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利用族群藥動學模式以單點血漿濃度評估酵素活性 PowerPoint PPT Presentation

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利用族群藥動學模式以單點血漿濃度評估酵素活性. 個人化治療目的是使藥物的給藥模式能因個人而調整,使得藥物的治療能針對每個個體發揮最佳療效及最低副作用的臨床效果。現今已知個人對於藥物治療效果的差異在於基因變異所導致的藥物代謝酵素、藥物標的或接受器,及藥物輸送蛋白等層次的分子修飾所造成的。因此為建構基因變異 (Genotyping) 與所導致的酵素活性的不同表現型 (Phenotyping) 之間的關聯性,就成為個人化給藥成功與否的一個重要關鍵。

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  • 個人化治療目的是使藥物的給藥模式能因個人而調整,使得藥物的治療能針對每個個體發揮最佳療效及最低副作用的臨床效果。現今已知個人對於藥物治療效果的差異在於基因變異所導致的藥物代謝酵素、藥物標的或接受器,及藥物輸送蛋白等層次的分子修飾所造成的。因此為建構基因變異 (Genotyping) 與所導致的酵素活性的不同表現型 (Phenotyping) 之間的關聯性,就成為個人化給藥成功與否的一個重要關鍵。

  • 在本實驗中我們致力於建構一適當之族群藥物動力學模式以關聯基因型與表現型而達到個人化藥物治療之目標。首先選用CYP2D6 為模式酵素並以dextromethorphan為探針藥物,故首先開發一靈敏度高、再現性佳的HPLC分析條件來偵測血漿中微量的dextromethorphan及其代謝物。而血漿中最低定量濃度為1nM。並且應用此分析條件進行人體試驗以定量分析12位受試者之血中濃度,進而了解其CYP2D6之酵素活性。另一方面,將dextromethorphan與葡萄柚汁併服,也觀察到葡萄柚汁對3A4酵素代謝之抑制作用。同時12位受試者的CYP2D6基因型表現則是利用聚合酶鏈鎖反應(PCR)及限制酶片段長度多型性(RFLP)之技術得知其遺傳突變因子的點突變而確立。之後將CYP2D6基因型分成四種等級以數字表示及生理因子(年齡、身高)當作共變數代入藥動學模式中。藥動學模式之設計是口服一階次吸收及排除的一室模式。利用SAS非線性迴歸模式模擬出一系列的數據資料庫。再以WinNonMix軟體將dextromethorphan臨床人體試驗所得之數據及模擬數據進行迴歸,並且建立一適當之族群藥物動力學模式。由臨床人體試驗之數據尋找出最適當之模式為Model 4且Between-Subject Covariance之設定為Block矩陣結構,數據經迴歸後可達到收斂並且可得到較小的OFV及-2LL值,分別為197.31及419.68。另一方面,SAS模擬數據組以GENE人群分佈、Model 1及Block/Sigma Squared之設定所得到的模式為最適當,其OFV及-2LL值分別為49480.93及21084.35。之後利用上述dextromethorphan之臨床數據所找出最適當的模式為Model 4、Between-Subject Covariance之設定為Block matrix進行單點預測。然而所得到的迴歸結果較差且較無法達到收斂,推測可能因為受試者人數過少而導致模式之建立難以進行。所以另外使用dextromethorphan與葡萄柚汁併服之血中濃度帶入相同模式中進行迴歸,由結果發現其藥動學參數較可被迴歸出來,而且數據也較易被收斂。

  • 由整體看來,雖然以單一抽血點來預測血中濃度變化之結果不盡理想,卻可觀察出此一族群藥物動力學模式之研究方向是可經由改善其他因素而逐漸建立。例如可增加受試者人數、發現更多影響CYP2D6代謝之突變遺傳基因以及模式程式之修正甚至於調整Residual Error Variance(Sigma Squared)及Between-Subject Covariance(Block/ Diagonal)之設定。如此一來可使族群藥物動力學模式之建立更為完善,進而根據個體所具備之基因型應用於臨床上以達到個人化藥物治療之目標。


Single-Point Plasma Concentration Profiling Enzymatic Activity Using Population Pharmacokinetic Modeling

  • The purpose of personalized drug therapy is to identify not only the best drug to be administered to a particular patient, but also the most effective and safest dosage from the outset of therapy. It is known that genetic variability in drug response occurs as a result of molecular alterations at the level of drug metabolizing enzymes, drug targets/receptors, and drug transport proteins. It would play a crucial role to correlate the relationship between the genotyping and the phenotyping in successfully accomplishing personalized drug therapy.

  • In this study, we are intent to construct population pharmacokinetic (PK) model to correlate the relationship between genotyping and phenotyping for the purpose of personalizing drug therapy. At first, dextromethorphan as a probing drug in plasma was analyzed by high-performance liquid chromatography. A sensitive and reproducible HPLC assay was developed for the simultaneous determination of dextromethorphan and its metabolites in plasma. Limits of quantitation in plasma were 1nM for all compounds. In the clinical trial, plasma concentrations of 12 volunteers were quantified with the HPLC method and to evaluated CYP2D6 activity. On the other hand, the inhibition of CYP3A4 by co-administration of grapefruit juice was explored. Genotyping for CYP2D6 was conducted in genomic DNA by polymerase chain reaction (PCR) and Restriction fragment length polymorphism (RFLP) for the allelic frequency distribution of single nucleotide polymorphisms (SNPs) at the same time. It was incorporated the number of the grades of CYP2D6 genotype and physiologic factors (age, height) as covariances into modeling study. The pharmacokinetic model was a 1-compartment model with first order absorption and elimination. The simulation database was generated by SAS nonlinear regression. Using our experimental data and simulation database to regression based on population model with covariances by WinNonMix, respectively.

  • The optimal model of experimental data was Model 4 and a full matrix structure (Block) for Between-Subject Covariance. The regressed data was achieved convergence and the minimum value of objective function (OFV) was 197.31, -2*ML Log Likelihood (-2LL) was 419.68. On the other hand, the proper model of SAS-simulation database considered GENE population distribution was settled on Model 1 with Block/ Sigma Squared error structure. And the following OFV was 197.31; -2LL was 419.68. Based on the above Model 4 and a full block matrix for Between- Subject Covariance, the full plasma concentration profile would be predicted by using single-point plasma concentration. However the output information was strained and sometimes could not achieved convergence. In addition, the plasma concentration of co-administration with grapefruit juice was regressed by the same model condition. The results appeared that the PK parameters were regressed easily and more achieved convergence.

  • Taken together, the population PK model could be improved by increasing subject number and CYP2D6 alleles influenced metabolism, altering the model equations, and even modifying the error structure of model. Based on individual genotype, the fittest population PK model will be constructed for personalized drug therapy.

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