N Archana1 
,
Antony Manoj Fh Benedict2,
J Niresh3
For correspondence:- N Archana Email: archana.nathan31@gmail.com
Accepted: 10 May 2018 Published: 30 June 2018
Citation: Archana N, Benedict AM, Niresh J. Chemotherapy drug regimen optimization using deterministic oscillatory search algorithm. Trop J Pharm Res 2018; 17(6):1135-1143 doi: 10.4314/tjpr.v17i6.21
© 2018 The authors.
This is an Open Access article that uses a funding model which does not charge readers or their institutions for access and distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0) and the Budapest Open Access Initiative (http://www.budapestopenaccessinitiative.org/read), which permit unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited..
Purpose: To schedule chemotherapy drug delivery using Deterministic Oscillatory Search algorithm, keeping the toxicity level within permissible limits and reducing the number of tumor cells within a predefined time period.
Methods: A novel metaheuristic algorithm, deterministic oscillatory search, has been used to optimize the Gompertzian model of the drug regimen problem. The model is tested with fixed (fixed interval variable dose, FIVD) and variable (variable interval variable dose, VIVD) interval schemes and the dosage presented for 52 weeks. In the fixed interval, the treatment plan is fixed in such a way that doses are given on the first two days of every seven weeks such as day 7, day 14, etc.
Results: On comparing the two schemes, FIVD provided a higher reduction in the number of tumor cells by 98 % compared to 87 % by VIVD after the treatment period. Also, a significant reduction in the number was obtained half way through the regimen. The dose level and toxicity are also reduced in the FIVD scheme. The value of drug concentration is more in FIVD scheme (50) compared to VIVD (41); however, it is well within the acceptable limits of concentration. The results proved the effectiveness of the proposed technique in terms of reduced drug concentration, toxicity, tumor size and drug level within a predetermined time period.
Conclusion: Artificial intelligent techniques can be used as a tool to aid oncologists in the effective treatment of cancer through chemotherapy.
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