Making in vitro release and formulation data AI-ready: A foundation for streamlined nanomedicine development

Area of Science:

• Pharmaceutical Sciences
• Nanomedicine
• Data Science

Background:

• Artificial intelligence (AI) and machine learning (ML) are revolutionizing pharmaceutical development, including drug discovery and nanomedicine.
• AI enables faster prediction of critical quality attributes (CQAs) in nanomedicine but is constrained by data quality and accessibility.
• The pharmaceutics field, especially nanomedicine, lacks comprehensive, open-access databases compared to other scientific domains.

Purpose of the Study:

• To address the lack of curated data in nanomedicine, this study curates an open-access local database for liposomal formulations.
• To identify and highlight challenges in current nanomedicine data reporting practices.
• To propose standardized data structures and reporting guidelines for nanomedicine formulation and in vitro release (IVR) data.

Main Methods:

• Curated an open-access local database containing formulation parameters, in vitro release (IVR) testing conditions, and digitized drug release data for liposomal formulations.
• Qualitatively and quantitatively evaluated the database entries to assess current data reporting standards.
• Analyzed data quality, completeness of reporting for formulation and IVR conditions, and consistency of drug release data formats.

Main Results:

• Identified significant challenges in current data reporting practices, including incomplete information on formulation and IVR conditions.
• Observed inconsistencies in the quality and format of digitized drug release data and plots.
• Established a foundation for data harmonization in nanomedicine formulation and IVR data through the curated database.

Conclusions:

• The developed open-access database and proposed data standards aim to enhance data accessibility and transparency in nanomedicine research.
• Improved data quality and standardization will facilitate the development of more robust AI models for predicting IVR and CQAs.
• Ultimately, this work seeks to streamline nanomedicine development by enabling reliable AI-driven predictions.