Development and Evaluation of Dissolving Microarray Patches for Co-administered and Repeated Intradermal Delivery of Long-acting Rilpivirine and Cabotegravir Nanosuspensions for Paediatric HIV Antiretroviral Therapy.

PURPOSE: Whilst significant progress has been made to defeat HIV infection, the efficacy of antiretroviral (ARV) therapy in the paediatric population is often hindered by poor adherence. Currently, two long-acting (LA) intramuscular injectable nanosuspensions of rilpivirine (RPV) and cabotegravir (CAB) are in clinical development for paediatric populations. However, administration requires access to healthcare resources, is painful, and can result in needle-stick injuries to the end user. To overcome these barriers, this proof-of-concept study was developed to evaluate the intradermal delivery of RPV LA and CAB LA via self-disabling dissolving microarray patches (MAPs). METHODS: Dissolving MAPs of two conformations, a conventional pyramidal and a bilayer design, were formulated, with various nanosuspensions of RPV and CAB incorporated within the respective MAP matrix. MAPs were mechanically robust and were capable of penetrating ex vivo skin with intradermal ARV deposition. RESULTS: In a single-dose in vivo study in rats, all ARV MAPs demonstrated sustained release profiles, with therapeutically relevant plasma concentrations of RPV and CAB detected to at least 63 and 28 d, respectively. In a multi-dose in vivo study, repeated MAP applications at 14-d intervals maintained therapeutically relevant plasma concentrations throughout the duration of the study. CONCLUSIONS: These results illustrate the potential of the platform to repeatedly maintain plasma concentrations for RPV and CAB. As such, these MAPs could represent a viable option to improve adherence in the paediatric population, one that is capable of being painlessly administered in the comfort of the patient's own home on a biweekly or less frequent basis.

Predicting Volume of Distribution in Humans: Performance of In Silico Methods for a Large Set of Structurally Diverse Clinical Compounds.

Volume of distribution at steady state (VD,ss) is one of the key pharmacokinetic parameters estimated during the drug discovery process. Despite considerable efforts to predict VD,ss, accuracy and choice of prediction methods remain a challenge, with evaluations constrained to a small set (<150) of compounds. To address these issues, a series of in silico methods for predicting human VD,ss directly from structure were evaluated using a large set of clinical compounds. Machine learning (ML) models were built to predict VD,ss directly and to predict input parameters required for mechanistic and empirical VD,ss predictions. In addition, log D, fraction unbound in plasma (fup), and blood-to-plasma partition ratio (BPR) were measured on 254 compounds to estimate the impact of measured data on predictive performance of mechanistic models. Furthermore, the impact of novel methodologies such as measuring partition (Kp) in adipocytes and myocytes (n = 189) on VD,ss predictions was also investigated. In predicting VD,ss directly from chemical structures, both mechanistic and empirical scaling using a combination of predicted rat and dog VD,ss demonstrated comparable performance (62%-71% within 3-fold). The direct ML model outperformed other in silico methods (75% within 3-fold, r 2 = 0.5, AAFE = 2.2) when built from a larger data set. Scaling to human from predicted VD,ss of either rat or dog yielded poor results (<47% within 3-fold). Measured fup and BPR improved performance of mechanistic VD,ss predictions significantly (81% within 3-fold, r 2 = 0.6, AAFE = 2.0). Adipocyte intracellular Kp showed good correlation to the VD,ss but was limited in estimating the compounds with low VD,ss SIGNIFICANCE STATEMENT: This work advances the in silico prediction of VD,ss directly from structure and with the aid of in vitro data. Rigorous and comprehensive evaluation of various methods using a large set of clinical compounds (n = 956) is presented. The scale of techniques evaluated is far beyond any previously presented. The novel data set (n = 254) generated using a single protocol for each in vitro assay reported in this study could further aid in advancing VD,ss prediction methodologies.