# Pharmacokinetic Equations Applied to Obtain New Topological Models in the Search of Antibacterial Compounds

## TL;DR

This paper introduces new topological models to identify antibacterial compounds with good pharmacokinetic properties, improving the success rate of finding effective antibacterials.

## Contribution

The paper presents novel discriminant functions and pharmacokinetic-based models for predicting antibacterial activity.

## Key findings

- Most antibacterial compounds have theoretical pharmacokinetic values within specific MRT, VD, and CL ranges.
- Applying these ranges to the model increased the success rate of identifying antibacterials to over 50%.
- Drug-like filters are not necessary when using these new models, as 89.9–100% of selected molecules show antibacterial activity.

## Abstract

Background: QSAR (Quantitative Structure–Activity Relationships) methods have been the basis for the design of new molecules with a certain activity. The great advantage of QSAR methods is that they can predict the pharmacological activity of compounds without the need to obtain or synthesize them previously. Currently, the development of antibiotic resistance by microorganisms is the most important issue in the treatment of infectious diseases. This elevated resistance is associated with expanded morbidity and mortality, as well as an increase in healthcare costs. The development of new molecules with antibacterial activity is therefore urgently needed. Methods: By means of molecular topology, we developed discriminant functions (DF1 and DF2) capable of predicting antibacterial activity. When applied to a database with 6373 chemicals, they selected 266 molecules as candidates, from which 41% have this activity, according to the bibliography. Regression equations determining pharmacokinetic properties such as mean residence time (MRT), volume of distribution (VD), and clearance (CL) were applied to the selected molecules. Results: We have observed that most antibacterial compounds have pharmacokinetic theoretical values in the intervals 20 > MRT > 0, 3 > VD > 0, and 500 > CL > 0. We have applied these intervals to our antibacterial model with the objective of finding new antibacterials with a good pharmacokinetic profile. We show that they are an effective tool for discriminating antibacterial compounds, increasing the bibliographic success rate to 50.8, 59, and 61.5%, respectively. When drug-like filters are applied to these new models, the vast majority (89.9–100%) of the selected molecules present antibacterial activity. Conclusions: Considering these results, these new models could avoid the application of drug-likeness filters when searching for new potential antibacterials. All of this proves the usefulness of these mathematical–topological models.