Abstract
Recent advances in instrumentation have moved analytical ultracentrifugation (AUC) closer to a possible validation in a Good Manufacturing Practices (GMP) environment. In order for AUC to be validated for a GMP environment, stringent requirements need to be satisfied; analysis procedures must be evaluated for consistency and reproducibility, and GMP capable data acquisition software needs to be developed and validated. These requirements extend to multiple regulatory aspects, covering documentation of instrument hardware functionality, data handling and software for data acquisition and data analysis, process control, audit trails and automation. Here we review the requirements for GMP validation of data acquisition software and illustrate software solutions based on UltraScan that address these requirements as far as they relate to the operation and data handling in conjunction with the latest analytical ultracentrifuge, the Optima AUC by Beckman Coulter. The software targets the needs of regulatory agencies, where AUC plays a critical role in the solutionbased characterization of biopolymers and macromolecular assemblies. Biopharmaceutical and regulatory agencies rely heavily on this technique for characterizations of pharmaceutical formulations, biosimilars, injectables, nanoparticles, and other soluble therapeutics. Because of its resolving power, AUC is a favorite application, despite the current lack of GMP validation. We believe that recent advances in standards, hardware, and software presented in this work manage to bridge this gap and allow AUC to be routinely used in a GMP environment. AUC has great potential to provide more detailed information, at higher resolution, and with greater confidence than other analytical techniques, and our software satisfies an urgent need for AUC operation in the GMP environment. The software, including documentation, are publicly available for free download from Github. The multi-platform software is licensed by the LGPL v.3 open source license and supports Windows, Mac and Linux platforms. Installation instructions and a mailing list are available from ultrascan.aucsolutions.com.
| Original language | English |
|---|---|
| Journal | PLoS Computational Biology |
| Volume | 16 |
| Issue number | 6 |
| DOIs | |
| State | Published - Jun 2020 |
Funding
Funding for this work was provided by: National Institutes of Health grant 1R01GM120600, NSERC DG-RGPIN-2019-05637, CIHR foundation grant (FDN 148469), and Canada 150 Research Chairs program C150-2017-00015 (All to BD). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Funding:Fundingforthisworkwasprovidedby: NationalInstitutesofHealthgrant1R01GM120600, NSERCDG-RGPIN-2019-05637,CIHRfoundation grant(FDN148469),andCanada150Research ChairsprogramC150-2017-00015(AlltoBD).The fundershadnoroleinstudydesign,datacollection andanalysis,decisiontopublish,orpreparationof themanuscript.
| Funders | Funder number |
|---|---|
| NSERCDG-RGPIN-2019-05637 | |
| R01GM120600 | |
| Canadian Institutes of Health Research | FDN148469 |
| DG-RGPIN-2019-05637 | |