A multilevel object-oriented modelling methodology for physiologically-based pharmacokinetics (PBPK): Evaluation with a semi-mechanistic pharmacokinetic model

Reig-López, Javier; Merino-Sanjuán, Matilde; Mangas-Sanjuan, Victor; Prado-Velasco, Manuel

doi:10.1016/j.cmpb.2020.105322

Cited by 6 publications

(7 citation statements)

References 9 publications

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“…The parameter values agree with those used in the first case of (Mangas-Sanjuan et al 2018, Fig. 2), excepting the division of the 100 mg PD dose in two separated 50 mg PD doses, which is applied now in agreement with the second step pointed in Methods to analyze a more complex scenario, after validating the accuracy of the model against the results of (Mangas-Sanjuan et al 2018) and (Reig-Lopez et al 2020).…”

Section: Mechanisms -Physiological Layerssupporting

confidence: 73%

“…This drug is metabolized in a principal metabolite (PM) and secondary metabolite (SM). The model is presented succinctly in the study case Section, although a detailed description is available (Mangas-Sanjuan et al 2018;Reig-Lopez et al 2020).…”

Section: Methodsmentioning

confidence: 99%

“…Figure 7 shows the diagram of the model defined in the Methods Section. A detailed description of this one appears in (Mangas-Sanjuan et al 2018), whereas the comparative analysis of the PhysPK vs NONMEM implementations may be seen in (Reig-Lopez et al 2020). The building of this model was performed using the GUI of openModelica 1.14.1 (diagram view), although the selection of non-default mechanisms for the physiological components were completed in the Modelica code view of the model, because openModelica 1.14 does not include this graphical function.…”

Section: Mechanisms -Physiological Layersmentioning

confidence: 99%

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In-silico virtual prototyping multilevel modeling system for Cyborgs (CybSim) as a novel approach for current challenges in biosciencies

Prado-Velasco¹

2021

Linköping Electronic Conference Proceedings

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There is a lack of Modeling and Simulation software systems in the bioscience arena that give both solutions compliant with current methodologies in drug discovery (pharmaceutic) and precision medicine (healthcare) fields, besides to support the addition of new biological mechanisms under a multilevel and multiformalism perspective, without penalize strongly the model sharing and reusing. A novel modeling and simulation software that tries to fill the previous gap has been designed (CybSim) and it is presented in this work. CybSim is a platform for multilevel modeling of physiological -cybernetic systems, compliant but not limited to Physiologically based-, Pharmacokinetic and Pharmacodynamic (PBPK/PK/PD) methodologies. This capability is governed through the Physiological Scope setting value. The main physiological components are mechanistic. The underlying mechanisms may be changed during the model building thanks to the separation between mechanisms and physiological instances. This capability is based on a multi-layer design. A preliminary version of CybSim has been implemented with OpenModelica (v1.14.1). A PBPK semiphysiological model published previously has been built as a case study to demonstrate the feasibility of CybSim. The accuracy of CybSim was verified during preliminary development phases. The two pointed out capabilities of Cyb-Sim demanded an object-oriented and acausal equationbased modeling language, able to support classes' redeclaration, connectors' causality, inner/outer scoping control and packages organization. These features are not supported by other modern acausal equation-based modeling languages like the EcosimPro language.

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Section: Mechanisms -Physiological Layerssupporting

confidence: 73%

Section: Methodsmentioning

confidence: 99%

Section: Mechanisms -Physiological Layersmentioning

confidence: 99%

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In-silico virtual prototyping multilevel modeling system for Cyborgs (CybSim) as a novel approach for current challenges in biosciencies

Prado-Velasco¹

2021

Linköping Electronic Conference Proceedings

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“…The mathematical models were implemented on multilevel object-oriented PhysPK biosimulation software. An evaluation of the accuracy and underlying methodology of PhysPK with respect to NONMEM software was recently published 27 . The model fitting, covariate analysis, statistics and optimisation procedures described in the next sections are also implemented in PhysPK.…”

Section: Methodsmentioning

confidence: 99%

Predictive engines based on pharmacokinetics modelling for tacrolimus personalized dosage in paediatric renal transplant patients

Prado-Velasco

Borobia

Carcas-Sansuan

2020

Sci Rep

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The development of predictive engines based on pharmacokinetic-physiological mathematical models for personalised dosage recommendations is an immature field. Nevertheless, these models are extensively applied during the design of new drugs. This study presents new advances in this subject, through a stable population of patients who underwent kidney transplantation and were prescribed tacrolimus. We developed 2 new population pharmacokinetic models based on a compartmental approach, with one following the physiologically based pharmacokinetic approach and both including circadian modulation of absorption and clearance variables. One of the major findings was an improved predictive capability for both models thanks to the consideration of circadian rhythms, both in estimating the population and in Bayesian individual customisation. This outcome confirms a plausible mechanism suggested by other authors to explain circadian patterns of tacrolimus concentrations. We also discovered significant intrapatient variability in tacrolimus levels a week after the conversion from a fast-release (Prograf) to a sustained-release formulation (Advagraf) using adaptive optimisation techniques, despite high adherence and controlled conditions. We calculated the intrapatient variability through parametric intrapatient variations, which provides a method for quantifying the mechanisms involved. We present a first application for the analysis of bioavailability changes in formulation conversion. The 2 pharmacokinetic models have demonstrated their capability as predictive engines for personalised dosage recommendations, although the physiologically based pharmacokinetic model showed better predictive behaviour.

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“…Another study by Zhou and colleagues evaluated the predictive performance of PopPK and PBPK for several compounds predominantly eliminated through the kidneys 49 . Furthermore, given increasing interest from both industry and regulatory agencies on quantitative tools for paediatric dosing recommendation, the use of both PopPK and PBPK models has been further evaluated within the last few years 50–54 . In conclusion, both model‐based approaches successfully predicted the clearance of the drugs, indicating that these two methodologies can complement each other for dose predictions in children older than 1 month.…”

Section: Discussionmentioning

confidence: 99%

Comparison between physiologically based pharmacokinetic and population pharmacokinetic modelling to select paediatric doses of gepotidacin in plague

Nguyen

Shaik

Tai

et al. 2021

Brit J Clinical Pharma

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Aims To develop physiologically based pharmacokinetic (PBPK) and population pharmacokinetic (PopPK) models to predict effective doses of gepotidacin in paediatrics for the treatment of pneumonic plague (Yersinia pestis). Methods A gepotidacin PBPK model was constructed using a population‐based absorption, distribution, metabolism and excretion simulator, Simcyp®, with physicochemical and in vitro data, optimized with clinical data from a dose‐escalation intravenous (IV) study and a human mass balance study. A PopPK model was developed with pooled PK data from phase 1 studies with IV gepotidacin in healthy adults. Results For both the PopPK and PBPK models, body weight was found to be a key covariate affecting gepotidacin clearance. With PBPK, ~90% of the predicted PK for paediatrics fell between the 5th and 95th percentiles of adult values except for subjects weighing ≤5 kg. PopPK‐simulated paediatric means for Cmax and AUC(0‐τ) were similar to adult exposures across various weight brackets. The proposed dosing regimens were weight‐based for subjects ≤40 kg and fixed‐dose for subjects >40 kg. Comparison of observed and predicted exposures in adults indicated that both PBPK and PopPK models achieved similar AUC and Cmax for a given dose, but the Cmax predictions with PopPK were slightly higher than with PBPK. The two models differed on dose predictions in children <3 months old. The PopPK model may be suboptimal for low age groups due to the absence of maturation characterization of drug‐metabolizing enzymes involved with clearance in adults. Conclusions Both PBPK and PopPK approaches can reasonably predict gepotidacin exposures in children.

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A multilevel object-oriented modelling methodology for physiologically-based pharmacokinetics (PBPK): Evaluation with a semi-mechanistic pharmacokinetic model

Cited by 6 publications

References 9 publications

In-silico virtual prototyping multilevel modeling system for Cyborgs (CybSim) as a novel approach for current challenges in biosciencies

In-silico virtual prototyping multilevel modeling system for Cyborgs (CybSim) as a novel approach for current challenges in biosciencies

Predictive engines based on pharmacokinetics modelling for tacrolimus personalized dosage in paediatric renal transplant patients

Comparison between physiologically based pharmacokinetic and population pharmacokinetic modelling to select paediatric doses of gepotidacin in plague

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