Quantitative Property-Property Relationship for Screening-Level Prediction of Intrinsic Clearance of Volatile Organic Chemicals in Rats and Its Integration within PBPK Models to Predict Inhalation Pharmacokinetics in Humans

The objectives of this study were (i) to develop a screening-level Quantitative property-property relationship (QPPR) for intrinsic clearance (CLint) obtained fromin vivoanimal studies and (ii) to incorporate it with human physiology in a PBPK model for predicting the inhalation pharmacokinetics of VOCs.CLint, calculated as the ratio of thein vivoVmax(μmol/h/kg bw rat) to theKm(μM), was obtained for 26 VOCs from the literature. The QPPR model resulting from stepwise linear regression analysis passed the validation step (R2=0.8; leave-one-out cross-validationQ2=0.75) forCLintnormalized to the phospholipid (PL) affinity of the VOCs. The QPPR facilitated the calculation ofCLint(L PL/h/kg bw rat) from the input data on logPow, log blood: water PC and ionization potential. The predictions of the QPPR as lower and upper bounds of the 95% mean confidence intervals (LMCI and UMCI, resp.) were then integrated within a human PBPK model. The ratio of the maximum (using LMCI forCLint) to minimum (using UMCI forCLint) AUC predicted by the QPPR-PBPK model was1.36±0.4and ranged from 1.06 (1,1-dichloroethylene) to 2.8 (isoprene). Overall, the integrated QPPR-PBPK modeling method developed in this study is a pragmatic way of characterizing the impact of the lack of knowledge ofCLintin predicting human pharmacokinetics of VOCs, as well as the impact of prediction uncertainty ofCLinton human pharmacokinetics of VOCs.

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Development of Physiologically Based Pharmacokinetic Model for Orally Administered Fexuprazan in Humans

Pharmaceutics ◽

10.3390/pharmaceutics13060813 ◽

2021 ◽

Vol 13 (6) ◽

pp. 813

Author(s):

Yoo-Seong Jeong ◽

Min-Soo Kim ◽

Nora Lee ◽

Areum Lee ◽

Yoon-Jee Chae ◽

...

Keyword(s):

Gastric Acid ◽

Pbpk Model ◽

Pbpk Modeling ◽

Drug Candidate ◽

Metabolite Formation ◽

Pbpk Models ◽

Physiologically Based Pharmacokinetic ◽

Physiologically Based

Fexuprazan is a new drug candidate in the potassium-competitive acid blocker (P-CAB) family. As proton pump inhibitors (PPIs), P-CABs inhibit gastric acid secretion and can be used to treat gastric acid-related disorders such as gastroesophageal reflux disease (GERD). Physiologically based pharmacokinetic (PBPK) models predict drug interactions as pharmacokinetic profiles in biological matrices can be mechanistically simulated. Here, we propose an optimized and validated PBPK model for fexuprazan by integrating in vitro, in vivo, and in silico data. The extent of fexuprazan tissue distribution in humans was predicted using tissue-to-plasma partition coefficients in rats and the allometric relationships of fexuprazan distribution volumes (VSS) among preclinical species. Urinary fexuprazan excretion was minimal (0.29–2.02%), and this drug was eliminated primarily by the liver and metabolite formation. The fraction absorbed (Fa) of 0.761, estimated from the PBPK modeling, was consistent with the physicochemical properties of fexuprazan, including its in vitro solubility and permeability. The predicted oral bioavailability of fexuprazan (38.4–38.6%) was within the range of the preclinical datasets. The Cmax, AUClast, and time-concentration profiles predicted by the PBPK model established by the learning set were accurately predicted for the validation sets.

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P21 PBPK modeling in pregnancy: achievements, shortcomings and future perspectives

Archives of Disease in Childhood ◽

10.1136/archdischild-2019-esdppp.59 ◽

2019 ◽

Vol 104 (6) ◽

pp. e25.2-e25

Author(s):

A Dallmann ◽

P Mian ◽

P Annaert ◽

M Pfister ◽

K Allegaert ◽

...

Keyword(s):

Pregnant Women ◽

Pbpk Model ◽

Multiple Pregnancy ◽

First Trimester ◽

Model Verification ◽

Pbpk Modeling ◽

Third Trimester ◽

The Past ◽

Pbpk Models

BackgroundPhysiologically-based pharmacokinetic (PBPK) models are considered a promising approach to better characterize and anticipate the effect of physiological changes on pharmacokinetics in pregnant women. Consequently, multiple pregnancy PBPK models have been developed and verified over the past years. Using acetaminophen (paracetamol) as example, PBPK modeling can provide specific insights into the expected pharmacokinetic changes throughout pregnancy.MethodsTo obtain an overview of pregnancy PBPK models, the scientific literature was systematically screened for publications with a focus on pharmaceutical applications using relevant keywords. Additionally, a pregnancy PBPK model for acetaminophen was developed with the Open Systems Pharmacology software suite (www.open-systems-pharmacology.org) following an established workflow. After model verification around gestational week 30, the model was scaled to earlier stages of pregnancy and molar dose fractions converted to acetaminophen metabolites were estimated for each trimester.ResultsOver the past years, more than 60 different pregnancy PBPK models for more than have 40 drugs been published. More than 70% of these models were developed for the third trimester, while few models have been applied to the first trimester. The developed PBPK model for acetaminophen indicated that the median dose fraction of acetaminophen converted to the reactive metabolite N-acetyl-p-benzoquinonimine (NAPQI) was 11%, 9.0% and 8.2% in the first, second and third trimester, respectively, while for non-pregnant women a value of 7.7% was simulated.ConclusionWhile the overall availability and quality of pregnancy PBPK models is varying considerably, the efforts to establish such models are promising in that they reflect an increased awareness of the necessity to better characterize pharmacokinetics during pregnancy. This is illustrated by the developed PBPK model for acetaminophen where information on NAPQI-formation in vivo is hitherto lacking. Although PBPK models are not a substitute for clinical trials, they constitute an important tool for clinicians in case of missing or incomplete information.Disclosure(s)Nothing to disclose

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In Vitro and In Vivo Bioequivalence Study of 3D-Printed Instant-Dissolving Levetiracetam Tablets and Subsequent Personalized Dosing for Chinese Children Based on Physiological Pharmacokinetic Modeling

Pharmaceutics ◽

10.3390/pharmaceutics14010020 ◽

2021 ◽

Vol 14 (1) ◽

pp. 20

Author(s):

Xianfu Li ◽

En Liang ◽

Xiaoxuan Hong ◽

Xiaolu Han ◽

Conghui Li ◽

...

Keyword(s):

3D Printing ◽

Pbpk Model ◽

Dose Range ◽

Chinese Children ◽

Pbpk Modeling ◽

Printing Technology ◽

3D Printing Technology ◽

Pbpk Models ◽

3D Printed

Recently, the development of Binder Jet 3D printing technology has promoted the research and application of personalized formulations, which are especially useful for children’s medications. Additionally, physiological pharmacokinetic (PBPK) modeling can be used to guide drug development and drug dose selection. Multiple technologies can be used in combination to increase the safety and effectiveness of drug administration. In this study, we performed in vivo pharmacokinetic experiments in dogs with preprepared 3D-printed levetiracetam instant-dissolving tablets (LEV-IDTs). Bioequivalence analysis showed that the tablets were bioequivalent to commercially available preparations (Spritam®) for dogs. Additionally, we evaluated the bioequivalence of 3D-printed LEV-IDTs with Spritam® by a population-based simulation based on the established PBPK model of levetiracetam for Chinese adults. Finally, we established a PBPK model of oral levetiracetam in Chinese children by combining the physiological parameters of children, and we simulated the PK (pharmacokinetics) curves of Chinese children aged 4 and 6 years that were administered the drug to provide precise guidance on adjusting the dose according to the effective dose range of the drug. Briefly, utilizing both Binder jet 3D printing technology and PBPK models is a promising route for personalized drug delivery with various age groups.

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Blood-brain Barrier Crossing using Magnetic Stimulated Nanoparticles

10.1101/2021.12.23.472846 ◽

2021 ◽

Author(s):

Jingfan Chen ◽

Muzhaozi Yuan ◽

Caitlin Madison ◽

Shoshana Eitan ◽

Ya Wang

Keyword(s):

Blood Brain Barrier ◽

Pbpk Model ◽

Superparamagnetic Iron Oxide ◽

Brain Barrier ◽

Control Group ◽

Pbpk Models ◽

Blood Brain ◽

The Impact ◽

The Brain

Due to the low permeability and high selectivity of the blood-brain barrier (BBB), existing brain therapeutic technologies are limited by the inefficient BBB crossing of conventional drugs. Magnetic nanoparticles (MNPs) have shown great potential as nano-carriers for efficient BBB crossing under the external static magnetic field (SMF). To quantify the impact of SMF on MNPs' in vivo dynamics towards BBB crossing, we developed a physiologically based pharmacokinetic (PBPK) model for intraperitoneal (IP) injected superparamagnetic iron oxide nanoparticles coated by gold and conjugated with poly(ethylene glycol) (PEG) (SPIO-Au-PEG NPs) in mice. Unlike most reported PBPK models that ignore brain permeability, we first obtained the brain permeabilities with and without SMF by determining the concentration of SPIO-Au-PEG NPs in the cerebral blood and brain tissue. This concentration in the brain was simulated by the advection-diffusion equations and was numerically solved in COMSOL Multiphysics. The results from the PBPK model after incorporating the brain permeability showed a good agreement (regression coefficient R2 = 0.825) with the in vivo results, verifying the capability of using the proposed PBPK model to predict the in vivo biodistribution of SPIO-Au-PEG NPs under the exposure to SMF. Furthermore, the in vivo results revealed that the brain bioavailability under the exposure to SMF (4.01%) is slightly better than the control group (3.68%). In addition, the modification of SPIO-Au-PEG NPs with insulin (SPIO-Au-PEG-insulin) showed an improvement of the brain bioavailability by 24.47 % in comparison to the non-insulin group. With the SMF stimulation, the brain bioavailability of SPIO-Au-PEG-insulin was further improved by 3.91 % compared to the group without SMF.

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1315. No Dose Adjustment of Metformin with Fostemsavir Coadministration Based on Mechanistic Static and Physiologically Based Pharmacokinetic Models

Open Forum Infectious Diseases ◽

10.1093/ofid/ofaa439.1497 ◽

2020 ◽

Vol 7 (Supplement_1) ◽

pp. S669-S669

Author(s):

Dung N Nguyen ◽

Xiusheng Miao ◽

Mindy Magee ◽

Guoying Tai ◽

Peter D Gorycki ◽

...

Keyword(s):

Dose Adjustment ◽

Pbpk Model ◽

Systemic Exposure ◽

Multidrug Resistant ◽

Pbpk Modeling ◽

Repeat Dose ◽

Physiologically Based Pharmacokinetic ◽

Physiologically Based

Abstract Background Fostemsavir (FTR) is an oral prodrug of the first-in-class attachment inhibitor temsavir (TMR) which is being evaluated in patients with multidrug resistant HIV-1 infection. In vitro studies indicated that TMR and its 2 major metabolites are inhibitors of organic cation transporters (OCT)1, OCT2, and multidrug and toxin extrusion transporters (MATEs). To assess the clinical relevance, of OCT and MATE inhibition, mechanistic static DDI prediction with calculated Imax,u/IC50 ratios was below the cut-off limits for a DDI flag based on FDA guidelines and above the cut-off limits for MATEs based on EMA guidelines. Methods Metformin is a commonly used probe substrate for OCT1, OCT2 and MATEs. To predict the potential for a drug interaction between TMR and metformin, a physiologically based pharmacokinetic (PBPK) model for TMR was developed based on its physicochemical properties, in vitro and in vivo data. The model was verified and validated through comparison with clinical data. The TMR PBPK model accurately described AUC and Cmax within 30% of the observed data for single and repeat dose studies with or without food. The SimCYP models for metformin and ritonavir were qualified using literature data before applications of DDI prediction for TMR Results TMR was simulated at steady state concentrations after repeated oral doses of FTR 600 mg twice daily which allowed assessment of the potential OCT1, OCT2, and MATEs inhibition by TMR and metabolites. No significant increase in metformin systemic exposure (AUC or Cmax) was predicted with FTR co-administration. In addition, a sensitivity analysis was conducted for either hepatic OCT1 Ki, or renal OCT2 and MATEs Ki values. The model output indicated that, a 10-fold more potent Ki value for TMR would be required to have a ~15% increase in metformin exposure Conclusion Based on mechanistic static models and PBPK modeling and simulation, the OCT1/2 and MATEs inhibition potential of TMR and its metabolites on metformin pharmacokinetics is not clinically significant. No dose adjustment of metformin is necessary when co-administered with FTR Disclosures Xiusheng Miao, PhD, GlaxoSmithKline (Employee) Mindy Magee, Doctor of Pharmacy, GlaxoSmithKline (Employee, Shareholder) Peter D. Gorycki, BEChe, MSc, PhD, GSK (Employee, Shareholder) Katy P. Moore, PharmD, RPh, ViiV Healthcare (Employee)

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Cutting Edge PBPK Models and Analyses: Providing the Basis for Future Modeling Efforts and Bridges to Emerging Toxicology Paradigms

Journal of Toxicology ◽

10.1155/2012/852384 ◽

2012 ◽

Vol 2012 ◽

pp. 1-10 ◽

Cited By ~ 17

Author(s):

Jane C. Caldwell ◽

Marina V. Evans ◽

Kannan Krishnan

Keyword(s):

Risk Assessment ◽

Model Development ◽

Pbpk Modeling ◽

Human Populations ◽

Pbpk Models ◽

Modeling Process ◽

Physiologically Based Pharmacokinetic ◽

State Of The Science

Physiologically based Pharmacokinetic (PBPK) models are used for predictions of internal or target dose from environmental and pharmacologic chemical exposures. Their use in human risk assessment is dependent on the nature of databases (animal or human) used to develop and test them, and includes extrapolations across species, experimental paradigms, and determination of variability of response within human populations. Integration of state-of-the science PBPK modeling with emerging computational toxicology models is critical for extrapolation betweenin vitroexposures,in vivophysiologic exposure, whole organism responses, and long-term health outcomes. This special issue contains papers that can provide the basis for future modeling efforts and provide bridges to emerging toxicology paradigms. In this overview paper, we present an overview of the field and introduction for these papers that includes discussions of model development, best practices, risk-assessment applications of PBPK models, and limitations and bridges of modeling approaches for future applications. Specifically, issues addressed include: (a) increased understanding of human variability of pharmacokinetics and pharmacodynamics in the population, (b) exploration of mode of action hypotheses (MOA), (c) application of biological modeling in the risk assessment of individual chemicals and chemical mixtures, and (d) identification and discussion of uncertainties in the modeling process.

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Interaction of Rifampin and Darunavir-Ritonavir or Darunavir-Cobicistat In Vitro

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.01776-16 ◽

2017 ◽

Vol 61 (5) ◽

Cited By ~ 4

Author(s):

Owain Roberts ◽

Saye Khoo ◽

Andrew Owen ◽

Marco Siccardi

Keyword(s):

High Performance ◽

Linear Regression Analysis ◽

Intrinsic Clearance ◽

Dependent Manner ◽

Concentration Dependent Manner ◽

Content Type ◽

Pbpk Models ◽

Physiologically Based Pharmacokinetic ◽

Dosing Regimens

ABSTRACT Treatment of HIV-infected patients coinfected with Mycobacterium tuberculosis is challenging due to drug-drug interactions (DDIs) between antiretrovirals (ARVs) and antituberculosis (anti-TB) drugs. The aim of this study was to quantify the effect of cobicistat (COBI) or ritonavir (RTV) in modulating DDIs between darunavir (DRV) and rifampin (RIF) in a human hepatocyte-based in vitro model. Human primary hepatocyte cultures were incubated with RIF alone or in combination with either COBI or RTV for 3 days, followed by coincubation with DRV for 1 h. The resultant DRV concentrations were quantified by high-performance liquid chromatography with UV detection, and the apparent intrinsic clearance (CLint.app.) of DRV was calculated. Both RTV and COBI lowered the RIF-induced increases in CLint.app. in a concentration-dependent manner. Linear regression analysis showed that log10 RTV and log10 COBI concentrations were associated with the percent inhibition of RIF-induced elevations in DRV CLint.app., where β was equal to −234 (95% confidence interval [CI] = −275 to −193; P < 0.0001) and −73 (95% CI = −89 to −57; P < 0.0001), respectively. RTV was more effective in lowering 10 μM RIF-induced elevations in DRV CLint.app. (half-maximal [50%] inhibitory concentration [IC50] = 0.025 μM) than COBI (IC50 = 0.223 μM). Incubation of either RTV or COBI in combination with RIF was sufficient to overcome RIF-induced elevations in DRV CLint.app., with RTV being more potent than COBI. These data provide the first in vitro experimental insight into DDIs between RIF and COBI-boosted or RTV-boosted DRV and will be useful to inform physiologically based pharmacokinetic (PBPK) models to aid in optimizing dosing regimens for the treatment of patients coinfected with HIV and M. tuberculosis.

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Reviewing Data Integrated for PBPK Model Development to Predict Metabolic Drug-Drug Interactions: Shifting Perspectives and Emerging Trends

Frontiers in Pharmacology ◽

10.3389/fphar.2021.708299 ◽

2021 ◽

Vol 12 ◽

Author(s):

Kenza Abouir ◽

Caroline F Samer ◽

Yvonne Gloor ◽

Jules A Desmeules ◽

Youssef Daali

Keyword(s):

Plasma Concentration ◽

Drug Interactions ◽

Pbpk Model ◽

Model Development ◽

Pbpk Modeling ◽

Maximum Plasma ◽

Extrinsic Factors ◽

Pbpk Models ◽

Emerging Trends ◽

Clearance Prediction

Physiologically-based pharmacokinetics (PBPK) modeling is a robust tool that supports drug development and the pharmaceutical industry and regulatory authorities. Implementation of predictive systems in the clinics is more than ever a reality, resulting in a surge of interest for PBPK models by clinicians. We aimed to establish a repository of available PBPK models developed to date to predict drug-drug interactions (DDIs) in the different therapeutic areas by integrating intrinsic and extrinsic factors such as genetic polymorphisms of the cytochromes or environmental clues. This work includes peer-reviewed publications and models developed in the literature from October 2017 to January 2021. Information about the software, type of model, size, and population model was extracted for each article. In general, modeling was mainly done for DDI prediction via Simcyp® software and Full PBPK. Overall, the necessary physiological and physio-pathological parameters, such as weight, BMI, liver or kidney function, relative to the drug absorption, distribution, metabolism, and elimination and to the population studied for model construction was publicly available. Of the 46 articles, 32 sensibly predicted DDI potentials, but only 23% integrated the genetic aspect to the developed models. Marked differences in concentration time profiles and maximum plasma concentration could be explained by the significant precision of the input parameters such as Tissue: plasma partition coefficients, protein abundance, or Ki values. In conclusion, the models show a good correlation between the predicted and observed plasma concentration values. These correlations are all the more pronounced as the model is rich in data representative of the population and the molecule in question. PBPK for DDI prediction is a promising approach in clinical, and harmonization of clearance prediction may be helped by a consensus on selecting the best data to use for PBPK model development.

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The Impact of Carboxylesterases in Drug Metabolism and Pharmacokinetics

Current Drug Metabolism ◽

10.2174/1389200219666180821094502 ◽

2019 ◽

Vol 20 (2) ◽

pp. 91-102 ◽

Cited By ~ 22

Author(s):

Li Di

Keyword(s):

Tissue Distribution ◽

Critical Role ◽

Individual Variability ◽

Human Pharmacokinetics ◽

Metabolizing Enzymes ◽

Soft Drugs ◽

Hydrolysis Of Esters ◽

The Impact

Background:Carboxylesterases (CES) play a critical role in catalyzing hydrolysis of esters, amides, carbamates and thioesters, as well as bioconverting prodrugs and soft drugs. The unique tissue distribution of CES enzymes provides great opportunities to design prodrugs or soft drugs for tissue targeting. Marked species differences in CES tissue distribution and catalytic activity are particularly challenging in human translation.Methods:Review and summarization of CES fundamentals and applications in drug discovery and development.Results:Human CES1 is one of the most highly expressed drug metabolizing enzymes in the liver, while human intestine only expresses CES2. CES enzymes have moderate to high inter-individual variability and exhibit low to no expression in the fetus, but increase substantially during the first few months of life. The CES genes are highly polymorphic and some CES genetic variants show significant influence on metabolism and clinical outcome of certain drugs. Monkeys appear to be more predictive of human pharmacokinetics for CES substrates than other species. Low risk of clinical drug-drug interaction is anticipated for CES, although they should not be overlooked, particularly interaction with alcohols. CES enzymes are moderately inducible through a number of transcription factors and can be repressed by inflammatory cytokines.Conclusion:Although significant advances have been made in our understanding of CESs, in vitro - in vivo extrapolation of clearance is still in its infancy and further exploration is needed. In vitro and in vivo tools are continuously being developed to characterize CES substrates and inhibitors.

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