Linezolid is an antibiotic used to treat infections caused by drug-resistant gram-positive organisms, including vancomycin-resistant Enterococcus faecium, multi-drug resistant Streptococcus pneumoniae, and methicillin-resistant Staphylococcus aureus. The adverse effects of linezolid can include thrombocytopenia and neuropathy, which are more prevalent with higher exposures and longer treatment durations. Although linezolid is traditionally administered at a standard 600 mg dose every 12 hours, the resulting exposure can vary greatly between patients and can lead to treatment failure or toxicity. The efficacy and toxicity of linezolid are determined by the exposure achieved in the patient; numerous clinical and population pharmacokinetics (popPK) studies have identified threshold measurements for both parameters. Several special populations with an increased need for linezolid dose adjustments have also been identified. Therapeutic Drug Monitoring (TDM) is a clinical strategy that assesses the response of an individual patient and helps adjust the dosing regimen to maximize efficacy while minimizing toxicity. Adaptive feedback control and model-informed precision dosing are additional strategies that use Bayesian algorithms and PK models to predict patient-specific drug exposure. TDM is a very useful tool for patient populations with sparse clinical data or known alterations in pharmacokinetics, including children, patients with renal insufficiency or those receiving renal replacement therapy, and patients taking co-medications known to interact with linezolid. As part of the clinical workflow, clinicians can use TDM with the thresholds summarized from the current literature to improve linezolid dosing for patients and maximize the probability of treatment success.

Rivaroxaban is a direct-acting oral anticoagulant approved to prevent strokes in patients with atrial fibrillation. Dosage recommendations are approved for all adult patients to receive either 15 mg or 20 mg once daily depending upon renal function. There are a number of reasons to believe rivaroxaban dosing could be more effective and/or safer for more patients if increased dosing precision is available. Because real-world patients are more diverse than those studied in phase III clinical trials, we evaluated the extremes of creatinine clearance (CrCl) on rivaroxaban clearance using a published population pharmacokinetic model and applying exposure variation limits (±20%) based on published literature. The proposed dosing recommendations are 10 mg once daily (CrCl 15-29 ml/min), 15 mg once daily (CrCl 30-69 ml/min), 10 mg twice daily (CrCl 70-159 ml/min), and 15 mg twice daily (CrCl 160-250 ml/min). These new dosing recommendations should be prospectively tested for predictive accuracy and to assess the impact on AF patient efficacy and safety.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a leading cause of death with 1.6 million deaths worldwide reported in 2021. Oral pyrazinamide (PZA) is an integral part of anti-TB regimens, but its prolonged use has the potential to drive development of PZA resistant Mtb. PZA is converted to the active moiety pyrazinoic acid (POA) by the Mtb pyrazinamidase encoded by pncA, and mutations in pncA are associated with the majority of PZA resistance. Conventional oral and parenteral therapies may result in subtherapeutic exposure in the lung, hence direct pulmonary administration of POA may provide an approach to rescue PZA efficacy for treating pncA-mutant PZA-resistant Mtb. The objectives of the current study were to i) develop novel dry powder POA formulations ii) assess their feasibility for pulmonary delivery using physicochemical characterization, iii) evaluate their pharmacokinetics (PK) in the guinea pig model and iv) develop a mechanism based pharmacokinetic model (MBM) using in vivo PK data to select a formulation providing adequate exposure in epithelial lining fluid (ELF) and lung tissue. We developed three POA formulations for pulmonary delivery and characterized their PK in plasma, ELF, and lung tissue following passive inhalation in guinea pigs. Additionally, the PK of POA following oral, intravenous and intratracheal administration was characterized in guinea pigs. The MBM was used to simultaneously model PK data following administration of POA and its formulations via the different routes. The MBM described POA PK well in plasma, ELF and lung tissue. Physicochemical analyses and MBM predictions suggested that POA maltodextrin was the best among the three formulations and an excellent candidate for further development as it has: (i) the highest ELF-to-plasma exposure ratio (203) and lung tissue-to-plasma exposure ratio (30.4) compared with POA maltodextrin and leucine (75.7/16.2) and POA leucine salt (64.2/19.3); (ii) the highest concentration in ELF (𝐶𝑚𝑎𝑥𝐸𝐿𝐹: 171 nM) within 15.5 minutes, correlating with a fast transfer into ELF after pulmonary administration (𝐾𝑃𝑀: 22.6 1/h). The data from the guinea pig allowed scaling, using the MBM to a human dose of POA maltodextrin powder demonstrating the potential feasibility of an inhaled product.

Population pharmacokinetic (PK)/pharmacodynamic models are commonly used to inform drug dosing; however, often real‐world patients are not well represented in the clinical trial population. We sought to determine how well dosing recommended in the rivaroxaban drug label results in exposure for real‐world patients within a reference area under the concentration–time curve (AUC) range. To accomplish this, we assessed the utility of a prior published rivaroxaban population PK model to predict exposure in real‐world patients. We used the model to predict rivaroxaban exposure for 230 real‐world patients using 3 methods: (1) using patient phenotype information only, (2) using individual post hoc estimates of clearance from the prior model based on single PK samples of rivaroxaban collected at steady state without refitting of the prior model, and (3) using individual post hoc estimates of clearance from the prior model based on PK samples of rivaroxaban collected at steady state after refitting of the prior model. We compared the results across 3 software packages (NONMEM, Phoenix NLME, and Monolix). We found that while the average patient‐assigned dosing per the drug label will likely result in the AUC falling within the reference range, AUC for most individual patients will be outside the reference range. When comparing post hoc estimates, the average pairwise percentage differences were all <10% when comparing the software packages, but individual pairwise estimates varied as much as 50%. This study demonstrates the use of a prior published rivaroxaban population PK model to predict exposure in real‐world patients.