研究目的：藥物基因體學是以基因的變異如何影響每一病患對於藥物的治療反應之研究。以單一位點核甘酸多型性(Single nucleotide polymorphisms)之偵測，可運用於評估臨床上病患對於藥物的反應或副作用與基因多型性的關聯性研究。本研究中，第一個研究目的為：在雙極性疾患(bipolar disorder)之病患中，以單一位點核甘酸多型性來探討與valproate有關之代謝異常的關聯性；第二個研究目的為：在重度憂鬱症(major depressive disorder)之病患中，運用單一位點核甘酸多型性來探討與抗憂鬱劑治療反應之關聯性，並預測病患之治療反應。
研究方法：本研究於國立成功大學醫學院附設醫院招募年滿18歲以上65歲以下符合第四版精神疾病的診斷（DSM-IV）定義為雙極性疾患、或重度憂鬱症之患者。第一部份研究，於雙極性疾患之病患中，檢測GNB3 C825T單一位點核甘酸多型性與代謝指標。第二部份研究中，於重度憂鬱症之病患中，檢測單一位點核甘酸多型性，包含與藥物療效相關基因：HTR2A T102C, HTR1A C(-1019)G, THP1 A218C, SLC6A4 rs25533, GNB3 C825T, BDNF G196A、及與藥物動力學相關基因：CYP 2C19*2 G681A, CYP 2C19*3 G636A, CYP 2D6*10 C100T, ABCB1 C3435T, ABCB1 G2677T, ABCB1 C1236T，同時檢測血中高度敏感C反應蛋白(high sensitive C-reactive protein)的濃度、與社會支持評量表(social support scales)。最後，將結合與治療反應相關的因子，以類神經網路預測重度憂鬱症病患之治療反應。
研究結果：第一部份研究中，首先確定valproate用於治療雙極性疾患會提高病患患有代謝異常的風險。且發現雙極性疾患之病患於GNB3 C825T單一位點核甘酸多型性帶有T allele者，對於valproate造成之代謝異常有較低的風險。第二部份研究中，首先發現TPH1 A218C 之單一位點核甘酸多型性的基因型頻率於健康受試者與重度憂鬱症患者中有顯著不同。與藥物療效相關基因中，經過藥物分組後BDNF G196A與TPH1 A218C之單一核苷酸多型性顯著才與治療反應有關；與藥物動力學相關基因中，不論病患是服用fluoxetine或venlafaxine ，ABCB1 G2677T與CYP 2C19*3 G636A之單一核苷酸多型性顯著與治療反應有關。此外，服藥前病患之高度敏感C反應蛋白之濃度高低、及社會支持量表之分數高低均與抗憂鬱劑之治療反應有關。此外，由建立類神經網路發現，病患於危急時獲得之社會支持、高度敏感C反應蛋白之濃度、與BDNF G196A之單一核苷酸多型性，為預測第二週抗憂鬱劑治療反應的主要敏感因子。
結論：運用藥物基因體學之方法，以情緒疾患為例，有助於探討其與藥物相關副作用或療效之關係。藉由藥物基因體學並結合相關因子，未來將可望達成個人化醫學的目標。

Objective: Pharmacogenomics is the study of how gene variations influence the responses of a patient to treatment with medications. Using single nucleotide polymorphisms (SNPs) is one of the approaches to determine the contribution of genes to adverse effects or effectiveness of medications. The first objective of the current study was to use information about genetic variants to minimize the potential adverse effects, valproate (VPA)-related metabolic abnormalities, in bipolar disorder (BD) patients. The secondary objective was to use genetic data to identify treatment response of antidepressant in patients with major depressive disorder (MDD).
Methods: All of the BD or MDD patients of the study, aged 18-65, were recruited from the National Cheng Kung University Hospital. In the first part, we measured metabolic indexes in BD patients, and investigated the possible associations between the polymorphism of GNB3 C825T and VPA-induced metabolic abnormalities. In the secondary part, we measured treatment response by using the change scores of the 21-item Hamilton Rating Scale for Depression biweekly. We also investigated the SNPs (pharmacodynamic (PD) genes: HTR2A T102C, HTR1A C(-1019)G, THP1 A218C, SLC6A4 rs25533, GNB3 C825T, and BDNF G196A; pharmacokinetic (PK) genes: CYP 2C19*2 G681A, CYP 2C19*3 G636A, CYP 2D6*10 C100T, ABCB1 C3435T, ABCB1 G2677T, and ABCB1 C1236T), high sensitive C-reactive protein (CRP), and social support scales (SSS). Further, the combination of theses factors to predict the individual antidepressant treatment responses was established by using artificial neural network (ANN).
Results: In the first part, VPA treatment for BD may increase the risk of metabolic disturbances, and BD patients who are carriers of T allele of the GNB3 C825T polymorphism have a lower risk for VPA-induced metabolic abnormalities compared to BD patients who are carriers of homozygous C allele. Moreover, VPA exposure and GNB3 C825T polymorphism have significant interactions with metabolic indexes. In the secondary part, the genotype and allele frequencies of TPH1 A218C polymorphism were different between healthy controls and MDD patients. Among PD genes, the genotype of BDNF G196A was marginally to associate with the response of venlafaxine treatment, while the polymorphisms of TPH1 A218C were associated with fluoxetine treatment. Among PK genes, ABCB1 G2677T, and CYP 2C19*3 were associated with response rates regardless of receiving fluoxetine or venlafaxine treatment. In addition to genetic factors, both of initial CRP levels and SSS were correlated with treatment response. Furthermore, a model of ANN indicated that available social support in crisis status, CRP, and genotypes of BDNF G196A were top sensitive to antidepressant treatment response in MDD patients at week 2.
Conclusion: The investigated phenotypes of medication-related adverse effects and effectiveness by using pharmacogenomic studies could be considered as a helpful assistance in selection and monitoring of treatment in mood disorders. Personalized medicine could be achieved in the future by pharmacogenomic tools integrated with environmental factors and biomarkers.

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