Evidence map›Paper›PMID 42467318›Full record

ReviewTherapeutic innovation & regulatory science2026

Quality by Design to Mitigate Aggregation: Mechanistic Insights and Analytical Strategies for Biopharmaceutical Manufacturing.

Anna Fedorko, Iryna Grynyuk, Jesús Alberto Afonso Urich

Abstract readReview
PubMed Publisher
In one paragraph

Review in Therapeutic innovation & regulatory science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Anna FedorkoResearch Center Pharmaceutical Engineering GmbH, Graz, 8010, Austria.ORCID http://orcid.org/0009-0003-0665-9746
Iryna GrynyukFaculty of Biotechnology and Biotechnics, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv, 03056, Ukraine.
Jesús Alberto Afonso UrichResearch Center Pharmaceutical Engineering GmbH, Graz, 8010, Austria. jesus.afonso@rcpe.at.ORCID http://orcid.org/0009-0009-2097-1764

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This review highlights the problem of protein molecule aggregation, which represents a significant challenge in the field of biopharmaceuticals. Protein aggregation is critical because it can affect the efficacy and safety of biopharmaceuticals, including those used to treat autoimmune diseases and various cancers. From a regulatory perspective, protein aggregation is recognized as a critical quality attribute (CQA) by health authorities such as the FDA and EMA, due to its potential impact on immunogenicity, product consistency, and patient safety. This article reviews various aggregation mechanisms, pathways, and strategies to minimize aggregation at various stages of drug development and manufacturing. Attention is paid to the latest analytical control techniques, addressing their applicability and limitations, as well as the possibility of integrating them into the QC flow, in alignment with current regulatory expectations for robust, science- and risk-based control strategies. The QbD principles emphasize their role in improving the understanding of manufacturing processes and enhancing the quality of finished pharmaceutical products, consistent with ICH Q8-Q11 guidelines and the regulatory push toward lifecycle-based pharmaceutical quality systems. The article offers valuable recommendations for the scientific community and manufacturers to effectively address protein aggregation and improve the safety and efficacy of biopharmaceuticals, while supporting regulatory compliance and facilitating more predictable interactions with health authorities.

Indexed as

BiopharmaceuticalsBiotechnologyDesign of experimentsIshikawa diagramMachine learningProtein aggregationQuality by designRegulatory complianceUp-scaling

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.