Structural regression modelling of peptide based drug delivery vectors for targeted anti-cancer therapy.

Abstract

Drug resistance in cancer poses a serious challenge in finding an effective remedy for cancer patients, because of the multitude of contributing factors influencing this complex phenomenon. One way to counter this problem is using a more targeted and dose-limiting approach for drug delivery, rather than relying on conventional therapies that exhibit multiple pernicious side-effects. Stability and specificity have traditionally been the core issues of peptide-based delivery vectors. In this study, we employed a structural regression modelling approach in the design, synthesis and characterization of a series of peptides that belong to approximately same topological cluster, yet with different electrostatic signatures encoded as a result of their differential positioning of amino acids in a given sequence. The peptides tagged with the fluorophore 5(6)-carboxyfluorescein, showed higher uptake in cancer cells with some of them colocalizing in the lysosomes. The peptides tagged with the anti-cancer drug methotrexate have displayed enhanced cytotoxicity and inducing apoptosis in triple-negative breast cancer cells. They also showed comparable uptake in side-population cells of lung cancer with stem-cell like properties. The most-optimized peptide showed accumulation in the tumor resulting in significant reduction of tumor size, compared to the untreated mice in in-vivo studies. Our results point to the following directives; (i) peptides can be design engineered for targeted delivery (ii) stereochemical engineering of peptide main chain can resist proteolytic enzymes and (iii) cellular penetration of peptides into cancer cells can be modulated by varying their electrostatic signatures.