Computational Predictions in the Design of Affinity-Based Drug Delivery

Overview

Affinity-mediated drug delivery utilizes electrostatic, hydrophobic, or other non-covalent interactions between pharmaceuticals and a delivery system to extend medication release and improve treatment effectiveness. Cyclodextrin polymers, chains of glucose rings, exhibit affinity interaction; however, experimentally testing drug candidates for affinity is time-consuming, making computational predictions a more effective approach. Currently, docking programs provide predictions of affinity, but lack reliability and scalability. Quantitative structure-activity relationship models (QSARs), which analyze statistical relationships between molecular properties, appear a promising alternative. Unfortunately, previously constructed QSARs are either not thoroughly verified or not publicly available, necessitating a robust and openly accessible model. Around 600 experimental affinities between cyclodextrin and guest molecules were cleaned and imported from published research. The software PaDEL-Descriptor calculated over 1000 chemical descriptors for each molecule, which were then analyzed with R to create several QSARs with different statistical methods. The QSARs that passed verification standards were then combined and averaged to create an ensemble model. These models proved highly time efficient, calculating in minutes what docking programs could accomplish in hours. Individually, QSARs reached R2 values of 0.6-0.8 on test sets, and ensemble calculation on external validation data (simulating predictions on completely new data) yielded an R2 of 0.7. Comparatively, docking predictions only reached an R2 of 0.2. Additionally, the affinity calculations from QSARs could also be used to create and modify drug release curves, allowing for fine-tuning of drug delivery. The speed, accuracy, and accessibility of these QSARs improve evaluation of individual drugs and facilitate screening of large datasets for potential candidates in cyclodextrin affinity-mediated delivery systems.

Competition history

• AJAS 2019 Category not listed

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