Characterization of anti-drug antibody dynamics using a bivariate mixed hidden-markov model by nonlinear-mixed effects approach.

IF 2.2 4区 医学 Q3 PHARMACOLOGY & PHARMACY Journal of Pharmacokinetics and Pharmacodynamics Pub Date : 2024-02-01 Epub Date: 2023-11-09 DOI:10.1007/s10928-023-09890-8
Ari Brekkan, Rocío Lledo-Garcia, Brigitte Lacroix, Siv Jönsson, Mats O Karlsson, Elodie L Plan
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Abstract

Biological therapies may act as immunogenic triggers leading to the formation of anti-drug antibodies (ADAs). Population pharmacokinetic (PK) models can be used to characterize the relationship between ADA and drug disposition but often rely on the ADA bioassay results, which may not be sufficiently sensitive to inform on this characterization.In this work, a methodology that could help to further elucidate the underlying ADA production and impact on the drug disposition was explored. A mixed hidden-Markov model (MHMM) was developed to characterize the underlying (hidden) formation of ADA against the biologic, using certolizumab pegol (CZP), as a test drug. CZP is a PEGylated Fc free TNF-inhibitor used in the treatment of rheumatoid arthritis and other chronic inflammatory diseases.The bivariate MHMM used information from plasma drug concentrations and ADA measurements, from six clinical studies (n = 845), that were correlated through a bivariate Gaussian function to infer about two hidden states; production and no-production of ADA influencing PK. Estimation of inter-individual variability was not supported in this case. Parameters associated with the observed part of the model were reasonably well estimated while parameters associated with the hidden part were less precise. Individual state sequences obtained using a Viterbi algorithm suggested that the model was able to determine the start of ADA production for each individual, being a more assay-independent methodology than traditional population PK. The model serves as a basis for identification of covariates influencing the ADA formation, and thus has the potential to identify aspects that minimize its impact on PK and/or efficacy.

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通过非线性混合效应方法,使用双变量混合隐马尔可夫模型表征抗药物抗体动力学。
生物疗法可以作为免疫原性触发因素,导致抗药物抗体(ADAs)的形成。群体药代动力学(PK)模型可用于表征ADA和药物处置之间的关系,但通常依赖于ADA生物测定结果,该结果可能不够敏感,无法告知该表征。在这项工作中,探索了一种有助于进一步阐明潜在ADA产生及其对药物处置的影响的方法。使用塞妥珠单抗聚乙二醇(CZP)作为试验药物,开发了一种混合隐马尔可夫模型(MHMM)来表征ADA对生物的潜在(隐藏)形成。CZP是一种聚乙二醇化的不含Fc的TNF抑制剂,用于治疗类风湿性关节炎和其他慢性炎症性疾病。双变量MHMM使用了来自六项临床研究(n = 845),它们通过二元高斯函数相关以推断大约两个隐藏状态;ADA的产生和没有产生影响PK。在这种情况下,个体间变异性的估计不受支持。与模型的观察部分相关的参数被合理地很好地估计,而与隐藏部分有关的参数则不那么精确。使用Viterbi算法获得的个体状态序列表明,该模型能够确定每个个体ADA产生的开始,是一种比传统群体PK更独立于测定的方法。该模型是识别影响ADA形成的协变量的基础,并且因此具有识别将其对PK和/或疗效的影响最小化的方面的潜力。
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来源期刊
CiteScore
4.90
自引率
4.00%
发文量
39
审稿时长
6-12 weeks
期刊介绍: Broadly speaking, the Journal of Pharmacokinetics and Pharmacodynamics covers the area of pharmacometrics. The journal is devoted to illustrating the importance of pharmacokinetics, pharmacodynamics, and pharmacometrics in drug development, clinical care, and the understanding of drug action. The journal publishes on a variety of topics related to pharmacometrics, including, but not limited to, clinical, experimental, and theoretical papers examining the kinetics of drug disposition and effects of drug action in humans, animals, in vitro, or in silico; modeling and simulation methodology, including optimal design; precision medicine; systems pharmacology; and mathematical pharmacology (including computational biology, bioengineering, and biophysics related to pharmacology, pharmacokinetics, orpharmacodynamics). Clinical papers that include population pharmacokinetic-pharmacodynamic relationships are welcome. The journal actively invites and promotes up-and-coming areas of pharmacometric research, such as real-world evidence, quality of life analyses, and artificial intelligence. The Journal of Pharmacokinetics and Pharmacodynamics is an official journal of the International Society of Pharmacometrics.
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