Abstract
Rifampicin exhibits complexities in its pharmacokinetics (PK), including high inter-occasion variability (IOV), which is challenging for dose individualization. Model-informed precision dosing (MIPD) can be used to optimize individual doses. In this simulation-based study we investigated the magnitude of IOV in rifampicin PK on an exposure level, the impact of not acknowledging IOV when performing MIPD, and the number of sampling occasions needed to forecast the dose. Subjects with drug-susceptible tuberculosis (TB) were simulated from a previously developed population PK model. To explore the magnitude of IOV, the area under the plasma concentration-time curve from time zero up to 24 h (AUC0–24h) after 35 mg/kg in the typical individual was simulated for 1,000 sampling occasions at steady-state. The impact of ignoring IOV for dose predictions was investigated by comparing the prediction error of a MIPD approach including IOV to an approach ignoring IOV. Furthermore, the number of sampling occasions needed to predict individual doses using a MIPD approach was assessed. The AUC0–24h in the typical individual varied substantially between simulated sampling occasions [95% prediction interval (PI): 122.2 to 331.2 h mg/L], equivalent to an IOV in AUC0–24h of 25.8%, compared to an inter-individual variability of 25.4%. The median of the individual prediction errors using a MIPD approach incorporating IOV was 0% (75% PI: −14.6% to 0.0%), and the PI for the individual prediction errors was narrower with than without IOV (median: 0%, 75% PI: −14.6% to 20.0%). The most common target dose in this population was forecasted correctly in 95% of the subjects when IOV was included in MIPD. In subjects where doses were not predicted optimally, a lower dose was predicted compared to the target, which is favorable from a safety perspective. Moreover, the imprecision (relative root mean square error) and bias in predicted doses using MIPD with IOV decreased statistically significant when a second sampling occasion was added (difference in imprecision: −9.1%, bias: −7.7%), but only marginally including a third (difference in imprecision: −0.1%, bias: −0.1%). In conclusion, a large variability in exposure of rifampicin between occasions was shown. In order to forecast the individual dose correctly, IOV must be acknowledged which can be achieved using a MIPD approach with PK information from at least two sampling occasions.
Highlights
Individualized dosing is important to improve treatment outcomes by avoiding toxicity while still achieving optimal efficacy in each individual patient
An average exposure (AUC0–24h at steady state: 235 h mg/L) corresponding to a high dose of 35 mg/kg in the PanACEA HIGHRIF1 trial, an open-label phase II multiple dose-rising trial registered at www.clinicaltrials.gov (NCT01392911) (Boeree et al, 2015; Svensson et al, 2018a; Svensson et al, 2019) is targeted, a dose that was found to be safe, while still resulting in high efficacy. In this simulation study we investigated the magnitude of interoccasion variability (IOV) compared to individual variability (IIV) in rifampicin PK on an exposure level, the impact of not acknowledging IOV when performing model-informed precision dosing (MIPD), the performance of the proposed MIPD approach, and the number of sampling occasions needed to predict individual doses accurately and precisely
Individualized dosing has been difficult for drugs with high IOV, since it is a phenomenon occurring completely at random, creating challenges in dose predictions (Karlsson and Sheiner, 1993; Wallin et al, 2010; Holford and Buclin, 2012; Liefaard and Chen, 2015)
Summary
Individualized dosing is important to improve treatment outcomes by avoiding toxicity while still achieving optimal efficacy in each individual patient. MIPD is an approach where information from a population pharmacokinetic (POPPK) or physiology-based pharmacokinetic (PBPK) model in combination with individually observed plasma drug concentrations is utilized to forecast the dose that leads to the most optimal exposure in an individual patient (Darwich et al, 2017; Keizer et al, 2018). MIPD can incorporate pharmacokinetics (PK) and efficacy and safety aspects in the individual dose prediction, i.e. predict the dose given a POPPK or PBPK model, and given pharmacokineticpharmacodynamic (PKPD) models This is superior to classical therapeutic drug monitoring (TDM), which does not predict an optimal individual dose, but only compares the individual exposure to a target and evaluates if the individual exposure is too high or too low. MIPD can be used to overcome the challenge of high IOV by optimizing a dose based on individually observed plasma concentrations, together with information from a POPPK, PBPK, or PKPD model to ensure efficacious individual dosages
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