An Optimal Control Framework for the Automated Design of Personalized Cancer Treatments

Angaroni, Fabrizio; Graudenzi, Alex; Rossignolo, Marco; Maspero, Davide; Calarco, Tommaso; Montangero, Simone; Antoniotti, Marco

doi:10.3389/fbioe.2020.00523

Cited by 20 publications

(13 citation statements)

References 93 publications

(192 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Instead of a selection of doses by trial and error, which invariably limits the possible choices, we formulate an optimization problem that leads to an optimal control formulation. Control theory has been used to optimize treatment in problems with available PK/PD models, such as HIV infections [29,30], cancer therapy [31][32][33][34][35], or to optimize strategies for the prevention of infectious diseases such as cholera [36], Zika virus infection [37] and COVID-19 [38]. For colistin administration, the optimization problem minimizes the deviations between the plasma concentration of colistin and its target value.…”

Section: Introductionmentioning

confidence: 99%

Optimal control for colistin dosage selection

Gontijo

Cavalieri

2021

J Pharmacokinet Pharmacodyn

View full text Add to dashboard Cite

Optimization of antibiotic administration helps minimizing cases of bacterial resistance. Dosages are often selected by trial and error using a pharmacokinetic (PK) model. However, this is limited to the range of tested dosages, restraining possible treatment choices, especially for the loading doses. Colistin is a last-resort antibiotic with a narrow therapeutic window; therefore, its administration should avoid subtherapeutic or toxic concentrations. This study formulates an optimal control problem for dosage selection of colistin based on a PK model, minimizing deviations of colistin concentration to a target value and allowing a specific dosage optimization for a given individual. An adjoint model was used to provide the sensitivity of concentration deviations to dose changes. A three-compartment PK model was adopted. The standard deviation between colistin plasma concentrations and a target set at 2 mg/L was minimized for some chosen treatments and sample patients. Significantly lower deviations from the target concentration are obtained for shorter administration intervals (e.g. every 8 h) compared to longer ones (e.g. every 24 h). For patients with normal or altered renal function, the optimal loading dose regimen should be divided into two or more administrations to attain the target concentration quickly, with a high first loading dose followed by much lower ones. This regimen is not easily obtained by trial and error, highlighting advantages of the method. The present method is a refined optimization of antibiotic dosage for the treatment of infections. Results for colistin suggest significant improvement in treatment avoiding subtherapeutic or toxic concentrations.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimal control for colistin dosage selection

Gontijo

Cavalieri

2021

J Pharmacokinet Pharmacodyn

View full text Add to dashboard Cite

show abstract

“…Mathematically, cancer chemotherapy can be formulated as an optimal control problem. This approach is more relevant than ever today, as testified by recent works (Schättler and Ledzewicz, 2015a , b ; Wang and Schättler, 2016 ; Angaroni et al, 2020 ; Jarrett et al, 2020 ; Sweilam et al, 2020 ). In literature, applications of optimal control theory to mathematical models of cancer biology and role of chemotherapy began to appear in the 1980s and have appeared with regularity in the following years to the present day.…”

Section: Introductionmentioning

confidence: 92%

“…A model for the objective function is given by the formalization of the aim therapeutic treatment, which is to kill the cancer cells while keeping the toxicity to the health cells acceptable. In literature there are many non-equivalent ways of modeling the objective function (Swan, 1990 ; Swierniak et al, 1996 ; Schättler and Ledzewicz, 2015b ; Wang and Schättler, 2016 ; Angaroni et al, 2020 ). A model that is largely adopted was proposed by Świerniak ( 1995 ), Świerniak et al ( 2016 ) with the objective to minimize the number of cancer cells at the end of a fixed therapy interval.…”

Section: Cell-cycle Specific Dynamic Modelmentioning

confidence: 99%

Control Theory and Cancer Chemotherapy: How They Interact

Lecca

2021

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

Control theory arises in most modern real-life applications, not least in biological and medical applications. In particular, in biological and medical contexts, the role of control theory began to take shape in the early 1980s when the first works appeared on the application of control theory in models of pharmacokinetics and pharmacodynamics for antitumor therapies. Forty years after those first works, the theory of control continues to be considered a mathematical analysis tool of extreme importance and usefulness, but the challenges it must overcome in order to manage the complexity of biological processes are in fact not yet overcome. In this article, we introduce the reader to the basic ideas of control theory, its aims and its mathematical formalization, and we review its use in cell phase-specific models for cancer chemotherapy. We discuss strengths and limitations of the control theory approach to the analysis pharmacokinetics and pharmacodynamics models, and we will see that most of them are strongly related to data availability and mathematical form of the model. We propose some future research directions that could prove useful in overcoming the these limitations and we indicate the crucial steps preliminary to a useful and informative application of control theory to cancer chemotherapy modeling.

show abstract

“…This approach was leveraged by Angaroni et al. to determine optimal personalized regimens of imatinib for CML, aiming to minimize drug exposure and cancer stem cell burden ( Angaroni et al., 2020 ). To this end, the authors developed an ODE model of CML, calibrated with longitudinal measurements of cancer cell burden, as well as personalized pharmacokinetic and pharmacodynamic models of imatinib.…”

Section: Emerging Applications For Practical Mathematical Modelingmentioning

confidence: 99%

“…Furthermore, optimal control theory has been gaining increasing attention to formulate personalized therapeutic regimens by integrating a biologically based mathematical model of cancer with knowledge of drug pharmacodynamics (Jarrett et al, 2020a). This approach was leveraged by Angaroni et al to determine optimal personalized regimens of imatinib for CML, aiming to minimize drug exposure and cancer stem cell burden (Angaroni et al, 2020). To this end, the authors developed an ODE model of CML, calibrated with longitudinal measurements of cancer cell burden, as well as personalized pharmacokinetic and pharmacodynamic models of imatinib.…”

Section: Emerging Applications For Practical Mathematical Modelingmentioning

confidence: 99%

Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology

et al. 2020

View full text Add to dashboard Cite

Summary We provide an overview on the use of biological assays to calibrate and initialize mechanism-based models of cancer phenomena. Although artificial intelligence methods currently dominate the landscape in computational oncology, mathematical models that seek to explicitly incorporate biological mechanisms into their formalism are of increasing interest. These models can guide experimental design and provide insights into the underlying mechanisms of cancer progression. Historically, these models have included a myriad of parameters that have been difficult to quantify in biologically relevant systems, limiting their practical insights. Recently, however, there has been much interest calibrating biologically based models with the quantitative measurements available from (for example) RNA sequencing, time-resolved microscopy, and in vivo imaging. In this contribution, we summarize how a variety of experimental methods quantify tumor characteristics from the molecular to tissue scales and describe how such data can be directly integrated with mechanism-based models to improve predictions of tumor growth and treatment response.

show abstract

An Optimal Control Framework for the Automated Design of Personalized Cancer Treatments

Cited by 20 publications

References 93 publications

Optimal control for colistin dosage selection

Optimal control for colistin dosage selection

Control Theory and Cancer Chemotherapy: How They Interact

Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology

Contact Info

Product

Resources

About