Nageshwar R. Budha, Sravanthi Cheeti, Sharmila Rajan, Mark J. Dresser, and Jin Y. Jin
Department of Clinical Pharmacology, Genentech, Inc
Objectives: Simcyp®, a population-based ADME simulator, allows investigating the differences in pharmacokinetics (PK) between different populations by integrating demographic and physiologic data [1-3]. This study is aimed at investigating potential differences in PK between healthy subjects and cancer patients using physiologically-based pharmacokinetic approach.
Methods: Demographic data such as age, sex, body weight, and body surface area and laboratory measurements such as albumin, alpha-1 acid glycoprotein, and hematocrit were collected in ~2100 cancer patients. A custom oncology population profile was built using the observed relationships between demographic variables and laboratory measurements in Simcyp®. The healthy volunteers profile in Simcyp® was used for comparison.
Results: Cancer patients were generally older when compared to healthy subjects age distribution in the Simcyp® healthy volunteers profile. The plasma protein albumin levels were lower and alpha-1 acid glycoprotein levels were higher in cancer patients. The custom oncology profile was used to investigate the differences in PK of two probe drugs, saquinavir and midazolam. The exposure of saquinavir is higher in cancer patients whereas the midazolam exposure was similar in cancer patients and healthy subjects. Differences in saquinavir PK are expected due to altered drug binding because of elevated alpha-1 acid glycoprotein levels in cancer patients. Similarly, lack of differences in midazolam PK supports the hypothesis that the CYP3A activity is not altered in cancer patients.
Conclusions: The above results suggest that the custom oncology profile in Simcyp® can be used to obtain reliable predictions of PK of drugs in cancer patients.
References:
[1] Jamei M, Marciniak S, Feng K, Barnett A, Tucker G, Rostami-Hodjegan A. The Simcyp population-based ADME simulator. Expert Opin Drug Metab Toxicol. 2009; 5:211-23.
[2] Huang SM, Rowland M. The role of physiologically based pharmacokinetic modeling in regulatory review. Clin Pharmacol Ther.2012; 91:542-9.
[3] Jamei M, Dickinson GL, Rostami-Hodjegan A. A framework for assessing inter-individual variability in pharmacokinetics using virtual human populations and integrating general knowledge of physical chemistry, biology, anatomy, physiology and genetics: A tale of ‘bottom-up’ vs ‘top-down’ recognition of covariates. Drug Metab Pharmacokinet. 2009; 24:53-75.
Reference: PAGE 21 () Abstr 2393 [www.page-meeting.org/?abstract=2393]
Poster: Oncology