Principle investigator:
The notion of “principle investigator” is not applicable in our case. We performed no studies with human subjects/patients. All data are derived from published clinical studies.
Word count: 4000 words.
Table count: No tables in the main text.
Figures count: 8 figures.
Statement 1 : What is already known about this subject:
Statement 2 : What this study adds:

Summary

Aims: Thrombocytopoenia is a common major side-effect of cytotoxic cancer therapies. A clinically relevant problem is to predict an individual’s thrombotoxicity in the next planned chemotherapy cycle in order to decide on treatment adaptation. To support this task, two dynamical mathematical models of thrombopoiesis under chemotherapy were proposed, a simple semi-mechanistic model and a comprehensive mechanistic model. In this study, we compare the performance of these models.
Methods: We consider close-meshed individual time series data of 135 non-Hodgkin’s lymphoma patients treated with six cycles of CHOP/CHOEP chemotherapies. Individual parameter estimates were derived on the basis of these data considering a varying number of cycles per patient. Parsimony assumptions were applied to optimize parameter identifiability. Models are compared by determining deviations of predicted and observed degrees of thrombocytopoenia in the next cycles.
Results: The mechanistic model results in superior fits of individual time series data. Moreover, prediction accuracy of future cycle toxicities by the mechanistic model is higher even if it used data of two cycles, while the semi-mechanistic model used data of five cycles for the corresponding calibrations.
Conclusions: We successfully established a quantitative and clinically relevant method for comparing prediction performance of biomathematical models of thrombopoiesis under chemotherapy. We showed that the more comprehensive mechanistic model outperforms the semi-mechanistic model. We aim at implementing the mechanistic model into clinical practice to assess its utility in real life clinical decision making.

Introduction

Thrombocytopoenia is a major side effect of many anti-cancer cytotoxic drugs and is dose-limiting in some occasions . However, severity of this condition is highly heterogeneous between patients with the effect that a possibly small proportion of patients expressing excessive toxicity limits the dosage for the entire patient population for safety reasons . It is therefore of clinical importance to predict thrombotoxicity on an individual level and to develop and apply personalized schedules of chemotherapy and ameliorative treatment such as platelet transfusion or growth factor applications.
A practically applied method to personalize chemotherapy are dose adjustments which could be done either a priori or a posteriori. A priori dose adjustment procedures are based on pharmacogenetic, demographic and clinical information with known predictive power regarding cytotoxic effects. However, although several of such risk factors are known, their predictive power is poor, still resulting in high heterogeneity within risk groups . Since current chemotherapy regimens are based on multiple cycles , aposteriori dose adjustment referring to therapy control on the basis of observed toxicity could be a promising alternative. A posteriori Bayesian-guided dosing for several anticancer drugs was applied by different groups as reviewed in Rousseau et al . Based on a semi-mechanistic model of haematopoiesis developed by Friberg et al. , a Bayesian a posteriori strategy of neutrophil-guided dose adaptation was proposed and a predictive tool was developed . However, it was shown, that inter-occasion variability (IOV) reduced effectiveness of dose adaptations . The authors treated IOV as a purely random effect affecting some of the individualized parameters. However, IOV of haematotoxicity could result from (non-random) long-range cumulative chemotherapy effects. To account for this effect, modifications of the Friberg model were proposed . Among these, the model of Henrich et al was designed to consider cumulative chemotherapy-induced haematotoxicity, based on slow bone-marrow exhaustion .
We recently proposed a comprehensive model of human thrombopoiesis under chemotherapy and developed a method to fit it to individual patient data using a Bayesian approach which exploits a large body of literature information including patient data of other studies . The major aim of the present work is to propose a framework to assess, improve and compare the performance of different models regarding prediction of next-cycle thrombocytopoenia at an individual level. We apply this framework to compare our model with that of Henrich et al .

Materials and methods

Modelling concepts to be compared

We compared the predictive potential of our comprehensive mechanistic thrombopoiesis model with the semi-mechanistic model of general haematopoiesis and bone marrow exhaustion proposed by Henrich et al . Figure 1 provides and overview of both modelling concepts.