Emerging screening platform characterises aminoquinoline structure-activity relationships with phospholipid layers

Area of Science:

• Biochemistry
• Pharmacology
• Analytical Chemistry

Background:

• Aminoquinolines (AQ) and substituted aminoquinolines (s-AQ) are classes of compounds with potential biological activity.
• Understanding their interaction with biological membranes is crucial for predicting their behavior and toxicity.
• Existing methods may not fully capture the nuanced interactions of these compounds with lipid bilayers.

Purpose of the Study:

• To investigate the interaction of aminoquinolines with supported phospholipid layers using electrochemical methods.
• To develop a quantitative measure of biomembrane affinity (log BAI) that accounts for charge and structural features.
• To explore the relationship between biomembrane affinity and compound toxicity.

Main Methods:

• Utilized High-Performance Liquid Chromatography (HPLC) with Immobilized Artificial Membranes (IAM) to monitor compound adsorption and partitioning.
• Employed electrochemistry to record interactions within dioleoyl phosphatidylcholine (DOPC) monolayers and IAMs.
• Integrated results with lipophilicity normalization to derive a log biomembrane affinity index (log BAI).
• Used ChimeraX molecular modeling for result interpretation and correlated log BAI with pKa and ToxScore values.

Main Results:

• The log BAI showed a linear relationship with aminoquinoline pKa values, indicating interaction is driven by molecular positive charge and the -NH2 group's properties.
• Deviations from linearity suggested variations in hydrogen bonding and location within the phospholipid layer.
• Substituted aminoquinolines exhibited a power correlation between log BAI and their ToxScore, linking membrane interaction to toxicity.

Conclusions:

• The developed HPLC-IAM method effectively quantifies aminoquinoline interactions with biomembranes, reflecting charge and structural contributions.
• Biomembrane affinity, as measured by log BAI, is a significant predictor of aminoquinoline toxicity.
• This approach provides valuable insights for drug design and safety evaluation of aminoquinoline derivatives.