Combining ‘Bottom-Up’ and ‘Top-Down’ Methods to Assess Ethnic Difference in Clearance: Bitopertin as an Example

• Published in: Clinical pharmacokinetics Vol. 55; no. 7; pp. 823 - 832
• Main Authors: Feng, Sheng, Shi, Jun, Parrott, Neil, Hu, Pei, Weber, Cornelia, Martin-Facklam, Meret, Saito, Tomohisa, Peck, Richard
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.07.2016; Springer Nature B.V
• Subjects: Adolescent; Adult; Asian Continental Ancestry Group; Body Mass Index; Computer Simulation; Dose-Response Relationship, Drug; European Continental Ancestry Group; Female; Humans; Internal Medicine; Male; Medicine; Medicine & Public Health; Metabolic Clearance Rate; Middle Aged; Models, Biological; Original; Original Research Article; Pharmacology/Toxicology; Pharmacotherapy; Piperazines - pharmacokinetics; Sulfones - pharmacokinetics; Young Adult; Ethnic Difference; Objective Function Value; PBPK Modelling; popPK Model; Visual Predictive Check
• ISSN: 0312-5963; 1179-1926
• DOI: 10.1007/s40262-015-0356-1

Background and Objectives We 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. Methods A 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. Results PBPK 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. Conclusion As 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.