Cancers (Basel). 2025 Nov 30;17(23):3850. doi: 10.3390/cancers17233850.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies. An unmet need exists for reliable biomarkers and in vitro models capable of predicting patient drug response to advance personalized medicine. Traditional models fail to represent the tumor's complexity and the role of the stromal environment in chemoresistance. Patient-derived organoids (PDOs) overcome these limitations, enabling multi-omics profiling and reliable drug testing for functional precision medicine. This review provides a comprehensive overview of PDAC PDO research, emphasizing the following major areas: (i) the genetic and phenotypic fidelity of PDOs, (ii) their predictive value for drug response and chemoresistance, (iii) the integration of the extracellular matrix and tumor microenvironment (TME) components, and (iv) emerging technologies. Studies confirm that PDOs faithfully represent the primary tumor's specific genetic features and retain intratumoral heterogeneity. PDO-based platforms have demonstrated a strong correlation between in vitro drug sensitivity and in vivo efficacy in xenograft models, validating their utility for identifying drug candidates, repurposing existing drugs, and determining effective combinations. Efforts are ongoing to integrate crucial TME components, like cancer-associated fibroblasts, using innovative co-culture platforms such as fused PDOs and InterOMaX, to better model desmoplasia and chemoresistance mechanisms. Furthermore, PDO technology is converging with microphysiological systems and artificial intelligence tools to facilitate high-throughput drug screening and dynamic, real-time monitoring of therapeutic effects. The integration of PDOs into biobanks and advanced screening platforms holds the potential to accelerate drug discovery and improve therapeutic outcomes for PDAC patients, if challenges related to protocol standardization and regulatory acceptance are addressed.

PMID:41375051 | PMC:PMC12690986 | DOI:10.3390/cancers17233850

Comput Biol Med. 2025 Dec 7;200:111372. doi: 10.1016/j.compbiomed.2025.111372. Online ahead of print.

ABSTRACT

The emergence of monkeypox virus (MPXV) as a global health threat has necessitated the rapid identification of novel antiviral therapeutics. Currently, no FDA-approved drugs are specifically designed against the disease. We used an in-house deep learning pharmacophore model for screening a library of 1974 FDA-approved drugs targeting the active site of MPXV DNA polymerase. Three drugs exhibited the strongest binding affinities, outperforming the control drug, Cidofovir diphosphate, and forming stable interactions with key active site residues. Among them, Paromomycin emerged as the most favourable drug, demonstrating stable, persistent, and adaptable interactions in molecular dynamics simulation. In parallel, we developed a novel automated peptide-generating AI pipeline that integrates active-site residues with knowledge-guided amino acid selection to generate and evaluate synthetic peptides. Cysteine-Phenylalanine-Cysteine (CFC), together with a panel of candidates, emerged through rational balancing of physicochemical properties and drug-likeness for accelerated therapeutic discovery. Synthetic peptides were evaluated to further understand the binding efficacies with DNA polymerase. CFC peptide demonstrated strong binding affinity (-8.08 kcal/mol) through stable interactions with key catalytic residues ASP549, ARG634 and LYS661, while MMGBSA analysis confirmed favourable binding energy (-33.02 kcal/mol). Consistent results in MD simulations indicate functional binding without destabilisation. Although ADMET predictions for CFC revealed limitations in permeability and oral bioavailability, its favourable binding profile and reduced predicted toxicity support its potential as a novel antiviral lead.

PMID:41360016 | DOI:10.1016/j.compbiomed.2025.111372