Research in Veterinary Science 2002, 73, 105–114 doi:10.1016/S0034-5288(02)00039-5, available online at http://www.idealibrary.com on REVIEW The pharmacokinetic–pharmacodynamic approach to a rational dosage regimen for antibiotics  LOU P L TOUTAIN*, J R E DEL CASTILLO, A BOUSQUET-ME UMR INRA de Physiopathologie et Toxicologie Exp erimentales, Ecole Nationale V et erinaire de Toulouse, 23 Chemin des Capelles, 31076 Toulouse cedex 03, France SUMMARY Pharmacokinetic–pharmacodynamic (PK/PD) surrogate indices (AUIC, AUC/MIC, Cmax /MIC, T > MIC) for measuring antibiotic efficacy are presented and reviewed As clinical trials are not sufficiently sensitive to establish a dosage regimen which guarantees total bacteriological cure (Pollyanna phenomenon), PK/PD indexes have been proposed from in vitro, ex vivo, and in vivo infection models and subsequently validated in retrospective or prospective human clinical trials The target value for time-dependent antibiotics (b-lactams, macrolides) is a time above the MIC (T > MIC) of 50–80% of the dosage interval, while for concentration-dependent antibiotics (quinolones and aminoglycosides), the area under the inhibitory curve (AUIC, or more simply AUC/MIC of about 125 h) is the best surrogate indicator of activity Using the latter drugs, high concentrations achieved early during therapy are desirable to prevent the development of resistance A Cmax /MIC ratio greater than 10–12 seems to be an appropriate target for aminoglycosides Ó 2002 Elsevier Science Ltd All rights reserved OVERUSE and misuse of antimicrobial drugs have favoured the growth of resistant organisms and resistance can spread to other microbial populations, jeopardizing humans and animals, including those not previously exposed to antimicrobial agents Among the documented misuses contributing to drug resistance are inappropriate dosage regimens (dose, dosage interval, duration of treatment, route and conditions of administration) (Anonymous 1998) Rational antibiotic therapy requires dosage regimens to be optimized, not only to guarantee clinical efficacy, but also to minimize the selection and spread of resistant pathogens Pharmacokinetics (PK) is the tool used to describe and predict drug concentration profiles in biological fluids (usually plasma) and combining PK and pharmacodynamic (PD) information (i.e., bacterial susceptibility to antibiotics) constitutes the PK/PD modelling approach to antibiotic efficacy The goal of this approach is to describe, predict, and if possible understand the time course of the antibiotic effect as a function of the drug dosage regimen In addition, the PK/PD approach addresses the two main sources (PK and PD) of inter- and intra-individual variability in therapy outcome and allows dual adaptation of the antibiotic regimen (Schentag et al 1985) The aim of this review is to indicate the limitations of clinical endpoints for selection of the dosage regimen of antibiotics and to demonstrate the advantages Corresponding author Fax: +33-561-19-39-17; E-mail: pl.toutain@envt.fr 0034-5288/02/$ - see front matter of the PK/PD alternative approach The potential contribution of PK/PD to the minimization of drug resistance development and determining breakpoints in veterinary susceptibility tests will also be addressed This review focuses mainly on antibiotic therapy for pathogens located in extracellular fluids where drug exchange with the plasma is not impeded by a diffusion barrier HOW TO DETERMINE A DOSAGE REGIMEN FOR AN ANTIBIOTIC As opposed to drugs acting on some physiological system of the host, the action of antibacterial drugs can only be investigated in spontaneously or experimentally infected and not in healthy animals Ideally, dose ranging and clinical trials in target species should explore responses to a variety of dosage regimens, including suboptimal schedules, but this is impractical for economic and not possible for ethical reasons In addition, the reliability of the results of clinical trials involving antibiotics is impaired by a large number of host and bacterial factors that cannot be controlled in a clinical setting Another problem with veterinary clinical trials is that many suffer from one or several flaws which limit their usefulness to evaluate the dosage regimen tested Van Donkersgoed (1992) performed a meta-analysis of field trials of prophylactic mass medication for bovine respiratory disease in feedlots Among 107 trials considered, all but 10 were excluded on account of major defects in experimental design or data analysis A Ó 2002 Elsevier Science Ltd All rights reserved 106 P L Toutain, J R E del Castillo, A Bousquet-Melou similar finding was reported in pigs (Bording 1990) and concerns over the informative value of published human clinical trials has led 13 leading medical journals to express their concern about sponsorship, authorship, and accountability of clinical trials and to revise their editorial policy (Anonymous 2001) CLINICAL OUTCOME AND THE POLLYANNA PHENOMENON In many infections, the ultimate goal of antibiotic therapy is not simply to guarantee a clinical success but to achieve it through a total bacteriological cure If bacterial eradication does not occur, less susceptible bacteria are likely to head the recolonization process after discontinuation of therapy and a more resistant population will become predominant (Dagan et al 2001) Therefore, it is essential to establish whether clinical success is a fully valid endpoint to compare the efficacy of two antibiotics or to assess the value of a dosage regimen for a bacteriological cure Investigation of this question has revealed the so-called ‘‘Pollyanna phenomenon’’ The Pollyanna phenomenon refers to the fact that if antibiotic efficacy is measured by symptomatic responses, drugs or dosing strategies with excellent antibacterial activity will not be as efficacious as anticipated, while the opposite will occur for antibiotics with poor antibacterial activity In otitis media in children, for instance, it was calculated that the clinical success rate will be high (89%) but not total when bacterial eradication is 100%, whereas a high clinical success (71%) may still be expected for a bacteriological cure of 27%, i.e., a probability of bacterial eradication which could be achieved with no antibiotic at all (placebo effect) (Marchant et al 1992) The lack of sensitivity of clinical trials to discriminate a ‘‘good’’ from a ‘‘bad’’ antibiotic is well illustrated by a study of bacteriological failures in this disease It was shown that two very different antibiotics in terms of their bacteriological failure rate, Cefaclor (32%) and Cefuroxime axetil (15%), may be expected to give rather similar clinical success (4% and 9%, respectively) Using the clinical outcome, 900 patients would be necessary to statistically discriminate between these two antibiotics, while 200 would be sufficient when using the bacteriological cure itself as the endpoint (Dagan et al 2001) The Pollyanna phenomenon can be encountered in veterinary therapeutics Yancey et al (1990) tested the efficacy of ceftiofur hydrochloride to treat experimental colibacillosis in neonatal swine in a large trial involving several hundred piglets This study highlights the possible discrepancy between dose-effect relationships based on bacteriological criteria (shedding of bacteria), a clinical endpoint (abnormal stool) or mortality as endpoints (Fig 1) Similarly, using the Escherichia coli model described by Charleston et al (1998) to test a quinolone in chickens, we computed very different ED50 values for mortality (8 mg/kg) and bacteriological cure (13 mg/kg) (Toutain, unpublished results) FIG 1: Efficacy of ceftiofur hydrochloride for the treatment of experimental colibacillosis in neonatal swine (Yancey et al (1990): response (mortality vs bacterial shedding) of E coli-infected pigs treated orally with ceftiofur HCl (0– 64 mg/kg) The lowest dose tested (0.5 mg/kg) reduced mortality, but excluding the placebo group there was no significant difference in mortality between doses (from 0.5 to 64 mg/kg) In contrast, bacterial shedding displayed a significant dose–effect relationship This illustrates the inability of a clinical outcome (mortality) to discriminate doses having possible different efficacies in terms of bacterial eradication This lack of sensitivity of clinical outcomes to find the best dosage regimen in terms of bacteriological cure, a prerequisite to minimize the risk of the emergence of resistance, opens the way to investigating the efficacy of an antibiotic using PK/PD approaches and surrogate indexes in healthy animals (Table 1) EVIDENCE FOR PK/PD RELATIONSHIPS: IN VITRO, EX VIVO AND IN VIVO MODELS IN EXPERIMENTAL SPECIES The relationships between antibiotic exposure, rate of bacterial killing, and possible regrowth of bacteria with increased MIC can be examined with in vitro systems mimicking the expected in vivo antibiotic concentration profiles in the target species (Murakawa et al 1980) These models have been used to determine the pharmacokinetic parameters (AUC, Cmax or times > MIC ) which correlate best with antibacterial activity and are predictive of the emergence of resistance In veterinary medicine, the response of Staphylococcus aureus to the simulated interstitial fluid pharmacokinetic profile of penicillin in sheep was obtained with an in vitro PD model (Koritz et al 1994) The limitation of in vitro models is that they simulate infection but not take into account the role of the immune system of the host Using a tissue cage model, inflammatory (exudate) and non-inflammatory (transudate) fluids can be collected and ex vivo antibacterial activity can be measured over a 24 h incubation period allowing the computation of the AUIC24 h corresponding to bacteriostasis (no change in bacterial count), bactericidal activity (99.9% reduction of bacterial count) or total bacterial eradication (Lees and Aliabadi 2002) Table gives ex vivo AUC24 h /MIC values for danofloxacin in serum for ruminant species As for in vitro investigation, these ex vivo AUICs24 h not take into account The pharmacokinetic–pharmacodynamic approach to antibiotics TABLE 1: 107 PK/PD vs dose titration or clinical trials Features Subjects Endpoints Validity (clinical relevance) Sensitivity to dose ranging Reliability Application to drug discovery and development Extrapolation (from in vitro models or other species) Dual dosage individualization Prediction of the emergence of resistance Breakpoint setting Population studies: PK or PD origin of variability Regulatory acceptance Cost Independent evaluation/objectivity Approach PK/PD Dose titration or clinical trials Healthy Surrogates: T > MIC, Cmax /MIC, and AUIC Need to be validated (prospectively or retrospectively) Yes High Early screening Infection models, patients Clinical outcome (cure, failure) bacteriological outcome (eradication, resistance) Gold standard but many possible drawbacks and Pollyanna phenomenon No (difficult to perform dose ranging in ill patients) Low Later, confirmatory Easy Difficult Yes Possible To be explored (promising) Yes No Possible Yes No, if only clinical outcomes are measured In progress Low Independent investigations possible Pivotal, designed to satisfy authorities but not to optimize treatments High Requires commercial funding TABLE 2: Critical ex vivo AUC2 4/MIC (h) values for Danofloxacin in serum to obtain a bacteriostasis, a bactericidal or a bacterial elimination in different ruminant species (Aliabadi and Lees 2001; Lees and Aliabadi 2002) AUC24 h/MIC Bacteriostatic Bactericidal Elimination Species Calf Sheep Goat Camel 15:9 Ỉ 2:0 18:1 Ỉ 1:9 33:5 Ỉ 3:5 17:8 Ỉ 1:7 20:2 Ỉ 1:7 28:7 Æ 1:8 22:6 Æ 1:7 29:6 Æ 2:5 52:4 Æ 8:1 17:2 Ỉ 3:6 21:2 Ỉ 3:7 68:7 Ỉ 15:6 Danofloxacin was administered intramuscularly to each species, at a dose rate of 1.25 mg/kg Ex vivo antibacterial activity was evaluated by bacterial count after 24 h of incubation The tested pathogens were Mannheimia haemolytica or E coli; the relationship between ex vivo AUIC24 h in serum and the log10 difference in bacterial count (CFU) was modelised by a Hill model; the AUIC24 h for bacteriostasis and bactericidal activity were defined as values that resulted in no change in bacterial count and the value that resulted in 99.9% reduction in bacterial count respectively The AUIC24 h for bacterial elimination was defined as the lowest value that resulted in the maximum antibacterial effect (actually the limit of detection i.e., 10 CFU/mL) Values are mean Ỉ SE of the mean (n = 6) host defence mechanisms but this approach was validated for danofloxacin in calves by comparing ex vivo data with findings obtained in vivo in a model of calf pneumonia (Lees and Aliabadi 2002) Murine thigh and lung infection models (e.g., with Klebsiella pneumoniae) have been used to describe the antibiotic dose–response curve and to generate and discriminate quantitative parameters of the in vivo antibiotic effect (Vogelman et al 1988; Leggett et al 1991) Neutropenic mice are inoculated and treated with one of a wide range of dosage regimens (varying dose and dosage interval) minimizing the interdependencies between the PD parameters tested (T > MIC, AUC/MIC, and Cmax /MIC) The mice are then serially killed and the numbers of bacterial cells remaining in the infected tissue (and other outcomes) are correlated with the different plasma drug profiles In these laboratory animal models, different PK/PD indices have been proposed for time- and concentration-dependent antibiotics The b-lactams exhibited a short-lived post-antibiotic effect (PAE) and minimal concentration-dependent killing, with optimal bactericidal action at a threshold of approximately  MIC, suggesting that the duration of plasma concentration exceeding the MIC (T > MIC) is the major PK/PD parameter determining their in vivo efficacy (Craig 1998).Thus, the daily dose of ceftazidime required to prevent death in 50% of the animals was 1.5 mg/kg for continuous infusion but 24.4 mg/kg for h intermittent injections (Roosendaal et al 1985) Moreover, T > MIC is the parameter which correlates best with efficacy for clindamycin and macrolides In the case of the macrolide spiramycin, PK/PD modelling of staphylococcal infections of the mammary gland in cows was used to predict efficacy (Renard et al 1996) and an optimal dosage regimen for the treatment of mastitis, showing the feasibility of this approach as proposed by Koritz and Bevill (1991) The dosage interval appears to be less important for the efficacy of aminoglycosides and quinolones In laboratory animal models, these antibiotics displayed major concentration-dependent killing, such that dosage regimens should aim at the highest (safe) plasma concentration The Cmax /MIC and/or AUC/MIC ratios are the main PK/PD parameters correlating with efficacy, although Cmax /MIC ratios may be more relevant in humans for infections where the risk of resistance development is significant (Craig 1998; Drusano et al 1993) The efficiency of spaced administration of large doses of quinolones or aminoglycosides is related to their prolonged and concentration-dependent PAE, which prevents bacterial regrowth when serum levels 108 P L Toutain, J R E del Castillo, A Bousquet-Melou fall below the MIC In veterinary medicine, the PK/PD relationships for danofloxacin and marbofloxacin have been investigated in ruminants using a tissue cage model allowing computation of the AUIC values producing bacteriostasis and bactericidal action (Aliabadi and Lees 1997) Although tetracyclines not exhibit concentrationdependent killing, the AUC/MIC ratio is the major PK/ PD parameter correlating with the therapeutic efficacy of these drugs (Craig 1998) PK/PD PREDICTIVE INDICES OF IN VIVO EFFICACY ARE BUILT ON (FREE) PLASMA ANTIBIOTIC CONCENTRATIONS, NOT TOTAL TISSUE ANTIBIOTIC LEVELS Most pathogens of clinical interest are located extracellularly and the biophase for antibiotics is the extracellular fluid (Schentag 1990) Except for plasma, extracellular fluids are difficult to sample, but if there is no barrier to impede drug diffusion, the concentration of free (unbound) antibiotic in plasma approximates its free concentration in the extracellular space The extravascular fluid penetration of free drug is complete, regardless of the extent of plasma protein binding (Schentag et al 1985), which makes it the best surrogate to free antibiotic in the biophase Therefore, the free drug concentration in plasma is the best drug-related predictor of clinical success, even for tissue infection (Schentag 1989; Cars 1997) In contrast, where there is a barrier to drug diffusion (central nervous system, eye, prostate .), the plasma concentration may be less useful to predict concentrations at the infection site Similarly, a discrepancy may exist between antibiotic concentrations in plasma and in a biophase if the normal rapid equilibration between plasma and infected site is impaired by a reduced blood supply (abscess, inflammatory debris, shock syndrome, sequestered bone fragments, tissue cage .) (Fig 2) The plasma binding of some classes of antibiotics (aminoglycosides, several fluoroquinolones) is low and the measured plasma concentration may be considered to be similar to the free concentration in the biophase Conversely, if drug binding is important (e.g., free fraction less than 20% of the total plasma concentration), a correction for binding is warranted It is noteworthy that the free concentration is controlled only by the intrinsic drug clearance Hence, even when the free fraction increases (e.g., displacement, low protein lev- FIG 2: Antibiotic access to the bacterial biophase Most bacteria (B) are located in extracellular fluid (plasma and interstitial fluids) Unbound (F) drug circulating in plasma is the only fraction which can gain access to the interstitial fluid through porous capillaries to combat infection due to an extracellular pathogen and a drug will develop antibacterial action if the free drug concentration exceeds the MIC Some tissues have permeability limitations at the capillary level and/or possess an efflux pump This impedes accumulation of drugs in the tissue (e.g., blood–brain barrier) and only lipophilic drugs can cross such barriers (e.g., quinolones) The blood perfusion rate can also be a limiting factor (clot, abscess) Some bacteria are located within cells (facultative or obligatory intracellular pathogens) Inside a cell (e.g., polymorphonuclear neutrophils), different locations are possible (cytosol, phagosome, and phagolysosome), where the antibiotic concentrations can be very different Macrolides for example are trapped in phagolysosomes which have a low pH (about 4–5) and this gives a ‘‘high total cell’’ concentration However, as the antibacterial potency of macrolides is pH dependent (low or no activity at acidic pH), a high local concentration is not synonymous with high activity The pharmacokinetic–pharmacodynamic approach to antibiotics 109 FIG 3: Differential influence of protein binding on the free concentration (Cfree ) and free fraction (fu) for a drug having a low extraction ratio The impact of protein binding on antibacterial activity causes confusion because the relationships between Cfree and the total antibiotic concentration (Ctot ) are fundamentally different in an in vitro (closed) and an in vivo (open) system Cfree is the only active fraction under in vivo or in vitro conditions In vitro, when an MIC is measured in broth (no binding), the antibiotic is entirely in its active form If the MIC is measured in a matrix able to bind antibiotics (e.g., serum), only free fraction is active and effects (e.g., inhibition diameter) decrease as the binding increases In vitro, a decrease in fu is synonymous with a decrease in Cfree In vivo, the situation is different and when discussing relationships between fu, Cfree and Ctot , the structural equation fu ¼ Cfree =Ctot (right, in vivo) should not be rearranged into Cfree ¼ fu à Ctot (left, in vitro) In vivo, an increase in fu is not synonymous to an increase in Cfree but rather to a decrease in Ctot Cfree is actually the independent variable which controls Ctot through Bmax and Kd, the maximum binding capacity and equilibrium association constant Ctot is only a dependent variable and competitive interaction decreases Ctot without increasing Cfree This is of relevance for drug monitoring as Ctot values are measured by analytical techniques In the case of competitive displacement (arrow), there is only a transient increase in Cfree , the small amount of antibiotic displaced from the transport protein being rapidly (within a few seconds) redistributed and eliminated whereas fu increases (e.g., from 0.2 to 0.4) els, .), this does not mean that more free drug is available for tissue distribution and drug action (see Fig for explanation) There is a persistent inclination in veterinary medicine to report ‘‘total tissue concentrations’’ for some antibiotics (especially macrolides) and to argue that this ‘‘tissue level’’ is better related to efficacy than the plasma concentration However, this point has been challenged because the total tissue concentration determined after homogenization may be very different from the biophase concentration whatever its location (intra- or extracellular) For further information about the irrelevance of tissue concentrations to predict antibiotic efficacy, see Barza (1994), Carbon (1990), Kneer (1993), Schentag (1989), Schentag (1990) and EMEA Points to consider (Anonymous 2000) PK/PD ENDPOINTS: ANALYTICAL PRESENTATION Various empirical PK/PD indices have been proposed (Aliabadi and Lees 1997, 2000; Hyatt et al 1995; Sanchez-Navarro and Sanchez Recio 1999; Schentag et al 1991) to predict the success or failure of therapy Three appear to be sufficient to predict drug effectiveness: T > MIC (therapeutic time) when antibiotics are time-dependent, AUIC (but also AUC/MIC), and Cmax / MIC (inhibitory ratio) when antibiotics are concentra- tion-dependent Fig shows how to compute these indices, while Figs and illustrate some of the difficulties encountered (discontinuity and non-discrimination between different pharmacokinetic profiles) AUIC, AUC/MIC, T > MIC, and Cmax /MIC are said to be PK/PD indices of efficacy because they comprise a PK parameter (AUC, T > MIC, Cmax ) and a common PD parameter, the MIC Hence they allow dual dosage individualization, based on a point consideration of the microbiological susceptibility and on the variation of the disposition kinetics One of the disadvantages of the MIC is that it is established over a 1:2 dilution scheme which has an inherent inaccuracy of 100% Forrest et al (1997) introduced the MIC mid-point, the mid-point between the recorded MIC, and the next lower value in the dilution series, to replace the conventional MIC when calculating the efficacy index Although MIC90 is the standard value, MIC50 is more precisely computable and has been suggested as a reasonable target to avoid administration of excessively high doses of antibiotics (Schentag 2000) AUIC is the partial AUC for the period of time during which concentrations are above the MIC divided by the MIC (Schentag et al 1991) and thus considers only the period when inhibitory activity is present AUIC should be calculated at the steady-state and over 24 h (Schentag et al 1996) It is very frequently 110 P L Toutain, J R E del Castillo, A Bousquet-Melou FIG 4: Computation of the main PK/PD indices for an MIC of 0.6 lg/mL AUIC considers the AUC between t1 and t2 (hatched area) Units should be in hours and these indexes should preferably be established under steady state conditions and for a 24 h dosing interval Data were simulated with a monocompartmental model (Vc ¼ 1.5 L/kg), for a rate constant of absorption (Ka) of 0:006 hÀ1 , a rate constant of elimination (K10 ) of 0:004 hÀ1 and a lag time of 60 The dose was mg/kg FIG 6: Lack of discrimination observed with AUIC The curves were simulated with the same total dose (5 mg/kg) as either a single IV bolus (5 mg/kg), or an IV loading bolus (0.45 mg/kg) followed by a 1011 infusion of 4.5 lg/ kg/min MIC was 0.3 lg/mL The AUIC values are very similar for both dosage regimens despite very dissimilar plasma profiles, while for a concentrationdependent antibiotic Cmax /MIC can discriminate between the two regimens At an MIC of over 0.30 lg/mL, AUIC becomes null during the infusion 24 h dosing interval should be about five times the MIC (actually 125/24 h) If it is acknowledged that AUC is determined only by plasma clearance and bioavailability and that free (not total) concentrations should be considered, a maintenance dose achieving a given AUIC (or AUC/MIC) is easily estimated with the following general equation: AUIC  MIC  Clðper dayÞ ; ð1Þ fu  F %  24 h where AUIC (or AUC/MIC) is the targeted endpoint in hours (e.g., 125 h), the MIC is the targeted pathogen, Cl the plasma (total) clearance in days, fu the free fraction of the drug in plasma (from to 1) and F% the bioavailability factor (from to 1) In Eq (1), AUIC/ 24 h may be viewed as the desired multiplicative factor for MIC Eq (1) can be simplified by ignoring fu when the free fraction is dominant (e.g., for aminoglycosides) and also F% for the IV route F ẳ 1ị Conversely, for drugs extensively bound in plasma fu should be taken into account (Hyatt et al 1995) The classical values reported for AUIC (125 and 250 h) were obtained for quinolones with low plasma binding However, for a quinolone extensively bound to plasma proteins, it is preferable to introduce fu into Eq (1) rather than to increase the targeted AUIC This parameter should be considered as a target for free, not total plasma AUC, as MICs are homogeneous to free, not total concentrations, and only free concentrations are microbiologically active (Cars 1997) The advantage of this approach is to use a single targeted AUIC value for all antibiotics of a given class, whatever the extent of the binding to plasma proteins Cmax /MIC (inhibitory ratio) is another PK/PD index, Cmax being a hybrid parameter influenced by plasma Doseper dayị ẳ FIG 5: Discontinuity of PK/PD indices The curves were simulated with a monocompartmental model for two close dosage regimens: a total dose of 1.28 (dose 1) or 0.92 mg/kg (dose 2), i.e., a loading dose of 0.525 (dose 1) or 0.375 mg/kg (dose 2), followed by a 720 infusion of 0.00105 lg/kg/min (dose 1) or 0.000750 lg/kg/min (dose 2) At an MIC of 0.3 lg/mL, the differences between the two regimens in terms of T > MIC and AUIC are very large and unlikely to reflect the actual clinical difference, whereas at a slightly lower MIC (0.25 lg/mL) these differences become minimal AUC/MIC is not subject to this discontinuity and may be preferred to AUIC reported in the literature as a dimensionless number (e.g., 125, and 250), but AUIC (or AUC/MIC) actually has a time dimension and saying that the AUIC should be 125 h to optimize efficacy is in practice equivalent to saying that the average plasma concentration over a The pharmacokinetic–pharmacodynamic approach to antibiotics clearance and bioavailability, and by the rate constants of absorption and elimination as well Thus Cmax /MIC reflects better than AUC/MIC the initial concentration build-up in plasma, which can be relevant if rapid attainment of a high concentration is desirable to optimize drug efficacy and minimize the emergence of resistance T > MIC (therapeutic time) is obtained by simple inspection of the simulated curve and generally expressed as a percentage of the dosage interval It is kinetically more complicated and is largely controlled by the terminal half-life, which is a hybrid process involving both plasma clearance and drug distribution or the rate constant of absorption (for long acting drug formulations undergoing a flip-flop process) These indices have been validated by various approaches ranging from in silico (computer) simulations to meta-analyses of clinical trials All methods found the different indices to be highly correlated (Preston et al 1998; Sanchez-Recio et al 2000) and it was difficult to determine which PK/PD parameter was most informative, as all three (T > MIC, AUIC, and Cmax / MIC) increased with increasing dose To circumvent this difficulty, Corvaisier et al (1998) using in silico simulations proposed a new composite index for the first 24 h, the weighted AUC (WAUC), which is the AUC/MIC weighted by the percentage of the total time for which plasma drug levels are above the MIC: T > MICðhÞ AUC h ; ð2Þ WAUCðhÞ ¼ MIC ðT > MICÞ maxðhÞ where (T > MICÞ max is equal to 24 h This index can be used for both concentration- and time-dependent antibiotics and has a high sensitivity to changes in MICs MAGNITUDE OF THE PK/PD PARAMETER REQUIRED FOR EFFICACY No PK/PD indices have yet been firmly validated in veterinary medicine, but as the differences can only reflect variations in species PK and MIC, it is reasonable to assume that the critical (breakpoint) values of these parameters to achieve efficacy will be similar in different animal species (Craig 1998) Thus, the results obtained in animal infection models or clinical human trials should be good starting points to design dosage regimens for a new antibacterial or a new species Studies of b-lactams in animal infection models have demonstrated that T > MIC does not need to be 100% of the dosage interval to develop a significant antibacterial effect In patients with otitis media (Streptococcus pneumoniae, Haemophilus influenza), a T > MIC of over 40% was required to achieve an 85–100% bacteriological cure rate with different b-lactams When mortality was selected as an endpoint for animals infected with S pneumoniae and treated for several days with penicillins or cephalosporins, mortality was close to 100% if T > MIC was 620% of the dosage interval, but 90–100% survival was reached when T > MIC was P40–50% of this interval (Craig 1998) 111 Finally, it can be recommended that T > MIC should be at least 50% and preferably P80% of the dosage interval to achieve an optimal bactericidal effect If the drug is extensively bound to plasma proteins, this recommendation holds for free, not total concentrations Using fluoroquinolones, in different models of infection with various species of gram-positive and gramnegative bacteria, AUC/MIC ratios of < 30 h were associated with >50% mortality but AUC/MIC values of P100 h with almost no mortality (Craig 1998) In seriously ill patients, a 24 h AUC/MIC value of P125 h for ciprofloxacin achieved a satisfactory outcome whereas lower values resulted in clinical and bacteriological cure rates of 250 h, 60% of patients became culture negative within one day When AUIC lay between 125 and 250 h, negative cultures were generally not achieved until the sixth day, while in patients whose AUIC was < 125 h, a second antibiotic was required (Schentag 2000) Preston et al (1998) showed that for levofloxacin the Cmax /MIC, AUC/MIC and T > MIC ratios were indistinguishable to predict a successful clinical outcome In contrast, Cmax /MIC was discriminant for microbiological outcomes and patients achieving a Cmax /MIC of P12:2 displayed 100% microbiological eradication In the case of aminoglycosides, in animal infection models the 24 h AUC/MIC ratio was a better predictor of therapeutic efficacy than the Cmax /MIC ratio, whereas the reverse was true in human clinical trials (Craig 1998) To obtain a clinical response of P90% and reduce the risk of emergence of resistance, Cmax / MIC needs to be 8–10 (Moore et al 1984) This is easily achieved with a single daily large dose of aminoglycosides, which also minimizes the consequences of adaptive resistance (Daikos et al 1991) Adaptive resistance is a phenotypic and reversible increase in MIC associated with a temporary lack of drug transport into the bacterial cells Its dissipation and restoration of microbial susceptibility requires a drug-free period, easily obtained with a once daily dosage regimen because the half-lives of aminoglycosides are short (about h) Once daily dosing also limits the incidence of nephrotoxicity and ototoxicity, as the tissular accumulation of aminoglycosides is saturable at clinically meaningful concentrations Additional work will be required to establish the magnitudes of the PK/PD parameters correlating with the efficacy of macrolides, azalides, clindamycin, tetracyclines, and glycopeptides (Craig 1998) For further information, see the comprehensive reviews of Hyatt et al (1995) and Craig (1998) PK/PD INDICES AND THE RISK OF RESISTANCE Study of the emergence of resistance is an integral part of the PK/PD approach aimed at limiting antimicrobial resistance (Anonymous 2000) and, according to Schentag et al (1996), the design of appropriate dosage 112 P L Toutain, J R E del Castillo, A Bousquet-Melou regimens may be the single most important contribution of clinical pharmacology to the resistance problem Resistance mechanisms can arise as the result of a single point mutation Since the frequency of occurrence is relatively high, the bacterial population is not homogeneous and behaves as a mixture of distinct populations having their own antibiotic susceptibility In this situation, exposure to antibiotics does not induce but selects resistance The emergence of resistance is only the predictable overgrowth of a pre-existing subpopulation with an initially lower level of susceptibility (Schentag et al 1998) and dosage regimens designed to rapidly eradicate this less susceptible subpopulation limit the risk of resistance (Schentag 2000) The most important risk factor for emergence of resistance is repeated exposure to suboptimal concentrations of antibiotics (Burgess 1999) In in vitro models simulating human PK of ciprofloxacin and sparfloxacin, high Cmax /MIC ratios were associated with a lower incidence of bacterial resistance for S pneumoniae (Thorburn and Edwards 2001) Similarly, in a mouse peritonitis model, less Pseudomonas aeruginosa resistance was observed for ciprofloxacin when Cmax /MIC was % 20 as compared to 10 (Michae-Hamzehpour et al 1987) Using ciprofloxacin against P aeruginosa in man, a single daily dose of 1200 mg triggered less resistance than 600 mg twice or 400 mg three times daily (Marchbanks et al 1993) In pneumococcal infection, Thomas et al (1998) investigated the probability of the development of resistant organisms in relation to the antibiotic dose After days treatment, approximately 50% of patients who had AUIC < 100 h developed resistance and this increased to 93% after weeks treatment On the contrary, among patients who had AUIC > 100 h, resistance developed in only 8% The concept of a mutant prevention concentration (MPC) is a possible application of the PK/PD approach for fluoroquinolones Briefly, two successive mutations (e.g., on gyrase and then on topoisomerase IV) result in mutant strains of high resistance In this framework of sequential mutations, there exists a concentration window lying between the MIC of wild bacteria (no mutation) and the MPC, a concentration which blocks the growth of first step mutants In this mutant selective window, the first step mutant population has an advantage over fully susceptible bacteria and increasing its population size increases the probability of having double mutants In contrast, above the MPC the probability that a wild bacterium will undergo the two resistance mutations is very low A practical strategy is to reduce the size of the mutant selective window, which can be achieved in different ways including by adjustment of the dosage regimen (Zhao and Drlica 2001) Large initial doses of quinolones and aminoglycosides are recommended to eradicate the resistant subpopulations Thus, for aminoglycosides a Cmax /MIC ratio of 10–12 and for quinolones an AUIC of >125– 250 h are desirable to minimize the survival and overgrowth of resistant strains Finally, for b-lactams increasing the duration of T > MIC should help to prevent the emergence of resistance PK/PD SUSCEPTIBILITY TESTING AND DOSAGE REGIMEN INDIVIDUALIZATION The objective of dual dosage regimen individualization is to adapt the antibiotic dosage regimen for the bacterial susceptibility (PD) and for the effect of the disease state (or other co-variables) on the antibiotic availability (PK) In the future, inexpensive methods of performing quantitative in vitro susceptibility tests would allow the practitioner to adapt the dosage regimen to the pathogen susceptibility as given by its measured MIC value The practitioner would then be in a position to determine himself the best dosage regimen (dose, interval of administration) to reach a given target endpoint Such an approach would require a knowledge of the relevant population kinetics, computation assistance and regulations allowing flexible labelling (i.e., antibiotics with a marketing authorization for a range of doses) (Martinez et al 1995) PK/PD AND VALIDATED CLINICAL BREAKPOINTS FOR VETERINARY MEDICINE The results of antimicrobial susceptibility tests are generally reported qualitatively, the isolate being designated as susceptible, intermediate or resistant This classification is based on breakpoint values, i.e., specific MICs allowing one to predict clinical efficacy or failure on the basis of an in vitro susceptibility test The clinical value of these tests for the guiding of an individual animal therapy remains unclear because most breakpoints were determined on the basis of human microbiological, pharmacological and clinical outcomes Recently, interpretive criteria for bacterial pathogens isolated from animals and breakpoints for several pathogen drug combinations for swine and cattle have been developed (for more information see nccls.org) Though performance standards and testing criteria of veterinary antimicrobial susceptibility tests have developed (NCCLS 1999a,b), true assessment of their predictive value of clinical outcome has seldom been addressed When such assessment has been carried out a posteriori, it appeared that the predictive value of susceptibility tests was less than ideal (see Shpigel et al 1998) Therefore, it is time to examine if this method for laboratory detection of resistance is as good for guiding an individual patient therapy than for providing resistance surveillance data (Gould 2000) Currently, approved interpretive criteria not formally take into account, in either human or veterinary medicine, the population concepts of PK/PD, i.e., they not combine kinetic variability in the animal population (including diseased animals) with what is known about the population distribution of MIC values for the target pathogen (not a single MIC value as currently done) A proposal for the determination of breakpoints having a clinical value should be carried out within the framework of population PK/PD as recently outlined by Ambrose and Grasela (2000) Briefly, a rational approach would consist of: (i) generating by simulation all possible drug exposures The pharmacokinetic–pharmacodynamic approach to antibiotics 113 get bacteria, i.e., without direct measurement of the antibiotic effect (in infection models) or efficacy (in clinical trials) The main advantages of this approach are summarized in Table In the case of a new antibiotic, a knowledge of the expected MIC for the target pathogens and pharmacokinetic parameters in healthy animals can give very early an order of magnitude of the future dosage regimen, without recourse to clinical trials or infection models which can be associated with difficulties in terms of validity (Pollyanna effect) The influence of antibiotic exposure on the bacterial efficacy of an antibiotic can be evaluated in in vitro kinetic models simulating in vivo situations and data are readily extrapolated from these models by means of PK/PD indices In a clinical setting, the PK/PD paradigm offers a rational approach for dual dosage adaptation, i.e., adjustment for variations in both antibiotic availability (PK) and bacterial susceptibility (PD) Consideration of PK and PD variability should also in the future be the best way to select appropriate breakpoints for susceptibility tests, whereby population studies will have a major influence on the prudent and rational use of antibiotics Finally, PK/PD for a given antibiotic can easily be determined by different independent groups, thus limiting the risk of conflicts of interest in commercial clinical drug trials FIG 7: Population AUIC breakpoint for marbofloxacin in dog The percentage of dogs having a serum area under the inhibitory curve (AUIC) > 125 h (A) and 48 h (B) for the selected MIC (lg/mL) are shown after a single marbofloxacin dose of 1, 2, 3, and mg/kg (curves 1, 2, 3, and 4, respectively) Dashed lines indicate critical MIC values to guarantee an AUIC of 125 or 48 h in 90% of a dog population Kinetic parameters were determined in a population of 63 dogs given a single intravenous dose of marbofloxacin (2 mg/kg) Serum data were analysed using a non-linear mixed effect regression model allowing to compute mean population parameters and the variance–covariance matrix of the individual parameters measuring the dispersion of the individual parameters in the population (Regnier et al unpublished data) (AUC, AUIC, Cmax /MIC, T > MIC) for the standard dosage regimen, which requires a knowledge of population parameters with typical (mean) values and their variance, (ii) establishing MIC distributions for clinically relevant pathogens, and (iii) generating random values across pharmacokinetic (e.g., AUC) and MIC distributions conform to their probabilities The resultant AUC/MIC probability distribution would allow one to examine the entire range of possible AUC/MIC ratios and the probability of achieving each ratio (Ambrose and Quintiliani 2000) Such an approach was used for marbofloxacin in dog (Fig 7) CONCLUSION The contribution of the PK/PD approach to the determination of an antibiotic 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DOSAGE REGIMEN INDIVIDUALIZATION The objective of dual dosage regimen individualization is to adapt the antibiotic dosage regimen for the bacterial susceptibility (PD) and for the effect of the. .. are readily extrapolated from these models by means of PK/PD indices In a clinical setting, the PK/PD paradigm offers a rational approach for dual dosage adaptation, i.e., adjustment for variations