Kiyomi Ito

漢方薬と西洋薬との併用に関する問題点や漢方薬の使用実態について明らかにするために、大学病院の漢方専門外来においてアンケート調査を実施した。外来患者475例を対象に、患者背景、漢方薬と西洋薬の併用状況、漢方薬の服用状況などについて調査を行った。440例から回答が得られた。原疾患は、更年期障害や月経不順などの産婦人科系疾患のために受診している患者が最も多く、次いでアトピー性皮膚炎などの皮膚科疾患、便秘などの消化器疾患で、この3疾患で患者のほぼ半数を占めた。漢方薬を1種類のみ服用している患者は121例であるのに対し、2種類以上服用している患者は210例であった。197例が西洋薬を1種類以上併用し、漢方薬と併用している西洋薬数は平均1.5種類であった。236例が漢方薬を食後に服用しており、食前あるいは食間に服用している患者より多かった。

The success of in vitro derived K-i values for predicting drug-drug interactions in vivo has been mixed. For example, the use of hepatic input concentration of inhibitor has resolved the negative and positive interactions on the qualitative level, eliminating false negative predictions. However, several examples of false positives and a high incidence of over-predictions of true positive interactions indicated a need for incorporation of additional factors. The aim of this study was to investigate the effect of parallel elimination pathways as a possible reason for false positives and over-predictions. Simulation studies indicated that the degree of interaction ( assessed by area under the plasma concentration-time curve ratio in the presence and absence of inhibitor) depends largely on the fraction of substrate metabolized by the particular P450 enzyme (fm(CYPi)) that is inhibited. The current analysis focused on CYP2D6 interactions due to the well documented genetic polymorphism and the ability to estimate fm(CYP2D6) readily from in vivo data obtained in extensive and poor metabolizers. Based on either a phenotype study or an alternative regression analysis approach, the fm(CYP2D6) values of 0.37 to 0.94 and 0.25 to 0.89, respectively, were obtained for nine substrates. Prediction of 44 drug-drug interaction studies was improved by the combination of parallel pathways of elimination and their susceptibility to inhibition. The overall success of predicting positive and negative interactions was increased from 54% to 84%, and the number of over-predictions was substantially reduced. It is concluded that incorporating parallel pathways provides a valuable step forward in making quantitative predictions of drug-drug interactions from in vitro data.