A hierarchical Bayesian approach for combining pharmacokinetic/pharmacodynamic modeling and Phase IIa trial design in orphan drugs: Treating adrenoleukodystrophy with Lorenzo's oil.
A hierarchical Bayesian approach for combining pharmacokinetic/pharmacodynamic modeling and Phase IIa trial design in orphan drugs: Treating adrenoleukodystrophy with Lorenzo's oil.
复制标题
一种将孤儿药物的药代动力学/药效学模型与 IIa 期试验设计相结合的分层贝叶斯方法：用洛伦佐油治疗肾上腺脑白质营养不良。
DOI：
10.1080/10543406.2016.1226326
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发表时间：
2016
影响因子：
1.1
通讯作者：
Carlin,BradleyP
中科院分区：
文献类型：
--
作者：
Basu,Cynthia;Ahmed,MariamA;Kartha,ReenaV;Brundage,RichardC;Raymond,GeraldV;Cloyd,JamesC;Carlin,BradleyP
X-连锁肾上腺脑白质营养不良（X-ALD）是一种罕见的，进行性的，典型的致命性神经退行性疾病。Lorenzo's oil（LO）是少数可用的X-ALD治疗方法之一，但几乎没有建立其临床疗效或使用适应症。在这篇文章中，我们分析了116例男性无症状的儿科患者谁是管理LO的数据。我们提供了一种分层贝叶斯统计方法，以了解LO的药代动力学（PK）和药效学（PD）的积累非常长链脂肪酸。我们实验与个人和观察水平的错误和各种选择的先验分布和处理的限制，只有一个观察每次给药的药物，而不是更常见的多次观察每次给药。我们通过PK建模将LO剂量与血浆芥酸浓度联系起来，然后通过PD建模将该浓度与生物标志物（C26，一种非常长链的脂肪酸）联系起来。接下来，我们设计了一个贝叶斯IIa期研究，以精确估计生物标志物的改善可以从各种LO剂量，同时建模的二元毒性终点。我们的贝叶斯自适应算法出现合理的鲁棒性和有效性，同时仍然保留良好的经典（频率论）的操作特性。未来的工作着眼于利用这项试验的结果来设计一项III期研究，将LO剂量与健康状况的实际改善联系起来，通过磁共振成像观察到的脑部病变的外观来衡量。
X-linked adrenoleukodystrophy (X-ALD) is a rare, progressive, and typically fatal neurodegenerative disease. Lorenzo’s oil (LO) is one of the few X-ALD treatments available, but little has been done to establish its clinical efficacy or indications for its use. In this article, we analyze data on 116 male asymptomatic pediatric patients who were administered LO. We offer a hierarchical Bayesian statistical approach to understand LO pharmacokinetics (PK) and pharmacodynamics (PD) resulting from an accumulation of very long-chain fatty acids. We experiment with individual- and observational-level errors and various choices of prior distributions and deal with the limitation of having just one observation per administration of the drug, as opposed to the more usual multiple observations per administration. We link LO dose to the plasma erucic acid concentrations by PK modeling, and then link this concentration to a biomarker (C26, a very long-chain fatty acid) by PD modeling. Next, we design a Bayesian Phase IIa study to estimate precisely what improvements in the biomarker can arise from various LO doses while simultaneously modeling a binary toxicity endpoint. Our Bayesian adaptive algorithm emerges as reasonably robust and efficient while still retaining good classical (frequentist) operating characteristics. Future work looks toward using the results of this trial to design a Phase III study linking LO dose to actual improvements in health status, as measured by the appearance of brain lesions observed via magnetic resonance imaging.