Conventional cancer treatments often fail due to the immunosuppressive tumor microenvironment, immune tolerance, and chronic inflammation. Therefore, new therapeutic approaches are urgently needed. Cancer vaccines can stimulate natural killer cells and cytotoxic T-lymphocytes, and induce long-lasting memory responses that help overcome the immunosuppressive tumor microenvironment. Recent advances in nucleic acid, peptide, and dendritic cell-based vaccines have improved antigen delivery and immune activation, while combinations with immune checkpoint inhibitors and ablative therapies enhance therapeutic efficacy and durability. Preclinical and clinical studies targeting tumor-associated antigens have shown promising outcomes. With poor survival rates and limited treatment options, hepatocellular carcinoma (HCC) appears to be the most prevalent cause of cancer-related deaths worldwide. Advances in antigen discovery, vaccine delivery systems, and synergistic combination strategies are paving the way for more effective and durable immune responses. By integrating molecular insights with clinical innovation, cancer vaccines hold the potential not only to improve treatment outcomes but also to redefine long-term disease management and survival in HCC.

Liposomal drug delivery systems have evolved into clinically relevant platforms for delivering small molecules, biologics, and vaccines. Nevertheless, a persistent translational gap remains between promising laboratory-scale formulations and reproducible pharmaceutical products. This review provides a practical framework linking liposome formulation, physicochemical characterization, manufacturing, and stability to pharmaceutical translation. Particular emphasis is placed on relationships between critical process parameters and critical quality attributes, including vesicle size, polydispersity index, zeta potential, morphology, lamellarity, encapsulation efficiency, drug loading, and in vitro release behavior. Preparation and cargo-loading strategies are discussed across conventional research-scale methods and approaches with potential for scalable manufacturing. Microfluidic production of conventional liposomes is distinguished from the manufacture of structurally distinct RNA-loaded lipid nanoparticles. Purification strategies and analytical determination of encapsulation efficiency are examined, with attention to the distinction between separation techniques and quantitative analytical methods. Lamellarity is highlighted as a structural determinant of encapsulation capacity, membrane organization, and release behavior, with complementary assessment by cryo-TEM, ³¹P-NMR, and small-angle X-ray scattering. Quality-control challenges associated with aggregation, fusion, cargo leakage, phospholipid hydrolysis, oxidation, and sterilization are also considered. Dry-state stabilization through lyophilization and spray drying is discussed for its potential to improve long-term stability and reduce cold-chain dependence. Ligand-targeted liposomes introduce additional requirements for surface characterization and biological evaluation, while approved products and investigational formulations illustrate achievements and limitations in clinical translation. Overall, this review emphasizes the importance of establishing relationships between formulation composition, processing, structural organization, stability, and functional performance to support more reproducible development and informed pharmaceutical translation.

Background/Objectives: Personalised neoantigen vaccines can now be manufactured within weeks, so what a patient receives is decided by which mutations are selected. Selection is organised around predicted peptide–MHC binding, yet presentation also requires that the mutation is transcribed, carried by enough of the tumour, and restricted by an HLA allele the malignant cells still express. A vaccine is also a set rather than a ranked list, and its epitopes must finally be joined into one construct. EvoSet-NeoVax carries these three problems through one label-blind workflow. Methods: Public neoantigen and non-small-cell lung cancer data were harmonised under a typed schema preserving missing values, partitioned under audited leakage control, and used to freeze six comparators. Results: A missingness-aware logistic model reached a precision–recall AUC of 0.869 on the internal test partition of 30 candidates, the best of the six on all five metrics, although no pairwise difference survives Holm correction across the fifteen comparisons. On the external TESLA set all six fell to chance, with receiver-operating-characteristic areas between 0.401 and 0.484. Combining predicted binding with within-patient expression, variant allele frequency and immunopeptidome evidence, twenty peptide records were selected for patient A144 across five retained HLA alleles and both HLA classes, resolving to nineteen mutations in nineteen genes, of which eighteen passed native-flank verification and entered the final modules; none matches the reviewed human proteome exactly. An independent-ranking top-K baseline cannot meet the composition constraints on this pool, and a retrospective comparison against the cohort’s 68 ELISpot outcomes separated neither ranking. Junction-specific linker optimisation reduced predicted linker-overlapping windows from 18 to 11 at high priority and from 88 to 42 at weak priority, and the optimised modules were encoded as three open reading frames of 744, 633 and 531 nucleotides passing exact translation back-check, folding to −268.20, −204.10 and −178.80 kcal/mol, and cloning in silico into an Addgene-verified T7 backbone with exact insert recovery. Conclusions: EvoSet-NeoVax provides a reproducible framework for translating tumour genomic evidence into sequence-verified, synthesis-ready neoantigen vaccine constructs.

Background/Objectives: The NLRP3 (NACHT, LRR and PYD domains-containing protein 3) inflammasome drives interleukin-1β maturation and pyroptosis in chronic inflammatory disease, and no direct inhibitor has been approved. Natural products are a plausible source of new chemical matter for this target, but such collections are too large to dock exhaustively, and the machine-learning rankings used to triage them are routinely acted on without stating the chemistry over which they hold. This study prioritises natural-product candidates for NLRP3 while quantifying that limit explicitly. Methods: In total, 610 compounds with reported NLRP3 IC50 values were curated from ChEMBL and used to benchmark five Morgan fingerprint classifiers across three repeated scaffold-based splits. A three-seed consensus of the calibrated best model ranked 2983 natural products, with a similarity-based applicability domain computed alongside every prediction. The highest-ranked compounds were docked into the NACHT domain inhibitor pocket of NLRP3 (PDB 7ALV), and five finalists entered 500 ns molecular dynamics simulations with molecular mechanics generalised Born surface area (MM-GBSA) interaction energy analysis. Results: XGBoost gave the highest mean held-out area under the receiver operating characteristic curve (AUROC), 0.868 ± 0.052, and its performance tracked chemical similarity: 0.857 ± 0.046 inside the applicability domain against 0.368 ± 0.116 outside it. Only 322 library compounds (11%) fell inside the domain. Feature attribution and R-group decomposition traced the ranking to a flavone series in which hydroxylation was tolerated or mildly favourable, and methoxylation was consistently detrimental. Five of the 48 docked compounds were simulated—the Morus prenylflavonoids kuwanon H, morusin and morusinol, together with the approved drugs vardenafil and carprofen—and none left the pocket. Kuwanon H gave the most favourable MM-GBSA interaction energy, −42.5 ± 4.8 against −28.4 ± 3.0 kcal mol⁻¹ for the crystallographic reference, with morusinol having the lowest ligand root-mean-square deviation (RMSD). Conclusions: Morus prenylflavonoids are prioritised candidates for NLRP3 ATPase, inflammasome activation and cytotoxicity assays. All five compounds lie outside the applicability domain (maximum Tanimoto 0.19–0.23), so the ranking served as triage rather than as a predicted activity, and the structure-based stages carry the prioritisation. The five therefore stand as prioritised candidates for experimental validation, through NLRP3 ATPase and target engagement assays, cellular inflammasome readouts and selectivity counter-screens.

Background: Streptococcus anginosus is an opportunistic bacterial pathogen associated with clinically significant infections, including brain abscesses, bacteremia, empyema, and infective endocarditis. The absence of an approved vaccine against this organism warrants investigation of potential preventive candidates. Accordingly, this study investigated the computational design and characterization of a multi-epitope vaccine targeting S. anginosus. Methods: Comparative analysis of 28 complete S. anginosus genomes was undertaken to distinguish core genes and identify suitable antigenic protein targets. The shortlisted proteins were examined for B-cell, CTL, and HTL epitopes, followed by the construction of a vaccine sequence containing the selected epitopes and β-defensin-3 as an adjuvant. Further analyses examined epitope conservancy, physicochemical characteristics, three-dimensional conformation, molecular docking with immune receptors, molecular dynamics simulations, and predicted immune responses. Results: Genomic comparison yielded 627 core genes, providing a basis for antigen selection. The resulting vaccine comprised 272 amino acid residues and showed favorable computational predictions for antigenicity, stability, solubility, and allergenicity. Structural assessment indicated acceptable stereochemical characteristics, while conservancy screening revealed complete sequence identity for several epitopes among their corresponding homologous proteins. Docking calculations identified possible contact interfaces with TLR2 and TLR4, with TLR2 receiving the more negative ClusPro score. Molecular dynamics trajectories characterized conformational variations within the vaccine–receptor complexes. The selected epitopes yielded an estimated worldwide population coverage of 99.23%. Immune-response modeling additionally predicted antibody-associated and cellular immune activity. Conclusions: The proposed vaccine displayed computational characteristics that support its further investigation as a preventive candidate against S. anginosus. Experimental studies are necessary to determine its safety, immunogenicity, and protective efficacy.