Mahmood Clinical Pharmacology Consultancy, Rockville, Maryland, USA.
Division of Clinical Evaluation and Pharmacology/Toxicology, Center for Biologics Evaluation and Research (CBER), Office of Tissues and Advanced Therapies (OTAT), Food and Drug Administration (FDA), Silver Spring, Maryland, USA.
J Clin Pharmacol. 2021 Jun;61 Suppl 1:S108-S116. doi: 10.1002/jcph.1846.
There is a growing interest in the use of physiologically based pharmacokinetic (PBPK) models as clinical pharmacology drug development tools. In PBPK modeling, not every organ or physiological parameter is required, leading to the development of a minimal PBPK (mPBPK) model, which is simple and efficient. The objective of this study was to streamline mPBPK modeling approaches and enable straightforward prediction of clearance of protein-based products in children. Four mPBPK models for scaling clearance from adult to children were developed and evaluated on Excel spreadsheets using (1) liver and kidneys; (2) liver, kidneys, and skin; (3) liver, kidneys, skin, and lymph; and (4) interstitial, lymph, and plasma volume. There were 35 therapeutic proteins with a total of 113 observations across different age groups (premature neonates to adolescents). For monoclonal and polyclonal antibodies, more than 90% of observations were within a 0.5- to 2-fold prediction error for all 4 methods. For nonantibodies, 79% to 100% of observations were within the 0.5- to 2-fold prediction error for the 4 different methods. Methods 1 and 4 provided the best results, >90% of the total observations were within the 0.5- to 2-fold prediction error for all 3 classes of protein-based products across a wide age range. The precision of clearance prediction was comparatively lower in children ≤2 years of age vs older children (>2 years of age) with methods 1 and 4 predicting 80% to 100% and 75% to 90% of observations within the 0.5- to 2-fold prediction error, respectively. The results of the study indicated that mPBPK models can be developed on spreadsheets, with acceptable performance for prediction of clearance.
人们越来越关注将基于生理学的药代动力学(PBPK)模型用作临床药理学药物开发工具。在 PBPK 建模中,并非需要所有器官或生理参数,因此开发了简化的 PBPK(mPBPK)模型,它简单高效。本研究的目的是简化 mPBPK 建模方法,并能够直接预测儿童体内蛋白质类产品的清除率。开发并评估了 4 种用于从成人到儿童的清除率比例的 mPBPK 模型,这些模型是在 Excel 电子表格中构建的,使用了(1)肝脏和肾脏;(2)肝脏、肾脏和皮肤;(3)肝脏、肾脏、皮肤和淋巴;以及(4)间质、淋巴和血浆体积。共涉及 35 种治疗性蛋白质,涵盖了不同年龄组(早产儿到青少年)的 113 个观察结果。对于单克隆和多克隆抗体,所有 4 种方法的超过 90%的观察结果都在 0.5-2 倍预测误差范围内。对于非抗体,4 种不同方法的 79%-100%的观察结果都在 0.5-2 倍预测误差范围内。方法 1 和 4 提供了最佳结果,在广泛的年龄范围内,对于所有 3 类蛋白质类产品,90%以上的总观察结果都在 0.5-2 倍预测误差范围内。使用方法 1 和 4,在儿童≤2 岁时,清除率预测的精度相对较低,分别有 80%-100%和 75%-90%的观察结果在 0.5-2 倍预测误差范围内。研究结果表明,mPBPK 模型可以在电子表格上进行开发,对于清除率的预测具有可接受的性能。