Effect of Streptococcus pneumoniae and Influenza A Virus on Middle Ear Antimicrobial Pharmacokinetics in Experimental Otitis Media

Antimicrobial treatment failures in children with acute otitis media and concomitant viral respiratory tract infection prompted us to study the effects of influenza A virus infection on middle ear antimicrobial drug penetration. Using a chinchilla model of Streptococcus pneumoniae we compared middle ear elimination rates in 4 groups of chinchillas: (1) control, (2) influenza A virus inoculation alone intranasally, (3) both influenza A and S. pneumoniae inoculation directly into the middle ear, and (4) S. pneumoniae inoculation alone into the middle ear. After infection was established, a solution containing amoxicillin, sulfamethoxazole, and trimethoprim was instilled into the middle ear and removed 4 hours later. The rate constant of elimination and half-life were calculated from measured drug concentrations initially and at 4 hours. S. pneumoniae infection alone significantly shortened the middle ear elimination half-life compared with the control group: amoxicillin, 2.65 ± 0.73 vs. 6.63 ± 2.55 hr; sulfamethoxazole, 1.75 ± 0.28 vs. 2.74 ± 0.6 hr; and trimethoprim, 1.06 ± 0.14 vs. 1.56 ± 0.34 hr (n = 16 ears, p values all <0.01). The combined influenza virus and S. pneumoniae infection significantly lengthened the half-life compared with the S. pneumoniae infection alone: amoxicillin, 5.65 ± 6.44 vs. 2.65 ± 0.73 hr; sulfamethoxazole, 2.5 ± 0.85 vs. 1.75 ± 0.28 hr; and trimethoprim, 1.26 ± 0.42 vs. 1.06 ± 0.14 hr (n = 16 ears, p values all <0.01). Influenza virus produced the longest half-lives for all 3 antimicrobials: amoxicillin 25.52 ± 14.96 hr; sulfamethoxazole, 5.46 ± 0.87 hr; and trimethoprim, 2.57 ± 0.75 hr. These effects demonstrate that influenza and S. pneumoniae infections alone and together affect middle ear antimicrobial penetration. The decreased penetration of antimicrobials that occurred with the combined viral and bacterial infection vs. the bacteria alone supports the clinical observation that patients with infections caused by both organisms may have decreased middle ear antimicrobial concentrations, producing treatment failures.     

The application of sample pooling methods for determining AUC, AUMC and mean residence times in pharmacokinetic studies

For high-throughput screening in drug development, methods that can reduce analytical work are desirable. Pooling of plasma samples from an individual subject in the time domain to yield a single sample for analysis has been used to estimate the area under the concentration-time curve (AUC). We describe a pooling procedure for the estimation of the area under the first moment curve (AUMC). The mean residence time (MRT), and where intravenous dosing has been used, the steady-state volume of distribution can then be determined. Plasma samples from pharmacokinetic studies in dogs and humans analyzed in our laboratory were used to validate the pooling approach. Each plasma sample containing a prokinetic macrolide and three of its metabolites was first analyzed separately, and AUCs and AUMCs were calculated using the linear trapezoidal rule. The procedures for the estimation of AUC by sample pooling have been reported by Riad et al. [Pharm. Res. (1991) vol. 8, pp. 541-543]. For the estimation of AUMC, the volume taken from each of n samples to form a pooled sample is proportional to t(n)(t(n+1) - t(n-1)), except at t0 where the aliquot volume is 0 and at t(last) where the aliquot volume is proportional to t(last)(t(last) - t((last)-1)). AUMC to t(last) is equal to C(pooled) x T2/2, where T is the overall experimental time (t(last) - t0). The ratio between AUMC and AUC yields the mean residence time (MRT). Bivariate (orthogonal) regression analysis was used to assess agreement between the pooling method and the linear trapezoidal rule. Bias and root mean square error were used to validate the pooling method. Orthogonal regression analysis of the AUMC values determined by pooling (y-axis) and those estimated by the linear trapezoidal rule (x-axis) yielded a slope of 1.08 and r2 of 0.994 for the dog samples; slope values ranged from 0.862 to 0.928 and r2 values from 0.838 to 0.988 for the human samples. Bias, expressed as percentage, ranged from -25.1% to 14.8% with an overall average of 1.40%. The results support the use of a pooled-sample technique in quantitating the average plasma concentration to estimate areas under the curve and areas under the first moment curve over the sampling time period. Mean residence times can then be calculated.     

Comparison of Two Otitis Media Models for the Study of Middle Ear Antimicrobial Pharmacokinetics

We compared two models of acute otitis media that estimate middle ear antimicrobial pharmacokinetics. Using a crossover study design, we compared a systemic drug administration model with a diffusion model we devised that measures the disappearance of antimicrobials from the middle ear. We induced acute otitis media in 14 chinchillas by inoculating S. pneumoniae into the middle ear, then administered 3 antimicrobials: amoxicillin, trimethoprim, and sulfamethoxazole. Next we collected middle ear fluid samples to analyze drug concentrations and compare rate constants for the systemic and diffusion models by analysis of variance. We found that amoxicillin K values were not affected by model testing sequence (p = 0.827) or model type (systemic versus diffusion, p = 0.310), nor were sulfamethoxazole K values: model testing sequence (p = 0.917), model type (p = 0.963). Trimethoprim K values were also not affected by model testing sequence (p = 0/760), but were by model type (p = 0.0001). Trimethoprim elimination from the diffusion model was faster (K = 0.33 ± 0.17 versus 0.57 ± 0.09 hr^–1) than from the systemic model, although it appears this was caused by sampling before drug distribution into the middle ear was complete. In conclusion, it appears K values derived from either systemic antimicrobial administration or direct middle ear instillation are similar for assessing middle ear anitmicrobial pharmacokinetics, and these models can be used interchangeably to study factors affecting otitis media treatment response.