2019 White Paper on Recent Issues in Bioanalysis: Chromatographic Assays (Part 1 – Innovation in Small Molecules and Oligonucleotides & Mass Spectrometric Method Development Strategies for Large Molecule Bioanalysis)
Bibliographic record
Abstract
The 2019 13th Workshop on Recent Issues in Bioanalysis (WRIB) took place in New Orleans, LA, USA on April 1–5, 2019 with an attendance of over 1000 representatives from pharmaceutical/biopharmaceutical companies, biotechnology companies, contract research organizations and regulatory agencies worldwide. WRIB was once again a 5-day, week-long event – a full immersion week of bioanalysis, biomarkers, immunogenicity and gene therapy. As usual, it was specifically designed to facilitate sharing, reviewing, discussing and agreeing on approaches to address the most current issues of interest including both small- and large-molecule bioanalysis involving LCMS, hybrid LBA/LCMS, LBA cell-based/flow cytometry assays and qPCR approaches. This 2019 White Paper encompasses recommendations emerging from the extensive discussions held during the workshop, and is aimed to provide the 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 2019 edition of this comprehensive White Paper has been divided into three parts for editorial reasons. This publication (Part 1) covers the recommendations on Innovation in Small Molecules and Oligonucleotides & Mass Spec Method Development Strategies for Large Molecules Bioanalysis. Part 2 (2018 FDA BMV Guidance, 2019 ICH M10 BMV Draft Guideline and regulatory agencies' input on bioanalysis, biomarkers, immunogenicity and gene therapy) and Part 3 (New Insights in Biomarkers Assays Validation, Current & Effective Strategies for Critical Reagent Management, Flow Cytometry Validation in drug discovery & development & CLSI H62, Interpretation of the 2019 FDA Immunogenicity Guidance and The Gene Therapy Bioanalytical Challenges) are published in volume 11 of Bioanalysis, issues 23 and 24 (2019), respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.031 | 0.030 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".