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疟疾感染中免疫反应和耐药性的动态变化。

Dynamics of immune response and drug resistance in malaria infection.

作者信息

Gurarie David, McKenzie F Ellis

机构信息

Department of Mathematics, Case Western Reserve University, Cleveland, OH 44106, USA.

出版信息

Malar J. 2006 Oct 11;5:86. doi: 10.1186/1475-2875-5-86.

DOI:10.1186/1475-2875-5-86
PMID:17034637
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC1629019/
Abstract

BACKGROUND

Malaria parasites that concurrently infect a host compete on the basis of their intrinsic growth rates and by stimulating cross-reactive immune responses that inhibit each others' growth. If the phenotypes also show different drug sensitivities ('sensitive' vs. 'resistant' strains), drug treatment can change their joint dynamics and the long-term outcome of the infection: most obviously, persistent drug pressure can permit the more resistant, but otherwise competitively-inferior, strains to dominate.

METHODS

Here a mathematical model is developed to analyse how these and more subtle effects of antimalarial drug use are modulated by immune response, repeated re-inoculation of parasites, drug pharmacokinetic parameters, dose and treatment frequency.

RESULTS

The model quantifies possible effects of single and multiple (periodic) treatment on the outcome of parasite competition. In the absence of further inoculation, the dosage and/or treatment frequency required for complete clearance can be estimated. With persistent superinfection, time-average parasite densities can be derived in terms of the basic immune-regulating parameters, the drug efficacy and treatment regimen.

CONCLUSION

The functional relations in the model are applicable to a wide range of conditions and transmission environments, allowing predictions to be made on both the individual and the community levels, and, in particular, transitions from drug-sensitive to drug-resistant parasite dominance to be projected on both levels.

摘要

背景

同时感染宿主的疟原虫会根据其内在生长速率以及通过刺激相互抑制生长的交叉反应性免疫反应进行竞争。如果这些表型还表现出不同的药物敏感性(“敏感”与“耐药”菌株),药物治疗会改变它们的联合动态以及感染的长期结果:最明显的是,持续的药物压力会使耐药性更强但在其他方面竞争力较弱的菌株占据主导地位。

方法

在此开发了一个数学模型,以分析抗疟药物使用的这些以及更细微的影响是如何受到免疫反应、寄生虫的反复再接种、药物药代动力学参数、剂量和治疗频率的调节的。

结果

该模型量化了单次和多次(周期性)治疗对寄生虫竞争结果的可能影响。在没有进一步接种的情况下,可以估计完全清除所需的剂量和/或治疗频率。对于持续的重复感染,可以根据基本免疫调节参数、药物疗效和治疗方案得出时间平均寄生虫密度。

结论

该模型中的函数关系适用于广泛的条件和传播环境,能够在个体和社区层面进行预测,特别是能够预测在两个层面上从药物敏感型寄生虫主导到耐药型寄生虫主导的转变。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92b3/1629019/c41d6ee18160/1475-2875-5-86-7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92b3/1629019/63122d0d9663/1475-2875-5-86-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92b3/1629019/ca9d33137344/1475-2875-5-86-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92b3/1629019/bad2df9d5844/1475-2875-5-86-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92b3/1629019/2d06f9cea106/1475-2875-5-86-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92b3/1629019/baf99fed98b8/1475-2875-5-86-5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92b3/1629019/d6d7f9ee74c4/1475-2875-5-86-6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92b3/1629019/c41d6ee18160/1475-2875-5-86-7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92b3/1629019/63122d0d9663/1475-2875-5-86-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92b3/1629019/ca9d33137344/1475-2875-5-86-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92b3/1629019/bad2df9d5844/1475-2875-5-86-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92b3/1629019/2d06f9cea106/1475-2875-5-86-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92b3/1629019/baf99fed98b8/1475-2875-5-86-5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92b3/1629019/d6d7f9ee74c4/1475-2875-5-86-6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92b3/1629019/c41d6ee18160/1475-2875-5-86-7.jpg

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