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

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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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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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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Investigations on the Influence of Zidovudine in the Pharmacokinetics of Isoniazid and Its Hepatotoxic Metabolites in Rats

Journal of Pharmacy Practice ◽

10.1177/0897190017735424 ◽

2017 ◽

Vol 32 (1) ◽

pp. 9-18

Author(s):

Raghu Ramanathan ◽

Karthikeyan Sivanesan

Keyword(s):

Plasma Concentration ◽

Drug Interactions ◽

Bacterial Infections ◽

Plasma Clearance ◽

Combined Therapy ◽

Maximum Plasma Concentration ◽

Pharmacokinetic Studies ◽

Maximum Plasma ◽

Group I ◽

Pharmacokinetic Drug

The HIV-infected patients are co-infected with many bacterial infections in which tuberculosis is most common found worldwide. These patients are often administered with combined therapy of anti-retroviral and anti-tubercular drugs which leads to several complications including hepatotoxicity or adverse drug interactions. The drug-drug interactions between the anti-retroviral and anti-tubercular drugs are not clearly defined and hence, this study was conducted to evaluate the pharmacokinetic drug-drug interactions of Zidovudine (AZT) with Isoniazid (INH) and its hepatotoxic metabolites. Seventy two rats were randomly divided into two major groups with their sub-groups each comprising 6 animals. The Group I received INH alone at a dose of 25 mg/kg; b.w and Group II received AZT (50 mg/kg; b.w) along with INH orally. Pharmacokinetic studies of INH and its metabolites i.e., acetyl hydrazine (ACHY) and hydrazine (HYD) shows that INH and ACHY attains maximum plasma concentration ( Cmax) within 30 minutes and HYD attains Cmax at 1 hour after INH administration and all these analytes disappear from plasma within 4 hours. Pharmacokinetic studies also revealed that AZT treatment did not showed any drug-drug interactions and have no effect on the T1/2, plasma clearance, AUC, Cmax and Tmax of INH and its hepatotoxic metabolites.

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A Physiologically-Based Pharmacokinetic (PBPK) Model Network for the Prediction of CYP1A2 and CYP2C19 Drug–Drug–Gene Interactions with Fluvoxamine, Omeprazole, S-mephenytoin, Moclobemide, Tizanidine, Mexiletine, Ethinylestradiol, and Caffeine

Pharmaceutics ◽

10.3390/pharmaceutics12121191 ◽

2020 ◽

Vol 12 (12) ◽

pp. 1191

Author(s):

Tobias Kanacher ◽

Andreas Lindauer ◽

Enrica Mezzalana ◽

Ingrid Michon ◽

Celine Veau ◽

...

Keyword(s):

San Francisco ◽

Drug Exposure ◽

Pbpk Modeling ◽

Whole Body ◽

Validation Dataset ◽

Extrinsic Factors ◽

Time Profiles ◽

Pbpk Models ◽

Physiologically Based Pharmacokinetic ◽

Physiologically Based

Physiologically-based pharmacokinetic (PBPK) modeling is a well-recognized method for quantitatively predicting the effect of intrinsic/extrinsic factors on drug exposure. However, there are only few verified, freely accessible, modifiable, and comprehensive drug–drug interaction (DDI) PBPK models. We developed a qualified whole-body PBPK DDI network for cytochrome P450 (CYP) CYP2C19 and CYP1A2 interactions. Template PBPK models were developed for interactions between fluvoxamine, S-mephenytoin, moclobemide, omeprazole, mexiletine, tizanidine, and ethinylestradiol as the perpetrators or victims. Predicted concentration–time profiles accurately described a validation dataset, including data from patients with genetic polymorphisms, demonstrating that the models characterized the CYP2C19 and CYP1A2 network over the whole range of DDI studies investigated. The models are provided on GitHub (GitHub Inc., San Francisco, CA, USA), expanding the library of publicly available qualified whole-body PBPK models for DDI predictions, and they are thereby available to support potential recommendations for dose adaptations, support labeling, inform the design of clinical DDI trials, and potentially waive those.

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Drug Interactions with the Direct-Acting Antiviral Combination of Ombitasvir and Paritaprevir-Ritonavir

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.01778-15 ◽

2015 ◽

Vol 60 (1) ◽

pp. 105-114 ◽

Cited By ~ 29

Author(s):

Prajakta S. Badri ◽

Sandeep Dutta ◽

Haoyu Wang ◽

Thomas J. Podsadecki ◽

Akshanth R. Polepally ◽

...

Keyword(s):

Plasma Concentration ◽

Drug Interactions ◽

Dose Adjustment ◽

Maximum Plasma ◽

Time Curve ◽

Geometric Means ◽

Direct Acting Antiviral ◽

Concentration Time ◽

Direct Acting ◽

Subtype 1B

ABSTRACTThe two direct-acting antiviral (2D) regimen of ombitasvir and paritaprevir (administered with low-dose ritonavir) is being developed for treatment of genotype subtype 1b and genotypes 2 and 4 chronic hepatitis C virus (HCV) infection. Drug-drug interactions were evaluated in healthy volunteers to develop dosing recommendations for HCV-infected subjects. Mechanism-based interactions were evaluated for ketoconazole, pravastatin, rosuvastatin, digoxin, warfarin, and omeprazole. Interactions were also evaluated for duloxetine, escitalopram, methadone, and buprenorphine-naloxone. Ratios of geometric means with 90% confidence intervals for the maximum plasma concentration and the area under the plasma concentration-time curve were estimated to assess the magnitude of the interactions. For most medications, coadministration with the 2D regimen resulted in a <50% change in exposures. Ketoconazole, digoxin, pravastatin, and rosuvastatin exposures increased by up to 105%, 58%, 76%, and 161%, respectively, and omeprazole exposures decreased by approximately 50%. Clinically meaningful changes in ombitasvir, paritaprevir, or ritonavir exposures were not observed. In summary, all 11 medications evaluated can be coadministered with the 2D regimen, with most medications requiring no dose adjustment. Ketoconazole, digoxin, pravastatin, and rosuvastatin require lower doses, and omeprazole may require a higher dose. No dose adjustment is required for the 2D regimen.

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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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Physiologically Based Pharmacokinetic Modeling of Cefadroxil in Mouse, Rat, and Human to Predict Concentration–Time Profile at Infected Tissue

Frontiers in Pharmacology ◽

10.3389/fphar.2021.692741 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhongxia Tan ◽

Youxi Zhang ◽

Chao Wang ◽

Le Sun

Keyword(s):

Plasma Concentration ◽

Pbpk Model ◽

Oral Absorption ◽

Time Profile ◽

Infected Tissue ◽

Dose Selection ◽

Pbpk Models ◽

Physiologically Based Pharmacokinetic ◽

Concentration Time ◽

Physiologically Based

The aim of this study was to develop physiologically based pharmacokinetic (PBPK) models capable of simulating cefadroxil concentrations in plasma and tissues in mouse, rat, and human. PBPK models in this study consisted of 14 tissues and 2 blood compartments. They were established using measured tissue to plasma partition coefficient (Kp) in mouse and rat, absolute expression levels of hPEPT1 along the entire length of the human intestine, and the transporter kinetic parameters. The PBPK models also assumed that all the tissues were well-stirred compartments with perfusion rate limitations, and the ratio of the concentration in tissue to the unbound concentration in plasma is identical across species. These PBPK models were validated strictly by a series of observed plasma concentration–time profile data. The average fold error (AFE) and absolute average fold error (AAFE) values were all less than 2. The models’ rationality and accuracy were further demonstrated by the almost consistent Vss calculated by the PBPK model and noncompartmental method, as well as the good allometric scaling relationship of Vss and CL. The model suggests that hPEPT1 is the major transporter responsible for the oral absorption of cefadroxil in human, and the plasma concentration–time profiles of cefadroxil were not sensitive to dissolution rate faster than T85% = 2 h. The cefadroxil PBPK model in human is reliable and can be used to predict concentration–time profile at infected tissue. It may be useful for dose selection and informative decision-making during clinical trials and dosage form design of cefadroxil and provide a reference for the PBPK model establishment of hPEPT1 substrate.

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Evaluation of potential drug interactions with sodium zirconium cyclosilicate: a single-center, open-label, one sequence crossover study in healthy adults

Clinical Kidney Journal ◽

10.1093/ckj/sfaa222 ◽

2020 ◽

Author(s):

Mats Någård ◽

William G Kramer ◽

David W Boulton

Keyword(s):

Plasma Concentration ◽

Drug Interactions ◽

Ph Sensitive ◽

Gastric Ph ◽

Healthy Adults ◽

Maximum Plasma ◽

Open Label ◽

Interaction Range ◽

Sodium Zirconium ◽

Sodium Zirconium Cyclosilicate

Abstract Background Sodium zirconium cyclosilicate (SZC; formerly ZS-9) is an oral potassium binder for the treatment of hyperkalemia in adults. SZC acts in the gastrointestinal tract and additionally binds hydrogen ions in acidic environments like the stomach, potentially transiently increasing gastric pH and leading to drug interactions with pH-sensitive drugs. This study assessed potential pharmacokinetic (PK) interactions between SZC and nine pH-sensitive drugs. Methods In this single-dose, open-label, single-sequence cross-over study in healthy adults, amlodipine, atorvastatin, clopidogrel, dabigatran, furosemide, glipizide, levothyroxine, losartan or warfarin were each administered alone and, following a washout interval, with SZC 10 g. Maximum plasma concentration (Cmax), area under the plasma concentration–time curve from 0 to the last time point (AUC0–t) and AUC extrapolated to infinity (AUCinf) were evaluated. No interaction was concluded if the 90% confidence interval for the geometric mean ratio (SZC coadministration versus alone) of the PK parameters was within 80–125%. Results During SZC coadministration, all PK parameters for amlodipine, glipizide, levothyroxine and losartan showed no interaction, while reductions in clopidogrel and dabigatran Cmax, AUC0–t and AUCinf (basic drugs) were <50% and increases in atorvastatin, furosemide and warfarin Cmax (acidic drugs) exceeded the no-interaction range by ˂2-fold. Conclusions SZC coadministration was associated with small changes in plasma concentration and exposure of five of the nine drugs evaluated in this study. These PK drug interactions are consistent with transient increases in gastric pH with SZC and are unlikely to be clinically meaningful.

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Investigation of the Impact of CYP3A5 and CYP2C19 Polymorphisms on Drug-Drug Interactions between Tacrolimus and Voriconazole Based on Physiologically-Based Pharmacokinetic Modeling

10.22541/au.163753625.53160390/v1 ◽

2021 ◽

Author(s):

Fei Gong ◽

Ying Ouyang ◽

Zhengzheng Liao ◽

Ying Kong ◽

Qingxian Li ◽

...

Keyword(s):

Pbpk Model ◽

Model Development ◽

Pbpk Modeling ◽

Physiologically Based Pharmacokinetic Modeling ◽

Final Model ◽

Physiologically Based Pharmacokinetic ◽

Significant Difference ◽

Physiologically Based ◽

The Impact ◽

Cyp2c19 Polymorphisms

ABSTRACT Aims: This study aimed to develop a PBPK model for tacrolimus incorporating CYP3A5 and CYP2C19 polymorphisms to predict the DDIs between tacrolimus and voriconazole. Methods: Pharmacokinetic (PK) data in rats and healthy subjects receiving tacrolimus with and without voriconazole were used for model development and evaluation. Then, we used the final model to simultaneously investigate the effect of CYP3A5 and CYP2C19 polymorphisms on the PK data of tacrolimus when combined with voriconazole. Results: The final results showed that the predicted Cmax in CYP3A5 nonexpressers was 1.5-fold higher than expressers, and the predicted AUC0-∞ was 1.92 to 1.96-fold higher in nonexpressers. However, the Cmax and AUC0-∞ of tacrolimus both have no significant difference between different CYP2C19 metabolizers. Conclusions: A physiologically-based pharmacokinetic (PBPK) model for tacrolimus integrated with CYP3A5 and CYP2C19 polymorphisms was successfully established, providing more insights regarding the DDIs between tacrolimus and voriconazole in patients with different CYP3A5 and CYP2C19 genotypes. Furthermore, this study highlights the feasibility of PBPK modeling to predict DDIs between these two drugs and the need to include CYP3A5 polymorphisms but not CYP2C19 polymorphisms.

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