Bosley J R, Björnson Elias, Zhang Cheng, Turkez Hasan, Nielsen Jens, Uhlen Mathias, Borén Jan, Mardinoglu Adil
Clermont Bosley LLC, Philadelphia, PA, United States.
Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden.
Front Pharmacol. 2021 May 13;12:643179. doi: 10.3389/fphar.2021.643179. eCollection 2021.
To determine how to set optimal oral L-serine (serine) dose levels for a clinical trial, existing literature was surveyed. Data sufficient to set the dose was inadequate, and so an ( = 10) phase I-A calibration trial was performed, administering serine with and without other oral agents. We analyzed the trial and the literature data using pharmacokinetic (PK) modeling and statistical analysis. The therapeutic goal is to modulate specific serine-related metabolic pathways in the liver using the lowest possible dose which gives the desired effect since the upper bound was expected to be limited by toxicity. A standard PK approach, in which a common model structure was selected using a fit to data, yielded a model with a single central compartment corresponding to plasma, clearance from that compartment, and an endogenous source of serine. To improve conditioning, a parametric structure was changed to estimate ratios (bioavailability over volume, for example). Model fit quality was improved and the uncertainty in estimated parameters was reduced. Because of the particular interest in the fate of serine, the model was used to estimate whether serine is consumed in the gut, absorbed by the liver, or entered the blood in either a free state, or in a protein- or tissue-bound state that is not measured by our assay. The PK model structure was set up to represent relevant physiology, and this quantitative systems biology approach allowed a broader set of physiological data to be used to narrow parameter and prediction confidence intervals, and to better understand the biological meaning of the data. The model results allowed us to determine the optimal human dose for future trials, including a trial design component including IV and tracer studies. A key contribution is that we were able to use human physiological data from the literature to inform the PK model and to set reasonable bounds on parameters, and to improve model conditioning. Leveraging literature data produced a more predictive, useful model.
为确定如何为临床试验设定最佳口服L-丝氨酸（丝氨酸）剂量水平，我们查阅了现有文献。但发现足以设定剂量的数据并不充分，因此开展了一项（n = 10）的I-A期校准试验，在有或没有其他口服药物的情况下给予丝氨酸。我们使用药代动力学（PK）建模和统计分析对试验和文献数据进行了分析。治疗目标是使用尽可能低的剂量来调节肝脏中特定的丝氨酸相关代谢途径，以达到预期效果，因为预计上限会受到毒性限制。一种标准的PK方法，即通过对数据拟合来选择通用模型结构，得到了一个具有单一中央室（对应于血浆）、该室清除率以及丝氨酸内源性来源的模型。为改善拟合效果，改变了参数结构以估计比率（例如生物利用度与体积之比）。模型拟合质量得到提高，估计参数的不确定性降低。由于对丝氨酸的去向特别感兴趣，该模型被用于估计丝氨酸是在肠道中被消耗、被肝脏吸收，还是以游离状态、或以我们的检测方法未测量的蛋白质或组织结合状态进入血液。PK模型结构的建立旨在代表相关生理学，这种定量系统生物学方法允许使用更广泛的生理数据来缩小参数和预测置信区间，并更好地理解数据的生物学意义。模型结果使我们能够确定未来试验的最佳人体剂量，包括一项包括静脉注射和示踪研究的试验设计部分。一个关键贡献是，我们能够利用文献中的人体生理数据为PK模型提供信息，为参数设定合理界限，并改善模型拟合。利用文献数据产生了一个更具预测性、更有用的模型。
