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Record W3121304865 · doi:10.4155/bio-2021-0005

2020 White Paper on Recent Issues in Bioanalysis: BAV Guidance, CLSI H62, Biotherapeutics Stability, Parallelism Testing, CyTOF and Regulatory Feedback ( <u>Part 2A</u> –Recommendations on Biotherapeutics Stability, PK LBA Regulated Bioanalysis, Biomarkers Assays, Cytometry Validation &amp; Innovation <u>Part 2B</u> –Regulatory Agencies’ Inputs on Bioanalysis, Biomarkers, Immunogenicity, Gene &amp; Cell Therapy and Vaccine)

2021· article· en· W3121304865 on OpenAlexaff
Susan Spitz, Yan Zhang, Sally Fischer, Kristina McGuire, Ülrike Sommer, Lakshmi Amaravadi, Abbas Bandukwala, Steven Eck, Fabio Garofolo, M. Rafiqul Islam, Gregor Jordan, Lindsay King, Yoshiro Saito, Giane Sumner, Linda M. Terry, Alessandra Vitaliti, Yow‐Ming Wang, Christine Grimaldi, Alison Joyce, Rachel Palmer, Matthew Andisik, Marcela Araya, Mitra Azadeh, Daniel Baltrukonis, Rebecca Elliott, Sam Haidar, Seema Kumar, Andrew P. Mayer, Florian Neff, Nisha Palackal, Kun Peng, Mohsen Rajabi Abhari, Christina Satterwhite, Natasha Savoie, Catherine Soo, Stephen Vinter, Jan Welink, Weili Yan, Kevin Maher, David Lanham, Sylvie Bertholet, Naveen Dakappagari, Christèle Gonneau, Cherie Green, Fabian Junker, Sumit Kar, Lisa Patti‐Diaz, Shyam Sarikonda, Megan McCausland, Priscila Camillo Teixeira, Vilma Decman, Jose Estevam, Michael N. Hedrick, Alberto Robert, Gregory Hopkins, Sandra Nuti, Shabnam Tangri, Richard Wnek, Suman Dandamudi, Arindam Dasgupta, Anna Edmison, Patrick J. Faustino, Michael McGuinness, Gustavo Mendes Lima Santos, Tahseen Mirza, Diaá M. Shakleya, Susan Stojdl, Nilufer Tampal, Jinhui Zhang, Elana Cherry, Isabelle Cludts, Andrew Exley, Akiko Ishii‐Watabe, Susan Kirshner, João Pedras-Vasconcelos, Meiyu Shen, Richard Siggers, Therese Solstad, Daniela Verthelyi, Haoheng Yan, Lucia Zhang

Bibliographic record

VenueBioanalysis · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBiosimilars and Bioanalytical Methods
Canadian institutionsHealth Canada
Fundersnot available
KeywordsBioanalysisExcellenceBiosimilarBiopharmaceuticalComputer scienceRegulatory scienceBusinessBiotechnologyPolitical scienceNanotechnologyMedicineBiology

Abstract

fetched live from OpenAlex

WRIB included three Main Workshops, seven Specialized Workshops that together spanned 11 days in order to allow exhaustive and thorough coverage of all major issues in bioanalysis, biomarkers, immunogenicity, gene therapy and vaccine. Moreover, a comprehensive vaccine assays track; an enhanced cytometry track and updated Industry/Regulators consensus on BMV of biotherapeutics by LCMS were special features in 2020. As in previous years, this year's WRIB continued to gather a wide diversity of international industry opinion leaders and regulatory authority experts working on both small and large molecules to facilitate sharing and discussions focused on improving quality, increasing regulatory compliance and achieving scientific excellence on bioanalytical issues. This 2020 White Paper encompasses recommendations emerging from the extensive discussions held during the workshop, and is aimed to provide the Global Bioanalytical Community with key information and practical solutions on topics and issues addressed, in an effort to enable advances in scientific excellence, improved quality and better regulatory compliance. Due to its length, the 2020 edition of this comprehensive White Paper has been divided into three parts for editorial reasons. This publication covers the recommendations on (Part 2A) BAV, PK LBA, Flow Cytometry Validation and Cytometry Innovation and (Part 2B) Regulatory Input. Part 1 (Innovation in Small Molecules, Hybrid LBA/LCMS & Regulated Bioanalysis), Part 3 (Vaccine, Gene/Cell Therapy, NAb Harmonization and Immunogenicity) are published in volume 13 of Bioanalysis, issues 4, and 6 (2021), respectively.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.028
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.062
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.027
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0030.003
Scholarly communication0.0130.006
Open science0.0050.004
Research integrity0.0180.010
Insufficient payload (model declined to judge)0.0620.103

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.072
GPT teacher head0.311
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations26
Published2021
Admission routes1
Has abstractyes

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