首页> 外文OA文献 >PREDICTION OF HUMAN SYSTEMIC, BIOLOGICALLY RELEVANT PHARMACOKINETIC (PK) PROPERTIES BASED ON QUANTITATIVE STRUCTURE PHARMACOKINETIC RELATIONSHIPS (QSPKR) AND INTERSPECIES PHARMACOKINETIC ALLOMETRIC SCALING (PK-AS)
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PREDICTION OF HUMAN SYSTEMIC, BIOLOGICALLY RELEVANT PHARMACOKINETIC (PK) PROPERTIES BASED ON QUANTITATIVE STRUCTURE PHARMACOKINETIC RELATIONSHIPS (QSPKR) AND INTERSPECIES PHARMACOKINETIC ALLOMETRIC SCALING (PK-AS)

机译:基于定量结构药动学关系(QSPKR)和种间药动学代谢谱(PK-AS)预测人类系统性,生物相关的药动学(PK)特性

摘要

This research developed validated QSPKR and PK-AS models for predicting human systemic PK properties of three, preselected, pharmacological classes of drugs, namely opioids, β-adrenergic receptor ligands (β-ARL) and β-lactam antibiotics (β-LAs) using pertinent human and animal systemic PK properties (fu,, CLtot, Vdss, fe) and their biologically relevant unbound counterparts from the published literature, followed by an assessment of the effect of different molecular descriptors on these PK properties and on the PK-AS slopes for CLtot and Vdss from two species (rat and dog). Lipophilicity (log (D)7.4) and molecular weight (MW) were found to be the most statistically significant and biologically plausible, molecular properties affecting the biologically relevant, systemic PK properties: For compounds with log (D)7.4 u3e -2.0 and MW u3c 350 D (e.g., most opioids and β-ARL), increased log (D)7.4 resulted in decreased fu and increased Vdssu, CLtotu and CLnonrenu, indicating the prevalence of hydrophobic interactions with biological membrane/proteins. As result, the final QSPKR models using log (D)7.4 provided acceptable predictions for fu, Vdssu, CLtotu and CLnonrenu. CLnonrenu and CLtotu. For both the datasets, inclusion of drugs undergoing extrahepatic clearance worsened the QSPKR predictions. For compounds with log (D)7.4 u3c -2.0 and MW u3e 350 D (e.g., β-LA), increased MW (leading to more hydrogen bond donors/acceptors) resulted in a decrease in fu, likely indicating hydrogen bonding interactions with plasma proteins. In general, it was more difficult to predict PK parameters for β-LAs, as their Vdssu approached plasma volume and CLrenu and CLnonrenu were low - as a result of their high hydrophilicity and large MW, requiring specific drug transporters for distribution and excretion. The PK-AS analysis showed that animal body size accounted for most of the observed variability (r2u3e 0.80) in systemic PK variables, with single species methods, particularly those using dog, gave the best predictions. The fu correction of PK variables improved goodness of fit and predictability of human PK. There were no apparent effects of molecular properties on the predictions. CLren, CLrenu, CLnonren, and CLnonrenu were the most difficult variables to predict, possibly due to the associated interspecies differences in the metabolism, renal and hepatobiliary drug transporters.
机译:这项研究开发了经过验证的QSPKR和PK-AS模型,用于预测使用以下三种预选药理学类别的药物的人类全身PK特性,即阿片类药物,β-肾上腺素受体配体(β-ARL)和β-内酰胺类抗生素(β-LAs)相关的人和动物系统性PK特性(fu,CLtot,Vdss,fe)及其与生物学相关的未结合文献,随后评估了不同分子描述符对这些PK特性和PK-AS斜率的影响用于来自两个物种(大鼠和狗)的CLtot和Vdss。发现亲脂性(log(D)7.4)和分子量(MW)在统计学上最显着且生物学上合理,分子特性影响与生物学相关的系统PK特性:对于具有log(D)7.4 u3e -2.0和MW 350 D(例如,大多数阿片类药物和β-ARL),log(D)7.4的增加导致fu的减少和Vdssu,CLtotu和CLnonrenu的增加,表明与生物膜/蛋白质的疏水性相互作用普遍存在。结果,使用log(D)7.4的最终QSPKR模型为fu,Vdssu,CLtotu和CLnonrenu提供了可接受的预测。 CLnonrenu和CLtotu。对于这两个数据集,包含经历肝外清除的药物都会加重QSPKR的预测。对于具有log(D)7.4 u3c -2.0和MW u3e 350 D的化合物(例如β-LA),增加的MW(导致更多的氢键供体/受体)导致fu降低,可能表明氢键相互作用与血浆蛋白。通常,由于β-LAs的Vdssu接近血浆体积,CLrenu和CLnonrenu较低,因此预测β-LAs的PK参数更加困难-由于其亲水性高和分子量大,需要特定的药物转运蛋白进行分布和排泄。 PK-AS分析表明,动物体型是系统性PK变量中观察到的大部分变异(r2 u3e 0.80),采用单一物种方法(尤其是使用狗的方法)给出了最佳预测。 PK变量的fu校正提高了人类PK的拟合优度和可预测性。分子特性对预测没有明显影响。 CLren,CLrenu,CLnonren和CLnonrenu是最难预测的变量,可能是由于代谢,肾脏和肝胆药物转运蛋白的种间差异所致。

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    Badri Prajakta;

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  • 年度 2010
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