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Prediction and Prevention of Emergence of Resistance of Clinically Used Antibacterials Fernando Baquero Dpt. Microbiology, Ramón y Cajal Hospìtal Madrid, Spain The basic process Variation: mutation rate Environment Selection of variants Evolution of Antibiotic Resistance Baquero, ICC 1999 House-keeping gene C O M p Genetic variation C O S T A1 Antibiotic selection - selective compartments Genetic variation - gene recombination C O M p C O S T A1 Genetic variation - gene recombination - accessory genetic elements X C O M p Antibiotic selection - selective compartments C O S T A1 A2 Antibiotic selection (Multiple) Genetic variation - linkage colonization factors Host NEW HOUSE-KEEPING GENE? Elements for Prediction • Antimicrobial agent (A) • Bacterial population/s (B) • In-host environment of A/B interaction • Ecology of host population Emergence of mutational resistance • Resistance is a function of the product of original inoculum, rate of reproduction and the mutation rate, divided by the negative growth rate (reduction in susceptibles). If high inoculum size resistance If no starting mutants, best S killer resistance If starting R mutants, best S killer resistance. (Lipsitch and Levin, AAC 1997; Austin et al., J. Theor. Biol., 1999) Complexity in prediction of mutation rate Target access mutations Target protective mutations Target structural mutations Target structural mutations (1) Antibiotic target-based mutation rate depends on: • Target gene/s structure Base composition determines possibility of mutation The higher the gene size, possibility mutation • Target permissivity Wide functional domains in the gene mutation rate • Target diversity Multiple targets mutation rate • Target cooperativity If inhibition of multiple targets are required for effect, mutation Target structural mutations (2) • Target determination If target is determined by multiple genes mutation • Target density High number of target molecules mutation • Target redundancy Multiple redundant genes encoding the target mutation • Target dominance If modified target is recessive mutation • Target essentiality Low cost target functional modifications mutation Prediction of antibiotic-resistance theoretical mutation rate • Mutation rate results from a multifactorial set of conditions • In-vitro mutation rate is only mutation rate in vitro Process of sequential selection of intermediate and resistant variants 100 100 90 80 70 10 S I R 60 %S 50 %R 40 %I 30 1 1 2 3 4 5 6 7 8 9 20 10 0 0.1 1 2 3 4 5 6 7 8 Reduction in viability after exposure to different antibiotics or concentrations. Effect on final proportion of different bacterial subpopulations 9 Antibiotic Gradients in Compartmentalized Habitats Concentration-Dependent Selection of TEM-12 over TEM-1 (mixed cultures1:100) Selection coefficient 7 6 5 4 3 2 1 0 0 0.004 0.008 0.015 0.03 0.06 cefotaxime (µg/ml) 0.12 0.25 0.5 Time-dependent Selection of TEM-12 and TEM12/OmpF over TEM-1 in mixed cultures Selection coefficient 10 8 6 4h 4 2 0 0 0 0.01 0.02 0.03 0.06 0.12 0.25 0.5 -2 cefotaxime (µg/ml) TEM-12 selection over TEM-1 in mice treated with cefotaxime: change in log TEM-12/TEM1 1.4 1.2 1 0.8 0.6 0.4 0.2 0 0 mg/k .25 mg/k 1 mg/k 4 mg/k 16 mg/k 64 mg/k 256 mg/k P. aeruginosa mutation rates in cystic fibrosis and bacteremic patients Mutation-rates Mutation-rates 1x10-5 1x10-5 3,6x10-6 1x10-6 1x10-6 1x10-7 1x10-7 2,9x10-8 2,4x10-8 <1x10-8 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 CF-Patients <1x10-8 0 5 10 15 20 25 30 35 40 45 50 Bacteremic-patients Antibiotic Resistance in mutator phenotype P. aeruginosa from cystic fibrosis patients % Resistance 90 80 70 60 50 40 30 20 10 0 Ticarcillin Imipenem Tobramycin Norfloxacin Ceftazidime Gentamicin Amikacin Fosfomycin Concentration-dependent E. coli mutS mutation rate (rifampicin-resistance) 4,00E-05 ) 0.5 0.4 0.3 0.2 0.1 37º/18 hours 2,00E-05 Mutation rate 0 1,20E-05 4,00E-7.5 0.5 0.4 0.3 0.2 0.1 CEFTAZIDIME (µg/ml) CAZ (µg/ml) 0 Why mutators do not predominate? mutator non-mutator Stressful Environment Exploitable Environment Biological Cost of Low-level Resistance may be Compensated before Evolution to High-level Resistancel HLR LLR Biological Cost Sörensen and Andersson, 1999 Conditions that increases the rate of antibiotic-R mutants (I) 1. High number of bacterial cells 2. Low antibiotic concentrations of the selective agent, exerced during a prolonged period 3. Antibiotic degradation or inactivation (spontaneous-binding-enzymatic) 4. Slow killing kinetics of the selective agent 5. Many different genes leading to resistance Conditions that increases the rate of antibiotic-R mutants (II) 6. Mutator phenotype (methyl-mismatch repair defficiencies and other mutator mechanisms) 7. Up-recombination systems 8. Bacterial stress; Slow bacterial growth 9. No significant decrease in fitness of R mutants 10. Physically structurated habitat Hungry predictive mathematical models • Models require the inclusion of important parameters for which no quantitative estimates are available for most host-bacteria-antibiotic interactions. • The use of models to design/evalute drug treatment regimes will depend on the availability of such data, and on how well the models predict observed outcomes. 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) . . . . . . . . R0 f ß µ z0 w y0 transmissibility of S or R genotypes rate of loss of carriage secondary cases per unit of time removal or death of cases initial frequency of R genotype fitness of S or R genotypes probability of selection of R genotype during therapy endemic prevalence as a function of antibiotic use The 15 essential components of the predictive modeling of development of antibiotic resistance (2) . . .m .a . . . TR erradication (lengh colonization/lengh therapy) superinfection fitness (colon. of S/R hosts with R/S) adquisition of resistance (mutation rate) prescription rate x lengh of treatment prescription rate per unit of time change in consumption of antibiotics time to reach a given frequency of resistance Some parameters used in the study of Iceland S. pneumoniae pen-R . R0 .f .µ . z0 . .m .a . . transmissibility R loss of carriage removal cases initial R frequency superinfection fitness mutation rate antibiotic pressure prescription/time change in consumption 2.1 cases per dase 2.6 months of carriage (1/f) 84 months of maintenance -3.1 (log10 z0) 1 (R S) not considered 38 DDDs/1,000 children 10 days -12.7 % (Austin, Kristinsson & Anderson, PNAS 96:1152, 1999) The patient and the community: the unified view Patient a. R proportional to total amount of antibiotic b. R proportional to multiple sequential treatments c. R proportional to persistance of R organism Community a'. R proportional to total usage of antibiotic b'. R proportional to number of treated patients c'. R proportional to endemicity of R organism