ArticleClinical pharmacokinetics2016
Combining 'Bottom-Up' and 'Top-Down' Methods to Assess Ethnic Difference in Clearance: Bitopertin as an Example.
Article in Clinical pharmacokinetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Inter-ethnic differences in pharmacokinetics-is there more that unites than divides?Pharmacology research & perspectives · 2021Pooled it
- Application of Physiologically-Based Pharmacokinetic Modeling for the Prediction of Tofacitinib Exposure in Japanese.The Kobe journal of medical sciences · 2017Trial
- Development, Verification, and Application of a Chinese Pediatric Physiologically Based Pharmacokinetic Model: Emphasis on CYP Metabolism and Renal Elimination.The AAPS journal · 2025Article
- In Silico Post-screening of Anti-polymerization Agents to Treat Sickle Cell Disease.bioRxiv : the preprint server for biology · 2025Article
- A new paradigm for drug discovery in the treatment of complex diseases: drug discovery and optimization.Chinese medicine · 2025Review
- Article
- Integrated Use of In Vitro and In Vivo Information for Comprehensive Prediction of Drug Interactions Due to Inhibition of Multiple CYP Isoenzymes.Clinical pharmacokinetics · 2023Article
- Pharmacokinetic profile of bitopertin, a selective GlyTNaunyn-Schmiedeberg's archives of pharmacology · 2023Article
- Asia-Inclusive Clinical Research and Development Enabled by Translational Science and Quantitative Clinical Pharmacology: Toward a Culture That Challenges the Status Quo.Clinical pharmacology and therapeutics · 2023Article
- Current trends in drug metabolism and pharmacokinetics.Acta pharmaceutica Sinica. B · 2019Review
- Application of Physiologically Based Pharmacokinetic Modeling to Predict Pharmacokinetics in Healthy Japanese Subjects.Clinical pharmacology and therapeutics · 2019Article
- Pharmacokinetic-Pharmacodynamic Modeling in Pediatric Drug Development, and the Importance of Standardized Scaling of Clearance.Clinical pharmacokinetics · 2019Review
- Combining "Bottom-up" and "Top-down" Approaches to Assess the Impact of Food and Gastric pH on Pictilisib (GDC-0941) Pharmacokinetics.CPT: pharmacometrics & systems pharmacology · 2017Article
- Translational Modeling and Simulation in Supporting Early-Phase Clinical Development of New Drug: A Learn-Research-Confirm Process.Clinical pharmacokinetics · 2017Article
- Recent Advances in Development and Application of Physiologically-Based Pharmacokinetic (PBPK) Models: a Transition from Academic Curiosity to Regulatory Acceptance.Current pharmacology reportsReview
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectivesWe propose a strategy for studying ethnopharmacology by conducting sequential physiologically based pharmacokinetic (PBPK) prediction (a 'bottom-up' approach) and population pharmacokinetic (popPK) confirmation (a 'top-down' approach), or in reverse order, depending on whether the purpose is ethnic effect assessment for a new molecular entity under development or a tool for ethnic sensitivity prediction for a given pathway. The strategy is exemplified with bitopertin.
methodsA PBPK model was built using Simcyp(®) to simulate the pharmacokinetics of bitopertin and to predict the ethnic sensitivity in clearance, given pharmacokinetic data in just one ethnicity. Subsequently, a popPK model was built using NONMEM(®) to assess the effect of ethnicity on clearance, using human data from multiple ethnic groups. A comparison was made to confirm the PBPK-based ethnic sensitivity prediction, using the results of the popPK analysis.
resultsPBPK modelling predicted that the bitopertin geometric mean clearance values after 20 mg oral administration in Caucasians would be 1.32-fold and 1.27-fold higher than the values in Chinese and Japanese, respectively. The ratios of typical clearance in Caucasians to the values in Chinese and Japanese estimated by popPK analysis were 1.20 and 1.17, respectively. The popPK analysis results were similar to the PBPK modelling results.
conclusionAs a general framework, we propose that PBPK modelling should be considered to predict ethnic sensitivity of pharmacokinetics prior to any human data and/or with data in only one ethnicity. In some cases, this will be sufficient to guide initial dose selection in different ethnicities. After clinical trials in different ethnicities, popPK analysis can be used to confirm ethnic differences and to support dose justification and labelling. PBPK modelling prediction and popPK analysis confirmation can complement each other to assess ethnic differences in pharmacokinetics at different drug development stages.
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