We investigate the personalisation and prediction accuracy of mathematical models for white blood cell (WBC) count dynamics during consolidation treatment using intermediate or high-dose cytarabine (Ara-C) in acute myeloid leukaemia (AML). Ara-C is the clinically most relevant cytotoxic agent for AML treatment. We extend a mathematical model of myelosuppression and a pharmacokinetic model of Ara-C with different hypotheses of Ara-C’s pharmacodynamic effects. We cross-validate the 12 model variations using dense WBC count measurements from 23 AML patients. Surprisingly, the prediction accuracy remains satisfactory in each of the models despite different modelling hypotheses. Therefore, we compare average clinical and calculated WBC recovery times for different Ara-C schedules as a successful methodology for model discrimination. As a result, a new hypothesis of a secondary pharmacodynamic effect on the proliferation rate seems plausible. Furthermore, we demonstrate the impact of treatment timing on subsequent nadir values based on personalised predictions as a possibility for influencing/controlling myelosuppression.

Acute lymphoblastic leukemia is the most common malignancy in childhood and requires prolonged oral maintenance chemotherapy to prevent disease relapse after remission induction with intensive intravenous chemotherapy. In maintenance therapy, drug doses of 6-mercaptopurine (6-MP) and methotrexate (MTX) are adjusted to achieve sustained antileukemic activity without excessive myelosuppression. However, uncertainty exists regarding timing and extent of drug dose responses and optimal dose adaptation strategies. We propose a novel comprehensive mathematical model for 6-MP and MTX pharmacokinetics, pharmacodynamics and myelosuppression in acute lymphoblastic maintenance therapy. We personalize and cross-validate the mathematical model using clinical data and propose a real-time algorithm to predict chemotherapy responses with a clinical decision support system as a potential future application.

Nonlinear model predictive control has been established as a powerful methodology to provide feedback for dynamic processes over the last decades. In practice it is usually combined with parameter and state estimation techniques, which allows to cope with uncertainty on many levels. To reduce the uncertainty it has also been suggested to include optimal experimental design into the sequential process of estimation and control calculation. Most of the focus so far was on dual control approaches, i.e., on using the controls to simultaneously excite the system dynamics (learning) as well as minimizing a given objective (performing). We propose a new algorithm, which sequentially solves robust optimal control, optimal experimental design, state and parameter estimation problems. Thus, we decouple the control and the experimental design problems. This has the advantages that we can analyze the impact of measurement timing (sampling) independently, and is practically relevant for applications with either an ethical limitation on system excitation (e.g., chemotherapy treatment) or the need for fast feedback. The algorithm shows promising results with a 36% reduction of parameter uncertainties for the Lotka-Volterra fishing benchmark example.

Background and Aims : Mouse and human data implicates the NOD1 and NOD2 sensors of the intestinal microbiome and the associated signal transduction via the RIPK2 kinase as a potentially key signaling node for the development of Inflammatory Bowel Disease (IBD) and an attractive target for pharmacologic intervention. The TRUC mouse model of IBD has been strongly indicated for evaluating the impact of RIPK2 antagonism on intestinal inflammation based on previous studies with NOD1, NOD2 and RIPK2 knockout mice. Methods. We identified and profiled the BI 706039 molecule as a potent and specific functional inhibitor of both human and mouse RIPK2 and with favorable pharmacokinetic properties. We dosed BI 706039 in the spontaneous TRUC mouse model from aged day 28 through aged day 56. Results : Oral, daily administration of BI 706039 caused dose-responsive and significant improvement in colonic histopathological inflammation, colon weight and terminal levels of protein normalized fecal lipocalin (all p< 0.001). These observations correlated with dose-responsively increasing systemic levels of the BI 706039 compound, splenic molecular target engagement of RIPK2 and modulation of inflammatory genes in the colon. Conclusions : A relatively low oral dose of a potent and selective RIPK2 inhibitor can block the signaling interface between the intestinal microbiome and the intestinal immune system and significantly improve disease associated intestinal inflammation.

Neutropenia is an adverse event commonly arising during intensive chemotherapy of acute myeloid leukemia (AML). It is often associated with infectious complications. Mathematical modeling, simulation, and optimization of the treatment process would be a valuable tool to support clinical decision making, potentially resulting in less severe side effects and deeper remissions. However, until now, there has been no validated mathematical model available to simulate the effect of chemotherapy treatment on white blood cell (WBC) counts and leukemic cells simultaneously. Methods: We developed a population pharmacokinetic/pharmacodynamic (PK/PD) model combining a myelosuppression model considering endogenous granulocyte-colony stimulating factor (G-CSF), a PK model for cytarabine (Ara-C), a subcutaneous absorption model for exogenous G-CSF, and a two-compartment model for leukemic blasts. This model was fitted to data of 44 AML patients during consolidation therapy with a novel Ara-C plus G-CSF schedule from a phase II controlled clinical trial. Additionally, we were able to optimize treatment schedules with respect to disease progression, WBC nadirs, and the amount of Ara-C and G-CSF. Results: The developed PK/PD model provided good prediction accuracies and an interpretation of the interaction between WBCs, G-CSF, and blasts. For 14 patients (those with available bone marrow blast counts), we achieved a median 4.2fold higher WBC count at nadir, which is the most critical time during consolidation therapy. The simulation results showed that relative bone marrow blast counts remained below the clinically important threshold of 5%, with a median of 60% reduction in Ara-C. Conclusion: These in silico findings demonstrate the benefits of optimized treatment schedules for AML patients. Significance: Until 2017, no new drug had been approved for the treatment of AML, fostering the optimal use of currently available drugs.