Toward Resolving Heterogeneous Mixtures of Nanocarriers in Drug Delivery Systems through Light Scattering and Machine...

Area of Science:

• Biomedical Engineering
• Materials Science
• Analytical Chemistry

Background:

• Nanocarriers (NCs) are crucial for targeted drug delivery, improving efficacy and reducing toxicity.
• Clinical translation of NCs is impeded by challenges in physicochemical characterization.
• Existing characterization methods are often time-consuming, costly, or destructive.

Purpose of the Study:

• To develop a cost-effective, high-throughput method for characterizing polydisperse nanoparticle mixtures.
• To overcome limitations of traditional characterization techniques for nanocarriers.
• To enable reliable and non-invasive assessment of nanocarrier-based drug delivery systems.

Main Methods:

• Utilized light scattering and optical forces combined with machine learning algorithms.
• Quantified relative concentration of nanoparticle suspensions by detecting refractive index and polydispersity changes.
• Employed a non-destructive approach requiring minimal sample preparation.

Main Results:

• Successfully characterized complex nanoparticle suspensions, including lipid-based NCs.
• Demonstrated accurate quantification of relative concentration and polydispersity.
• Validated the method using synthetic nanoparticles and a liposomal Doxorubicin formulation (Doxoves).

Conclusions:

• The developed methodology provides a rapid, cost-effective, and non-invasive solution for nanocarrier characterization.
• This approach can significantly accelerate the quality control and clinical translation of nanocarrier therapeutics.
• Advances in characterization are essential for ensuring the safety and efficacy of nanomedicines.