Prediction and Prevention of Emergence of Resistance of Clinically Used Antibacterials

Fernando BaqueroDpt. Microbiology, Ramón y Cajal Hospìtal

Madrid, Spain

The basic process

Variation: mutation rate

Environment

Selection of variants

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

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

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

Antibiotic Gradients in Compartmentalized Habitats

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

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

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

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

Antibiotic Resistance in mutator phenotype

P. aeruginosa from cystic fibrosis patients

0

10

20

30

40

50

60

70

80

90

TicarcillinCeftazidime

ImipenemGentamicin

TobramycinAmikacin

NorfloxacinFosfomycin

% Resistance

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)

Why mutators do not predominate?

Stressful Environment Exploitable Environment

mutator

non-mutator

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.

(Free version of Levin and Anderson, 1999)

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)

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

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

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)

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