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Partitioning of ionizing molecules between aqueous buffers and phospholipid vesicles
R P Austin1, A M Davis, C N Manners
1Department of Physical Chemistry, Astra Charnwood, Loughborough, Leicestershire, United Kingdom.
Journal of Pharmaceutical Sciences
|October 1, 1995
Summary
This study reveals that charged molecules can penetrate phospholipid bilayers, unlike in simple oil-water systems. This finding is crucial for understanding drug behavior and designing new therapeutics.
Area of Science:
- Membrane biophysics
- Physical chemistry
- Drug delivery
Background:
- Understanding molecule distribution across biological membranes is key for drug development.
- The partitioning of ionizing molecules in lipid bilayers is complex and not fully understood.
- Previous models often rely on oil-water partition coefficients, which may not accurately reflect membrane behavior.
Purpose of the Study:
- To investigate the pH-dependent distribution of ionizing molecules between dimyristoylphosphatidylcholine (DMPC) vesicles and aqueous buffers.
- To compare partitioning behavior in a model membrane system with the traditional 1-octanol-water system.
- To elucidate the mechanisms of charged molecule partitioning into phospholipid bilayers.
Main Methods:
- Utilized ultrafiltration to measure the distribution of four ionizing molecules.
- Employed small unilamellar vesicles (SUVs) of dimyristoylphosphatidylcholine (DMPC) as a model membrane system.
- Varied the pH of aqueous buffers to study pH-dependent partitioning.
Main Results:
- Observed that pH-distribution behavior differs between DMPC vesicles and 1-octanol-water systems.
- Demonstrated that charged forms of some molecules can partition into the phospholipid bilayer.
- Confirmed that this partitioning is not due to ion pairing.
Conclusions:
- Charged species can exhibit significant membrane affinity, challenging conventional models.
- Protonated amines possess a higher membrane affinity than previously assumed.
- Findings have direct implications for optimizing drug design and predicting drug behavior.