journal article·2024-07Open access

Oxidized mRNA Lipid Nanoparticles for In Situ Chimeric Antigen Receptor Monocyte Engineering

Alvin J. Mukalel, Alex G. Hamilton, Margaret M. Billingsley, Jacqueline Li, Ajay S. Thatte, Xuexiang Han, Hannah C. Safford, Marshall S. Padilla, Tyler Papp, Hamideh Parhiz, Drew Weissman, Michael J. Mitchell

Advanced Functional Materials· Volume 34· Issue 27

Abstract

Abstract Chimeric antigen receptor (CAR) monocyte and macrophage therapies are promising solid tumor immunotherapies that can overcome the challenges facing conventional CAR T cell therapy. mRNA lipid nanoparticles (mRNA‐LNPs) offer a viable platform for in situ engineering of CAR monocytes with transient and tunable CAR expression to reduce off‐tumor toxicity and streamline cell manufacturing. However, identifying LNPs with monocyte tropism and intracellular delivery potency is difficult using traditional screening techniques. Here, ionizable lipid design and high‐throughput in vivo screening are utilized to identify a new class of oxidized LNPs with innate tropism and mRNA delivery to monocytes. A library of oxidized (oLNPs) and unoxidized LNPs (uLNPs) is synthesized to evaluate mRNA delivery to immune cells. oLNPs demonstrate notable differences in morphology, ionization energy, and pKa, thereby enhancing delivery to human macrophages, but not T cells. Subsequently, in vivo library screening with DNA barcodes identifies an oLNP formulation, C14‐O2, with innate tropism to monocytes. In a proof‐of‐concept study, the C14‐O2 LNP is used to engineer functional CD19‐CAR monocytes in situ for robust B cell aplasia (45%) in healthy mice. This work highlights the utility of oxidized LNPs as a promising platform for engineering CAR macrophages/monocytes for solid tumor CAR monocyte therapy.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: CAR-T cell therapy research · RNA Interference and Gene Delivery · Immune Cell Function and Interaction

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Bioengineering University of Pennsylvania Philadelphia PA 19104 USA

Department of Medicine University of Pennsylvania Philadelphia PA 19104 USA

Abramson Cancer Center Perelman School of Medicine University of Pennsylvania Philadelphia PA 19104 USA

Institute for Immunology Perelman School of Medicine University of Pennsylvania Philadelphia PA 19104 USA

Cardiovascular Institute Perelman School of Medicine University of Pennsylvania Philadelphia PA 19104 USA

Institute for Regenerative Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA 19104 USA

Funding

National Institutes of Health · National Science Foundation · American Cancer Society · Congressionally Directed Medical Research Programs

References (96)

Show source-provided references
1. 10.1056/NEJMoa170856610.1056/NEJMoa1708566 ↗
2. 10.1056/NEJMoa170991910.1056/NEJMoa1709919 ↗
3. 10.1038/nrclinonc.2017.14810.1038/nrclinonc.2017.148 ↗
4. 10.1056/NEJMoa170986610.1056/NEJMoa1709866 ↗
5. CAR T Cells: Engineering Immune Cells to Treat Cancer – NCI https://www.cancer.gov/about‐cancer/treatment/research/car‐t‐cells (accessed: August 2023).
6. 10.1136/jitc-2021-00272310.1136/jitc-2021-002723 ↗
7. 10.3390/cancers1209256710.3390/cancers12092567 ↗
8. Ukidve A. · Drug Deliv · 2021 · 11 · 2276
9. 10.1016/j.pharmthera.2016.06.01010.1016/j.pharmthera.2016.06.010 ↗
10. 10.1038/s41571-019-0297-y10.1038/s41571-019-0297-y ↗
11. Global Manufacturing of CAR T Cell Therapy: Molecular Therapy – Methods & Clinical Development https://www.cell.com/molecular‐therapy‐family/methods/fulltext/S2329‐0501(16)30202‐9?sf77801175=1 (accessed: August 2023).
12. 10.1002/eji.20225003910.1002/eji.202250039 ↗
13. 10.1038/nrd.2018.16910.1038/nrd.2018.169 ↗
14. 10.1038/s41417-023-00600-710.1038/s41417-023-00600-7 ↗
15. 10.1038/s41577-022-00737-w10.1038/s41577-022-00737-w ↗
16. 10.1186/s13045-017-0430-210.1186/s13045-017-0430-2 ↗
17. 10.1186/s13045-022-01335-y10.1186/s13045-022-01335-y ↗
18. 10.1016/j.omto.2022.02.01910.1016/j.omto.2022.02.019 ↗
19. 10.1002/JLB.4RI0818-311R10.1002/JLB.4RI0818-311R ↗
20. 10.3389/fonc.2021.78836510.3389/fonc.2021.788365 ↗
21. 10.1016/j.immuni.2014.06.01010.1016/j.immuni.2014.06.010 ↗
22. 10.1126/scitranslmed.300635310.1126/scitranslmed.3006353 ↗
23. 10.1002/btm2.1028510.1002/btm2.10285 ↗
24. 10.15252/emmm.20201359810.15252/emmm.202013598 ↗
25. 10.1126/scitranslmed.abl410610.1126/scitranslmed.abl4106 ↗
26. 10.1136/jitc-2021-00395910.1136/jitc-2021-003959 ↗
27. Deyle D. R. · Curr. Opin. Mol. Ther. · 2009 · 11 · 442
28. 10.1146/annurev.genet.37.110801.14371710.1146/annurev.genet.37.110801.143717 ↗
29. 10.2174/15665230878684802110.2174/156652308786848021 ↗
30. 10.1038/d41573-021-00017-710.1038/d41573-021-00017-7 ↗
31. 10.1038/s41467-020-19505-210.1038/s41467-020-19505-2 ↗
32. 10.1002/btm2.1025810.1002/btm2.10258 ↗
33. 10.1158/2326-6066.CIR-17-021110.1158/2326-6066.CIR-17-0211 ↗
34. 10.1007/s00262-017-2034-710.1007/s00262-017-2034-7 ↗
35. 10.1038/mt.2010.2410.1038/mt.2010.24 ↗
36. 10.1089/hum.2017.04810.1089/hum.2017.048 ↗
37. 10.1128/jvi.70.11.8098-8108.199610.1128/jvi.70.11.8098-8108.1996 ↗
38. 10.1182/bloodadvances.202100460310.1182/bloodadvances.2021004603 ↗
39. 10.1056/NEJMoa203538910.1056/NEJMoa2035389 ↗
40. 10.1056/NEJMoa202790610.1056/NEJMoa2027906 ↗
41. 10.1016/j.jconrel.2015.08.00710.1016/j.jconrel.2015.08.007 ↗
42. 10.1038/s41467-021-26714-w10.1038/s41467-021-26714-w ↗
43. 10.1158/2326-6066.CIR-13-017010.1158/2326-6066.CIR-13-0170 ↗
44. 10.1097/CJI.000000000000012610.1097/CJI.0000000000000126 ↗
45. 10.1021/acsbiomaterials.1c0153210.1021/acsbiomaterials.1c01532 ↗
46. 10.1126/science.abm059410.1126/science.abm0594 ↗
47. 10.1021/acs.nanolett.9b0424610.1021/acs.nanolett.9b04246 ↗
48. Bernstein M. · Clinical Pharmacology Considerations for Antibody‐Drug Conjugates · 2022
49. 10.1002/advs.20230192910.1002/advs.202301929 ↗
50. 10.1016/j.addr.2022.11411610.1016/j.addr.2022.114116 ↗
51. 10.1016/j.jconrel.2019.04.01810.1016/j.jconrel.2019.04.018 ↗
52. 10.1038/ncomms406510.1038/ncomms4065 ↗
53. 10.1038/s41467-020-16439-710.1038/s41467-020-16439-7 ↗
54. 10.3390/biomedicines908091510.3390/biomedicines9080915 ↗
55. 10.1155/2020/524530810.1155/2020/5245308 ↗
56. 10.1007/978-1-60327-029-8_2410.1007/978-1-60327-029-8_24 ↗
57. 10.1021/nn301922x10.1021/nn301922x ↗
58. 10.1073/pnas.162087411410.1073/pnas.1620874114 ↗
59. 10.1016/j.cobme.2018.08.00110.1016/j.cobme.2018.08.001 ↗
60. 10.1021/acs.nanolett.2c0113310.1021/acs.nanolett.2c01133 ↗
61. 10.1002/adtp.20200011110.1002/adtp.202000111 ↗
62. 10.1002/smll.20180509710.1002/smll.201805097 ↗
63. 10.1038/s41467-022-33157-410.1038/s41467-022-33157-4 ↗
64. 10.1038/s41467-020-14527-210.1038/s41467-020-14527-2 ↗
65. 10.1073/pnas.230106712010.1073/pnas.2301067120 ↗
66. 10.1002/adma.20230337010.1002/adma.202303370 ↗
67. 10.1073/pnas.060099710310.1073/pnas.0600997103 ↗
68. 10.1021/acsmacrolett.6b0041910.1021/acsmacrolett.6b00419 ↗
69. 10.1016/j.jconrel.2010.07.11610.1016/j.jconrel.2010.07.116 ↗
70. 10.3389/fimmu.2020.60794510.3389/fimmu.2020.607945 ↗
71. 10.1038/s42003-021-02441-210.1038/s42003-021-02441-2 ↗
72. 10.1002/adfm.20210672710.1002/adfm.202106727 ↗
73. 10.1021/acsnano.3c0118610.1021/acsnano.3c01186 ↗
74. 10.1016/j.jconrel.2019.10.02810.1016/j.jconrel.2019.10.028 ↗
75. 10.1021/acs.nanolett.8b0110110.1021/acs.nanolett.8b01101 ↗
76. 10.1073/pnas.210925611810.1073/pnas.2109256118 ↗
77. 10.1038/s41565-019-0499-610.1038/s41565-019-0499-6 ↗
78. 10.1016/j.omtm.2018.12.00910.1016/j.omtm.2018.12.009 ↗
79. 10.1016/j.immuni.2020.07.01110.1016/j.immuni.2020.07.011 ↗
80. 10.1093/jmcb/mjaa04510.1093/jmcb/mjaa045 ↗
81. 10.1101/gr.209601.11610.1101/gr.209601.116 ↗
82. R.Core Team R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing 2022).
83. D.Robinson A.Hayes S.Couch broom: Convert Statistical Objects into Tidy Tibbles2022.
84. 10.18637/jss.v096.i0110.18637/jss.v096.i01 ↗
85. H.Wickham R.François L.Henry K.Müller dplyr: A Grammar of Data Manipulation2022.
86. H.Wickham forcats: Tools for Working with Categorical Variables (Factors)2021.
87. E.Clarke S.Sherrill‐Mix ggbeeswarm: Categorical Scatter (Violin Point) Plots2017.10.32614/CRAN.package.ggbeeswarm ↗
88. 10.1007/978-3-319-24277-410.1007/978-3-319-24277-4 ↗
89. K.Slowikowski ggrepel: Automatically Position Non‐Overlapping Text Labels with ‘ggplot2’2021.
90. B.Auguie gridExtra: Miscellaneous Functions for ‘Grid’ Graphics2017.
91. T. L.Pedersen patchwork: The Composer of Plots2020.10.32614/CRAN.package.patchwork ↗
92. Wickham H. · readxl: Read Excel Files · 2022
93. B.Han Rncc: To find connected components from graph built from string hamming distances2018.
94. H.Wickham stringr: Simple Consistent Wrappers for Common String Operations2019.
95. Wickham H. · tidyr: Tidy Messy Data · 2022
96. E.Dolstra https://dspace.library.uu.nl/handle/1874/7540 (accessed: July 2023).