Department of Mathematics, North Carolina State University, Box 8205, Raleigh, NC, 27695, USA.
Bull Math Biol. 2024 Jan 3;86(2):12. doi: 10.1007/s11538-023-01234-4.
Physiologically-based pharmacokinetic (PBPK) modeling is important for studying drug delivery in the central nervous system, including determining antibody exposure, predicting chemical concentrations at target locations, and ensuring accurate dosages. The complexity of PBPK models, involving many variables and parameters, requires a consideration of parameter identifiability; i.e., which parameters can be uniquely determined from data for a specified set of concentrations. We introduce the use of a local sensitivity-based parameter subset selection algorithm in the context of a minimal PBPK (mPBPK) model of the brain for antibody therapeutics. This algorithm is augmented by verification techniques, based on response distributions and energy statistics, to provide a systematic and robust technique to determine identifiable parameter subsets in a PBPK model across a specified time domain of interest. The accuracy of our approach is evaluated for three key concentrations in the mPBPK model for plasma, brain interstitial fluid and brain cerebrospinal fluid. The determination of accurate identifiable parameter subsets is important for model reduction and uncertainty quantification for PBPK models.
基于生理学的药代动力学（PBPK）模型对于研究药物在中枢神经系统中的传递非常重要，包括确定抗体暴露量、预测目标位置的化学浓度以及确保准确的剂量。PBPK 模型的复杂性涉及许多变量和参数，需要考虑参数可识别性，即哪些参数可以从特定浓度集的数据中唯一确定。我们在针对抗体治疗的脑最小药代动力学（mPBPK）模型的背景下引入了基于局部敏感性的参数子集选择算法的使用。该算法通过基于响应分布和能量统计的验证技术进行增强，为在指定的感兴趣时间域内确定 PBPK 模型中可识别的参数子集提供了一种系统且稳健的技术。我们的方法的准确性针对血浆、脑间质液和脑脑脊液中 mPBPK 模型的三个关键浓度进行了评估。准确确定可识别的参数子集对于模型简化和 PBPK 模型的不确定性量化非常重要。
