Department of Clinical Pharmacology, Genentech, Inc., South San Francisco, California, United States of America.
Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America.
PLoS Comput Biol. 2020 Jul 23;16(7):e1007620. doi: 10.1371/journal.pcbi.1007620. eCollection 2020 Jul.
Myelosuppression is one of the most common and severe adverse events associated with anti-cancer therapies and can be a source of drug attrition. Current mathematical modeling methods for assessing cytopenia risk rely on indirect measurements of drug effects and primarily focus on single lineage responses to drugs. However, anti-cancer therapies have diverse mechanisms with varying degrees of effect across hematopoietic lineages. To improve predictive understanding of drug-induced myelosuppression, we developed a quantitative systems pharmacology (QSP) model of hematopoiesis in vitro for quantifying the effects of anti-cancer agents on multiple hematopoietic cell lineages. We calibrated the system parameters of the model to cell kinetics data without treatment and then validated the model by showing that the inferred mechanisms of anti-proliferation and/or cell-killing are consistent with the published mechanisms for three classes of drugs with different mechanisms of action. Using a set of compounds as a reference set, we then analyzed novel compounds to predict their mechanisms and magnitude of myelosuppression. Further, these quantitative mechanisms are valuable for the development of translational in vivo models to predict clinical cytopenia effects.
骨髓抑制是与抗癌治疗相关的最常见和最严重的不良事件之一，也是药物淘汰的一个原因。目前用于评估细胞减少风险的数学建模方法依赖于药物作用的间接测量，主要侧重于对药物的单一谱系反应。然而，抗癌疗法具有不同的机制，对造血谱系的影响程度也不同。为了提高对药物诱导的骨髓抑制的预测理解，我们开发了一种体外造血的定量系统药理学 (QSP) 模型，用于量化抗癌药物对多种造血细胞谱系的影响。我们对无治疗情况下的细胞动力学数据进行了模型系统参数校准，然后通过表明推断的抗增殖和/或细胞杀伤机制与三种作用机制不同的药物类别的已发表机制一致，验证了该模型。使用一组化合物作为参考集，我们分析了新的化合物以预测它们的骨髓抑制机制和程度。此外，这些定量机制对于开发转化体内模型以预测临床细胞减少效应非常有价值。
