Machine Learning Approach for Determining the Formation of β-Lactam Antibiotic Complexes with Cyclodextrins Using Multispectral Analysis.

Mikołaj Mizera,Judyta Cielecka-Piontek,Kornelia Lewandowska,Andrzej Miklaszewski

doi:10.3390/molecules24040743

Abstract

The problem of determining the formation of complexes of β-lactam antibiotics with cyclodextrins (CDs) and the interactions involved in this process were addressed by machine learning on multispectral images. Complexes of β-lactam antibiotics, including cefuroxime axetil, cefetamet pivoxil, and pivampicillin, as well as CDs, including αCD, βCD, γCD, hydroxypropyl-αCD, methyl-βCD, hydroxypropyl-βCD, and hydroxypropyl-γCD, were prepared in all combinations. Thermograms confirming the formation of cyclodextrin complexes were obtained using differential scanning calorimetry. Transmission Fourier-transform infrared (tFTIR) and complementary attenuated total reflectance FTIR (ATR) coupled with machine learning were techniques chosen as a nondestructive alternative. The machine learning algorithm was used to determine the formation of complexes in samples using solely their tFTIR and ATR spectra at the prediction stage. Parameterized method 7 (PM7) was used to support the analysis by molecular modeling of the complexes. The model developed through machine learning properly distinguished samples with formed complexes form noncomplexed samples with a cross-validation accuracy of 90.4%. Analysis of the contribution of spectral bands to the model indicated interactions of ester groups of β-lactam antibiotics with CDs, as well as some interactions of cephem ring in cefetamet pivoxil and penam moiety in pivampicillin. Molecular modeling with PM7 helped to explain experimental results and allowed to propose possible binding modes.

Highlights

The active pharmaceutical ingredients (APIs) systems with macromolecules such as cyclodextrins (CDs) are a promising way of enhancing physicochemical properties of well-established drugs
The aim of this work was to prepare complexes of prodrug β-lactam analogs and CDs and to confirm complex formation based on analysis of spectral and thermal measurements, followed by a development of a machine-learning-based model for the determination of complex formation in samples, based solely on attenuated total reflectance FTIR (ATR) and Transmission Fourier-transform infrared (tFTIR) spectra with simultaneous identification of domains involved in the creation of the complex
Active pharmaceutical ingredients—cefuroxime axetil (CA), cefetamet pivoxil (CT), and pivampicillin (PA)—were synthesized in the Institute of Biotechnology and Antibiotics

Summary

Introduction

The active pharmaceutical ingredients (APIs) systems with macromolecules such as cyclodextrins (CDs) are a promising way of enhancing physicochemical properties of well-established drugs. The analysis of such systems is the major challenge in the field of quality control due to a problem with the possible different stoichiometric ratio between reagents. As a model, APIs in this work as esters of selected derivatives of β-lactam analogs. The benefits of combining prodrugs of β-lactam analogs with cyclodextrins include the possibility of modifying their solubility, increasing absorption, masking the specific taste, and even increasing bactericidal effectiveness [1,2]

Objectives

Methods

Results

Discussion

Conclusion