pk/pd modelling : clinical implications
DESCRIPTION
Pk/Pd modelling : Clinical Implications. JW Mouton Dept Medical Microbiology Canisius Wilhelmina Hospital Nijmegen, The Netherlands. infections are treated with the same dosing regimen irrespective of the absolute susceptibility of the micro-organism. Susceptible = Susceptible. - PowerPoint PPT PresentationTRANSCRIPT
Pk/Pd modelling :Clinical Implications
JW MoutonDept Medical Microbiology
Canisius Wilhelmina Hospital
Nijmegen, The Netherlands
infections are treated with the same
dosing regimen irrespective of the absolute
susceptibility of the micro-organism
Susceptible
=
SusceptibleMIC = .016 mg/L
MIC = 2.0 mg/L
Pharmacokineticsconc. vs time
Con
c.
Time0 25
0.0
0.4
PK/PDeffect vs time
Time
Eff
ect
0
1
0 25
Pharmacodynamicsconc. vs effect
0
1
10-4 10-3Conc (log)
Eff
ect
Immunesystem Severity Infection
Forrest, 1993
Predictors of Clinical Response
AUIC =125 : a magical number??
• PK models : 125
• Animal studies granulocytopenic : 100
• Animal studies immunocompetent : 40
• Human studies: peak/MIC ratio: > 1:10, AUC/MIC
•There is a clear relation between pharmacodynamic index and effect.
•At some concentration there is a maximum effect
•Ideally, a dose should be chosen to obtain a maximum effect
Example Target Controlled DosingIxacin
• Patient 60 yr, pneumonia and suspected bacteraemia/sepsis
• Ixacin 400 mg IV q8h
• Gram negative rod, E-test MIC=0.01 mg/L
• Adjust dose to 100 mg/day
Mouton & Vinks, PW 134:816
Use of Pharmacodynamics in setting breakpoints
• Known concentration effect relationship– microbiology– clinical effect
• Standard dose used clinically– pharmacokinetic profile– patient/disease specifics
Breakpoints based on AUC/MIC =100
•Three studies have shown AUC/MIC predictive for outcome
•One prospective study Peak/MIC more predictive
Quinolones : dose optimalization
To peak or not to Peak?
•For most quinolones, because of the relatively long half-life, there is a strong correlation between AUC/MIC and Peak/MIC
Pharmacodynamics of fluoroquinolones against Pseudomonas infection in neutropenic rats
Drusano et.al., AAC, 1993, 37: 483-90.
• Survival linked to Peak/MIC when
ratio > 10/1
• Survival linked to AUC/MIC when
ratio < 10/1
! ! !
• Proteinbinding in animals/humans
• Duration of treatment
• Extended dosing interval
• Immune system
• Severity of infection
• Bacteriological vs Clinical cure
T>MIC: how long??breakpoints
• Free fractions of the drug (Fu)?• The same for all micro-organisms?• The same for all beta-lactams?• Efficacy vs emergence of resistance• The same for all infections?• Value in combination therapy?• Variance pk in population?• Static dose vs maximum effect?
• If maximum effect, which max effect?
Same all beta-lactams?Same all m.o.?
Drug Enterobacteriaceae S. pneumoniae
Ceftriaxone (F) 38 (34-42) 39 (37-41)
Cefotaxime 38 (36-40) 38 (36-40)
Ceftazidime 36 (27-42) 39 (35-42)
Cefpirome 35 (29-40) 37 (33-39)
MK-0826 32 (20-39)
Meropenem 22 (18-28)
Imipenem 24 (17-28)
Linezolid 40 (33-59)
T> MIC for static effect
Predicted efficacy of ticarcillin and tobramycinpseudomonas
-6 -5 -4 -3 -2 -1 0 1 2 3 4-6
-4
-2
0
2
4
predicted : t > 0.25 * mic ticarcillin and log auc tobramycin
Slope
Y-intercept
X-intercept
1/slope
0.9285 ± 0.07826
-2.815 ± 0.1250
3.032
1.077
r² 0.7830
dcfu predicted
dcfu
measu
red
Mouton ea., aac 1999
Concentration-time profile of beta-lactamVd = 20 L, Ka = 1.2 h-1, Ke = 0.3 h-1
0.20
1.20
2.20
3.20
4.20
5.20
6.20
7.20
8.20
9.20
10.20
11.20
12.20
13.20
14.20
15.20
16.20
17.20
18.20
19.20
0.00
1.25
2.50
3.75
5.00
6.25
7.50
8.75
10.0
0
11.2
5
12.5
0
13.7
5
15.0
0
16.2
5
17.5
0
18.7
5
20.0
0
21.2
5
22.5
0
23.7
5
0.95-1.00
0.90-0.95
0.85-0.90
0.80-0.85
0.75-0.80
0.70-0.75
0.65-0.70
0.60-0.65
0.55-0.60
0.50-0.55
0.45-0.50
0.40-0.45
0.35-0.40
0.30-0.35
0.25-0.30
0.20-0.25
0.15-0.20
0.10-0.15
0.05-0.10
0.00-0.05
Conc
Time
Prob
Monte Carlo Simulation of beta-lactamVd = 20 L, Ka = 1.2 h-1, Ke = 0.3 h-1, VC=20%
4h
10h
Mouton, Int J Antimicrob Agents april 2002
Monte Carlo Simulations in pk/pd (1)
• Estimates of pk parameter values and a measures of dispersion (usually SD)
• Simulate pk curves based here-on
Target Attainment Rates
• Pharmacokinetics of piperacillin– Population pk using npem2– ‘validation’using classical pk analysis (winnonlin)
• Use parameter estimates for Monte Carlo Simulation– Using sd’s only– Using correlation matrix
• Obtain TARS at various T>MICs
0 25 50 75 1000
20
40
60
80
100
LoHi
Average
T>MIC %
PR
OB
Monte Carlo Simulations in pk/pd (2)
• Estimates of pk parameter values and a measures of dispersion (usually SD)
• Obtain covariance matrix or correlation matrix
• Simulate pk curves based here-on :• The result will be SMALLER 95% CI because of
correlation between parameter estimates
0 25 50 75 1000
20
40
60
80
100
LoHi
Average
T>MIC %
PR
OB
* 1
00
Needed for TAR
• Pharmacokinetic profile of the drug
• Variation of PK parameters– Estimates of VC
– Population analysis, correlation matrix
Cumulative Fraction of Response
• TAR at each MIC• Distribution of MICs• Multiply, determine fraction and cum fraction
• Beware !!!!! For prediction, a ‘true’ MIC distribution is needed, NOT a biased collection of strains!!!!!
0.0625 0.125 0.25 0.5 1 2 40.0
0.1
0.2
0.3
0.4
MIC mg/L
frac
tio
n
After Drusano et al
Pharmacodynamic properties of beta-lactams
• Efficacy dependent on time above mic
• Relatively concentration independent
Target for concentration profiles optimizing time > mic
Avoid high peak concentrations
Target Concentrationcontinuous infusion
• Maximum effect time-kill at 4 x MIC• Maximum effect in vitro model 4 x MIC
(Mouton et al 1994)• Effect in endocarditis model 4 x MIC
(Xiong et al 1994)• Effect in pneumonia model dependent on
severity of infection (Roosendaal et al 1985,86)
Efficacy in Relation to MICcontinuous infusion ceftazidime, K. pneumoniae rat pneumonia
Roosendaal et al. 1986, AAC 30:403; Roosendaal et al. 1985, JID 152:373
Continuous InfusionPharmacokinetic Considerations
• Protein binding
• Linear relationship between clearance and dose
• Linear relationship between protein binding and dose
• Third compartment effects (CNS)
Dose Calculationscontinuous infusion
• Total Clearance estimate
• Volume of Distribution
• Elimination rate constant
TBC = Ke x Vd
Normogram Continuous Infusion
0 20 40 60 80 100 120 140 160 180 2000
1000
2000
3000
4000
5000
6000
64
32
16
8
4
clearance mL/min
do
se m
g/d
ay
Mouton & Vinks, JAC 1996
Example Target Controlled Dosingcefticostix
• Patient 60 yr, UTI and suspected bacteraemia/sepsis
• Cefticostix 1 g IV q8h• Gram negative rod, E-test MIC=0.12 mg/L• Adjust dose to 30 mg/day CI based on
patient clearance
Mouton & Vinks, PW 134:816
Example Cost EfficiencyContinuous Infusion Beta-lactam
Therapeutic Drug Monitoringcontinuous infusion
• Clearance estimate
• Variable clearance (ICU)
• Non-linear clearance
Ceftazidime concentrationsICU patients
Piperacillin concentrationscontinuous vs intermittent