Prediction and Prevention of Emergence of Resistance of Clinically Used Antibacterials

03/02/00

Click here to start to view slide by slide


Download a PDF file to get the whole presentation (needs Acrobat reader 4.0)
(if you do not have Acrobat Reader 4.0, get it here)


 

Table of Contents

Prediction and Prevention of Emergence of Resistance of Clinically Used Antibacterials

The basic process

Evolution of Antibiotic Resistance 

Elements for Prediction

Emergence of mutational resistance

Complexity in prediction of mutation rate

Target structural mutations (1) 

Target structural mutations (2)

Prediction of antibiotic-resistance theoretical mutation rate

Process of sequential selection of intermediate and resistant variants

Antibiotic Gradients in Compartmentalized Habitats

Concentration-Dependent Selection of TEM-12 over TEM-1 (mixed cultures1:100) 

Time-dependent Selection of TEM-12 and TEM-12/OmpF over TEM-1 in mixed cultures

TEM-12 selection over TEM-1 in mice treated with cefotaxime: change in log TEM-12/TEM1

P. aeruginosa mutation rates in cystic fibrosis and bacteremic patients

Antibiotic Resistance in mutator phenotype P. aeruginosa from cystic fibrosis patients

PPT Slide

Why mutators do not predominate?

Biological Cost of Low-level Resistance may be Compensated before Evolution to High-level Resistancel

Conditions that increases the rate of antibiotic-R mutants (I)

Conditions that increases the rate of antibiotic-R mutants (II)

Hungry predictive mathematical models

Hungry models for resistance: what do we need? Most models are based on: 1. Duration of infectiousness of infected individuals 2. Incidence of drug treatment 3. Extent to which treatment of susceptible population reduces the transmission of the infection 4. Degree of reduction in fitness of the resistant bacteria in the absence of treatment (cost) 5. Probability of acquisition of resistance during therapy. (Science, 283:808, 1999) 

The 15 essential components in the predictive modeling of development of antibiotic resistance (1) 

The 15 essential components of the predictive modeling of development of antibiotic resistance (2) 

Some parameters used in the study of Iceland S. pneumoniae pen-R

The patient and the community: the unified view 

Author: F. Baquero (G) 

Email: webmaster@isap.org

Home Page: http://www.isap.org/2000/Bangalore/intro.htm