Defining Golden Batches in Biomanufacturing Processes From Internal Metabolic Activity to Detect Process Changes That May Affect Product Quality.

Xin Bush, Erica J Fratz-Berilla, Casey L Kohnhorst, Roberta King, Cyrus Agarabi, David N Powers, Nicholas Trunfio

Abstract read

In one paragraph

Article in Biotechnology and bioengineering, 2025. 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

7 authors.

Xin BushU.S. Food and Drug Administration, Center for Drug Evaluation and Research, Office of Pharmaceutical Quality, Office of Pharmaceutical Quality Research, Division Pharmaceutical Quality Research VI, Silver Spring, Maryland, USA.ORCID 0000-0002-5568-5938

Erica J Fratz-BerillaU.S. Food and Drug Administration, Center for Drug Evaluation and Research, Office of Pharmaceutical Quality, Office of Pharmaceutical Quality Research, Division Pharmaceutical Quality Research VI, Silver Spring, Maryland, USA.ORCID 0000-0002-9259-0540

Casey L KohnhorstU.S. Food and Drug Administration, Center for Drug Evaluation and Research, Office of Pharmaceutical Quality, Office of Pharmaceutical Quality Research, Division Pharmaceutical Quality Research III, Silver Spring, Maryland, USA.

Roberta KingDepartment of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, Kingston, Rhode Island, USA.

Cyrus AgarabiU.S. Food and Drug Administration, Center for Drug Evaluation and Research, Office of Pharmaceutical Quality Research, Immediate Office, Silver Spring, Maryland, USA.

David N PowersU.S. Food and Drug Administration, Center for Drug Evaluation and Research, Office of Pharmaceutical Quality, Office of Pharmaceutical Quality Research, Division Pharmaceutical Quality Research VI, Silver Spring, Maryland, USA.

Nicholas TrunfioU.S. Food and Drug Administration, Center for Drug Evaluation and Research, Office of Pharmaceutical Quality, Office of Pharmaceutical Quality Research, Division Pharmaceutical Quality Research VI, Silver Spring, Maryland, USA.

Funding

This project was supported in part by the Internship/Research Participation Program at the Office of Biotechnology Products, US Food and Drug Administration, administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the US Department of Energy and FDA.

6 · The paper itself

Abstract

Cellular metabolism plays a role in the observed variability of a drug substance's Critical Quality Attributes (CQAs) made by biomanufacturing processes. Therefore, here we describe a new approach for monitoring biomanufacturing processes that measures a set of metabolic reaction rates (named Critical Metabolic Parameters (CMP) in addition to the macroscopic process conditions currently being used as Critical Process Parameters (CPP) for biomanufacturing. Constraint-based systems biology models like Flux Balance Analysis (FBA) are used to estimate metabolic reaction rates, and metabolic rates are used as inputs for multivariate Batch Evolution Models (BEM). Metabolic activity was reproducible among batches and could be monitored to detect a deliberately induced macroscopic process shift (i.e., temperature change). The CMP approach has the potential to enable "golden batches" in biomanufacturing processes to be defined from the internal metabolic activity and to aid in detecting process changes that may impact the quality of the product. Overall, the data suggested that monitoring of metabolic activity has promise for biomanufacturing process control.

Indexed as

Systems BiologyAnimalsCHO CellsCricetulusModels, BiologicalQuality Controlbioprocessingconstraint‐based systems biologymultivariate analysis

Identifiers

PMID39462977

DOI10.1002/bit.28873

PMCPMC11718427

What OpenQuestion holds

Textmetadata

LicenceCC BY

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.