标题： 使用群体方法的体内建模挑战 显示英文标题

标题： In-host modeling challenges using population approach methods

摘要： 非线性混合效应模型被广泛用于制药行业、美国监管机构和学术界在药物计量学领域估计参数。参数估计的精确度使用相对标准误差（RSE）来评估，RSE阈值小于50%被认为是“精确估计”。 在这里，我们研究了单独使用这一指标在Monolix中校准最近发表的一种针对丁型肝炎病毒（HDV）的体内数学模型的方法，并将其与我们从接受HDV进入抑制剂布莱韦肽（BLV）单药治疗长达96周的患者中获得的纵向数据进行了对比。 尽管Monolix参数的RSE小于50%，表明模型参数估计具有精确度，但我们发现Monolix校准输出、测量的纵向数据和HDV模型之间存在显著差异。这表明尽管参数估计精确，但模型仍存在问题。 令人惊讶的是，虽然根据RSE<50%，Monolix建议参数估计是精确的，但在Monolix中的相关矩阵显示BLV疗效与HDV感染细胞的丢失率之间存在强烈的负相关，这引发了可识别性问题。 此外，拟合结果未能在大多数患者中准确再现HDV动力学（即，拟合优度差）。 最后，估计的治疗前血清HDV水平与测量观察值存在显著差异。 总之，我们证明即使RSE小于50%，还需要检查相关矩阵、置信区间以及个体和群体层面的拟合优度，以确保模型的准确性，从而接受或改进一个提议的模型。 显示英文摘要

摘要： Non-linear mixed effects models are widely used to estimate parameter estimates in the field of pharmacometrics across pharmaceutical industry, US regulatory agencies and academia. The preciseness of the parameter estimate is evaluated using relative standard error (RSE) with a threshold of <50% considered as 'precisely estimated'. Here we investigate the use of this metric alone in Monolix to calibrate a recently published in-host mathematical model for hepatitis D virus (HDV) with our own longitudinal data obtained from patients treated with HDV-entry inhibitor bulevirtide (BLV) monotherapy for up to 96 weeks. We identified substantial discordance between Monolix calibration output, measured longitudinal data and the HDV model despite the fact that Monolix parameters had a RSE <50%, suggesting that model parameters were estimated with precision. Surprisingly, while Monolix suggested precise parameter estimates based on RSE<50%, the correlation matrix in Monolix indicated a strong inverse correlation between BLV efficacy and the loss rate of HDV-infected cells raising identifiability issues. Furthermore, the fits failed to reproduce HDV kinetics accurately in the majority of patients (i.e., poor goodness of fit). Lastly, the estimated pretreatment serum HDV level varied significantly from measured observations. In summary, we demonstrate that even when RSE was <50%, other outputs such as the correlation matrix, confidence intervals and goodness of fit at both the individual and population level need to be checked for accuracy to accept or refine a proposed model.