School of Pharmacy, University of Otago, Dunedin, New Zealand.
Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
Clin Pharmacokinet. 2020 Sep;59(9):1161-1170. doi: 10.1007/s40262-020-00883-1.
Fat-free mass (FFM)-based dose scaling is increasingly being adopted in clinical pharmacology. Given the complexities with the measurement of FFM in clinical practice, choosing an appropriate equation for FFM is critical for accurate dose scaling. Janmahasatian's FFM model (FFM) has largely remained the preferred choice because of its mechanistic basis and good predictive properties. This model was, however, developed from a largely European cohort and has been shown to give biased predictions of FFM in Indian people.
The objective of this work was to derive an extended version of the FFM model (FFM) that accounts for the variation in body composition due to ethnicity, and to demonstrate its application by developing an extended FFM model in an Indian population (FFM).
The fundamental assumption of FFM model development was a linear relationship between bioimpedance and body mass index. In this extension to Janmahasatian's work, this assumption was extended to allow for potential non-linear relationships. While the original Z model parameters were kept fixed, a set of body composition-related parameters [Formula: see text] were incorporated, where [Formula: see text] and [Formula: see text] were the ethnicity factors to the intercept and the linear coefficient, respectively, and [Formula: see text] a non-linear exponent. The model was then applied to data arising from a south Indian population and the [Formula: see text] parameters were estimated by standard non-linear regression. The data were generated from a reference model for FFM for the Indian population, which was known to provide unbiased estimates for this population.
The parameter estimates (%RSE) of the final FFM model were [Formula: see text] (fixed), [Formula: see text] (3.2%) for male patients, 0.70 (3.3%) for female patients, and [Formula: see text] (12.4%). The final model predictions were in good agreement with the reference model predictions.
An FFM model has been achieved by extending the original FFM model assumptions to account for inter-ethnic differences in body composition. The extended model can be applied to any ethnic population by estimating a set of body composition-related parameters [Formula: see text]. This can be performed using bioimpedance data without the need for formal FFM measurements.
在临床药理学中,越来越多地采用无脂肪质量(FFM)为基础的剂量调整。鉴于在临床实践中测量 FFM 的复杂性,选择合适的 FFM 方程对于准确的剂量调整至关重要。Janmahasatian 的 FFM 模型(FFM)因其机械基础和良好的预测特性而在很大程度上仍然是首选。然而,该模型是从一个主要是欧洲的队列中开发出来的,并且已经表明它对印度人的 FFM 预测存在偏差。
这项工作的目的是推导出一种扩展的 FFM 模型(FFM),该模型考虑了由于种族而导致的身体成分变化,并通过在印度人群中开发扩展的 FFM 模型(FFM)来证明其应用。
FFM 模型开发的基本假设是生物阻抗与体重指数之间存在线性关系。在对 Janmahasatian 工作的扩展中,该假设扩展为允许潜在的非线性关系。虽然保留了原始 Z 模型参数不变,但引入了一组与身体成分相关的参数[公式:见正文],其中[公式:见正文]和[公式:见正文]分别是截距和线性系数的种族因素,[公式:见正文]是非线性指数。然后将该模型应用于来自印度南部人群的数据,并通过标准非线性回归估计[公式:见正文]参数。数据是从印度人群的 FFM 参考模型中生成的,该模型已知对该人群提供无偏估计。
最终 FFM 模型的参数估计值(%RSE)为[公式:见正文](固定),男性患者为[公式:见正文](3.2%),女性患者为[公式:见正文](3.3%),[公式:见正文](12.4%)。最终模型的预测与参考模型的预测吻合良好。
通过将原始 FFM 模型假设扩展到考虑身体成分的种族差异,实现了 FFM 模型。可以通过估计一组与身体成分相关的参数[公式:见正文]将扩展模型应用于任何种族人群。这可以使用生物阻抗数据完成,而无需进行正式的 FFM 测量。