A Multi-Compartmental Model Generally Applicable to Physiologically-Based Pharmacokinetics

Arundel, Philip A.

doi:10.1016/s1474-6670(17)44557-5

Cited by 18 publications

(28 citation statements)

References 7 publications

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“…30 The equations used to calculate fu inc are also presented in Appendix (Eqs. [24][25][26]. In addition, it should be noted that for the combined set of compounds for which there were experimental fu inc values in human, we did not observe a significant improvement in the prediction accuracy of the plasma concentration-time profiles compared to when calculated fu inc values were used in the current PBPK modeling approach (not shown).…”

Section: In Vitro-based Modeling Of CLmentioning

confidence: 72%

“…• Rodgers and Rowland elaborated model (TC K pu ). and those that rely on preclinical in vivo kinetics 11,25,26 :…”

Section: Methods Overall Work Flowmentioning

confidence: 99%

“…Compartments are switched off by setting the corresponding flow rates to zero for the six-compartment model used for the Arundel method. 11,25 The remaining six active compartments have the sum of blood flows and physical volumes for the tissue group associated with them ( Figure 3). The differential equations for the PBPK model are of the generic structure found in most articles on this subject 21 (see Appendix).…”

Section: Base Pbpk-model Framework For Humanmentioning

confidence: 99%

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PHRMA CPCDC initiative on predictive models of human pharmacokinetics, part 5: Prediction of plasma concentration–time profiles in human by using the physiologically‐based pharmacokinetic modeling approach

Poulin¹,

Jones

et al. 2011

Journal of Pharmaceutical Sciences

157

181

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Section: In Vitro-based Modeling Of CLmentioning

confidence: 72%

“…• Rodgers and Rowland elaborated model (TC K pu ). and those that rely on preclinical in vivo kinetics 11,25,26 :…”

Section: Methods Overall Work Flowmentioning

confidence: 99%

Section: Base Pbpk-model Framework For Humanmentioning

confidence: 99%

See 1 more Smart Citation

PHRMA CPCDC initiative on predictive models of human pharmacokinetics, part 5: Prediction of plasma concentration–time profiles in human by using the physiologically‐based pharmacokinetic modeling approach

Poulin¹,

Jones

et al. 2011

Journal of Pharmaceutical Sciences

157

181

View full text Add to dashboard Cite

“…The volume of distribution derived noncompartmentally is often used to estimate partition coefficients for PBPK models 22, 27, 28. Failure to use the correct estimate of volume may result in a suboptimal set of partition coefficients being used in the PBPK model.…”

Section: Discussionmentioning

confidence: 99%

On the Volume of Distribution at Steady State and Its Relationship With Two‐Compartmental Models

Yates

Arundel

2008

Journal of Pharmaceutical Sciences

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“…The in vivo K p values for pafuramidine and furamidine were not available for human tissues. Unbound K p values in rats and humans were assumed to be similar (K p, u, rat ϭ K p, u, human ) (Arundel, 1997). Because no significant species difference in plasma binding of pafuramidine/furamidine was observed (Table 1), K p values derived from the rat model were applied to describe the distribution of pafuramidine/furamidine in humans.…”

Section: Semi-pbpk Modeling To Predict Prodrug/metabolite Exposurementioning

confidence: 99%

A Semiphysiologically Based Pharmacokinetic Modeling Approach to Predict the Dose-Exposure Relationship of an Antiparasitic Prodrug/Active Metabolite Pair

Yan¹,

Generaux²,

Yoon³

et al. 2011

Drug Metab Dispos

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ABSTRACT:Dose selection during antiparasitic drug development in animal models and humans traditionally has relied on correlations between plasma concentrations obtained at or below maximally tolerated doses that are efficacious. The objective of this study was to improve the understanding of the relationship between dose and plasma/tissue exposure of the model antiparasitic agent, pafuramidine, using a semiphysiologically based pharmacokinetic (semi-PBPK) modeling approach. Preclinical and clinical data generated during the development of pafuramidine, a prodrug of the active metabolite, furamidine, were used. A whole-body semi-PBPK model for rats was developed based on a whole-liver PBPK model using rat isolated perfused liver data. A whole-body semi-PBPK model for humans was developed on the basis of the whole-body rat model. Scaling factors were calculated using metabolic and transport clearance data generated from rat and human sandwichcultured hepatocytes. Both whole-body models described pafuramidine and furamidine disposition in plasma and predicted furamidine tissue (liver and kidney) exposure and excretion profiles (biliary and renal). The whole-body models predicted that the intestine contributes significantly (30-40%) to presystemic furamidine formation in both rats and humans. The predicted terminal elimination half-life of furamidine in plasma was 3-to 4-fold longer than that of pafuramidine in rats (170 versus 47 h) and humans (64 versus 19 h). The dose-plasma/tissue exposure relationship for the prodrug/active metabolite pair was determined using the wholebody models. The human model proposed a dose regimen of pafuramidine (40 mg once daily) based on a predefined efficacysafety index. A similar approach could be used to guide doseranging studies in humans for next-in-class compounds.

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A Multi-Compartmental Model Generally Applicable to Physiologically-Based Pharmacokinetics

Cited by 18 publications

References 7 publications

PHRMA CPCDC initiative on predictive models of human pharmacokinetics, part 5: Prediction of plasma concentration–time profiles in human by using the physiologically‐based pharmacokinetic modeling approach

PHRMA CPCDC initiative on predictive models of human pharmacokinetics, part 5: Prediction of plasma concentration–time profiles in human by using the physiologically‐based pharmacokinetic modeling approach

On the Volume of Distribution at Steady State and Its Relationship With Two‐Compartmental Models

A Semiphysiologically Based Pharmacokinetic Modeling Approach to Predict the Dose-Exposure Relationship of an Antiparasitic Prodrug/Active Metabolite Pair

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