Background: Salivary therapeutic drug monitoring of linezolid has been proposed as a non-invasive alternative to blood sampling for pharmacokinetic assessment. However, the salivary disposition and pharmacokinetics of the two major linezolid metabolites, PNU-142300 and PNU-142586, have not been characterized. Objective: To evaluate the salivary disposition and serum protein binding of PNU-142300 and PNU-142586 using pharmacokinetic analysis. Methods: In this exploratory study, six healthy adult volunteers received a single intravenous dose of linezolid. Serial blood and saliva samples were collected for 10 h after dosing. The total and protein-unbound concentrations of linezolid and its two major metabolites, PNU-142300 and PNU-142586, were quantified using validated high-performance liquid chromatography methods. Results: Linezolid was consistently quantifiable in saliva, and its salivary concentration–time profile closely followed that of protein-unbound serum exposure. PNU-142300 and PNU-142586 were readily quantifiable in blood but remained below the lower limit of quantification in saliva in all participants at all sampling time points. Conclusions: Under the conditions studied, linezolid was quantifiable in saliva, whereas its two major metabolites were not, indicating different salivary disposition profiles for the parent drug and its metabolites. Salivary concentrations were consistent with protein-unbound serum exposure to linezolid under the conditions studied, whereas exposure to PNU-142300 and PNU-142586 could not be assessed using saliva and required blood-based measurements in the present study.

Background: Hyperuricemia is an adverse effect frequently observed during favipiravir treatment. The time course, from uric acid elevation to recovery, and quantitative relationship between drug exposure and changes in serum uric acid levels remain insufficiently characterized. We investigated the pharmacodynamic mechanism of uric acid elevation and described its time course by population pharmacokinetic and pharmacodynamic modelling. Methods: Patients who received favipiravir for coronavirus disease 2019 or severe fever with thrombocytopenia syndrome were retrospectively evaluated. The pharmacokinetics of favipiravir were described by a one-compartment model with first-order absorption and elimination. Metabolite concentrations were predicted based on previously reported values. Changes in serum uric acid levels were described by a turnover model with zero-order production and first-order elimination. The drug effect was implemented as inhibition of the uric acid elimination process. Simulations based on the final model were performed for 10 consecutive days after the clinical regimen, with a 21-day follow-up. Results: The final model supported the inhibition of uric acid elimination by favipiravir and its metabolite. Regarding simulations, serum uric acid levels reached a median peak of 6.93 mg/dL at 6.7 days after treatment initiation and returned to pre-treatment levels within 4.0 days after treatment discontinuation. Conclusions: This combined population pharmacokinetic and pharmacodynamic turnover model quantified favipiravir-associated increases in serum uric acid levels and showed a transient profile with rapid recovery after drug discontinuation. These findings underscore the need for monitoring serum uric acid levels during favipiravir treatment, particularly in patients at a higher risk of gout.