Abstract
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.
Highlights
Drug-induced myelosuppression is one of the most severe adverse events (AEs) associated with anti-cancer therapies[1]
As we endeavored to understand the mechanistic effects of drugs on hematopoietic cell populations, we constructed an in vitro quantitative systems pharmacology (QSP) model describing hematopoiesis, both in the control condition as well as in the presence of drug treatment (Fig 2) using a system of ordinary differential equations (ODEs) in MATLAB
The model represents the cell populations measured in the multi-lineage toxicity assay (MLTA), their lineage relationships, and the processes of proliferation, differentiation vs renewal, branching, and death
Summary
Drug-induced myelosuppression is one of the most severe adverse events (AEs) associated with anti-cancer therapies[1]. Understanding patient propensity for AEs is required for clinical optimization of both drug selection and dose schedules. The Friberg model describes the in vivo development of neutrophils using multiple transit compartments where drug treatment can affect the self-renewal and proliferation of immature cell types[8]. These models have supported safety-mitigating strategies in the clinic. Semi-mechanistic modeling combined with clinical data sufficiently captured G-CSF response and neutrophil loss after chemotherapy[9] and identified an optimal blood monitoring schedule during palbociclib treatment[10]
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