Modelling chemotherapy effects on granulopoiesis

Individual hematotoxicity prediction of further chemotherapy cycles by dynamic mathematical models in patients with gastrointestinal tumors

PURPOSE

Hematotoxicity is a common side-effect of cytotoxic gastrointestinal (GI) cancer therapies. An unsolved problem is to predict the individual risk therefore to decide on treatment adaptions. We applied an established biomathematical prediction model and primarily evaluated its predictive value in patients undergoing chemotherapy for GI cancers in curative intent.

METHODS

In a prospective, observational multicenter study on patients with gastro-esophageal or pancreatic cancer (n = 28) receiving myelosuppressive adjuvant or neoadjuvant chemotherapy (FLO(T) or FOLFIRINOX), individual model parameters were learned based on patients' observed laboratory values during the first chemotherapy cycle and further external data resources. Grades of hematotoxicity of subsequent cycles were predicted by model simulation and compared with observed data.

RESULTS

The most common high-grade hematological toxicity was neutropenia [19/28 patients (68%)]. For the FLO(T) regimen, individual grades of thrombocytopenia and leukopenia could be well predicted for cycles 2-4, as well as grades of neutropenia for cycle 2. Prediction accuracy for neutropenia in the third and fourth cycle differed by one toxicity grade on average. For the FOLFIRINOX-regimen, thrombocytopenia predictions showed a maximum deviation of one toxicity grade up to the end of therapy (8 cycles). Deviations of predictions were less than one degree on average up to cycle 4 for neutropenia, and up to cycle 6 for leukopenia.

CONCLUSION

The biomathematical model showed excellent short-term and decent long-term prediction performance for all relevant hematological side effects associated with FLO(T)/FOLFIRINOX. Clinical utility of this precision-medicine approach needs to be further investigated in a larger cohort.

A whole-body circulatory neutrophil model with application to predicting clinical neutropenia from in vitro studies

A circulatory model of granulopoiesis and its regulation is presented that includes neutrophil trafficking in the lungs, liver, spleen, bone marrow, lymph nodes, and blood. In each organ, neutrophils undergo transendothelial migration from vascular to interstitial space, clearance due to apoptosis, and recycling via the lymphatic flow. The model includes cell cycling of progenitor cells in the bone marrow, granulocyte colony-stimulating factor (G-CSF) kinetics and its neutrophil regulatory action, as well as neutrophil margination in the blood. From previously reported studies, 111 In-labeled neutrophil kinetic data in the blood and sampled organs were used to estimate the organ trafficking parameters in the model. The model was further developed and evaluated using absolute neutrophil count (ANC), band cell, and segmented neutrophil time course data from healthy volunteers following four dose levels of pegfilgrastim (r2  = 0.77-0.99), along with ANC time course responses following filgrastim (r2  = 0.96). The baseline values of various cell types in bone marrow and blood, as well as G-CSF concentration in the blood, predicted by the model are consistent with available literature reports. After incorporating the mechanism of action of both paclitaxel and carboplatin, as determined from an in vitro bone marrow studies, the model reliably predicted the observed ANC time course following paclitaxel plus carboplatin observed in a phase I trial of 46 patients (r2  = 0.70). The circulatory neutrophil model may provide a mechanistic framework for predicting multi-organ neutrophil homeostasis and dynamics in response to therapeutic agents that target neutrophil dynamics and trafficking in different organs.

Predicting Chemotherapy-Induced Neutropenia and Granulocyte Colony-Stimulating Factor Response Using Model-Based In Vitro to Clinical Translation

The ability to predict the incidence of chemotherapy-induced neutropenia in early drug development can inform risk monitoring and mitigation strategies, as well as decisions on advancing compounds to clinical trials. In this report, a physiological model of granulopoiesis that incorporates the drug's mechanism of action on cell cycle proliferation of bone marrow progenitor cells was extended to include the action of the cytotoxic agents paclitaxel, carboplatin, doxorubicin, and gemcitabine. In vitro bone marrow studies were conducted with each compound, and results were used to determine the model's drug effect parameters. Population simulations were performed to predict the absolute neutrophil count (ANC) and incidence of neutropenia for each compound, which were compared to results reported in the literature. In addition, using the single agent in vitro study results, the model was able to predict ANC time course in response to paclitaxel plus carboplatin in combination, which compared favorably to the results reported in a phase 1 clinical trial of 46 patients (r2 = 0.70). Model simulations were used to compare the relative risk (RR) of neutropenia in patients with high baseline ANCs for five chemotherapeutic regimens: doxorubicin (RR = 0.59), paclitaxel plus carboplatin combination (RR = 0.079), carboplatin (RR = 0.047), paclitaxel (RR = 0.031), and gemcitabine (RR = 0.013). Finally, the model was applied to quantify the reduced incidence of neutropenia with coadministration of pegfilgrastim or filgrastim, for both paclitaxel and the combination of paclitaxel plus carboplatin. The model provides a framework for predicting clinical neutropenia using in vitro bone marrow studies of anticancer agents that may guide drug development decisions.

Oral Neutrophils: Underestimated Players in Oral Cancer

The composition of the oral milieu reflects oral health. Saliva provides an environment for multiple microorganisms, and contains soluble factors and immune cells. Neutrophils, which rapidly react on the changes in the microenvironment, are a major immune cell population in saliva and thus may serve as a biomarker for oral pathologies. This review focuses on salivary neutrophils in the oral cavity, their phenotype changes in physiological and pathological conditions, as well as on factors regulating oral neutrophil amount, activation and functionality, with special emphasis on oral cancer and its risk factors.

A physiological model of granulopoiesis to predict clinical drug induced neutropenia from in vitro bone marrow studies: with application to a cell cycle inhibitor

Neutropenia is one of the most common dose-limiting toxocities associated with anticancer drug therapy. The ability to predict the probability and severity of neutropenia based on in vitro studies of drugs in early drug development will aid in advancing safe and efficacious compounds to human testing. Toward this end, a physiological model of granulopoiesis and its regulation is presented that includes the bone marrow progenitor cell cycle, allowing for a mechanistic representation of the action of relevant anticancer drugs based on in vitro studies. Model development used data from previously reported tracer kinetic studies of granulocyte disposition in healthy humans to characterize the dynamics of neutrophil margination in the presence of endogenous granulocyte-colony stimulating factor (G-CSF). In addition, previously published data from healthy volunteers following pegfilgrastim and filgrastim were used to quantify the regulatory effects of support G-CSF therapies on granulopoiesis. The model was evaluated for the cell cycle inhibitor palbociclib, using an in vitro system of human bone marrow mononuclear cells to quantify the action of palbociclib on proliferating progenitor cells, including its inhibitory effect on G1 to S phase transition. The in vitro results were incorporated into the physiological model of granulopoiesis and used to predict the time course of absolute neutrophil count (ANC) and the incidence of neutropenia observed in three previously reported clinical trials of palbociclib. The model was able to predict grade 3 and 4 neutropenia due to palbociclib treatment with 86% accuracy based on in vitro data.

Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology

Background

Individualization and patient-specific optimization of treatment is a major goal of modern health care. One way to achieve this goal is the application of high-resolution diagnostics together with the application of targeted therapies. However, the rising number of different treatment modalities also induces new challenges: Whereas randomized clinical trials focus on proving average treatment effects in specific groups of patients, direct conclusions at the individual patient level are problematic. Thus, the identification of the best patient-specific treatment options remains an open question. Systems medicine, specifically mechanistic mathematical models, can substantially support individual treatment optimization. In addition to providing a better general understanding of disease mechanisms and treatment effects, these models allow for an identification of patient-specific parameterizations and, therefore, provide individualized predictions for the effect of different treatment modalities.

Results

In the following we describe a software framework that facilitates the integration of mathematical models and computer simulations into routine clinical processes to support decision-making. This is achieved by combining standard data management and data exploration tools, with the generation and visualization of mathematical model predictions for treatment options at an individual patient level.

Conclusions

By integrating model results in an audit trail compatible manner into established clinical workflows, our framework has the potential to foster the use of systems-medical approaches in clinical practice. We illustrate the framework application by two use cases from the field of haematological oncology.

Modeling individual time courses of thrombopoiesis during multi-cyclic chemotherapy

Background

Thrombocytopenia is a major side-effect of cytotoxic cancer therapies. The aim of precision medicine is to develop therapy modifications accounting for the individual’s risk.

Methodology/Principle findings

To solve this task, we develop an individualized bio-mechanistic model of the dynamics of bone marrow thrombopoiesis, circulating platelets and therapy effects thereon. Comprehensive biological knowledge regarding cell differentiation, amplification, apoptosis rates, transition times and corresponding regulations are translated into ordinary differential equations. A model of osteoblast/osteoclast interactions was incorporated to mechanistically describe bone marrow support of quiescent cell stages. Thrombopoietin (TPO) as a major regulator is explicitly modelled including pharmacokinetics and–dynamics of TPO injections. Effects of cytotoxic drugs are modelled by transient depletions of proliferating cells.

To calibrate the model, we used population data from the literature and close-meshed individual data of N = 135 high-grade non-Hodgkin’s lymphoma patients treated with CHOP-like chemotherapies. To limit the number of free parameters, several parsimony assumptions were derived from biological data and tested via Likelihood methods. Heterogeneity of patients was explained by a few model parameters. The over-fitting issue of individual parameter estimation was successfully dealt with a virtual participation of each patient in population-based experiments.

The model qualitatively and quantitatively explains a number of biological observations such as the role of osteoblasts in explaining long-term toxic effects, megakaryocyte-mediated feedback on stem cells, bi-phasic stimulation of thrombopoiesis by TPO, dynamics of megakaryocyte ploidies and non-exponential platelet degradation. Almost all individual time series could be described with high precision. We demonstrated how the model can be used to provide predictions regarding individual therapy adaptations.

Conclusions

We propose a mechanistic thrombopoiesis model of unprecedented comprehensiveness in both, biological mechanisms considered and experimental data sets explained. Our innovative method of parameter estimation allows robust determinations of individual parameter settings facilitating the development of individual treatment adaptations during chemotherapy.

Chemotherapy is ubiquitously used to treat cancer diseases. Due to general toxicity of the drugs, chemotherapy results in a number of side effects especially with respect to blood formation. Here we study the loss of platelets during chemotherapy which is dose limiting in many situations. However, this side-effect greatly varies between patients with respect to both, severity and necessity of clinical countermeasures.We therefore developed a mathematical model to predict the time course of platelets of patients under chemotherapy and to propose possible treatment adaptations in cases of intolerable toxicity. The model is based on available biological knowledge and data of platelet formation and therapeutic effects thereon. As a major result, we could describe individual time series data of 135 patients under chemotherapy. Conversely, the model can be used to make predictions regarding alternative therapy schedules such as postponement of therapy or chemotherapy dose reductions. Our model is intended to support clinical decision making on an individual patient level.

Pharmacokinetic-pharmacodynamic modelling of neutrophil response to G-CSF in healthy subjects and patients with chemotherapy-induced neutropenia

AIM

The objective of the present study was to use pharmacokinetic-pharmacodynamic modelling to characterize the effects of chemotherapy on the granulopoietic system and to predict the absolute neutrophil counts (ANCs) for patients with chemotherapy-induced neutropenia treated with filgrastim and pegfilgrastim.

METHODS

Data were extracted from 10 phase I-III studies conducted in 110 healthy adults, and 618 adult and 52 paediatric patients on chemotherapy following administration of filgrastim or pegfilgrastim. The structural model accounted for ANC dynamics and the effects of filgrastim and pegfilgrastim, chemotherapy and corticosteroids. The impact of neutrophils on drug disposition was based on a drug receptor-binding model that assumed quasi-equilibrium and stimulation of the production and maturation of neutrophils upon treatment. The chemotherapy and corticosteroid effects were represented by kinetic-pharmacodynamic-type models, where chemotherapy stimulated elimination of neutrophil precursors at the mitotic stage, and corticosteroids stimulated neutrophil production.

RESULTS

The systemic half-lives of filgrastim (2.6 h) and pegfilgrastim (10.1 h) were as expected. The effective half-life of chemotherapy was 9.6 h, with a 2-day killing effect. The rate of receptor elimination from mitotic compartments exhibited extreme interindividual variability (% coefficient of variation >200), suggesting marked differences in sensitivity to chemotherapy effects on ANCs. The stimulatory effects of pegfilgrastim were significantly greater than those of filgrastim. Model qualification confirmed the predictive capability of this model.

CONCLUSION

This qualified model simulates the time course of ANC in the absence or presence of chemotherapy and predicts nadir, time to nadir and time of recovery from different grades of neutropenia upon treatment with filgrastim and pegfilgrastim.

Model-based optimization of G-CSF treatment during cytotoxic chemotherapy

PURPOSE

Although G-CSF is widely used to prevent or ameliorate leukopenia during cytotoxic chemotherapies, its optimal use is still under debate and depends on many therapy parameters such as dosing and timing of cytotoxic drugs and G-CSF, G-CSF pharmaceuticals used and individual risk factors of patients.

METHODS

We integrate available biological knowledge and clinical data regarding cell kinetics of bone marrow granulopoiesis, the cytotoxic effects of chemotherapy and pharmacokinetics and pharmacodynamics of G-CSF applications (filgrastim or pegfilgrastim) into a comprehensive model. The model explains leukocyte time courses of more than 70 therapy scenarios comprising 10 different cytotoxic drugs. It is applied to develop optimized G-CSF schedules for a variety of clinical scenarios.

RESULTS

Clinical trial results showed validity of model predictions regarding alternative G-CSF schedules. We propose modifications of G-CSF treatment for the chemotherapies 'BEACOPP escalated' (Hodgkin's disease), 'ETC' (breast cancer), and risk-adapted schedules for 'CHOP-14' (aggressive non-Hodgkin's lymphoma in elderly patients).

CONCLUSIONS

We conclude that we established a model of human granulopoiesis under chemotherapy which allows predictions of yet untested G-CSF schedules, comparisons between them, and optimization of filgrastim and pegfilgrastim treatment. As a general rule of thumb, G-CSF treatment should not be started too early and patients could profit from filgrastim treatment continued until the end of the chemotherapy cycle.