Aim Oral flucloxacillin may be co-administered with probenecid to increase flucloxacillin concentrations and increase attainment of pharmacokinetic-pharmacodynamic (PK-PD) targets. The aims of this study were to describe outcomes of patients treated with oral flucloxacillin plus probenecid as follow-on therapy from initial intravenous treatment, and to identify optimal dosing regimens when treating infections caused by susceptible Gram-positive organisms. Methods We performed a prospective observational study of adults treated with oral flucloxacillin 1000 mg and probenecid 500 mg 8-hourly (with food) for proven or probable staphylococcal infections. We developed a population pharmacokinetic model of free flucloxacillin concentrations within Monolix, in order to estimate probability of PK-PD target attainment (fT>MIC), and used Monte Carlo simulation to explore optimal dosing regimens. Results The 45 patients (73% male) had a median (range) age of 49 years (20 – 74), weight of 90 kg (59 – 167), fat free mass (Janmahasatian) of 65 kg (38 – 89) and eGFR (CKD-EPI) of 89 mL/min/1.73m2 (41 – 124). The most common infections were osteomyelitis (n=18, 40%) and septic arthritis (n=12, 27%). Forty patients (89%) were cured 30 days after completion of therapy. 10 (22%) experienced nausea which did not require treatment alternation. Free flucloxacillin clearance depended on allometrically-scaled fat free mass, and increased by 1% for each unit increase in eGFR. Conclusion Oral flucloxacillin and probenecid was well-tolerated and efficacious. Patients with higher fat free mass and eGFR may require four times daily dosing and/or therapeutic drug monitoring to ensure PK-PD target attainment.

AIM: We propose the use of in silico mathematical models to provide insights that optimize therapeutic interventions designed to eradicate respiratory infection during a pandemic. A modelling and simulation framework is provided using SARS-CoV-2 as an example, considering applications of both treatment and prophylaxis. METHODS: A target cell-limited model was used to quantify the viral infection dynamics of SARS-CoV-2 in a pooled population of 105 infected patients. Parameter estimates from the resulting model were used to simulate and compare the impact of various interventions against meaningful viral load endpoints. RESULTS: Robust parameter estimates were obtained for the basic reproduction number, viral release rate and infected-cell mortality from the infection model. These estimates were informed by the largest dataset currently available for SARS-CoV-2 viral time course. The utility of this model was demonstrated using simulations, which hypothetically introduced inhibitory or stimulatory drug mechanisms at various target sites within the viral life-cycle. We show that early intervention is crucial to achieving therapeutic benefit when monotherapy is administered. In contrast, combination regimens of two or three drugs may provide improved outcomes if treatment is initiated late. The latter is relevant to SARS-CoV-2, where the period between infection and symptom onset is relatively long. CONCLUSIONS: The use of in silico models can provide viral load predictions that can rationalize therapeutic strategies against an emerging viral pathogen.