Model predictive control for optimal oral anticoagulant drug administration

An Adaptive Model Predictive Controller to Address the Biovariability in Blood Clotting Response During Therapy With Warfarin

OBJECTIVE

Effective dosing of anticoagulants aims to prevent blood clot formation while avoiding hemorrhages. This complex task is challenged by several disturbing factors and drug-effect uncertainties, requesting frequent monitoring and adjustment. Biovariability in drug absorption and action further complicates titration and calls for individualized strategies. In this paper, we propose an adaptive closed-loop control algorithm to assist in warfarin therapy management.

METHODS

The controller was designed and tested in silico using an established pharmacometrics model of warfarin, which accounts for inter-subject variability. The control algorithm is an adaptive Model Predictive Control (a-MPC) that leverages a simplified patient model, whose parameters are updated with a Bayesian strategy. Performance was quantitatively evaluated in simulations performed on a population of virtual subjects against an algorithm reproducing medical guidelines (MG) and an MPC controller available in the literature (l-MPC).

RESULTS

The proposed a-MPC significantly (p 0.05) lowers rising time (2.8 vs. 4.4 and 11.2 days) and time out of range (3.3 vs. 7.2 and 12.9 days) with respect to both MG and l-MPC, respectively. Adaptivity grants a significantly (p 0.05) lower number of subjects reaching unsafe INR values compared to when this feature is not present (8.9% vs.15% of subjects presenting an overshoot outside the target range and 0.08% vs. 0.28% of subjects reaching dangerous INR values).

CONCLUSION

The a-MPC algorithm improve warfarin therapy compared to the benchmark therapies.

SIGNIFICANCE

This in-silico validation proves effectiveness of the a-MPC algorithm for anticoagulant administration, paving the way for clinical testing.

MPC based optimization applied to treatment of HCV infections

BACKGROUND AND OBJECTIVE

The recent introduction of antivirals for the treatment of the hepatitis C virus opens new frontiers but also poses a significant burden on public health systems. This paper presents a simulation study in which model predictive control (MPC) is proposed for optimizing the therapy aiming to obtain a reduction of the costs of therapy, while maintaining the best pharmacological control of the infection.

METHODS

A dynamic model describing the evolution of hepatitis C is deployed as internal model for MPC implementation, using nominal values of parameters. Different closed-loop simulations are presented both in nominal and in mismatch conditions. In addition, a more easily implementable treatment is proposed, which is based on a discrete dosage approach, where days on/off therapy are considered instead of continuous therapy modulation.

RESULTS

Results show that therapy modulation allows one to achieve the same infection evolution as with full therapy, with a reduction of drug consumption between 10% and 40%. The alternative discrete dosage approach shows similar results achieved with therapy modulation, both in terms of therapy effectiveness and drug consumption reduction.

CONCLUSIONS

The proposed model predictive control therapy optimization strategies appear to be effective, implementable and robust to model errors. It therefore represents a potentially useful approach to alleviate the burden of HCV therapy cost on national health systems.

Procoagulant control strategies for the human blood clotting process

This paper describes the comparison between two drug control strategies to hemophilia A. To emulate blood clotting and the pathological condition of hemophilia, a mathematical model composed by 14 ordinary differential equations is considered. We adopt a variable structure non-linear PID approach and a Model Predictive Control in order to control the dosage of procoagulant factor used in the treatment of hemophiliac patient. The two control actions are sampled for a practical application. Finally, we discuss and compare the results of the two control approaches, introducing a suited control index (eINR).

Abnormality detection in noisy biosignals

Although great strides have been achieved in computer-aided diagnosis (CAD) research, a major remaining problem is the ability to perform well under the presence of significant noise. In this work, we propose a mechanism to find instances of potential interest in time series for further analysis. Adaptive Kalman filters are employed in parallel among different feature axes. Lung sounds recorded in noisy conditions are used as an example application, with spectro-temporal feature extraction to capture the complex variabilities in sound. We demonstrate that both disease indicators and distortion events can be detected, reducing long time series signals into a sparse set of relevant events.

Monitoring of anticoagulant therapy applying a dynamic statistical model

Patients with an increased risk of thrombosis may require treatment with vitamin K-antagonists such as warfarin. Treatment with warfarin has been reported difficult mainly due to high inter- and intraindividual variability in response to the drug [1]. Using predictive models that can predict International Normalised Ratio (INR) values enables for a higher degree of individualised warfarin dosing regime. This paper reports the outcome of the development of a dynamic prediction model. It takes warfarin intake and INR values as inputs, and uses an individual sensitivity parameter to model response to warfarin intake. The model is set on state-space form and uses Kalman filtering technique to optimise individual parameters. Retrospective test of the model proved robustness to choices of initial parameters, and feasible prediction results of both INR values and suggested warfarin dosage, which may prove beneficial for both patients and healthcare takers.

Combining pharmacological therapy and vaccination in Chronic Myeloid Leukemia via model predictive control

This paper describes a simulation study which aims at optimizing the therapy for the control of Chronic Myeloid Leukemia according to the following objectives: the reduction of the administered drug and vaccine amounts, the establishment of a auto-immune response and the long-term control of disease without reducing the effective of therapy with respect to the full treatment. A therapy optimization method is developed defining and solving a Model Predictive Control algorithm, preceded by an accurate Initial Guess search based on Monte-Carlo like approach. Simulation results show that the suggested procedure achieves the proposed goals.