effect, transit, and absorption processes. PhysPK has shown to be a tool with strong prediction accuracy, similar to the obtained by ODE-based PBPK reference software, and the results obtained with the Phys-DAT model for oral administered drugs showed predictive reliability in healthy volunteers, setting the basis to determine the interchangeability of the acausal MOOM methodology with other modeling approaches.
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Affiliation(s)
- Marina Cuquerella-Gilabert
- Department of Pharmacy and Pharmaceutical Technology and Parasitology, University of Valencia, Valencia, Spain; Interuniversity Research Institute for Molecular Recognition and Technological Development, Polytechnic University of Valencia-University of Valencia, Valencia, Spain; Simulation Department, Empresarios Agrupados Internacional S.A., Madrid, Spain
| | - Javier Reig-López
- Department of Pharmacy and Pharmaceutical Technology and Parasitology, University of Valencia, Valencia, Spain; Interuniversity Research Institute for Molecular Recognition and Technological Development, Polytechnic University of Valencia-University of Valencia, Valencia, Spain
| | - Jenifer Serna
- Simulation Department, Empresarios Agrupados Internacional S.A., Madrid, Spain
| | | | - Matilde Merino-Sanjuan
- Department of Pharmacy and Pharmaceutical Technology and Parasitology, University of Valencia, Valencia, Spain; Interuniversity Research Institute for Molecular Recognition and Technological Development, Polytechnic University of Valencia-University of Valencia, Valencia, Spain
| | - Victor Mangas-Sanjuan
- Department of Pharmacy and Pharmaceutical Technology and Parasitology, University of Valencia, Valencia, Spain; Interuniversity Research Institute for Molecular Recognition and Technological Development, Polytechnic University of Valencia-University of Valencia, Valencia, Spain.
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Dinh J, Johnson TN, Grimstein M, Lewis T. Physiologically Based Pharmacokinetics Modeling in the Neonatal Population-Current Advances, Challenges, and Opportunities. Pharmaceutics 2023; 15:2579. [PMID: 38004559 PMCID: PMC10675397 DOI: 10.3390/pharmaceutics15112579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 10/24/2023] [Accepted: 10/29/2023] [Indexed: 11/26/2023] Open
Abstract
Physiologically based pharmacokinetic (PBPK) modeling is an approach to predicting drug pharmacokinetics, using knowledge of the human physiology involved and drug physiochemical properties. This approach is useful when predicting drug pharmacokinetics in under-studied populations, such as pediatrics. PBPK modeling is a particularly important tool for dose optimization for the neonatal population, given that clinical trials rarely include this patient population. However, important knowledge gaps exist for neonates, resulting in uncertainty with the model predictions. This review aims to outline the sources of variability that should be considered with developing a neonatal PBPK model, the data that are currently available for the neonatal ontogeny, and lastly to highlight the data gaps where further research would be needed.
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Affiliation(s)
- Jean Dinh
- Certara UK Limited, Sheffield S1 2BJ, UK; (J.D.); (T.N.J.)
| | | | - Manuela Grimstein
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD 20903, USA
| | - Tamorah Lewis
- Pediatric Clinical Pharmacology & Toxicology, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
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Shenkoya B, Yellepeddi V, Mark K, Gopalakrishnan M. Predicting Maternal and Infant Tetrahydrocannabinol Exposure in Lactating Cannabis Users: A Physiologically Based Pharmacokinetic Modeling Approach. Pharmaceutics 2023; 15:2467. [PMID: 37896227 PMCID: PMC10610403 DOI: 10.3390/pharmaceutics15102467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/05/2023] [Accepted: 10/11/2023] [Indexed: 10/29/2023] Open
Abstract
A knowledge gap exists in infant tetrahydrocannabinol (THC) data to guide breastfeeding recommendations for mothers who use cannabis. In the present study, a paired lactation and infant physiologically based pharmacokinetic (PBPK) model was developed and verified. The verified model was used to simulate one hundred virtual lactating mothers (mean age: 28 years, body weight: 78 kg) who smoked 0.32 g of cannabis containing 14.14% THC, either once or multiple times. The simulated breastfeeding conditions included one-hour post smoking and subsequently every three hours. The mean peak concentration (Cmax) and area under the concentration-time curve (AUC(0-24 h)) for breastmilk were higher than in plasma (Cmax: 155 vs. 69.9 ng/mL; AUC(0-24 h): 924.9 vs. 273.4 ng·hr/mL) with a milk-to-plasma AUC ratio of 3.3. The predicted relative infant dose ranged from 0.34% to 0.88% for infants consuming THC-containing breastmilk between birth and 12 months. However, the mother-to-infant plasma AUC(0-24 h) ratio increased up to three-fold (3.4-3.6) with increased maternal cannabis smoking up to six times. Our study demonstrated the successful development and application of a lactation and infant PBPK model for exploring THC exposure in infants, and the results can potentially inform breastfeeding recommendations.
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Affiliation(s)
- Babajide Shenkoya
- Center for Translational Medicine, University of Maryland School of Pharmacy, Baltimore, MD 21201, USA
| | - Venkata Yellepeddi
- Division of Clinical Pharmacology, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT 84112, USA
- Department of Molecular Pharmaceutics, College of Pharmacy, University of Utah, Salt Lake City, UT 84112, USA
| | - Katrina Mark
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of Maryland School of Medicine, 11 S Paca, Suite 400, Baltimore, MD 21042, USA
| | - Mathangi Gopalakrishnan
- Center for Translational Medicine, University of Maryland School of Pharmacy, Baltimore, MD 21201, USA
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Hudson RE, Metz TD, Ward RM, McKnite AM, Enioutina EY, Sherwin CM, Watt KM, Job KM. Drug exposure during pregnancy: Current understanding and approaches to measure maternal-fetal drug exposure. Front Pharmacol 2023; 14:1111601. [PMID: 37033628 PMCID: PMC10076747 DOI: 10.3389/fphar.2023.1111601] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 03/13/2023] [Indexed: 04/11/2023] Open
Abstract
Prescription drug use is prevalent during pregnancy, yet there is limited knowledge about maternal-fetal safety and efficacy of this drug use because pregnant individuals have historically been excluded from clinical trials. Underrepresentation has resulted in a lack of data available to estimate or predict fetal drug exposure. Approaches to study fetal drug pharmacology are limited and must be evaluated for feasibility and accuracy. Anatomic and physiological changes throughout pregnancy fluctuate based on gestational age and can affect drug pharmacokinetics (PK) for both mother and fetus. Drug concentrations have been studied throughout different stages of gestation and at or following delivery in tissue and fluid biospecimens. Sampling amniotic fluid, umbilical cord blood, placental tissue, meconium, umbilical cord tissue, and neonatal hair present surrogate options to quantify and characterize fetal drug exposure. These sampling methods can be applied to all therapeutics including small molecule drugs, large molecule drugs, conjugated nanoparticles, and chemical exposures. Alternative approaches to determine PK have been explored, including physiologically based PK modeling, in vitro methods, and traditional animal models. These alternative approaches along with convenience sampling of tissue or fluid biospecimens can address challenges in studying maternal-fetal pharmacology. In this narrative review, we 1) present an overview of the current understanding of maternal-fetal drug exposure; 2) discuss biospecimen-guided sampling design and methods for measuring fetal drug concentrations throughout gestation; and 3) propose methods for advancing pharmacology research in the maternal-fetal population.
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Affiliation(s)
- Rachel E. Hudson
- Division of Clinical Pharmacology, Department of Pediatrics, School of Medicine, The University of Utah, Salt Lake City, UT, United States
| | - Torri D. Metz
- Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, School of Medicine, The University of Utah, Salt Lake City, UT, United States
| | - Robert M. Ward
- Division of Clinical Pharmacology, Department of Pediatrics, School of Medicine, The University of Utah, Salt Lake City, UT, United States
| | - Autumn M. McKnite
- Department of Pharmacology and Toxicology, College of Pharmacy, The University of Utah, Salt Lake City, UT, United States
| | - Elena Y. Enioutina
- Division of Clinical Pharmacology, Department of Pediatrics, School of Medicine, The University of Utah, Salt Lake City, UT, United States
| | - Catherine M. Sherwin
- Department of Pediatrics, Boonshoft School of Medicine, Wright State University, Dayton, OH, United States
| | - Kevin M. Watt
- Division of Clinical Pharmacology, Department of Pediatrics, School of Medicine, The University of Utah, Salt Lake City, UT, United States
| | - Kathleen M. Job
- Division of Clinical Pharmacology, Department of Pediatrics, School of Medicine, The University of Utah, Salt Lake City, UT, United States
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Stamatopoulos K, O’Farrell C, Simmons MJH, Batchelor HK, Mistry N. Use of In Vitro Dynamic Colon Model (DCM) to Inform a Physiologically Based Biopharmaceutic Model (PBBM) to Predict the In Vivo Performance of a Modified-Release Formulation of Theophylline. Pharmaceutics 2023; 15:882. [PMID: 36986743 PMCID: PMC10058579 DOI: 10.3390/pharmaceutics15030882] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 02/25/2023] [Accepted: 03/07/2023] [Indexed: 03/11/2023] Open
Abstract
A physiologically based biopharmaceutic model (PBBM) of a modified-release formulation of theophylline (Uniphyllin Continus® 200 mg tablet) was developed and implemented to predict the pharmacokinetic (PK) data of healthy male volunteers by integrating dissolution profiles measured in a biorelevant in vitro model: the Dynamic Colon Model (DCM). The superiority of the DCM over the United States Pharmacopeia (USP) Apparatus II (USP II) was demonstrated by the superior predictions for the 200 mg tablet (average absolute fold error (AAFE): 1.1-1.3 (DCM) vs. 1.3-1.5 (USP II). The best predictions were obtained using the three motility patterns (antegrade and retrograde propagating waves, baseline) in the DCM, which produced similar PK profiles. However, extensive erosion of the tablet occurred at all agitation speeds used in USP II (25, 50 and 100 rpm), resulting in an increased drug release rate in vitro and overpredicted PK data. The PK data of the Uniphyllin Continus® 400 mg tablet could not be predicted with the same accuracy using dissolution profiles from the DCM, which might be explained by differences in upper gastrointestinal (GI) tract residence times between the 200 and 400 mg tablets. Thus, it is recommended that the DCM be used for dosage forms in which the main release phenomena take place in the distal GI tract. However, the DCM again showed a better performance based on the overall AAFE compared to the USP II. Regional dissolution profiles within the DCM cannot currently be integrated into Simcyp®, which might limit the predictivity of the DCM. Thus, further compartmentalization of the colon within PBBM platforms is required to account for observed intra-regional differences in drug distribution.
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Affiliation(s)
| | - Connor O’Farrell
- School of Chemical Engineering, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Mark J. H. Simmons
- School of Chemical Engineering, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Hannah K. Batchelor
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, 161 Cathedral Street, Glasgow G4 0RE, UK
| | - Nena Mistry
- Biopharmaceutics, DPD, MDS, GSK, David Jack Centre, Park Road, Ware SG12 0DP, UK
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Zhang M, Sychterz C, Chang M, Huang L, Schmidt BJ, Gaohua L. A perspective on the current use of the phase distribution model for predicting milk-to-plasma drug concentration ratio. CPT Pharmacometrics Syst Pharmacol 2022; 11:1547-1551. [PMID: 36181346 PMCID: PMC9755927 DOI: 10.1002/psp4.12865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/09/2022] [Accepted: 09/12/2022] [Indexed: 11/07/2022] Open
Abstract
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.
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Affiliation(s)
- Mian Zhang
- QSP and PBPK, Clinical Pharmacology & PharmacometricsBristol Myers SquibbLawrencevilleNew JerseyUSA
| | - Caroline Sychterz
- QSP and PBPK, Clinical Pharmacology & PharmacometricsBristol Myers SquibbLawrencevilleNew JerseyUSA
| | - Ming Chang
- QSP and PBPK, Clinical Pharmacology & PharmacometricsBristol Myers SquibbLawrencevilleNew JerseyUSA
| | - Lu Huang
- QSP and PBPK, Clinical Pharmacology & PharmacometricsBristol Myers SquibbLawrencevilleNew JerseyUSA
| | - Brian J. Schmidt
- QSP and PBPK, Clinical Pharmacology & PharmacometricsBristol Myers SquibbLawrencevilleNew JerseyUSA
| | - Lu Gaohua
- QSP and PBPK, Clinical Pharmacology & PharmacometricsBristol Myers SquibbLawrencevilleNew JerseyUSA
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Allegaert K, Abbasi MY, Annaert P, Olafuyi O. Current and future physiologically based pharmacokinetic (PBPK) modeling approaches to optimize pharmacotherapy in preterm neonates. Expert Opin Drug Metab Toxicol 2022; 18:301-312. [PMID: 35796504 DOI: 10.1080/17425255.2022.2099836] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION There is a need for structured approaches to inform on pharmacotherapy in preterm neonates. With their proven track record up to regulatory acceptance, physiologically based pharmacokinetic (PBPK) modeling and simulation provide such a structured approach, and hold the promise to support drug development in preterm neonates. AREAS COVERED Compared to the general and pediatric use of PBPK modeling, its use to inform pharmacotherapy in preterms is limited. Using a systematic search (PBPK + preterm), we retained 25 records (20 research papers, 2 letters, 3 abstracts). We subsequently collated the published information on PBPK software packages (PK-Sim®, Simcyp®), and their applications and optimization efforts in preterm neonates. It is encouraging that these applications cover a broad range of scenarios (pharmacokinetic-dynamic analyses, drug-drug interactions, developmental pharmacogenetics, lactation related exposure) and compounds (small molecules, proteins). Furthermore, specific compartments (cerebrospinal fluid, tissue) or (patho)physiologic processes (cardiac output, biliary excretion, first pass metabolism) are considered. EXPERT OPINION Knowledge gaps exist, giving rise to various levels of model uncertainty in PBPK applications in preterm neonates. To improve this setting, we need cross talk between clinicians and modelers to generate and integrate knowledge (PK datasets, system knowledge, maturational physiology and pathophysiology) to further refine PBPK models.
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Affiliation(s)
- Karel Allegaert
- Department of Pharmaceutical and Pharmacological Sciences.,Department of Development and Regeneration, and.,Leuven Child and Youth Institute, KU Leuven, Leuven Belgium.,Department of Clinical Pharmacy, Erasmus MC, Rotterdam, the Netherlands
| | - Mohammad Yaseen Abbasi
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Pieter Annaert
- Department of Pharmaceutical and Pharmacological Sciences
| | - Olusola Olafuyi
- School of Life Sciences, University of Nottingham, Nottingham, United Kingdom
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