Prediction and Prevention of Emergence of Resistance of Clinically Used Antibacterials
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Prediction and Prevention of Emergence of Resistance of Clinically Used Antibacterials
Fernando BaqueroDpt. Microbiology, Ramón y Cajal Hospìtal
Madrid, Spain
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The basic process
Variation: mutation rate
Environment
Selection of variants
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Baquero, ICC 1999
NEW HOUSE-KEEPING GENE?
Host
X
COST
COMp
COST
COMp
COST
COMp
A1
A1
A2
A1
House-keeping gene
Genetic variation
Antibiotic selection- selective compartments
Genetic variation- gene recombination
Genetic variation- gene recombination- accessory genetic elements
Antibiotic selection- selective compartments
Antibiotic selection(Multiple)
Genetic variation- linkage colonization factors
Evolution of Antibiotic Resistance
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Elements for Prediction
• Antimicrobial agent (A)
• Bacterial population/s (B)
• In-host environment of A/B interaction
• Ecology of host population
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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)
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Complexity in prediction of mutation rate
Target access mutations
Target protective mutations
Target structural mutations
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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
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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
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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
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Process of sequential selection of intermediate and resistant variants
0.1
1
10
100
1 2 3 4 5 6 7 8 9
S
I
R
0
10
20
30
40
50
60
70
80
90
100
1 2 3 4 5 6 7 8 9
%S
% R
%I
Reduction in viability after exposure to different antibiotics or concentrations. Effect on final proportion of different bacterial
subpopulations
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Antibiotic Gradients in Compartmentalized Habitats
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Concentration-Dependent Selection of TEM-12 over TEM-1 (mixed
cultures1:100)
0
1
2
3
4
5
6
7
0 0.004 0.008 0.015 0.03 0.06 0.12 0.25 0.5
cefotaxime (µg/ml)
Sel
ectio
n co
effic
ient
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Time-dependent Selection of TEM-12 and TEM-12/OmpF over TEM-1 in mixed cultures
-2
0
2
4
6
8
10
0 0 0.01 0.02 0.03 0.06 0.12 0.25 0.5
cefotaxime (µg/ml)
Sel
ectio
n co
effic
ient
4 h
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TEM-12 selection over TEM-1 in mice treated with cefotaxime: change in log TEM-12/TEM1
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 mg/k .25 mg/k 1 mg/k 4 mg/k 16 mg/k 64 mg/k 256 mg/k
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P. aeruginosa mutation rates in cystic fibrosisand bacteremic patients
<1x10-8
1x10-7
1x10-6
1x10-5
0 5 10 15 20 25 30 35 40 45 50
Bacteremic-patients
Mutation-rates
2,4x10-8
<1x10-8
1x10-7
1x10-6
1x10-5
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
CF-Patients
Mutation-rates
2,9x10-8
3,6x10-6
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Antibiotic Resistance in mutator phenotype
P. aeruginosa from cystic fibrosis patients
0
10
20
30
40
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60
70
80
90
TicarcillinCeftazidime
ImipenemGentamicin
TobramycinAmikacin
NorfloxacinFosfomycin
% Resistance
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Concentration-dependent E. coli mutS mutation rate (rifampicin-resistance)
Mu
tati
on
ra
te
CAZ (µg/ml)
0.5 0.4 0.3 0.2 0.1 0)
37º/18 hours
0.5 0.4 0.3 0.2 0.1 0
1,20E-05
4,00E-05
2,00E-05
4,00E-7.5
CEFTAZIDIME (µg/ml)
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Why mutators do not predominate?
Stressful Environment Exploitable Environment
mutator
non-mutator
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Biological Cost of Low-level Resistance may be Compensated before Evolution to High-level
Resistancel
HLR
LLR
Biological Cost
Sörensen and Andersson, 1999
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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
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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
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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)
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Hungry models for resistance:what do we need?
Most models are based on:
1. Duration of infectiousness of infected individuals2. Incidence of drug treatment3. Extent to which treatment of susceptible population reduces the transmission of the infection4. 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)
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The 15 essential components in the predictive modeling of development of antibiotic resistance
(1)
. R0 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
. z0 initial frequency of R genotype
. w fitness of S or R genotypes
. probability of selection of R genotype during therapy
. y0 endemic prevalence as a function of antibiotic use
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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
. TR time to reach a given frequency of resistance
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Some parameters used in the study of Iceland S. pneumoniae pen-R
. R0 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
. z0 initial R frequency -3.1 (log10 z0)
. 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)
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The patient and the community: the unified view
Patienta. R proportional to total amount of antibiotic
b. R proportional to multiple sequential treatments
c. R proportional to persistance of R organism
Communitya'. R proportional to total usage of antibiotic
b'. R proportional to number of treated patients
c'. R proportional to endemicity of R organism