title: Modeling and predicting drug resistance rate and strength.
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.
authors.affiliation: Department of Applied Research, Applied-Research Center for True Development, 4016 rue Préfontaine, Montréal, Québec, H1W 0A3, Canada. fullybright@gmail.com.
authors.forename: R
authors.initials: R
authors.lastname: Fullybright
authors.affiliation: Division of Biostatistics and Epidemiology, Department of Biomedical Sciences, Texas Tech University Health Sciences Center, 4801 Alberta Avenue, El Paso, TX, 79905, USA.
authors.forename: A
authors.initials: A
authors.lastname: Dwivedi
authors.affiliation: Division of Biostatistics and Epidemiology, Department of Biomedical Sciences, Texas Tech University Health Sciences Center, 4801 Alberta Avenue, El Paso, TX, 79905, USA.
authors.forename: I
authors.initials: I
authors.lastname: Mallawaarachchi
authors.affiliation: Laboratory of Applied Ecology, School of Agronomic Sciences, University of Abomey-Calavi, 05 BP 1752, Cotonou, Republic of Benin.
authors.forename: B
authors.initials: B
authors.lastname: Sinsin
mesh_terms: D000900:Anti-Bacterial Agents / Q000494:pharmacology*; D001419:Bacteria / Q000187:drug effects; D001424:Bacterial Infections / Q000382:microbiology*; D021821:Communicable Diseases, Emerging / Q000382:microbiology*; D024881:Drug Resistance, Bacterial; D006801:Humans; D008826:Microbial Sensitivity Tests; D015233:Models, Statistical
chemical_list: D000900:Anti-Bacterial Agents
keywords: Antibiotic; Drug; Infectious diseases; Layer; Model; Pharmaceutical; Rate; Resistance; Strength