Quantifying Drug-Induced Bone Marrow Toxicity Using a Novel Haematopoiesis Systems Pharmacology Model

Abstract

Haematological toxicity associated with cancer therapeutics is monitored by changes in blood cell count, and their primary effect is on proliferative progenitors in the bone marrow. Using observations in rat bone marrow and blood, we characterize a mathematical model that comprises cell proliferation and differentiation of the full haematopoietic phylogeny, with interacting feedback loops between lineages in homeostasis as well as following carboplatin exposure. We accurately predicted the temporal dynamics of several mature cell types related to carboplatin-induced bone marrow toxicity and identified novel insights into haematopoiesis. Our model confirms a significant degree of plasticity within bone marrow cells, with the number and type of both early progenitors and circulating cells affecting cell balance, via feedback mechanisms, through fate decisions of the multipotent progenitors. We also demonstrated cross-species translation of our predictions to patients, applying the same core model structure and considering differences in drug-dependent and physiology-dependent parameters.

Figure 1

Carboplatin toxicity in the bone marrow is mechanistically linked with effects in the blood. The diagram provides a graphical representation of our quantitative systems pharmacology model defined in Eqs. (1), (2), (3), (4), (5), (6), (7), (8), (9). The formation of every mature blood cell is pictured as a path through the haematopoiesis differentiation tree, with haematopoietic stem cells at its top. Haematopoietic stem cells mature into MPPs, which then commit to specific lineages giving rise to fully differentiated blood cells. Red triangles highlight populations affected by carboplatin. Rat and human symbols mark model variables for which observations in rat and human were available. Cell surface markers used to quantify bone marrow progenitors in rats are also reported in the diagram. Proliferative cells are affected by carboplatin. Proliferation and maturation in the bone marrow are regulated by the interplay of feedback mechanisms from different circulating cells (dotted arrows). CD90+/Lin‐, CD90 positive and lineage negative cells; CMP, common‐myeloid progenitor; MEP, megakaryocyte–erythrocyte progenitor; Mono, monocytes; Neut, neutrophils; MPP, multipotent progenitor; Plt, platelets; RBCs, red blood cells; Ret, reticulocytes; T1, T2, T3, transit‐compartments.

Figure 2

Graphical representation of our theoretical approach developed to investigate drug‐induced haematological toxicities. The first step consisted of defining a novel global and quantitative system pharmacology model able to integrate data from different sources (i.e., rat carboplatin PK, bone marrow effects, and peripheral blood counts) and describe carboplatin‐induced myelosuppression profiles in rats. Then, we considered the cross‐species differences between rat and human to update model parameter values. Last, we generated clinical predictions. In addition, when clinical data are available, back‐translation can also be performed (dotted gray arrow). BM, bone marrow; PK, pharmacokinetic.

Figure 3

Overall myelosuppression profile induced by repeated cycles of carboplatin 40 mg/kg. Rats were dosed with carboplatin 40 mg/kg on day 1 of repeated 14‐day cycles (Table S1 ). Plots show the time course of carboplatin effects across the main bone marrow progenitor populations (multipotent progenitors, common‐myeloid progenitors, megakaryocyte–erythrocyte progenitors) and mature blood cells (neutrophils, monocytes, platelets, reticulocytes, red blood cells). Multipotent progenitor, common‐myeloid progenitor, megakaryocyte–erythrocyte progenitor, neutrophil, monocyte, and red blood cell total counts go down over multiple cycles, whereas reticulocyte and platelet recovery to baseline (after rebound) is still possible. Points show the observations, red lines show average model predictions, and blue shadow areas show percentiles in the simulated data. Visual predictive checks were generated from 1,000 simulations using the log‐additive residual errors reported in Table S4 . d, days.

Figure 4

Clinical predictions of carboplatin‐induced neutropenia and thrombocytopenia were generated translating our quantitative systems pharmacology model of carboplatin‐induced myelotoxicity from rat to human. Clinical predictions (solid red lines) generated using the same slope values as those estimated from the rat (a) and predictions generated using slope values adjusted for species‐specific drug sensitivity, Eq. (10), Table  2 (b), were compared with clinical data of carboplatin‐induced neutropenia and thrombocytopenia described in ref.5 which are represented by solid (means) and dotted (percentiles) blue lines. Blue shadow areas show percentiles in our simulations (1,000 individuals), dotted horizontal black lines are thresholds for grades 3 and 4 (neutropenia and thrombocytopenia). Two cycles of 21 days of carboplatin were simulated. Doses were calculated with the Calvert formula,51 targeting an AUC _(0–24) of 5 (mg/mL) minutes (Supplementary Text S11 ). The predicted percentages of patients with neutropenia (top) and thrombocytopenia (bottom) grades with those in ref.5 are compared (c). d, days. AUC(0‐24), area under the curve in the 24 h time interval; IC50, half maximal inhibitory concentration.