Abstract:Across multiple sectors, including food, cosmetics and pharmaceutical industries, there is a need to predict the potential effects of xenobiotics. These effects are determined by the intrinsic ability of the substance, or its derivatives, to interact with the biological system, and its concentration–time profile at the target site. Physiologically based kinetic (PBK) models can predict organ-level concentration–time profiles — however, the models are time and resource intensive to generate de novo. Read-across… Show more
“…After model development, a test set was used to evaluate the model. A review of PBPK models by Thompson et al 42 was used to select the test set. From this review, PBPK models published between 2018 and 2021 provided five drugs for which IV data and the required in vitro parameters were available.…”
To improve predictions of concentration‐time (C‐t) profiles of drugs, a new physiologically based pharmacokinetic modeling framework (termed ‘PermQ’) has been developed. This model includes permeability into and out of capillaries, cell membranes, and intracellular lipids. New modeling components include (i) lumping of tissues into compartments based on both blood flow and capillary permeability, and (ii) parameterizing clearances in and out of membranes with apparent permeability and membrane partitioning values. Novel observations include the need for a shallow distribution compartment particularly for bases. C‐t profiles were modeled for 24 drugs (7 acidic, 5 neutral, and 12 basic) using the same experimental inputs for three different models: Rodgers and Rowland (RR), a perfusion‐limited membrane‐based model (K
p,mem
), and PermQ. K
p,mem
and PermQ can be directly compared since both models have identical tissue partition coefficient parameters. For the 24 molecules used for model development, errors in V
ss
and
t
1/2
were reduced by 37% and 43%, respectively, with the PermQ model. Errors in C‐t profiles were reduced (increased EOC) by 43%. The improvement was generally greater for bases than for acids and neutrals. Predictions were improved for all 3 models with the use of parameters optimized for the PermQ model. For five drugs in a test set, similar results were observed. These results suggest that prediction of C‐t profiles can be improved by including capillary and cellular permeability components for all tissues.
“…After model development, a test set was used to evaluate the model. A review of PBPK models by Thompson et al 42 was used to select the test set. From this review, PBPK models published between 2018 and 2021 provided five drugs for which IV data and the required in vitro parameters were available.…”
To improve predictions of concentration‐time (C‐t) profiles of drugs, a new physiologically based pharmacokinetic modeling framework (termed ‘PermQ’) has been developed. This model includes permeability into and out of capillaries, cell membranes, and intracellular lipids. New modeling components include (i) lumping of tissues into compartments based on both blood flow and capillary permeability, and (ii) parameterizing clearances in and out of membranes with apparent permeability and membrane partitioning values. Novel observations include the need for a shallow distribution compartment particularly for bases. C‐t profiles were modeled for 24 drugs (7 acidic, 5 neutral, and 12 basic) using the same experimental inputs for three different models: Rodgers and Rowland (RR), a perfusion‐limited membrane‐based model (K
p,mem
), and PermQ. K
p,mem
and PermQ can be directly compared since both models have identical tissue partition coefficient parameters. For the 24 molecules used for model development, errors in V
ss
and
t
1/2
were reduced by 37% and 43%, respectively, with the PermQ model. Errors in C‐t profiles were reduced (increased EOC) by 43%. The improvement was generally greater for bases than for acids and neutrals. Predictions were improved for all 3 models with the use of parameters optimized for the PermQ model. For five drugs in a test set, similar results were observed. These results suggest that prediction of C‐t profiles can be improved by including capillary and cellular permeability components for all tissues.
“…However, this requires more initiatives for data sharing amongst different sectors. Along these lines, the European Partnership for Alternative Approaches to Animal Testing (EPAA) is currently working on assessing the chemical space coverage of existing PBK models (Thompson et al 2021) and determining methods to identify 'similar' chemicals in support of read across.…”
With an increasing need to incorporate new approach methodologies (NAMs) in chemical risk assessment and the concomitant need to phase out animal testing, the interpretation of in vitro assay readouts for quantitative hazard characterisation becomes more important. Physiologically based kinetic (PBK) models, which simulate the fate of chemicals in tissues of the body, play an essential role in extrapolating in vitro effect concentrations to in vivo bioequivalent exposures. As PBK-based testing approaches evolve, it will become essential to standardise PBK modelling approaches towards a consensus approach that can be used in quantitative in vitro-to-in vivo extrapolation (QIVIVE) studies for regulatory chemical risk assessment based on in vitro assays. Based on results of an ECETOC expert workshop, steps are recommended that can improve regulatory adoption: (1) define context and implementation, taking into consideration model complexity for building fit-for-purpose PBK models, (2) harmonise physiological input parameters and their distribution and define criteria for quality chemical-specific parameters, especially in the absence of in vivo data, (3) apply Good Modelling Practices (GMP) to achieve transparency and design a stepwise approach for PBK model development for risk assessors, (4) evaluate model predictions using alternatives to in vivo PK data including read-across approaches, (5) use case studies to facilitate discussions between modellers and regulators of chemical risk assessment. Proof-of-concepts of generic PBK modelling approaches are published in the scientific literature at an increasing rate. Working on the previously proposed steps is, therefore, needed to gain confidence in PBK modelling approaches for regulatory use.
“…Multi-compartment models reveal sometimes absolutely necessary, for example in finely deciphering the internal contamination routes of specific chemical compounds that cause damages to only target organs [7, 1, 13]. Additionally, PBK models can be of crucial importance to predict organ-level concentration–time profiles in situation where animal testing is now prohibited, using PBK model information from one chemical substance to inform the development or evaluation of a PBK model for a similar chemical substance [31]. For such purpose, but also to enlarge the use of PBK models, there is today a clear need for user-friendly tools facilitating the implementation of any PBK models, whatever the required number of compartments to consider, whatever the number of connections to account for between compartments in pairs of between compartment and the external medium, and whatever the species-compound combination of interest.…”
Section: Introductionmentioning
confidence: 99%
“…Multi-compartment models reveal sometimes absolutely necessary, for example in order to finely decipher the internal contamination routes of specific chemical compounds causing damages to only specific organs (Brinkmann et al, 2014; Allen and Weihrauch, 2021; Gestin et al, 2021). Additionally, PBK models can be of crucial importance to predict organ-level concentration–time profiles in situation where animal testing is now prohibited, using PBK model information from one chemical substance to inform the development or evaluation of a PBK model for a similar chemical substance (Thompson et al, 2021). In the perspective to enlarge and facilitate the use of PBK models, namely to include more compartments, to better estimate parameter values from data and to better support a fine deciphering of underlying contamination processes after chemical exposure, there is today a clear need for user-friendly tools.…”
FigureIncrease the confidence in using in vitro and in silico data to aid the chemical risk assessment process is one, if not the most, important challenge currently facing regulatory authorities. A particularly crucial concern is to fully take advantage of scientifically valid physiologically-based kinetic (PBK) models. Nevertheless, risk assessors remain still unwilling in employing PBK models within their daily work. Indeed, PBK models are not often included in current official guidance documents. In addition, most users have limited a limited experience in using modelling in general. So, the complexity of PBK models, together with a lack to evaluation methods of their performances, certainly contributes to their under-use.This paper proposes an innovative and unified modelling framework, in both the writing of PBK equations as matrix ordinary differential equations (ODE), and in its exact solving simply expressed with matrix products. This generic PBK solution allows to consider as many as state-variables as needed to quantify chemical absorption, distribution, metabolism and excretion processes within living organisms when exposed to chemical substances. This generic PBK model makes possible any compartmentalisation of the model to be considered, as well as all appropriate inter-connections between compartments and with the external medium.We first introduce our PBK modelling framework, with all intermediate steps from the matrix ODE until the exact solution. Then we apply this framework to bioaccumulation testing, before illustrating its concrete use through complementary case studies in terms of species, compound and model complexity. Upon our findings, we finally propose to consider this approach to eventually be part of a future revision of the current regulation.
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