scholarly journals Optimal control of acute myeloid leukaemia

2018 ◽  
Author(s):  
Jesse A Sharp ◽  
Alexander P Browning ◽  
Tarunendu Mapder ◽  
Kevin Burrage ◽  
Matthew J Simpson
Keyword(s):  
Optimal Control ◽  
Immune Response ◽  
Acute Myeloid Leukaemia ◽  
Myeloid Leukaemia ◽  
Cell Model ◽  
Control Strategies ◽  
Treatment Strategies ◽  
Numerical Convergence ◽  
Stem Cell Model ◽  
Acute Myeloid

AbstractAcute myeloid leukaemia (AML) is a blood cancer affecting haematopoietic stem cells. AML is routinely treated with chemotherapy, and so it is of great interest to develop optimal chemotherapy treatment strategies. In this work, we incorporate an immune response into a stem cell model of AML, since we find that previous models lacking an immune response are inappropriate for deriving optimal control strategies. Using optimal control theory, we produce continuous controls and bang-bang controls, corresponding to a range of objectives and parameter choices. Through example calculations, we provide a practical approach to applying optimal control using Pontryagin’s Maximum Principle. In particular, we describe and explore factors that have a profound influence on numerical convergence. We find that the convergence behaviour is sensitive to the method of control updating, the nature of the control, and to the relative weighting of terms in the objective function. All codes we use to implement optimal control are made available.

scholarly journals Effects of Immune Response and Time Delays in Models of Acute Myeloid Leukaemia

2021 ◽  
Author(s):  
Weigang Sun ◽  
Lei Yang ◽  
Min Luo
Keyword(s):  
Immune Response ◽  
Acute Myeloid Leukaemia ◽  
Myeloid Leukaemia ◽  
Time Delays ◽  
Complex Dynamics ◽  
Feedback Regulation ◽  
Local Bifurcation ◽  
Hematopoietic Lineage ◽  
Proliferation And Differentiation ◽  
Acute Myeloid

Abstract In this paper, we propose a general acute myeloid leukaemia (AML) model and introduce an immune response and time delays into this model to investigate their effects on the dynamics. Based on the existence, stability and local bifurcation of three types of equilibria, we show that the immune response is a best strategy for the control of the AML on the condition that the rates of proliferation and differentiation of the hematopoietic lineage exceed a threshold. In particular, a powerful immune response leads to bi-stability of the steady states, and a stronger response wipes out all the leukaemia cells. In addition, we further reveal that the time delays existing in the feedback regulation and immune response process induce a series of oscillations around the steady state, which shows that the leukaemia cells can hardly be eliminated. Our work in this paper aims to investigate the complex dynamics of this AML model with the immune response and time delays on the basis of mathematical models and numerical simulations, which may provide a theoretical guidance for the treatments of the AML.

scholarly journals Optimal control of acute myeloid leukaemia

2019 ◽  
Vol 470 ◽  
pp. 30-42 ◽  
Author(s):  
Jesse A. Sharp ◽  
Alexander P Browning ◽  
Tarunendu Mapder ◽  
Kevin Burrage ◽  
Matthew J Simpson
Keyword(s):  
Optimal Control ◽  
Acute Myeloid Leukaemia ◽  
Myeloid Leukaemia ◽  
Acute Myeloid

Acute leukaemias

2016 ◽  
pp. 699-753
Author(s):  
Adele K. Fielding ◽  
Charles G. Mullighan ◽  
Dieter Hoelzer ◽  
Eytan M. Stein ◽  
Ghada Zakout ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Acute Myeloid Leukaemia ◽  
Acute Lymphoblastic Leukaemia ◽  
Myeloid Leukaemia ◽  
Lymphoblastic Leukaemia ◽  
Treatment Strategies ◽  
Response To Treatment ◽  
Current Standard ◽  
Two Phases ◽  
Acute Myeloid

This chapter covers acute myeloid leukaemia (AML) and acute lymphoblastic leukaemia (ALL), and includes information on prognostic factors, current standard of care, basic biology, epidemiology, clinical presentation, diagnosis, pathophysiology, aetiology, and management. Although the majority of patients with acute myeloid leukaemia (AML) achieve complete remission with induction chemotherapy, relapse after achievement of clinical remission remains the most critical clinical challenge facing AML patients and clinicians today, with a pressing need to improve prognostication. Prognostic factors in acute lymphoblastic leukaemia is to stratify patients into good- and poor-risk groups and to adapt different treatment strategies accordingly. There are principally two phases to evaluating prognostic factors; the first is the patient characteristics at diagnosis and the second is the response to treatment.

The acute myeloid leukaemia market

2019 ◽  
Vol 19 (4) ◽  
pp. 233-234
Author(s):  
Jorrit Schaefer ◽  
Sorcha Cassidy ◽  
Rachel M. Webster
Keyword(s):  
Acute Myeloid Leukaemia ◽  
Myeloid Leukaemia ◽  
Acute Myeloid
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