Coxon, CR

Downing, J

Ehtezazi, T

Islam, Y

Leach, AG

Platt, M

Pluchino, S

LJMU Research Online (Liverpool John Moores University)

SMART MICELLAR SYSTEMS FOR BRAIN DRUG DELIVERYY. Islam1, A. Leach1, C. Coxon1, J. Downing1, M. Platt2, S. Pluchino3, T. Ehtezazi11School of Pharmacy and Biomolecular Sciences, James Parsons Building, Byrom Street, Liverpool, L3 3AF2 Department of Chemistry, Loughborough University, Loughborough LE11 3TU, United Kingdom3 Department of Clinical Neurosciences, Centre for Brain Repair, University of Cambridge, Robinson Way CB2 0PY, UK.1. INTRODUCTION: Neurodegenerative disorders (like dementia, MS, PD, AD, stroke andtraumatic brain injury) cost directly to healthcare system over £134 billionper annum in the UK and USA. These disorders affect the quality of life ofthe patient. Brain matrix metalloproteinase-9 (MMP-9) levels increase inneurodegenerative disorders. An enzyme that plays an important role inmany pathological processes of brain injuries and inflammation.The Challenge Conventional drug delivery systems have number of limitations such asroute of administration (intracranial injection), bioavailability (permeabilityacross the BBB), and the safety of the carrier material.Our approach We aim to develop a safe and smart material that forms a micellar drugdelivery system with the ability to target the brain specifically and release apayload at the suitable region. Our approach is to apply state-of-the-artintegrated experimental and computational methods to identify the smartenzyme responsive materials (Figure 1). The key advantages of our smart biomaterial are; permeability across theBBB and being responsive to MMP-9.2. METHODS: Experimental data was analysed by applying statistical models; Lamort,Kridel , Kcat/KM and Ratnikov datasets in relation to MMP-9 cleavablepeptides. Molecular docking was applied to identify MMP-9 cleavable peptides withdesired sensitivity. Synthesis of suitable peptides using a CEM Liberty Blue synthesizer, andcharacterization via Liquid chromatography–mass spectrometry (LC-MS). Conjugation of Cholesteryl chloroformate with peptide, using N,N-Diisopropylethylamine (DIPEA) and Dimethylformamide (DMF). Characterizing the self-assembled nano-micelles using transmissionelectron microscopy (TEM).3. RESULTS: Y_scores were calculated as average of Lamort, Ratnikov and Kridel forliterature known sequences (Table 1). Y_score against log Kcat/KM values of known peptides gave apolynomial regression curve with R2 = 0.9048 (Figure 2). The frequency distribution of amino acids in natural materials that areresponsive to MMP-9 is shown in Figure 3. This identifies key features ofknown substrates. Figure 4 shows how a good substrate for MMP-9 is predicted to beaccommodated in the enzyme active site. This provides an explanationfor many of the preferences shown in Figure 3. Table 2 presents a list of novel MMP-9 cleavable peptides with predictedY_score and Kcat/KM. These are from a database of 1.2 million MMP-9cleavable peptides that were generated by applying the above statisticaland molecular modelling techniques. TEM image of self-assembled nano-micelle with MMP-9 cleavablepeptide and a short rabies virus glycoprotein (sRVG) shuttle peptide(Figure 5). The size of nano-micelles ranged from 50-200 nm.REFERENCES: 1. Kumar, P., Wu, H., McBride, J.L., Jung, K.E., Kim, M.H., Davidson, B.L., Lee, S.K., Shankar, P. and Manjunath, N., 2007. Transvascular delivery of small interfering RNA to the central nervous system. Nature, 448(7149), pp.39-43.2. Alvarez-Erviti, L., Seow,Y.,Yin, H., Betts, C., Lakhal, S. and Wood, M.J., 2011. Delivery of siRNA to the mouse brain by systemic injection of targeted exosomes. Nature biotechnology, 29(4), pp.341-345.3. Javed, H., Menon, S.A., Al-Mansoori, K.M., Al-Wandi, A., Majbour, N.K., Ardah, M.T., Varghese, S., Vaikath, N.N., Haque, M.E., Azzouz, M. and El-Agnaf, O.M., 2015. Development of nonviral vectors targeting the brain as a therapeutic approach for Parkinson’s disease and other brain disorders. MolecularTherapy.051015202530354045X A R N D C Q E G H I L K M F P S T W Y VP4 P3 P2 P1 P1' P2' P3' P4'Figure 3. Frequency distribution of  natural amino acids in known MMP-9 substratesP4 P3 P2 P1P1’P2’P3’P4’Cleavage position4. CONCLUSIONS: Novel MMP-9 cleavable peptides were generated byapplying computational methods. Self assembled nano-micelles were formulated withMMP-9 cleavable and brain targeting peptides.Table 1. A list of known MMP-9 substrates, processed using statistical techniques to assign cleavability scores. Figure 2. A plot of Y_score and log Kcat/KM values  Figure 4. Characterising suitable biomaterial by docking using a structure of the enzyme taken from the protein databank. A peptide (PEGLW) after autodocking is shown in cyan;  it can be observed that “Leu” extends into the  S1’ pocket and “Trp” wraps around the bridge over the S1’ pocket.Figure 1. Schematic presentation of the core of the smart biomaterial and mechanism of action of Smart-MMP-9 Responsive drug delivery system..  y = 0.0047x3 - 0.2107x2 + 3.1435x - 13.781R² = 0.90480.00.51.01.52.02.50.0 5.0 10.0 15.0 20.0 25.0Y_ScoreLog Kcat/KMSequence Probability Lamort Ratnikov Kridel Kcat/KM Y_ScoreKAG-LLC 4.3636 0.47 -0.11 251 0.18KG-LC 8.22765 0.47 358 0.47SGRR-LLSRTA 0.51 3.61 0.50 0.12 1,200 1.41RPLA-LWRSQ 4.12 4.03 0.69 0.49 2,000 1.74RGLA-LWRSQ 0.36 3.37 0.56 0.47 2,000 1.47GPLE-LEEAQ 29.73 4.06 0.55 0.45 2,000 1.69EAAPSS-VIAATE 24.05 4.13 0.53 0.46 2,000 1.71SGLRPAK-STA 23.23 3.74 0.64 0.00 5,800 1.46PLG-LEEA 46.53 3.87 0.66 0.46 8,000 1.66GPQG-IAGQ 1449.48 4.00 0.62 0.27 8,400 1.63SGQPHY-LTTA 27.00 3.84 0.65 0.37 8,800 1.62IPES-LRAG 38.64 4.07 0.66 0.36 12,600 1.70SGFGSRY-LTA 52.20 3.76 0.63 0.11 13,200 1.50GPLG-LERAQ 160.15 4.11 0.62 0.51 14,000 1.75GAMF-LEAIP 6.26 4.06 0.60 0.26 15,000 1.64RPLA-LEESQ 4.08 3.99 0.60 0.44 21,000 1.68KPAG-LLGC 191.40 4.08 0.62 0.45 24,000 1.72SGPLF-YSVTA 11.48 4.10 0.67 0.37 25,600 1.71PLA-LWRSQ 5.64 4.03 0.68 0.49 51,000 1.73SGPRA-VSTTA 74.98 4.17 0.65 0.35 61,000 1.72RPLG-LEEA 34.02 3.86 0.66 0.46 62,000 1.66RPLE-LWRSQ 1.59 4.02 0.63 0.45 67,000 1.70SGKIPRT-LTA 52.94 4.16 0.68 0.20 67,500 1.68RPLG-LGAAQ 237.75 4.23 0.74 0.38 133,000 1.78RPLA-LWEEQ 9.19 3.96 0.71 0.39 143,000 1.69GPLG-LREAQ 566.23 4.15 0.71 0.50 160,000 1.79SGKIPRR-LTA 29.81 4.09 0.72 0.18 160,000 1.66SGKGPRQ-ITA 170.54 4.15 0.65 0.23 188,000 1.68RPLA-LWESQ 5.67 3.99 0.69 0.49 198,000 1.72RPLG-LGGAQ 638.64 4.24 0.74 0.38 270,000 1.79RPLA-LRRSQ 7.63 4.12 0.69 0.44 291,000 1.75RPLG-LWGAQ 482.56 4.19 0.74 0.51 750,000 1.81 Sequence Y_score Kcat/KMPFM-AG 2.59 1,462,495 PFR-AG 2.51 1,264,381 PFR-LG 2.04 405,685 PKR-LG 2.03 389,159 PFR-FG 2.00 345,901 Table 2. Novel MMP-9 cleavable peptides with predicted cleavability (Kcat/KM)  Figure 5. TEM image of MMP-9 nano-micelle with targeting ligand

Smart micellar systems for brain drug delivery

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Smart micellar systems for brain drug delivery.

Smart micellar systems for brain drug delivery.

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