The use of bile salt micelles for the prediction of human intestinal absorption

Human intestinal absorption (HIA) will dictate biopharmaceutical performance through its influence on absorption, distribution, metabolism, and elimination and can vary significantly depending upon the nature of the compound under consideration. In this study, an in vitro assay method is proposed for the prediction of HIA through the measurement of drug solubility in an aqueous phase containing micellar bile salt, namely sodium deoxycholate. A series of twenty compounds, displaying a range of physicochemical properties and known HIA values, were analyzed using UV spectroscopy to determine a solubilization ratio for each compound. A micelle/water partition coefficient (Kxm/a) was calculated and then used to develop an equation through simple linear regression; logit HIA = −0.919 + 0.4618 logKxm/a (R2 = 0.85). From this equation, a value for % HIA was determined which compared well with literature. Furthermore, 4 additional drugs were then analyzed using the developed equation and found to match well with literature, confirming the suitability of the method. Using a simple, economic, and robust UV bile salt assay allows prediction of HIA and avoids many of the disadvantages of other techniques, such as animal-based methods

Distribution of cortical activation during visuospatial n-back tasks as revealed by functional magnetic resonance imaging

International audienc

Combinatorial QSAR Modeling of human Intestinal Absorption

Intestinal drug absorption in humans is a central topic in drug discovery. In this study, we use a broad selection of machine learning and statistical methods for the classification and numerical prediction of this key endpoint. Our dataset is based on a selection of 458 small drug-like compounds with FDA approval. Using easily available tools, we calculated one- to three-dimensional physicochemical descriptors and used various methods of feature selection (best-first backward selection, correlation analysis, and decision tree analysis). We then used decision tree induction (DTI), fragment-based lazy-learning (LAZAR), support vector machine classification, multilayer perceptrons, random forests, k-nearest neighbor and Naïve Bayes analysis to model absorption ratios and binary classification (well absorbed and poorly absorbed compounds). Best performance for classification was seen with DTI using the chi-squared analysis interaction detector (CHAID) algorithm yielding corrected classification rate of 88%, (Matthews correlation coefficient of 75%). In numeric predictions, the multilayer perceptron performed best achieving root mean squared error of 25.823 and a correlation coefficient of 0.6. In line with current understanding is the importance of descriptors such as lipophilic partition coefficients (logP) and hydrogen bonding. However, we are able to highlight the utility of gravitational indices and moments of inertia, reflecting the role of structural symmetry in oral absorption. Our models are based on s diverse dataset of marketed drugs representing a broad chemical space. These models therefore contribute substantially to the molecular understanding of human intestinal drug absorption and qualify for a generalized use in drug discovery and lead optimization