Inhalation Absorption Prediction (IAP) Model for Predicting Medicinal Cannabis Phytochemical Pharmacokinetics

Abstract

Introduction: The medicinal benefits from inhalation of Cannabis sativa phytochemicals have been extensively reported. Whilst in-silico models are available for prediction of phytochemical pharmacokinetics post-ingestion, no models are available to accurately predict inhalation pharmacokinetics. Therefore, the aim of this study was to explore the relationship between phytochemical physicochemical properties and inhalation pharmacokinetics and to develop an in-silico model for predicting the time of maximal compound concentration in plasma (T max) and compound elimination half-life (T ½), following inhalation. Methods: A training set of compound pharmacokinetic data was collated from previous publications and compared to physicochemical parameters using regression analyses. Physicochemical parameters that correlated with T max and T ½ were combined to develop a statistical model, which constructs functional fingerprints predicting compound concentrations in plasma post inhalation. Predicted functional fingerprints for three cannabis bioactive compounds were constructed and biomatched against previously reported physiological effects. Results: Inhalation T max was predicted (r 2 = 0.84) by compound volume (Vol), topological surface area (TPSA) and molecular weight (MW), whilst T ½ was predicted (r 2 = 0.87) by molecular weight, volume and number of rotatable bonds (nrot). The resulting inhalation absorption prediction (IAP) model was achieved by combining T max and T ½ predictions. The IAP model was applied to cannabis metabolites which accurately predicted decay functions in-vivo and biomatching with associated physiological effects. Conclusion: The IAP model was applied successfully to cannabis phytochemicals to explore the pharmacokinetics underpinning their medicinal effects. This study demonstrates the utility of the IAP model and highlights its applicability during the investigation of medicinal plants and their modes of action.

Utilisation of QSPR ODT modelling and odour vector modelling to predict Cannabis sativa odour

“…Statistical modelling can be used to predict chemical activities or properties from their physicochemical data, which is known as quantitative structure–activity/property relationship (QSAR/QSPR) modelling. This process and relationship has been demonstrated for a range of properties including time of maximal phytochemical concentration in circulation following ingestion and inhalation [ 36 , 37 ], blood-to-liver partition coefficients of volatile compounds [ 38 ], intestinal bioavailability and antioxidant activity [ 39 ], and perception of chemical odour following inhalation [ 34 ]. Generation of these QSPR models is useful, particularly for measures such as ODT which are laborious to measure and are required for OI calculation which is an input for vector modelling [ 40 ].…”