An in vitro quantitative systems pharmacology approach for deconvolving mechanisms of drug-induced, multilineage cytopenias.

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 sample 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. Author summary: Reduced bone marrow activity and levels of mature blood cells is an undesirable side effect of many anti-cancer therapies. Selecting promising lead compounds for further development requires understanding of potential myelosuppressive effects. However, existing preclinical experiments and modeling formulations fail to consider drug effects on multiple blood cell types or the mechanistic differences between how drugs induced myelosuppression. Here we developed a quantitative systems pharmacology (QSP) model that estimates a drug candidate's effect on multiple precursor and mature blood cell lineages and further distinguishes how the drug affects these populations—through cell-killing or anti-proliferation mechanisms. This modeling formalism is valuable for vetting compounds for therapeutic development and for further translational modeling to anticipate the clinical effects of compounds. [ABSTRACT FROM AUTHOR]