Compound 9f exhibited IC50 values of 2.3 nM over EGFR and 234 nM over HER2, which is 38-fold of staurosporine and 10-fold of TAK-285 over EGFR. Compound 9f also showed high selectivity profile when tested over a small kinase panel. Compounds 9a–h showed IC50 values in the range of 1.0–7.3 nM and 0.8–2.8 nM against PC3 and 22RV1 prostate carcinoma cell lines, respectively. Cell cycle analysis, apoptotic induction, molecular docking, dynamics, and MM-GBSA studies confirmed the plausible mechanism(s) of compound 9f as a potent EGFR/HER2 dual inhibitor with an effective antiproliferative action against prostate carcinoma.Epidermal growth factor receptor (EGFR) and human epidermal growth factor receptor 2 (HER2) protein tyrosine kinases co-expressed in various cancers such as ovarian, breast, colon, and prostate subtypes. Herein, new TAK-285 derivatives ( The IC50 values of the highly active candidate were evaluated over both kinases. In addition, to verify the selectivity of the most active candidate, a small kinase panel was then employed. Cell-based antiproliferative evaluation was carried out over two prostate carcinoma cell lines. Moreover, comprehensive simulation analyses were accomplished to recognise the binding affinities and direction of the final small molecules.Up to date, many potential antitumor small molecule inhibitors bearing 2-nitroimidazole moiety have been discovered2a–d via reacting 2-nitroimidazole (1) with different 1,n-dibromoalkanes in DMF solvent and K2CO3 at 60 °C. In 3) reacted with 3,4-dichlorophenol in the presence of K2CO3 and acetonitrile solvent for 3 h at 85 °C to afford 1,2-dichloro-4-(2-chloro-4-nitrophenoxy)benzene (4). The nitro group of intermediate 4 was then reduced via 10% platinum on carbon and H2 gas at room temperature to afford 3-chloro-4-aniline (5). The second aniline reagent (3-chloro-4-(3-(trifluoromethyl)phenoxy)aniline) was purchased. In 6) reacted separately with the two aniline reagents to form intermediates 7a and 7b. Intermediates 8a and 8b were then produced by hydrolysis of the acetate group of compounds 7a and 7b utilising 28% aqueous ammonia in methanol solvent. The free phenolic group in compounds 8a and 8b was allowed to react with the imidazoles 2a–d in DMF solvent and K2CO3 at 80 °C to generate the desired TAK-285 derivatives (9a–h) (s (9a–h) .9a–h, various spectroscopic approaches were used including 1H NMR, 13C NMR, and HRMS. Also, their purity was acquired via the HPLC system where they showed purity higher than 95%. Since all final compounds have a methoxy group, a singlet peak at the 3.95–3.92 ppm range was observed in the 1H NMR spectra. In addition, the specific peak of C2 position proton of quinazoline scaffold was found at 8.54–8.51 ppm (see supporting information). Furthermore, the extended alkyl linkers of all final compounds (n = 2–5) were confirmed where their protons and carbons were found in relatively low chemical shifts. In the 1H NMR spectrum, compound 9a showed the C2 of quinazoline at 8.52 ppm, and three protons of the methoxy group at 3.92 ppm as a singlet peak. Additionally, the O-alkylation reaction between the OH group and the imidazole linker (step vi) was proved by the disappearance of the OH peak. Like compound 9a, the target compounds 9b, 9c, and 9d have also shown a similar pattern, and their extending protons and carbons were found in the range of 2.04–1.34 ppm in 1H NMR spectra and 80.00–20.00 ppm in 13C NMR spectra, respectively. The 3-chloro-4-(3-(trifluoromethyl)phenoxy)aniline derivative 9e was identified by one proton of benzene ring at 8.22 ppm as a doublet peak with a J coupling constant of 4.0 Hz. In addition, there is a singlet peak at 3.95 ppm attributable to the three protons of the methoxy group in the 1H NMR spectrum, while its carbon was acquired at 56.35 ppm in the 13C NMR spectrum. These findings provided evidence proving the successful synthesis of the desired TAK-285 derivatives 9a–h.To elucidate the chemical structure of compounds 9a–h) were assessed over EGFR and HER2 kinases at Reaction Biology Co.  via “HotSpotSM” assay at 10 µM concentration in the presence of 10 µM of ATP. The results were obtained as the % remaining kinase activity of test samples in comparison to the DMSO vehicle. The rates of % kinase inhibition of compounds 9a–h over both kinases were computed and described in 9a–h over EGFR kinase. Incorporation of 3-chloro-4-anilino group in compounds 9a–d demonstrated an inhibitory range of 88.90–94.25%, which is lower than the inhibitory range expressed by derivatives with the hydrophobic moiety of TAK-285 (3-chloro-4-(3-(trifluoromethyl)phenoxy)aniline, 9e–h) that demonstrated an outstanding range of % inhibition over EGFR (96.75–99.33%). In the case of HER2, it was observed that most tested compounds showed lower inhibition rates. Nevertheless, a comparable SAR model was revealed over HER2 kinase. While derivatives 9a–d exhibited a modest inhibitory range of 69.83–80.26%, TAK-285 derivatives 9e–h revealed a higher inhibitory activity range (81.74–97.95%). Among all, compound 9f exhibited the best dual inhibition values over EGFR and HER2 with 99.33 and 97.95% inhibition over both kinases, respectively. Accordingly, compound 9f was selected for further investigations.All the newly synthesised TAK-285 derivatives  encouraged us to do further assessment of compound  kinases . The IC59f), an in vitro screening assay was performed over a small panel of cancer-related kinases including fibroblast growth factor receptor 1 (FGFR1), vascular endothelial growth factor receptor 2 (VEGFR2), cyclin-dependent kinase 2 (CDK2), c-mesenchymal-epithelial transition factor (c-MET), and p38α mitogen-activated protein kinase (MAPK14) in a single-dose concentration of 10 µM. As shown in 9f with its nanomolar potency against both kinases (EGFR and HER2) compared to the other tested kinase targets.To assess the selectivity and the kinase inhibition profile of the most active dual inhibitor of this new series  was measured using MTT assay against two human prostate carcinoma cell lines (PC3 and 22RV1). 9a–h showed IC50 values in the range of 1.0–7.3 nM and 0.8–2.8 nM against PC3 and 22RV1, respectively. Compound 9f was the most potent derivative over PC3 cell line with an IC50 value of 1.0 nM, while compound 9e showed the best IC50 value over 22 RV1 cell line (0.8 nM). The potent nanomolar cell-based activity of this series of compounds over the tested prostate carcinoma cell line could be attributed to other possible targets in addition to EGFR and HER2 receptors. The presence of the 2-nitroimidazole moiety in the chemical structure of compounds 9a–h could develop possible covalent bonds between the nitroimidazole moiety and some cellular proteinsSome inhibitors of the ErbB (EGFR/HER2/ErbB3/ErbB4) family, notably the dual EGFR/HER2 inhibitor lapatinib, failed in phase II clinical trials despite overexpression of this family in castration-resistant prostate cancer (CRPC)50 values.Three independent experiments were performed in triplicate. Standard deviation (±SD) of the mean was also obtained for all experiments along with the IC9f, was chosen for testing its outcomes on the cell cycle profile and apoptosis. PC3 and 22RV1 cells were treated with compound 9f at its IC50. The comparison data in 9f (test 2) arrested the cell cycle of 22RV1 and PC3 cells at the G2/M phase by 62.74% and 49.43%, respectively (9f treated sample has arrested it at G2/M phases as indicated by higher number of counts (%) in these phases of both cell cycle studies.Cell cycle progression is responsible for normal cell growth and proliferation. DNA damage can result in apoptosis, which causes cell death, or DNA repair. At specific checkpoints that serve as control mechanisms to guarantee correct cell division, the state of the cells is evaluated. Checkpoints in the cell cycle include the G1 (restriction), S (metaphase), and G2/Mectively . Also, t9f-treated 22RV1 cells underwent early apoptosis (13.20%), and simultaneously, some cells had prominently progressed to the late apoptosis phase (15.38%) (9f-treated PC3 cells went through early apoptosis (6.27%), and late apoptosis phase (16.94%)  . We foun(15.38%) . Moreove16.94%)  and 6. L.94%  and(16.94%)  and PC3 (16.94%)  cells coAnnexin V conjugated to fluorochromes (PE) retains its high affinity for phosphatidylserine (PS), making it a sensitive probe for flow cytometric analysis of apoptotic cells. PE Annexin V staining occurs prior to the loss of membrane integrity that occurs in the final stages of cell death caused by either apoptotic or necrotic processes. As a result, PE Annexin V staining is typically used in conjunction with a vital dye such as propidium iodide (PI) or DAPI to allow the investigator to identify early apoptotic cells . Viable cells with intact membranes exclude DAPI, whereas dead and damaged cells’ membranes are permeable to DAPI. When apoptosis is measured over time, cells can often be tracked from PE Annexin V and DAPI negative , to PE Annexin V positive and DAPI negative (early apoptosis), and finally to PE Annexin V and DAPI positive (end stage apoptosis and death). The progression of cells through these three stages suggests apoptosis.9a–h) towards EGFR ) of EGFR and HER2 were inserted as reference standards. Since all docked compounds (9a–h) showed promising results, derivatives 9f and 9g, which were biologically superior, were selected for deep investigations compared to the co-crystallised inhibitor in each case.To validate the dual inhibitory activities of the newly designed TAK-285 derivatives (9f showed the formation of one H-bond with Lys692 (2.95 Å) and three pi–H bonds with Cys773, Val702, and Arg817 . Its binding score was found to be −8.79 kcal/mol (RMSD = 1.13), besides compound 9g binding score was −8.46 kcal/mol (RMSD = 1.45), indicating superior binding affinities for both derivatives compared to that of the docked AQ4 inhibitor. Also, compound 9g formed one H-bond with Met769 (3.16 Å) and two pi–H interactions with Cys773 and Leu694 , as depicted in The binding pocket of EGFR receptor  achieved two H-bonds with Met801 and Cys805 . Besides, compound 9g  got stabilised through the formation of four H-bonds with Met801 (1), Lys753 (2), and Phe731 (1) at 3.52, 3.01, 3.20, and 3.39 Å, respectively, as represented in 9f and 9g members) towards the binding pockets of both EGFR and HER2 receptors.On the other hand, the binding site of the HER2 receptor  and HER2 (3RCD) tyrosine kinase domains. The protein conformational change was monitored via the difference in the position of the Cα atoms of the protein backbone and was reported in Å. The positional change of the Cα for both protein complexes was plotted as a function of simulation time in Molecular dynamic simulations were applied to simulate the behaviour of the hit compounds in a cell-like environment. Compounds 9f-1M17 complex showed an RMSD of about 3.00 Å, which is considered acceptable for such protein. The 9g-1M17 and the Co-1M17 complexes showed a fluctuation at around 4.00 and 5.00 Å, respectively; such instability is due to the presence of the hinge as it is playing a critical rule whenever the ligand tries to orient itself inside the active site, the salt bridge between the residue Glu734 and Lys851 will break, the protein subunits will move apart, and the hinge will open which results in higher fluctuation. The distance between the Glu734 and Lys851 for the 9g-1M17 complex is depicted in 9f, the co-crystal is illustrated in Figure SI 1 (SI).The RMSD of the 1M17 complexes is plotted in 9f-3RCD and the Co-3RCD were stable and fluctuated at around 2.00 Å. On the other hand, the 9g-3RCD complex fluctuates at around 4.00 Å, which comes from the unfolded loop of the Arg756, Glu757, Asn758, Thr759, and Ser760, coloured red in In the case of the 3RCD complexes, the 9g was unstable inside the active site. This instability is a result of the moving of the imidazole ring out of the active site, and this leads to relocating the ligand within the active site. The butyl bridge rotatability plays a critical role in this instability. Compound 9f, on the other hand, showed much more stability; the compound was stable during the simulation time, at around 155–170 ns, a fluctuation of 8.00 Å was observed; this fluctuation is again due to the presence of the alkyl bridge, the imidazole group moved out of the protein active site during this period, before it goes back inside the active site as described in With regards to 1M17, compound 9f showed a fluctuation between 2.00 and 4.00 Å; the compound imidazole group was shifting its position inside the active site, which led to this RMSD. Compound 9g also showed similar behaviour, and the pentyl bridge gives the imidazole flexibility to move out/in the active site, which results in an RMSD of 3.00–4.00 Å. The co-crystal ligand holds tight inside the active site with an RMSD of ∼2.00 Å during the simulation.In the case of 3RCD, compound 9f-1M17 and 9g-3RCD with the active site residues was carried out. The interactions of these ligands with protein residue were plotted using the simulation interaction diagram panel of Maestro software.Further, a deep analysis of the interactions of the compounds Figure SI 3. Compound 9f interactions histogram , Phe731 (95%), Ala751 (75%), Phe1004 (45%), and Leu852 (40%). In addition, Thr862 formed a water bridge hydrogen bond almost 65% of the simulation time, along with Lys753 (35%). The residues that were able to form interactions more than 30% of the simulation time are reported in Figure SI 5.In the case of 3RCD, compound istogram , the Metistogram . Compoun9f and 9g showed lower binding energy compared to the co-crystal ligand in the case of 1M17, while in the case of 3RCD, compound 9g showed superiority over 9f, which in turn, showed higher binding energy compared to the co-crystal ligand.The average MM-GBSA binding energy over the last 50 ns was generated using the thermal_mmgbsa.py python script provided by Schrödinger, which also generates Coulomb energy, covalent binding energy, van der Waals energy, lipophilic energy, generalised Born electrostatic solvation energy, and hydrogen-bonding energy. All the obtained data are shown in 2a–d were synthesised in our recently published report4 and 5 were synthesised following the earlier report1H NMR spectra were added in the Supplementary File.The general protocols utilised for the chemical synthesis, structure elucidation, and purity of the synthesised compounds were provided in the Supplementary File. Compounds 6, 1.0 equiv.) and the appropriate aniline reagent (1.2 equiv.) in isopropyl alcohol (i-PrOH). The mixture was refluxed for 4 h and cooled to rt after completion of the reaction. The reaction mixture was filtered using i-PrOH to give the target intermediate, which was used in the next step without further purification.A round-bottom flask was charged with 4-chloro-7-methoxyquinazolin-6-yl acetate phenyl)amino)-7-methoxyquinazolin-6-yl acetate (7a). Ivory solid. Yield: 100% . 1H NMR  δ 11.10 , 8.92 , 8.61 , 8.13 , 7.76 , 7.64 , 7.43 , 7.35 , 7.31 , 6.97 , 3.99 , and 2.38 .4-((3-Chloro-4-(3-(trifluoromethyl)phenoxy)phenyl)amino)-7-methoxyquinazolin-6-yl acetate (7b). Ivory solid. Yield: 100% . 1H NMR  δ 10.99 , 8.91 , 8.57 , 8.15 , 7.78 , 7.63 , 7.51 , 7.41 , 7.36 , 7.29–7.24 , 3.99 , and 2.38 .7, 3–4 mmol) and an excess amount of aqueous ammonia solution (28% in H2O) in methanol (CH3OH) were put into the round-bottom flask and stirred for 4 h at rt. The excess solvent of the resulting mixture was partially concentrated, filtered with chilled methanol, and dried to give the required intermediates without further purification.The appropriate acetate intermediate phenyl)amino)-7-methoxyquinazolin-6-ol (8a). Ivory solid. Yield: 94.3% . 1H NMR  δ 9.70 , 9.52 , 8.48 , 8.29 , 7.91 , 7.79 , 7.60 , 7.29 , 7.22–7.21 , 6.91 , and 3.96 . 13C NMR  δ 157.25 (quinazoline-C4), 156.21 (quinazoline-C7), 154.41 (Ar-C), 152.35 (quinazoline-C2), 147.25 (quinazoline-C6), 146.73 (quinazoline-C8a), 145.12 (Ar-C), 138.83 (Ar-C), 132.46 (Ar-C), 132.00 (Ar-C), 125.27 (Ar-C), 125.23 (Ar-C), 123.10 (Ar-C), 122.95 (Ar-C), 121.96 (Ar-C), 118.70 (Ar-C), 117.05 (Ar-C), 110.10 (quinazoline-C4a), 107.65 (quinazoline-C8), 105.74 (quinazoline-C5), and 56.40 (OCH3). HRMS (ESI) m/z calculated for C21H15Cl3N3O3 [M + H]+: 462.0179, found: 462.0170.4-((3-Chloro-4-(3-(trifluoromethyl)phenoxy)phenyl)amino)-7-methoxyquinazolin-6-ol (8b). Ivory solid. Yield: 64% . 1H NMR  δ 9.71 , 9.53 , 8.49 , 8.30 , 7.92 , 7.79 , 7.60 , 7.45 , 7.30 , 7.21–7.18 , 3.96 . 13C NMR  δ 158.23 (quinazoline-C4), 156.23 (quinazoline-C7), 154.41 (Ar-C), 152.36 (quinazoline-C2), 147.25 (quinazoline-C6), 146.73 (quinazoline-C8a), 145.11 (Ar-C), 138.79 (Ar-C), 131.85 (Ar-C), 131.28 (Ar-C), 125.43 (Ar-C), 123.14 (Ar-C), 123.08 (Ar-C), 122.81 (Ar-C), 122.04 (Ar-C), 120.49 (Ar-C), 119.76 (Ar-C), 113.11 (Ar-C), 110.10 (quinazoline-C4a), 107.65 (quinazoline-C8), 105.75 (quinazoline-C5), and 56.39 (OCH3). HRMS (ESI) m/z calculated for C22H16ClF3N3O3 [M + H]+: 462.0832, found: 462.0821.8a, 1.0 equiv.) in DMF, potassium carbonate (2.0 equiv.) and the appropriate bromoalkyl imidazole  were added at rt. The reaction mixture was stirred for 4 h at 80 °C and partitioned using EtOAc and water. The collected organic layer was dried over MgSO4, filtered, and concentrated in vacuo. The crude was purified by flash column chromatography (CH2Cl2:CH3OH = 20:1) to afford the desired final TAK-285 derivatives.To the intermediate phenyl)-7-methoxy-6-(3-(2-nitro-1H-imidazol-1-yl)propoxy)quinazolin-4-amine (9a). Yellow solid . m.p.: 237–238 °C. HPLC purity: 96.95% . 1H NMR  δ 9.55 , 8.52 , 8.18 , 7.85 , 7.81 , 7.67 , 7.62 , 7.32 , 7.23 , 7.21 , 7.17 , 6.93 , 4.62 , 4.19 , 3.92 , 2.42–2.35 . 13C NMR  δ 157.14 (quinazoline-C4), 156.38 (quinazoline-C7), 155.02 (Ar-C), 153.18 (imidazole-C2), 148.40 (quinazoline-C2), 147.66 (quinazoline-C6), 145.60 (quinazoline-C8a), 138.35 (Ar-C), 132.48 (Ar-C), 132.04 (Ar-C), 128.34 (Ar-C), 128.24 (imidazole-C5), 125.36 (imidazole-C4), 125.27 (Ar-C), 123.69 (Ar-C), 122.87 (Ar-C), 122.47 (Ar-C), 118.87 (Ar-C), 117.21 (Ar-C), 109.23 (quinazoline-C4a), 107.81 (quinazolie-C8), 103.35 (quinazoline-C5), 66.54 (propyl-CH2), 56.36 (OCH3), 47.43 (propyl-CH2), and 29.61 (propyl-CH2). HRMS (ESI) m/z calculated for C27H22Cl3N6O5 [M + H]+: 615.0717, found: 615.0723.N-phenyl)-7-methoxy-6-(4-(2-nitro-1H-imidazol-1-yl)butoxy)quinazolin-4-amine (9b). Yellow solid . m.p.: 101–102 °C. HPLC purity: 96.17% (RT = 15.333 min). 1H NMR  δ 9.56 , 8.52 , 8.19 , 7.86 , 7.81 , 7.76 , 7.62 , 7.32 , 7.24 , 7.21 , 7.19 , 6.93 , 4.50 , 4.18 , 3.92 , 2.04–1.97 , and 1.86–1.79 . 13C NMR  δ 157.14 (quinazoline-C4), 156.35 (quinazoline-C7), 155.00 (Ar-C), 153.07 (imidazole-C2), 148.66 (quinazoline-C2), 147.49 (quinazoline-C6), 145.59 (quinazoline-C8a), 138.36 (Ar-C), 132.48 (Ar-C), 132.04 (Ar-C), 128.35 (Ar-C), 128.31 (imidazole-C5), 125.36 (imidazole-C4), 125.26 (Ar-C), 123.71 (Ar-C), 122.85 (Ar-C), 122.50 (Ar-C), 118.86 (Ar-C), 117.21 (Ar-C), 109.30 (quinazoline-C4a), 107.77 (quinazoline-C8), 103.15 (quinazoline-C5), 68.96 (butyl-CH2), 56.36 (OCH3), 49.70 (butyl-CH2), 27.15 (butyl-CH2), and 25.88 (butyl-CH2). HRMS (ESI) m/z calculated for C28H24Cl3N6O5 [M + H]+: 629.0874, found: 629.0881.N-phenyl)-7-methoxy-6-((5-(2-nitro-1H-imidazol-1-yl)pentyl)oxy)quinazolin-4-amine (9c). Yellow solid . m.p.: 94–95 °C. HPLC purity: 97.95% (RT = 15.902 min). 1H NMR  δ 9.55 , 8.51 , 8.19 , 7.89 , 7.80 , 7.71 , 7.62 , 7.32 , 7.24 , 7.20 , 7.18 , 6.93 , 4.43 , 4.13 , 3.92 , 1.92–1.83 , and 1.51–1.44 . 13C NMR  δ 157.15 (quinazoline-C4), 156.33 (quinazoline-C7), 154.98 (Ar-C), 153.02 (imidazole-C2), 148.79 (quinazoline-C2), 147.45 (quinazoline-C6), 145.57 (quinazoline-C8a), 138.38 (Ar-C), 132.48 (Ar-C), 132.04 (Ar-C), 128.35 (Ar-C), 128.30 (imidazole-C5), 125.35 (imidazole-C4), 125.26 (Ar-C), 123.72 (Ar-C), 122.85 (Ar-C), 122.51 (Ar-C), 118.85 (Ar-C), 117.20 (Ar-C), 109.31 (quinazoline-C4a), 107.74 (quinazoline-C8), 102.89 (quinazoline-C5), 69.04 (pentyl-CH2), 56.33 (OCH3), 49.75 (pentyl-CH2), 30.00 (pentyl-CH2), 28.56 (pentyl-CH2), and 23.00 (pentyl-CH2). HRMS (ESI) m/z calculated for C29H26Cl3N6O5 [M + H]+: 643.1030, found: 643.1031.N-phenyl)-7-methoxy-6-((6-(2-nitro-1H-imidazol-1-yl)hexyl)oxy)quinazolin-4-amine (9d). Yellow solid . m.p.: 90–91 °C. HPLC purity: 95.31% (RT = 16.266 min). 1H NMR  δ 9.55 , 8.51 , 8.19 , 7.87 , 7.79 , 7.69 , 7.61 , 7.32 , 7.23 , 7.19 , 7.16 , 6.92 , 4.38 , 4.12 , 3.92 , 1.86–1.77 , 1.54–1.46 , and 1.41–1.34 . 13C NMR  δ 157.15 (quinazoline-C4), 156.33 (quinazoline-C7), 154.99 (Ar-C), 153.00 (imidazole-C2), 148.85 (quinazoline-C2), 147.44 (quinazoline-C6), 145.57 (quinazoline-C8a), 138.39 (Ar-C), 132.48 (Ar-C), 132.04 (Ar-C), 128.28 (Ar-C), 128.25 (imidazole-C5), 125.35 (imidazole-C4), 125.25 (Ar-C), 123.72 (Ar-C), 122.85 (Ar-C), 122.52 (Ar-C), 118.85 (Ar-C), 117.21 (Ar-C), 109.32 (quinazoline-C4a), 107.74 (quinazoline-C8), 102.86 (quinazoline-C5), 69.12 (hexyl-CH2), 56.33 (OCH3), 49.80 (hexyl-CH2), 30.17 (hexyl-CH2), 28.92 (hexyl-CH2), 26.05 (hexyl-CH2), and 25.56 (hexyl-CH2). HRMS (ESI) m/z calculated for C30H28Cl3N6O5 [M + H]+: 657.1187, found: 657.1184.N-(3-Chloro-4-(3-(trifluoromethyl)phenoxy)phenyl)-7-methoxy-6-(3-(2-nitro-1H-imidazol-1-yl)propoxy)quinazolin-4-amine (9e). Yellow solid . m.p.: 232–233 °C. HPLC purity: 98.08% (RT = 14.336 min). 1H NMR  δ 9.58 , 8.54 , 8.22 , 7.88 , 7.84 , 7.69 , 7.63 , 7.49 , 7.36 , 7.25–7.23 , 7.19 , 4.64 , 4.22 , 3.95 , and 2.43–2.38 . 13C NMR  δ 158.14 (quinazoline-C4), 156.39 (quinazoline-C7), 155.02 (Ar-C), 153.18 (imidazole-C2), 148.39 (quinazoline-C2), 147.65 (quinazoline-C6), 145.58 (quinazoline-C8a), 145.20 (Ar-C), 138.31 (Ar-C), 131.89 (Ar-C), 131.30 (Ar-C), 130.98 (imidazole-C5), 128.34 (imidazole-C4), 128.23 (Ar-C), 125.45 (CF3), 123.75 (Ar-C), 123.02 (Ar-C), 122.55 (Ar-C), 120.65 (Ar-C), 119.87 (Ar-C), 113.22 (quinazoline-C4a), 109.23 (Ar-C), 107.80 (quinazoline-C8), 103.34 (quinazoline-C5), 66.54 (propyl-CH2), 56.35 (OCH3), 47.43 (propyl-CH2), and 29.61 (propyl-CH2). HRMS (ESI) m/z calculated for C28H23ClF3N6O5 [M + H]+: 615.1371, found: 615.1371.N-(3-Chloro-4-(3-(trifluoromethyl)phenoxy)phenyl)-7-methoxy-6-(4-(2-nitro-1H-imidazol-1-yl)butoxy)quinazolin-4-amine (9f). Yellow solid . m.p.: 103–104 °C. HPLC purity: 95.07% (RT = 14.794 min). 1H NMR  δ 9.57 , 8.52 , 8.20 , 7.87 , 7.82 , 7.75 , 7.61 , 7.47 , 7.34 , 7.23–7.19 , 4.50 , 4.18 , 3.92 , 2.03–1.97 , and 1.85–1.79 . 13C NMR  δ 157.60 (quinazoline-C4), 155.83 (quinazoline-C7), 154.47 (Ar-C), 152.55 (imidazole-C2), 148.12 (quinazoline-C2), 146.98 (quinazoline-C6), 145.04 (quinazoline-C8a), 144.52 (Ar-C), 137.78 (Ar-C), 131.35 (Ar-C), 130.75 (Ar-C), 130.43 (imidazole-C5), 127.80 (imidazole-C4), 127.76 (Ar-C), 124.89 (CF3), 123.24 (Ar-C), 122.47 (Ar-C), 122.06 (Ar-C), 120.11 (Ar-C), 119.33 (Ar-C), 112.62 (quinazoline-C4a), 108.75 (Ar-C), 107.26 (quinazoline-C8), 102.62 (quinazoline-C5), 68.41 (butyl-CH2), 55.82 (OCH3), 49.15 (butyl-CH2), 26.60 (butyl-CH2), and 25.33 (butyl-CH2). HRMS (ESI) m/z calculated for C29H25ClF3N6O5 [M + H]+: 629.1527, found: 629.1528.N-(3-Chloro-4-(3-(trifluoromethyl)phenoxy)phenyl)-7-methoxy-6-((5-(2-nitro-1H-imidazol-1-yl)pentyl)oxy)quinazolin-4-amine (9g). Yellow solid . m.p.: 90–91 °C. HPLC purity: 95.59% (RT = 15.254 min). 1H NMR  δ 9.56 , 8.51 , 8.20 , 7.87 , 7.80 , 7.71 , 7.61 , 7.47 , 7.34 , 7.22–7.18 , 4.43 , 4.13 , 3.92 , 1.91–1.83 , and 1.52–1.44 . 13C NMR  δ 158.15 (quinazoline-C4), 156.34 (quinazoline-C7), 154.98 (Ar-C), 153.03 (imidazole-C2), 148.79 (quinazoline-C2), 147.45 (quinazoline-C6), 145.56 (quinazoline-C8a), 145.00 (Ar-C), 138.35 (Ar-C), 131.89 (Ar-C), 131.30 (Ar-C), 130.98 (imidazole-C5), 128.34 (imidazole-C4), 128.29 (Ar-C), 125.43 (CF3), 123.78 (Ar-C), 123.00 (Ar-C), 122.60 (Ar-C), 120.65 (Ar-C), 119.86 (Ar-C), 113.20 (quinazoline-C4a), 109.31 (Ar-C), 107.75 (quinazoline-C8), 102.90 (quinazoline-C5), 69.04 (pentyl-CH2), 56.32 (OCH3), 49.75 (pentyl-CH2), 30.00 (pentyl-CH2), 28.56 (pentyl-CH2), and 23.00 (pentyl-CH2). HRMS (ESI) m/z calculated for C30H27ClF3N6O5 [M + H]+: 643.1684, found: 643.1693.N-(3-Chloro-4-(3-(trifluoromethyl)phenoxy)phenyl)-7-methoxy-6-((6-(2-nitro-1H-imidazol-1-yl)hexyl)oxy)quinazolin-4-amine (9h). Yellow solid . m.p.: 84–85 °C. HPLC purity: 96.10% (RT = 15.657 min). 1H NMR  δ 9.56 , 8.51 , 8.20 , 7.88 , 7.81 , 7.69 , 7.61 , 7.47 , 7.33 , 7.22–7.20 , 7.16 , 4.38 , 4.13 , 3.93 , 1.87–1.78 , 1.54–1.47 , and 1.42–1.34 . 13C NMR  δ 158.15 (quinazoline-C4), 156.33 (quinazoline-C7), 154.97 (Ar-C), 153.00 (imidazole-C2), 148.84 (quinazoline-C2), 147.44 (quinazoline-C6), 145.54 (quinazoline-C8a), 144.98 (Ar-C), 138.36 (Ar-C), 131.87 (Ar-C), 131.30 (Ar-C), 130.98 (imidazole-C5), 128.28 (imidazole-C4), 128.23 (Ar-C), 125.43 (CF3), 123.77 (Ar-C), 122.97 (Ar-C), 122.59 (Ar-C), 120.63 (Ar-C), 119.88 (Ar-C), 113.16 (quinazoline-C4a), 109.32 (Ar-C), 107.73 (quinazoline-C8), 102.84 (quinazoline-C5), 69.11 (hexyl-CH2), 56.30 (OCH3), 49.80 (hexyl-CH2), 30.17 (hexyl-CH2), 28.92 (hexyl-CH2), 26.06 (hexyl-CH2), and 25.56 (hexyl-CH2). HRMS (ESI) m/z calculated for C31H29ClF3N6O5 [M + H]+: 657.1840, found: 657.1841.SM service from Reaction Biology Co.  was used to screen the tested compounds. The HotSpot assay platform includes specific kinase/substrate pairs and required cofactors. The base reaction buffer consisted of 20 mM Hepes (pH 7.5), 10 mM MgCl2, 1 mM EGTA, 0.02% Brij35, 0.02 mg/mL BSA, 0.1 mM Na3VO4, 2 mM DTT, and 1% DMSO. The tested compounds were dissolved in 100% DMSO to a specific concentration, and serial dilution was conducted. The reaction mixture containing the tested compound and 33P-ATP was incubated at room temperature for 2 h, and radioactivity was detected using the filter-binding method.Enzyme inhibitory assays were carried out as described in our previous reports in the literature2 with a 95% humid atmosphere.Prostate cancer cell lines (PC3 and 22RV1) of American Type Culture Collection (ATCC) were obtained from Korean Cell Center (KCL). PC3 and 22RV1 cell lines were cultured in Roswell Park Memorial Institute Medium (RPMI) 1640 and Dulbecco’s modified Eagle’s medium (DMEM)  supplemented with 1% penicillin–streptomycin and 10% foetal bovine serum (FBS). The cells were maintained at 37 °C in a 5% CO4 cells per well) and then incubated for 24 h at 37 °C in a 5% CO2-containing atmosphere. Subsequently, the medium from the culture plate was removed, and 100 µL of RPMI media containing different concentrations of compounds  were then added into the cell lines in each well and incubated for 24 h in a 5% CO2 humidified incubator at 37 °C. Afterwards, MTT solution  was added to each well, replacing the media-containing compounds. After 4 h of incubation, the MTT reagent was removed, and 200 μL of DMSO was then added to dissolve the formazan crystals, and colour intensity was measured at a wavelength of 540 nm using a BioTek Synergy H1 instrument . IC50 was calculated according to the equation of Boltzmann sigmoidal concentration–response curve using Graph Pad Prism 5 .To further analyse the intracellular cytotoxicity effect of the as-synthesised compounds, MTT assay was performed. Moreover, the experiments were divided into subcategories. In each group, cancer cells were first seeded in 96-well plates (4 × 106 cells per well) and then incubated for 24 h at 37 °C in a 5% CO2-containing atmosphere. Subsequently, the medium from the culture plate was removed, and 3 mL of RPMI and DMEM media containing concentrations of compound 9f at its IC50 value were then added into the cells in each well and incubated for 24 h in a 5% CO2 humidified incubator at 37 °C. Afterward, the media was removed following the washing once with PBS and the 25 µL cell sample mixtures were stained with 25 µL PI stain. 25 µL of sample mixture was loaded into ADAMII LS assay slide, then the slide was incubated at room temperature for 1 min in the dark and run on the ADAMII LS fluorescence cell analyser. Cell cycle distribution was calculated using ADAMII LS software .To determine the effect of the tested compound on the cell cycle distribution of PC3 and 22RV1 cell lines. Cell cycles can be analysed with checking the cell cycle change compared to the control group. Control cells without any treatment were used as a reference point for determining the cell cycle arrest phase for the test sample. Cell cycle analysis was performed using the Cell Cycle Analysis Kit . Briefly, cancer cells were first seeded in six-well plates  after washing with phosphate-buffered saline (PBS). The cell was resuspended in 100 µL 1X Annexin V binding buffer, and 5 µL Annexin V-PE reagent was added afterward and incubated for 15 min at room temperature. The sample was subsequently centrifuged, and the pallet was resuspended again with 500 µL 1X Annexin V binding buffer and added 1.25 µL DAPI reagent. The slide loaded with the prepared sample was incubated at room temperature for 1 min in the dark and run on the ADAMII LS fluorescence cell analyser. The comparative data including dot plot graphs and images were calculated and analysed using ADAMII LS software .The apoptotic effect of a potent compound on PC3 and 22RV1 cell lines was assessed using apoptosis analysis with Annexin V-PE, DAPI solution . Early and late apoptotic effects were analysed compared to the control. Cells were seeded in a six-well plate with cells density 0.8 × 10The statistical analysis of the data was done by standard deviations and all values represent the mean ± SD of three independent experiments.9a–h) towards the two target receptors (EGFR and HER2) using the MOE 2019.0102,9a–h) were sketched using the ChemDraw program, transferred into the MOE window, and prepared to be inserted into the same database besides the co-crystallised inhibitor in each case, as previously discussed,,,Two molecular docking processes were performed for the newly designed and synthesised derivatives (The MD simulations and MM-GBSA study were performed following same procedures found in our earlier report9a–h) were synthesised and assessed as EGFR/HER2 dual inhibitors. Applying “HotSpotSM” assay at 10 µM concentration in the presence of 10 µM of ATP, all compounds 9a–h exhibited encouraging percentage inhibition ranges against both kinases. The most active candidate (9f) showed nanomolar IC50 values of 2.3 nM over EGFR and 234 nM over HER2. A kinase selectivity panel of derivative 9f indicated a potential selective profile. Potent nanomolar IC50 values of compounds were obtained against PC3 and 22RV1 cell lines. The cell cycle of 22RV1 and PC3 cells was inhibited by compound 9f by 62.74 and 49.43% in the G2/M phase, respectively. Also, cells treated with compound 9f underwent early apoptosis. Molecular simulation studies revealed insights about the binding mode of the designed compounds. Overall, we report compound 9f as a new potent dual EGFR/HER2 inhibitor with significant cytotoxic activity against 22RV1 and PC3 prostate carcinoma cell lines and could be a potent anticancer drug for future medication.EGFR and HER2 have been co-expressed and recognised in numerous solid tumours. Successfully, new TAK-285 derivatives (Click here for additional data file.


Triple-negative breast cancers (TNBC) frequently harbor defects in DNA double-strand break repair through homologous recombination (HR), such as BRCA1 dysfunction. However, less than 15% of TNBC patients were found to carry BRCA1 mutation, indicating that there are other mechanisms regulating BRCA1-deficient in TNBC. In the current study, we shown that overexpression of TRIM47 correlates with progression and poor prognosis in triple-negative breast cancer. Moreover, we demonstrated that TRIM47 directly interacts with BRCA1 and induces ubiquitin-ligase-mediated proteasome turnover of BRCA1, subsequently leads to a decrease of BRCA1 protein levels in TNBC. Moreover, the downstream gene expression of BRCA1, such as p53, p27, p21 was significantly reduced in the overexpression of TRIM47 cell lines but increased in TRIM47-deleted cells. Functionally, we found that overexpression of TRIM47 in TNBC cells confers an exquisite sensitivity to olaparib, an inhibitor of poly-(ADP-ribose)-polymerase (PARP), but TRIM47 inhibition significantly confers TNBC cells resistance to olaparib both in vitro and in vivo. Furthermore, we showed that overexpression of BRCA1 significant increase the olaparib resistance in TRIM47-overexpression-induced PARP inhibitions sensitivity. Taken together, our results uncover a novel mechanism for BRCA1-deficient in TNBC and targeting TRIM47/BRCA1 axis may be a promising prognostic factor and a valuable therapeutic target for TNBC. Breast cancer is the most common cancer among women. There are about 2.1 million newly diagnosed female breast cancer cases in 2018, which accounting for almost 1 in 4 cancer cases among women worldwide  (ab16780). For the use of these clinical materials for research purposes, prior patient consent and approval from the Institutional Research Ethics Committee were obtained. The 39 specimens were histopathologically and clinically diagnosed in Integrated Hospital of Traditional Chinese Medicine, Southern Medical University. 10 freshly TNBC tissues were histopathologically and clinically diagnosed in the First Affiliated Hospital of Sun Yat-sen University were frozen and stored in liquid nitrogen until further use.pMSCV/TRIM47 recombinant plasmid was generated by subcloning the PCR-amplified human TRIM47 coding sequence into pMSCV vector. pMSCV/BRCA1 recombinant plasmid was generated by subcloning the PCR-amplified human BRCA1 coding sequence into pMSCV vector. TRIM47 ring-domain mutant (C9S/C12S) plasmid was generated by using QuikChange ® Site-Directed Mutagenesis Kit according to manufacturer’s instructions. To silence endogenous TRIM47, two siRNA oligonucleotides were cloned to generate pSuper-retro-shTRIM47#1, shTRIM47#2, respectively. The primers using in this study were listed in Supplementary Table Immunoprecipitation assay was performed according to described previously . LysatesThe indicated proteins were immunoprecipitated by Flag-tag affinity gel  and resolved by SDS-PAGE, and the proteins were transferred to PVDF membrane. The membrane was then blocked in 10% skimmed milk for 1 h at 4 °C. As indicated, recombinant His-TRIM47 or Flag-BRCA1 was added at 1 μg/ml and incubated at 4 °C for 18 h. The blot was then washed 6 times with TBST and subjected to WB analysis using anti-His or anti-Flag antibody .Western blotting was performed using antibodies against Anti-TRIM47 antibody (ab72234), Anti-p53 antibody [PAb 240] (ab26), Anti-p21 antibody [EPR362] (ab109520), Anti-p27 KIP 1 antibody [Y236] (ab32034), Anti-rH2AX antibody [SIGMA] (H5912), Anti-BARD1 antibody [2059C4a] (ab50984), Anti-Rad51 antibody [EPR4030(3)] (ab133534), Anti-GFP antibody (ab290), and Anti-BRCA1 antibody [MS110] (ab16780). The membranes were re-probed with an Anti-GAPDH antibody [6C5] - Loading Control (ab8245) as the loading control.Total RNA was isolated with TRIzol reagent (Invitrogen) according to manufacturer’s instructions. RNA was reverse-transcribed into cDNA and carried out via Real-time PCR with SYBR Green Master (Roche). The data were assessed based on the threshold cycle (Ct), and calculated as IF staining was carried out on indicated cells grown on coverslips using Anti-Rad51 antibody [EPR4030(3)] (ab133534), Anti-rH2AX antibody [SIGMA] (H5912), and FITC-conjugated goat anti-mouse secondary antibody (Jackson Immuno Research). The images were captured using the AxioVision Rel.4.6 computerized image analysis system (Carl Zeiss).2 at a temperature of 37 °C for 2 weeks. The colonies were stained with 1.0% crystal violet for 30 s after fixation with 10% formaldehyde for 5 min. Then the cells were counted and analyzed.Colony formation assay was performed according to described previously . IndicatLuciferase activity assay was performed according to described previously . Lucifer6 indicated treated cells were collected and washed with PBS and the Annexin-V binding solution, subsequently added 150 μl of an Annexin-V antibody in Binding Buffer and incubated for 15 min, followed by addition of 1.5 μl of PI at 1 mg/ml and a further incubation for 5 min at room temperature in the dark. Cell morphology was assessed by phase-contrast microscopy. Percentage of apoptosis was analyzed with an EPICS XL flow cytometer (Beckman-Coulter). Each sample was analyzed in triplicate.Annexin-V assay was performed according to described previously . For evaSix-week-old BALB/c-nu mice were purchased from the Center of Experimental Animal of Guangdong Province , and approval for the experiments was obtained from the Institutional Animal Care and Use Committee.7) were injected subcutaneously in the back under aseptic conditions with or without Olaparib. Tumor size was measured using callipers every week, and tumor volume was calculated according to the following equation: (long axis × short axis2)/2. The xenograft tumors were removed on the sixth week after the injection and weighed after dissection.To form tumor xenografts in female six-week-old BALB/c nude mice, Vector or TRIM47-transfected-MDA-MB-231 cells  was used. The datasets are available in The Cancer Genome Atlas (TCGA) (https://tcga-data.nci.nih.gov/tcga/). Gene set enrichment analysis (GSEA) was performed on GSEA 2.0.9 (http://www.broadinstitute.org/gsea/).For the relationship between TRIM47 and the OS, RFS, and MFS of breast cancers, Kaplan–Meier Plotter  was used to analyze the data. Differences between two variables were evaluated using Student’s t-test. The relationship between TRIM47 expression and the clinicopathological characteristics were carried out using the chi-squared test, log-rank analysis, the Kaplan–Meier method, and the Cox regression model. supplemental figure 1supplemental figure 2supplemental figure 3supplemental figure 4supplemental figure 5supplemental figure 6supplemental figure 7supplemental figure 8supplemental figure 9supplemental figure 10supplemental figure 11Supplementary Informations


The morbidity of type 1 diabetes mellitus (T1DM) in children is increasing and diabetic peripheral neuropathy (DPN) is one of the main microvascular complications of T1DM. The aim of this study was to explore sympathetic skin response (SSR) characteristics in children with T1DM and analyze the value of early diagnosis and follow-up in T1DM complicated with DPN.Our prospective study enrolling 85 participants diagnosed with T1DM and 30 healthy controls (HCs) in the Children’s Hospital of Hebei Province from 2017 to 2020. Compared the outcomes of SSR and nerve conduction study (NCS) in T1DM, and evaluated the variations in SSR and NCS of different durations, as well as changes after six months of therapy.p < 0.05). The SSR test was more sensitive than the NCS test in the early diagnosis of T1DM with DPN (p < 0.05). The abnormal rates of SSR and NCS in long duration of disease were higher than those in short duration of disease (p < 0.05). Among 65 participants with diabetic neuropathy, the onset latencies of SSR were shortened and the NCS were improved after treatment (p < 0.05).SSR latency of T1DM group showed statistical difference as compared to HCs (SSR could provide the accurate early diagnosis and follow-up of pediatric diabetic peripheral neuropathy. Type 1 diabetes mellitus (T1DM) is the major type of pediatric diabetes, affecting about two million children and adolescents globally, the annual incidence estimates are 98,200  new cases in the under 14 years old . Nerve cWe prospectively recruited T1DM participants who were admitted to endocrinology department between October 2017 to March 2020. Inclusion criteria for the T1DM group were as follows: (I) participants aged ≥ four years and ≤ 14 years; and (II) those who fulfilled the WHO diagnostic criteria of type 1 diabetes mellitus : particiOther children (aged ≥ five years and ≤ 14 years) diagnosed with Tourette’s syndrome were selected as the healthy controls (HCs). All HCs had unremarkable cranial magnetic resonance imaging findings, were not taking any drugs with anti-cholinergic effects, and they did not present with clinical features of nerve system damage or any other autoimmune and oncological diseases.Legal guardians of all participants provided written informed consent before inclusion. This study was approved by the Ethics Committee of the Children’s Hospital of Hebei Province (202222-59).All subjects lay comfortably on the bed in a quiet room. The temperature of the room and the limbs of the patient were maintained at 23–26 °C and 32.0–36.0 °C, respectively. We recorded the SSR of the upper and lower limbs using a electromyogram evoked potential system MEB2306C (Japan). Recording electrodes were placed on the palmar and plantar surfaces and reference electrodes were placed on the dorsum of the hands and feet. An electrical stimulus  was applied to the contralateral median nerve at the wrist. To overcome habituation and the ensuing variability, a 40–60 s inter-stimulus interval was delivered randomly, and the intensity of the stimulation was increased progressively. At least five consecutive SSR potentials were recorded, and the mean of all the recorded SSR potentials was analyzed. SSR are made up of negative wave and positive wave and the parameters of SSR consist of the latency  and the amplitude.The onset latency was measured from the beginning of the stimulus to the first continuous deflection from the baseline, N latency was measured from the stimulus to negative deflection, and P latency was measured from the stimulus to positive deflection, as shown in Fig. 1-A. Numerous studies have shown that the amplitude of SSR was highly variable and was an unreliable parameter due to the influence of habituation and age, whereas the latency was a stable parameter, free from repeated stimuli . To avoiAll procedures were performed on the upper and lower limbs. The NCS study should include four motor nerves  and three sensory nerves . The parameters included motor conduction velocity (MCV), distal and proximal compound muscle action potentials (d/pCMAP), sensory conduction velocity (SCV) and sensory nerve action potential (SNAP). Results outside the 2.5 percentiles of values obtained in the age-matched healthy controls were considered anomaly.Participants can be stratified after the definitive diagnosis of T1DM , the sho12 treatment for six months.Participants were defined as diabetic peripheral neuropathy when participants complained with the signs and/or symptoms of small and large fiber dysfunction or when the tests of NCS were abnormal. All participants with DPN received multiple insulin injections and oral vitamin BStatistical analyses were performed using IBM SPSS Statistics version 24 . The distribution of all continuous variables was tested using the Kolmogorov–Smirnov normality test. Continuous variables with non-normal distributions were expressed as medians with interquartile ranges (IQR) and were compared using the Mann–Whitney U test. Categorical data of clinical features are shown as proportions and were compared using the chi-square test. Comparisons of SSR and NCS results between early diagnosis of T1DM were performed using McNemar’s tests. Statistical significance was set at 0.05.p > 0.05). Other clinical features of T1DM participants and HCs were summarized in Table A total of 85 participants with T1DM including 36 males and 49 females were recruited, with a median age of 10 years (IQR:8 ~ 12.5). The HCs were consisted of 30 participants, including 14 males and 16 females, with a median age of 11 years (IQR:8.8 ~ 12.3). There were no significant differences in terms of sex and age between T1DM and HCs  presented with classic diabetic symptoms  and 22 participants (25.9%) presented with diabetic ketoacidosis upon admission, of which five participants experienced onset symptoms of diabetic ketoacidosis. Twenty-four participants (28.2%) complained with the signs and/or symptoms of DPN.p < 0.05)  (Table The SSR latencies (including onset and N/P wave) of T1DM group in the upper and lower limbs were prolonged as compared to HCs  .There were 65 participants with diabetic peripheral neuropathy, the SSR test was more sensitive than the NCS test in the early diagnosis of T1DM with DPN (χp < 0.05). Moreover, the parameters of NCS were also improved after treatment (p < 0.001)  are mainly used to assess the large myelinated motor and sensory nerves of limbs in the clinical environment , and areAutonomic dysfunction is an important factor of morbidity and mortality among children living with diabetes , 24, theThere are many recommended guidelines at present, some experts advocate commencing screening five years after the definitive diagnosis of T1DM in children , while sThis study had some limitations. Firstly, most enrolled pediatric diabetic participants showed no signs or symptoms of peripheral neurology. We did not conduct cohort studies to determine whether patients with subclinical peripheral neurology would exhibit obvious clinical manifestations in future. Secondly, our study did not conduct other autonomic function tests used in children living with diabetes.In conclusion, SSR could evaluate the small unmyelinated sympathetic fibers and NCS could assess the large myelinated peripheral nerves, as they were both generally well-tolerated by children. The SSR tests were superior to the NCS tests in the early diagnosis of neuropathy, therefore, SSR could provide the accurate early diagnosis as well as follow-up of neuropathy.


The difference in angular accuracy, however, was not significantly different. The overall average distance between the bony fiducial markings and the AR projected fiducials was 1.7 ± 0.6 mm. With direct intraoperative reference, AR-guided surgery enhances bone conduction implant placement while reduces operative time when compared to conventional surgical planning.Exact placement of bone conduction implants requires avoidance of critical structures. Existing guidance technologies for intraoperative placement have lacked widespread adoption given accessibility challenges and significant cognitive loading. The purpose of this study is to examine the application of augmented reality (AR) guided surgery on accuracy, duration, and ease on bone conduction implantation. Five surgeons surgically implanted two different types of conduction implants on cadaveric specimens with and without AR projection. Pre- and postoperative computer tomography scans were superimposed to calculate centre-to-centre distances and angular accuracies. Wilcoxon signed-rank testing was used to compare centre-to-centre (C-C) and angular accuracies between the control and experimental arms. Additionally, projection accuracy was derived from the distance between the bony fiducials and the projected fiducials using image guidance coordinates. Both operative time (4.3 ± 1.2 min. vs. 6.6 ± 3.5 min.,  By direct vibratory stimulation of the inner ear, it is widely employed for conductive or mixed hearing loss associated with abnormalities of the middle or external ear in which traditional amplification devices cannot be worn3. Examples include the BoneBridge floating mass transducer (BB-FMT) made by MED-EL  and the Osia osseointegrated steady-state implant (O-OSI) made by Cochlear™ . Although conceptually similar, design and surgical procedures differ.Transcutaneous bone conduction devices (BCDs) are implantable devices that enable acoustic transmission via a magnetic interface with an external sound processor5. The use of three-dimensional (3D) planning software allows surgeons to accurately determine ideal BCD positioning while safeguarding against potential injuries and potentially reducing operating times. However, a chasm exists between preoperative planning and surgical performance as manual measurements based off anatomical landmarks introduce room for error1.Exact placement of BCDs requires careful evaluation of the temporal bone contour and thickness. More importantly, critical structures such as the sigmoid sinus or underlying dura should be avoided. Sigmoid sinus injury can lead to substantial bleeding and air embolisms, while dural injury can result in CSF leaks and the development of arteriovenous malformations3. However, widespread adoption is lacking possibly due to cost, accessibility, and familiarity with 3D printing. Surgical simulation software paired with intraoperative navigation (IN) tools have also been utilized6. A major challenge of IN technologies is the omission of relevant information from the surgical field requiring surgeons to switch their focus to and from the surgical field7. Augmented reality (AR) guided surgery has been proposed to overcome this by placing relevant information directly into the surgical field8.Several technologies for pre-operative planning and placement of BCDs have been explored. 3D printed transparent stencils interfacing with the surface of the one translated into a mean accuracy near 1.0 mm10. Given the prospect of underdeveloped anatomy, lack of soft tissue landmarks, and subsurface critical anatomy, AR may be poised to improve BCD surgical performance. We assessed the accuracy of a novel augmented reality (AR) technology to guide placement on cadaveric specimens11.From guiding bony cuts in oncologic maxillofacial surgery to outlining intradural spinal tumor margins during resection, projected AR technology can help guide surgeons where difficult, atypical anatomy is encounteredWhole cadaveric heads were prepared with bilateral curvilinear post-auricular incisions with elevation of a soft tissue flap for exposure of the zygomatic root, posterior external auditory canal, and the mastoid tip. Eight 2 mm bone wells were drilled outside of the surgical field to act as fiducial references for eventual image guidance calibration within the experimental arm. Areas of placement included the zygomatic root, bony external auditory canal, and the mastoid tip.12. Scans were evaluated for abnormal anatomy or evidence of previous surgery. Both the O-OSI and BB-FMT devices were imaged for surgical modelling by creating the virtual rendering of hearing device for projecting the overlay during the procedure. Materialise Mimics Medical 19.0  was used to identify optimal placement of the devices with creation of virtual heads rendered from CT imaging using pre-set bony segmentation sequencing.Using a prototype intraoperative cone-beam computed tomography scanner , the cadaver heads were obtained, with an isotropic voxel size of 0.78 mmImplants were imported into Materalise Mimics as optimized triangulated surface meshes that moved independently from the bone. The experimental design is outlined in Fig. 11. The AR setup is demonstrated in Fig. Implantation followed a standardized protocol beginning with the control arm followed by the experimental AR arm Fig. . Within Following implant placement, CT scans were obtained of the cadaveric heads to capture the location of implantation for eventual 3D coordinates measurement analysis. Each surgeon performed four O-OSI placements followed by four BB-FMTs.16. The developed AR system has demonstrated the projection accuracy at 0.55 ± 0.33 mm and has been widely adapted to the domains of Otolaryngologic and Orthopedic oncologic operations20. Recently, the software has evolved to include AR integration9.The integrated AR surgical navigation system consists of a PicoPro projector , a Polaris Spectra stereoscopic infrared optical tracker , a USB 2.0-megapixel camera , and a standard computer. A 3D printed PicoPro projector enclosure enabled the attachment of four tracking markers, which provide real-time three-dimensional tracking information  camera and instrument tracker, (2) camera and projector, which are both are outlined by Chan et al.(R) and a 3 × 1 translational vector (t). The rotational parameter was represented with Euler angles 21. This calibration serves as the extrinsic parameter of the camera.The camera and tracking tool calibration defines the relationship between the camera’s center and the tracking tool coordinates by creating a homogeneous transformation matrix, (A) of the camera include the principal point 24. This is denoted as:The intrinsic parameters R t) with intrinsic (A) parameters, three-dimensional space s is defined as the scale factors. This is represented by: When combining the extrinsic  scores for the six domains and subsequently weighted workload scores were generated27.All participants completed a NASA Task Load Index (TLX) questionnaire assessing the use of AR in addition to providing feedback in an open-ended questionnaireContinuous data was examined for normality by reviewing histograms, quantile–quantile plots, and the Shapiro–Wilk test for normality. Given the lack of normality and repeated measurements, Wilcoxon signed-rank testing was used for centre-to-centre (C-C) and angular accuracies comparisons between the control and experimental arms. All analyses were performed using SPSS 26 .Project Identification Number: 3541). Informed consent was obtained from all subjects and/or their legal guardian(s) by way of the University of Toronto’s Division of Anatomy–Body Donation Program. All subjects provided consent in the publication of identifying images in an online open-access publication.All methods were carried out in accordance with relevant guidelines and regulations. This study was approved by the Sunnybrook Health Sciences Centre Research Ethics Board . Similarly, distributions of angle accuracies were not significantly different (p = 0.561).A Kruskal–Wallis H test was run to determine if there were differences in accuracies between the five surgeons. Distributions of accuracies were not similar for all groups, as assessed by visual inspection of a boxplot. However, the distributions of C-C accuracies lacked statistically significant difference between groups . This pattern is consistent when comparing within O-OSI surgeries (p < 0.005), but not BB-FMT surgeries (p = 0.169).All O-OSI devices were placed in a mastoid position while BB-FMT placement varied with seven FMTs placed in the mastoid, seven in a middle fossa position, and four in a retrosigmoid position. Outlined in Table p < 0.001). This trend was seen for both devices with C-C distances consistently less within the experimental arm. For BB-FMT surgeries, the difference between the two arms was 9.8 ± 3.9 mm (p < 0.007). For O-OSI surgeries, this difference was less pronounced at 4.5 ± 2.8 mm (p = 0.007). In contrast, angular accuracies were not statistically significant between AR and control arms (p = 0.490). A significant difference between the two arms when stratifying by device was not evident.Overall C-C distances between the two arms were significantly different  scores for each domain are shown in Fig. Other comments with the technology highlighted the concerns associated with the projector’s light intensity and clarity of the projection borders. Suggestions to overcome blocking of optical tracking markers with bone dust or surgeon placement were to draw projections prior to performing the surgery.11. The projected image automatically adjusts with any head movement and provides real-time intraoperative reference. Despite being a prototype setup, the AR platform utilizes widely available image-guidance tracking software. Moreover, a central projector prevents the need for wearable technology and allows for multiple viewers from all angles.AR guided projection poses a novel technique to bridge the divide between pre-operative planning software and performance of bone conduction implantation surgery Fig. . When co5. Given the ease of sterilization and the added benefit of attaching the guide to the surgical field, these stencils have achieved a high degree of accuracy. Following a successful cadaveric feasibility study, the BB-STAMP (Bone Bridge Surface template-assisted marker positioning) has been performed on three patients with a high degree of C-C accuracy of 0.3 ± 0.1 mm and a rotational accuracy of 4.1 ± 4.7°5. A key distinguishing factor, however, was the number of hours dedicated to simulation on physical models and the multi-day process model creation. In contrast, AR projection circumvents the need for a 3D printer or the need for sterilization processing. Moreover, the ability to project critical anatomy such as the sigmoid sinus provides an added layer of patient safety.Other existing technologies that aim to address pre- and intra-operative planning include 3D printed stencils that provide guidance for intraoperative placement2.For both BB-STAMP and AR projection technologies, rotational accuracy remains a domain requiring continuing improvement. Further iterations of AR projection will include drill trajectory information from optical markers attached to the drill hand pieces as well as a tracking probe that are linked to color projections indicating adequate or inadequate trajectories. Kong et al. described a case study in which CT-guided navigation utilizing an electromagnetic tracker as another means of adapting intraoperative planning for intraoperative reference. However, accuracy and time comparisons were not established8. Advantages of HMD technology over the AR projection includes the convergence of the surgeon’s perspective and field of view without manipulation of the projector or occupying valuable, physical space29. Although OST technology preserves the direct view of the world within the HMD, a fixed focal length challenges a user’s ability to maintain focus of both AR and real-world content with more proximal tasks30. Moreover, performance of precise manual tasks with OST HMD guidance is inferior to performance without AR assistance30. The inability to wear surgical loupes or a headlight, battery life concerns, patient privacy, and signal interference have challenged widespread adoption29. The use of a projector addresses these shortcomings by enabling multiple viewers from numerous angles and avoiding the need for wearables.Other AR technologies have been applied to the head and neck including the use of optical see-through (OST) head-mounted devices (HMD) including the HoloLens™ Gen 1 and 2 Several challenges exist with AR projection including the need for pre-operative registration to pre-established surface landmarks. In this investigation, 2 mm wells served as bony landmarks for registration. Further research will aim to eliminate this pre-operative preparation with surface registration only. A potential confounder of our results may be attributed to the design of our study order. Specifically, the experimental arm followed the control arm for both implant surgeries. This may have primed surgeons with techniques or nuances that translated to the experimental arm to improve performance.8. When a surgeon’s perspective and the projection axis are misaligned, the accuracy of surgical implantation accuracy is threatened. The parallax effect, however, was minimized in our AR setup by allowing for surgeons to freely adjust the projector to their viewpoint since the projector’s image would automatically adjust for any movement given its attached location tracker. As a result, recalibration and reregistration were not required following displacement. Given the placement of bone conduction devices along surface anatomy, the parallax has a reduced effect8.One of the inherent challenges facing AR projection arises from parallax errorThe impact of cognitive loading from this novel technology remains consistent across surgeons that AR projection remains low on within the mental, physical, temporal cognitive demands. Both effort and frustration were considered very low with performance considered high. Technological adaptation challenges remain low, enabling surgeons to focus on the surgical task with minimal disruption to their cognitive load.31. Future development and research will focus on surface anatomy registration to exclude the need for surface fiducials as well.Future directions include projection of critical anatomy beyond the sigmoid sinus and enable periodic reference of the facial nerve, inner ear, or even the internal auditory canal during other temporal bone procedures Fig. . AdditioProjection of pre-operative planning onto the surgical field proves to be an exciting opportunity to interact with medical information in a meaningful way. With direct intraoperative reference, AR guided surgery enhances surgical precision when compared to conventional surgical planning while maintaining a low cognitive load on surgeons. In comparison to existing BCD planning technologies such as 3D printed stencils, AR projection does not require time-consuming model production and circumvents the need for sterilization. Its integration into existing surgical navigation platforms while avoiding the challenges associated with head-mounted AR technology reinforces its promise as a useful intraoperative tool.Supplementary Legends.Supplementary Video 1.


While historically viewed as an insulin insensitive organ, it is now accepted that insulin has a role in brain physiology. Changes in brain insulin and IGF1 signaling have been associated with neurological diseases, however the molecular factors regulating brain insulin sensitivity remain uncertain. In this study, we proposed that a recently described protein, termed Inceptor, may play a role in brain insulin and IGF1 resistance. We studied Inceptor in healthy and diseased nervous tissue to understand the distribution of the protein and examine how it may change in states of insulin resistance. We found that Inceptor is in fact present in cerebellum, hippocampus, hypothalamus, and cortex of the brain in neurons, with higher levels in cortex of female compared to male mice. We also confirmed that Inceptor colocalized with IR and IGF1R in brain. We saw little difference in insulin receptor signaling following Inceptor knockdown in neuron cultures, or in Inceptor levels with high-fat diet in mouse or Alzheimer’s disease in mouse or human tissue. These results all provide significant advancements to our understanding of Inceptor in the brain.https://osf.io/9q8sw.The Stage 1 registered report manuscript was accepted-in-principle on 9 August 2022. This manuscript was registered through Open Science Forum (OSF) on 24 August 2022 and is available here:  Cellular resistance to insulin signaling, a prominent feature of type 2 diabetes, has been studied extensively in peripheral tissues. Brain insulin and IGF1 resistance have been described in association with impaired brain function, including Alzheimer’s disease4. The factors regulating brain insulin and IGF1 resistance remain poorly understood.The brain contains an abundance of insulin receptor (IR) and the highly homologous insulin-like growth factor-1 receptor (IGF1R). IR is primarily implicated in glucose metabolism, while IGF1R is important for cellular growth. Both dimeric receptors function by binding to their ligand, either insulin or IGF1, at the surface of the cellular membrane. This results in tyrosine kinase activity and the initiation of an intracellular signaling cascade. There is significant overlap between the signaling cascades of the two receptors and they frequently exist as heterodimers of IR and IGF1R5. While previous research has shown Inceptor  is present in some cancer cell lines and has effects on autophagy, its role as a regulator of insulin signaling was only recently described9. Inceptor was found to regulate the insulin sensitivity of pancreatic beta cells by directly interacting with IR and IGF1R to decrease the abundance of these receptors at the cell surface. This resulted in reduced receptor available for cell signaling, but did not impact total receptor levels in the cell. Of further interest, unlike IR and IGFR which are broadly expressed, Inceptor expression appears to be restricted to specific tissues, including pancreas, gastrointestinal tract, and brain. We proposed that Inceptor may play a role in regulating brain insulin and IGF1 signaling. The purpose of this study was to understand the brain region and cell specificity of Inceptor expression in the mouse brain. Moreover, we investigated Inceptor protein differences across sexes, as well as in high-fat diet and Alzheimer’s disease, two models which exhibit brain insulin resistance.Recent studies have identified a single pass transmembrane protein, termed Inceptor, as a regulator of insulin and IGF1 signaling in the endocrine pancreasFinally, we compared Inceptor protein levels in autopsy specimens from humans with and without Alzheimer’s disease Table .Table 1D5. We examined brain tissue for the presence and distribution of Inceptor protein using a commercially-available antibody.Inceptor protein has been detected in cancer tissues and pancreas. Expression in the hypothalamus has been demonstrated, but its presence in other brain regions has not been characterized5. Tissue lysates were measured for Inceptor level by SDS-PAGE and immunoblot. Our preliminary data supported the performance of this antibody as specific for Inceptor and the presence of Inceptor in the brain, as well as the validity of our controls. Inceptor migrates near its 130 kD predicted molecular weight and is absent in liver and kidney (negative controls) [Supplementary Figure The MIN6 pancreatic insulinoma cell line was used as a positive control in measurements of InceptorTo determine which region of the brain contains the greatest abundance of Inceptor protein, we dissected cortex, hippocampus, cerebellum, and hypothalamus from male and female mice and compared relative Inceptor protein abundance by western blot. We predicted that Inceptor would have a heterogeneous brain distribution and that hypothalamic tissue would have the highest Inceptor expression, due to the hypothalamus’ important role in sensing peripheral insulin and regulating feeding behavior. If Inceptor protein levels were found to be equivalent in all brain regions, this could suggest that Inceptor brain expression is equally important for regulation of the highly-expressed IGF1R and IR.Inceptor mRNA were measured by quantitative PCR. The housekeeping gene Tata-binding protein (Tbp) was used to normalize expression across samples. The MIN6 pancreatic beta cell-derived cell line was used as a positive control in these experiments. In the event we did not detect Inceptor in cultured cells, this would suggest that Inceptor expression is more abundant in vivo, or not present in these particular CNS cell types. It is also possible that circulating factors which are not present in culture media regulate expression of Inceptor. We explored this possibility in greater detail below. Based on available brain RNA sequencing databases, we predicted neurons would have the highest Inceptor gene expression11.Our preliminary data demonstrated that Inceptor is present in the brain [Supplementary Figure 9. Given that estrogen is known to influence Inceptor levels in other contexts, we hypothesized that Inceptor abundance will differ across male and female mouse brains. To test this, 14-week-old C57BL6/J mouse brain tissues were divided into cortex, hippocampus, cerebellum, and hypothalamus. Each brain region from male and female mouse brains was compared directly for Inceptor content by SDS-PAGE immunoblot. In the event we did not detect Inceptor in particular brain regions during the experiments described above, they were excluded from our analysis. We predicted female brains would have higher Inceptor levels than male brains, likely driven by increased exposure to estrogen.Previous research identified Inceptor in cancer cells and termed the gene EIG121, or estrogen induced gene 121In the event we could not detect changes in brain Inceptor levels across sex, we would have concluded that sex is not a significant factor in regulating brain Inceptor levels. In the event that only some brain regions were affected by sex, this could suggest that estrogen differentially affects these brain regions.As stated above, Inceptor has been shown to directly interact with the insulin receptor in the pancreas to regulate insulin signaling. We tested whether this association also occurs in nervous tissue.To test if Inceptor directly regulates brain tissue insulin sensitivity, we knocked down Inceptor with shRNA introduced via lentivirus particle transduction to the cell type(s) that express it in culture. This experiment was conditional and only proceeded because we were able to detect Inceptor in cultured cells. Following stable uptake of shRNA against Inceptor, cells were treated with 10 or 100 nM insulin and activation of the insulin signaling cascade was measured by western blot.. In an attempt to promote this potential interaction, mice were dosed with 50 uL of recombinant human insulin (Humulin R) into the inferior vena cava 10 min prior to brain tissue harvest. Tissues were collected by dissecting the mouse brain region with the most abundant Inceptor levels and immunoprecipitating IR/IGF1R from the tissue. We then probed for Inceptor interaction by SDS-PAGE/western blot. If a particular sex was determined to have a higher Inceptor expression in the CNS, then that sex was chosen for Co-IP experiments. We predicted that Inceptor would have similar functions in the brain as has been described in the pancreas. We also predicted Inceptor knockdown would increase insulin sensitivity in cultured cells, and more Inceptor would Co-IP with the insulin and IGF1 receptors after insulin stimulation.We tested if Inceptor directly interacts with IR and IGF1R in vivo12.Studies have shown that prolonged dietary insults can affect the brain adversely. One commonly-utilized metabolic insult is the high fat with high sugar diet (HFD). This diet has been shown to promote both brain and peripheral insulin resistance. We have conducted a 12 week HFD feeding experiment in mice to generate tissue samples which are archived in our laboratory. Following this diet, we performed both glucose tolerance and insulin tolerance tests to confirm the effects of the diet on peripheral metabolic parameters [Supplementary Figure post mortem3. Our lab maintains the 3xTg mouse model of Alzheimer’s Disease13. This model develops significant metabolic perturbations as a function of age15. We tested 40-week-old female 3xTg AD mice and B6129SF2/J control mice from our colony to confirm these differences [Supplementary Figure Brain insulin resistance has been demonstrated in samples of human Alzheimer’s disease (AD) tissue Finally, to further validate any potential findings in 3xTg animals we measured Inceptor in human Alzheimer’s disease frontal cortex and hippocampus autopsy specimens (see table in Design). We predicted that the hippocampus of human specimens with Alzheimer’s disease would show reduced Inceptor expression compared to controls.Animal studies were performed in accordance with ARRIVE guidelines and as approved by the Institutional Animal Care and Use Committee at University of Virginia. Human tissue was used as was approved by the Institutional Review Board for Health Sciences Research at the University of Virginia. Samples were obtained with consent from the families of the deceased. All methods were carried out in accordance with relevant guidelines and regulations.The C57BL6/J (#000664), B6129SF2/J (#101045), and 3xTg AD (34830-JAX) animals used in this study were purchased from Jackson Labs. Tissues from these animals were flash frozen in liquid nitrogen and stored at − 80 °C.Human frontal cortex and hippocampus brain samples were previously acquired via generous donation from the University of Virginia Brain Resource Facility and have been continuously stored at − 80 °C since receipt. Portioned human brain samples collected in 1.5 ml Eppendorf tubes were thawed on ice and processed as described in the western blot section below, with the exception that full homogenization sometimes required more than two bullet blender cycles depending on sample size.p = 0.28) or PMI (p = 0.1315) between the groups.As shown in Table Mice on a C57BL6/J background were fed a high fat and high sugar diet  or control chow  for 12 weeks. Body weight was recorded each week. At the conclusion of the diet, ITT and GTT testing were performed. The mice were then sacrificed and tissues were harvested and flash frozen.For glucose tolerance testing, mice were fasted overnight prior to testing. Mice were weighed and had their baseline blood glucose measured (t = 0) before receiving an IP injection of 10% dextrose at 2 mU/g body weight. Blood glucose was measured by tail clip at 15, 30, 60, 90, and 120 min post-injection. For insulin tolerance testing, mice were fasted for 4 h. Mice were weighed and had their baseline blood glucose measured (t = 0) before receiving an IP injection of Humulin-R insulin at 1 mU/g body weight. Blood glucose levels were measured at 15, 30, 60, 90, and 120 min post-injection by tail clip.Samples were processed by addition of lysis buffer  containing protease and phosphatase inhibitors on ice. Samples were homogenized using a NextAdvance™ bullet blender for 2 cycles, and then sonicated. Samples were then centrifuged at 14,000×g for 12 min to remove debris. Supernatant was collected and total protein content was measured using the Pierce™ 660 Protein Assay from ThermoFisher Scientific. Samples were prepared for western blot by addition of Laemmli buffer and denatured by 90 °C heat for 5 min before SDS-PAGE and immunoblot. Proteins were resolved using 10–20 ug of protein loaded in 4–16% acrylamide stain-free gels from Biorad. After SDS-PAGE, the gels were imaged for protein in the stain-free gel before transfer. Proteins were then transferred to LF-PVDF membranes and blocked using T20 protein blocking buffer for 1 h. Primary antibodies were diluted at a 1:500 concentration in 5% milk solution and applied overnight at 4 °C. Following primary incubation, membranes were washed 3 times in PBST and secondary antibodies were applied at a 1:4000 concentration in 5% BSA-TBST for 2 h. Membranes were then washed 4 times and imaged. All western blot data was normalized to actin as a loading control. Each western blot sample was a biological replicate from an individual animal.Inceptor: F-CACAGGTTCCAGGTGGAGG, R-TGCAAGAGAAGGAGCACTCG. TBP F-ACCCTTCACCAATGACTCCTATG, R-TGACTGCAGCAAATCGCTTGG. RNA was isolated from cells using TRIzol extraction and cDNA was synthesized using Applied Biosystems High-Capacity cDNA Reverse Transcription Kit. qPCR was performed using SYBR Green dye.The following primer pairs were used in this study; The Inceptor/EIG121 antibody was purchased from Novus Biologicals (NBP2-57699). Actin fluorescent conjugated antibody was purchased from BioRad (4568044). Insulin receptor antibody (3025S), IGF1R antibody (3027S), ERK antibody (9102L), pERK antibody (9106L), and pAKT antibody (9271S) were purchased from Cell Signaling technology. AKT antibody was purchased from Invitrogen (AHO1112).p < 0.05 considered significant.Data analysis was performed using GraphPad Prism. T-Tests, one- or two-way ANOVA were performed as appropriate with The MIN6 cell line utilized in this study was generously donated by Dr. David Castle (University of Virginia). These cells are a positive control for the measurement of Inceptor in this study. These cells were cultured using high glucose DMEM supplemented with 15% FBS, 1% pen strep, and 50uM beta-mercaptoethanol (BME).Primary embryonic neuron cultures were generated using standard methods. Cultures were grown from day 17 embryos. The embryonic cortex was dissected from the brain, mechanically dissociated in ice cold neurobasal media, and then digested in trypsin supplemented with DNAse for 30 min. The digestion was terminated using MEM supplemented with 10% FBS. The cells were pelleted by centrifugation and resuspended in MEM + 10%FBS + Pen Strep and plated on poly-L-lysine-coated plastic 6-well plates. After 2 h, the media was replaced with neurobasal media supplemented with B27 and pen strep. The cells were grown for 12 days before RNA collection or shRNA knockdown.16. Day 1-Day 4 neonatal mouse cortical tissue was cleared of meninges and disassociated in trypsin with DNAse for 30 min. Cells were then pelleted by centrifugation. The pellets were then resuspended and seeded in p75 culture flasks for growth. After 1–2 weeks, microglia were separated from the culture using an orbital shaker rotating at 400 RPM for at least 4 h. The microglia were then pelleted from the media to collect RNA. The astrocytes were removed from the culture flask by mild trypsinization and seeded in multi-well cell culture plates for experiments.Primary cultures of mixed glia were generated using standard protocolsIR and IGF1R were immunoprecipitated from freshly dissected mouse brain tissue. Anesthetized mice were treated with 5u of recombinant human insulin (Humulin R) 10 min prior to brain tissue collection. The anatomical regions dissected were the regions with the highest Inceptor abundance. Tissue was gently homogenized in RIPA buffer containing protease and phosphatase inhibitors. The homogenate was centrifuged for 20 min at 12,000 RPM and the supernatant was collected. This supernatant was quantified for its protein content. 100–500 µg of lysate was then incubated with IR or IGF1R antibodies overnight at 4 °C. The next day, protein-AG beads were added to the lysate to bind select antibodies for 4 h. Beads were centrifuged to pellet and washed 3 times with lysis buffer. Laemmli buffer was then added to samples for western blot to measure Inceptor.Inceptor, along with scrambled negative control shRNA, and GFP encoding lentivirus as a positive control to confirm transduction efficiency. shRNA was transduced to cultured cells followed by puromycin selection until stable knockdown of at least 80% of cells was achieved. In the unlikely event that adequate knockdown of Inceptor could not be achieved via lentiviral transduction, we would use a morpholino construct to achieve knockdown. Following successful knockdown of Inceptor in the cells, media was replaced with B27 minus insulin for 4 h prior to the start of the experiment. Cells were then left untreated or treated with 10 or 100 nM insulin for 30 min. Cell lysate was collected and activation of insulin signaling was assessed by western blot.We developed 3 lentiviruses encoding various shRNA against The MIN6 pancreatic beta cell line was used as a positive control in measurements of Inceptor  0.8. The Type I error probability associated with this test of this null hypothesis is 0.05.Determination of the number of cell cultures for Inceptor levels by cell type: Based on our preliminary data we expected a > 70% difference in expression between cell types. If the response is normally distributed with a standard deviation of 0.15, we would have needed 2 cultures per group. We expanded this to 3 cultures per group to allow us to assess for outliers.We assumed the response within each subject group was normally distributed with standard deviation 0.2. Based on an expected true difference in the experimental and control means of 0.4, we studied 5 mice of each sex to be able to reject the null hypothesis that the population means of the experimental and control groups are equal with probability (power) 0.8. The Type I error probability associated with this test of this null hypothesis is 0.05.5, we predicted a strong effect with a difference in means of 0.5. Thus, we studied 4 control and 4 Inceptor knockdown samples to be able to reject the null hypothesis that the population means of the experimental and control groups are equal with probability (power) 0.8. The Type I error probability associated with this test of this null hypothesis is 0.05.We planned to compare the effects of insulin stimulation on control and Inceptor knockdown (shRNA treated) cultures. We expected the response within each subject group to be normally distributed with standard deviation 0.2. Based on the report by Ansrullah et al.We planned a study of a continuous response variable from independent control and experimental subjects with 1 control per experimental subject. Our preliminary data suggested the response within each subject group was normally distributed with standard deviation ~ 0.15. Expecting a true difference in the experimental and control means of 0.3, we studied 5 experimental subjects and 5 control subjects to be able to reject the null hypothesis that the population means of the experimental and control groups are equal with probability (power) 0.8. The Type I error probability associated with this test of this null hypothesis is 0.05.For experiments involving measurements of Inceptor in human AD and control frontal cortex and hippocampus samples, the samples described in the Table of Human Samples (see Design) were used. We expected that human AD samples would have decreased Inceptor abundance in tissue relative to controls.p-value of < 0.05 was considered significant and denoted with (*) or the actual p value where appropriate.Data was analyzed for statistical significance between groups using GraphPad Prism software. In the event potential outliers were observed in measurements, we performed a Grubbs test to determine if these outliers were significant. Outlier data was thrown out prior to the onset of analysis and were clearly marked in all raw data. Values obtained in experiments were compared using t-test, one or two-way ANOVA with Tukey’s post hoc test where appropriate. A p = 0.9739, t = 0.034, df = 8; hippocampus, p = 0.9801, t = 0.026, df = 8). Also in accordance with our original prediction, Inceptor levels in the hypothalamus were the highest of any brain region [Fig. p = 0.3744, t = 0.941, df = 8).Following collection of at least 5 male and 5 female 14-week-old C57Bl/6J mice, we prepared tissue lysate from cortex, hippocampus, cerebellum, and hypothalamus for comparison. We also collected pancreas and liver samples from these mice as positive and negative controls, respectively. Pancreas has been demonstrated to be positive for Inceptor [See ref. 5 and Supplementary Figure p = 0.0001, t = 6.870, df = 8). In fact, for female mice Inceptor levels in cortex were statistically equivalent to levels in the hypothalamus , and the Inceptor levels of both these regions were significantly higher than those in cerebellum or hippocampus [Fig. p = 0.0060, vs. hippocampus p = 0.0017; hypothalamus vs. cerebellum p = 0.0003, versus hippocampus p = 0.0001, all df = 8). For males, hypothalamus showed significantly more Inceptor than all other regions [Fig. p = 0.0003; by Tukey’s multiple comparisons test hypothalamus vs. cerebellum p = 0.0013, vs. hippocampus p = 0.0005, vs. cortex p = 0.0005, all df = 8). For all of the data in this section, normality assumptions were met.Notably, in the cortex, we found that Inceptor levels were significantly higher in females as compared to male mice Fig. a, b (unpp < 0.0001 by one-way ANOVA with Tukey’s for multiple comparisons). Because astrocyte and microglia data did not meet normality assumptions, we performed a follow-up Kruskal–Wallis test which yielded similar results .To determine which cell types in brain have measurable levels of Inceptor, we cultured neurons, astrocytes, and microglia from C57BL/6 J mice, as well as the rat insulinoma cell line MIN6. MIN6 and neuron samples yielded measurable levels of Inceptor via qPCR, while astrocytes and microglia did not Fig. a (neuronWe next performed immunoprecipitation (IP) assays to look for interactions between Inceptor and the Insulin receptor or IGF-1 receptor in the brain. We first collected lysate from the pancreas, liver, cortex, and hypothalamus of C57BL/6 J female mice using the protocol as described in Methods. We chose females due to our previous finding that 14-week females had high levels of Inceptor in both the cortex and hypothalamus [Fig. 5. We found in most cortex samples that Inceptor was also pulled down in both IR and IGF1R IP, but in a few cases Inceptor was not pulled down in the IR IP. We also noted that Inceptor was inconsistently pulled down in the hypothalamus. A representative image of data for one complete animal is provided in Fig. For both IR and IGF1R, we successfully co-immunoprecipitated (CO-IP) Inceptor in the pancreas, as had previously been demonstrated by Ansarullah et al.p < 0.0001, t = 7.264, df = 11). For this knockdown experiment, we utilized neuron cultures since this was the only cell type in the brain with detectable levels of Inceptor in both qPCR and Western blot [Fig. p = 0.6068, KD p = 0.0312, insulin p = 0.0021; for pAKT/AKT, interaction term p = 0.2489, KD p = 0.3777, insulin p < 0.0001. In addition, by Tukey’s multiple comparisons test, pERK/ERK, scr vs. scr + ins p = 0.1480; KD vs. KD + ins p = 0.0233, scr + ins vs. KD + ins p = 0.2035; pAKT/AKT scr vs. scr + ins p = 0.0037; KD vs. KD + ins p = 0.0001, scr + ins vs. KD + ins p = 0.4628; all df = 10). Results for virus constructs 2 and 4 with 10 nM insulin and for all constructs with 100 nM insulin showed similar trends and are provided in Supplementary Figure To measure the effects of Inceptor knockdown on Insulin and IGF1 signaling, we next developed three lentiviral constructs and validated their knockdown efficiency compared to GFP and a scramble control [Fig. p = 0.0191, df = 10). However, none of the other lentivirus / insulin treatment combinations showed significant differences between these groups. In some cases, levels of pERK/ERK for insulin-treated cells also appeared to increase even further in the Inceptor knockdown neurons, although this did not reach statistical significance. We note, however, a trend towards significance for lentivirus construct 2 and 10 nM insulin treatment . For hypothalamus, data did not meet assumptions of normality, so we also performed a follow-up Wilcoxon matched-paired signed rank test, which remained not significant with p = 0.1250). Similarly, and even more surprisingly, there were no measured differences in the pancreas samples for HFD mice versus controls [Fig. p = 0.0887, t = 1.937, df = 8).To study the effects of metabolic disturbances on Inceptor, we next tested whether Inceptor levels differed in the brain regions and pancreas of C57BL/6 J mice subjected to a 12-week high-fat and high-sugar diet compared to control animals. We have previously demonstrated that these high-fat diet fed animals are both insulin resistant and glucose intolerant [Supplementary Figure 13. In addition, these mice have been shown to have insulin resistance and glucose intolerance15 [Supplemental Figure 15 , we previously saw that Inceptor levels were significantly decreased for 3xTg mice compared to controls , while total levels of insulin receptor and IGF1 receptor remained unchanged . In contrast, the hippocampus and cortex, which also express the transgenes, showed no such differences in Inceptor for AD mice versus controls . Except as noted above for HFD hypothalamus, for all data in this section, normality assumptions were met.We also tested hypothalamus, hippocampus and cortex samples from 40-week old female 3xTg mice versus controls. 3xTg is a commonly used Alzheimer’s disease model utilizing three transgenic insertions which confer both amyloid and tau pathology to the mice, though not neuronal lossp = 0.3253, t = 1.030, df = 11; frontal cortex p = 0.7437, t = 0,3353, df = 11). Normality assumptions were met for all human data. This study was not sufficiently powered to examine differences by sex.Finally, we prepared lysate from human male and female hippocampus and frontal cortex tissue (see Table of Human Samples in Design) from both control and Alzheimer’s disease patients and performed western blotting. Again somewhat unexpectedly, we found no significant differences in the level of Inceptor for AD versus controls for either brain region Fig. a–d  for its upregulation in endometrium in response to estrogenAs we originally hypothesized, the hypothalamus did indeed have the highest level of Inceptor in both male and female mice in our experiment comparing Inceptor levels by brain region. However, it remains to be seen whether these levels are highest as a result of the hypothalamic role in feeding regulation and peripheral insulin sensing, or for some other reason.CO-IP experiments showed that Inceptor does likely interact with IR and IGF1R in the cortex, and possibly the hypothalamus as well. Whereas we consistently achieved CO-IP of Inceptor in pancreas samples, the same was not true for the hypothalamus and cortex. One obvious reason is although Inceptor is highest in these brain regions, the levels are low relative to pancreas, which could make it difficult to detect by western blot in some cases. In addition, as nothing is yet known about the kinetics of the activity of Inceptor interaction with IR and IGF1R in the brain, we may miss some interactions. Likewise, a possible explanation is that our insulin treatment of the animals did not adequately stimulate an IGF1R/Inceptor or hybrid receptor/Inceptor interaction, and we are therefore precipitating Inceptor for homeostatic interactions only. Finally, the primary role of Inceptor in the brain may not be to regulate IR and IGF1R. It is possible that there are other receptors or even non-receptor proteins that are regulated by Inceptor in brain.18. It is possible that there may be differences in Inceptor influence based on which insulin receptor isoform is present. Altogether, these initial experiments leave many unanswered questions about how Inceptor might regulate insulin/IGF1 signaling and might itself be regulated in the brain.It came as some surprise that following the knockdown of Inceptor in cultured neurons, the induction of phosphokinases following insulin stimulation remained essentially the same as in cell cultures with intact Inceptor, including at higher insulin treatment doses when both insulin and IGF1 receptors are more completely activated. It is worth noting that insulin and IGF1 receptors are found as either homodimers or a hybrid insulin receptor/IGF1 receptor heterodimer in the cell membrane. It is thought that much of the insulin receptor in the brain is part of these hybrid receptors rather than homodimers. It is unknown if Inceptor will have the same impact on hybrid receptors as homodimers. In addition, there are two isoforms of the insulin receptor, IR-A and IR-B. While neurons contain IR-B, pancreatic beta-cells contain both IR-A and IR-B19. And as has been shown previously (Ref.5), we saw robust levels of Inceptor in our pancreas samples from mice. However, there was no measurable difference in Inceptor levels in the pancreas of mice with a high-fat diet induced insulin resistant state compared to controls, which was somewhat unexpected as these mice have body-wide insulin resistance and metabolic abnormalities. Since we did see a trend towards decreased Inceptor in pancreas from these HFD mice, this suggests that our sample size might be too small to detect subtle differences. The direction of effect also differs from our hypothesis that these levels would increase, and suggests that any differences in Inceptor levels are an effect and not a driver of this resistance.Previous research in pancreatic neuroendocrine tumors found that higher levels of Inceptor are correlated with better prognosis and outcomes15. Therefore, more important is the fact that the cortex and hippocampus, areas more relevant to human AD, show no differences in Inceptor levels. This suggests that the insulin resistance seen in AD brains is a result of mechanisms not related to Inceptor. Our human data, wherein we also saw no significant differences in Inceptor levels for hippocampus and cortex samples from subjects with AD versus control brains, suggest the same thing.In the AD model mice, which have transgene-induced metabolic abnormalities and insulin resistance in addition to their neurodegenerative disease phenotypes, only the hypothalamus showed any difference in Inceptor levels between transgenic animals and controls. We note that due to the specific transgenics of this model there is deposition of amyloid and tau in the hypothalamus, a phenotype not replicated in humansOur study provides initial evidence for the presence of Inceptor in the brain with variability by sex, brain region, and cell type. We further showed that Inceptor interacts with IR and IGF1R to perform its functions. Further experiments showed that at least in our experimental models, downstream insulin signaling markers did not greatly differ following knockdown of Inceptor, and that Inceptor levels do not significantly vary following high fat diet in mice, or in AD in both mice and humans. These results further highlight that much more remains to be learned regarding the kinetics and mechanisms of Inceptor interactions and regulation, as well as the differences between Inceptor action in the brain versus the periphery.Supplementary Information.