Quality by Design for Preclinical In Vitro Assay Development.

CRISPR JMP assay optimization design of experiments design space screening

Journal

Pharmaceutical statistics
ISSN: 1539-1612
Titre abrégé: Pharm Stat
Pays: England
ID NLM: 101201192

Informations de publication

Date de publication:
24 Sep 2024
Historique:
revised: 10 05 2024
received: 02 10 2023
accepted: 08 07 2024
medline: 25 9 2024
pubmed: 25 9 2024
entrez: 24 9 2024
Statut: aheadofprint

Résumé

Quality by Design (QbD) is an approach to assay development to determine the design space, which is the range of assay variable settings that should result in satisfactory assay quality. Typically, QbD is applied in manufacturing, but it works just as well in the preclinical space. Through three examples, we illustrate the QbD approach with experimental design and associated data analysis to determine the design space for preclinical assays.

Identifiants

pubmed: 39317677
doi: 10.1002/pst.2430
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2024 AstraZeneca. Pharmaceutical Statistics published by John Wiley & Sons Ltd.

Références

D. Murray and M. Wigglesworth, High Throughput Screening Methods: Evolution and Refinement, eds. J. A. Bittker and N. T. Ross (London, UK: The Royal Society of Chemistry, 2016).
C. Bock, P. Datlinger, F. Chardon, et al., “High‐Content CRISPR Screening,” Nature Reviews Methods Primers 2 (2022): 8.
S. N. Deming, “Quality by Design 1,” ChemTech 18, no. 9 (1988): 560–566.
E.M. Agency, “ICH Guideline Q8 (R2) on Pharmaceutical Development 2017,” Step 5.
I. M. Fukuda, C. F. F. Pinto, C. S. Moreira, A. M. Saviano, and F. R. Lourenço, “Design of Experiments (DoE) Applied to Pharmaceutical and Analytical Quality by Design (QbD),” Brazilian Journal of Pharmaceutical Sciences 54 (2018): e01006.
J. M. Juran and J. Juran, Juran on Quality by Design: The New Steps for Planning Quality Into Goods and Services (New York, NY: Simon and Schuster, 1992).
K. Pramod, M. A. Tahir, N. A. Charoo, S. H. Ansari, and J. Ali, “Pharmaceutical Product Development: A Quality by Design Approach,” International Journal of Pharmaceutical Investigation 6 (2016): 129–138.
L. Peltonen, “Design Space and QbD Approach for Production of Drug Nanocrystals by Wet Media Milling Techniques,” Pharmaceutics 10 (2018): 104.
M. Sha'at, A. F. Spac, I. Stoleriu, et al., “Implementation of QbD Approach to the Analytical Method Development and Validation for the Estimation of Metformin Hydrochloride in Tablet Dosage Forms by HPLC,” Pharmaceutics 14 (2022): 1187.
M. Saha, P. Sikder, A. Saha, et al., “QbD Approach Towards Robust Design Space for Flutamide/Piperineself‐Emulsifying Drug Delivery System With Reduced Liver Injury,” AAPS PharmSciTech 23 (2022): 62.
L. Gurba‐Bryskiewicz, U. Dawid, D. A. Smuga, et al., “Implementation of QbD Approach to the Development of Chromatographic Methods for the Determination of Complete Impurity Profile of Substance on the Preclinical and Clinical Step of Drug Discovery Studies,” International Journal of Molecular Sciences 23 (2022): 10720.
M. I. Sadowski, C. Grant, and T. S. Fell, “Harnessing QbD, Programming Languages, and Automation for Reproducible Biology,” Trends in Biotechnology 34 (2016): 214–227.
S. Adam, D. Suzzi, C. Radeke, and J. G. Khinast, “An Integrated Quality by Design (QbD) Approach Towards Design Space Definition of a Blending Unit Operation by Discrete Element Method (DEM) Simulation,” European Journal of Pharmaceutical Sciences 42 (2011): 106–115.
M. L. Guerriero, A. Corrigan, A. Bornot, et al., “Delivering Robust Candidates to the Drug Pipeline Through Computational Analysis of Arrayed CRISPR Screens,” SLAS Discovery 25 (2020): 646–654.
S. Bonner, I. P. Barrett, Y. Cheng, et al., “Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery,” Artificial Intelligence in the Life Sciences 2 (2022): 100036.
S. Markossian, A. Grossman, K. Brimacombe, et al., eds., Assay Guidance Manual (Bethesda, MD: Eli Lilly & Company and the National Center for Advancing Translational Sciences, 2004).
X. D. Zhang, M. Ferrer, A. S. Espeseth, et al., “The Use of Strictly Standardized Mean Difference for Hit Selection in Primary RNA Interference High‐Throughput Screening Experiments,” Journal of Biomolecular Screening 12 (2007): 497–509.
X. D. Zhang, “Strictly Standardized Mean Difference, Standardized Mean Difference and Classicalt‐Test for the Comparison of Two Groups,” Statistics in Biopharmaceutical Research 2 (2010): 292–299.
J. Gilman, L. Walls, L. Bandiera, and F. Menolascina, “Statistical Design of Experiments for Synthetic Biology,” ACS Synthetic Biology 10 (2021): 1–18.
F. C. Onyeogaziri and C. Papaneophytou, “A General Guide for the Optimization of Enzyme Assay Conditions Using the Design of Experiments Approach,” SLAS Discovery 24 (2019): 587–596.
J. J. Peterson and M. Yahyah, “A Bayesian Design Space Approach to Robustness and System Suitability for Pharmaceutical Assays and Other Processes,” Statistics in Biopharmaceutical Research 1, no. 4 (2009): 441–449.
JMP, JMP Statistical Software. Version 17 (Cary, NC: SAS Institute Inc., 1989–2023).
P. H. Tewson, S. Martinka, N. C. Shaner, T. E. Hughes, and A. M. Quinn, “New DAG and cAMP Sensors Optimized for Live‐Cell Assays in Automated Laboratories,” Journal of Biomolecular Screening 21, no. 3 (2016): 298–305, https://doi.org/10.1177/1087057115618608.
C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning (Cambridge, MA: The MIT Press, 2006).
S. N. Wood, N. Pya, and B. Saefken, “Smoothing Parameter and Model Selection for General Smooth Models (With Discussion),” Journal of the American Statistical Association 111 (2016): 1548–1575.

Auteurs

Jonathan Jones (J)

Data Sciences and Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK.

Bairu Zhang (B)

Data Sciences and Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK.

Xiang Zhang (X)

Data Sciences and Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden.

Peter Konings (P)

Data Sciences and Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden.

Pia Hansson (P)

Bioscience Cardiovascular, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.

Anna Backmark (A)

Bioscience Cardiovascular, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.

Alessia Serrano (A)

Functional Genomics, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK.

Ulrike Künzel (U)

Functional Genomics, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK.

Steven Novick (S)

Data Sciences and Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Gaithersburg, Maryland, USA.

Classifications MeSH