Risk Assessment of Perfluorooctane Sulfonate (PFOS) using Dynamic Age Dependent Physiologically based Pharmacokinetic Model (PBPK) across Human Lifetime

Deepika, Deepika; Sharma, Raju Prasad; Schuhmacher, Marta; Kumar, Vikas

doi:10.1016/j.envres.2021.111287

Cited by 16 publications

(12 citation statements)

References 74 publications

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“…Multi-compartment models often provide detailed information about various tissues and, most importantly, they include intrinsic physiological processes such as liver metabolism, gut metabolism, kidney transporters and elimination, reabsorption and enterohepatic recirculation (EHR) to capture the linear and non-linear kinetics along with anatomy ( Figure 3 ) [ 3 , 4 , 15 , 16 , 17 ]. However, a point worth mentioning is that adding a lot of compartments often makes the model complex and impractical, since values are required for multiple parameters.…”

Section: Unravelling the Art Of Developing Pbpk Modelsmentioning

confidence: 99%

“…PBPK models are mathematical models encompassing multiple compartments with physiology, anatomy, biochemical and physicochemical parameters for describing ADME (absorption, distribution, metabolism and excretion) of xenobiotics and their metabolites ( Figure 1 ) [ 1 , 2 , 3 , 4 ]. These models vary from empirical, semi-mechanistic to compartmental models based on the complexity of the problem [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

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The Role of “Physiologically Based Pharmacokinetic Model (PBPK)” New Approach Methodology (NAM) in Pharmaceuticals and Environmental Chemical Risk Assessment

Deepika

Kumar

2023

IJERPH

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Physiologically Based Pharmacokinetic (PBPK) models are mechanistic tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognized by regulatory authorities for predicting organ concentration–time profiles, pharmacokinetics and daily intake dose of xenobiotics. The extension of PBPK models to capture sensitive populations such as pediatric, geriatric, pregnant females, fetus, etc., and diseased populations such as those with renal impairment, liver cirrhosis, etc., is a must. However, the current modelling practices and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology and calculation of biochemical parameters for integrating knowledge and refining existing PBPK models. Specific PBPK covering compartments such as cerebrospinal fluid and the hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints such as developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in silico models where experimental data are unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building of qAOPs and use of machine learning for improving existing models, along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.

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Section: Unravelling the Art Of Developing Pbpk Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Role of “Physiologically Based Pharmacokinetic Model (PBPK)” New Approach Methodology (NAM) in Pharmaceuticals and Environmental Chemical Risk Assessment

Deepika

Kumar

2023

IJERPH

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“…flow. There are some literatures available where authors have collected the physiological data or developed equations for human lifetime (Brown et al, 1997;Deepika et al, 2021). Mass balance equations are often used to explain blood flows and other parameters connecting all tissues.…”

Section: Unravelling the Art Of Developing Pbpkmentioning

confidence: 99%

“…improved the prediction performance of PBPK, assessing agespecific risk. Human lifetime PBPK model was developed for forever chemical like PFOS to evaluate long term risk and disposition in organs for improving risk assessment (Deepika et al, 2021) By incorporating pathophysiological changes occurring in a disease, PBPK model are being extended for drug-disease model. They can be used for predicting the ADME of a given drug or chemical in case of diseased population like liver or renal failure.…”

Section: Pbpk Model Capturing Sensitive and Diseased Populationmentioning

confidence: 99%

“…PBPK models are mathematical models encompassing of multiple compartments with physiology, anatomy, biochemical and physicochemical parameters for describing ADME (absorption, distribution, metabolism and excretion) of xenobiotics and its metabolites (Fig 1) (Deepika et al, 2020(Deepika et al, , 2021Sharma et al, 2017a). These models vary from empirical, semi-mechanistic to compartmental models based on the complexity of the problem (Espié et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

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The Role of “Physiologically Based Pharmacokinetic Model (PBPK)” New Approach Methodology (NAMs) in Pharmaceuticals and Environmental Chemical Risk Assessment

Deepika¹,

Kumar²

2022

Preprint

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Physiologically Based Pharmacokinetic Models (PBPK) are mechanistical tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognised by regulatory authorities for predicting organ concentration-time profile, pharmacokinetic and daily intake dose of xenobiotics. Extension of PBPK models to capture sensitive populations like pediatric, geriatric, pregnant females, fetus etc. and diseased population like renal impairment, liver cirrhosis etc. is a must. However, the current modelling practice and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology, and calculation of biochemical parameters for integrating the knowledge and refining existing PBPK models. Specific PBPK covering compartments like cerebrospinal fluid, and hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints like developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in-silico models where experimental data is unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building qAOPs, use of machine learning for improving existing models along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.

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Estimating the dynamic early life exposure to PFOA and PFOS of the HELIX children: Emerging profiles via prenatal exposure, breastfeeding, and diet

Ratier,

Casas,

Grazuleviciene

et al. 2024

Environment International

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Risk Assessment of Perfluorooctane Sulfonate (PFOS) using Dynamic Age Dependent Physiologically based Pharmacokinetic Model (PBPK) across Human Lifetime

Cited by 16 publications

References 74 publications

The Role of “Physiologically Based Pharmacokinetic Model (PBPK)” New Approach Methodology (NAM) in Pharmaceuticals and Environmental Chemical Risk Assessment

The Role of “Physiologically Based Pharmacokinetic Model (PBPK)” New Approach Methodology (NAM) in Pharmaceuticals and Environmental Chemical Risk Assessment

The Role of “Physiologically Based Pharmacokinetic Model (PBPK)” New Approach Methodology (NAMs) in Pharmaceuticals and Environmental Chemical Risk Assessment

Estimating the dynamic early life exposure to PFOA and PFOS of the HELIX children: Emerging profiles via prenatal exposure, breastfeeding, and diet

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