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Aminoglycosides are bactericidial antibiotics with a serum concentration-dependent activity. They are mainly eliminated by the kidneys and the main difficulty arising in clinical use is their uptake by the renal cortex which leads to nephrotoxicity. An ototoxicity is also reported. We propose a PK/PD modelling of aminoglycoside nephrotoxicity which unifies more fourty years of physiological knowledge. This deterministic model successively describes the pharmacokinetics of aminoglycosides, their storage into renal cortex, their effect on renal cells, their consequences on the renal function through tubuloglomerular feedback and the changes in the serum concentrations of creatinine that is considered as a toxicity marker. The simulation of the model displays the leading effect of the shape and daily-time of administration schedule on the search for minimizing toxicity.

Transfer messenger RNA (tmRNA) directs the modification of proteins of which the biosynthesis has stalled or has been interrupted. Here, we report that aminoglycosides can interfere with this quality control system in bacteria, termed trans-translation. Neomycin B is the strongest inhibitor of tmRNA aminoacylation with alanine (K(i) value of approximately 35 micro m), an essential step during trans-translation. The binding sites of neomycin B do not overlap with the identity determinants for alanylation, but the aminoglycoside perturbs the conformation of the acceptor stem that contains the aminoacylation signals. Aminoglycosides reduce the conformational freedom of the transfer RNA-like domain of tmRNA. Additional contacts between aminoglycosides and tmRNA are within the tag reading frame, probably also disturbing reprogramming of the stalled ribosomes prior protein tagging. Aminoglycosides impair tmRNA aminoacylation in the presence of all of the transfer RNAs from Escherichia coli, small protein B, and elongation factor Tu, but when both proteins are present, the inhibition constant is 1 order of magnitude higher. SmpB and elongation factor Tu have RNA chaperone activities, ensuring that tmRNA adopts an optimal conformation during aminoacylation.

The main constraints on the administration of aminoglycosides are the risks of nephrotoxicity and ototoxicity, which can lead to acute, renal, vestibular, and auditory toxicities. In the present study we focused on nephrotoxicity. No reliable predictor of nephrotoxicity has been found to date. We have developed a deterministic model which describes the pharmacokinetic behavior of aminoglycosides (with a two-compartment model), the kinetics of aminoglycoside accumulation in the renal cortex, the effects of aminoglycosides on renal cells, the resulting effects on renal function by tubuloglomerular feedback, and the resulting effects on serum creatinine concentrations. The pharmacokinetic parameter values were estimated by use of the NPEM program. The estimated pharmacodynamic parameter values were obtained after minimization of the least-squares objective function between the measured and the calculated serum creatinine concentrations. A simulation program assessed the influences of the dosage regimens on the occurrence of nephrotoxicity. We have also demonstrated the relevancy of modeling of the circadian rhythm of the renal function. We have shown the ability of the model to fit with 49 observed serum creatinine concentrations for a group of eight patients treated for endocarditis by comparison with 49 calculated serum creatinine concentrations (r(2) = 0.988; P < 0.001). We have found that for the same daily dose, the nephrotoxicity observed with a thrice-daily administration schedule appears more rapidly, induces a greater decrease in renal function, and is more prolonged than those that occur with less frequent administration schedules (for example, once-daily administration). Moreover, for once-daily administration, we have demonstrated that the time of day of administration can influence the incidence of aminoglycoside nephrotoxicity. The lowest level of nephrotoxicity was observed when aminoglycosides were administered at 1:30 p.m. Clinical application of this model might make it possible to adjust aminoglycoside dosage regimens by taking into account both the efficacies and toxicities of the drugs.

Objective: To develop a model that relates the probability of occurrence of nephrotoxicity to the cumulative area under the curve (AUC) of amikacin serum concentration., Design and Patients: This was a retrospective study of two groups of patients in whom nephrotoxicity was observed after administration of amikacin. The first group consisted of patients treated with once-daily administration (ODA) [n = 13]. The second group consisted of patients treated with twice-daily administration (TDA) [n = 22]., Main Outcome Measures: The probability of nephrotoxicity occurrence., Results: The model is a powerful tool to represent and describe the influence of the dosage regimen on aminoglycoside nephrotoxicity. The onset of nephrotoxicity is delayed in the ODA group (p = 0.01) for the same total daily dose among the two groups. The cumulative serum AUC values at onset of nephrotoxicity were greater for the ODA group (p = 0.029). In addition, for the same probability of nephrotoxicity occurrence (50%), the cumulative AUC for the ODA dosage regimen is 2 613 mg. h/L versus only 1 521 mg. h/L for the TDA dosage regimen. The difference in nephrotoxicity between ODA and TDA is greatest for a cumulative AUC of 2 495 mg. h/L, which corresponds to standard therapy with amikacin 900 mg/day during a 7-day period, i.e. 15 mg/kg/day for a 60kg patient with normal renal function (initial creatinine clearance >80 mL/min). For an AUC above 2 495 mg. h/L, the difference in nephrotoxicity decreases slowly to zero. This result means that ODA is especially justified when the treatment is administered over a short duration, i.e. less than 7 days., Conclusions: The utility of selecting ODA in order to obtain less nephrotoxicity in comparison with TDA is therefore not established when the treatment is prolonged. In clinical use, the choice of the dosage regimen is not clear-cut, and both expected efficacy and expected toxicity must be taken into account in order to obtain an overall optimisation of each patient's therapy.

Objective: Pharmacokinetic (PK) interindividual variability in amikacin has been shown to be wide in neonates. This study evaluated the evolution of this variability with gestational age (GA) at birth in relation to renal maturation., Methods: Population PK values of amikacin were studied in 131 newborns (postnatal age 1 day, GA 24-41 weeks) divided into 16 groups, defined by GA, from 24 to 41 weeks (with a mean of 8.2 infants per group). PK variables were Kel/Vol, Ks/Vs, Cl/Vol. Cls/ where: Kel = Kslope x GA + Kintercept, Cl = Clslope x GA + Clintercept, and Vol = Vs x body weight. Ki and Cli were held as constants. The nonparametric distribution of the probability density function (PDF) was obtained, as were mean, median, and SD values of each PK variable for each GA group., Results: Amikacin elimination increased linearly with GA, showing that GA is a good covariate of renal elimination. Amikacin volume of distribution increased with body weight up to a GA of about 38 weeks and then decreased for highest GA values. However, the PDF for the individual GA groups showed a multimodal PK distribution. Kel, Vol, Vs, Cl, and Cl, standard deviations increased linearly with GA, showing differential renal maturation. The higher the GA, the more interindividual PK variability increased., Conclusions: These results show that amikacin elimination and the volume of distribution are dependent upon GA, and that differential renal maturation in neonates is responsible for the wider PK interindividual variability with high GA. Dosage regimens of amikacin and other aminoglycosides should be revised in newborns with high GA. Bayesian adaptive control of therapeutics might be particularly indicated to obtain efficacy for each neonate as early as the first dose.

Various suggestions have been made for empirical pharmacodynamic indices of antibiotic effectiveness, such as areas under the drug concentration-time curve in serum (AUC), AUC > MIC, AUC/MIC, area under the inhibitory curve (AUIC), AUC above MIC, and time above MIC (T > MIC). In addition, bacterial growth and killing models, such as the Zhi model, have been developed. The goal of the present study was to compare the empirical behavior of the Zhi model of bacterial growth and killing with the other empirical pharmacodynamic indices described above by using simulated clinical data analyzed with the USC*PACK PC clinical programs for adaptive control of drug therapy, with one model describing a concentration-dependent antibiotic (tobramycin) and another describing a concentration-independent antibiotic (ticarcillin). The computed relative number of CFU was plotted against each pharmacodynamic index, with each axis parameterized over time. We assumed that a good pharmacodynamic index should present a clear and continuous relationship between the time course of its values and the time course of the bacterial killing as seen with the Zhi model. Preliminary work showed that some pharmacodynamic indices were very similar. A good sensitivity to the change in the values of the MIC was shown for AUC/MIC and also for T > MIC. In addition, the time courses of some other pharmacodynamic indices were very similar. Since AUC/MIC is easily calculated and shows more sensitivity, it appeared to be the best of the indices mentioned above for the concentration-dependent drug, because it incorporated and used the MIC the best. T > MIC appeared to be the best index for a concentration-independent drug. We also propose a new composite index, weighted AUC (WAUC), which appears to be useful for both concentration-dependent and concentration-independent drugs.