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

Poulin, Patrick; Jones, Rhys D.O.; Jones, Hannah M.; Gibson, Christopher R; Rowland, Malcolm; Chien, Jenny Y.; Ring, Barbara J.; Adkison, Kimberly K.; Ku, M. Sherry; He, Handan; Vuppugalla, Ragini; Marathe, Punit; Fischer, Volker; Dutta, Sandeep; Sinha, Vikash; Bjornsson, Thorir D.; Lavé, Thierry; Yates, James

doi:10.1002/jps.22550

Cited by 157 publications

(187 citation statements)

References 47 publications

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“…This concept has been widely accepted as a standard practice for in vitro assays used for clinical DILI prediction (Dykens et al 2008;Xu et al 2008;Khetani et al 2013). While a drug's C max is commonly determined in human safety and efficacy studies, such information is not available at the preclinical developmental stage; nonetheless, it might be predicted as reported elsewhere (Poulin et al 2011). For example, a physiologically based pharmacokinetic modeling approach was demonstrated to predict C max using in silico and in vitro data with reasonable accuracy (67 % of drugs fall in an average-fold error of < twofold) (Jones et al 2006).…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, the C max data used in the present study were retrieved from human studies, while the HCS assays were designed for preclinical study where the C max data for the drug candidates are unknown. A recent publication (Jones et al 2006) indicated that the C max may be predicted based on preclinical data with reasonable accuracy; however, the results from a recent study initialized by Pharmaceutical Research and Manufacturers of America (PhRMA) indicated that the pharmacokinetic models still suffer from a general underestimation of drug exposure (Poulin et al 2011). Therefore, the utility of the predicted C max for the in vitro assay-based models requires further evaluation.…”

Section: Discussionmentioning

confidence: 99%

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A testing strategy to predict risk for drug-induced liver injury in humans using high-content screen assays and the ‘rule-of-two’ model

et al. 2014

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Drug-induced liver injury (DILI) is a major cause of drug failures in both the preclinical and clinical phase. Consequently, improving prediction of DILI at an early stage of drug discovery will Correspondence to: Weida Tong. Jürgen Borlak and Weida Tong have contributed equally to the manuscript.Disclaimer: The views presented in this article do not necessarily reflect current or future opinion or policy of the US Food and Drug Administration. Any mention of commercial products is for clarification and not intended as endorsement. Electronic supplementary materialThe online version of this article (doi:10.1007/s00204-014-1276-9) contains supplementary material, which is available to authorized users. HHS Public AccessAuthor manuscript Arch Toxicol. Author manuscript; available in PMC 2018 January 04.Published in final edited form as:Arch Toxicol. 2014 July ; 88(7): 1439-1449. doi:10.1007/s00204-014-1276-9. Author Manuscript Author ManuscriptAuthor ManuscriptAuthor Manuscript reduce the potential failures in the subsequent drug development program. In this regard, highcontent screening (HCS) assays are considered as a promising strategy for the study of DILI; however, the predictive performance of HCS assays is frequently insufficient. In the present study, a new testing strategy was developed to improve DILI prediction by employing in vitro assays that was combined with the RO2 model (i.e., 'rule-of-two' defined by daily dose ≥100 mg/day & logP ≥3). The RO2 model was derived from the observation that high daily doses and lipophilicity of an oral medication were associated with significant DILI risk in humans. In the developed testing strategy, the RO2 model was used for the rational selection of candidates for HCS assays, and only the negatives predicted by the RO2 model were further investigated by HCS. Subsequently, the effects of drug treatment on cell loss, nuclear size, DNA damage/fragmentation, apoptosis, lysosomal mass, mitochondrial membrane potential, and steatosis were studied in cultures of primary rat hepatocytes. Using a set of 70 drugs with clear evidence of clinically relevant DILI, the testing strategy improved the accuracies by 10 % and reduced the number of drugs requiring experimental assessment by approximately 20 %, as compared to the HCS assay alone. Moreover, the testing strategy was further validated by including published data (Cosgrove et al. in Toxicol Appl Pharmacol 237:317-330, 2009) on drug-cytokine-induced hepatotoxicity, which improved the accuracies by 7 %. Taken collectively, the proposed testing strategy can significantly improve the prediction of in vitro assays for detecting DILI liability in an early drug discovery phase.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A testing strategy to predict risk for drug-induced liver injury in humans using high-content screen assays and the ‘rule-of-two’ model

et al. 2014

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“…That is, urinary concentrations are assumed to be directly related in a mathematical sense to intake rates over the previous short time window. An intake dose (D, mg/day) is calculated based on a measured spot urinary concentration (C), assumptions regarding 24-h urinary volume or creatinine excretion (V 24 or Cr 24 ), and data on the fraction of ingested parent compound excreted in urine (F UE , either as parent or as the measured metabolite) 6 :…”

Section: Introductionmentioning

confidence: 99%

Interpreting variability in population biomonitoring data: Role of elimination kinetics

Aylward

Kirman²,

Adgate

et al. 2012

J Expo Sci Environ Epidemiol

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Biomarker concentrations in spot samples of blood and urine are implicitly interpreted as direct surrogates for long-term exposure magnitude in a variety of contexts including (1) epidemiological studies of potential health outcomes associated with general population chemical exposure, and (2) cross-sectional population biomonitoring studies. However, numerous factors in addition to exposure magnitude influence biomarker concentrations in spot samples, including temporal variation in spot samples because of elimination kinetics. The influence of half-life of elimination relative to exposure interval is examined here using simple first-order pharmacokinetic simulations of urinary concentrations in spot samples collected at random times relative to exposure events. Repeated exposures were modeled for each individual in the simulation with exposure amounts drawn from lognormal distributions with varying geometric standard deviations. Relative variation in predicted spot sample concentrations was greater than the variation in underlying dose distributions when the half-life of elimination was shorter than the interval between exposures, with the degree of relative variation increasing as the ratio of half-life to exposure interval decreased. Results of the modeling agreed well with data from a serial urine collection data set from the Centers for Disease Control. Data from previous studies examining intra-class correlation coefficients for a range of chemicals relying upon repeated sampling support the importance of considering the half-life relative to exposure frequency in design and interpretation of studies using spot samples for exposure classification and exposure estimation. The modeling and data sets presented here provide tools that can assist in interpretation of variability in cross-sectional biomonitoring studies and in design of studies utilizing biomonitoring data as markers for exposure.

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“…PBPK models have showed, in 69% of the cases, a medium to high degree of accuracy in simulating the time course of the drug plasma concentration in human following intravenous administration. On the contrary, the accuracy decreases to 23% in case of oral administration [5]. Accordingly, the authors of this study concluded that i) PBPK models can be a useful basis for a more rational selection of first-in-human phase dose ranges and that ii) there is a need for better predictions of human pharmacokinetics following oral administration.…”

Section: Introductionmentioning

confidence: 84%

A physiologically oriented mathematical model for the description of in vivo drug release and absorption

Cont¹,

Abrami²,

Hasa³

et al. 2014

ADMET DMPK

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This paper focuses on a physiologically-oriented mathematical model aimed at studying the in vivo drug release, absorption, distribution, metabolism and elimination (ADME). To this purpose, the model accounts for drug delivery from an ensemble of non-eroding poly-disperse polymeric particles and the subsequent ADME processes. The model outcomes are studied with reference to three widely used drugs: theophylline, temazepam and nimesulide. One of the most important results of this study is the quantitative evaluation of the interplay between the release kinetics and the subsequent ADME processes. Indeed, it is usually assumed that in vivo drug release coincides with in vitro so that the effect exerted by the ADME processes is neglected. In addition, the proposed model may be an important tool for the design of delivery systems since, through proper changes, it could also account for different oral delivery systems.

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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

Cited by 157 publications

References 47 publications

A testing strategy to predict risk for drug-induced liver injury in humans using high-content screen assays and the ‘rule-of-two’ model

A testing strategy to predict risk for drug-induced liver injury in humans using high-content screen assays and the ‘rule-of-two’ model

Interpreting variability in population biomonitoring data: Role of elimination kinetics

A physiologically oriented mathematical model for the description of in vivo drug release and absorption

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