Suppr超能文献

通过优化给药方案解决肿瘤化疗问题。

Tackling the problems of tumour chemotherapy by optimal drug scheduling.

作者信息

Remesh Ambili

机构信息

Associate Professor, Department of Pharmacology and Therapeutics, Dr SMCSI Medical College , Karakonam, India .

出版信息

J Clin Diagn Res. 2013 Jul;7(7):1404-7. doi: 10.7860/JCDR/2013/6223.3144. Epub 2013 May 31.

Abstract

INTRODUCTION

There are various strategies for overcoming the major pitfalls of cancer chemotherapy, such as toxicity and drug resistance. The scientific computing of drug scheduling by optimisation before drug administration is one among them. In a majority of these strategies, the pharmacodynamic variations are given more importance than the pharmacokinetic variations. This study was meant to analyse the importance of the pharmacokinetic parameters (φ) of the individual patients in cancer chemotherapy scheduling, along with the pharmacodynamic factors.

METHODS

A mathematical model is developed and it is implemented in the open source OCTAVE GNU LINUX. Optimisation is done by using an optimization tool in OCTAVE. The present study was aimed at evaluating the daily drug dosaging and cyclic chemotherapy which are commonly practised in the chemotherapy scheduling. Four cases were analyzed with and without considering the pharmacokinetic parameters. The optimal therapy was meant to reduce the number of cancer cells to a minimum at the end of the therapy and to minimise the emergence of resistant cancer cells. Since the dose was within tolerable limits, the toxic effects could also be minimised.

RESULTS

Even with the consideration of a 1 per cent effect (φ), the maximum possible dose and the performance index were increased in the daily scheduling. But in the cyclic therapy, even though the maximum tolerated dose or the performance index was not altered, the cumulative toxicity was greatly reduced.

CONCLUSION

Daily scheduling and cyclic chemotherapy can be applied alternatively more effectively, by considering the interindividual variations in the pharmacokinetic effect (φ).

摘要

引言

克服癌症化疗的主要缺陷(如毒性和耐药性)有多种策略。给药前通过优化进行药物给药方案的科学计算就是其中之一。在大多数这些策略中,药效学变化比药代动力学变化更受重视。本研究旨在分析个体患者的药代动力学参数(φ)在癌症化疗方案制定中的重要性,以及药效学因素。

方法

开发了一个数学模型,并在开源的OCTAVE GNU LINUX中实现。通过使用OCTAVE中的优化工具进行优化。本研究旨在评估化疗方案中常用的每日药物剂量和周期化疗。分析了4种情况,分别考虑和不考虑药代动力学参数。最佳治疗方案旨在在治疗结束时将癌细胞数量降至最低,并尽量减少耐药癌细胞的出现。由于剂量在可耐受范围内,毒性作用也可降至最低。

结果

即使考虑1%的效应(φ),每日给药方案中的最大可能剂量和性能指标也会增加。但在周期化疗中,即使最大耐受剂量或性能指标没有改变,累积毒性也会大大降低。

结论

通过考虑药代动力学效应(φ)中的个体差异,可以更有效地交替应用每日给药方案和周期化疗。

相似文献

1
Tackling the problems of tumour chemotherapy by optimal drug scheduling.通过优化给药方案解决肿瘤化疗问题。
J Clin Diagn Res. 2013 Jul;7(7):1404-7. doi: 10.7860/JCDR/2013/6223.3144. Epub 2013 May 31.
7
Automating the drug scheduling of cancer chemotherapy via evolutionary computation.
Artif Intell Med. 2002 Jun;25(2):169-85. doi: 10.1016/s0933-3657(02)00014-3.

本文引用的文献

1
A history of cancer chemotherapy.癌症化疗史。
Cancer Res. 2008 Nov 1;68(21):8643-53. doi: 10.1158/0008-5472.CAN-07-6611.
2
Mathematical modeling of cancer progression and response to chemotherapy.癌症进展及化疗反应的数学建模
Expert Rev Anticancer Ther. 2006 Oct;6(10):1361-76. doi: 10.1586/14737140.6.10.1361.
3
Automating the drug scheduling of cancer chemotherapy via evolutionary computation.
Artif Intell Med. 2002 Jun;25(2):169-85. doi: 10.1016/s0933-3657(02)00014-3.
4
How to calculate the dose of chemotherapy.如何计算化疗剂量。
Br J Cancer. 2002 Apr 22;86(8):1297-302. doi: 10.1038/sj.bjc.6600139.
10
Drug kinetics and drug resistance in optimal chemotherapy.最佳化疗中的药物动力学与耐药性
Math Biosci. 1995 Feb;125(2):191-209. doi: 10.1016/0025-5564(94)00027-w.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验