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The Annals of Pharmacotherapy: Vol. 36, No. 1, pp. 83-86. DOI 10.1345/aph.19114
© 2002 Harvey Whitney Books Company.
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Research Articles

Pharmacokinetic model for tobramycin in acinetobacter meningitis

PV Chicano-Pia, AC Cercos-Lleti, and E Roma-Sanchez

OBJECTIVE: To report a case of acinetobacter meningitis treated with a once-daily intravenous dose of tobramycin and to propose a pharmacokinetic model for the drug disposition. CASE SUMMARY: A 28-year-old man with chronic hydrocephalus was admitted with a diagnosis of intracranial hypertension. Acinetobacter spp. was detected in the cerebrospinal fluid (CSF); it was sensitive to tobramycin, ampicillin/sulbactam, and colistin. Based on the culture report, multiple daily-dose therapy with tobramycin was started. As the infectious symptoms remained, once-daily therapy was recommended; the optimal dose was calculated with nonlinear regression by least-squares analysis and a Bayesian method, using plasma and CSF samples. The infection was resolved, tobramycin therapy was discontinued, and the patient was discharged from the intensive care unit. DISCUSSION: We use once-daily intravenous tobramycin therapy because, although the intrathecal administration of drugs is generally well tolerated, the presence of preservatives may be a source of central nervous system adverse effects. Pharmacokinetic parameters were calculated with plasma and CSF concentration values obtained during the first once-daily dose by using a compartment-effect model which allows fitting of simultaneous plasma and CSF concentrations. The prediction level was determined by the estimation of drug concentrations during the fourth once-daily dose. CSF concentrations of drug were enough to eradicate the clinical signs of infection. CONCLUSIONS: Therapy using once-daily intravenous administration of tobramycin may be an adequate alternative for acinetobacter infections in neurosurgical patients when an intrathecal route is initially not recommended. The development of a compartment-effect model can be useful to predict drug concentrations.





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