UMR 181 Physiopathologie et Toxicologie Expérimentales ECOLE NATIONALE VETERINAIRE T O U L O U S E Antibiotic dosage regimen based on PK-PD concepts and the possible minimization of resistance PL Toutain UMR 181 Physiopathologie et Toxicologie Expérimentales INRA/ENVT 24th World Buiatric Congress. France. 15. October 2006

Why to optimize dosage regimen for antibiotics To optimize efficacy Reduce the emergence and selection of resistance

Dosage regimen and antibioresistance The design of appropriate dosage regimens may be the single most important contribution of clinical pharmacology to the resistance problem Schentag, Annal. Pharm. 1996 Little attention has been focused on delineating the correct drug dose to suppress the amplification of less susceptible mutant bacterial sub-populations Drusano et al (2005)

Selecting a dosage regimen for a particular animal or group of animals Individual animal (or herd) issues Probability of “cure” without side effects Public health issues Probability for avoiding enriching a resistant bacterial subpopulation Two general issues arise when selecting a treatment plan for a given patient. With respect to the individual patient, the physician is concerned with the probability that the treatment will provide a cure without serious side effects. The probability that resistance will arise in that patient is generally quite low. Indeed, for most treatments it is so low that numbers are not available (for example, with multidrug treatment of tuberculosis and directly observed therapy, resistance has been reported to arise in only 0.01% of the cases). The public health issues are quite different, since many patients are considered: for some diseases millions of prescriptions are written each year. From a public health standpoint even very rare events can be significant over the course of many patients and many years of treatment. In the case of antibiotics, resistance is considered to be largely irreversible. (In principle, resistant mutants could revert to wild-type cells if resistance has a selective disadvantage in the absence of the drug; however, there is little evidence that true reversion occurs. Instead suppressor mutations tend to be acquired that overcome a growth disadvantage while retaining the resistance allele.) Thus, each resistant case erodes the usefulness of an agent. Since new classes of agent are unlikely to be available in the near future, antimicrobials must be considered non-renewable resources. The prescribing physician is the steward of a valuable resource and must consider both individual health issues as well as public health ones. The prescribing veterinarian is the steward of a valuable resource and must consider both individual health issues as well as public health ones Possible conflict of interest between the two goals

Why to optimize dosage regimen for antibiotics To optimize efficacy Reduce the emergence and selection of resistance Target pathogen: efficacy issue Non target pathogen: human safety issue Zoonotic bacteria (food borne pathogens) Commensal flora (resistance gene reservoir)

Biophases & antibiorésistance G.I.T Proximal Distal AB: oral route 1-F% Gut flora Zoonotic (salmonella, campylobacter commensal ( enterococcus) F% Food chain Environmental exposure Blood Man Target biophase Bug of vet interest Résistance = public health concern Résistance = lack of efficacy

Biophases & antibiorésistance G.I.T Proximal Distal Gut flora Zoonotic (salmonella, campylobacter commensal ( enterococcus) Intestinal secretion Bile Quinolones Macrolides Tétracyclines Food chain Environmental exposure Systemic administration Blood Man Biophase Bug of vet interest Résistance = public health concern Résistance = lack of efficacy

Public Health Concerns : Human pathogenic bacteria spreading from animal reservoirs Current main concerns: Resistance emerging to commonly used empiric therapies for acute GI tract infections Salmonella Fluoroquinolone-resistance 3rd gen. Cephalosporin-resistance Campylobacter Macrolide-resistance One area of concern relating to the use of antimicrobials in food animals is resistance emerging to commonly used empiric therapies for acute GI tract infections, such as fluoroquinolone-resistance and 3rd gen. cephalosporin-resistance in Salmonella and fluoroquinolone-resistance and macrolide-resistance in Campylobacter.

Emergence of quinolone resistance in Salmonella typhimurium DT104 in UK following licensing of fluoroquinolones for use in food animals Stöhr & Wegener, Drug resistance Updates, 2000, 3:207-209

Dosage regimen and resistance: Epidemiological evidences

Dosage regimen and prevention of resistance Many factors (e.g.; broad vs. narrow spectrum…) can contribute to the development of bacteria resistance the most important risk factor is repeated exposure to inappropriate antibiotic concentrations (exposure) dosage regimen should minimize the likelihood of exposing pathogens to sublethal drug levels

Drug factors influencing resistance Regimen Route of administration, dose, interval of administration, duration of treatment

Nb of days of b-lactam use Effect on Penicillin resistance in pneumococcus isolates (n=465) of duration of b-lactam use, 6 months before swab collection Nb of days of b-lactam use 1-7 8-14 >14 Odd ratios 0.86 1.5 2.5 95% CI 0.37- 2.02 0.73- 3.06 1.3 - 4.82 Nasrin et al. BMJ, 2002

Dosage regimen and antibiotic resistance Treatments were initiated 7 days post challenge (E. coli) and continued for 14 days Treatment Control Label use Gradient Pulse Rotation with dissimilar antimicrobials Rotation with similar antimicrobials Dosing scheme No antibiotics Apramycin, 150g/ton of feed for 14 days Apramycin, 50g/ton of feed for 5 days, then 100g/ton for 5 days then 150g/ton for 4 days Apramycin, 150g/ton of feed for 3 days, then 3 days without antibiotics, sequence repeated throughout the 14-day period Apramycin, 150g/ton of feed for 5 days, then sulfamethazine formulated for 118g/kg BW in drinking water for 5 days then carbadox, 50g/ton of feed for 4 days Apramycin, 150g/ton of feed for 5 days, then gentamicin 6.6 mg/L of drinking water for 5 days then neomycin formulated for 22mg/kg BW in drinking water for 4 days Mathew, 2003

AB dosing day post challenge regimen 3 6 10 13 17 31 Effect of 14-day antibiotic dosing regimen on sensitivity (MIC, µg/mL) to apramycin by E. coli recovered AB dosing day post challenge regimen 3 6 10 13 17 31 Control (no AB) 4.3 3.9 3.5 3.1 2.3 2.6 Label 5.9 41.1 56 49 50 6.6 Rotation Similar AB 3.5 4.2 200 182 141 7.6 Rotation Dissimilar AB 2.6 38.8 44 14 14.0 3.8 Gradient 50, 100, 150 3.5 3.5 3.5 68.5 109.9 2.8 Pulse (3 days) 5.2 4.3 3.6 4.0 7.0 3.7 Mathew, 2003

How to determine a dosage regimen that is both efficacious and that minimizes the risk to promote resistance

How to find and confirm a dose (dosage regimen) Dose titration Animal infectious model Clinical trial PK/PD

The dose-titration

The dose-titration: experimental infectious model Severe not representative of the real world Prophylaxis vs. metaphylaxis vs. curative power of the design generally low for large species influence of the endpoints

How to find and confirm a dose (dosage regimen) Dose titration Animal infectious model Clinical trial PK/PD

Bacteriological vs clinical success: the pollyanna phenomenon

The Pollyanna phenomenon If efficacy is measured by symptomatic response, drugs with excellent antibacterial activity will appear less efficacious than they really are and drugs with poor antibacterial activity will appear more efficacious than they really are. The clinical efficacy does not always indicate bacteriological efficacy making it difficult to distinguish between antimicrobials on clinical outcomes only

The Pollyanna effect Otitis media Discrepency between clinical and bacteriological results Otitis media Antibiotic effect 89% 27% 74% 100% 0% 20% 40% 60% 80% Efficacy (%) Clinical success Placebo effect Bacteriological cure Merchant et al. Pediatrics 1992

The Pollyanna effect Ceftiofur – oral Response % 90 Mortality Bacterial 60 shedding Response % 30 0.5 2 16 64 Dose (mg/kg) Yancey et al. 1990 Am. J. Vet.Res.

EFFICACY OF ORAL PRADOFLOXACIN AND AMOXYCILLIN/CLAVULANATE IN CANINE CYSTITIS AND PROSTATITIS Treatment Number of dogs Clinical Cure (%) Reduction of Total Clinical Score (%) Bacteriological cure Pradofloxacin 85 89.3 96.8 85.3 Amoxicillin/ Clavulanate 77 83.9 93.4 48.0* P=0.002 Data from Bayer Animal Health (VERAFLOX SYMPOSIUM)

The Pollyanna phenomenon The clinical efficacy does not always indicate bacteriological efficacy and a good clinical efficacy is not enough to validate an appropriate dosage regimen

The role of antibiotics is to eradicate the causative organisms from the site of infection Jacobs. Istambul, 2001

How to find and confirm a dose (dosage regimen) Dose titration Animal infectious model Clinical trial PK/PD

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

What is PK/PD?

Dose titration PK/PD Black box Dose PK PD Response clinical Dose Body pathogen Dose Response Plasma concentration

PK/PD: in vitro In vitro Medium concentration Response MIC Test tube MIC is very variable from pathogen to pathogen and should be acknowledged The idea at the back of the PK/PD indices were to develop surrogates able to predict clinical success by scaling a PK variable by the MIC

A plasma concentration variable scaled by MIC Dose titration Dose Response clinical Black box PK/PD PK PD Dose Body pathogen Response A plasma concentration variable scaled by MIC

Dose titration vs. PK/PD : the explicative variable A PK/PD SURROGATE Effect Effect effect Dose AUC AUC/MIC, DOSE (external dose) EXPOSURE (internal dose) Exposure scaled by MIC

PK/PD indices as indicator of antibiotic efficacy

The surrogates (predictors) of antibiotic efficacy AUC/MIC, T>MIC, Cmax/MIC

PK/PD predictors of efficacy Cmax/MIC : aminoglycosides AUC/MIC : quinolones, tetracyclines, azithromycins, T>MIC : penicillins, cephalosporins, macrolides, Cmax Cmax/MIC AUC MIC AUIC = Concentrations MIC Time 24h T>CMI

Why these indices are termed PK/PD AUC CMI Dose / Clearance CMI50(90) AUIC # = PD  Dual dosage regimen adaptation

Relationship between dose and PK/PD predictors of efficacy Breakpoint value e.g. 125 PD PK Bioavailability Free fraction

Why plasma concentration The site of infection Update : 17 avril 2017

Only the free (non-bound) fraction (concentration) of the drug can interact with bacterial receptors Only the concentration of free drug that is of concern for its PK/PD relationship

MIC is a reasonable approximate of the concentration of free drug needed at the site of infection

Most infections of interest are located extra-cellularly and direct comparisons to total tissue concentration with PD parameters are meaningless Cars, 1991

Where are located the pathogens ECF Most bacteria of clinical interest - respiratory infection - wound infection - digestive tract inf. Cell (in phagocytic cell most often) Legionnella spp mycoplasma (some) chlamydiae Brucella Cryptosporidiosis Listeria monocytogene Salmonella Mycobacteria Meningococci Rhodococcus equi

When there is no barrier to penetration, the level of free drug in serum is an adequate surrogate marker for biophase concentration Cars, 1991

(S. aureus, Brucella, Salmonella) Barrier, efflux pump Porous capillaries Plasma Interstitial fluid Brain, retina, prostate Surrogate marker (T>MIC, AUIC, Cmax/MIC) Biophase for most bacteria of veterinary therapeutical interest Tissular barrier B Mannhemia, Pasteurella Haemophilus, Streptococcus, Staphylococcus, Coli, Klebsiella Bound  F Bound  F B lipophilicity F Efflux pump Total concentration Biophase for facultative and obligatory intracellular pathogens Bound Cytosol (Listeria, Shigella) B F Phagosome (Chlamydiae) Cell Cell membrane Bound B F B B Obligatory or facultative bacteria Phagolysosome (S. aureus, Brucella, Salmonella)

Tissue concentrations According to EMEA "unreliable information is generated from assays of drug concentrations in whole tissues (e.g. homogenates)" EMEA 2000

Magnitude of PK/PD parameter required for efficacy Istambul, 2001

Relationship Between T>MIC and Efficacy for Carbapenems (Red), Penicillins (Aqua) and Cephalosporins (Yellow)

Relationship between PK/PD parameters and efficacy for cefotaxime against Klebsiella pneumoniae in a pneumonia model 10 10 10 R² = 94% 9 9 9 8 8 8 Log10 CFU per lung at 24 h 7 7 7 6 6 6 5 5 5 01 1 10 100 1000 10000 3 10 30 100 300 1000 3000 20 40 60 80 100 24 h AUC/MIC ratio Peak MIC ratio Time above MIC (%) Craig CID, 1998

Efficacy index: clinical validation Bacteriological cure versus time above MIC in otitis media (from Craig and Andes 1996) 100 S. pneumoniae Penicillin cephalosporins 50 Bacteriologic cure (%) H. influenzae Penicillin cephalosporins 50 100 Time above MIC (%) Free serum concentration need to exceed the MIC of the pathogen for 40-50% of the dosing interval to obtain bacteriological cure in 80% of patients

PK/PD parameters: -lactams Time above MIC is the important parameter determining efficacy of the -lactams T>MIC required for static dose vary from 25-40% of dosing interval for penicillins and cephalosporins. Free drug levels of penicillins and cephalosporins need to exceed the MIC for 40-50% of the dosing interval to produce maximum survival Graig

Betalactam Goal: to maximize the duration of exposure over which free drug levels in biophase exceed the MIC no further significant reduction in bacteria count when concentration exceed 4 MIC

Comparison of relationships between 24-hr AUC/MIC and efficacy against Pneumococci for fluoroquinolones in animals and patients Patients with CAP and AECB 58 patients enrolled in a comparative trial of levofloxacin vs. gatifloxacin Free-drug 24-hr AUC/MIC < 33.7, the probability of a microbiologic cure was 64% Free-drug 24-hr AUC/MIC>33.7, the probability of a microbiologic cure was 100% Andes & Craig Int. J. Antimicrob. Agents, 2002, 19: 259

AUIC = 125 h as a consensus descriptor of antibiotic action Roughly speaking AUIC = 125 h is equivalent to say that the mean concentration should be 5 times the MIC over 80% of the dosage interval (24h) Schentag et al. 1990

Efficacy index: clinical validation Relationship between the maximal peak plasma level to MIC ratio and the rate of clinical response in 236 patients with Gram-negative bacterial infections treated with aminoglycosides (gentamicin, tobramycin, amikacin) 100 80 Response rate (%) 60 2 4 6 8 10 12 Maximum peak/MIC ratio Moor et al. 1984 J. Infect. Dis.

Modern interests in pharmacodynamics Establish the PK/PD target required for effective antimicrobial therapy Identify which PK/PD parameter (T>MIC, AUC/MIC, peak/MIC) best predicts in vivo antimicrobial activity Determine the magnitude of the PK/PD parameter required for in vivo efficacy (static effect, 1 or 2 log kill) Define resistance for those situations where one cannot attain the target required for efficacy

Magnitude of PK/PD parameter required for efficacy: the case of quinolones for calf

Bacterial growth in serum containing danofloxacin for incubation periods of 0.25 to 6h Conc. 0.02 1.E+09 0.04 0.06 0.08 1.E+06 Log cfu/ml 0.12 0.16 1.E+03 0.20 0.24 1.E+00 0.28 1 2 3 4 5 6 0.32 Incubation time (h) P. Lees

Bacteriostatic AUIC24h = 18 h Sigmoidal Emax relationship for bacterial count vs ex vivo AUIC24h in goat 1 serum Observed Predicted 1 Bacteriostatic AUIC24h = 18 h -1 Bactericidal AUIC24h = 39 h -2 Log cfu/ml difference -3 Elimination AUIC24h = 90 h -4 -5 -6 -7 50 100 150 200 250 300 AUIC24h P. Lees

Parameter Danofloxacin Marbofloxacin Ex vivo AUC24h/MIC (h) values for danofloxacin and marbofloxacin in calf serum Parameter Danofloxacin Marbofloxacin Bacteriostatic 15.9 ± 2.0 37.3 ± 6.9 Bactericidal 18.1 ± 1.9 46.5 ± 6.8 Elimination 33.5 ± 3.5 119.6 ± 10.9 Slope 17.3 ± 4.2 11.5 ± 3.3 Values are mean ± sem (n=6) P. Lees

PK/PD indices Determination of breakpoint values To optimize efficacy To minimize resistance Update: 17/05/2004

Effectiveness of PK/PD indices as predictor for the development of antimicrobial resistance

There is evidence that the likelihood for the selection of bacteria with mutation conferring resistance can be predicted on basis of PK/PD relationship

Impact of dosage regimen on the emergence of resistance: Experimental evidences

AUIC and bacterial eradication Nosocomial pneumonia treated with IV ciprofloxacin AUIC was highly predictive of time to bacterial eradication If AUIC >250 h/day : eradication of organism on day 1 of therapy good target for nosocomial pneumonia and compromised host defense 100 AUIC < 125 % patients remaining culture positive 50 AUIC 125-250 AUIC > 250 4 8 12 Days after start of therapy Schentag Symposium, 1999

Suboptimal antibiotic dosage as a risk factor for selection of penicillin-resistant Streptococcus pneumoniae : in vitro kinetic model Odenholt et al. (2003) Antimicrobial Agents and Chemotherapy, 47: 518-523

Material and Methods Mixed culture of Stretococcus pneumoniae containing ca. 90% susceptible, 9% intermediate and 1% resistant bacteria In vitro kinetic model Exposure to Penicillin : T>MIC varied from S = 46 to 100 % I = 6 to 100 % R = 0 to 48 % Odenholt, 2003

Selection by penicillin of resistant bacteria in a mixed population of S.pneumoniae: control Odenholt, 2003

Selection by penicillin of resistant bacteria in a mixed population of S.pneumoniae Odenholt, 2003

Selection by penicillin of resistant bacteria in a mixed population of S.pneumoniae Odenholt, 2003

Selection by penicillin of resistant bacteria in a mixed population of S.pneumoniae Odenholt, 2003

Selection by penicillin of resistant bacteria in a mixed population of S.pneumoniae Odenholt, 2003

Selection by penicillin of resistant bacteria in a mixed population of S.pneumoniae Odenholt, 2003

Tam, V.H. et al (2005) Antimicrob.Agents Chemother. 49, 4920 Optimisation of Meropenem minimum concentration/MIC ratio to suppress in vitro resistance of Pseudomonas aeruginosa Determined bactericidal activity of Meropenem and ability to suppress P.aeruginosa resistance In vitro hollow fibre infection model (HFIM) inoculated with dense inoculum (1x108 cfu/mL) and subjected to various Meropenem exposures over 5 days Doses administered every 8h to achieve the same Cmax but escalating unbound Cmin concentrations Tam, V.H. et al (2005) Antimicrob.Agents Chemother. 49, 4920

T>MIC 100% & Cmin/MIC=1.7+ tobramycin Optimisation of Meropenem minimum concentration/mic ratio to suppress in vitro resistance of Pseudomonas aeruginosa Placebo 12 10 T>MIC 100% & Cmin/MIC=6 8 8 Log 10 cfu/mL 6 Log 10 cfu/mL 4 4 2 Time (days) Time (days) 5 1 2 3 4 5 1 2 3 4 T>MIC 84% T>MIC 100% & Cmin/MIC=10 10 10 8 8 Log 10 cfu/mL 6 4 Log 10 cfu/mL 4 Time (days) 2 1 2 3 4 5 Time (days) 1 2 3 4 5 T>MIC 100% & Cmin/MIC=1.7 T>MIC 100% & Cmin/MIC=1.7+ tobramycin 10 10 8 8 Log 10 cfu/mL 6 4 Log 10 cfu/mL 4 Time (days) 2 1 2 3 4 5 Time (days) 1 2 3 4 5

Resistance emerged when Optimisation of Meropenem minimum concentration/MIC ratio to suppress in vitro resistance of pseudomonas aeruginosa results Resistance emerged when (a) T>MIC = 84% (b) T>MIC = 100% and Cmin/MIC = 1.7 Resistance avoidance when (a) T>MIC = 100% and Cmin/MIC = 6.0 or (b) T>MIC = 100% and Cmin/MIC = 1.7 plus tobramycin

Optimisation of Meropenem minimum concentration/MIC ratio to suppress in vitro resistance of Pseudomonas aeruginosa conclusions Breakpoint to prevent resistance different of those selected for clinical efficacy Because of the ceiling effect for T>MIC this variable may not be satisfactory when the breakpoint exceeds 100% Cmin/MIC of Meropenem can be optimized to suppress the emergence of non-plasmid-mediated P aeruginosa resistance Meropenem exposure necessary to avoid resistance may not be achievable with conventional doses NOTE: The experimental conditions represent a very conservative situation in a clinical setting (neutropenia and high bacterial burden)

Surrogate indices and emergence of resistance : Ceftizoxime in vivo In vivo murine study using mixed infection model related mutation frequency to T>MIC (as percentage of dosing interval) for ceftizoxime No resistance when T>MIC was <40% or = 100% Mutation frequency very low when T>MIC  87% Peak mutation frequency for T>MIC = 70% For optimal efficacy the usual value quoted is T>MIC = 40-60% Stearne et al (2002)

Predictive value of PK/PD indices for emergence of resistance: time dependent antibiotic T>MIC should be 40-60% of the dosing interval for clinical efficacy BUT Plasma concentrations should be 3-4 times the MIC to optimally prevent resistance

T>MIC for 40-50% of the dosing interval: Daily dosing vs T>MIC for 40-50% of the dosing interval: Daily dosing vs. long-acting drug Daily formulation Long-acting drug/formulation MIC Both treatments ensure plasma concentrations above MIC for 50% of the dosing interval (1 or 14 days) but they are not equivalent

Impact of dosage regimen on the emergence of resistance: Experimental evidences for quinolones

AUIC (AUC/MIC) and bacterial resistance Ciprofloxacin AUIC predicts bacterial resistance in nosocomial pneumoniae 100 Resistance for AUIC < 100 day % 4 50 20 93 AUC/MIC > 101 75 50 Probability of remaining susceptible AUC/MIC< 100 25 5 10 15 20 No.Days after start of therapy AUIC < 100 = suboptimal Schentag-Symposium 1999

PK/PD and resistance development Data drawn from studies of five different treatment regimens for nosocomial pneumonia have suggested that the probability of selecting for resistant organisms increase when AUIC < 100 (ciprofloxacin) EMEA 2000

Bacterial population responses to drug selective pressure : examination of Garenoxacin’s effect on Pseudomonas aeruginosa (1) Determined influence of Garenoxacin on ability to suppressG P. aeruginosa resistance In vitro hollow fibre infection model inoculated with dense inoculum (2.4 x 108 cfu/ml) and subject to various Garenoxacin exposures of 2-3 days Doses administered once daily over 1h period to achieve constant targetted Cmax at 1, 25 and 49h and AUC24 /MIC ratios of 0, 10, 50, 75, 100 and 200h. Tam, V.H. et al. J. Infect. Dis. 2005 192, 420

Bacterial population responses to drug selective pressure : examination of Garenoxacin’s effect on Pseudomonas aeruginosa Control AUC/MIC=10 AUC/MIC=48 AUC/MIC=89 AUC/MIC=108 AUC/MIC=201

MIC of resistant mutants at 48h 4-16x greater than wild type Bacterial population responses to drug selective pressure : examination of garenoxacin’s effect on Pseudomonas aeruginosa (2) AUC24/MIC ratios used (10 to 200h) based on steady state kinetics of unbound garenoxacin in humans MIC of resistant mutants at 48h 4-16x greater than wild type Replacement of (a) majority of susceptible organisms by resistant mutants when AUC/MIC = 10, 48 and 89h (b) all susceptible organisms by mutants when AUC/MIC = 108 and 137h No increase in resistant mutants when AUC/MIC = 201h Modelling data gave AUC24/MIC ratio of 190h to avoid amplification of resistant sub-populations The resistance suppression breakpoint

Firsov et al. (2003) Antimicrob.Agents Chemother. 47, 1604. In vitro pharmacodynamic evaluation of the mutant selection window hypothesis using four fluoroquinolones against Staph. aureus In vitro model to simulate human pharmacokinetics of 4 fluoroquinolones (monoexponential decline) Inoculum of 108 cfu/ml Cmax (a) = MIC (b) >MIC <MPC (within MSW) (c) >MPC Resulting AUC24/MIC values = 13 to 244h Determination of MIC at 0 and 72h Absence of WBCs Firsov et al. (2003) Antimicrob.Agents Chemother. 47, 1604.

The MPC hypothesis for 4 Quinolones against S aureus As a test of the window idea Firsov and Zinner carried out a pharmacodynamic study in which moxifloxacin concentration was varied so that it was either above, within, or below the selection window throughout treatment using an in vitro model The dynamic model contained Staphylococcus aureus, and at the times indicated by the arrows moxifloxacin was added and samples were taken for analysis. Determination of MIC showed that resistant mutants were enriched only when the moxifloxacin concentration was inside the selection window for at least 20% of the time.

Firsov et al (2003). Antimicrob. Agents Chemother. 47, 1604 The MPC hypothesis for 4 Quinolones against S aureus DRUGS Cmax AUC24/MIC (h) Change in MIC Gatifloxacin & Ciprofloxacin >MIC 15 to 16 Slight increase Moxifloxacin & Levofloxacin MIC 13 to 17 None All 4 drugs 24 to 62* Greatest increase 107 to 123 Small increase All drugs >> MIC 201 to 244** *Concentrations within MSW over most of dose interval **Concentrations >MPC over most of dose interval Firsov et al (2003). Antimicrob. Agents Chemother. 47, 1604

The MPC hypothesis for 4 Quinolones against S aureus CONCLUSIONS Resistant mutants selectivity enriched when antibiotic concentrations fall within MSW MIC72/MIC0 peak at AUC24/MIC of 43 Only moxifloxacin may protect against resistance at normal clinical doses

Predictive value of PK/PD indices for emergence of resistance: concentration dependent antibiotic More clearly established than for time dependent antibiotics For quinolones, the development of resistance is mostly attributable to the primary resistance pathway (mutation) Concepts of selection window and AUIC are convergent

Drusano G.L. (2003) CID, 36 Suppl 1. 342-350 Conditions for counter selective dosing to avoid emergence of resistance The total organism burden substantially exceeds the inverse of the mutational frequency to resistance There is a high probability of a resistant clone being present at baseline The step size of change in MIC of the mutated population is relatively small (<10-fold) Appropriate dosing then able to suppress the parent/sensitive population and also suffices to inhibit the mutant sub-population Drusano G.L. (2003) CID, 36 Suppl 1. 342-350

Cmax/MIC and resistance Enoxacin Staphylococcus aureus, Klebsiella pneumoniae, E.coli, P. aeruginosa Cmax = 3 MIC >99% reduction of initial inoculous regrowth at 24h unless Cmax/MIC >8 if regrowth, MIC for the regrowing bacteria was 4-8 fold that of parent strain Conclusion : there was selection of a resistant subpopulation Cmax correlates with suppression of emergence of resistance of organisms Blaser et al. 1987 Antimicrob. Agent Chemother.

PK/PD parameters vs. emergence of resistance for fluoroquinolones Resistance developed 24-hr AUC/MIC P.aeruginosa Other GNB <100 – monotherapy 80% 100% >100 – monotherapy 33% 10% Combinations 11% 0% 25% 12% Thomas et al. AAC, 1998, 42:521

AUIC > 250 h Veterinary application: one shot Bacterial killing is extremely fast with eradication averaging 1.9 days regardless the species of bacteria Veterinary application: one shot

What is the concentration needed to prevent mutation and/or selection of bacteria with reduced susceptibility? Beta-lactams: stay always above the 4xMIC Aminoglycosides: achieve a peak of 8x the MIC at least Fluoroquinolones: AUC/MIC > 200 and peak/MIC > 8

Mutant Prevention Concentration (MPC) and the Selection Window (SW) hypothesis This lecture was prepared on April 5, 2003. Literature cited is listed as a note to the last slide.

Traditional explanation for enrichment of mutants Concentration MIC Placing MIC near the lower boundary of the selection window contradicts traditional medical teaching in which resistant mutants are thought to be selected primarily when drug concentrations are below MIC (shown in a figure taken from a book published in 2002 (2)). This distinction is important because traditional dosing recommendations to exceed MIC are likely to place drug concentrations inside the selection window where they will enrich resistant mutant subpopulations. While low drug concentrations do not enrich resistant mutants, they do allow pathogen population expansion; consequently, low drug doses indirectly foster the generation of new mutants that will be enriched by subsequent antimicrobial challenge. Selective Pressure Time

Traditional Explanation for Enrichment of Mutants Placing MIC near the lower boundary of the selection window contradicts traditional medical teaching in which resistant mutants are thought to be selected primarily when drug concentrations are below MIC This distinction is important because traditional dosing recommendations to exceed MIC are likely to place drug concentrations inside the selection window where they will enrich resistant mutant subpopulations. While low drug concentrations do not enrich resistant mutants, they do allow pathogen population expansion; consequently, low drug doses indirectly foster the generation of new mutants that will be enriched by subsequent antimicrobial challenge

The selection window hypothesis Mutant prevention concentration (MPC) (to inhibit growth of the least susceptible, single step mutant) MIC Selective concentration (SC) to block wild-type bacteria Mutant Selection window Plasma concentrations All bacteria inhibited Growth of only the most resistant subpopulation Growth of all bacteria

Without antibiotics With antibiotics Blocking Growth of Single Mutants Forces Cells to Have a Double Mutation to Overcome Drug Without antibiotics 10-8 single mutant population 10-8 Wild pop With antibiotics 10-8 single mutant population Wild population éradication sensible single mutant Double mutant

Mutants are not selected at concentrations below MIC or above the MPC For emphasis we restate that as a rule mutants are not selectively enriched at drug concentrations below MIC. As an aside, we note some selective pressure exists at concentrations below the standard MIC because it measures inhibition of growth of a large number of cells (100,000). Indeed, some enrichment of mutants does occur upon repeated serial passage of a strain (6). These data stress that the bottom boundary of the window can be fuzzy. That is why we define it to be MIC(99), the minimal concentration that blocks growth of 99% of the cells in a culture. MIC(50) would be a more precise lower boundary, but it is more difficult to determine experimentally.

(number of bacteria during infection: < 1010) Blocking Growth of Single Mutants Forces Cells to Have a Double Mutation to Overcome Drug attack by drug frequency ~ 10-7 frequency ~ 10-7 wild type double mutant single mutant frequency ~ 10-14 In the early 1990s New York City experienced an outbreak of MDR tuberculosis. At the time we were studying DNA topoisomerases to better understand chromosome structure. We had used quinolones as tools, and so we were familiar with them. During the outbreak it became clear that the use of ciprofloxacin quickly led, sometimes within a month, to ciprofloxacin-resistant tuberculosis (13, 17). At the time it seemed that the problem was to find a way to halt the development of resistance. From a naïve microbiological point of view we thought that we should look for new quinolones that would readily attack resistant mutants. We had gyrase mutants available for several bacterial species, and we obtained some investigational compounds from John Domagala at Parke-Davis. The general idea, as shown in the slide, was to find a compound that would be exceptionally active against a resistant mutant. Such a compound, if used against wild-type cells, would require a double mutation for growth. Since the mutation frequency for fluoroquinolones is on the order of 10-7, a pair of independent mutations would arise at frequency of 10-14. Infections rarely have more than 1010 cells, so a mutant-active compound would prevent the selective enrichment of mutants. The story that emerged can be divided into several subtopics, as shown on the next slide. (number of bacteria during infection: < 1010)

The selection window Selection of a resistant subpopulation between selective concentrations (SC) and mutant preventive concentrations (MPC) fluoroquinolones and M. tuberculosis fluoroquinolones, chloramphenicol, aminoglycosides, vancomycin and S. aureus b-lactam antibiotics (cefotaxime and amoxicillin) and E. coli

Strategies for Restricting the Development of Resistance

Strategies for Restricting the Development of Resistance Three possible strategies for restricting the development of antimicrobial resistance. To keep concentrations above the MPC To narrow the selection window. To use combination therapy in which pharmacokinetic mismatch is avoided.

Strategies for Restricting the Development of Resistance Dose above MPC Narrow the window MPC Serum drug concentration MPC~MIC We can imagine three strategies for restricting the development of antimicrobial resistance. One is to keep concentrations above the MPC. Another is to narrow the selection window. A third is to use combination therapy in which pharmacokinetic mismatch is avoided. Each of these strategies will be briefly discussed. MIC Time post-administration

A goal for the developers of new antimicrobial compounds. Closing the Window: A goal for the developers of new antimicrobial compounds. Another strategy is to close the selection window.

What is the concentration needed to prevent mutation and/or selection of bacteria with reduced susceptibility? Beta-lactams: we do not know but most likely stay always above the MIC… Aminoglycosides: achieve a peak of 8x the MIC at least Fluoroquinolones: AUC/MIC > 100 h and peak/MIC > 8

Population approach to determine a dosage regimen for antibiotics

Why a population approach Development of resistance is a collective phenomenon

Population dosage regimen: (The regulator point of view) Population model (info) To predict the single dosage regimen for most animals in the population Empirical antibiotherapy: The dose controlling 90% of the overall population whatever the susceptibility of the bug, the breed, age, sex etc

Population dosage regimen: flexible dosing regimens Population model (info) Covariates : Sex, husbandry, breed... To predict the best dosage regimen for a subgroup of animals (breed, age, health status…) Targeted antibiotherapy: The dose controlling 90% of the overall population when the susceptibility (MIC) of the bug is known Ethopharmacology/pharmacogenetics: doses may be tailored according to genotypes or any other covariates

Why Population PK/PD To take into account, explicitly ,variability (and uncertainty) when selecting a dosage regimen. Variability is not noise

Not only the mean but also the dispersion (variance) around the mean are needed to predict a population dosage regimen for antibiotics

The main goal of population kinetics is to document sources of variabilities

Why a population approach The fact: Underexposure in only few animals within a herd or a flock may lead to the establishment in these animals of a less susceptible sub-population of bugs that subsequently may transmit resistance horizontally to other animals The risk factor: inter-animal variability (age, breed, sex, health status….) that is not documented in conventional preclinical studies. Development of resistance is a collective phenomenon

Why a population approach The solution: population PK/PD investigations and Monte-Carlo simulations The ultimate Goal: An empirical population dosage regimen controlling a given quantile (e.g. 90%) of a population and not an average dosage regimen

What is Monte Carlo simulations Roulette wheels, dice.. exhibit random behavior and may be viewed as a simple random number generator MCs is the term applied to stochastic simulations that incorporate random variability into a model Monte-Carlo (Monaco) Nice

The so-called target attainment rate (TAR) Type of questions solved by Monte Carlo investigations for the prudent use of antibiotics What is the dose of an antibiotic to be administrated to a group of cattle to guarantee that at least 90% of these cattle will achieve an AUC/MIC ratio (the selected PK/PD index) value of 125 in the framework of an empirical antibiotherapy? The so-called target attainment rate (TAR)

Monte Carlo simulation: applied to PK/PD models Model: AUC/MIC PDF of AUC Generate random AUC and MIC values across the AUC & MIC distributions that conforms to their probabilities PDF of MIC Calculate a large number of AUC/MIC ratios PDF of AUC/MIC Plot results in a probability chart % target attainment (AUC:MIC, T>MIC) Adapted from Dudley, Ambrose. Curr Opin Microbiol 2000;3:515−521

AUC distribution for an hypothetical antibiotic

PK Variability Doxycycline n = 215

MIC distribution Pasteurella multocida 40 35 30 25 Pathogens % 20 15 10 SUSCEPTIBLE 5 0.0625 0.125 0.25 0.5 1 2 4 MIC ( m g/mL)

Dosage regimen: application of PK/PD concepts The 2 sources of variability : PK and PD PK: exposure PD: MIC Distribution of PK/PD surrogates (AUC/MIC) Monte-Carlo approach

A working example to illustrate what is Monte Carlo simulation

Type of questions solved by Monte Carlo investigations for the prudent use of antibiotics What is the dose to be administrated to guarantee that 90% of the cattle population will actually achieve an AUC/MIC of 80 (metaphylaxis) or 125 (curative treatment) for an empirical (MIC unknown) or a targeted antibiotherapy ( MIC determined)

2 conditions for an optimal dosing regimen Probability of “cure” = POC = 0.90 Time out of the MSW should be higher than e.g.12h/day (50% of the dosing interval) in 90% of cattle

Solving the structural model to compute the dose for my new quinolone With point estimates (mean, median, best-guess value…) With range estimates Typically calculate 2 scenarios: the best case & the worst case (e.g. MIC90) Can show the range of outcomes By Monte Carlo Simulations Based on probability distribution Give the probability of outcomes

Computation of an “average dose” Computation of the dose with point estimates (mean clearance and F%, MIC90) MIC90 (worst case scenario) Breakpoint value Mean value Bioavailability (Mean value) Computation of an “average dose”

An add-in design to help Excel spreadsheet modelers perform Monte Carlo simulations Others features Search optimal solution (e.g. dose) by finding the best combination of decision variables for the best possible results

Computation of the dose using Monte Carlo simulation (Point estimates are replaced by distributions) Log normal distribution: 9±2.07 mL/Kg/h Observed distribution Breakpoint value Dose to POC=0.9 Uniform distribution: 0.3-0.70

Metaphylaxis: dose to achieve a POC of 90% i. e Metaphylaxis: dose to achieve a POC of 90% i.e. an AUC/MIC of 80 (empirical antibiotherapy) Dose distribution

Hypothetical antibiotic : selection of an empirical (initial) dose for Pasteurella multocida x mg/kg 2x mg/kg 4x mg/kg 100% 90% 80% 60% % of cattle above the breakpoint 40% 20% 0% 24 48 72 96 120 144 168 192 AUC/MIC ratio (h)

Sensitivity analysis Analyze the contribution of the different variables to the final result (predicted dose) Allow to detect the most important drivers of the model

Sensitivity analysis Metaphylaxis, empirical antibiotherapy Contribution of the MIC distribution

The second criteria to determine the optimal dose: the MSW & MPC

Dosage regimen: implication for drug resistance The presence of sublethal concentrations of a drug exerts selective pressure on population of pathogens without eradicating it Under those circumstances, mutant strains that possess a degree of drug resistance are favored minimize the time that suboptimal drug levels are present

Kinetic disposition for an hypothetical antibiotic & for a selected metaphylactic dose (3.8 mg/kg) (monocompartmental model, oral route) Log normal distribution: 9±2.07 mL/kg/h F% Uniform distribution: 0.3-0.70 Slope=Cl/Vc=0.09 per h (T1/2=7.7h) MPC MIC concentrations MSW

Time>MPC for the POC 90% for metaphylaxis (dose 3 Time>MPC for the POC 90% for metaphylaxis (dose 3.8 mg/kg, empirical antibiotherapy)

Sensitivity analysis (dose of 3 Sensitivity analysis (dose of 3.8mg/kg, curative treatment empirical antibiotherapy) Clearance Clearance (slope) is the only influential factor of variability for T>MPC not bioavailability as for metaphylaxis

Computation of the dose using Monte Carlo simulation Targeted antibiotherapy

(metaphylaxis, targeted antibiotherapy) Sensitivity analysis (metaphylaxis, targeted antibiotherapy) F%

Computation of the dose (mg/kg): metaphylaxis vs. curative treatment Monte Carlo curative metaphylaxis Efficacy 3.379 3.803 To guarantee T>MPC in 90% of pigs for 50% the dosage interval 3.8 7.1

Jumbe et al. (2003) J. Clin. Invest. 112, 275 Applications of a mathematical model to prevent in vivo amplification of antibiotic-resistant bacterial population during therapy. Granulocyte containing mouse thigh infection model based on Pseudomonas aeruginosa (1x107 or 1x108* cfu/ml in 0.1 ml) Effect of escalating doses of levafloxacin on amplification/suppression of susceptible and resistant populations over 24h Mathematical modelling to predict effect of dose and therapy duration on resistance emergence Prediction of drug dose selection to minimise resistance emergence in clinical patients using Monte Carlo simulations. * 108 organisms harbored 50-1,000 spontaneously drug resistant mutants Jumbe et al. (2003) J. Clin. Invest. 112, 275

Maximal amplification of resistant mutants for AUC24/MIC = 52h Applications of a mathematical model to prevent in vivo amplification of antibiotic-resistant bacterial population during therapy. Maximal amplification of resistant mutants for AUC24/MIC = 52h No amplification of resistant mutants for AUC24/MIC = 157h 10,000 subject Monte Carlo simulation indicated a target attainment rate of 61% for a 750 mg dose of levofloxacin (and predicted attainment rates of 25 and 62% for ciprofloxacin doses of 200 mg b.i.d. and 400 mg t.i.d.) for patients with nosocomial pneumonia

The weak link in MCs is Absence of a priori knowledge on PK & PD distribution Population PK are needed to document influence of different factors on drug exposure Health vs. disease; age; sex; breed… PD: MIC distributions Truly representative of real world (prospective rather than retrospective sampling) Possibility to use diameters distribution if the calibration curve is properly defined

Conclusion

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 and for an adequate duration to guarantee a cure (clinical, bacteriological) and without favoring antibioresistance

PK/PD cannot replace confirmatory clinical trials of efficacy but allow to arrive more quickly to a better dosage regimen recommendation EMEA 2000

CONCLUSIONS In vivo and in vitro studies in recent years have addressed the question of dosage to avoid the emergence of resistance “The approach is quite general and may be applied for any new drug to determine the optimal doses that minimise emergence of resistance” Jumbe et al (2003) There is now a need to conduct similar studies with veterinary pathogens and drugs used in veterinary therapy