- Han, Kelong;
- Baker, Mark;
- Lovern, Mark;
- Paul, Prokash;
- Xiong, Yuan;
- Patel, Parul;
- Moore, Katy P;
- Seal, Ciara S;
- Cutrell, Amy G;
- D'Amico, Ronald D;
- Benn, Paul D;
- Landovitz, Raphael J;
- Marzinke, Mark A;
- Spreen, William R;
- Ford, Susan L
Aim
To characterize cabotegravir population pharmacokinetics using data from phase 1, 2 and 3 studies and evaluate the association of intrinsic and extrinsic factors with pharmacokinetic variability.Methods
Analyses were implemented in NONMEM and R. Concentrations below the quantitation limit were modelled with likelihood-based approaches. Covariate relationships were evaluated using forward addition (P < .01) and backward elimination (P < .001) approaches. The impact of each covariate on trough and peak concentrations was evaluated through simulations. External validation was performed using prediction-corrected visual predictive checks.Results
The model-building dataset included 23 926 plasma concentrations from 1647 adult HIV-1-infected (72%) and uninfected (28%) subjects in 16 studies at seven dose levels (oral 10-60 mg, long-acting [LA] intramuscular injection 200-800 mg). A two-compartment model with first-order oral and LA absorption and elimination adequately described the data. Clearances and volumes were scaled to body weight. Estimated relative bioavailability of oral to LA was 75.6%. Race and age were not significant covariates. LA absorption rate constant (KALA ) was 50.9% lower in females and 47.8% higher if the LA dose was given as two split injections. KALA decreased with increasing BMI and decreasing needle length. Clearance was 17.4% higher in current smokers. The impact of any covariate was ≤32% on trough and peak concentrations following LA administration. The final model adequately predicted 5097 plasma concentrations from 647 subjects who were not included in the model-building dataset.Conclusions
A cabotegravir population pharmacokinetic model was developed that can be used to inform dosing strategies and future study design. No dose adjustment based on subject covariates is recommended.