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. 2021 Dec;67(12):e17459.
doi: 10.1002/aic.17459. Epub 2021 Sep 22.

Data Management Schema Design for Effective Nanoparticle Formulation for Neurotherapeutics

Hawley Helmbrecht  1 , Nuo Xu  1 , Rick Liao  1 , Elizabeth Nance  1   2   3   4
Affiliations

Data Management Schema Design for Effective Nanoparticle Formulation for Neurotherapeutics

Hawley Helmbrecht et al. AIChE J. 2021 Dec.

Abstract

Translation of nanotherapeutics from preclinical research to clinical application is difficult due to the complex and dynamic interaction space between the nanotherapeutic and the brain environment. To improve translation, increased insight into nanoformulation-brain interactions in preclinical research is necessary. We developed a nanoformulation-brain database and wrote queries to connect the complex physical, chemical, and biological features of neurotherapeutics based on experimental data. We queried the database to select nanoformulations based on specific physical characteristics that enable effective penetration within the brain, including size, polydispersity index, and zeta potential. Additionally, we demonstrate the ability to query the database to return select nanoformulation characteristics, including nanoformulation methodology or methodological variables such as surfactant, polymer, drug loading, and sonication times. Finally, we show the capacity of our database to produce correlations relating nanoparticle formulation parameters to biological outcomes, including nanotherapeutic impact on cell viability in cultured brain slices.

Keywords: Biochemicals; Bioengineering; Biofuels; Biomolecular Engineering; Food; database; nanoformulation; neurotherapeutics; polymer; query.

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Figures

Figure 1.
Figure 1.
Entity Relationship diagram for the database. The diagram relates experimental components with tables researcher, experiment, and collaborator (purple), biological components with tables pup_info and ldh_assay (green), nanoformulations components with nanoformulations (orange), including single_emulsion, double_emulsion, nanoprecipitation, reverse_formulations tables, and np_charc tables.
Figure 2.
Figure 2.
Database evaluation according to the six V’s of big data. (A) Volume describes the number of independent researchers, features, rows, and quantity of data. (B) Variety describes the four major types of data with examples. (C) Velocity has example timelines with upload points for data and time scale values bolded below each major methodology step. (D) Value describes the time, life, and insight value of the database. (E) Variability shows the biological factors that affect the fate of nanoparticles and the methodological factors that affect the features of the nanoparticle. (F) Veracity describes the complexity of errors that can be introduced to the database. (G) Time for queries to run on a local server compared to Snowflake. Created with BioRender.com.
Figure 3.
Figure 3.
Entity-relationship diagram with query labels. The query numbers are included in every table they access. Underlined query labels designate that the query both accessed that table and placed some constraint on the results from that table.
Figure 4.
Figure 4.
(A) Nanoformulation characteristic values for PDI, ZP, and size for the first seven queries developed. (B) Number-average size and ZP for nanoprecipitation formulations obtained with Query 8 for varying surfactants: cholic acid sodium salt (CHA), polyvinyl alcohol (PVA), Pluronic F68 (F68), and varying polymer amounts. (C) Number-average size and ZP for two different polymer types with 1% P80 or DI Water as the nanoprecipitation sink.
Figure 5.
Figure 5.
Heatmap of nanoprecipitation methodology variables, dynamic light scattering characterization results, and LDH assay results for (A) nanoprecipitation and (B) double emulsion experiments in brain slices with Pearson correlation as the colormap and explicitly stated in each box. (C) Heatmap of nanoformulation methodology data for double emulsions with varying sonication times vs. dynamic light scattering characterization data, nanoparticle activity (NP Act), and supernatant activity (Sup Act) with color associated with Pearson correlation as colormap and explicitly stated in each box.
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    Joseph A, Nance E. Joseph A, et al. Annu Rev Chem Biomol Eng. 2022 Jun 10;13:325-346. doi: 10.1146/annurev-chembioeng-092220-030853. Epub 2022 Mar 23. Annu Rev Chem Biomol Eng. 2022. PMID: 35320699 Free PMC article. Review.

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