Silk-Elastinlike Polymers Enhance the Anti-inflammatory and Analgesic Properties of Semisynthetic Glycosaminoglycans
Rapid clearance, mucosal barriers, and tight epithelium create strong natural barriers within the bladder and bowel, reducing the efficacy of biologics and small molecule therapeutics. Silk-like and Elastin-like motifs were genetically recombined to create silk-elastinlike protein polymers (SELPs) that transition to solid matrices from injectable solutions in response to body temperature. Semi-synthetic glycosaminoglycan ethers (SAGEs) are a novel therapeutic class of polysaccharides that have both anti-inflammatory and analgesic properties. We hypothesized that SELP could be leveraged to overcome physiological barriers and enhance delivery and efficacy of water-soluble therapeutics, such as SAGE, to bladder and rectal tissues (see Figure 1).
Material properties of SELP 815K, SELP 415K, Poloxamer 407 (PLX), and Poly(lactide-co-glycolide)-Poly(ethylene glycol)-Poly(lactide-co-glycolide) (PLGA-PEG-PLGA) triblock copolymer based thermoresponsive delivery systems were evaluated using gelation time assays, rheology, scanning electron microscopy, in vivo delivery of fluorescently labeled SAGE, and enhancement of SAGE efficacy to ameliorate radiation-induced inflammation (RII) or LL-37 induced cystitis (IC) in murine models. Therapeutic efficacy was assessed using gross anatomical inspection, histology, ELISA for MPO activity, and other inflammatory markers, along with mechanical pain sensitization via Von Frey filament testing.
SELP 815K at 12% (w/w) solution in saline transitioned from an injectable fluidic solution to form a robust gel within 3 minutes after heating to 37 °C. SELP released SAGE over a 24 hr period in a controlled and tunable fashion. Both SELPs had lower injection viscosities but sill formed stronger gels compared to PLGA-PEG-PLGA and PLX. SELP enhanced the accumulation of SAGE in both rectal and bladder tissues and significantly improve the amelioration of pain and inflammatory responses in both models (P