QSAR analysis of the partitioning of terpenes and terpenoids into human milk

Abstract

Studies have shown that olfactory experience during breastfeeding plays an important role in the later development of certain food preferences in life. Thus, the aim of this study was to predict partitioning of odorous terpenes and terpenoids into breast milk from a predictive QSAR model for drug transfer. A large heterogenous data set based on drugs and their active metabolites that were used to build a QSAR was collected from the literature. Due to the vast structural diversity of these compounds and possibly different mechanisms involved in M/P partitioning, a non-linear artificial neural network (ANN) model was used to develop a predictive QSAR model. The value of the correlation coefficient of predicted versus experimentally measured M/P values for the final model (14-2-1) was high (R = .82). The descriptors selected in the final model (14-2-1) belong to 3 main categories: (a) solubility/permeability descriptors (dipole moment, polar surface area, aromatic ring count and hydroxyl group count), (b) reactivity descriptors (i.e. HOMO energy) and (c) shape descriptors (different ring size counts, counts of methyl groups and molecular depth). Results of this study predict that many volatile terpenes from the essential oils are transferred into breast milk selectively. The highest M/P values (>3.5) were predicted for β-caryophyllene, aromadendrene, alloaromadendrene, and 1,4-and 1,8-cineole, high values for carvacrol (M/P = 3.2), eugenol (M/P = 3.0) and thymol (M/P = 3.6), and moderate values for α-pinene (M/P = 2.3) and low values (M/P = 0.4) for phellandrene and limonene. Our model helps to explain and expand on the current knowledge of volatile compounds in breast milk by predicting that a variety of volatile terpenoids can be found in breast milk.