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 = MIC = .016 mg/L MIC = 2.0 mg/L

Severity Infection Immunesystem Pharmacokinetics Pharmacodynamics conc. vs time Conc. Time 25 0.0 0.4 Pharmacodynamics conc. vs effect 1 10 -4 -3 Conc (log) Effect PK/PD effect vs time Time Effect 1 25 Severity Infection Immunesystem

Predictors of Clinical Response Forrest, 1993

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 Dosing Ixacin 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

Quinolones : dose optimalization Three studies have shown AUC/MIC predictive for outcome One prospective study Peak/MIC more predictive 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

Survival linked to Peak/MIC when ratio > 10/1 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.? T> MIC for static effect 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)

Predicted efficacy of ticarcillin and tobramycin pseudomonas Mouton ea., aac 1999

Concentration-time profile of beta-lactam Vd = 20 L, Ka = 1.2 h-1, Ke = 0.3 h-1

Monte Carlo Simulation of beta-lactam Vd = 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

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

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!!!!!

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 Concentration continuous 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 MIC continuous infusion ceftazidime, K. pneumoniae rat pneumonia Roosendaal et al. 1986, AAC 30:403; Roosendaal et al. 1985, JID 152:373

Continuous Infusion Pharmacokinetic Considerations Protein binding Linear relationship between clearance and dose Linear relationship between protein binding and dose Third compartment effects (CNS)

Dose Calculations continuous infusion Total Clearance estimate Volume of Distribution Elimination rate constant TBC = Ke x Vd

Normogram Continuous Infusion Mouton & Vinks, JAC 1996

Example Target Controlled Dosing cefticostix 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 Efficiency Continuous Infusion Beta-lactam

Therapeutic Drug Monitoring continuous infusion Clearance estimate Variable clearance (ICU) Non-linear clearance

Ceftazidime concentrations ICU patients

Piperacillin concentrations continuous vs intermittent