Prediction of Milk/Plasma Concentration Ratios of Drugs and Environmental Pollutants Using In Silico Tools: Classification and Regression Based QSARs and Pharmacophore Mapping

Abstract

A large set of 185 compounds with diverse molecular structures and different mechanisms of therapeutic actions was used to develop and validate statistically significant classification and regression based QSTR models for predicting partitioning of drugs/chemicals into breast milk. Pharmacophore mapping was also carried out which showed four important features required for lower risk of secretion into milk: (i) hydrophobic group (HYD), (ii) ring aromatic group (RA), (iii) negative ionizable (NegIon) and (iv) hydrogen bond donor (HBA). QSTR and pharmacophore models were rigorously validated internally as well as externally to check the possibilities of any chance correlation and judge the predictive potential of the models. Pharmacological distribution diagrams (PDDs) were used for the classification model as a visualizing technique for the identification and selection of chemicals with lower partitioning into milk. Our in silico models enable to identify the essential structural attributes and quantify the prime molecular pre-requisites which were chiefly responsible for secretion into milk. The developed models were also implemented to screen milk/plasma partitioning potential for a huge number DrugBank database (http://www.drugbank.ca/) compounds.

Modeling drug passage into human milk

“…A recent study from India used a dataset of 185 drugs and environmental compounds obtained from previous compilations of drugs used to build models and sorted them into drugs with M/P ratios less than or greater than 1 . Ninety‐seven drugs were used as a training set and the remaining 88 compounds were used as a test set.…”

“…A recent study from India used a dataset of 185 drugs and environmental compounds obtained from previous compilations of drugs used to build models and sorted them into drugs with M/P ratios less than or greater than 1. 54 Ninety-seven drugs were used as a training set and the remaining 88 compounds were used as a test set. They obtained molecular descriptors from the online Drug Bank database and used them in three different models: a classification-based model, a regression-based model, and a 3D pharmacophore model.…”

Health-Relevant Phenotypes in the Offspring of Mice Given CAR Activators Prior to Pregnancy

“…Such drugs and chemicals can be identified by a combination of small molecular weight, high logarithmic partition coefficient (logP), and logarithmic acid dissociation constant (pKa) ,7 (Moor et al, 1992), which also correlate with the probability of transfer via milk (Howard and Lawrence, 2001;Kar and Roy, 2013). Highly lipophilc drugs (logP .…”

Prediction model for milk transfer of drugs by primarily evaluating the area under the curve using QSAR/QSPR

“…The QSAR/QSPR approach aims to find correlations between structural features or physicochemical constants of a drug and its biological activity, and can be applied to predict physical and chemical properties by means of descriptors that explain changes in the physical or chemical properties of that drug group. A number of linear regression models have been reported for QSAR models predicting M/P ratios [17][18][19][20][21][22][23], but because M/P ratio data are collected from individual reports, uncertainties in subjects, measurement methods, and variations in the number of cases may affect the models. A classification model was also constructed based on the idea that prediction by linear regression is not realistic [24,25].…”