(764d) Building a queryable nanotherapeutic database to advance nanomedicine translation to the clinic

Many nanomedicine-focused labs produce 100s to 1000s, or more, of individual nanoparticle formulations across the lab lifetime. Ideally, when developing and optimizing our nanoformulations, we rapidly identify a formulation most likely to translate to the clinic for a specific indication. Each formulation represents an experimental data point intended to contribute to our growing understanding and implementation of nanomedicine for the treatment of disease. Yet, the time it takes to develop a nanotherapeutic to the point of translation often results in testing of many formulations by a number of researchers over many years across a range of experimental conditions and in multiple biological models. In addition, it is important for translation to understand the formulation-dependent interactions between nanomedicine platforms and the complex biological environment in which they are applied. To investigate dynamic nanoparticle-biology interactions with a range of nano-based delivery systems, nanomedicine labs may find it useful to develop a database management schema that can organize and integrate key experimental variables and factors that might influence the effectiveness of nanotherapeutics in the target setting.

In this talk, we explore the development of a queryable database management system using our labs 6 years of nanotherapeutic formulations for treating brain disease. Database management systems already commonly exist in clinical medical applications. For example, The Brain Database is a clinical neuroanatomy database developed over three decades ago. However, pre-clinical data management schemas have different complexities than clinical applications. Pre-clinical databases need management schemas that logically connect experimental methodologies, which can be highly repetitive, vary in quality, are prone to rapid iteration and evolution, and often research lab or facility-specific. There is limited or non-existent literature on effective database management for nanoformulations used preclinically, but similar approaches for utilizing databases exist in computational cell biology for managing data-rich biological cell process data. A successful standardized data management schema for pre-clinical nanotherapeutic experimental pipelines will have three main effects: (1) increased sustainability for data organization and storage, (2) increased insight into related nanotherapeutic parameters and their biological interactions, and (3) increased searchability of specific results or methods.

To show the efficacy of a well-designed pre-clinical database management schema for nanoformulation data, we create a database using PostgreSQL that performs three functions: (1) Obtains the nanoparticle formulation variables that produce specific nanoparticle sizes and charges; (2) Connects nanoformulations to all related experimental elements, including nanoformulation methodology, biological specimen prep, and biological specimen characterization; and (3) Defines a standard lab protocol for regularly updating the database as new nanoformulation experiments are carried out. We show we can query our nanoformulations in this database to obtain nanoformulation batches with specific physicochemical properties or to identify relationships between nanoformulations and specific biological outcomes, such as impact on brain cell viability. This talk highlights the proof-of-concept that our experimental data can benefit from a well-structured database management schema. It also lays the foundation to develop standard practices for discussing, planning, and participating in better data management. These practices will lead to better data utilization and data longevity, and greater reproducibility and interpretability of datasets generated from independent researchers.

A standard data management schema for experimental nanoformulation experiments decreases the need for repetitive experimentation, connects nanoformulation variables with biological outcome in an interpretable and efficient way, and enables science-informed querying of any standardized variables. The overall benefits of a standardized database contribute to efficient experimentation and cross-experimental insight which will ultimately improve pre-clinical to clinical translation of nanoformulations.