Predicting human exposure of active drug after oral prodrug administration, using a joined in vitro/in silico–in vivo extrapolation and physiologically-based pharmacokinetic modeling approach

Malmborg, Jonas; Ploeger, Bart

doi:10.1016/j.vascn.2012.12.002

Cited by 19 publications

(11 citation statements)

References 45 publications

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“…The concentration–time profile of MPA shows a biphasic decay due to enterohepatic recycling of MPAG (Malmborg & Ploeger, ; Matsunaga et al, , ). So, the PBPK model (Figure ) included biliary excretion of MPAG via multidrug resistance‐associated protein 2 (MRP‐2) (Matsunaga et al, ); the secretion of MPAG from hepatocytes back into the systemic circulation via MRP‐3 (Matsunaga et al, ); the uptake of MPAG into the hepatocytes via organic anion transport 1B3 (OATP1B3) (Matsunaga et al, ); and first order hydrolysis of MPAG into MPA in the lumen of lower small intestine and large intestine via beta glucuronidase (GUSB) enzymes (Saitoh et al, ).…”

Section: Methodsmentioning

confidence: 99%

“…Using the predictive power of whole body PBPK models, the effect of variability in anatomical, physiological, and enzymes and transporters expression levels on the variability of drug pharmacokinetics can be predicted a priori using virtual population PBPK (Pop‐PBPK) models (Willmann et al, ). The prediction of MPA plasma levels was reported before using a PBPK model in healthy subjects and those with reduced renal function (Joshi, Venkataramanan, & Kalluri, ; Malmborg & Ploeger, ).…”

Section: Introductionmentioning

confidence: 99%

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The development of a population physiologically based pharmacokinetic model for mycophenolic mofetil and mycophenolic acid in humans using data from plasma, saliva, and kidney tissue

Alsmadi

Alfarah

Albderat

et al. 2019

Biopharm & Drug Disp

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Background Mycophenolic acid (MPA) is used widely to prevent graft rejection in kidney‐transplant patients. Therapeutic drug monitoring (TDM) in plasma requires an invasive procedure that is inconvenient, especially in pediatric patients. TDM in saliva is a more convenient non‐invasive alternative compared with plasma. Methods A population physiologically based pharmacokinetic (Pop‐PBPK) model of mycophenolate mofetil (MMF) and MPA with enterohepatic recycling was built and verified using previously published plasma, saliva, and kidney biopsy data in healthy and kidney‐transplant adult patients. The verified model was then used to predict experimentally observed plasma and saliva MMF and MPA TDM data in Jordanian pediatric kidney transplant patients measured using LC‐MS/MS. A correlation was established between plasma and saliva exposures in pediatrics. Results The developed LCMS was sensitive to both MMF and MPA in plasma and saliva. The developed Pop‐PBPK model predicted well the previously reported MMF and MPA levels in plasma, saliva, and kidney tissue and those observed in the current study (more than 75% of observed data points were within 90% predictive interval of population simulations). A statistically significant correlation was found between plasma and saliva exposures for both MMF (Pop‐PBPK predicted and observed) and MPA (Pop‐PBPK predicted). Conclusion Both MPA and MMF can be classified as class III compounds in the Salivary Excretion Classification System. Saliva is an alternative body fluid to plasma that can be used for TDM of MPA and MMF in kidney‐transplant patients in pediatrics. Exposure to MPA and MMF in plasma, saliva, and kidney tissue was reliably predicted using the developed Pop‐PBPK model.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The development of a population physiologically based pharmacokinetic model for mycophenolic mofetil and mycophenolic acid in humans using data from plasma, saliva, and kidney tissue

Alsmadi

Alfarah

Albderat

et al. 2019

Biopharm & Drug Disp

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“…reported a PBPK model linked to an in vitro/in silico in vivo extrapolation (IVIVE) approach after oral administration of MMF [65]. The PBPK model was developed according to the lumping principle and was a six-compartment model representing the following: the lung; heart, brain and kidney; gut, stomach, spleen and pancreas; the liver; muscle, bone, skin and testes; and adipose tissue.…”

Section: Mycophenolic Acidmentioning

confidence: 99%

“…The reported model also adequately described that rapid conversion and absorption of MMF resulted in an early MPA T max , which is also influenced by the fast absorption and the high CL of MPA itself. However, the observed PK profile of MMF with a rapid decay phase followed by a much slower terminal elimination phase was not captured by the model [65]. …”

Section: Mycophenolic Acidmentioning

confidence: 99%

Pharmacokinetic modeling of therapies for systemic lupus erythematosus

Yang

Sherwin

Tian

et al. 2015

Expert Review of Clinical Pharmacology

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With the increasing use of different types of therapies in treating autoimmune diseases such as systemic lupus erythematosus (SLE), there is a need to utilize pharmacokinetic (PK) strategies to optimize the clinical outcome of these treatments. Various PK analysis approaches, including population PK modeling and physiologically based PK modeling, have been used to evaluate drug PK characteristics and population variability or to predict drug PK profiles in a mechanistic manner. This review outlines the PK modeling of major SLE therapies including immunosuppressants (methotrexate, azathioprine, mycophenolate and cyclophosphamide, among others) and immunomodulators (intravenous immunoglobulin). It summarizes the population PK modeling, physiologically based PK modeling and model-based individualized dosing strategies to improve the therapeutic outcomes in SLE patients.

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“…Most of the current in silico models within CNS drug development programs lack molecular descriptors of important biological functions of the BBB such as active drug transport, drug metabolism, endothelial enzymatic activity, and drug–drug interactions which hinder their translational significance. However, advances in this rapidly evolving field including the use of multiple linear regression (MLR) models incorporating additional molecular descriptors (such as plasma protein binding ratio -PPBR and high affinity P-glycoprotein substrate probability - HAPSP) (61), in vitro/in silico-in vivo data extrapolation (IVIVE) (62) and the use of bayesian statistic models (63) are closing the gap (64). At this stage, in silico models cannot be considered as standalone tools since in vivo and in vitro studies are required to validate the results and/or refine the working hypotheses the original computational algorithm(s) were built upon (65) (see also Fig.…”

Section: Predictive Non-cell Based Modelsmentioning

confidence: 99%

In Vitro Cerebrovascular Modeling in the 21st Century: Current and Prospective Technologies

et al. 2014

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The blood-brain barrier (BBB) maintains the brain homeostasis and dynamically responds to events associated with systemic and/or rheological impairments (e.g., inflammation, ischemia) including the exposure to harmful xenobiotics. Thus, understanding the BBB physiology is crucial for the resolution of major central nervous system CNS) disorders challenging both health care providers and the pharmaceutical industry. These challenges include drug delivery to the brain, neurological disorders, toxicological studies, and biodefense. Studies aimed at advancing our understanding of CNS diseases and promoting the development of more effective therapeutics are primarily performed in laboratory animals. However, there are major hindering factors inherent to in vivo studies such as cost, limited throughput and translational significance to humans. These factors promoted the development of alternative in vitro strategies for studying the physiology and pathophysiology of the BBB in relation to brain disorders as well as screening tools to aid in the development of novel CNS drugs. Herein, we provide a detailed review including pros and cons of current and prospective technologies for modelling the BBB in vitro including ex situ, cell based and computational (in silico) models. A special section is dedicated to microfluidic systems including micro-BBB, BBB-on-a-chip, Neurovascular Unit-on-a-Chip and Synthetic Microvasculature Blood-Brain Barrier.

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Predicting human exposure of active drug after oral prodrug administration, using a joined in vitro/in silico–in vivo extrapolation and physiologically-based pharmacokinetic modeling approach

Cited by 19 publications

References 45 publications

The development of a population physiologically based pharmacokinetic model for mycophenolic mofetil and mycophenolic acid in humans using data from plasma, saliva, and kidney tissue

The development of a population physiologically based pharmacokinetic model for mycophenolic mofetil and mycophenolic acid in humans using data from plasma, saliva, and kidney tissue

Pharmacokinetic modeling of therapies for systemic lupus erythematosus

In Vitro Cerebrovascular Modeling in the 21st Century: Current and Prospective Technologies

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