scholarly journals Imperfect drug penetration leads to spatial monotherapy and rapid evolution of multi-drug resistance

2014 ◽  
Author(s):  
Stefany Moreno-Gamez ◽  
Alison L Hill ◽  
Daniel I.S. Rosenbloom ◽  
Dmitri A. Petrov ◽  
Martin A Nowak ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Rapid Evolution ◽  
Public Health Problem ◽  
Antimicrobial Treatment ◽  
The Body ◽  
Drug Coverage ◽  
Multi Drug Resistance ◽  
Resistance Risk ◽  
Combination Treatments ◽  
Microbial Infections

Infections with rapidly evolving pathogens are often treated using combinations of drugs with different mechanisms of action. One of the major goals of combination therapy is to reduce the risk of drug resistance emerging during a patient's treatment. While this strategy generally has significant benefits over monotherapy, it may also select for multi-drug resistant strains, which present an important clinical and public health problem. For many antimicrobial treatment regimes, individual drugs have imperfect penetration throughout the body, so there may be regions where only one drug reaches an effective concentration. Here we propose that mismatched drug coverage can greatly speed up the evolution of multi-drug resistance by allowing mutations to accumulate in a stepwise fashion. We develop a mathematical model of within-host pathogen evolution under spatially heterogeneous drug coverage and demonstrate that even very small single-drug compartments lead to dramatically higher resistance risk. We find that it is often better to use drug combinations with matched penetration profiles, although there may be a trade-off between preventing eventual treatment failure due to resistance in this way, and temporarily reducing pathogen levels systemically. Our results show that drugs with the most extensive distribution are likely to be the most vulnerable to resistance. We conclude that optimal combination treatments should be designed to prevent this spatial effective monotherapy. These results are widely applicable to diverse microbial infections including viruses, bacteria and parasites.

scholarly journals Imperfect drug penetration leads to spatial monotherapy and rapid evolution of multidrug resistance

2015 ◽  
Vol 112 (22) ◽  
pp. E2874-E2883 ◽  
Author(s):  
Stefany Moreno-Gamez ◽  
Alison L. Hill ◽  
Daniel I. S. Rosenbloom ◽  
Dmitri A. Petrov ◽  
Martin A. Nowak ◽  
...  
Keyword(s):  
Multidrug Resistance ◽  
Rapid Evolution ◽  
Public Health Problem ◽  
Multidrug Resistant ◽  
Term Treatment ◽  
Antimicrobial Treatment ◽  
The Body ◽  
Drug Coverage ◽  
Chronic Infections ◽  
Long Term Treatment

Infections with rapidly evolving pathogens are often treated using combinations of drugs with different mechanisms of action. One of the major goal of combination therapy is to reduce the risk of drug resistance emerging during a patient’s treatment. Although this strategy generally has significant benefits over monotherapy, it may also select for multidrug-resistant strains, particularly during long-term treatment for chronic infections. Infections with these strains present an important clinical and public health problem. Complicating this issue, for many antimicrobial treatment regimes, individual drugs have imperfect penetration throughout the body, so there may be regions where only one drug reaches an effective concentration. Here we propose that mismatched drug coverage can greatly speed up the evolution of multidrug resistance by allowing mutations to accumulate in a stepwise fashion. We develop a mathematical model of within-host pathogen evolution under spatially heterogeneous drug coverage and demonstrate that even very small single-drug compartments lead to dramatically higher resistance risk. We find that it is often better to use drug combinations with matched penetration profiles, although there may be a trade-off between preventing eventual treatment failure due to resistance in this way and temporarily reducing pathogen levels systemically. Our results show that drugs with the most extensive distribution are likely to be the most vulnerable to resistance. We conclude that optimal combination treatments should be designed to prevent this spatial effective monotherapy. These results are widely applicable to diverse microbial infections including viruses, bacteria, and parasites.

scholarly journals Artificial Intelligence to Get Insights of Multi-Drug Resistance Risk Factors during the First 48 Hours from ICU Admission

Antibiotics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 239
Author(s):  
Inmaculada Mora-Jiménez ◽  
Jorge Tarancón-Rey ◽  
Joaquín Álvarez-Rodríguez ◽  
Cristina Soguero-Ruiz
Keyword(s):  
Artificial Intelligence ◽  
Risk Factors ◽  
Drug Resistance ◽  
Multi Drug Resistance ◽  
Time Interval ◽  
Health Records ◽  
Resistance Risk ◽  
Icu Admission ◽  
Selection Strategies ◽  
Patient Will

Multi-drug resistance (MDR) is one of the most current and greatest threats to the global health system nowadays. This situation is especially relevant in Intensive Care Units (ICUs), where the critical health status of these patients makes them more vulnerable. Since MDR confirmation by the microbiology laboratory usually takes 48 h, we propose several artificial intelligence approaches to get insights of MDR risk factors during the first 48 h from the ICU admission. We considered clinical and demographic features, mechanical ventilation and the antibiotics taken by the patients during this time interval. Three feature selection strategies were applied to identify statistically significant differences between MDR and non-MDR patient episodes, ending up in 24 selected features. Among them, SAPS III and Apache II scores, the age and the department of origin were identified. Considering these features, we analyzed the potential of machine learning methods for predicting whether a patient will develop a MDR germ during the first 48 h from the ICU admission. Though the results presented here are just a first incursion into this problem, artificial intelligence approaches have a great impact in this scenario, especially when enriching the set of features from the electronic health records.

scholarly journals 2583. Short-term Impact of Antimicrobial Exposure on Fecal Carriage of Resistant Microorganisms

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S897-S897
Author(s):  
Abdul Ahmad ◽  
Carla Amundson ◽  
Connie Clabots ◽  
Stephen Porter ◽  
James R Johnson ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Antimicrobial Resistance ◽  
Antimicrobial Treatment ◽  
Multi Drug Resistance ◽  
Antimicrobial Use ◽  
Fecal Flora ◽  
Short Term ◽  
Paired Samples ◽  
Fecal Carriage ◽  
Stool Samples

Abstract Background The relationship between antimicrobial use and subsequent resistance is complicated; this study assesses the short-term impact of antimicrobial use on fecal carriage of resistant microorganisms. This is a sub-study of an ongoing trial comparing 7 vs. 14 days of antimicrobial treatment for male urinary tract infection. This analysis quantifies the effect of 1–2 weeks of systemic antimicrobial use on the fecal flora within 1 week of completing therapy. Methods The parent study has enrolled 216 subjects, with 178 enrolled in the optional resistance sub-study. Subjects received either ciprofloxacin or trimethoprim/sulfamethoxazole (SXT), randomized to 7 vs. 14 days therapy. Subjects provided 2 stool specimens, 1 during treatment and 1 a week after completing study medication. Samples were plated on media for Gram-positive and negative growth, including T-7 plates with ciprofloxacin and SXT added to select for resistant organisms. Resistance to 22 antimicrobials was assessed, with resistance reported by: number of isolates with any antimicrobial resistance, total number of resistant drugs/isolate, and number of isolates with multi-drug resistance (resistance to 3 or more different antimicrobial classes). Results Overall, 143 (80%) subjects provided 2 stool samples, with 104 (73%) having growth from at least 1 of the samples. Fifty-one of 143 (36%) had microbial growth from both samples. From these 51 paired samples, there were 255 total strains isolated (117 from the first sample, 138 from the second), with some yielding multiple organisms (range, 1–5). From sample 1, 110/117 (94%) isolates had any antimicrobial resistance, vs. 131/138 (95%) from sample 2 (P = .79). Mean number of resistant drugs/isolate was 7.4 in sample 1 and 5.8 in sample 2 (P = .009). Multi-drug resistance was seen in 102/117 (87%) isolates from sample 1 vs. 85/138 (62%) isolates in sample 2 (P < .001). Conclusion The fecal flora of patients on antimicrobial therapy for UTI has a significant increase in resistant microorganisms compared with samples obtained shortly after antimicrobial completion. This may reflect repopulation of the fecal flora with less-resistant strains after the selection pressure of therapy has been removed. After unblinding, we will assess if differences in resistance are affected by therapy duration. Disclosures All authors: No reported disclosures.

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