RT Journal Article
SR Electronic
T1 Analysis and Prediction of Drug Transfer into Human Milk Taking into Consideration Secretion and Reuptake Clearances across the Mammary Epithelia
JF Drug Metabolism and Disposition
JO Drug Metab Dispos
FD American Society for Pharmacology and Experimental Therapeutics
SP 2370
OP 2380
DO 10.1124/dmd.111.040972
VO 39
IS 12
A1 Koshimichi, Hiroki
A1 Ito, Kousei
A1 Hisaka, Akihiro
A1 Honma, Masashi
A1 Suzuki, Hiroshi
YR 2011
UL http://dmd.aspetjournals.org/content/39/12/2370.abstract
AB Medication use during lactation is a matter of concern due to unnecessary exposure of infants to drugs. Although some studies have predicted the extent of drug transfer into milk from physicochemical parameters, drug concentration-time profiles in milk have not been predicted or even analyzed yet. In the present study, a drug transfer model was constructed by defining secretion and reuptake clearances (CLsec and CLre, respectively) between milk and plasma based on unbound drug concentrations. Through the use of this model, drug concentration-time profiles were analyzed in human milk and plasma based on data collected from the literature. CLsec and CLre values were obtained successfully for 49 drugs. Because the CLsec and CLre values were in general similar for each drug, transport across the mammary epithelia was mediated by passive diffusion in most cases. This study demonstrated that the logarithmically transformed values of CLsec and CLre can be predicted from physicochemical parameters with adjusted R2 values of 0.705 and 0.472, respectively. Moreover, 66.7 and 77.8% of predicted CLsec and CLre values were within 3-fold error ranges of the observed values for 45 and 27 drugs, respectively. Finally, time profiles of drug concentrations in milk were simulated from physicochemical parameters. The milk-to-plasma area under the concentration-time curve ratios also were predicted successfully within 3-fold error ranges of the observed values for 71.9% of the drugs analyzed. The method described herein therefore may be useful in predicting drug concentration-time profiles in human milk for newly developed drugs.