ReviewBiotechnology progress
Artificial intelligence and machine learning-assisted digital applications for biopharmaceutical manufacturing.
Review in Biotechnology progress. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence and automation are no longer just buzzwords in the biopharmaceutical industry. The manufacturing of a class of biologics, comprising monoclonal antibodies, cell therapies, and gene therapies, is far more complex than that of traditional small molecule drugs. Therefore, applications based on artificial intelligence are essential for successfully manufacturing this new class of biologics more quickly and more economically. Some biologics manufacturers, academic researchers, and young entrepreneurs have already begun implementing artificial intelligence-based applications to increase operational efficiency, enhance process understanding, improve process monitoring, and achieve better regulatory compliance. Regulatory guidance from health agencies on the use of artificial intelligence and machine learning is acting as a catalyst in the adoption process of these new technologies by the biopharmaceutical industry. Research in artificial intelligence and machine learning has also advanced significantly in the last decade. At the same time, new cloud technologies have made the development and deployment of machine learning applications much easier. Several examples of artificial intelligence and machine learning applications in monoclonal antibodies manufacturing already exist. Cell and gene therapy, which present the future of medicine, will also benefit from this new technology. Overall, advancements in this domain will essentially help better serve patients' needs.
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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.