ReviewmAbs
Harnessing the potential of machine learning for advancing "Quality by Design" in biomanufacturing.
Review in mAbs. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Data-driven optimization of salt shock for high-efficiency ectoine production inSynthetic and systems biotechnology · 2027Article
- Review
- Article
- A Review on Applications of Artificial Intelligence (AI) in Monoclonal Antibody (mAb) Manufacturing.Molecules (Basel, Switzerland) · 2026Review
- From Polyphenols to Prodrugs: Bridging the Blood-Brain Barrier with Nanomedicine and Neurotherapeutics.International journal of molecular sciences · 2026Review
- Monoclonal Antibodies and Derivatives: Therapeutic Tools for Cancer.Oncology research · 2026Review
- Industry-defined opportunities for advancing big data capabilities in biopharmaceutical manufacturing.Frontiers in bioengineering and biotechnology · 2026Review
- Artificial intelligence in experimental and clinical in vitro analysis: applications, limitations, and future directions.Biotechnology notes (Amsterdam, Netherlands) · 2026Review
- Bioprocess Engineering for Cultivated Meat.Advances in biochemical engineering/biotechnology · 2026Review
- Recent Developments in Pharmaceutical Spray Drying: Modeling, Process Optimization, and Emerging Trends with Machine Learning.Pharmaceutics · 2025Review
- Review
- AutoML-Driven Soft Sensors for Real-Time Monitoring of Amino Acids in Mammalian Perfusion Cultures.Biotechnology and bioengineering · 2025Article
- AI-enhanced bioprocess technologies: machine learning implementations from upstream to downstream operations.World journal of microbiology & biotechnology · 2025Review
- Regulating the AI-enabled ecosystem for human therapeutics.Communications medicine · 2025Review
- Machine Learning-Powered Optimization of a CHO Cell Cultivation Process.Biotechnology and bioengineering · 2025Article
- Advancing Monoclonal Antibody Manufacturing: Process Optimization, Cost Reduction Strategies, and Emerging Technologies.Biologics : targets & therapy · 2025Review
- Computational methods for biofabrication in tissue engineering and regenerative medicine - a literature review.Computational and structural biotechnology journal · 2024Review
- Antibody glycan quality predicted from CHO cell culture media markers and machine learning.Computational and structural biotechnology journal · 2024Article
- Biosimilars in the Era of Artificial Intelligence-International Regulations and the Use in Oncological Treatments.Pharmaceuticals (Basel, Switzerland) · 2024Review
- Hybrid modeling for in silico optimization of a dynamic perfusion cell culture process.Biotechnology progressArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ensuring consistent high yields and product quality are key challenges in biomanufacturing. Even minor deviations in critical process parameters (CPPs) such as media and feed compositions can significantly affect product critical quality attributes (CQAs). To identify CPPs and their interdependencies with product yield and CQAs, design of experiments, and multivariate statistical approaches are typically used in industry. Although these models can predict the effect of CPPs on product yield, there is room to improve CQA prediction performance by capturing the complex relationships in high-dimensional data. In this regard, machine learning (ML) approaches offer immense potential in handling non-linear datasets and thus are able to identify new CPPs that could effectively predict the CQAs. ML techniques can also be synergized with mechanistic models as a 'hybrid ML' or 'white box ML' to identify how CPPs affect the product yield and quality mechanistically, thus enabling rational design and control of the bioprocess. In this review, we describe the role of statistical modeling in Quality by Design (QbD) for biomanufacturing, and provide a generic outline on how relevant ML can be used to meaningfully analyze bioprocessing datasets. We then offer our perspectives on how relevant use of ML can accelerate the implementation of systematic QbD within the biopharma 4.0 paradigm.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.