Bridging in vitro dissolution and in vivo exposure for acalabrutinib. Part II. A mechanistic PBPK model for IR formulation comparison, proton pump inhibitor drug interactions, and administration with acidic juices.

Acalabrutinib (Calquence®) 100 mg (bid) has received accelerated approval by FDA for the treatment of adult patients with mantle cell lymphoma (MCL) who have received at least one prior therapy. Acalabrutinib is a substrate of PgP and CYP3A4, with a significant fraction of drug metabolized by first pass gut extraction and 25% absolute bioavailability. The absorption of acalabrutinib is affected by stomach pH, with lower pharmacokinetic exposure observed following co-administration with proton pump inhibitors. During dissolution at pH values below its highest basic pKa, the two basic moieties of acalabrutinib react with protons from the aqueous solution, leading to a higher pH at the drug surface than in the bulk solution. A batch-specific product particle size distribution (P-PSD), was derived from dissolution data using a mechanistic model that was based on the understanding of surface pH and the contribution of micelles to the dissolution rate. P-PSD values obtained for various batches of acalabrutinib products in simple buffers, or in complex fluids such as fruit juices, were successfully integrated into a physiologically based pharmacokinetic (PBPK) model developed using GastroPlus v9.0™. The integrated model allowed the prediction of clinical pharmacokinetics under normal physiological stomach pH conditions as well as following treatment with proton pump inhibitors. The model also accounted for lower pharmacokinetic exposure that was observed when acalabrutinib was co-administered with the acidic beverages, grapefruit juice, (which contains CYP3A inhibitors), and orange drink (which does not contain CYP3A inhibitors), relative to administration with water. The integration of dissolution data in the PBPK model enables mechanistic understanding and the establishment of more robust in vitro-in vivo correlations (IVIVC) under a variety of conditions. The model can then distinguish the interplay between dissolution and first pass extraction and how in vivo stomach pH, saturation of gut PgP, and saturation or inhibition of gut CYP3A4, will impact the pharmacokinetics of acalabrutinib.

Bridging in vitro dissolution and in vivo exposure for acalabrutinib. Part I. Mechanistic modelling of drug product dissolution to derive a P-PSD for PBPK model input.

Drug product dissolution for four batches of acalabrutinib 100 mg capsules were analyzed with in vitro dissolution in various pH conditions and in media containing synthetic surfactant micelles or biorelevant micelles. Non-sink conditions, where the drug is unionized, were used to derive a batch specific drug product particle size distribution (P-PSD). The purpose of this P-PSD is to serve as an input in physiological based pharmacokinetic (PBPK) models to calculate in vivo dissolution in various administration conditions. The P-PSD was used to predict dissolution in all other conditions tested, introducing a different Unstirred Water Layer (UWL) thickness for free- and micelle-bound drug and the calculation of surface solubility using a theoretical model. With the proposed P-PSD approach and proposed model inputs, percent dissolved at all time points and for all conditions and batches were adequately anticipated with an 11% overprediction. In contrast, the use of drug substance laser diffraction particle size data with equivalent inputs to the models led to an underprediction of observed percent dissolved by 31% overall. Finally, the use of bulk solubility instead of surface solubility led to an overall 48% overprediction of the dissolution data. Batch specific P-PSD were used to predict in vivo dissolution of acalabrutinib drug products with PBPK models. The current limitations of PBPK models for integration of in vitro dissolution are also discussed and improvements are suggested to improve future predictions.