1. Prediction and Prevention of Emergence of Resistance of Clinically Used Antibacterials Fernando Baquero Dpt. Microbiology, Ramón y Cajal Hospìtal Madrid, Spain

2. The basic process Variation: mutation rate Environment Selection of variants

3. Evolution of Antibiotic Resistance

4. Elements for Prediction A ntimicrobial agent (A) B acterial population/s (B) I n-host environment of A/B interaction E cology of host population

5. 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)

6. Complexity in prediction of mutation rate Target access mutations Target protective mutations Target structural mutations

7. 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

8. 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

9. 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

10. Process of sequential selection of intermediate and resistant variants Reduction in viability after exposure to different antibiotics or concentrations. Effect on final proportion of different bacterial subpopulations

11. Antibiotic Gradients in Compartmentalized Habitats

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

13. Time-dependent Selection of TEM-12 and TEM- 12/OmpF over TEM-1 in mixed cultures 4 h

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

15. P. aeruginosa mutation rates in cystic fibrosis and bacteremic patients <1x10 -8 1x10 -7 1x10 -6 1x10 -5 05101520253035404550 Bacteremic-patients Mutation-rates 2,4x10 -8

16. Antibiotic Resistance in mutator phenotype P. aeruginosa from cystic fibrosis patients 0 10 20 30 40 50 60 70 80 90 Ticarcillin Ceftazidime Imipenem Gentamicin Tobramycin Amikacin Norfloxacin Fosfomycin % Resistance

17. Concentration-dependent E. coli mutS mutation rate (rifampicin-resistance) Mutation rate CAZ (µg/ml) 0.50.40.30.20.10 ) 37º/18 hours 0.50.40.30.20.10 1,20E-05 4,00E-05 2,00E-05 4,00E-7.5 CEFTAZIDIME (µg/ml)

18. Why mutators do not predominate? Stressful Environment Exploitable Environment mutator non-mutator

19. Biological Cost of Low-level Resistance may be Compensated before Evolution to High-level Resistancel HLR LLR Biological Cost Sörensen and Andersson, 1999

20. 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

21. 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

22. 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. (Free version of Levin and Anderson, 1999)

23. 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)

24. The 15 essential components in the predictive modeling of development of antibiotic resistance (1). R 0 transmissibility of S or R genotypes. f rate of loss of carriage. ß secondary cases per unit of time. µ removal or death of cases. z 0 initial frequency of R genotype. w fitness of S or R genotypes.  probability of selection of R genotype during therapy. y 0 endemic prevalence as a function of antibiotic use

25. The 15 essential components of the predictive modeling of development of antibiotic resistance (2).  erradication (lengh colonization/lengh therapy).  superinfection fitness (colon. of S/R hosts with R/S). m adquisition of resistance (mutation rate). a prescription rate x lengh of treatment.  prescription rate per unit of time.  change in consumption of antibiotics. T R time to reach a given frequency of resistance

26. Some parameters used in the study of Iceland S. pneumoniae pen-R. R 0 transmissibility R 2.1 cases per dase. f loss of carriage 2.6 months of carriage (1/f). µ removal cases 84 months of maintenance. z 0 initial R frequency -3.1 (log 10 z 0 ).  superinfection fitness 1 (R  S). m mutation rate not considered. a antibiotic pressure 38 DDDs/1,000 children.  prescription/time 10 days.  change in consumption -12.7 % (Austin, Kristinsson & Anderson, PNAS 96:1152, 1999)

27. 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