Multi-objective particle swarm optimisation for phase specific cancer drug scheduling

Mohammad S. Alam; Saleh Algoul; M. Alamgir Hossain; M. A.Azim Majumder

doi:10.1007/978-3-642-16750-8_16

Multi-objective particle swarm optimisation for phase specific cancer drug scheduling

Mohammad S. Alam
, Saleh Algoul
, M. Alamgir Hossain
, M. A.Azim Majumder

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

An effective chemotherapy drug scheduling requires adequate balancing of administration of anti-cancer drugs to reduce the tumour size as well as toxic side effects. Conventional clinical methods very often fail to balance between these two parameters due to their inherent conflicting nature. This paper presents a method of phase specific drug scheduling using a close-loop control method and multi-objective particle swarm optimisation algorithm (MOPSO) that can provide solutions for trading-off between the cell killing and toxic side effects. 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 MOPSO is used to find suitable parameters of the controller. A phase specific cancer tumour model is used for this work to show the effects of drug on tumour. Results show that the proposed method can generate very efficient drug scheduling that trade-off between cell killing and toxic side effects and satisfy associated design goals, for example lower drug doses and lower drug concentration. Moreover, our approach can reduce the number of proliferating and quiescent cells up to 72% and 60% respectively; maximum reduction with phase-specific model compared to reported work available so far.

Original language	English
Title of host publication	Computational Systems-Biology and Bioinformatics - First International Conference, CSBio 2010, Proceedings
Pages	180-192
Number of pages	13
DOIs	https://doi.org/10.1007/978-3-642-16750-8_16
State	Published - 2010
Externally published	Yes
Event	1st International Conference on Computational Systems Biology and Bioinformatics, CSBio 2010 - Bangkok, Thailand Duration: 3 Nov 2010 → 5 Nov 2010

Publication series

Name	Communications in Computer and Information Science
Volume	115 CCIS
ISSN (Print)	1865-0929

Conference

Conference	1st International Conference on Computational Systems Biology and Bioinformatics, CSBio 2010
Country/Territory	Thailand
City	Bangkok
Period	3/11/10 → 5/11/10

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Cancer chemotherapy
Cell compartment
Feedback control
Multi-objective optimisation
Particle Swarm Algorithm
Phase specific scheduling

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

10.1007/978-3-642-16750-8_16

Cite this

Alam, M. S., Algoul, S., Hossain, M. A., & Majumder, M. A. A. (2010). Multi-objective particle swarm optimisation for phase specific cancer drug scheduling. In Computational Systems-Biology and Bioinformatics - First International Conference, CSBio 2010, Proceedings (pp. 180-192). (Communications in Computer and Information Science; Vol. 115 CCIS). https://doi.org/10.1007/978-3-642-16750-8_16

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title = "Multi-objective particle swarm optimisation for phase specific cancer drug scheduling",

abstract = "An effective chemotherapy drug scheduling requires adequate balancing of administration of anti-cancer drugs to reduce the tumour size as well as toxic side effects. Conventional clinical methods very often fail to balance between these two parameters due to their inherent conflicting nature. This paper presents a method of phase specific drug scheduling using a close-loop control method and multi-objective particle swarm optimisation algorithm (MOPSO) that can provide solutions for trading-off between the cell killing and toxic side effects. 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 MOPSO is used to find suitable parameters of the controller. A phase specific cancer tumour model is used for this work to show the effects of drug on tumour. Results show that the proposed method can generate very efficient drug scheduling that trade-off between cell killing and toxic side effects and satisfy associated design goals, for example lower drug doses and lower drug concentration. Moreover, our approach can reduce the number of proliferating and quiescent cells up to 72\% and 60\% respectively; maximum reduction with phase-specific model compared to reported work available so far.",

keywords = "Cancer chemotherapy, Cell compartment, Feedback control, Multi-objective optimisation, Particle Swarm Algorithm, Phase specific scheduling",

author = "Alam, \{Mohammad S.\} and Saleh Algoul and Hossain, \{M. Alamgir\} and Majumder, \{M. A.Azim\}",

year = "2010",

doi = "10.1007/978-3-642-16750-8\_16",

language = "English",

isbn = "3642167497",

series = "Communications in Computer and Information Science",

pages = "180--192",

booktitle = "Computational Systems-Biology and Bioinformatics - First International Conference, CSBio 2010, Proceedings",

note = "1st International Conference on Computational Systems Biology and Bioinformatics, CSBio 2010 ; Conference date: 03-11-2010 Through 05-11-2010",

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Alam, MS, Algoul, S, Hossain, MA & Majumder, MAA 2010, Multi-objective particle swarm optimisation for phase specific cancer drug scheduling. in Computational Systems-Biology and Bioinformatics - First International Conference, CSBio 2010, Proceedings. Communications in Computer and Information Science, vol. 115 CCIS, pp. 180-192, 1st International Conference on Computational Systems Biology and Bioinformatics, CSBio 2010, Bangkok, Thailand, 3/11/10. https://doi.org/10.1007/978-3-642-16750-8_16

Multi-objective particle swarm optimisation for phase specific cancer drug scheduling. / Alam, Mohammad S.; Algoul, Saleh; Hossain, M. Alamgir et al.
Computational Systems-Biology and Bioinformatics - First International Conference, CSBio 2010, Proceedings. 2010. p. 180-192 (Communications in Computer and Information Science; Vol. 115 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Multi-objective particle swarm optimisation for phase specific cancer drug scheduling

AU - Alam, Mohammad S.

AU - Algoul, Saleh

AU - Hossain, M. Alamgir

AU - Majumder, M. A.Azim

PY - 2010

Y1 - 2010

N2 - An effective chemotherapy drug scheduling requires adequate balancing of administration of anti-cancer drugs to reduce the tumour size as well as toxic side effects. Conventional clinical methods very often fail to balance between these two parameters due to their inherent conflicting nature. This paper presents a method of phase specific drug scheduling using a close-loop control method and multi-objective particle swarm optimisation algorithm (MOPSO) that can provide solutions for trading-off between the cell killing and toxic side effects. 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 MOPSO is used to find suitable parameters of the controller. A phase specific cancer tumour model is used for this work to show the effects of drug on tumour. Results show that the proposed method can generate very efficient drug scheduling that trade-off between cell killing and toxic side effects and satisfy associated design goals, for example lower drug doses and lower drug concentration. Moreover, our approach can reduce the number of proliferating and quiescent cells up to 72% and 60% respectively; maximum reduction with phase-specific model compared to reported work available so far.

AB - An effective chemotherapy drug scheduling requires adequate balancing of administration of anti-cancer drugs to reduce the tumour size as well as toxic side effects. Conventional clinical methods very often fail to balance between these two parameters due to their inherent conflicting nature. This paper presents a method of phase specific drug scheduling using a close-loop control method and multi-objective particle swarm optimisation algorithm (MOPSO) that can provide solutions for trading-off between the cell killing and toxic side effects. 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 MOPSO is used to find suitable parameters of the controller. A phase specific cancer tumour model is used for this work to show the effects of drug on tumour. Results show that the proposed method can generate very efficient drug scheduling that trade-off between cell killing and toxic side effects and satisfy associated design goals, for example lower drug doses and lower drug concentration. Moreover, our approach can reduce the number of proliferating and quiescent cells up to 72% and 60% respectively; maximum reduction with phase-specific model compared to reported work available so far.

KW - Cancer chemotherapy

KW - Cell compartment

KW - Feedback control

KW - Multi-objective optimisation

KW - Particle Swarm Algorithm

KW - Phase specific scheduling

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DO - 10.1007/978-3-642-16750-8_16

M3 - Conference contribution

AN - SCOPUS:78649509372

SN - 3642167497

SN - 9783642167492

T3 - Communications in Computer and Information Science

SP - 180

EP - 192

BT - Computational Systems-Biology and Bioinformatics - First International Conference, CSBio 2010, Proceedings

T2 - 1st International Conference on Computational Systems Biology and Bioinformatics, CSBio 2010

Y2 - 3 November 2010 through 5 November 2010

ER -

Multi-objective particle swarm optimisation for phase specific cancer drug scheduling

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

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Cite this