BOUSQUET-M EELOU UMR INRA de Physiopathologie et Toxicologie Expeerimentales, Ecole Nationale Veeteerinaire de Toulouse, 23 Chemin des Capelles, 31076 Toulouse cedex 03, France SUMMARY P
Trang 1The pharmacokinetic–pharmacodynamic approach to a
rational dosage regimen for antibiotics
P L TOUTAIN*, J R E DEL CASTILLO, A BOUSQUET-M EELOU UMR INRA de Physiopathologie et Toxicologie Expeerimentales, Ecole Nationale Veeteerinaire de Toulouse,
23 Chemin des Capelles, 31076 Toulouse cedex 03, France
SUMMARY Pharmacokinetic–pharmacodynamic (PK/PD) surrogate indices (AUIC, AUC/MIC, C max /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
C max /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
resis-tance can spread to other microbial populations,
jeop-ardizing 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)
Ra-tional antibiotic therapy requires dosage regimens to
be optimized, not only to guarantee clinical efficacy,
but also to minimize the selection and spread of
resis-tant pathogens
Pharmacokinetics (PK) is the tool used to describe
and predict drug concentration profiles in biological
fluids (usually plasma) and combining PK and
phar-macodynamic (PD) information (i.e., bacterial
suscep-tibility to antibiotics) constitutes the PK/PD modelling
approach to antibiotic efficacy The goal of this
ap-proach is to describe, predict, and if possible
under-stand 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
regi-men of antibiotics and to demonstrate the advantages
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 experimen-tally infected and not in healthy animals Ideally, dose ranging and clinical trials in target species should ex-plore responses to a variety of dosage regimens, in-cluding suboptimal schedules, but this is impractical for economic and not possible for ethical reasons In ad-dition, the reliability of the results of clinical trials in-volving 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 con-sidered, all but 10 were excluded on account of major defects in experimental design or data analysis A
Corresponding author Fax: +33-561-19-39-17;
Trang 2similar finding was reported in pigs (Bording 1990) and
concerns over the informative value of published
hu-man 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
re-sponses, drugs or dosing strategies with excellent
anti-bacterial 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
eradi-cation 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
anti-biotics 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
pa-tients would be necessary to statistically discriminate
between these two antibiotics, while 200 would be
suf-ficient 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
experimen-tal colibacillosis in neonaexperimen-tal swine in a large trial
in-volving several hundred piglets This study highlights
the possible discrepancy between dose-effect
relation-ships 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 ED50values for mortality (8 mg/kg) and
bacteriological cure (13 mg/kg) (Toutain, unpublished
results)
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 emer-gence 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 ac-tivity and are predictive of the emergence of resistance
In veterinary medicine, the response of Staphylococcus aureus to the simulated interstitial fluid pharmacoki-netic 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 do 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 col-lected and ex vivo antibacterial activity can be mea-sured over a 24 h incubation period allowing the computation of the AUIC24 h corresponding to bacte-riostasis (no change in bacterial count), bactericidal activity (99.9% reduction of bacterial count) or total bacterial eradication (Lees and Aliabadi 2002) Table 2 gives ex vivo AUC24 h/MIC values for danofloxacin in serum for ruminant species As for in vitro investiga-tion, these ex vivo AUICs do not take into account
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 exclud-ing 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.
Trang 3host defence mechanisms but this approach was
vali-dated 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
interdepen-dencies 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
concentra-tion-dependent antibiotics The b-lactams exhibited a
short-lived post-antibiotic effect (PAE) and minimal
concentration-dependent killing, with optimal
bacteri-cidal action at a threshold of approximately 4 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 6 h intermittent injections (Roosendaal et al 1985)
Moreover, T > MIC is the parameter which corre-lates best with efficacy for clindamycin and macrolides
In the case of the macrolide spiramycin, PK/PD mod-elling 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 dos-age 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 effi-cacy, 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
TABLE 1: PK/PD vs dose titration or clinical trials
PK/PD Dose titration or clinical trials Subjects Healthy Infection models, patients
Endpoints Surrogates: T > MIC, C max /MIC, and
AUIC
Clinical outcome (cure, failure) bacteriological outcome (eradication, resistance)
Validity (clinical relevance) Need to be validated (prospectively or
retrospectively)
Gold standard but many possible drawbacks and Pollyanna phenomenon
Sensitivity to dose ranging Yes No (difficult to perform dose ranging in ill patients)
Application to drug discovery and
development
Early screening Later, confirmatory Extrapolation (from in vitro models
or other species)
Easy Difficult Dual dosage individualization Yes No
Prediction of the emergence of resistance Possible Possible
Breakpoint setting To be explored (promising) Yes
Population studies: PK or PD origin of
variability
Yes No, if only clinical outcomes are measured Regulatory acceptance In progress Pivotal, designed to satisfy authorities but not to optimize treatments
Independent evaluation/objectivity Independent investigations possible Requires commercial funding
TABLE 2: Critical ex vivo AUC 2 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)
Calf Sheep Goat Camel Bacteriostatic 15:9 2:0 17:8 1:7 22:6 1:7 17:2 3:6
Bactericidal 18:1 1:9 20:2 1:7 29:6 2:5 21:2 3:7
Elimination 33:5 3:5 28:7 1:8 52:4 8:1 68:7 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 AUIC 24 h in serum and the log 10 difference in bacterial count (CFU) was modelised by a Hill model; the AUIC 24 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 AUIC 24 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 SE of the mean (n = 6).
Trang 4fall 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
pro-ducing bacteriostasis and bactericidal action (Aliabadi
and Lees 1997)
Although tetracyclines do not exhibit
concentration-dependent 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
tracellularly and the biophase for antibiotics is the
ex-tracellular 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
ex-travascular 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 nor-mal 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 concentra-tion), a correction for binding is warranted It is note-worthy 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
Trang 5els, .), this does not mean that more free drug is
available for tissue distribution and drug action (see
Fig 3 for explanation)
There is a persistent inclination in veterinary
medi-cine 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
de-termined 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
an-tibiotic 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
pro-posed (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
effective-ness: 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 4 shows how to compute these in-dices, while Figs 5 and 6 illustrate some of the difficulties encountered (discontinuity and non-dis-crimination between different pharmacokinetic pro-files)
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 con-sideration 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) in-troduced the MIC mid-point, the mid-point between the recorded MIC, and the next lower value in the di-lution 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 di-vided by the MIC (Schentag et al 1991) and thus con-siders 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
FIG 3: Differential influence of protein binding on the free concentration (C free ) 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 C free and the total antibiotic concentration (C tot ) are fundamentally different in an in vitro (closed) and an in vivo (open) system C free 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 C free In vivo, the situation is different and when discussing relationships between fu, C free and C tot , the structural equation fu ¼ C free =C tot (right, in vivo) should not be rearranged into
C free ¼ fu C tot (left, in vitro) In vivo, an increase in fu is not synonymous to an increase in C free but rather to a decrease in C tot C free is actually the independent variable which controls C tot through Bmax and Kd, the maximum binding capacity and equilibrium association constant C tot is only a dependent variable and competitive interaction decreases C tot without increasing C free This is of relevance for drug monitoring as C tot values are measured by analytical techniques In the case of competitive displacement (arrow), there is only a transient increase in C free , 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).
Trang 6reported 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
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 mainte-nance dose achieving a given AUIC (or AUC/MIC) is easily estimated with the following general equation: Doseðper dayÞ ¼AUIC MIC Clðper dayÞ
fu F % 24 h ; ð1Þ 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 0 to 1) and F% the bioavailability factor (from 0 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 routeðF ¼ 1Þ 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 concen-trations, and only free concentrations are microbio-logically 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,
C being a hybrid parameter influenced by plasma
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 min 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
differ-ences 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.
FIG 6: Lack of discrimination observed with AUIC The curves were simu-lated 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 min 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 concentration-dependent antibiotic C max /MIC can discriminate between the two regimens At
an MIC of over 0.30 lg/mL, AUIC becomes null during the infusion FIG 4: Computation of the main PK/PD indices for an MIC of 0.6 lg/mL.
AUIC considers the AUC between t 1 and t 2 (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
mono-compartmental model (V c ¼ 1.5 L/kg), for a rate constant of absorption (Ka) of
0:006 h1, a rate constant of elimination (K 10 ) of 0:004 h1and a lag time of
60 min The dose was 5 mg/kg.
Trang 7clearance 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
at-tainment of a high concentration is desirable to
opti-mize drug efficacy and miniopti-mize the emergence of
resistance
T >MIC (therapeutic time) is obtained by simple
inspection of the simulated curve and generally
ex-pressed 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
in-volving 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
ap-proaches 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
in-formative, 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:
WAUCðhÞ¼AUC
MICh T >MICðhÞ
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
reason-able 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
antibac-terial effect In patients with otitis media (Streptococcus
pneumoniae, Haemophilus influenza), a T > MIC of
over 40% was required to achieve an 85–100%
bacte-riological cure rate with different b-lactams
When mortality was selected as an endpoint for
an-imals infected with S pneumoniae and treated for
sev-eral 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)
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 ef-fect If the drug is extensively bound to plasma pro-teins, this recommendation holds for free, not total concentrations
Using fluoroquinolones, in different models of in-fection with various species of positive and gram-negative bacteria, AUC/MIC ratios of < 30 h were as-sociated 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 <50% (Forrest et al 1993)
In treatment with quinolones, for an AUIC of
>250 h, 60% of patients became culture negative within one day When AUIC lay between 125 and 250 h, neg-ative 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 indis-tinguishable to predict a successful clinical outcome In contrast, Cmax/MIC was discriminant for microbiologi-cal 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 aminoglyco-sides, which also minimizes the consequences of adaptive resistance (Daikos et al 1991) Adaptive re-sistance 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 be-cause the half-lives of aminoglycosides are short (about
2 h) Once daily dosing also limits the incidence of nephrotoxicity and ototoxicity, as the tissular accumu-lation 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, tet-racyclines, 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 antimi-crobial resistance (Anonymous 2000) and, according to Schentag et al (1996), the design of appropriate dosage
Trang 8regimens may be the single most important
contribu-tion of clinical pharmacology to the resistance problem
Resistance mechanisms can arise as the result of a
single point mutation Since the frequency of
occur-rence 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
sub-population 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
concen-trations of antibiotics (Burgess 1999) In in vitro models
simulating human PK of ciprofloxacin and sparfloxacin,
high Cmax/MIC ratios were associated with a lower
in-cidence of bacterial resistance for S pneumoniae
(Thorburn and Edwards 2001) Similarly, in a mouse
peritonitis model, less Pseudomonas aeruginosa
resis-tance 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
re-sistance than 600 mg twice or 400 mg three times daily
(Marchbanks et al 1993)
In pneumococcal infection, Thomas et al (1998)
in-vestigated the probability of the development of
resis-tant organisms in relation to the antibiotic dose After 5
days treatment, approximately 50% of patients who
had AUIC < 100 h developed resistance and this
increased to 93% after 3 weeks treatment On the
contrary, among patients who had AUIC > 100 h,
re-sistance 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
win-dow lying between the MIC of wild bacteria (no
mu-tation) 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
ad-vantage over fully susceptible bacteria and increasing its
population size increases the probability of having
double mutants In contrast, above the MPC the
prob-ability that a wild bacterium will undergo the two
re-sistance 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
adjust-ment of the dosage regimen (Zhao and Drlica 2001)
Large initial doses of quinolones and
aminoglyco-sides are recommended to eradicate the resistant
sub-populations 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
over-growth of resistant strains Finally, for b-lactams
in-creasing 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 individual-ization 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 regi-men 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, com-putation assistance and regulations allowing flexible labelling (i.e., antibiotics with a marketing authoriza-tion 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 des-ignated 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 clin-ical value of these tests for the guiding of an individual animal therapy remains unclear because most break-points were determined on the basis of human micro-biological, 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 de-veloped (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 provid-ing resistance surveillance data (Gould 2000)
Currently, approved interpretive criteria do not formally take into account, in either human or veteri-nary medicine, the population concepts of PK/PD, i.e., they do 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 re-cently outlined by Ambrose and Grasela (2000) Briefly, a rational approach would consist of: (i) gen-erating by simulation all possible drug exposures
Trang 9(AUC, AUIC, Cmax/MIC, T > MIC) for the standard
dosage regimen, which requires a knowledge of
popu-lation parameters with typical (mean) values and their
variance, (ii) establishing MIC distributions for
clini-cally 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
possi-ble 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 dosage regimen relies on
the paradigm that the dosage regimen can be
approx-imated from plasma levels during antibiotic exposure in
healthy animals scaled by the susceptibility of the
tar-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 1
In the case of a new antibiotic, a knowledge of the expected MIC for the target pathogens and pharmac-okinetic parameters in healthy animals can give very early an order of magnitude of the future dosage reg-imen, 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., ad-justment 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
ACKNOWLEDGMENTS The authors wish to thank Prof Peter Lees for his valuable critical comments The work was supported by
a grant from INRA (Project entitled ‘‘Transversalitee: utilisation raisonneee des antibiotiquesaa viseee the era-peutique en eelevage’’)
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