MetStabOn-Online Platform for Metabolic Stability Predictions.

Sabina Podlewska,Rafał Kafel

doi:10.3390/ijms19041040

Abstract

Metabolic stability is an important parameter to be optimized during the complex process of designing new active compounds. Tuning this parameter with the simultaneous maintenance of a desired compound’s activity is not an easy task due to the extreme complexity of metabolic pathways in living organisms. In this study, the platform for in silico qualitative evaluation of metabolic stability, expressed as half-lifetime and clearance was developed. The platform is based on the application of machine learning methods and separate models for human, rat and mouse data were constructed. The compounds’ evaluation is qualitative and two types of experiments can be performed—regression, which is when the compound is assigned to one of the metabolic stability classes (low, medium, high) on the basis of numerical value of the predicted half-lifetime, and classification, in which the molecule is directly assessed as low, medium or high stability. The results show that the models have good predictive power, with accuracy values over 0.7 for all cases, for Sequential Minimal Optimization (SMO), k-nearest neighbor (IBk) and Random Forest algorithms. Additionally, for each of the analyzed compounds, 10 of the most similar structures from the training set (in terms of Tanimoto metric similarity) are identified and made available for download as separate files for more detailed manual inspection. The predictive power of the models was confronted with the external dataset, containing metabolic stability assessment via the GUSAR software, leading to good consistency of results for SMOreg and Naïve Bayes (~0.8 on average). The tool is available online.

Highlights

During the drug design process, attention is initially placed on obtaining the desired affinity with the appropriate receptors
The summary describes the compound representation used, the predictive model applied, the number of input compounds and the number of compounds assigned to a particular metabolic stability class
Detailed results are gathered in a table with the structure and simplified molecular-input line-entry system (SMILES) of a compound, the predicted value of half-lifetime and the metabolic stability class to which a compound was assigned

Summary

Introduction

During the drug design process, attention is initially placed on obtaining the desired affinity with the appropriate receptors. Failures of compounds at later stages of drug development are connected with other unfavorable physicochemical, pharmacokinetic, or toxic properties. The proper evaluation of these properties in silico is just as important as the development of computational tools for accurate activity predictions [1,2,3,4]. It is important to analyze whether the compound will bind to the plasma proteins [9] as well as evaluate the half-life time or potential metabolic routes [3]. Predictions most often concern the possible interactions of the examined compound with other therapeutics and the possible undesirable modulations of other protein activities, such as hERG potassium channels [10] leading to the compound’s cardio toxicity [11,12]

Methods

Results

Conclusion