Journal:Laboratory information management system for COVID-19 non-clinical efficacy trial data

Full article title	Laboratory information management system for COVID-19 non-clinical efficacy trial data
Journal	Laboratory Animal Research
Author(s)	Yoon, Suhyeon; Noh, Hyuna; Jin, Heejin; Lee, Sugyoung; Han, Soyul; Kim, Sung-Hee; Kim, Jiseon; Seo, Jung S.; Kim, Jeong J.; Park, In H.; Oh, Jooyeon; Bae, Joon-Yong; Lee, Gee E.; Woo, Sun-Je; Seo, Sun-Min; Kim, Na-Won; Lee, Youn W.; Jang, Hui J.; Hong, Seung-Min; An, Se-Hee; Lyoo, Kwang-Soo; Yeom, Minjoo; Lee, Hanbyeul; Jung, Bud; Yoon, Sun-Woo; Jang, Hung-Ah; Seok, Sang-Hyuk; Lee, Yu J.; Kim, Seo Y.; Kim, Young B.; Hwang, Ji-Yeon; On, Dain; Lim, Soo-Yeon; Kim, Sol P.; Jang, Ji Y.; Lee, Ho; Kim, Kyoungmi; Lee, Hyo-Jung; Kim, Hong B.; Park, Hun W.; Jeong, Dae G.; Song, Daesub; Choi, Kang-Seuk; Lee, Ho-Young; Choi, Yang-Kyu; Choi, Jung-ah; Song, Manki; Park, Man-Seong; Seo, Jun-Yeong; Nam, Ki T.; Shin, Jeon-Soo; Won, Sungho; Yun, Jun-Won; Seong, Je K.
Author affiliation(s)	Seoul National University, RexSoft Corp., Yonsei University College of Medicine, Korea University College of Medicine, International Vaccine Institute, Konkuk University, Seoul National University Bundang Hospital, Chonbuk National University, Korea University, Korea Research Institute of Bioscience and Biotechnology, Kangwon National University, National Cancer Center, Dongguk University, Yonsei University College of Medicine,
Primary contact	snumouse at snu dot ac dot kr
Year published	2022
Volume and issue	38
Article #	17
DOI	10.1186/s42826-022-00127-2
ISSN	2233-7660
Distribution license	Creative Commons Attribution 4.0 International
Website	https://labanimres.biomedcentral.com/articles/10.1186/s42826-022-00127-2
Download	https://labanimres.biomedcentral.com/track/pdf/10.1186/s42826-022-00127-2.pdf (PDF)

Abstract

Background: As the number of large-scale research studies involving multiple organizations producing data has steadily increased, an integrated system for a common interoperable data format is needed. For example, in response to the coronavirus disease 2019 (COVID-19) pandemic, a number of global efforts are underway to develop vaccines and therapeutics. We are therefore observing an explosion in the proliferation of COVID-19 data, and interoperability is highly requested in multiple institutions participating simultaneously in COVID-19 pandemic research.

Results: In this study, a laboratory information management system (LIMS) has been adopted to systemically manage, via web interface, various COVID-19 non-clinical trial data—including mortality, clinical signs, body weight, body temperature, organ weights, viral titer (viral replication and viral RNA), and multi-organ histopathology—from multiple institutions. The main aim of the implemented system is to integrate, standardize, and organize data collected from laboratories in multiple institutes conducting COVID-19 non-clinical efficacy testing. Six animal Biosafety level 3 (BSL-3) institutions proved the feasibility of our system. Substantial benefits were shown by maximizing collaborative high-quality non-clinical research.

Conclusions: This LIMS platform can be used for current and future outbreaks, leading to accelerated medical product development through the systematic management of extensive data from non-clinical animal studies.

Keywords: SARS-CoV-2, COVID-19, non-clinical, laboratory information management system, data

Background

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in the serious respiratory-related coronavirus disease 2019 (COVID-19) pandemic. It was first reported in Wuhan, the capital of Hubei, China, in late December 2019 and has attracted international attention. [1] After four months, it spread to multiple countries, leading to more than one million confirmed cases worldwide. [2] SARS-CoV-2, a single-stranded RNA-enveloped virus [3], enters the host cells by binding its structural spike (S) protein to cell surface receptors, angiotensin-converting enzyme 2 (ACE2.) [4] After entering the host cells, the virus hijacks the cell to undergo viral replication. [5] During this process, an aberrant host immune condition called a cytokine storm can occur in response to SARS-CoV-2 infection, which is characterized by high concentrations of pro-inflammatory cytokines and chemokines, including tumor necrosis factor-α, interleukin-1, and interleukin-6 [6], resulting in severe COVID-19 associated with excessive inflammation. [7]

Along with understanding the mechanism of transmissibility and pathogenesis of SARS-CoV-2, significant efforts are being devoted to developing novel vaccines and therapeutic agents to prevent or treat COVID-19. In particular, various COVID-19-related research has been conducted with the participation of multiple organizations. Among such studies, animal models of monkeys, cats, ferrets, hamsters, and human angiotensin converting enzyme 2 (hACE2) transgenic mice [8], capable of conducting controlled experiments, have made significant contributions to vaccine development. [9,10,11,12,13] (Note: Though the U.S. Food and Drug Administration doesn't consider animal studies to be part of "non-clinical laboratory studies," many research organizations with animal lab capabilities still use "non-clinical" to include and describe these sorts of animal modelling studies.) Because various parameters such as mortality, clinical signs, body weight, organ weights, viral titer (viral replication and viral RNA), and multi-organ histopathology should be comprehensively analyzed for a reliable interpretation of the results, there is an increasing need for an integrated system in which multiple institutions can apply a streamlined analysis and input the results simultaneously when conducting large-scale non-clinical efficacy tests of a COVID-19 vaccine.

When experimental data are collected from multiple institutions, “organization-level effects” may occur owing to non-standardization in the data coding process and coding errors, which can act as a batch effect in a data analysis. In large-scale experimental multicenter research, it is necessary to prevent the loss of reliability in the data analysis caused by batch effects; however, this is a difficult goal to achieve without an integrated system. An effective approach to overcoming the limitations caused by the prior-mentioned batch effects is to use a laboratory information management system (LIMS). [14] A LIMS systematizes the research process by automating data-related tasks that were conducted manually using information technology, and simultaneously realizes various needs, such as the standardization of experimental data, sharing, problem tracking, and the creation of reports based on document templates. The accumulated data stored in a LIMS can be useful in understanding the current clinical knowledge and making decisions, which is a remarkable advantage in research on rapidly changing epidemics such as COVID-19.

Despite the advantages of a LIMS, no systems allowing multiple institutions to participate simultaneously in research related to COVID-19 have yet been proposed among developed commercial off-the-shelf (COTS) LIMSs. Given that, we aimed to clearly understand the standard procedures of non-clinical trials for COVID-19 therapeutics and vaccines and to design and implement a LIMS suitable for these research purposes.

Notes

This presentation is faithful to the original, with only a few minor changes to presentation, grammar, and spelling. In some cases important information was missing from the references, and that information was added. The authors don't define "non-clinical trial" or "non-clinical research" in the original; for this version, a presumed definition and accompanying citation is provided to the reader for additional context.