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Population PK/PD and the rational design of an antimicrobial dosage regimen in veterinary medicine

NATIONAL VETERINARY S C H O O L T O U L O U S E. Population PK/PD and the rational design of an antimicrobial dosage regimen in veterinary medicine. UMR 181 Physiopathologie & Toxicologie Expérimentales. Pierre-Louis Toutain AAVM Congress - Ottawa June 2004. Co-workers. Industry

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Population PK/PD and the rational design of an antimicrobial dosage regimen in veterinary medicine

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  1. NATIONAL VETERINARY S C H O O L T O U L O U S E Population PK/PD and the rational design of an antimicrobial dosage regimen in veterinary medicine UMR 181 Physiopathologie & Toxicologie Expérimentales Pierre-Louis Toutain AAVM Congress - Ottawa June 2004

  2. Co-workers • Industry • Horse study • Vetoquinol (France) • M. Schneider • Pig study • SOGEVAL (France) • C. Zemirline • P. Pomie • VIRBAC (France) • E. Bousquet • INTERVET (germany) • E Thomas • Academia • Horse study • A. Bousquet-Mélou • M. Doucet • D. Concordet • M. Peyrou • Pig study • J. del Castillo • V. Laroute • D. Concordet • P. Sanders • M. Laurentie • H. Morvan

  3. "The design of appropriate dosage regimens may be the single most important contribution of clinical pharmacology to the resistance problem" Schentag et al. Annals of Pharmacotherapy, 30: 1029-1031

  4. Dosage regimen and prevention of resistance • Many factors can contribute to the development of bacteria resistance • the most important risk factor is repeated exposure to suboptimal antibiotic concentrations • dosage regimen should minimize the likelihood of exposing pathogens to sublethal drug levels

  5. Ranking (Low, Medium, High) of extent of antibiotic drug use in animal based on duration and method of administration Individual Groups or pens Flocks, herds animal of animal of animals Duration Short <6 days L M H Medium 6-21 d L M H Long >21 days M H H

  6. What is the contribution of the kineticist to the prudent use of antibiotics • To assist the clinicians designing an optimal dosage regimen • To ensure that the selected antibiotic reach the site of infection at an appropriate effective concentration, for an adequate duration and for all (or most) animals under treatmentto guarantee a cure (clinical, bacteriological) and without favoring antibioresistance

  7. The application of population pharmacokinetic modelling to optimize antibiotic therapy

  8. How to ensure that a dosage regimen minimizes the likelihood of exposing pathogens to sub-clinical drug levels Individual animals groups or pens vs flocks/herds  population approach

  9. Reminder • Traditional vs populational PK/PD approaches • What is PK/PD for antibiotics and how to determine a dosage regimen using PK/PD predictors • see P. Lees presentation

  10. Traditional veterinary PK • Study performed in experimental setting • elaborate design • limited number of animals • rich data • Data analysis: two stages 1- modelling individuals  samples of individual estimates • Cl, Vss, F%, t1/2 2- statistical analysis • mean - SD • search for difference between subgroups (ANOVA), for associations (regression…)

  11. Limits of traditional PK • Experimental conditions • may be not representative of the real world • consider variability as a nuisance • Data analysis • variance and covariance often badly estimated and explained • Solution: the population approach

  12. How to determine a dosage regimen using PK/PD predictors

  13. PK PD Response Dose Plasma concentration Dose titration Dose Response Black box PK/PD

  14. The main goal of a PK/PD trial in veterinary pharmacology • To be an alternative to dose-titration studies to discover an optimal dosage regimen (will be presented by P. Lees)

  15. Contributions of the PK/PD approach to the population determination of a dosage regimen The separation of PK and PD variabilities

  16. PK/PD variabilities for antibiotics • Consequence for dosage determination PK PD Effect BODY Pathogens Dose Plasma concentration • Physiological/constitutional variables • Breed, sex, age • Kidney function • Liver function... • Clinical covariables • pathogens susceptibility (MIC) • disease severity or duration PK/PD population approach

  17. T>MIC : penicillins, cephalosporins, macrolides, oxazolidinones • Cmax/MIC : aminoglycosides • AUIC (or 24h AUC/MIC) : quinolones, tetracyclines, ketolides, azithromycins, streptogramins AUC MIC Cmax/MIC AUIC = Units = Time (h) T>CMI PK/PD predictors of efficacy Cmax Concentrations MIC Time 24h

  18. AUIC: an attempt to combine PK and PD properties of antibiotics AUIC # = = critical breakpoint value Capacity to eliminate the drug PK Dose / Clearance MIC90 orMIC50 AUC MIC • Fixed endpoint related to Emax and EC50 PD Application : fluoroquinolones

  19. Computation of dose using a PK/PD predictor • Dose = x x Clearance (24h) PD Breakpoint to be achieved AUIC 24h MIC fu x F% bioavailability Free fraction PK

  20. MIC90 (average) (average) Computation of dose using a PK/PD predictor • Dose = x x Clearance MIC50 : average PD Breakpoint to be achieved PK AUIC 24h MIC F% average (pop) PK

  21. Dispersion of variance around the mean may be the most relevant parameter to predict a population dosage regimen for antibiotics

  22. Ingested dose Selection of resistance Experimental setting MIC gut flora Field conditions oral Dose gut flora 1-F% Resistance: zoonotic, commensal Side effects Undesirable concentration Target biophase Therapeutic window MIC90 Suboptimal exposure  resistance Variability and the likelihood of resistance F% Resistance: pathogens of interest

  23. Ingested dose Selection of resistance Experimental setting MIC gut flora 1-F% Field conditions gut flora oral Dose F% Side effects Undesirable concentration Therapeutic window Target biophase MIC90 Suboptimal exposure  resistance Variability and the likelihood of resistance Resistance: zoonotic, commensal Resistance: pathogens of interest

  24. Examples of population approaches for antibiotics in veterinary medicine • Identification and explanation of PK variability • marbofloxacin in horse • Determining drug PK characteristics in tissues using sparse sampling • marbofoxacin in ocular fluid in dog • Dosage regimen determining • doxycyclin in pig

  25. Marbofloxacin in horses A. Bousquet-Mélou et al.

  26. Marbofloxacin in horses: PK • A fluoroquinolone • No marketing authorization in horses • Conventional PK study • data analysis using the two-stage approach • clearance = 4.15 ± 0.75 mL/kg/min CV = 18% • Vss = 1.48 ± 0.3 L/kg • t1/2 = 7.56 ± 1.99 h

  27. Marbofloxacin in horses: PK/PD integration (oral route) • Value of efficacy index (AUIC24h) and Cmax/MIC calculated from PK parameters obtained after the administration of 2 mg/kg BW in 6 horses • MIC90 = 0.027 µg/mL (enterobacteriaceae) • “average” PK/PD index • AUIC24h = 155 ± 21 • Cmax/MIC = 31 ± 4.5

  28. Population PK approach for marbofloxacin in horses: objective • To measure the interindividual variability of systemic exposure to marbofloxacin in horses • To identify covariates explaining a part of this variability Body clearance The only determinant of AUC

  29. Materials and Methods (1) • Animals • patients from the Equine Clinic of the Veterinary School • healthy horses from the Riding School • Covariates record • demographic, physiological, disease • not all covariates presented • IV administration of marbofloxacin (2 mg.kg-1) • Nonlinear mixed-effects modelling • Kinepop software (D. Concordet)

  30. 4 samples 5 samples Materials and Methods (2) Sampling design selection • Number of samples per animal and selection of sampling times • D - optimal design to maximize the precision of AUC[0-24h] • previous informations : AUC[0-24h] Mean and Standard Deviation • Bousquet-Melou et al., Equine Vet J, 34, 2002 AUC imprecision Sampling windows: 30min windows centred around 1.5, 3, 5, 7 and 19.5 h post-administration Sampling design

  31. Materials and Methods (3) • PK model : - biexponential equation • - parameterisation in volumes of distribution and clearances • Statistical model : - lognormal distribution of PK parameters Model 1 : no covariate Model 2 : with covariates for body clearance

  32. Bousquet-Melou et al., Equine Vet J, 34, 2002 Results: conventional vs pop kinetics • 52 horses, 253 blood samples 10 1 Marbofloxacin (mg/mL) 0.1 0.01 0.001 0 4 8 12 16 20 24 Time (h)

  33. Clearance (pop) 2.5 population mean = 3.88 mL/kg/min 2 Inter-individual variability CV(%) = 50 % 1.5 predicted concentrations (mg/mL) 1 0.5 0 0 0.5 1 1.5 2 2.5 observed concentrations (mg/mL) Variability: model without covariable

  34. 2.5 2.5 2 2 1.5 1.5 1 1 0.5 0.5 0 0 0 0.5 1 1.5 2 2.5 0 0.5 1 1.5 2 2.5 Variability: model with covariables Without covariable With covariables predicted concentrations (mg/mL) observed concentrations (mg/mL)

  35. Age NS Disease NS Sex NS Weight P=0.001 R2 = 0.33 The body weight explains about 33% of marbofloxacin clearance variability Variability: explicative covariable Covariables for body clearance expressed in L.kg-1.h-1 Note: dose was 2 mg/kg BW i.e. already scaled to BW

  36. Body weight (kg) 0 200 400 600 0 0.6 -1 0.4 Ln (Clearance) Clearance (L/kg/h) -2 0.2 -3 0 0 200 400 600 Body weight (kg) Marbofloxacin: the body weight is a covariable Allometric relationship with an allometric exponent >1

  37. Discussion • Marbofloxacin clearance in horses Population trial Classical trials * Mean (L.kg-1.h-1) 0.233 0.19 - 0.246 CV (%) 50 18 - 21 * Carretero et al., Equine Vet J, 34, 2002 Bousquet-Melou et al., Equine Vet J, 34, 2002 • Influence of body weight In the range of observed weights : about 3-fold variation in body clearance expressed per kilogram

  38. Conclusion • High interindividual variability of marbofloxacin body clearance in horses • Underestimated in classical PK trials • Influence of body weight • Consequences on systemic exposure • Clinical relevance for efficacy and resistance ? • Current trial • Multicentric experiment (Montreal, Toulouse, Utrecht, Vienna) • Increased number of covariates • Further trials • Assessment of variability of PD origin

  39. Population PK/PD determination of a dosage regimen for an antiobiotic

  40. Objectives • Document, with population PK/PD approach, the dosage regimen for antibiotics in pig • Ultimate goal : make recommendations • to determine a dosage regimen • to establish MIC breakpoints • to establish PK/PD predictor breakpoints

  41. Population trial (INRA/SOGEVAL/CTPA)J. del Castillo et al. • Antibiotic: doxycyclin • Britain (2 settings) • 215 pigs (30 to 110 kg BW) • oral (soup) • pens of 12-15 pigs (unit of treatment)

  42. Population trial • Decision of treatment : metaphylaxis • prevalence of disease>10% (tachypnee, body temperature > 40°C) • Treatments : • Doxycyclin (5 mg/kg) or • Doxycyclin + paracetamol (15 mg/kg) • 2 meals apart from 24h • Measure of covariables (rectal temperature /clinical signs etc.) • Blood samplings (4 or 5 after the 2nd dose) • Dosage HPLC (doxy, paracetamol+metabolite)

  43. PK Variability Doxycycline n = 215

  44. PK doxycyclin variability analysis

  45. Doxycycline : sex effect Sexe 0 Sexe 1 Doxycycline Time (h)

  46. Doxycycline : body temperature effect Doxycycline Rectal temperature

  47. Doxycycline : disease effect healthy diseased Concentrations (µg/mL) Time (h)

  48. Variability analysis: AUC vs. body weight

  49. How to make use of PK/PD population knowledge to predict how well will doxycyclin perform clinically?

  50. The use of MonteCarlo simulation Dose selection at the population level Determination of breakpoints: PK/PD MIC

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