Multi-objective multi-drug scheduling schemes for cell cycle specific cancer treatment

M. S. Alam, M. A. Hossain, S. Algoul, M. A A Majumader, M. A. Al-Mamun, G. Sexton, R. Phillips

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

This paper presents an investigation into the development of an optimal chemotherapy drug(s) scheduling scheme to control the drug doses to be infused to the patient's body. The current standard of practice of treatment is based on empirical evidence gathered from preclinical and clinical trials carried out during the drug development process. In general, most chemotherapy drugs used in cancer treatments are toxic agents and usually have narrow therapeutic indices; dose levels at which these drugs significantly kill the cancerous cells are close to those levels at which harmful toxic side effects occur. Therefore, an effective chemotherapy treatment protocol requires advanced automation and treatment design tools for use in clinical practice and the challenges inherent to complex biomedical systems and clinical deployment of technology (Parker, 2009). An optimum but effective drug scheduling requires suitable balancing between the beneficial and toxic side effects. Conventional clinical methods very often fail to find right drug doses that balance between these two constraints due to their inherent conflicting nature. A Multi-objective Genetic Algorithm Optimization (MOGA) process is employed to find the desired drug concentration at tumour sites that trade-off between the conflicting objectives. A close-loop control method, namely Integral-Proportional-Derivative (I-PD) is designed to control the drug to be infused to the patient's body and MOGA is used to find suitable/acceptable drug concentration at tumour site and parameters of the controller. Cell cycle specific cancer tumour models have been used in this work to show the effects of drug(s) on different cell populations, drug concentrations and toxic side effects. Results show that the applied multi-objective optimization approach can produce a wide range of solutions that trade-off between cell killing and toxic side effects and satisfy associated goals of chemotherapy treatment. Depending on the physiological state of the patient and state of the cancer, the oncologist can pick the right solution suitable for the patient. The chemotherapy drug schedules obtained by the proposed treatment protocols appears to be continuous on the time (day) scale, i.e., specific amount of drugs to be administered to the patient on daily basis which can be termed as Metronomics in nature. The dose duration and the interval period between dose applications can be adjusted in the proposed scheme either by setting the sampling time of closed-loop I-PD controller to any value depending on the state of the patient and disease (model parameters) or by using genetic optimization process aiming to minimize/maximize treatment objectives and satisfying treatment constraints. Regarding the total duration of the treatment, clinical knowledge can be utilized giving emphasis on physiological state of the patient, state of the tumour and disease. Moreover, the total duration of the treatment can also be found/determined for specific values of model parameters describing physiological state of the patient, state of the tumour and disease through multi-objective optimization process. It is noted that the proposed scheme offered the best treatment performance as compared to the reported work available so far. Moreover, robustness analysis shows that the control scheme is highly stable and robust despite the model uncertainties; from small to wide range, and the percentage of proliferating cell reduction is almost same as it is found with optimum model parameters without having any uncertainty.

Original languageEnglish
Pages (from-to)14-32
Number of pages19
JournalComputers and Chemical Engineering
Volume58
Early online date18 Jun 2013
DOIs
Publication statusPublished - 11 Nov 2013
Externally publishedYes

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Oncology
Chemotherapy
Tumors
Scheduling
Cells
Pharmaceutical Preparations
Multiobjective optimization
Poisons
Genetic algorithms
Derivatives
Controllers
Automation
Sampling

Cite this

Alam, M. S. ; Hossain, M. A. ; Algoul, S. ; Majumader, M. A A ; Al-Mamun, M. A. ; Sexton, G. ; Phillips, R. / Multi-objective multi-drug scheduling schemes for cell cycle specific cancer treatment. In: Computers and Chemical Engineering. 2013 ; Vol. 58. pp. 14-32.
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Multi-objective multi-drug scheduling schemes for cell cycle specific cancer treatment. / Alam, M. S.; Hossain, M. A.; Algoul, S.; Majumader, M. A A; Al-Mamun, M. A.; Sexton, G.; Phillips, R.

In: Computers and Chemical Engineering, Vol. 58, 11.11.2013, p. 14-32.

Research output: Contribution to journalArticle

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T1 - Multi-objective multi-drug scheduling schemes for cell cycle specific cancer treatment

AU - Alam, M. S.

AU - Hossain, M. A.

AU - Algoul, S.

AU - Majumader, M. A A

AU - Al-Mamun, M. A.

AU - Sexton, G.

AU - Phillips, R.

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N2 - This paper presents an investigation into the development of an optimal chemotherapy drug(s) scheduling scheme to control the drug doses to be infused to the patient's body. The current standard of practice of treatment is based on empirical evidence gathered from preclinical and clinical trials carried out during the drug development process. In general, most chemotherapy drugs used in cancer treatments are toxic agents and usually have narrow therapeutic indices; dose levels at which these drugs significantly kill the cancerous cells are close to those levels at which harmful toxic side effects occur. Therefore, an effective chemotherapy treatment protocol requires advanced automation and treatment design tools for use in clinical practice and the challenges inherent to complex biomedical systems and clinical deployment of technology (Parker, 2009). An optimum but effective drug scheduling requires suitable balancing between the beneficial and toxic side effects. Conventional clinical methods very often fail to find right drug doses that balance between these two constraints due to their inherent conflicting nature. A Multi-objective Genetic Algorithm Optimization (MOGA) process is employed to find the desired drug concentration at tumour sites that trade-off between the conflicting objectives. A close-loop control method, namely Integral-Proportional-Derivative (I-PD) is designed to control the drug to be infused to the patient's body and MOGA is used to find suitable/acceptable drug concentration at tumour site and parameters of the controller. Cell cycle specific cancer tumour models have been used in this work to show the effects of drug(s) on different cell populations, drug concentrations and toxic side effects. Results show that the applied multi-objective optimization approach can produce a wide range of solutions that trade-off between cell killing and toxic side effects and satisfy associated goals of chemotherapy treatment. Depending on the physiological state of the patient and state of the cancer, the oncologist can pick the right solution suitable for the patient. The chemotherapy drug schedules obtained by the proposed treatment protocols appears to be continuous on the time (day) scale, i.e., specific amount of drugs to be administered to the patient on daily basis which can be termed as Metronomics in nature. The dose duration and the interval period between dose applications can be adjusted in the proposed scheme either by setting the sampling time of closed-loop I-PD controller to any value depending on the state of the patient and disease (model parameters) or by using genetic optimization process aiming to minimize/maximize treatment objectives and satisfying treatment constraints. Regarding the total duration of the treatment, clinical knowledge can be utilized giving emphasis on physiological state of the patient, state of the tumour and disease. Moreover, the total duration of the treatment can also be found/determined for specific values of model parameters describing physiological state of the patient, state of the tumour and disease through multi-objective optimization process. It is noted that the proposed scheme offered the best treatment performance as compared to the reported work available so far. Moreover, robustness analysis shows that the control scheme is highly stable and robust despite the model uncertainties; from small to wide range, and the percentage of proliferating cell reduction is almost same as it is found with optimum model parameters without having any uncertainty.

AB - This paper presents an investigation into the development of an optimal chemotherapy drug(s) scheduling scheme to control the drug doses to be infused to the patient's body. The current standard of practice of treatment is based on empirical evidence gathered from preclinical and clinical trials carried out during the drug development process. In general, most chemotherapy drugs used in cancer treatments are toxic agents and usually have narrow therapeutic indices; dose levels at which these drugs significantly kill the cancerous cells are close to those levels at which harmful toxic side effects occur. Therefore, an effective chemotherapy treatment protocol requires advanced automation and treatment design tools for use in clinical practice and the challenges inherent to complex biomedical systems and clinical deployment of technology (Parker, 2009). An optimum but effective drug scheduling requires suitable balancing between the beneficial and toxic side effects. Conventional clinical methods very often fail to find right drug doses that balance between these two constraints due to their inherent conflicting nature. A Multi-objective Genetic Algorithm Optimization (MOGA) process is employed to find the desired drug concentration at tumour sites that trade-off between the conflicting objectives. A close-loop control method, namely Integral-Proportional-Derivative (I-PD) is designed to control the drug to be infused to the patient's body and MOGA is used to find suitable/acceptable drug concentration at tumour site and parameters of the controller. Cell cycle specific cancer tumour models have been used in this work to show the effects of drug(s) on different cell populations, drug concentrations and toxic side effects. Results show that the applied multi-objective optimization approach can produce a wide range of solutions that trade-off between cell killing and toxic side effects and satisfy associated goals of chemotherapy treatment. Depending on the physiological state of the patient and state of the cancer, the oncologist can pick the right solution suitable for the patient. The chemotherapy drug schedules obtained by the proposed treatment protocols appears to be continuous on the time (day) scale, i.e., specific amount of drugs to be administered to the patient on daily basis which can be termed as Metronomics in nature. The dose duration and the interval period between dose applications can be adjusted in the proposed scheme either by setting the sampling time of closed-loop I-PD controller to any value depending on the state of the patient and disease (model parameters) or by using genetic optimization process aiming to minimize/maximize treatment objectives and satisfying treatment constraints. Regarding the total duration of the treatment, clinical knowledge can be utilized giving emphasis on physiological state of the patient, state of the tumour and disease. Moreover, the total duration of the treatment can also be found/determined for specific values of model parameters describing physiological state of the patient, state of the tumour and disease through multi-objective optimization process. It is noted that the proposed scheme offered the best treatment performance as compared to the reported work available so far. Moreover, robustness analysis shows that the control scheme is highly stable and robust despite the model uncertainties; from small to wide range, and the percentage of proliferating cell reduction is almost same as it is found with optimum model parameters without having any uncertainty.

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KW - Multi-objective optimization

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