Dai Guowei, Pfister Marc, Blackwood-Chirchir Anne, Roy Amit
Strategic Modeling & Simulation Group, Discovery Medicine & Clinical Pharmacology, Route 206 & Province Line Rd, Bristol-Myers Squibb R&D, Princeton, NJ 08543, USA.
J Clin Pharmacol. 2008 Nov;48(11):1254-69. doi: 10.1177/0091270008320604. Epub 2008 Sep 8.
Characterizing the key determinants of variability in the exposure of orally administered drugs may be important in understanding the implications of exposure variability on clinical responses. In particular, partitioning overall variability into interoccasion variability (IOV) and interindividual variability (IIV) allows a better assessment of the clinical importance of exposure variability. The IOV characterizes the dose-to-dose variability in exposure within a subject and is likely to be less clinically relevant than IIV for chronically administered drugs as the effect of IOV averages out over repeated dosing. The main aims of this model-based analysis were (1) to characterize the IOV and IIV of dasatinib, a novel, orally administered, multitargeted kinase inhibitor of BCR-ABL and SRC family kinases that is indicated for the treatment of chronic myeloid leukemia and Philadelphia-positive acute lymphoblastic leukemia and (2) to demonstrate using simulated data that it is possible to estimate IIV and IOV in relative bioavailability (F(R)) of an orally administered drug, given an adequate sampling scheme. Variability in dasatinib exposure was estimated to be mainly due to IOV in F(R) (44% coefficient of variation [CV]) and, to a lesser extent, due to IIV in F(R) and IIV in clearance (32% and 25% CV, respectively). The IIV is expected to be more clinically relevant than IOV for chronically administered oral drugs such as dasatinib, as the overall variability in cumulative exposure will be mainly due to IIV. The analysis of simulated data demonstrated that models ignoring either IIV or IOV in F(R) resulted in upwardly biased estimates of interindividual or residual variability. Thus, it may be important to account for both IIV and IOV in F(R), particularly for orally administered agents that exhibit absorption-related variability in exposure.
确定口服药物暴露变异性的关键决定因素,对于理解暴露变异性对临床反应的影响可能至关重要。特别是,将总体变异性分为给药间隔变异性(IOV)和个体间变异性(IIV),有助于更好地评估暴露变异性的临床重要性。IOV表征个体内暴露的剂量间变异性,对于长期给药的药物,与IIV相比,其临床相关性可能较低,因为IOV的影响在重复给药过程中会平均化。基于模型的分析的主要目的是:(1)确定达沙替尼(一种新型口服多靶点BCR-ABL和SRC家族激酶抑制剂,用于治疗慢性髓性白血病和费城染色体阳性急性淋巴细胞白血病)的IOV和IIV;(2)通过模拟数据证明,在有足够采样方案的情况下,有可能估计口服药物相对生物利用度(F(R))中的IIV和IOV。达沙替尼暴露的变异性估计主要归因于F(R)中的IOV(变异系数[CV]为44%),在较小程度上归因于F(R)中的IIV和清除率中的IIV(分别为32%和25% CV)。对于达沙替尼这类长期口服药物,预计IIV比IOV更具临床相关性,因为累积暴露的总体变异性将主要归因于IIV。对模拟数据的分析表明,忽略F(R)中IIV或IOV的模型会导致个体间或残差变异性的估计值偏高。因此,在F(R)中同时考虑IIV和IOV可能很重要,特别是对于暴露表现出与吸收相关变异性的口服药物。