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. 2023 Apr 12;24(8):7126.
doi: 10.3390/ijms24087126.

Strategy for Pre-Clinical Development of Active Targeting MicroRNA Oligonucleotide Therapeutics for Unmet Medical Needs

Affiliations

Strategy for Pre-Clinical Development of Active Targeting MicroRNA Oligonucleotide Therapeutics for Unmet Medical Needs

Marc Thibonnier et al. Int J Mol Sci. .

Abstract

We present here an innovative modular and outsourced model of drug research and development for microRNA oligonucleotide therapeutics (miRNA ONTs). This model is being implemented by a biotechnology company, namely AptamiR Therapeutics, in collaboration with Centers of Excellence in Academic Institutions. Our aim is to develop safe, effective and convenient active targeting miRNA ONT agents for the metabolic pandemic of obesity and metabolic-associated fatty liver disease (MAFLD), as well as deadly ovarian cancer.

Keywords: MAFLD; active targeted delivery; diabetes; microRNAs; obesity; oligonucleotide therapeutics; ovarian cancer.

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Conflict of interest statement

The authors declare no conflict of interest. Thibonnier was involved in the design of study protocols, analyses and interpretation of data, the writing of the manuscript and the decision to publish the results. Marc Thibonnier is the founder and a shareholder of AptamiR Therapeutics, Inc.

Figures

Figure 1
Figure 1
AptamiR Therapeutics’ Modular Parallel and Iterative Strategy of Drug Discovery and Development.
Figure 2
Figure 2
Medical consequences of adipose tissue hypertrophy, hyperplasia, inflammation and necrosis (adapted from Reference [50], an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://www.ncbi.nlm.nih.gov/pubmed/31262098 accessed on 20 February 2023)).
Figure 3
Figure 3
Conserved interaction between hsa-miR-22-3p and the 3′-UTR region of the KDM3A gene (TargetScanHuman 8.0, www.targetscan.org/vert_80/ (accessed on 20 February 2023)).
Figure 4
Figure 4
Prediction of protein–protein interaction networks related to miR-22 using the protein–protein interaction-networks functional enrichment analysis tool String (https://string-db.org (accessed on 3 July 2021)).
Figure 5
Figure 5
Tissue/organ distribution of miR-22-3p using the TissueAtlas2 program (https://www.ccb.uni-saarland.de/tissueatlas2 (accessed on 20 February 2023)) [58], an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/, accessed on 20 February 2023). Adipocyte, myocardium and skeletal muscle samples are identified by red arrows.
Figure 6
Figure 6
Histologic appearance of livers (H&E staining) at the end of 12 weeks of treatment in mice receiving SC injections of saline or the APT-110 miR-22-3p inhibitor (two samples from each group are shown), reprinted from Reference [60], an open-access article distributed in accordance with the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) license.
Figure 7
Figure 7
Comparison of mRNA (Panels a) and protein (Panels b) expression levels in various tissues and organs of the asialoglycoprotein receptor 1 (ASGR1) and the fatty acid translocase (FAT) membrane transporter (nTPM = normalized transcript per million) (www.proteinatlas.org (accessed on 27 March 2023)). The figures are shown in “Expression Level” mode in decreasing order from left to right. The tissues/organs with little or no expression are not shown for sake of readability.
Figure 8
Figure 8
Graphic representation of an 18 mer miR-22-3p antagomir with a PNA (Pna) backbone coupled with C32:6 dotriacontahexaenoic fatty acid (5′-C32-S-S-PnaC-PnaT-PnaT-PnaC-PnaT-PnaT-PnaC-PnaA-PnaA-PnaC-PnaT-PnaG-PnaG-PnaC-PnaA-PnaG-PnaC-PnaT-3′).
Figure 9
Figure 9
Graphic representation of an 18 mer miR-22-3p antagomir with a PNA backbone coupled with the hexapeptide hexarelin (5′-Hex-S-S-PnaC-PnaT-PnaT-PnaC-PnaT-PnaT-PnaC-PnaA-PnaA-PnaC-PnaT-PnaG-PnaG-PnaC-PnaA-PnaG-PnaC-PnaT-3′).
Figure 10
Figure 10
Pathway over-representation analysis of candidate miRNA target genes relevant to ovarian cancer. Analysis was performed via Webgestalt (http://www.webgestalt.org/ (accessed on 7 November 2022)) against Panther pathways and using whole human genome as background. The x-axis refers to the enrichment ratio for each pathway (all pathways were significantly enriched at FDR < 5%).
Figure 11
Figure 11
miRNA-mRNA networks in ovarian cancer. Network nodes represent miRNAs and genes (mRNAs), whereas edges represent an association between a miRNA and a gene. miRNAs are represented by orange squares and genes are shown in blue circles. miRNA node size is proportional to the number of genes it is predicted to interact with. Network was created in Cytoscape (https://cytoscape.org/ (accessed on 20 January 2023)).
Figure 12
Figure 12
Tissue/organ distribution of FOLR1 in normal tissues and ovarian cancer, reprinted from [87], an article licensed under Creative Commons Attribution 4.0 International License.
Figure 13
Figure 13
Gene-expression profile of FOLR1 in human normal and tumor tissue samples using the gene-expression profiling interactive analysis tool (http://gepia.cancer-pku.cn/ (accessed on 17 February 2023)). The cancer and normal ovarian samples are shown in the green rectangle. The height of the bar represents the median expression of the certain tumor type or normal tissue.
Figure 14
Figure 14
mRNA (top panel) and protein (bottom panel) expression profile of FOLR1 in various cancers (www.proteinatlas.org (accessed on 17 February 2023)). The red rectangle on the mRNA panel highlights the ovarian cancer samples.
Figure 15
Figure 15
Various roles of adipocytes in the creation of the metabolic tumor microenvironment (TME) in the omentum during ovarian cancer metastasis, reprinted from Reference [92], an open-access article distributed under the terms and conditions of Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (accessed on 17 February 2023)).

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