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PubMed2026

Rational design of high-productivity perfusion processes for CHO Cells: From growth inhibitory strategies to model-driven optimization.

While perfusion culture for Chinese hamster ovary (CHO) cells offers advantages such as continuous operation and flexibility, it suffers from product loss through cell bleeding and difficulties in reaching high productivity due to sustained rapid cell growth. Growth inhibitory strategies are widely used to enhance productivity in fed‑batch processes; however, their practical implementation and comparative effectiveness in perfusion processes remain insufficiently explored. Meanwhile, process development often relies on costly trial‑and‑error approaches. Here, we systematically compared three growth inhibitory strategies in perfusion culture-low cell‑specific perfusion rate (CSPR), sodium butyrate, and mild hypothermia-with respect to cell growth, metabolism, productivity, and product quality. Genome‑scale metabolic flux sampling analysis revealed that low‑CSPR and sodium butyrate induce a convergent up‑regulation of energy metabolism, correlating with greater gains in specific productivity (qp). Building on this insight, we developed a growth‑kinetic model for the combined low‑CSPR + butyrate strategy, incorporating parameter uncertainty. This model‑guided framework enabled the rational design of two distinct high‑productivity perfusion processes: a sustained mode that achieved robust long‑term stability alongside substantial productivity gains, and a high‑intensity mode that pushed qp and daily volumetric titer to their maxima, with increases of up to 108.94% and 190.36%, respectively, in a model CHO cell line with a moderate baseline productivity. Our study provides a proof‑of‑concept framework for perfusion intensification, from strategy selection to rational process design.

Animals↗
PubMed2026

Beyond antigen matching: compatibility intelligence theory for transfusion as an emergent biological system.

BACKGROUND: Despite major advances in serologic testing, extended phenotyping, and blood group genomics, clinically similar transfusion exposures may result in markedly different immune and clinical outcomes. Existing compatibility strategies do not fully explain this biological variability. OBJECTIVES: To examine transfusion compatibility as an emergent donor-recipient biological state and propose a systems-level conceptual framework that integrates established biological determinants into a testable model for future precision transfusion medicine. METHODS: This narrative review critically synthesizes current evidence from blood group genomics, recipient immunobiology, inflammation, disease-specific biology, transfusion medicine, and computational prediction. The proposed framework distinguishes Compatibility Intelligence Theory (CIT) as a biological interpretation from Precision Transfusion Intelligence (PTI) as its potential clinician-supervised translational application. RESULTS: The review argues that transfusion compatibility is shaped by interactions among donor genetics, recipient immune biology, inflammatory physiology, disease context, transfusion history, and longitudinal adaptation rather than by antigen matching alone. CIT provides an organizational framework for integrating these determinants, whereas PTI describes a possible clinician-supervised translation. To address current feasibility, the revised framework separates variables into routinely measurable, contextually available but incompletely standardized, and research-stage domains, and proposes a staged strategy for deriving rather than assuming their quantitative weights. Any clinical implementation would require comparative validation against current serologic, phenotypic, and genotype-based practice. CONCLUSIONS: Compatibility Intelligence Theory offers a testable systems-level framework for understanding transfusion compatibility without replacing established transfusion practices. The framework is not presented as a ready-to-use score: currently measurable variables can be organized for structured risk review, whereas inflammatory, immunogenetic, and multi-omic inputs require prospective standardization and validation. If future studies demonstrate incremental predictive and patient-centered benefit, CIT-informed PTI could support an adaptive, evidence-based extension of current precision transfusion practice.

Humans↗
PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗
PubMed2026

Long-term (>7-year) parental consumption of genetically modified maize (Cry1Ab/Cry2Aj and EPSPS) induces no adverse sperm DNA methylation alterations across two generations of cynomolgus monkeys.

This study assessed the long-term safety of genetically modified (GM) maize from a male reproductive perspective, using a non-human primate model. We analyzed the sperm DNA methylation profiles in cynomolgus monkeys fed GM maize, non-GM parental maize, or a conventional diet over two generations (F0/F1). Whole-genome bisulfite sequencing (WGBS) revealed no significant differences in global methylation levels among groups. The identified differentially methylated regions (DMRs) were short, enriched in non-regulatory genomic areas, and did not cluster after treatment. Functional enrichment analysis showed that DMR-associated genes were consistently involved in the same core biological pathways (e.g., mTOR and Wnt signaling) across all dietary comparisons. These findings indicate that GM maize consumption did not induce specific adverse epigenetic alterations in sperm, with the observed changes reflecting common physiological adaptations to dietary variations rather than GM-related effects.

Animals↗
PubMed2026

Enrichment of Lysobacter in a long-term organically managed agricultural field with low soilborne disease incidence.

Disease-suppressive soils, in which soilborne pathogens are naturally suppressed, offer a promising model for sustainable crop protection, particularly in organic farming systems where chemical disease control options are limited. Although disease suppression in these soils is considered to rely on biological control, the underlying mechanisms remain poorly understood. In this study, we investigated soil from a long-term organically managed field in Shiga Prefecture, Japan, where soilborne disease incidence has remained consistently low, to identify bacterial community features potentially associated with this field. The 16S rRNA gene amplicon sequencing indicated that this soil harbored a bacterial community distinct from those of nearby agricultural soils. Following the application of organic compounds, the genus Lysobacter, a taxon with known antagonistic activity against plant pathogens, was markedly enriched in response to proteinaceous organic inputs. This enrichment was consistent across sampling times and specific to certain proteinaceous organic inputs, whereas minimal effects were observed on chitin, N-acetyl-d-glucosamine, or cysteine. Broader soil surveys indicated that Lysobacter enrichment was not strictly associated with whether soils had been managed under organic or conventional farming practices. Stepwise multiple regression analysis identified 10 co-occurring bacterial genera that were strongly associated with Lysobacter abundance. These findings highlight condition-dependent Lysobacter enrichment as a characteristic microbial response to proteinaceous organic amendments in this low-disease-incidence field and provide microbial insights that may inform microbiome-based strategies for sustainable soil management.

Lysobacter↗
PubMed2026

Systemic biomarkers of treatment response to methotrexate in people with painful knee osteoarthritis: A biological substudy of the PROMOTE randomised controlled clinical trial.

OBJECTIVE: Stratification of therapeutic responses may help identify efficacious therapies for osteoarthritis (OA). In the PROMOTE randomised trial, participants with elevated baseline high-sensitivity C-reactive protein (hs-CRP) showed greater pain reduction after methotrexate treatment. We set out to interrogate a broader panel of serum/plasma inflammatory response markers relevant to methotrexate actions as potential biomarkers of therapeutic effect. Our objectives were to: (i) characterize changes in these systemic markers during methotrexate treatment; determine whether (ii) baseline levels or (iii) changes in any marker during treatment were associated with treatment response; and (iv) compare these findings with the more established clinical inflammatory marker, hs-CRP. DESIGN: Plasma/serum samples from participants in PROMOTE's biological substudy were analysed for 35 inflammatory markers at baseline (pre-treatment) and at 6-months (post-treatment), by MesoScale V-plex multiplex assay. Those with paired biological and clinical data at both baseline and 6-months were included in the substudy analysis set. Relationships between markers and overall data structure were assessed by Pearson correlation and Principal Component analysis. Associations between markers (baseline levels or change over time) and change in average knee pain severity in past week (numerical rating scale, NRS) were evaluated by univariable linear regression, adjusting for baseline age, sex, and body mass index. Least Absolute Shrinkage and Selection Operator (LASSO) regression with bootstrap resampling enabled marker selection. Benjamini-Hochberg correction adjusted for multiple testing (Padj). RESULTS: 87 participants with paired blood marker and clinical data were eligible for substudy analysis. 18/35 markers were quantifiable and analysed. Systemic IL-8 and TNF-α levels decreased (Padj=0.015, 0.048 respectively) while IL-15 increased (Padj=0.033) with methotrexate treatment over 6-months. Analysing within this active treatment randomised arm, higher baseline IFN-γ was associated with greater reduction in NRS pain change (0.66 [0.01, 1.31], P=0.047), as was decreasing TNF-α over 6-months (2.25 [0.00, 4.5], P=0.049). LASSO identified higher IFN-γ, lower plasma IL-15 and IL-16, and younger age as the most important baseline predictors of pain improvement. hs-CRP was highly selected by LASSO for treatment response in both arms. In a secondary univariate treatment arm-by-biomarker interaction analysis, of the 19 markers, only hs-CRP showed consistent effects in adjusted models (at baseline, coeffic. 2.34 [0.53, 4.15], P=0.001; change over 6-months, (0.36 [0.06, 0.66], P=0.018). CONCLUSIONS: Blood measurement of IFN-γ, TNF-α, IL-15 and IL-16 as well as hs-CRP could act as potential markers to stratify the treatment response by average knee pain to methotrexate in knee osteoarthritis.

Humans↗
PubMed2026

ORBIT: Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space for cancer driver gene identification.

Accurate identification of cancer driver genes is crucial for precision oncology but remains challenging due to the complexity of integrating heterogeneous data and modeling dynamic biological systems. To address these limitations, we propose ORBIT (Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space). Our framework synergistically fuses multi-omics profiles with functional network data using a context-adaptive graph reweighting mechanism to capture cancer-specific dynamics. The model employs a bi-prototype contrastive learning strategy within hyperbolic space, which aligns gene representations around distinct driver and non-driver semantic anchors while preserving the intrinsic hierarchy of biological networks. Comprehensive evaluations demonstrate that ORBIT achieves highly competitive stability in pan-cancer analysis while consistently outperforming state-of-the-art methods in cancer-specific predictions. Furthermore, functional enrichment analysis confirms that the model effectively segregates core cancer pathways, and drug sensitivity profiling validates the clinical relevance of the identified drivers. By integrating hyperbolic geometry with context-adaptive learning, ORBIT offers a robust and interpretable paradigm for precision medicine. The source codes and datasets are publicly accessible at https://github.com/spcho-dev/ORBIT.

Humans↗
PubMed2026

Does high fructose consumption trigger microglia activation and neuroinflammation? A systematic review.

This systematic review evaluated the effects of fructose intake on neuroinflammatory markers in rodent models. The search terms Fructose AND neuroinflammation OR Neurodegeneration OR chemokines OR interleukins OR microglia OR behaviour OR memory OR cognition were used in Google Scholar, Scopus and Web of Science. Thirteen animal studies investigating fructose-induced neuroinflammation that matched the eligibility criteria were included in the study. Across the studies, 16 inflammatory markers were identified and significantly altered following exposure to fructose. The findings consistently demonstrated elevated expression of pro-inflammatory cytokines, TNF-α, IL-6, and IL-1β, following fructose administration. Fructose consumption also dysregulated MCP-1, fractalkine, and CX3CR1 levels, thereby promoting inflammatory signalling and microglial activation. Furthermore, fructose exposure significantly increased IBA-1 and CD11b, indicating sustained neuroimmune activation. Alterations in important inflammatory pathways involving TLR4, NLRP3, NF-κB, MyD88, iNOS, and cyclooxygenases (COX-1 and COX-2) were also observed. In contrast, expression of the anti-inflammatory regulator peroxisome proliferator-activated receptor gamma (PPARγ) was reduced after fructose treatment. Overall, the findings suggest that chronic fructose consumption induces neuroinflammation through multiple inflammatory and immune-related mechanisms in the brain. These effects appear to be dose- and duration-dependent and may contribute significantly to neurodegeneration and cognitive impairment.

Microglia↗
PubMed2026

Meta-PseU: A meta-classifier for robust prediction of RNA pseudouridine modification sites from long sequences.

BACKGROUND AND OBJECTIVES: Pseudouridine (Ψ) represents one of the most abundant and conserved RNA modifications. Ψ provides an additional hydrogen-bond donor that enhances RNA structural stability and modulates translation. It participates in diverse biological processes, including RNA-protein interactions, splicing, translational control, and stress responses. Aberrant pseudouridylation is implicated in cancer, neurodegenerative disorders, and autoimmune diseases. Despite its biological importance, experimental identification of Ψ sites remains time-consuming and costly, limiting the feasibility of transcriptome-wide profiling. Computational approaches have therefore become essential complements to experimental techniques. However, state-of-the-art machine-learning and deep-learning predictors often suffer from limited generalizability due to small training datasets. To overcome these issues, we aim at constructing new long-sequence datasets and developing a novel Ψ site predictor. METHODS: New long-sequence datasets were constructed as benchmarks for RNA Ψ-site prediction. The Ψ modification sites in RMBase 3.0 were mapped to the reference genomes across three species of human, mouse, and yeast, and the RNA sequences with a length of 201 were generated by extending the upstream and downstream from the mapped, central sites. To eliminate sequence redundancy, the sequences were clustered using CD-HIT with a 70% sequence identity threshold. We developed Meta-PseU, a logistic regression-based meta-classifier that considered 118 machine learning and deep learning classifiers. The datasets and programs are freely accessible at https://github.com/kuratahiroyuki/MetaPseU. RESULTS: By optimizing model configuration, we proposed the Meta-PseU model stacking 32 machine learning and deep learning classifiers out of 118 classifiers. Meta-PseU substantially improved model generalizability, overcoming a key limitation of existing approaches. It greatly outperformed state-of-the-art predictors and achieved increasing accuracy with increasing sequence length. CONCLUSIONS: Long-sequence datasets were newly constructed as benchmarks for RNA Ψ-site prediction. Meta-PseU offers a new framework for robust Ψ-site identification by using long sequences.

Pseudouridine↗
PubMed2026

Fluoride as a Modifier of Metallome Homeostasis: A Systematic Review of Animal Studies.

Fluoride is widely used for caries prevention due to its effects on mineralized tissues, yet its potential role as a modifier of systemic metal homeostasis remains insufficiently explored. This systematic review synthesizes preclinical evidence on the association between fluoride exposure and changes in metal and semi-metal concentrations across biological matrices. A comprehensive search strategy was conducted across major databases without language or date restrictions, following SyRF, CAMARADES and PRISMA 2020 guidelines. Thirty-one animal studies were included, encompassing multiple species, exposure conditions and analytical approaches. Despite substantial methodological heterogeneity, consistent patterns emerged. Fluoride exposure was associated with element-specific redistribution of the metallome rather than uniform change. Essential elements were predominantly depleted, most consistently zinc, copper and manganese, whereas the toxic metals lead and cadmium tended to be retained. This contrast between homeostatically regulated essential elements that are lost and non-regulated toxic metals that accumulate supports the hypothesis that fluoride differentially modifies the distribution and retention of co-existing elements. The novelty of this review lies in integrating metallomic outcomes across experimental models, highlighting fluoride as a potential systemic modulator rather than a tissue-specific agent. Although variability in study design and risk of bias limits causal inference, the consistent directionality of findings across models reinforces their biological plausibility and translational relevance.

Animals↗
PubMed2026

Targeted Nanoparticle Delivery CRISPR/Cas9: overcoming biological barriers, enhancing stability, and improving therapeutic precision.

Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) has emerged as a promising gene-editing platform for genetic disorders; however, its in vivo application remains limited by low delivery efficiency and biological barriers. Many CRISPR payloads fail to reach target sites due to extracellular degradation, immune clearance, and intracellular trafficking limitations. This review examines the interplay between biological barriers and nanoparticle engineering strategies for CRISPR/Cas9 delivery. A barrier-oriented engineering approach is proposed as a central framework, encompassing ligand-based surface modification for enhanced targeting and uptake, improved circulation stability via PEGylation and biomimetic coatings, and optimized payload release through endosomal escape strategies. Stimulus-responsive nanoparticle systems further enable spatiotemporal control over payload release. Nuclear targeting strategies, including optimization of nuclear localization signals (NLS) and exploitation of endogenous trafficking pathways, are highlighted as key factors for improving genome-level editing efficiency. Despite these advances, major challenges-including limited intracellular delivery efficiency, insufficient targeting precision, and safety concerns-continue to hinder clinical translation. Future directions highlight artificial intelligence-driven nanoparticle design, personalized delivery systems, and next-generation CRISPR platforms. Overall, an integrated, barrier-oriented engineering strategy is essential for advancing CRISPR/Cas9 delivery toward clinical applications, ultimately advancing global good health and well-being.

CRISPR/Cas9↗
PubMed2026

Construction of an infectious clone of Spodoptera frugiperda densovirus and its biological characteristics.

Densoviruses are highly pathogenic to their insect hosts and have great potential for biocontrol. Spodoptera frugiperda densovirus (SfDV) was isolated from diseased larvae of Spodoptera frugiperda, while its biological functions remain unclear. Herein, we successfully constructed an infectious clone of SfDV. The S. frugiperda larvae transfected with the infectious clone exhibited anorexia, stunted growth, and reduced activity. Histopathological analysis further showed that the epidermis, fat body and trachea were infected instead of muscle and midgut tissues. Transmission electron microscopy (TEM) revealed that numerous virions of about 22 nm were distributed within both the nucleoplasm and cytoplasm of epidermal cells. Moreover, many virions were also found contained within vesicles in the cytoplasm. The replication kinetics of the rescued SfDV (rSfDV) was similar to that of the parental SfDV. The median lethal dose (LD50) and median lethal time (LT50) values of rSfDV were 6.63 × 107 viral genome copies (vgc), 5.23 d, respectively, which were also comparable to those of the parental SfDV. Taken together, the infectious clone of SfDV provides an important tool for further exploring the genome function, pathogenesis, and interactions with its hosts.

Animals↗
PubMed2026

Biological mechanisms of atropine in myopia control (Review).

Myopia is now recognized as a progressive, potentially sight‑threatening disease rather than just a refractive error, with its prevalence rising rapidly worldwide due to its high occurrence, major vision losses and huge public health cost. The World Health Organization estimates that 2.6 billion individuals in the world were myopic in 2020 this figure is projected to increase to 3.364 billion by 2030. Although myopia may be better controlled in its early stages, it may not be completely reversed at this time. Of all of the methods for controlling myopia, atropine, a muscarinic receptor antagonist, remains an effective pharmacological option for slowing myopia progression in children. However, the mechanisms of action of atropine remain to be fully elucidated. This review provided a systematic review for myopia epidemiology, pathogenesis, the effects and side effects, as well as up‑to‑date possible mechanisms, in the hope of facilitating that researchers in this field elucidate its underlying mechanisms so that clinical ophthalmologists may be able to better control this disease.

Humans↗
PubMed2026

The Meaning and Significance of Breastfeeding for Biological Mothers of Children with Cleft Lip and/or Palate.

INTRODUCTION: Given the functional, emotional, and symbolic challenges imposed by cleft lip and/or palate (CL/P) on exclusive breastfeeding (EBF), mothers often experience early breastfeeding cessation with impacts on maternal identity and the mother-infant bond. This study aimed to explore the meanings, feelings, and experiences of mothers of children with CL/P regarding breastfeeding in the face of these adversities. METHODS: This qualitative study applied the Clinical-Qualitative Method as proposed by Turato. Six in-depth semistructured interviews were conducted with biological mothers of children with CL/P, recruited from a specialized craniofacial reference center in Brazil. Data were analyzed using a qualitative content analysis approach, grounded in psychodynamic concepts from the Medical Psychology theoretical framework. RESULTS: Based on the analysis of the collected material, three analytical categories were identified and constructed: (1) "Existential conflict regarding the inability to fulfill the ideal maternal role"; (2) "Duality between the need and the fear of caregiving"; and (3) "The anguish experienced between tangible and intangible support during breastfeeding."Conclusions:Although the inability to EBF in children with CL/P generates emotional distress, motherhood is reimagined through adaptive forms of care and bonding, highlighting gaps in institutional support and the need for more humanized, emotionally sensitive health practices.

Humans↗
PubMed2026

Ancient DNA and Human Physiology.

Ancient DNA (aDNA) enables the reconstruction of chronologically sampled genomes from ancient humans, animals, plants, pathogens, and microorganisms, as well as environmental DNA, providing a record of biological changes through time. Improvements in short and degraded DNA extraction methods and low-cost sequencing now enable the generation of broad, cross-regional datasets that expand evolutionary analyses from past population demography to biological mechanisms. By tracking temporal shifts of allele frequencies, integrating functional genomics resources (e.g., gene expression, chromatin structure variation), modeling population demography to separate selection from genetic drift, and aligning genetic changes with archaeological, cultural, and climatic data, aDNA has the potential to link sequence variation to physiological function within their temporal and environmental contexts. In this review, we summarize illustrative case studies from aDNA research spanning complex traits, dietary adaptations, and responses to pathogens and other environmental changes, showing how human biology has evolved under multiple selective pressures through time. These dated signals help triage experimental work and expose mechanisms that are rare or absent in living cohorts. Although some challenges remain, such as geographic and temporal sampling disparities, limitations in data resolution and variant detection, and genotype-phenotype uncertainties, rapid methodological progress and stronger ethical frameworks are expanding what can be inferred, making aDNA a promising tool for refining physiological pathways, their timing, and their drivers.

Humans↗
PubMed2026

A multi-model genome-wide association study identifies genetic variants underlying resistance to Largemouth Bass Ranavirus (LMBV) in Micropterus salmoides.

Largemouth bass (Micropterus salmoides) is an economically important freshwater aquaculture species, yet recurrent outbreaks of Largemouth Bass Ranavirus (LMBV) continue to impair production and cause substantial losses. The genetic basis of host variation in LMBV resistance remains insufficiently characterized. Here, we applied a multi-model genome-wide association study (GWAS) to identify loci associated with resistance following a controlled challenge with the LMBV-23PY strain. Whole-genome resequencing was performed for 146 phenotyped fish, including 72 susceptible and 74 resistant individuals. After stringent quality control, 877,262 high-quality variants were retained and tested using six GWAS models. Across binary survival status and survival time phenotypes, 32 shared suggestive variants were consistently detected across models, representing suggestive loci for LMBV-23PY resistance. Genes within ±50 kb of these loci were annotated, and functional enrichment highlighted immune- and redox-related biological processes. Three prioritized candidates-GSTT3L (glutathione S-transferase theta-3-like), CGRP2 (calcitonin gene-related peptide 2), and NPPC (natriuretic peptide C)-were associated with pathways involved in oxidative stress responses and immune regulation. Collectively, these results provide insight into the genetic architecture of LMBV-23PY resistance in largemouth bass and identify suggestive variants and associated candidate genes for downstream validation, functional interrogation, and the development of marker-assisted and genome-enabled breeding strategies.

Animals↗
PubMed2026

Understanding FDA's reasons for nonapproval: A systematic review of complete response letters in cell and gene therapy.

BACKGROUND AIMS: Cell and gene therapy products face unique regulatory challenges due to their biological complexity and the stringent expectations for manufacturing control, analytical testing, and clinical assessment. As the pipeline grows, understanding the drivers of FDA non‑approval is increasingly important for improving first‑cycle success and reducing development delays. In this study, we aimed to characterize the frequency and impact of refuse‑to‑file actions, major amendments, and complete response letters issued for biologics licensing applications; and to identify recurring patterns of deficiencies contributing to delayed approval. METHODS: Publicly available documents were extracted from the FDA website. Data from each approved cell and gene therapy product's regulatory history-including complete responses, major amendments, inspection timing, and cited deficiencies-were compiled and categorized across clinical, quality, and labeling domains. RESULTS: Analysis showed that informational deficiencies were common across modalities. Major amendments occurred in roughly half of applications, and complete responses had the most significant impact. Quality deficiencies appeared in all complete responses and were the predominant barrier, while clinical and labeling issues were less frequent but meaningful when present. CONCLUSION: Overall, these findings highlight the need for proactive FDA engagement, comprehensive readiness, and early inspection preparation to reduce regulatory risk and improve first cycle approval outcomes.

Humans↗