Nanomedicine and its Potential in Managing Diabetes View PDF

*Ariel Pablo Lopez
Department Of Genetics, Molecular Biology Laboratory, Universidad De Buenos Aires, Argentina

*Corresponding Author:
Ariel Pablo Lopez
Department Of Genetics, Molecular Biology Laboratory, Universidad De Buenos Aires, Argentina
Email:aplopez@prensamedica.com.ar

Published on: 2020-05-19

Abstract

Strategies based on nanotechnology hold substantial capability for enhancing diabetes patient safety. Nanoparticles are being acquired to aid in the early treatment of type 1 diabetes as imaging contrast operatives. Glucose Nanosensors are integrated into implantable tools that allow for more precise and patient-friendly actual-time monitoring of blood glucose levels and also provide the basis for glucose-responsive nanoparticles that better mimic the adaptive insulin requirements of the body.

Keywords

Nanomedicine; Diabetes; Glucose Nano Sensors

Introduction

Strategies based on nanotechnology hold substantial capability for enhancing diabetes patient safety. Nanoparticles are being acquired to aid in the early treatment of type 1 diabetes as imaging contrast operatives. Glucose Nano sensors are integrated into implantable tools that allow for more precise and patient-friendly actual-time monitoring of blood glucose levels and also provide the basis for glucose-responsive nanoparticles that better mimic the adaptive insulin requirements of the body. Eventually, nanotechnology is used in non-invasive methodologies to transport systems and to engineer more appropriate type 1 diabetes vaccine, cell, and gene therapy. Addressed the present situation of these strategies, and explained fundamental problems for their clinical practice translation.

Diabetes Mellitus

Diabetes mellitus is classified as a bunch of high blood glucose autoimmune diseases. Diabetes occurrence is increasing [1] the number of people impacted globally is projected to grow from more than 280 million adults today to more than 400 million adults by 2030 [2]. The estimated global expected costs involved with diabetes care and its consequences amount to US$ 500 billion [3], not counting the administrative costs related to the loss of work time. Type 1 diabetes, constitutes 10 percent of all instances of diabetes mellitus [4]. Which effects from an insulin deficiency-a 51-amino-acid peptide generated by the β-cells of the Langerhans islets in the pancreas-that manages blood glucose levels by triggering liver and muscle cells to occupy blood glucose [5]. This deficiency stems from an autoimmune response in affected individuals contributing to the T-cell-mediated degradation of β-cells and eventual hypoinsulinemia and hyperglycemia. Type 2 diabetes is also known as a lifestyle disorder [6] and is linked to obesity and lack of exercise, unlike type 1 diabetes. Patients with type 2 diabetes develop insulin resistance- that is, they blunted their response to insulin provided by β-cells, contributing to hyperglycaemia again [7]. Persistent glycaemic management for diabetes clients is a primary determinant of future consequences [8]. Type 1 and Type 2 diabetes treatment aim is to control blood glucose levels inside acceptable normoglycaemic ranges (70–140 mg per dl, or 4–8 mM) [9]. Continuous hyperglycemia can result in blindness, kidney and heart disease, nerve degeneration, and enhanced sensitivity to infection if left untreated [10]. Alternatively, overdiagnosis with insulin can cause hypoglycemia, leading to seizures, unconsciousness, or death [11]. Insulin replacement therapy is recommended for people with type 1 diabetes, to imitate common variations in insulin levels across the day. Standard practice involves long-acting insulin doses to accommodate a basal insulin dose, which is combined with fast-acting insulin bolus injections at mealtime [12,13]. The primary therapy for type 2 diabetes concentrates on slowing the improvement of illness through exercise and meal regulation. Patients are often given oral and/or injectable medications that enhance insulin generation and function.

Due to the gastrointestinal tract's harsh environment, insulin and other macromolecular diabetic treatments must be subcutaneously administered which can be uncomfortable and unpleasant, contributing to low patient acquiescence [14]. Also, this modern type of insulin replacement therapy is open loop, meaning it relies on a historical understanding of the specific blood glucose profile of the patient in response to various meals and insulin treatments to determine insulin dosages [8]. Several technologies have been developed to address the injection therapy disadvantages by dynamically monitoring the insulin levels with real-time data [15], thus raising the treatment-related patient burden. These devices cover insulin pumps as well as constant glucose sensors. Another important example is the double hormone glycaemic regulation system for the bionic pancreas, which was newly tested in a Phase II trial of patients with type 1 diabetes [16]. This method has been shown to boost the glycaemic regulation dramatically while raising the cycle of hypoglycaemic episodes.

Given these technical advancements, the vast majority of patients often find it difficult to maintain optimal levels of glucose using insulin replacement therapy. A longitudinal analysis of diabetes clients found that approximately 50 percent of patients do not meet their glycaemic goal during the day [17]. Possible factors include the open-loop nature of existing treatments by injecting insulin into the subcutaneous space; the perception of subcutaneous fluid as having the same blood glucose concentration; and insufficient compliance with patients. Possible therapies need to be sufficient to use while performing stronger glycaemic regulation, improved safety profiles, and, preferably, a minimized cost of manufacturing and introducing them into clinical practice to provide clinical improvements. Research is focused on such objectives to encourage different routes of insulin management [18], to improve insulin pharmacokinetics, and to create new therapeutic objects [19]. In the past 20 years, nanotechnology has advanced in many therapeutic areas, including oncology and cardiology [20-23], both diagnostics and treatment. Yes, there are several physical, chemical, and biological features of nanoparticles and nanoscaled materials that make them attractive for biomedical applications [24]. Nanoparticles are used for the delivery of both small and large macromolecular therapies and the detection and monitoring of disease sequence [25]. A plethora of novel compositions of nanoparticles with different structures was manufactured for biomedical applications, such as liposomes, polymer nanoparticles, nanostructures, metallic nanoparticles, stimulus-responsive nanoparticles, and nanofabricated tools [26-33]. The nanotechnology’s evolving role in diabetes management, from diagnosis and disease monitoring to therapeutics [34]. We’re concentrating on the most advanced innovations in each category we believe would most definitely affect diabetes care shortly. Molecular imaging and biomedical imaging devices for patients with type 1 or type 2 diabetes are providing new possibilities for early diagnosis, scheduling, and tracking of disease progressions [35]. Prematurely diabetes diagnosis and disease progression prediction are fundamental parts of controlling the condition. For evidence, as diabetes progresses, the mass of β-cells and their corresponding insulin production and release are decreasing [36]. While the estimation of functional β-cells can allow practitioners to recommend more effective therapies and encourage research to develop improved β-cell-targeted therapies, accurate determination of β-cell mass is impractical because post-mortem autopsy is needed. Possibilities to test β-cell mass utilizing imagery have developed in recent years with the production of β-cell-targeting peptide dyes and antibody - dye conjugates [37], but these are typically reserved for excised tissue samples that require invasive procedures. Additionally, nanoprobes are being produced with β-cell specificity and high contrast [38], which may enable clinicians and scientists to measure non-invasively the endogenous β-cell mass [39] in vivo, the existence of exogenous transplanted islets [40] and the efficiency of islet cells in cell substitution treatment [41-43]. Numerous non-invasive imaging methods for the analysis of β-cell mass are being studied, such as computed tomography, positron discharge tomography, and magnetic resonance imaging [44]. A few other magnetic nanoparticles probes were produced for β-cell imaging [45] as contrast agents. Super paramagnetic iron oxide nanoparticles are especially desirable in that they are biocompatible and can diminish to iron and oxygen. The super paramagnetic properties make it possible to target these nanoparticles using magnetism, to monitor them utilizing MRI, and to use them as magnetic triggers for drug release [46]. SPIONs have been designed to track the infiltration of the immune cells and eventual pancreatitis as an early diagnostic method for diabetes. In a pilot clinical trial, a commercially permitted SPION-based MRI contrast-imaging agent, ferumoxtran-10-a dextran-coated iron oxide nanoparticle, was injected with non-diabetic healthy volunteers and patients with latest-onset diabetes, which is readily absorbed by macrophages due to its size and surface characteristics and scanned using a 1.5 T clinical MRI tool to track pancreatitis [47]. The research allowed pancreatic representation and, more significantly, showed a double variation in the pancreas relaxation time of T2 in diabetic patients versus healthy volunteers due to continued inflammation of the islets. Direct imaging of β-cell mass growing iron oxide nanoparticles can also be used to monitor islet cells that are endogenous and exogenously transplanted. Ferrimagnetic iron oxide nanocubes have a high degree of relaxation, which increases MRI resolution, making it possible to visualize single cells in pancreatic islets using a clinical MRI device. While significant advances have been made in the creation of imaging samples to track inflammation and β-cell biomass, there is still a need for molecularly focused samples that can report directly on in vivo islet functionality.

The design of nanotechnology for diabetes control has only recently started but is progressing at an accelerated pace due to motivation and conversion from advances in other disease therapies. These incorporate cancer. Which obtained FDA approval in 1995 for the first nanoparticle-based therapy, a pegylated liposome nanoparticle formulation loaded with the chemotherapeutic agent doxorubicin. The pipeline of nanomedicines for cancer evidence has since grown greatly, with more than 20 many formulations presently under clinical research. Also, nanotechnology was developed to control cardiovascular disease. Nanoparticles were utilized to administer MRI contrast factors to human cases for the detection of acute myocardial infractions. As a consequence, there is previously a comprehensive toolbox of innovative nanotechnology-based formulations that are clinically relevant. Between the developments identified to date is non-invasive monitoring of disease sequence and blood glucose levels, glucose-responsive and patient-friendly insulin, and enhanced immune regulation for cell-based treatments. Despite this, the bar to success is strong, as diabetes administration is well known in outpatient clinical trials using sophisticated monitoring systems using consecutive glucose monitors and insulin pumps. Long-term nanotechnology health is also under greater scrutiny.

Since then, the FDA released guidelines to help facilitate the safe production of nanotechnology-based, clinical-use materials. In the development of diabetes therapeutics and diagnostics, safety and long-term performance must be fully assessed, particularly for products that are not damaged or removed from the body. Recent studies envisage encouraging a chance to grow closed-loop glucose detecting and formulations of nanoparticles that provide insulin. Some more innovation of modern glucose-sensing compounds which can fulfil as triggering

processes for both sensors and glucose-responsive components will be key to advancing this technique. To illustrate improved sensitivity and accuracy to glucose, the next phase of nanosensors and incorporated glucose-mediated insulin distribution formulations would need to. In particular, the extended lag times for reply to increased blood glucose levels continue a major challenge. In developing new glucose-responsive moieties with more potent interaction properties, as well as products that contain these binding domains, a possible solution will rest. The next phase of technology for glucose sensors would require to be more stable and accurate, with less drift originating from sensor failure or fault. Cell-based treatment is another field where nanotechnology can play a major role in reducing the immune response to the new cells that produce insulin. Nanoparticles show significant ability as operatives for the delivery of nucleic acid therapy, and progress is likely to be produced with the development of advanced approaches for cellular targeting. This can be beneficial in treatments that include reprogramming endogenous cells into islet-like cells, as well as in transplant islet growth. In brief, anticipate nanotechnology to play a major role in developing diabetes control in the next decade. It is promoting the advent of FDA-approved nanotechnology formulations combined with the clinical success of insulin-providing technologies through the pulmonary route. In our opinion, the greatest need and also the highest clinical potential for diabetes therapy based on nanotechnology is the production of robust glucose-sensitive nanoparticles and nanodevices for sensor assimilation and the addition of integrated glucose-sensing and insulin-providing nanoformulations.

References

  1. American Diabetes Association. Standards of medical care in diabetes-2014. Diabetes Care 37:S14.https://doi.org/10.2337/dc14-s014
  2. Whiting DR, Guariguata L, Weil C, Shaw J (2011) IDF diabetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030. Diabet Res Clin Prac 94:311-321.https://doi.org/10.1016/j.diabres.2011.10.029
  3. Zimmet PZ, Magliano DJ, Herman WH, Shaw JE (2014) Diabetes: a 21st century challenge. Lancet Diabet Endocrinol 2:56-64.https://doi.org/10.1016/S2213-8587(13)70112-8
  4. Du Toit G, Van der Merwe MT (2003) The epidemic of childhood obesity. S Afr Med J 93: 49-50.
  5. Abulafia-Lapid R, Elias D, Raz I, Keren-Zur Y, Atlan H, et al. (1999) T cell proliferative responses of type 1 diabetes patients and healthy individuals to human hsp60 and itspeptides. J Autoimmun 12:121-129.https://doi.org/10.1006/jaut.1998.0262
  6. Riddle MC (2005) Glycemic management of type 2 diabetes: an emerging strategy with oral agents, insulins, and combinations. Endocrinol Metabol Clin 34:77-98.https://doi.org/10.1016/j.ecl.2004.12.002
  7. Zozulinska D, Wierusz-Wysocka B (2006) Type 2 diabetes mellitus as inflammatory disease. Diabet Res Clin Pract 74:S12-S16.https://doi.org/10.1016/j.diabres.2006.06.007
  8. Pickup JC (2012) Management of diabetes mellitus: is the pump mightier than the pen?. Nat Rev Endocrinol 8:425.https://doi.org/10.1038/nrendo.2012.28
  9. American Diabetes Association. Standards of medical care indiabetes-2013. Diabetes Care 36:S11-S66.https://doi.org/10.2337/dc13-S011
  10. Emerging Risk Factors Collaboration. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet 375:2215-2222.https://doi.org/10.1016/S0140-6736(10)60484-9
  11. Robinson LE, van Soeren MH (2004) Insulin resistance and hyperglycemia in critical illness: role of insulin in glycemic control. AACN Adv Crit Care 15:45-62.https://doi.org/10.1097/00044067-200401000-00004
  12. Berenson DF, Weiss AR, Wan ZL, WeissMA (2011) Insulin analogs for the treatment of diabetes mellitus: therapeutic applications of protein engineering. Ann N Y Acad Sci 1243:E40.https://dx.doi.org/10.1111/j.1749-6632.2012.06468.x
  13. Hirsch IB (1998) Intensive treatment of type 1 diabetes. Med Clin N 82:689-719.https://doi.org/10.1016/S0025-7125(05)70020-1
  14. Heinemann L. New ways of insulin delivery. Int J Clin Pract 65:31-46.https://doi.org/10.1111/j.1742-1241.2010.02577.x
  15. Pickup JC, Renard E (2008) Long-acting insulin analogs versus insulin pump therapy for the treatment of type 1 and type 2 diabetes. Diabetes Care 31:S140-S145.https://doi.org/10.2337/dc08-s235
  16. Russell SJ, El-Khatib FH, Nathan DM, Magyar KL, Jiang J, et al. (2012) Blood glucose control in type 1 diabetes with a bihormonal bionic endocrine pancreas. Diabetes Care 35:2148-2155.https://doi.org/10.2337/dc12-0071
  17. Resnick HE, Foster GL, Bardsley J, Ratner RE (2006) Achievement of American Diabetes Association clinical practice recommendations among US adults with diabetes, 1999-2002: The National Health and Nutrition Examination Survey. Diabetes Care 29:531-537.https://doi.org/10.2337/diacare.29.03.06.dc05-1254
  18. Gómez-Pérez FJ, Rull JA (2005) Insulin therapy: current alternatives. Arch Med Res 36:258-272.https://doi.org/10.1016/j.arcmed.2005.03.012
  19. Mehanna A (2013) Antidiabetic agents: past, present and future. Fut Med Chem 5:411-430.https://doi.org/10.4155/fmc.13.13
  20. Weissleder R, Pittet MJ (2008) Imaging in the era of molecular oncology. Nature 452:580-589.https://doi.org/10.1038/nature06917
  21. Whitesides GM (2003) The'right'size in nanobiotechnology. Nature Biotechnol 21:1161-1165.https://doi.org/10.1038/nbt872
  22. Dvir T, Timko BP, Kohane DS, Langer R (2011) Nanotechnological strategies for engineering complex tissues. Nature Nanotechnol 6: 13-22.https://doi.org/10.1038/nnano.2010.246
  23. Gao Y, K Shen J, Milane L, J Hornicek F, M Amiji M, et al. (2015) Targeted cancer therapy; nanotechnology approaches for overcoming drug resistance. Curr Med Chem 22:1335-1347.https://doi.org/10.2174/0929867322666150209151851
  24. LaVan DA, Lynn DM, Langer R (2002) Moving smaller in drug discovery and delivery. Nat Rev Drug Disc 1:77-84.https://doi.org/10.1038/nrd707
  25. Chakraborty M, Jain S, Rani V (2011) Nanotechnology: emerging tool for diagnostics and therapeutics. App Biochem Biotechnol 165:1178-1187.https://doi.org/10.1007/s12010-011-9336-6
  26. Yan W, Huang L (2007) Recent advances in liposome?basednanoparticles for antigen delivery. J Macromol Sci Part C: Polymer Rev 47:329-344.https://doi.org/10.1080/15583720701455020
  27. Stinchcombe TE (2007) Nanoparticle albumin-bound paclitaxel: a novel Cremphor-EL®-free formulation of paclitaxel. Fut Med 2:415-423.https://doi.org/10.2217/17435889.2.4.415
  28. Barbas AS, Mi J, Clary BM, White RR (2010) Aptamer applications for targeted cancer therapy. Fut Oncol 6:1117-1126.https://doi.org/10.2217/fon.10.67
  29. Chiu GN, Wong MY, Ling LU, Shaikh IM, Tan KB, et al. (2009) Lipid-based nanoparticulate systems for the delivery of anti-cancer drug cocktails: implications on pharmacokinetics and drug toxicities. Curr Drug Metabol 10:861-874.https://doi.org/10.2174/138920009790274531
  30. Ozpolat B, Sood AK, Lopez-Berestein G (2014) Liposomal siRNA nanocarriers for cancer therapy. Adv Drug Deliv Rev 66:110-116.https://doi.org/10.1016/j.addr.2013.12.008
  31. Sahoo SK, Labhasetwar V (2003) Nanotech approaches todrug delivery and imaging. Drug Disc Today 8:1112-1120.https://doi.org/10.1016/S1359-6446(03)02903-9
  32. Boisselier E, Astruc D (2009) Gold nanoparticles in nanomedicine: preparations, imaging, diagnostics, therapies and toxicity. Chem Soci Rev 38:1759-1782.https://doi.org/10.1039/B806051G
  33. Peng X, Peng L, Wu C, Xie Y (2014) Two dimensional nanomaterials for flexible supercapacitors. Chem Soci Rev 43:3303-3323.https://doi.org/10.1039/C3CS60407A
  34. Zhi ZL, Khan F, Pickup JC (2013) Multilayer nanoencapsulation: A nanomedicine technology for diabetes research and management. Diabet Res Clin Pract 100:162-169.https://doi.org/10.1016/j.diabres.2012.11.027
  35. Naesens M, Sarwal MM (2010) Molecular diagnostics in transplantation. Nat Rev Nephrol 6:614.https://doi.org/10.1038/nrneph.2010.113
  36. Matveyenko AV, Butler PC (2008) Relationship between β?cell mass and diabetes onset. Diabet Obes Metabol 10:23-31.https://doi.org/10.1111/j.1463-1326.2008.00939.x
  37. Moore A, Bonner-Weir S, Weissleder R (2001) Noninvasive invivo measurement of β-cell mass in mouse model of diabetes. Diabetes 50:2231-2236.https://doi.org/10.2337/diabetes.50.10.2231
  38. Malaisse WJ, Maedler K (2012) Imaging of the β-cells of the islets of Langerhans. Diabet Res Clin Pract 98:11-18.https://doi.org/10.1016/j.diabres.2012.07.001
  39. Denis MC, Mahmood U, Benoist C, Mathis D, Weissleder R(2004) Imaging inflammation of the pancreatic islets in type 1 diabetes. Proceed Nat Acad Sci 101:12634-12639.https://doi.org/10.1073/pnas.0404307101
  40. Leibiger IB, Caicedo A, Berggren PO (2012) Non?invasive in vivo imaging of pancreatic β?cell function and survival–a perspective. Acta physiol 204:178-185.https://doi.org/10.1111/j.1748-1716.2011.02301.x
  41. Fu W, Wojtkiewicz G, Weissleder R, Benoist C, Mathis D (2012) Early window of diabetes determinism in NOD mice, dependent on thecomplement receptor CRIg, identified by noninvasive imaging. Nature Immunol 13:361-368.https://doi.org/10.1038/ni.2233
  42. Medarova Z, Evgenov NV, Dai G, Bonner-Weir S, Moore A (2006) In vivo multimodal imaging of transplanted pancreatic islets. Nature Protoc 1:429-435.https://doi.org/10.1038/nprot.2006.63
  43. Evgenov NV, Medarova Z, Dai G, Bonner-Weir S, Moore A (2005) In vivo imaging of islet transplantation. Nature Med 12:144-148.https://doi.org/10.1038/nm1316
  44. Andralojc K, Srinivas M, Brom M, Joosten L, De Vries IJ, et al. (2012) Obstacles on the way to the clinical visualisation of beta cells: looking for the Aeneas of molecular imaging to navigate between Scylla and Charybdis. Diabetologia 55:1247-1257.https://doi.org/10.1007/s00125-012-2491-7
  45. Laurent D, Vinet L, Lamprianou S, Daval M, Filhoulaud G, et al.(2016) Pancreatic β?cell imaging in humans: fiction or option?. Diabet Obes Metabol 18:6-15.https://doi.org/10.1111/dom.12544
  46. Josephson L (2006) Magnetic nanoparticles for MR imaging. InBioMEMS Biomed Nanotechnol 227-237.https://doi.org/10.1007/978-0-387-25842-3_8
  47. Gaglia JL, Guimaraes AR, Harisinghani M, Turvey SE, Jackson R, et al. (2011) Noninvasive imaging of pancreatic islet inflammation in type 1A diabetes patients. J Clin Invest 121: 442-445.https://doi.org/10.1172/JCI44339
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