臺灣博碩士論文加值系統

嚴重的藥物交互作用 (DDI) 會危害病人並導致藥品下市，因此新藥的研發會於臨床前試驗期 (preclinical stage)，藉由預測模式 (prediction models) 來增進對藥品的了解，協助臨床試驗的決策以降低進入後可能造成的風險與成本。在臨床前試驗期，會以動物實驗或體外試驗 (in vitro tests) 來探討可能產生DDI的情形。動物實驗提供新藥在進入人體試驗前初步的藥動學資訊，然而不同生物體之間存在代謝上的差異，動物之藥動學交互作用數據 (如AUC ratio，藥品併用與單獨使用下AUC比) 是否與人體之數據具關聯性尚無明確的論斷。體外DDI試驗主要於分離之細胞或酵素系統進行代謝抑制實驗 (metabolic inhibition studies)，酵素抑制劑參數如IC50或Ki值常用來定量預測體內的AUC ratio (AUCR) ，以判斷發生DDI的可能性。以P-gp為例，FDA建議以假定腸道濃度 (hypothetical intestinal concentration) 與IC50的比值來預測體內AUCR，然而在過去許多研究中，研究人員對於切點 (cutoff point) 的適合度及各項預測率的準確度提出了許多的想法與質疑，至今仍未能得到最適合的結論。
因此，本研究針對兩個研究問題進行研究，首先探討以藥物強度因子 (drug intensity factor, DIF = Dose/IC50) 是否能提升P-gp型交互作用體外預測體內之預測率，由PubMed資料庫收集交互作用配對藥品之AUC資料，並以receiver operating characteristic (ROC) curve進一步探討DIF切點。此外，也以數據探勘的方式蒐集動物與人體在藥品併用與未併用CYP抑制劑下的AUC數據，探討動物與人體DDI數據間之相關性。
研究結果共取得126組AUCR數據，經進一步處理後共91組AUCR進行分析。共29組配對，由28個受質及36個抑制劑組成。FDA指標與DIF切點分別為45及5，DIF顯示能比FDA建議指標有更高的整體預測率(82%與74%)與專一度，與過去研究之切點相比，也顯示DIF能夠好的整體預測率及專一度，並且有較強的概似比(4.47)，顯示DIF能夠有更精準預測P-gp抑制劑的情形。CYP型交互作用之動物與人類體內數據共取得54個AUCR，22組配對，6種動物及5種CYP亞型。結果顯示兩者的相關性低，而小鼠有低估的情形，猴子有高估的情形。
提高前臨床試驗與臨床試驗期的DDI預測，能夠降低臨床試驗的風險及藥品開發時的成本消耗，DIF能夠提供好的整體預測率及專一度，能夠協助P-gp型交互作用抑制劑的預測，而動物與人體之研究由於數據的缺乏，無法有確定的結論，更多動物與人類的差異性可能還需了解。

Severe drug-drug interactions (DDI) endanger patient’s health and may cause market withdrawal of drugs. Therefore, in the preclinical stage, DDI prediction becomes crucial, because it helps the decision-making process to ensure the reduction of the risks and costs that may possibly occur in clinical phases. In vitro tests and in vivo animal studies are commonly used to explore the potential of DDI in the preclinical stage. Animal studies provide preliminary pharmacokinetic information of new drugs before entering human trials. However, given the differences in metabolic systems between species, it is not clear whether DDI studies in animals are relevant to human. Currently, the DDI potential of a new molecular entity is generally determined on the basis of in-vitro enzyme-inhibition studies. Parameters derived from in vitro studies, such as IC50 or Ki values, were used to predict possible DDI occurrence and exclude severe DDI drug pairs from entering human clinical trial. For P-gp, the US FDA recommends the use of a hypothetical intestinal concentration and IC50 to predict the AUC ratio in the context of drug coadministration; however, the method has not been validated and there is no consensus about the appropriate cutoff value.
In this research of Chapter 2, a drug intensity factor (DIF) is described for predicting the potential of P-gp-mediated DDI. The DIF parameter, defined as inhibitor dose/IC50, can be realized as a quantitative measure of the potential of a p-gp inhibitor at a given dose to cause DDI. We tried to evaluate if DIF can predict potential P-gp-mediated DDI. In Chapter 3, the aim was to examine the research question as to how DDI information obtained from animal studies correlates with the acutal DDI in human. Based on the available database for CYP-mediated DDI, the correlation of animal and human AUC data were analyzed.
Comprehensive literature search for AUC data in the context of DDI studies has been conducted using PubMed to investigate the two researches. The AUC ratio (AUCR) was colleted or calculated as the ratio of AUC with substrate/inhibitor coadministration to AUC without coadministration. Receiver operating characteristic (ROC) analysis has been performed for the determination of the DIF cutoff point.
The result of chaper 2 shows that DIF is a better predictor of clinical p-gp-mediated DDI with higher overall prediction performance (82% and 74%), comparing with the FDA-suggested index. However, in chapter 3, only limited data can be obtained of animal data, which account for only 22 drug pairs. We also observe that animal DDI information seems may result in either over- or under- estimation of human DDI, depending on the species studied (e.g. monkey vs. rat).
The imporvement of prediction of DDI is crutial to understand the safety of medication. DIF can provide better overall accurancy that might asist the prediction of P-gp-mediated inhibitor. Although a quantitiative relationship between animal and human data cannot be estabished, it is pinpointted that the further evaluation of similarities of human and animal are warranted.

第一章 緒論
第一節 新藥開發過程與藥物交互作用預測概念
壹、 藥品交互作用在臨床及藥品市場之影響
貳、 前臨床試驗的目的
參、 美國藥品食品檢驗局（FDA）對藥品交互作用之論點
肆、 藥廠/臨床對藥品交互作用之論點
第二節 藥品交互作用的類型
壹、 代謝型藥品交互作用
貳、 轉運蛋白型藥品交互作用
參、 多種型藥品交互作用
第三節 藥品交互作用之預測方法
壹、 體外預測體內
貳、 藥物交互作用預測的標準
第四節 體外試驗
壹、 體外試驗目的
貳、 體外實驗方法
參、 過去P-gp體外預測體內實驗回顧與爭議
肆、 小結
第五節 動物實驗
壹、 動物實驗的目的
貳、 物種之間的差距
第六節 討論
第二章 藥物交互作用預測：以體外預測體內
第一節 藥物強度因子(drug intensive factor, DIF)概念
第二節 研究目的
第三節 研究方法
第四節 研究結果
一、 前導研究結果
二、 數據探勘研究結果
三、 與FDA標準比較
四、 同樣AUCR標準，各ROC間的比較
五、 不同AUCR標準下，各ROC預測結果
六、 各抑制劑間差異
七、 各受質間差異
八、 依藥理分類分析
第五節 討論
第三章 藥品交互作用預測：以體內預測體內
第一節 前言與研究目的
第二節 研究方法
第三節 研究結果
一、 結果
二、 物種之間的差異
第四節 討論
第四章 結論
第一節 體外預測體內-P-gp型交互作用
第二節 體內預測體內：CYP型交互作用
第三節 總結與建議
第五章 參考資料

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