Development of a Hybrid Physiologically Based Pharmacokinetic Model with Drug‐Specific Scaling Factors in Rat to Improve Prediction of Human Pharmacokinetics

Sayama, Hiroyuki; Kono, Hiroshi; Kogayu, Motohiro; Iwaki, Masahiro

doi:10.1002/jps.23726

Cited by 15 publications

(6 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More recently, translational research, which involves the translation of efficacy and PK data from preclinical to clinical studies via modeling and simulation cycles, occupies an important position in drug development (31,32). We have proposed the tiered approach, which consists of four steps based on modeling and simulation of PBPK models to predict human PK from drug discovery to first-in-human studies (20). In the last step, the measured plasma concentration profile in HV allows us to optimize the PBPK model by the integration of comprehensive in vitro and in vivo information.…”

Section: Discussionmentioning

confidence: 99%

“…S1 and Supplementary Table S3, respectively. The framework of the PBPK model had previously been reported (20), that is, the model is composed of 11 tissue compartments (lungs, adipose tissue, bones, brain, heart, muscles, kidneys, spleen, liver, skin, and small intestine), which are linked by venous and arterial blood pools. Perfusion ratelimited kinetics were assumed, and each tissue was represented by a single well-stirred compartment.…”

Section: Prediction Of Plasma Concentrations In Ckd Patientsmentioning

confidence: 99%

“…The K p values for HV in each tissue was calculated by the tissue composition-based equations proposed by Rodgers et al (21,22), using clogP, pK a , and f p . A uniform factor obtained from the comparison of the predicted and observed V ss was applied to the calculated K p values for the adjustment of PK profiles in HV (20). The SFs for f p , CL UintH , and CL R were then introduced to obtain the parameters under CKD conditions, and the plasma concentrations in patients were simulated.…”

Section: Prediction Of Plasma Concentrations In Ckd Patientsmentioning

confidence: 99%

See 2 more Smart Citations

Application of a Physiologically Based Pharmacokinetic Model Informed by a Top-Down Approach for the Prediction of Pharmacokinetics in Chronic Kidney Disease Patients

Sayama

Takubo

Kono

et al. 2014

AAPS J

Self Cite

View full text Add to dashboard Cite

Quantitative prediction of the impact of chronic kidney disease (CKD) on drug disposition has become important for the optimal design of clinical studies in patients. In this study, clinical data of 151 compounds under CKD conditions were extensively surveyed, and alterations in pharmacokinetic parameters were evaluated. In CKD patients, the unbound hepatic intrinsic clearance decreased to a similar extent for drugs eliminated via hepatic metabolism by cytochrome P450, UDP-glucuronosyltransferase, and other mechanisms. Renal clearance showed a similar decrease to glomerular filtration rate, irrespective of the contribution of tubular secretion. The scaling factor (SF) obtained from the interquartile range of the relative change in each parameter was applied to the well-stirred model to predict clearance in patients. Hepatic and renal clearance could be successfully predicted for approximately half and two-thirds, respectively, of the applied compounds, showing the high utility of SFs. SFs were also introduced to a physiologically based pharmacokinetic (PBPK) model, and the plasma concentration profiles of 12 model compounds with different elimination pathways were predicted for CKD patients. The PBPK model combined with SFs provided good predictability for plasma concentration. The developed PBPK model with information on SFs would accelerate translational research in drug development by predicting pharmacokinetics in CKD patients.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Prediction Of Plasma Concentrations In Ckd Patientsmentioning

confidence: 99%

Section: Prediction Of Plasma Concentrations In Ckd Patientsmentioning

confidence: 99%

See 1 more Smart Citation

Application of a Physiologically Based Pharmacokinetic Model Informed by a Top-Down Approach for the Prediction of Pharmacokinetics in Chronic Kidney Disease Patients

Sayama

Takubo

Kono

et al. 2014

AAPS J

Self Cite

View full text Add to dashboard Cite

show abstract

“…The issue of poor prediction with the PBPK approach is not new. Prediction of the PK of a single drug (i.e., not in the context of DDI) from in vitro data with a PBPK model was shown to be inaccurate in many instances [39][40][41]. The problem is usually solved either by fitting the PBPK model to the clinical data to estimate some parameters [42] or by incorporating scaling factors from animal experiments into the model [39,40].…”

Section: Discussionmentioning

confidence: 99%

“…Prediction of the PK of a single drug (i.e., not in the context of DDI) from in vitro data with a PBPK model was shown to be inaccurate in many instances [39][40][41]. The problem is usually solved either by fitting the PBPK model to the clinical data to estimate some parameters [42] or by incorporating scaling factors from animal experiments into the model [39,40]. Although these approaches work quite well: first, they introduce an empirical adjustment, which is in contradiction with the philosophy of the PBPK model; second, they question the ability of the PBPK approach to make quantitative predictions solely from in vitro data.…”

Section: Discussionmentioning

confidence: 99%

Comparison of the Static In Vivo Approach to a Physiologically Based Pharmacokinetic Approach for Metabolic Drug–Drug Interactions Prediction

Tod

Pierrillas

Bourguignon

et al. 2016

Int. J. Pharmacokinet.

View full text Add to dashboard Cite

Background:The in vivo mechanistic static model (IMSM) and the physiologically based pharmacokinetic (PBPK) model are two approaches used to predict the magnitude of drug-drug interactions (DDIs). The aim of this study was to evaluate the performance of IMSM and to compare IMSM with the PBPK approach implemented in Simcyp. Methods:The predictive performances of IMSM were evaluated on a panel of 628 DDIs. Subsequently, the IMSM and PBPK approaches were compared on a set of 104 DDIs. Results: The IMSM yielded 85% of predictions within 1.5-fold of the observed value on the 628 DDIs panel. The predictive performances of IMSM were better than those of the PBPK approach (median fold error 1 vs 0.86 on 104 studies; p = 0.02). Conclusion:The IMSM approach is an alternative tool for metabolic DDIs prediction.

show abstract

Physiologically Based Pharmacokinetic Modelling for First-In-Human Predictions: An Updated Model Building Strategy Illustrated with Challenging Industry Case Studies

et al. 2019

View full text Add to dashboard Cite

Physiologically based pharmacokinetic modelling is well established in the pharmaceutical industry and is accepted by regulatory agencies for the prediction of drug-drug interactions. However, physiologically based pharmacokinetic modelling is valuable to address a much wider range of pharmaceutical applications, and new regulatory impact is expected as its full power is leveraged. As one example, physiologically based pharmacokinetic modelling is already routinely used during drug discovery for in-vitro to in-vivo translation and pharmacokinetic modelling in preclinical species, and this leads to the application of verified models for first-inhuman pharmacokinetic predictions. A consistent cross-industry strategy in this application area would increase confidence in the approach and facilitate further learning. With this in mind, this article aims to enhance a previously published first-inhuman physiologically based pharmacokinetic model-building strategy. Based on the experience of scientists from multiple companies participating in the GastroPlus™ User Group Steering Committee, new Absorption, Distribution, Metabolism and Excretion knowledge is integrated and decision trees proposed for each essential component of a first-inhuman prediction. We have reviewed many relevant scientific publications to identify new findings and highlight gaps that need to be addressed. Finally, four industry case studies for more challenging compounds illustrate and highlight key components of the strategy.

show abstract

Development of a Hybrid Physiologically Based Pharmacokinetic Model with Drug‐Specific Scaling Factors in Rat to Improve Prediction of Human Pharmacokinetics

Cited by 15 publications

References 26 publications

Application of a Physiologically Based Pharmacokinetic Model Informed by a Top-Down Approach for the Prediction of Pharmacokinetics in Chronic Kidney Disease Patients

Application of a Physiologically Based Pharmacokinetic Model Informed by a Top-Down Approach for the Prediction of Pharmacokinetics in Chronic Kidney Disease Patients

Comparison of the Static In Vivo Approach to a Physiologically Based Pharmacokinetic Approach for Metabolic Drug–Drug Interactions Prediction

Physiologically Based Pharmacokinetic Modelling for First-In-Human Predictions: An Updated Model Building Strategy Illustrated with Challenging Industry Case Studies

Contact Info

Product

Resources

About