Quentin Renou(1), Nadège Néant(2), Alexandre Destere(3), Jennifer Lagoutte-Renosi(4,5), Matthieu Grégoire(6,7), François Parant(8), Florian Lemaitre(9), Peggy Gandia(10), Nicolas Venisse(11), Stéphane Bouchet(12), Minh P Lê(13, 14), Patrice Muret(5), Gilles Peytavin(12,15), Caroline Solas(2), Sihem Benaboud(16, 17)
1. Unité des Virus Émergents (UVE : Aix-Marseille Univ-IRD 190-Inserm 1207), Marseille, France 2. Aix-Marseille Univ, APHM, UVE IRD190-Inserm 1207, Hôpital La Timone, Laboratoire de Pharmacocinétique et Toxicologie, Marseille, France 3. Department of Pharmacology and Pharmacovigilance Center, Côte d'Azur University Medical Center, Nice, France 4. Université de Franche-Comté, MPFRPV, F-25000 Besançon, France 5. Service de Pharmacologie Clinique et Toxicologie, CHU Besançon, F-25000 Besançon, France 6. Nantes Université, CHU Nantes, Cibles et médicaments des infections et de l’immunité, 9 IICiMed, UR 1155, F-44000 Nantes, France 7. Nantes Université, CHU Nantes, Service de Pharmacologie Clinique, F-44000 Nantes, France 8. Hospices Civils de Lyon - Groupement Hospitalier Sud - Service de Biochimie et Biologie Moléculaire - UM Pharmacologie-Toxicologie - 69495 Pierre-Bénite 9. Univ Rennes, CHU Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes, France 10. CHU de Toulouse, Laboratoire de Pharmacocinétique et Toxicologie Clinique, Institut Fédératif de Biologie, 330, avenue de Grande-Bretagne, 31059, Toulouse cedex 9, France. 11. CHU Poitiers, Laboratoire de Toxicologie et Pharmacocinétique ; CIC Inserm 1402 ; EBI, UMR CNRS 7267, Poitiers, France 12. Laboratoire de Pharmacologie et Toxicologie, Service de Pharmacologie Médicale, CHU Pellegrin, INSERM U1219, Bordeaux, France 13. AP-HP Nord, Pharmacology Department, Bichat Claude-Bernard University Hospital, Paris, France 14. Université Paris Cité, INSERM - S 1144, Paris, France 15. Université Paris Cité, INSERM - UMR 1137, IAME, Paris, France 16. EA7323, Evaluation des Thérapeutiques et Pharmacologie Périnatale et Pédiatrique, Université Paris Cité, Paris, France. 17. Service de Pharmacologie Clinique, Hôpital Cochin, AP-HP, Université Paris Cité, Paris, France.
Objectives:
Cabotegravir is a potent integrase strand transfer inhibitor recently approved as a long-acting (LA) intramuscular (IM) injection in combination with rilpivirine (RPV) for the maintenance treatment of HIV-1 infection. These new long-acting ART hold great promise for the treatment of HIV, but their PK variability differs from that known to date for oral treatments, raising new questions about target concentrations. Low plasma concentrations (Cpl) of CAB and RPV 4 weeks after the first injection have been associated with virological failure in clinical trials, as have high BMI or HIV-1 subtype A6/A1. An oral/intramuscular CAB POP-PK model was developed by Han et al. using data from phase 1, 2 and 3 trials in order to describe CAB LA PK. The aim of this study was to perform an external evaluation of this model using routine follow-up data to evaluate the currently recommended dosing regimen and ultimately integrate it into an interactive adaptation tool intend for therapeutic drug monitoring (TDM).
Methods:
Data from the multicenter (n=12) and observational ANRS0255 CARLAPOP study of patients receiving the LA CAB-RPV regimen who had CAB Cpl determined as routine TDM from January 2022 to December 2022 were used to evaluate the model. Immuno-virologic, therapeutic (last ART treatment/comedications) and clinical data were collected at baseline and during the follow-up. The Han et al. model has two compartments with first order oral and IM absorption and elimination. With clearances and volumes scaled to body weight and smoking status also having an influence on the clearance. In addition, LA absorption rate constant is driven by sex, BMI, needle length and whether the dose is given as two split injections. MONOLIX software was used for data analysis. Graphical analysis of visual predictive checks (VPC) was performed. The mean prediction error (MPE), the distribution of prediction error (PE), and relative Root Mean Squared Error (rRMSE) were calculated and described. Predictive performance was evaluated by bias and precision.
Results:
A total of 735 patients were included in the study, with over 86% being followed for six months or longer and 54% for more than a year. A total of 2194 CAB concentrations were analysed (298 oral, 1896 IM). Both groups were similar in demographics and covariate distribution. Graphical analysis of VPC and concentration corrected VPC plots showed a significant number of outliers, particularly at the 90th percentile of concentration levels. The MPE values were 0.41 and 0.44 and rRMSE values were 1.9 and 9.6 for oral and IM, respectively, indicating a significant bias and poor precision. Although we observed an improvement in the predictive performance of the model after removing concentrations considered as outliers, this improvement was not significant enough to validate the model.
Conclusions:
The aim of this study was to evaluate this model within the context of adaptation tools development. The accuracy and precision of the pharmacokinetic predictions were not good enough to apply the model to TDM of patients with LA CAB-RPV regimen. The model provided acute prediction for a part of our population. The potential factors influencing model predictability for patients with poorer predictions will be investigated.
Reference: PAGE 32 (2024) Abstr 11128 [www.page-meeting.org/?abstract=11128]
Poster: Drug/Disease Modelling - Infection