ReviewBioengineering (Basel, Switzerland)2023
rAAV Manufacturing: The Challenges of Soft Sensing during Upstream Processing.
Review in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Paradigm shift: from pulmonary vasodilation to cardiopulmonary dual-track therapy-a comprehensive review of pathophysiology and advances in pulmonary hypertension-associated right heart failure.Respiratory research · 2026Review
- Batch Bayesian auto-tuning for nonlinear Kalman estimators.Scientific reports · 2025Article
- Chromatography in Downstream Processing of Recombinant Adeno-Associated Viruses: A Review of Current and Future Practises.Biotechnology and bioengineering · 2025Review
- Rapid At-Line AAVX Affinity HPLC: Enabling Process Analytical Technology for Bioprocess Development of Adeno-Associated Virus Vectors.Biotechnology journal · 2025Article
- Advancing AAV vector manufacturing: challenges, innovations, and future directions for gene therapy.Frontiers in molecular medicine · 2025Review
- Review
- How Not to Make the Joint Extended Kalman Filter Fail with Unstructured Mechanistic Models.Sensors (Basel, Switzerland) · 2024Article
- Status and future of recombinant adeno-associated virus vector manufacturing.Biotechnology progressReview
- A recurrent neural network for soft sensor development using CHO stable pools in fed-batch process for SARS-CoV-2 spike protein production as a vaccine antigen.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
4 authors.
Funding
Abstract
Recombinant adeno-associated virus (rAAV) is the most effective viral vector technology for directly translating the genomic revolution into medicinal therapies. However, the manufacturing of rAAV viral vectors remains challenging in the upstream processing with low rAAV yield in large-scale production and high cost, limiting the generalization of rAAV-based treatments. This situation can be improved by real-time monitoring of critical process parameters (CPP) that affect critical quality attributes (CQA). To achieve this aim, soft sensing combined with predictive modeling is an important strategy that can be used for optimizing the upstream process of rAAV production by monitoring critical process variables in real time. However, the development of soft sensors for rAAV production as a fast and low-cost monitoring approach is not an easy task. This review article describes four challenges and critically discusses the possible solutions that can enable the application of soft sensors for rAAV production monitoring. The challenges from a data scientist's perspective are (i) a predictor variable (soft-sensor inputs) set without AAV viral titer, (ii) multi-step forecasting, (iii) multiple process phases, and (iv) soft-sensor development composed of the mechanistic model.
Indexed as
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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.