title: Molecular descriptors that influence the amount of drugs transfer into human breast milk.
abstract: Most drugs are excreted into breast milk to some extent and are bioavailable to the infant. The ability to predict the approximate amount of drug that might be present in milk from the drug structure would be very useful in the clinical setting. The aim of this research was to simplify and upgrade the previously developed model for prediction of the milk to plasma (M/P) concentration ratio, given only the molecular structure of the drug. The set of 123 drug compounds, with experimentally derived M/P values taken from the literature, was used to develop, test and validate a predictive model. Each compound was encoded with 71 calculated molecular structure descriptors, including constitutional descriptors, topological descriptors, molecular connectivity, geometrical descriptors, quantum chemical descriptors, physicochemical descriptors and liquid properties. Genetic algorithm was used to select a subset of the descriptors that best describe the drug transfer into breast milk and artificial neural network (ANN) to correlate selected descriptors with the M/P ratio and develop a QSAR. The averaged literature M/P values were used as the ANN's output and calculated molecular descriptors as the inputs. A nine-descriptor nonlinear computational neural network model has been developed for the estimation of M/P ratio values for a data set of 123 drugs. The model included the percent of oxygen, parachor, density, highest occupied molecular orbital energy (HOMO), topological indices (chiV2, chi2 and chi1) and shape indices (kappa3, kappa2), as the inputs had four hidden neurons and one output neuron. The QSPR that was developed indicates that molecular size (parachor, density) shape (topological shape indices, molecular connectivity indices) and electronic properties (HOMO) are the most important for drug transfer into breast milk. Unlike previously reported models, the QSPR model described here does not require experimentally derived parameters and could potentially provide a useful prediction of M/P ratio of new drugs only from a sketch of their structure and this approach might also be useful for drug information service. Regardless of the model or method used to estimate drug transfer into breast milk, these predictions should only be used to assist in the evaluation of risk, in conjunction with assessment of the infant's response.
authors.affiliation: School of Pharmaceutical, Molecular and Biomedical Science, University of South Australia, North Terrace, Adelaide 5000, Australia. nena.kustrin@unisa.edu.au
authors.forename: S
authors.initials: S
authors.lastname: Agatonovic-Kustrin
authors.forename: L H
authors.initials: LH
authors.lastname: Ling
authors.forename: S Y
authors.initials: SY
authors.lastname: Tham
authors.forename: R G
authors.initials: RG
authors.lastname: Alany
mesh_terms: D000818:Animals; D006801:Humans; D007408:Intestinal Absorption; D008895:Milk, Human / Q000737:chemistry*; D016571:Neural Networks, Computer; D004364:Pharmaceutical Preparations / Q000097:blood* / Q000378:metabolism; D010599:Pharmacokinetics; D013329:Structure-Activity Relationship
chemical_list: D004364:Pharmaceutical Preparations