(272a) Computational and Experimental Studies on the Self-Assembly of Novel Cancer Drug Peptide-Based Carrier Systems

Self-assembling peptides comprise an emerging class of attractive nanomaterials with highly promising applications in drug delivery due to their potential to facilitate drug release and/or stability while reducing the risk of adverse reactions¹. According to a recent perspective of our labs, metal coordination, structure stabilization, and cyclization, as well as minimalism are key aspects that can be fruitfully combined and taken into consideration in the design of peptide self-assembled nanocarriers for cancer drug delivery¹. We previously used computational and experimental approaches, showing that Cyclo-His-His (Cyclo-HH) can constitute a promising novel drug delivery system for cancer drug Epirubicin, combining drug self-encapsulation, enhanced fluorescence, and the ability to transport the drug into cells, acting as a real-time optical monitor for the drug release process ^2,3. Driven by our earlier studies, and the importance of developing novel cancer drug nanocarriers for different cancer drugs ¹, here, we investigated computationally, using primarily multi-µs simulations, and experimentally, using a series of assays, the self-assembly properties of peptides with a variety of cancer drugs. Simulations were utilized as a means to investigate the early stages of peptide and drug self-assembly in solution, and the detection of molecular nanoparticles by the corresponding building blocks. Motivated by our lab’s previously published computational methodology to study such systems⁴, the presentation will focus on the further development of computational tools integrating structural, dynamical, and thermodynamic calculations, in conjunction with machine learning approaches, as a means to analyze the simulations, and computationally predict as well as delineate the properties of different cancer drug nanocarriers. Our computational results comply with experiments for all drugs investigated, and in combination, reveal a highly promising anti-cancer perspective for particular drug nanocarriers investigated. Additionally, such computational approaches could be used to accurately predict the properties of novel self-assembled peptide-based cancer drug nanocarrier systems.

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2. Chen Y et al. ACS Nano 2020, 14 (3): 2798-2807.
3. Tao K et al. Adv Funct Mater 2020, 30 (10): 1909614.
4. Orr AA et al. Methods Mol Biol 2022, 2405: 179-203.