pH-sensitive polymer-modified liposome based immunity-inducing system Effects of inclusion of cationic lipid and CpG-DNA

Purpose: Efficient cancer vaccine carriers need both antigen delivery to dendritic cells (DCs) and DC activation. Previous pH-sensitive polymer-modified liposomes delivered antigen to DC cytosol but had limited adjuvant potency, and combining cationic lipid TRX with MPLA reduced antitumor effects, suggesting a need for alternative adjuvant combinations and controlled CpG-DNA delivery to endosomal TLR9.

Hypothesis: Including CpG-DNA into cationic lipid-containing, pH-sensitive polymer-modified liposomes—especially via a suitable complexation method—will deliver CpG-DNA to DC endosomes, enhance DC activation through TLR9, promote antigen-specific cellular immunity, and improve antitumor effects compared with conventional pH-sensitive polymer-modified liposomes. Pre-mix and Post-mix methods will produce different intracellular distributions and immune-activation profiles.

Aims: Prepare pH-sensitive polymer (MGlu-HPG)-modified liposomes containing cationic lipid TRX and CpG-DNA using Pre-mix and Post-mix methods. - Characterize size, zeta potential, CpG-DNA binding, and antigen loading. - Evaluate cellular association, intracellular distribution, and endosomal delivery in DC2.4 cells. - Assess in vitro DC activation: cytokine production and co-stimulatory/MHC molecule expression. - Evaluate in vivo antigen-specific T-cell responses and antitumor effects in E.G7-OVA tumor-bearing mice.

Delivery system: Platform: pH-sensitive polymer-modified liposomes. - Lipids: Egg yolk phosphatidylcholine (EYPC), with or without 30 mol% TRX (3,5-didodecyloxybenzamide, a cationic lipid). - Polymer: MGlu-HPG (3-methylglutarylated hyperbranched poly(glycidol)), lipids/polymer = 7/3 w/w. - Payload: Model antigen ovalbumin (OVA); CpG-DNA ODN 1826 as TLR9 ligand; fluorescent labels Rh-PE and FITC-CpG-DNA for tracking. - CpG-DNA complexation methods: - Pre-mix: thin lipid film dispersed with OVA/CpG-DNA mixture. - Post-mix: CpG-DNA added to pre-formed liposomes. - Formulations: Lip, TRX-Lip, Pre-mix TRX−, Pre-mix TRX+, Post-mix TRX−, Post-mix TRX+. - Targeting ligand: None; anionic surface intended for scavenger receptor–mediated DC uptake. - Size/zeta: ~88–110 nm; zeta potentials: Lip −18 mV, TRX-Lip −63 mV, Pre-mix TRX+ −65 mV, Post-mix TRX+ −60 mV.

Approach: In vitro model: DC2.4 immature murine DC line; EG7-OVA cells. - In vivo model: Female C57BL/6 mice. - Immunization: 50 µg OVA-loaded liposomes subcutaneously, twice at one-week intervals; splenocytes restimulated with OVA; IFN-γ measured by ELISA. - Tumor model: E.G7-OVA cells (1 × 10⁶) subcutaneously; immunized on days 5 and 12 with 50 µg OVA-loaded liposomes and 1 µg CpG-DNA; tumor volume and survival monitored; groups n = 4. - Late-stage tumor: Treatment started day 9 when tumors reached ~500 mm³. - Controls: PBS, free CpG-DNA, Lip, TRX-Lip, Pre-mix TRX−, Post-mix TRX−, separate injections of TRX-Lip and CpG-DNA.

Key methods: Dynamic light scattering and zeta potential for liposome characterization. - ssDNA assay for CpG-DNA content and stability. - Flow cytometry for cellular association of liposomes and CpG-DNA. - Confocal laser scanning microscopy with LysoTracker for endosomal colocalization. - ELISA for TNF-α, IL-12, and IFN-γ. - Flow cytometry for MHC class I/II and CD80 expression. - Tumor volume measurement and Kaplan–Meier survival analysis.

Key results: CpG-DNA binding: Pre-mix TRX+ bound >80% of CpG-DNA in feed; Pre-mix TRX− bound <13%. After 24 h, only 3.7 ± 1.3% of CpG-DNA was detected in supernatant, indicating >96% stable binding. - OVA loading: TRX inclusion increased OVA content from 150 to 240 g/mol lipid; CpG-DNA inclusion did not significantly change OVA content. - Cellular association: TRX-containing liposomes showed >10-fold higher DC2.4 cellular fluorescence than TRX-free liposomes. Pre-mix TRX+ showed ~3.5 relative fluorescence units for FITC-CpG-DNA vs ~1 for free CpG-DNA; Post-mix formulations were lower. - Intracellular distribution: Pre-mix TRX+ delivered CpG-DNA to endosomes with >80% colocalization with LysoTracker. Post-mix TRX+ also internalized CpG-DNA but with lower endosomal colocalization, suggesting partial cytosolic release. - DC activation in vitro: TRX-Lip induced high TNF-α and IL-12. Pre-mix TRX+ strongly promoted both cytokines; Post-mix did not. Free CpG-DNA produced TNF-α but no IL-12. Post-mix TRX+ showed higher CD80 expression than Pre-mix. - In vivo immune response: Pre-mix TRX+ and Post-mix TRX+ induced significantly stronger OVA-specific IFN-γ production than Pre-mix TRX− and TRX-Lip. Post-mix TRX+ gave the highest IFN-γ levels. - Antitumor effect: In early treatment, Pre-mix TRX+ showed strongest tumor suppression and survival; tumors shrank after 3 days and disappeared. Post-mix TRX+ showed earlier tumor regression (day 13) than Pre-mix (day 20), but survival difference was not significant. Separate injection of TRX-Lip and CpG-DNA at different sites produced lower survival than Post-mix TRX+. In late-stage tumors, Post-mix TRX+ produced stronger antitumor effects and prolonged survival (p = 0.0311) versus Lip.

Interpretation: The authors conclude that multiple adjuvant molecules—cationic lipid TRX and CpG-DNA—combined with pH-sensitive polymer-modified liposomes can strongly induce antigen-specific cellular immunity and antitumor effects. The CpG-DNA complexation method is critical: Pre-mix favors endosomal TLR9 stimulation and cytokine production, while Post-mix promotes co-stimulatory molecule expression and in vivo cellular immunity. This design is a promising vaccine carrier strategy for cancer immunotherapy.

Limitations: In vitro and mouse studies only; no large-animal or human data. - Small tumor-treatment group size (n = 4). - No systematic toxicity or safety evaluation reported. - Only one model antigen (OVA) and one tumor model (E.G7-OVA). - Post-mix mechanism of CpG-DNA binding in culture medium is not fully resolved. - Long-term survival benefit remains limited. - Clinical translation would require further optimization and safety assessment.