Alkylammonium Modified Photosensitizers Achieve Lysosome-Precision Photodynamic Therapy: Implications from Molecular Dynamics Simulations

Xi He

doi:10.62051/y9e6ah53

Authors

Xi He

DOI:

https://doi.org/10.62051/y9e6ah53

Keywords:

Cancer treatment; organelle-precision; photodynamic therapy; molecular dynamics simulations.

Abstract

Photodynamic therapy (PDT) is one of the developing treatment modalities that has been clinically approved for cancer treatment. It uses an organic photosensitizer that harnesses the light energy and converts intracellular oxygens to reactive cytotoxic species. The resulting chemicals can then damage cells and lead to cancer cell death. However, the existing photosensitizers do not show cancel cell or organelle specificity, which lowers their treatment outcomes. In this project, we used molecular dynamic simulations to demonstrate that a straightforward alkylammonium modification on photosensitizers is able to endow conventional photosensitizers with the ability to target and accumulate in lysosomes. This acquired lysosome specificity may achieve the lysosome-precision PDT experimentally, which is expected to enhance the PDT outcomes. More importantly, such organelle-precision modifications have the potential to be extended beyond PDT treatment and make contribution to drug discovery and eventually cancer treatment.

Downloads

Download data is not yet available.

References

Siegel, R. L., Miller, K. D., Fuchs, H. E. & Jemal, A. Cancer statistics, 2022. CA Cancer J Clin 72, 7 - 33, doi:10.3322/caac.21708 (2022).

Chandra, R. A., Keane, F. K., Voncken, F. E. M. & Thomas, C. R., Jr. Contemporary radiotherapy: present and future. Lancet 398, 171 - 184, doi: 10.1016/S0140 - 6736 (21)00233 - 6 (2021).

Luo, D. et al. Pharmacokinetics and pharmacodynamics of liposomal chemo phototherapy with short drug-light intervals. J Control Release 297, 39-47, doi: https://doi.org/10.1016/j.jconrel.2019.01.030 (2019).

Wang, K. & Tepper, J. E. Radiation therapy-associated toxicity: Etiology, management, and prevention. CA Cancer J Clin 71, 437 - 454, doi: 10.3322/caac.21689 (2021).

Gunaydin, G., Gedik, M. E. & Ayan, S. Photodynamic Therapy-Current Limitations and Novel Approaches. Frontiers in Chemistry 9, doi: ARTN 69169710.3389/fchem.2021.691697 (2021).

Abrahamse, H. & Hamblin, M. R. New photosensitizers for photodynamic therapy. Biochem J 473, 347 - 364, doi:10.1042/BJ20150942 (2016).

Niu, N. et al. A cell membrane-targeting AIE photosensitizer as a necroptosis inducer for boosting cancer theranostics. Chem Sci 13, 5929-5937, doi:10.1039/d2sc01260j (2022).

Yang, J., Griffin, A., Qiang, Z. & Ren, J. Organelle-targeted therapies: a comprehensive review on system design for enabling precision oncology. Signal Transduct Target Ther 7, 379, doi:10.1038/s41392 - 022 - 01243 - 0 (2022).

Saminathan, A., Zajac, M., Anees, P. & Krishnan, Y. Organelle-level precision with next-generation targeting technologies. Nature Reviews Materials 7, 355 - 371, doi: 10.1038/s41578 - 021 - 00396 - 8 (2022).

Wang, R., Li, X. & Yoon, J. Organelle-Targeted Photosensitizers for Precision Photodynamic Therapy. ACS Appl Mater Interfaces 13, 19543-19571, doi:10.1021/acsami.1c02019 (2021).

Qiu, J. & Xia, Y. Killing cancer cells by rupturing their lysosomes. Nat Nanotechnol 15, 252-253, doi: 10.1038/s41565 - 020 - 0639 - z (2020).

Kaufmann, A. M. & Krise, J. P. Lysosomal sequestration of amine-containing drugs: analysis and therapeutic implications. J Pharm Sci 96, 729 - 746, doi: 10.1002/jps.20792 (2007).

Grote, F. & Lyubartsev, A. P. Optimization of Slipids Force Field Parameters Describing Headgroups of Phospholipids. J Phys Chem B 124, 8784 - 8793, doi: 10.1021/acs.jpcb.0c06386 (2020).

Wang, J., Wang, W., Kollman, P. A. & Case, D. A. Automatic atom type and bond type perception in molecular mechanical calculations. Journal of Molecular Graphics and Modelling 25, 247-260, doi: https://doi.org/10.1016/j.jmgm.2005.12.005 (2006).

Wang, J., Wolf, R. M., Caldwell, J. W., Kollman, P. A. & Case, D. A. Development and testing of a general amber force field. J Comput Chem 25, 1157 - 1174, doi: 10.1002/jcc.20035 (2004).

Hirohara, M., Saito, Y., Koda, Y., Sato, K. & Sakakibara, Y. Convolutional neural network based on SMILES representation of compounds for detecting chemical motif. BMC Bioinformatics 19, 526, doi: 10.1186/s12859 - 018 - 2523 - 5 (2018).

Gaussian 16 Rev. C.01 (Wallingford, CT, 2016).

Yang, Y., Weaver, M. N. & Merz, K. M., Jr. Assessment of the “6-31+G** + LANL2DZ” Mixed Basis Set Coupled with Density Functional Theory Methods and the Effective Core Potential: Prediction of Heats of Formation and Ionization Potentials for First-Row-Transition-Metal Complexes. The Journal of Physical Chemistry A 113, 9843 - 9851, doi: 10.1021/jp807643p (2009).

Sobot, D. et al. Conjugation of squalene to gemcitabine as unique approach exploiting endogenous lipoproteins for drug delivery. Nature Communications 8, 15678, doi: 10.1038/ncomms15678 (2017).

Trivedi, P. C., Bartlett, J. J. & Pulinilkunnil, T. Lysosomal Biology and Function: Modern View of Cellular Debris Bin. Cells 9, doi:10.3390/cells9051131 (2020).

Martinez, L., Andrade, R., Birgin, E. G. & Martinez, J. M. PACKMOL: a package for building initial configurations for molecular dynamics simulations. J Comput Chem 30, 2157 - 2164, doi: 10.1002/jcc.21224 (2009).

Abraham, M. J. et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1-2, 19 - 25, doi: https: //doi.org/10.1016/j.softx.2015.06.001 (2015).

Harrach, M. F. & Drossel, B. Structure and dynamics of TIP3P, TIP4P, and TIP5P water near smooth and atomistic walls of different hydroaffinity. J Chem Phys 140, 174501, doi: 10.1063/1.4872239 (2014).

Bernetti, M. & Bussi, G. Pressure control using stochastic cell rescaling. J Chem Phys 153, 114107, doi: 10.1063/5.0020514 (2020).

Bussi, G., Donadio, D. & Parrinello, M. Canonical sampling through velocity rescaling. The Journal of Chemical Physics 126, doi: 10.1063/1.2408420 (2007).

Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. J Mol Graph 14, 33 - 38, 27 - 38, doi: 10.1016/0263 - 7855 (96) 00018 - 5 (1996).

27 You, W., Tang, Z. & Chang, C.-e. A. Potential Mean Force from Umbrella Sampling Simulations: What Can We Learn and What Is Missed? Journal of Chemical Theory and Computation 15, 2433 - 2443, doi: 10.1021/acs.jctc.8b01142 (2019).

Hub, J. S., de Groot, B. L. & van der Spoel, D. g_wham—A Free Weighted Histogram Analysis Implementation Including Robust Error and Autocorrelation Estimates. Journal of Chemical Theory and Computation 6, 3713 - 3720, doi: 10.1021/ct100494z (2010).

Iulianna, T., Kuldeep, N. & Eric, F. The Achilles’ heel of cancer: targeting tumors via lysosome-induced immunogenic cell death. Cell Death & Disease 13, 509, doi:10.1038/s41419-022-04912 - 8 (2022).

Ubah, O. C. & Wallace, H. M. Cancer therapy: Targeting mitochondria and other sub-cellular organelles. Curr Pharm Des 20, 201 - 222, doi: 10.2174/13816128113199990031 (2014).

Li, Z., Zou, J. & Chen, X. In Response to Precision Medicine: Current Subcellular Targeting Strategies for Cancer Therapy. Adv Mater 35, e2209529, doi:10.1002/adma.202209529 (2023).

Choi, N. E., Lee, J. Y., Park, E. C., Lee, J. H. & Lee, J. Recent Advances in Organelle-Targeted Fluorescent Probes. Molecules 26, doi:10.3390/molecules26010217 (2021).