Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 3 appraisals
mAbs
Optimization strategies for monoclonal antibody production: advances in simulation and artificial intelligence in bioprocessing
The rapid growth of monoclonal antibody (mAb) therapies has increased the need for efficient, scalable, and affordable manufacturing processes. However, mAb production remains complex because of nonlinear upstream cell culture behavior, expensive downstream purification, especially Protein-A chromatography, and plant-level bottlenecks that can increase cost, cycle time, and manuacturing uncertainty. This review examines recent developments in mAb manufacturing with focus on process simulation, mathematical optimization, and artificial intelligence/machine learning (AI/ML) across upstream processing (USP), downstream processing (DSP), and integrated plant-level operation. In USP, media optimization, dynamic feeding, high-density cultures, and continuous perfusion bioreactors are discussed in relation to productivity and critical quality attributes (CQAs). In DSP, alternative and intensified purification strategies are reviewed with a focus on recovery, impurity clearance, scalability, cost, and technology maturity. AI/ML applications are also discussed from early-stage development and cell-line screening to upstream control, CQA prediction, chromatography optimization, and downstream decision support. Despite these advancements, challenges such as data heterogeneity, limited standardized datasets, model transferability, and regulatory constraints remain important barriers to implementation. Overall, this review uniquely connects simulation and AI/ML approaches to practical optimization across the full mAb manufacturing workflow, including design, scheduling, debottlenecking, purification, monitoring, and quality prediction. The combination of process simulation, continuous bioprocessing, and AI/ML-based decision support may enable more flexible, reliable, and cost-effective mAb manufacturing. However, these benefits depend on validation through robust models, process-specific case studies, and technoeconomic analysis.
25 July 2026
Read appraisal →Inflammation research : official journal of the European Histamine Research Society ... [et al.]
Lipopolysaccharide-mediated macrophage polarization, conserved pathogenesis, and implications for peripheral neuropathy: a systematic review.
OBJECTIVE AND DESIGN: This systematic review synthesized evidence for a conserved lipopolysaccharide (LPS)-mediated pathogenic mechanism across diverse tissues and evaluated its potential relevance to peripheral neuropathy. METHODS: Studies were identified in which LPS was the independent exposure and pro-inflammatory, M1-like macrophage activation/polarization was an outcome. Structured evidence mapping was used to code in-vivo studies for direct measurement of prespecified steps along a proposed pathway: gut perturbation→barrier disruption→circulating LPS→systemic inflammation→tissue interface disruption→innate immune activation→M1-like macrophage skew→tissue dysfunction. Conditional concordance and downstream chain completeness scores were calculated. RESULTS: Mechanistic patterns were conserved between pulmonary, cardiac, renal, lymphatic, gastrointestinal, central nervous, adipose, osseous, urologic, dental, hepatic, uterine, and pancreatic tissues. Conditional concordance with the proposed pathway was high (mean 0.984 ± 0.053). Eleven studies assessed all downstream steps from LPS exposure to tissue dysfunction, each demonstrating full chain completeness. M1 macrophage skew (87%), innate immune activation (87%), and circulating LPS (82.6%) were the most frequently reported steps. CONCLUSIONS: These findings demonstrate conservation of LPS-driven M1-like macrophage polarization and tissue injury across systems, supporting the need to further investigate the biological plausibility of a gut-immune-nerve axis contributing to peripheral neuropathy.
18 July 2026
Read appraisal →PloS one
Transcriptomic characterization of key psoriasis-associated genes based on single-cell RNA-seq and machine learning
BACKGROUND: Psoriasis is a multifaceted skin and systemic disorder driven by a complex interplay of genetic, immunological, and environmental factors. Genetic predisposition plays a pivotal role, with the IL-17/IL-23 immune axis recognized as a central pathogenic pathway. Ongoing research, however, continues to uncover additional critical drivers, cytokines, intracellular signaling networks, and potential therapeutic targets. METHODS: Single-cell RNA sequencing (scRNA-seq) datasets comprising both psoriatic and healthy samples were obtained from the Gene Expression Omnibus (GEO). Cell-type proportions were estimated using Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), and weighted gene co-expression network analysis (WGCNA) was applied to explore correlations between cell types and gene signatures. Machine learning algorithms were subsequently employed to identify four psoriasis-associated key genes: DEFB4A, GJB2, SERPINB3, and SERPINB13. Their expression was validated in bulk RNA-seq datasets. Using scRNA-seq data, we further investigated the lesional regulatory roles of these genes and their associated pathway alterations, and we proposed targeted therapeutic strategies. RESULTS: A series of algorithms identified 271 hub genes significantly associated with psoriasis lesions and basal cells. Machine learning analysis refined this set to four key genes in psoriasis: DEFB4A, GJB2, SERPINB3, and SERPINB13. CONCLUSIONS: These four psoriasis-associated driver genes were upregulated in lesional skin. We also screened small-molecule compounds targeting these genes, offering potential therapeutic strategies.
14 July 2026
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