Individualised dosing algorithm and personalised treatment of high-dose rifampicin for tuberculosis.

Aims: To propose new exposure targets for Bayesian dose optimisation suited for high-dose rifampicin and to apply them using measured plasma concentrations coupled with a Bayesian forecasting algorithm allowing predictions of future doses, considering rifampicin's auto-induction, saturable pharmacokinetics and high interoccasion variability.

Methods: Rifampicin exposure targets for Bayesian dose optimisation were defined based on literature data on safety and anti-mycobacterial activity in relation to rifampicin's pharmacokinetics i.e. highest plasma concentration up to 24 hours and area under the plasma concentration-time curve up to 24 hours (AUC ). Targets were suggested with and without considering minimum inhibitory concentration (MIC) information. Individual optimal doses were predicted for patients treated with rifampicin (10 mg/kg) using the targets with Bayesian forecasting together with sparse measurements of rifampicin plasma concentrations and baseline rifampicin MIC.

Results: The suggested exposure target for Bayesian dose optimisation was a steady state AUC of 181-214 h × mg/L. The observed MICs ranged from 0.016-0.125 mg/L (mode: 0.064 mg/L). The predicted optimal dose in patients using the suggested target ranged from 1200-3000 mg (20-50 mg/kg) with a mode of 1800 mg (30 mg/kg, n = 24). The predicted optimal doses when taking MIC into account were highly dependent on the known technical variability of measured individual MIC and the dose was substantially lower compared to when using the AUC -only target.

Conclusions: A new up-to-date exposure target for Bayesian dose optimisation suited for high-dose rifampicin was derived. Using measured plasma concentrations coupled with Bayesian forecasting allowed prediction of the future dose whilst accounting for the auto-induction, saturable pharmacokinetics and high between-occasion variability of rifampicin.