Ever noticed how people from different ethnic backgrounds respond differently to drugs? For example, you may enjoy having a few drinks with friends on the weekend. When your friends with Eastern Asian heritage drink alcohol, it’s not uncommon for their faces to turn red. This happens because many East Asians possess an enzyme deficiency for aldehyde dehydrogenase 2 (ALDH2). Alcohol is metabolized to acetaldehyde which is further broken down into acetate by ALDH2. When people with the inactive ALDH2 variant drink alcohol, acetaldehyde accumulates in their body causing facial flushing, nausea, and a rapid heartbeat.
Ethnic diversity in drug response and its impact on dosing has been well described for some drugs.1 A recent study of the most widely prescribed proprietary drugs in the US showed that, in around half of all cases, the recommended doses in Japan were considerably lower than both the US and European doses.2 Investigating the potential impact of ethnicity on pharmacokinetics often involves repeating clinical studies in different populations, which may be unnecessary in some cases. Physiologically-based pharmacokinetic (PBPK) modeling and simulation in virtual populations can uncover changes in drug disposition due to ethnic differences, providing supporting information for regulatory review and helping identify and optimize essential bridging studies.
Developing safe and effective drugs for patients of different ethnicities
Reducing the total number of clinical studies undertaken to secure regulatory approval without compromising patient safety is a major goal for both pharmaceutical companies and regulatory agencies worldwide. A major global pharmaceutical company – which had identified China as a strategically important market – approached us to develop a virtual Chinese population so that potential differences in pharmacokinetics between populations could be simulated to assist with decision-making regarding clinical trials. This builds on the prior capabilities of the Simcyp® Simulator in capturing differences in clearance observed between Japanese and North European Caucasian subjects.3
Building virtual populations for different ethnic groups
Demographic, physiological and genetic data were gathered from literature sources and from a Pfizer® database of Chinese healthy volunteers. The information was used to build a virtual Chinese population within the Simcyp Simulator. Significant differences between Chinese and Caucasian populations were noted for liver weight, the frequency of CYP2D6 poor and intermediate metabolizers, the frequency of CYP2C19 and CYP3A5 poor metabolizers and the hepatic abundance of CYP2C19. Simulations showed good agreement with clinical data when the model was tested using drugs metabolized predominantly by specific CYP enzymes with minimal impact of transporters and low biliary or renal clearance.4
Scientists at Janssen® Pharmaceuticals Inc, have also used the Simcyp Simulator to investigate variability and ethnicity differences in pharmacokinetics as part of its New Drug Application for OlysioTM (simeprevir). Simulations showed a 2.2- fold higher exposure in virtual healthy Chinese subjects compared with healthy Caucasians – a very close match to the 2-fold increase observed in clinical studies. Although no dosage recommendations for specific subpopulations are made in product labeling for Olysio, subsequent simulations in virtual Caucasian, Japanese and Chinese populations were requested by FDA reviewers to provide additional information on the impact of disease state and ethnicity on drug exposure.5
Leveraging biosimulation technology to expedite drug approvals
In silico predictions may decrease the need to repeat pharmacokinetic studies in different ethnic groups3 and expedite the regulatory approval process in regions remote to where original clinical development took place. Furthermore, accessing distinct virtual Japanese and Chinese populations within the Simcyp Simulator can overcome some of the difficulties that arise when clinical data collected in Japanese, Korean and Chinese individuals are combined and reported for a single ‘Asian’ population.4
Accelerating the regulatory review process in different global regions brings new medicines to patients faster and can have considerable financial benefit for pharmaceutical companies.
 Yasuda SU, Zhang L, Huang SM. The role of ethnicity in variability in response to drugs: Focus on clinical pharmacology studies. Clinical Pharmacology & Therapeutics. 2008; 84(3):417-23.
 Malinowski HJ, Westelinck A, Sato J, Ong T. Same drug, different dosing: Differences in dosing for drugs approved in the United States, Europe, and Japan. Journal of Clinical Pharmacology. 2008;48(8):900-8.
 Inoue S, Howgate EM, Rowland-Yeo K, Shimada T, Yamazaki H, Tucker GT, Rostami-Hodjegan A. Prediction of in vivo drug clearance from in vitro data. II: Potential inter-ethnic differences. Xenobiotica. 2006; 36(6):499-513.
 Barter ZE, Tucker GT, Rowland-Yeo K. Differences in cytochrome p450-mediated pharmacokinetics between Chinese and Caucasian populations predicted by mechanistic physiologically based pharmacokinetic modelling. Clinical Pharmacokinetics. 2013; 52(12):1085-100.
 US FDA Clinical Pharmacology and Biopharmaceutics Review—Simeprevir http://www.access-data.fda.gov/drugsatfda_docs/nda/2013/205123Orig1s000ClinPharmR.pdf
All information presented derive from public source materials.
Understanding the mechanisms of complex drug-drug interactions
PBPK modeling is used to predict the influence of ethnicity on drug disposition. This approach is also valuable in investigating and identifying the underlying mechanistic determinants of drug-drug interactions. Read this case study to learn how Certara scientists applied PBPK modeling to understand complex DDIs, both for drugs in development and following marketing approval.