Abstract
Drug resistance has been worsening in human infectious diseases medicine over the past several decades. Our ability to successfully control resistance depends to a large extent on our understanding of the features characterizing the process. Part of that understanding includes the rate at which new resistance has been emerging in pathogens. Along that line, resistance data covering 90 infectious diseases, 118 pathogens, and 337 molecules, from 1921 through 2007, are modeled using various statistical tools to generate regression models for the rate of new resistance emergence and for cumulative resistance build-up in pathogens. Thereafter, the strength of the association between the number of molecules put on the market and the number of resulting cases of resistance is statistically tested. Predictive models are presented for the rate at which new resistance has been emerging in infectious diseases medicine, along with predictive models for the rate of cumulative resistance build-up in the aggregate of 118 pathogens as well as in ten individual pathogens. The models are expressed as a function of time and/or as a function of the number of molecules put on the market by the pharmaceutical industry. It is found that molecules significantly induce resistance in pathogens and that new or cumulative drug resistance across infectious diseases medicine has been arising at exponential rates.
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Acknowledgments
Special thanks to Professors and Mentors Gregory Pasternack, Carlos Puente, and Marc Grismer, respectively professors of Geomorphology and Watershed Hydrology, Statistical Hydrology, and Hydrology and Biological/Agricultural Engineering at the University of California, Davis, USA, for providing key advice eventually leading to the publication of this great work.
We acknowledge the expert command of computer systems by Franck Kouyami, Fawaz Tairou, Sandrine Ouensou, et al., for the reliable computer assistance they have offered multiple times along the way.
Many thanks to Sayane Gouroubéra for his many encouragements and capacity to see beyond the usual.
Much gratitude is expressed to the French Agence Universitaire de la Francophonie, a French government agency, for making the Campus Numérique Francophone computer resources center available—thereby making this research possible.
Author contributions
RF: conceived and designed the study, collected the data, and wrote the manuscript.
AD and IM: conducted the statistical analysis.
BS and RF: revised the manuscript for critical intellectual content.
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This work was not funded. The authors received no funding for this work. As a result, no funding body had any role in the study design, decision to publish, or preparation of the manuscript.
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Electronic supplementary material
Below are the links to the electronic supplementary material files.
Supplementary Material 1
Characterization of drug resistance as a multi-layered, stepwise occurrence (PDF 163 kb)
Supplementary Material 2
Supporting rationale for the algorithm (PDF 79.3 kb)
Supplementary Material 3
Detailed methodology (PDF 138 kb)
Supplementary Material 4
First-layer resistance: year and number of occurrences (PDF 1.33 mb)
Supplementary Material 5
Literature references to resistance occurrence between pathogens and single-molecule drugs (PDF 532 kb)
Supplementary Material 6
Second-layer resistance: year and number of occurrences (PDF 173 kb)
Supplementary Material 7
Molecules introduction years (PDF 472 kb)
Supplementary Material 8
Dataset for the statistical analysis (PDF 664 kb)
Supplementary Material 9
Detailed statistical results (PDF 148 kb)
Supplementary Material 10
Figures (PDF 780 kb)
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Fullybright, R., Dwivedi, A., Mallawaarachchi, I. et al. Modeling and predicting drug resistance rate and strength. Eur J Clin Microbiol Infect Dis 35, 1259–1267 (2016). https://doi.org/10.1007/s10096-016-2659-z
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DOI: https://doi.org/10.1007/s10096-016-2659-z