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用当前的方法一直控制耐药性面临的棘手难题。

The slippery difficulty of ever containing drug resistance with current practices.

作者信息

Fullybright R

机构信息

Department of Applied Research, Applied-Research Center for True Development, 4016 rue Prefontaine, Montréal, Québec, H1W 0A3, Canada.

出版信息

Eur J Clin Microbiol Infect Dis. 2017 Apr;36(4):603-609. doi: 10.1007/s10096-016-2855-x. Epub 2016 Nov 28.

DOI:10.1007/s10096-016-2855-x
PMID:27896497
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5366174/
Abstract

It has previously been shown that the rate of drug resistance emergence in medicine is exponential, while we have been producing drugs at a much lower rate. Our ability to successfully contain resistance at any one time is function of how many drugs we have at our disposal to counter new resistances from pathogens. Here, we assess our level of preparedness through a mathematical comparison of the drug manufacture rate by the pharmaceutical industry with the resistance emergence rate in pathogens. To that effect, changes in the rates of growth of the drugs production and resistance emergence processes are computed over multiple time segments and compared. It is found that new resistance emergence rate in infectious diseases medicine remains mathematically and permanently ahead of the drugs production rate by the pharmaceutical industry. Consequently, we are not in a position to ever contain current or future strengths of resistance from pathogens. A review of current practices is called for.

摘要

此前已有研究表明,医学中耐药性出现的速率呈指数增长,而我们生产药物的速率要低得多。我们在任何时候成功控制耐药性的能力取决于我们可用于对抗病原体新耐药性的药物数量。在此,我们通过对制药行业的药物生产速率与病原体耐药性出现速率进行数学比较,来评估我们的准备程度。为此,计算了多个时间段内药物生产和耐药性出现过程的增长率变化并进行比较。结果发现,传染病医学中新耐药性出现的速率在数学上始终领先于制药行业的药物生产速率。因此,我们无法控制病原体当前或未来的耐药性强度。有必要对当前做法进行审查。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b7c8/5366174/1ec5defb37c8/10096_2016_2855_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b7c8/5366174/1ec5defb37c8/10096_2016_2855_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b7c8/5366174/1ec5defb37c8/10096_2016_2855_Fig1_HTML.jpg

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本文引用的文献

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Pathogens. 2019 May 31;8(2):73. doi: 10.3390/pathogens8020073.
2
Modeling and predicting drug resistance rate and strength.建模与预测耐药率及强度。
Eur J Clin Microbiol Infect Dis. 2016 Aug;35(8):1259-67. doi: 10.1007/s10096-016-2659-z. Epub 2016 May 21.
3
Prospects for new antibiotics: a molecule-centered perspective.新型抗生素的前景:以分子为中心的视角。
J Antibiot (Tokyo). 2014 Jan;67(1):7-22. doi: 10.1038/ja.2013.49. Epub 2013 Jun 12.
4
Novel classes of antibiotics or more of the same?新型抗生素还是更多的同类抗生素?
Br J Pharmacol. 2011 May;163(1):184-94. doi: 10.1111/j.1476-5381.2011.01250.x.
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Resistance to antibiotics: are we in the post-antibiotic era?抗生素耐药性:我们处于后抗生素时代吗?
Arch Med Res. 2005 Nov-Dec;36(6):697-705. doi: 10.1016/j.arcmed.2005.06.009.
6
Cytosine arabinoside (ara-C) resistance confers cross-resistance or collateral sensitivity to other classes of anti-leukemic drugs.阿糖胞苷(ara-C)耐药会导致对其他类抗白血病药物产生交叉耐药或 collateral 敏感性。 (注:这里“collateral”可能是“协同”之类的意思,但根据要求未做进一步解释,推测可能是专业术语中不太常见准确中文表述的词)
Anticancer Res. 2000 Jan-Feb;20(1A):139-50.
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Emergence of Shigella sonnei resistant to kanamycin and to nalidixic acid, without exposure to these drugs.
J Med Microbiol. 1969 Nov 4;2(4):457-61. doi: 10.1099/00222615-2-4-457.