ATLAS mPBPK: A MATLAB‐Based Tool for Modeling and Simulation of Minimal Physiologically‐Based Pharmacokinetic Models

Mavroudis, Panteleimon D.; Ayyar, Vivaswath S.; Jusko, William J.

doi:10.1002/psp4.12441

Cited by 6 publications

(6 citation statements)

References 33 publications

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“…Recently, minimal-PBPK models have made an impact in pharmacometrics as powerful tools to bridge top-down and bottom-up approaches ( Cao and Jusko, 2012 ; Mavroudis et al, 2019 ; Bloomingdale et al, 2021 ; Mehta et al, 2023 ). By retaining essential compartments for absorption, metabolism, and clearance, our mPBPK has demonstrated its capability to accurately predict the PK dynamics of eleven anti-TB compounds with diverse MOAs and across several drug classes, regardless of their chemical characteristics or development stage.…”

Section: Discussionmentioning

confidence: 99%

A minimal PBPK model to accelerate preclinical development of drugs against tuberculosis

Reali,

Fochesato,

Kaddi

et al. 2024

Front. Pharmacol.

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Introduction: Understanding drug exposure at disease target sites is pivotal to profiling new drug candidates in terms of tolerability and efficacy. Such quantification is particularly tedious for anti-tuberculosis (TB) compounds as the heterogeneous pulmonary microenvironment due to the infection may alter lung permeability and affect drug disposition. Murine models have been a longstanding support in TB research so far and are here used as human surrogates to unveil the distribution of several anti-TB compounds at the site-of-action via a novel and centralized PBPK design framework.Methods: As an intermediate approach between data-driven pharmacokinetic (PK) models and whole-body physiologically based (PB) PK models, we propose a parsimonious framework for PK investigation (minimal PBPK approach) that retains key physiological processes involved in TB disease, while reducing computational costs and prior knowledge requirements. By lumping together pulmonary TB-unessential organs, our minimal PBPK model counts 9 equations compared to the 36 of published full models, accelerating the simulation more than 3-folds in Matlab 2022b.Results: The model has been successfully tested and validated against 11 anti-TB compounds—rifampicin, rifapentine, pyrazinamide, ethambutol, isoniazid, moxifloxacin, delamanid, pretomanid, bedaquiline, OPC-167832, GSK2556286 - showing robust predictability power in recapitulating PK dynamics in mice. Structural inspections on the proposed design have ensured global identifiability and listed free fraction in plasma and blood-to-plasma ratio as top sensitive parameters for PK metrics. The platform-oriented implementation allows fast comparison of the compounds in terms of exposure and target attainment. Discrepancies in plasma and lung levels for the latest BPaMZ and HPMZ regimens have been analyzed in terms of their impact on preclinical experiment design and on PK/PD indices.Conclusion: The framework we developed requires limited drug- and species-specific information to reconstruct accurate PK dynamics, delivering a unified viewpoint on anti-TB drug distribution at the site-of-action and a flexible fit-for-purpose tool to accelerate model-informed drug design pipelines and facilitate translation into the clinic.

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Section: Discussionmentioning

confidence: 99%

A minimal PBPK model to accelerate preclinical development of drugs against tuberculosis

Reali,

Fochesato,

Kaddi

et al. 2024

Front. Pharmacol.

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“…All fitting calculations of the TMDD‐PBPK model were performed using the R package of CGNM version 0.6.2 23,29,30 . CGNM is an algorithm that finds multiple parameter sets from a wide initial range that minimizes the sum of squared residuals (SSR), as shown in Equation ().

\begin{matrix} left SSR = \sum \{{()(, \log_{10} y_{obs} - \log_{10} y_{sim})}^{2} \times w\} \\ left + \{\sum {()(, \log_{10} y_{obs, in vitro} - \log_{10} y_{sim, italicin vitro})}^{2} \times w\} \end{matrix}

where y obs , y sim , y obs,in vitro , y sim,in vitro , and w represent the observed in vivo value (blood concentration or amount excreted in feces), simulated in vivo value, observed in vitro value, simulated in vitro values, and the weight, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…All fitting calculations of the TMDD-PBPK model were performed using the R package of CGNM version 0.6.2. 23,29,30 CGNM is an algorithm that finds multiple parameter sets from a wide initial range that minimizes the sum of squared residuals (SSR), as shown in Equation ( 3).…”

Section: Top-down and Middle-out Analysis By Cgnmmentioning

confidence: 99%

Elucidating nonlinear pharmacokinetics of telmisartan: Integration of target‐mediated drug disposition and OATP1B3‐mediated hepatic uptake in a physiologically based model

Tsuchitani,

Tomaru,

Aoki

et al. 2024

CPT Pharmacom & Syst Pharma

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Telmisartan, a selective inhibitor of angiotensin II receptor type 1 (AT1), demonstrates nonlinear pharmacokinetics (PK) when orally administered in ascending doses to healthy volunteers, but the underlying mechanisms remain unclear. This study presents a physiologically based pharmacokinetic model integrated with target‐mediated drug disposition (TMDD‐PBPK model) to explore the mechanism of its nonlinear PK. We employed the Cluster‐Gauss Newton method for top‐down analysis, estimating the in vivo Km,OATP1B3 (Michaelis–Menten constant for telmisartan hepatic uptake via Organic Anion Transporting Polypeptide 1B3) to be 2.0–5.7 nM. This range is significantly lower than the reported in vitro value of 810 nM, obtained in 0.3% human serum albumin (HSA) conditions. Further validation was achieved through in vitro assessment in plated human hepatocytes with 4.5% HSA, showing a Km of 4.5 nM. These results underscore the importance of albumin‐mediated uptake effect for the hepatic uptake of telmisartan. Our TMDD‐PBPK model, developed through a “middle‐out” approach, underwent sensitivity analysis to identify key factors in the nonlinear PK of telmisartan. We found that the nonlinearity in the area under the concentration–time curve (AUC) and/or maximum concentration (Cmax) of telmisartan is sensitive to Km,OATP1B3 across all dosages. Additionally, the dissociation constant (Kd) for telmisartan binding to the AT1 receptor, along with its receptor abundance, notably influences PK at lower doses (below 20 mg). In conclusion, the nonlinear PK of telmisartan appears primarily driven by hepatic uptake saturation across all dose ranges and by AT1‐receptor binding saturation, notably at lower doses.

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“…There are few MATLAB‐based open‐source GUIs that are capable of PK/PD simulations, including gPKPDsim 1 (for any model implemented in SimBiology), A4S 2 (for the predefined model library), MatVPC 3 (for any model implemented in MATLAB; focus on visual predictive checks), and ATLAS mPBPK 4 (for minimal physiologically‐based PD model). GPKPDsim and ATLAS mPBPK also have data fitting capabilities.…”

Section: Introductionmentioning

confidence: 99%

gPKPDviz: A flexible R shiny tool for pharmacokinetic/pharmacodynamic simulations using mrgsolve

Lu,

Poon,

Brooks

et al. 2023

CPT Pharmacom & Syst Pharma

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GPKPDviz is a Shiny application (app) dedicated to real‐time simulation, visualization, and assessment of the pharmacokinetic/pharmacodynamic (PK/PD) models. Within the app, gPKPDviz is capable of generating virtual populations and complex dosing and sampling scenarios, which, together with the streamlined workflow, is designed to efficiently assess the impact of covariates and dosing regimens on PK/PD end points. The actual population data from clinical trials can be loaded into the app for simulation if desired. The app‐generated dosing regimens include single or multiple dosing, and more complex regimens, such as loading doses or intermittent dosing. When necessary, the dosing regimens can be defined externally and loaded to the app for simulation. Using mrgsolve as the simulation engine, gPKPDviz is typically used for population simulation, however, with a slight modification of the mrgsolve model, gPKPDviz is capable of performing individual simulations with individual post hoc parameters, individual dosing logs, and individual sampling timepoints through an external dataset. A built‐in text editor has a debugging feature for the mrgsolve model, providing the same error messages as model compilation in R. GPKPDviz has had stringent validation by comparing simulation results between the app and using mrgsolve in R. GPKPDviz is a member of the suite of Modeling and Simulation Shiny apps developed at Genentech to facilitate the typical modeling work in Clinical Pharmacology. For broader access to the Pharmacometric community, gPKPDviz has been published as an open‐source application in GitHub under the terms of GNU General Public License.

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ATLAS mPBPK: A MATLAB‐Based Tool for Modeling and Simulation of Minimal Physiologically‐Based Pharmacokinetic Models

Cited by 6 publications

References 33 publications

A minimal PBPK model to accelerate preclinical development of drugs against tuberculosis

A minimal PBPK model to accelerate preclinical development of drugs against tuberculosis

Elucidating nonlinear pharmacokinetics of telmisartan: Integration of target‐mediated drug disposition and OATP1B3‐mediated hepatic uptake in a physiologically based model

gPKPDviz: A flexible R shiny tool for pharmacokinetic/pharmacodynamic simulations using mrgsolve

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