Development and validation of PBPK models for genistein and daidzein for use in a next-generation risk assessment

被引:2
作者
Najjar, A. [1 ]
Lange, D. [1 ]
Genies, C. [2 ]
Kuehnl, J. [1 ]
Zifle, A. [3 ]
Jacques, C. [2 ]
Fabian, E. [4 ]
Hewitt, N. [5 ]
Schepky, A. [1 ]
机构
[1] Beiersdorf AG, Hamburg, Germany
[2] Pierre Fabre Dermo Cosmet & Personal Care, Toulouse, France
[3] Kao Germany GmbH, Darmstadt, Germany
[4] BASF SE, Ludwigshafen, Germany
[5] Cosmet Europe, Brussels, Belgium
关键词
daidzein; genistein; PBPK; validation; safety assessment; PHARMACOKINETICS; ABSORPTION; METABOLISM; TOXICITY; PLASMA; RAT; CLEARANCE; HUMANS; LIVER;
D O I
10.3389/fphar.2024.1421650
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
Introduction All cosmetic ingredients must be evaluated for their safety to consumers. In the absence of in vivo data, systemic concentrations of ingredients can be predicted using Physiologically based Pharmacokinetic (PBPK) models. However, more examples are needed to demonstrate how they can be validated and applied in Next-Generation Risk Assessments (NGRA) of cosmetic ingredients. We used a bottom-up approach to develop human PBPK models for genistein and daidzein for a read-across NGRA, whereby genistein was the source chemical for the target chemical, daidzein.Methods An oral rat PBPK model for genistein was built using PK-Sim (R) and in vitro ADME input data. This formed the basis of the daidzein oral rat PBPK model, for which chemical-specific input parameters were used. Rat PBPK models were then converted to human models using human-specific physiological parameters and human in vitro ADME data. In vitro skin metabolism and penetration data were used to build the dermal module to represent the major route of exposure to cosmetics.Results The initial oral rat model for genistein was qualified since it predicted values within 2-fold of measured in vivo PK values. This was used to predict plasma concentrations from the in vivo NOAEL for genistein to set test concentrations in bioassays. Intrinsic hepatic clearance and unbound fractions in plasma were identified as sensitive parameters impacting the predicted Cmax values. Sensitivity and uncertainty analyses indicated the developed PBPK models had a moderate level of confidence. An important aspect of the development of the dermal module was the implementation of first-pass metabolism, which was extensive for both chemicals. The final human PBPK model for daidzein was used to convert the in vitro PoD of 33 nM (from an estrogen receptor transactivation assay) to an external dose of 0.2% in a body lotion formulation.Conclusion PBPK models for genistein and daidzein were developed as a central component of an NGRA read-across case study. This will help to gain regulatory confidence in the use of PBPK models, especially for cosmetic ingredients.
引用
收藏
页数:15
相关论文
共 50 条
[21]   Multilaboratory assessment of metagenomic next-generation sequencing for unbiased microbe detection [J].
Han, Dongsheng ;
Diao, Zhenli ;
Lai, Huiying ;
Han, Yanxi ;
Xie, Jiehong ;
Zhang, Rui ;
Li, Jinming .
JOURNAL OF ADVANCED RESEARCH, 2022, 38 :213-222
[22]   Development and Application of Performance Assessment Criteria for Next-Generation Sequencing-Based HIV Drug Resistance Assays [J].
Becker, Michael G. ;
Liang, Dun ;
Cooper, Breanna ;
Le, Yan ;
Taylor, Tracy ;
Lee, Emma R. ;
Wu, Sutan ;
Sandstrom, Paul ;
Ji, Hezhao .
VIRUSES-BASEL, 2020, 12 (06)
[23]   Desiderata for the development of next-generation electronic health record phenotype libraries [J].
Chapman, Martin ;
Mumtaz, Shahzad ;
Rasmussen, Luke, V ;
Karwath, Andreas ;
Gkoutos, Georgios, V ;
Gao, Chuang ;
Thayer, Dan ;
Pacheco, Jennifer A. ;
Parkinson, Helen ;
Richesson, Rachel L. ;
Jefferson, Emily ;
Denaxas, Spiros ;
Curcin, Vasa .
GIGASCIENCE, 2021, 10 (09)
[24]   Development of "smart cell" construction platform for next-generation microbial breeding [J].
Hasunuma, Tomohisa .
2018 IEEE CPMT SYMPOSIUM JAPAN (ICSJ), 2018, :75-76
[25]   Next-generation Metabolomics in the Development of New Antidepressants: Using Albiflorin as an Example [J].
Han, Jian ;
Xia, Yonghong ;
Lin, Lejun ;
Zhang, Zuoguang ;
Tian, Hui ;
Li, Kefeng .
CURRENT PHARMACEUTICAL DESIGN, 2018, 24 (22) :2530-2540
[26]   Clinical Validation of KRAS, BRAF, and EGFR Mutation Detection Using Next-Generation Sequencing [J].
Lin, Ming-Tseh ;
Mosier, Stacy L. ;
Thiess, Michele ;
Beierl, Katie F. ;
Debeljak, Marija ;
Tseng, Li-Hui ;
Chen, Guoli ;
Yegnasubramanian, Srinivasan ;
Ho, Hao ;
Cope, Leslie ;
Wheelan, Sarah J. ;
Gocke, Christopher D. ;
Eshleman, James R. .
AMERICAN JOURNAL OF CLINICAL PATHOLOGY, 2014, 141 (06) :856-866
[27]   Validation of Variant Assembly Using HAPHPIPE with Next-Generation Sequence Data from Viruses [J].
Gibson, Keylie M. ;
Steiner, Margaret C. ;
Rentia, Uzma ;
Bendall, Matthew L. ;
Perez-Losada, Marcos ;
Crandall, Keith A. .
VIRUSES-BASEL, 2020, 12 (07)
[28]   Next-generation sequencing workflows in veterinary infection biology: towards validation and quality assurance [J].
Van Borm, S. ;
Wang, J. ;
Granberg, F. ;
Colling, A. .
REVUE SCIENTIFIQUE ET TECHNIQUE-OFFICE INTERNATIONAL DES EPIZOOTIES, 2016, 35 (01) :67-81
[29]   A hypothetical skin sensitisation next generation risk assessment for coumarin in cosmetic products [J].
Reynolds, G. ;
Reynolds, J. ;
Gilmour, N. ;
Cubberley, R. ;
Spriggs, S. ;
Aptula, A. ;
Przybylak, K. ;
Windebank, S. ;
Maxwell, G. ;
Baltazar, M. T. .
REGULATORY TOXICOLOGY AND PHARMACOLOGY, 2021, 127
[30]   In vitro to in vivo extrapolation to support the development of the next generation risk assessment (NGRA) strategy for nanomaterials [J].
Jagiello, Karolina ;
Ciura, Krzesimir .
NANOSCALE, 2022, 14 (18) :6735-6742