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Abstract：
BACKGROUND: Pharmacogenetic profiling and therapeutic drug monitoring (TDM) have both been proposed to manage inter-individual variability (IIV) in drug exposure. However, determining the most effective approach for estimating exposure for a particular drug remains a challenge. This study aimed to quantitatively assess the circumstances in which pharmacogenetic profiling may outperform TDM in estimating drug exposure, under three sources of variability (IIV, inter-occasion variability [IOV], and residual unexplained variability [RUV]).
METHODS: Pharmacokinetic models were selected from the literature corresponding to drugs for which pharmacogenetic profiling and TDM are both clinically considered approaches for dose individualization. The models were used to simulate relevant drug exposures (trough concentration or area under the curve [AUC]) under varying degrees of IIV, IOV, and RUV.
RESULTS: Six drug cases were selected from the literature. Model-based simulations demonstrated that the percentage of patients for whom pharmacogenetic exposure prediction is superior to TDM differs for each drug case: tacrolimus (11.0%), tamoxifen (12.7%), efavirenz (49.2%), vincristine (49.6%), risperidone (48.1%), and 5-fluorouracil (5-FU) (100%). Generally, in the presence of higher unexplained IIV in combination with lower RUV and IOV, exposure was best estimated by TDM, whereas, under lower unexplained IIV in combination with higher IOV or RUV, pharmacogenetic profiling was preferred.
CONCLUSIONS: For the drugs with relatively low RUV and IOV (e.g., tamoxifen and tacrolimus), TDM estimated true exposure the best. Conversely, for drugs with similar or lower unexplained IIV (e.g., efavirenz or 5-FU, respectively) combined with relatively high RUV, pharmacogenetic profiling provided the most accurate estimate for most patients. However, genotype prevalence and the relative influence of genotypes on the PK, as well as the ability of TDM to accurately estimate AUC with a limited number of samples, had an impact. The results could be used to support clinical decision making when considering other factors, such as the probability for severe side effects.
METHODS: Pharmacokinetic models were selected from the literature corresponding to drugs for which pharmacogenetic profiling and TDM are both clinically considered approaches for dose individualization. The models were used to simulate relevant drug exposures (trough concentration or area under the curve [AUC]) under varying degrees of IIV, IOV, and RUV.
RESULTS: Six drug cases were selected from the literature. Model-based simulations demonstrated that the percentage of patients for whom pharmacogenetic exposure prediction is superior to TDM differs for each drug case: tacrolimus (11.0%), tamoxifen (12.7%), efavirenz (49.2%), vincristine (49.6%), risperidone (48.1%), and 5-fluorouracil (5-FU) (100%). Generally, in the presence of higher unexplained IIV in combination with lower RUV and IOV, exposure was best estimated by TDM, whereas, under lower unexplained IIV in combination with higher IOV or RUV, pharmacogenetic profiling was preferred.
CONCLUSIONS: For the drugs with relatively low RUV and IOV (e.g., tamoxifen and tacrolimus), TDM estimated true exposure the best. Conversely, for drugs with similar or lower unexplained IIV (e.g., efavirenz or 5-FU, respectively) combined with relatively high RUV, pharmacogenetic profiling provided the most accurate estimate for most patients. However, genotype prevalence and the relative influence of genotypes on the PK, as well as the ability of TDM to accurately estimate AUC with a limited number of samples, had an impact. The results could be used to support clinical decision making when considering other factors, such as the probability for severe side effects.
摘要：
背景：药物遗传学分析和治疗药物监测（TDM）都被提出来管理药物暴露中的个体间差异（IIV）。然而,确定估计特定药物暴露的最有效方法仍然是一个挑战.本研究旨在定量评估药物基因图谱在估计药物暴露方面可能优于TDM的情况。在三个变异性来源下(IIV，间断性[IOV]，和残余无法解释的变异性[RUV])。
方法：药代动力学模型选自对应于药物的文献，药物遗传学分析和TDM都是临床上认为的剂量个体化方法。模型用于模拟不同程度IIV下的相关药物暴露（谷浓度或曲线下面积[AUC]）。IOV,和RUV。
结果：从文献中选取6例药物病例。基于模型的模拟表明，每个药物病例的药物遗传暴露预测优于TDM的患者百分比不同：他克莫司（11.0%），他莫昔芬（12.7%），efavirenz(49.2%),长春新碱(49.6%),利培酮(48.1%),和5-氟尿嘧啶(5-FU)(100%)。一般来说,在存在较高的无法解释的IIV以及较低的RUV和IOV的情况下，暴露最好通过TDM估计，然而,在较低的无法解释的IIV与较高的IOV或RUV组合下，药物遗传学分析是首选。
结论：对于RUV和IOV相对较低的药物（例如，他莫昔芬和他克莫司)，TDM估计真实暴露最好。相反，对于具有相似或更低原因不明IIV的药物(例如，efavirenz或5-FU，分别)与相对较高的RUV相结合，药物遗传学分析为大多数患者提供了最准确的评估.然而,基因型患病率和基因型对PK的相对影响，以及TDM用有限数量的样本准确估计AUC的能力，产生了影响。在考虑其他因素时，结果可用于支持临床决策，比如严重副作用的概率。
方法：药代动力学模型选自对应于药物的文献，药物遗传学分析和TDM都是临床上认为的剂量个体化方法。模型用于模拟不同程度IIV下的相关药物暴露（谷浓度或曲线下面积[AUC]）。IOV,和RUV。
结果：从文献中选取6例药物病例。基于模型的模拟表明，每个药物病例的药物遗传暴露预测优于TDM的患者百分比不同：他克莫司（11.0%），他莫昔芬（12.7%），efavirenz(49.2%),长春新碱(49.6%),利培酮(48.1%),和5-氟尿嘧啶(5-FU)(100%)。一般来说,在存在较高的无法解释的IIV以及较低的RUV和IOV的情况下，暴露最好通过TDM估计，然而,在较低的无法解释的IIV与较高的IOV或RUV组合下，药物遗传学分析是首选。
结论：对于RUV和IOV相对较低的药物（例如，他莫昔芬和他克莫司)，TDM估计真实暴露最好。相反，对于具有相似或更低原因不明IIV的药物(例如，efavirenz或5-FU，分别)与相对较高的RUV相结合，药物遗传学分析为大多数患者提供了最准确的评估.然而,基因型患病率和基因型对PK的相对影响，以及TDM用有限数量的样本准确估计AUC的能力，产生了影响。在考虑其他因素时，结果可用于支持临床决策，比如严重副作用的概率。
