In Silico Models Using Simple Molecular Descriptors Predict Placental and Breast Milk Transfer of Cannabinoids from Cannabis sativa.

Despite an increasing interest in the pharmacology of cannabinoids from Cannabis sativa, little is known to date about their ability to cross the placenta and to be secreted into breast milk, and in this study, we sought to fill this gap. In total, 126 phytocannabinoids previously detected in Cannabis sativa were investigated for their transplacental transfer and secretion into breast milk. Placental transport was predicted using novel multiple linear regression (MLR), artificial neural network (ANN), boosted trees (BT), and support vector regression (SVR) models, based on a reference set of 84 compounds for which the placental clearance index (CI) relative to antipyrine is known. Secretion into breast milk was predicted using newly developed classification models based on soft independent modeling of class analogies (SIMCA) and One-Class Partial Least Squares (OC-PLS) algorithms. Analysis of the Q vs. Hotelling's T2 plot for the cannabinoids indicated that they are similar in their physicochemical properties to compounds empirically demonstrated to enter breast milk ("in-class"); only 7 of 126 compounds were borderline (with elevated Q but not T2); no compounds were classified as "out-of-class". The mean predicted CI values for phytocannabinoids investigated in this study ranged from 0.4 to 0.85. It was concluded that all the cannabinoids in the studied group might cross the placenta (although their passage might be expected to be more difficult than that of antipyrine) and enter breast milk. These results should support informed risk assessment and prioritization of cannabinoids for future experimental testing.