Sara Bettonte (1,2), Mattia Berton (1,2), Felix Stader (3), Manuel Battegay (1,2), Catia Marzolini (1,2,4)
(1) University Hospital Basel, Switzerland, (2) University of Basel, Switzerland, (3) Certara UK Limited, United Kingdom, (4) University of Liverpool, United Kingdom
Introduction: Combined antiretroviral treatments have transformed HIV infection into a chronic disease. However, the efficacy of antiretroviral treatments relies on high levels of adherence to daily oral regimens. The recent approval of cabotegravir and rilpivirine as the first long-acting injectable antiretroviral drugs represents a major milestone for the treatment of HIV infection. These long-acting injectables are formulated to slowly release the drug over months after the injection and therefore have the potential to improve adherence with less frequent dosing [1]. Although injectable antiretroviral drugs hold great promises, a number of questions remain unresolved including notably their pharmacokinetics in special populations or the management of drug-drug interactions. Physiologically based pharmacokinetic (PBPK) modelling is a useful tool to simulate unstudied clinical scenarios [2]. Only a few PBPK models for intramuscular administration have been published [3, 4], however they rely on clinical data to estimate the release constant of the drug from the intramuscular depot making them specific to the population studied. We overcame this limitation by developing a mechanistic intramuscular model integrating the dissolution process after drug injection.
Objectives: The aim of this study was to evaluate the predictive performance of our mechanistic intramuscular PBPK model to simulate the pharmacokinetics of cabotegravir.
Methods: The mechanistic intramuscular framework was implemented our in-house PBPK model built using Matlab® [2]. A daily oral lead-in phase is generally recommended before initiating intramuscular cabotegravir to verify its tolerability. Cabotegravir drug parameters were obtained from published data both after oral and intramuscular administration as well as during the switch period between the two routes of administration. Observed data were extracted from the literature using GetData Graph digitizer®. Non-compartmental analysis was conducted to calculate the primary pharmacokinetic parameters if they were not reported. The predictive performance of the model to simulate the pharmacokinetics of cabotegravir was judged by the visual inspection of concentration-time profiles after single and multiple oral and intramuscular administrations. Furthermore, the achievement of pharmacokinetic parameters within the 1.25-fold, 1.5-fold, 2-fold limits of observed data (as per FDA guidance) was verified using observed clinical data [5].
Results: The model simulations were in close agreement with the observed clinical data and the ratios for predicted versus observed pharmacokinetics parameter were all within the 1.5-fold limit. After the administration of a single oral dose (30mg) of cabotegravir, the AUC0-∞ ratio for predicted versus observed was 1.28; while, for multiple oral doses the AUC0-τ ratio was 1.19. In the case of intramuscular administration, the AUC0-τ ratio was 0.93 for a monthly single dose (400 mg) and 0.87 for multiple monthly doses (1x 800mg followed by 3x 400mg, all doses were administered at 1 month interval). Lastly, the model was able to predict the pharmacokinetics of cabotegravir switch from oral to intramuscular since the AUC0-τ ratio was 0.88 after the first intramuscular dose.
Conclusion: Our developed mechanistic intramuscular PBPK model was able to correctly predict long-acting cabotegravir pharmacokinetics after single, multiple doses, and after transitioning from oral to intramuscular administration. Therefore, the current model represents a useful tool to investigate unstudied clinical scenarios with injectable long-acting antiretrovirals.
References:
[1] Owen, A. and S. Rannard, Adv Drug Deliv Rev, 2016. 103: p. 144-156.
[2] Stader, F., et al., CPT Pharmacometrics Syst Pharmacol, 2019.
[3] Rajoli, R.K., et al., Clin Pharmacokinet, 2015. 54(6): p. 639-50.
[4] Lin, Z., et al., J Pharm Sci, 2015. 104(1): p. 233-43.
[5] Landovitz, R.J., et al., PLoS Med, 2018. 15(11): p. e1002690.
Reference: PAGE 30 (2022) Abstr 10016 [www.page-meeting.org/?abstract=10016]
Poster: Drug/Disease Modelling - Absorption & PBPK