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Sharma S, Bhatia V. Nanoscale Drug Delivery Systems for Glaucoma: Experimental and In Silico Advances. Curr Top Med Chem 2021; 21:115-125. [PMID: 32962618 DOI: 10.2174/1568026620666200922114210] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 07/21/2020] [Accepted: 07/23/2020] [Indexed: 12/25/2022]
Abstract
In this review, nanoscale-based drug delivery systems, particularly in relevance to the antiglaucoma drugs, have been discussed. In addition to that, the latest computational/in silico advances in this field are examined in brief. Using nanoscale materials for drug delivery is an ideal option to target tumours, and the drug can be released in areas of the body where traditional drugs may fail to act. Nanoparticles, polymeric nanomaterials, single-wall carbon nanotubes (SWCNTs), quantum dots (QDs), liposomes and graphene are the most important nanomaterials used for drug delivery. Ocular drug delivery is one of the most common and difficult tasks faced by pharmaceutical scientists because of many challenges like circumventing the blood-retinal barrier, corneal epithelium and the blood-aqueous barrier. Authors found compelling empirical evidence of scientists relying on in-silico approaches to develop novel drugs and drug delivery systems for treating glaucoma. This review in nanoscale drug delivery systems will help us understand the existing queries and evidence gaps and will pave the way for the effective design of novel ocular drug delivery systems.
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Affiliation(s)
- Smriti Sharma
- Department of Chemistry, Miranda House, University of Delhi, Delhi, India
| | - Vinayak Bhatia
- ICARE Eye Hospital and Postgraduate Institute, Noida, UP, India
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Thomford NE, Senthebane DA, Rowe A, Munro D, Seele P, Maroyi A, Dzobo K. Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery. Int J Mol Sci 2018; 19:E1578. [PMID: 29799486 PMCID: PMC6032166 DOI: 10.3390/ijms19061578] [Citation(s) in RCA: 549] [Impact Index Per Article: 91.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Revised: 05/16/2018] [Accepted: 05/18/2018] [Indexed: 12/12/2022] Open
Abstract
The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of "active compound" has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of 'organ-on chip' and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug development. This review discusses plant-based natural product drug discovery and how innovative technologies play a role in next-generation drug discovery.
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Affiliation(s)
- Nicholas Ekow Thomford
- Pharmacogenomics and Drug Metabolism Group, Division of Human Genetics, Department of Pathology and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Anzio Road, Observatory, Cape Town 7925, South Africa.
- School of Medical Sciences, University of Cape Coast, PMB, Cape Coast, Ghana.
| | - Dimakatso Alice Senthebane
- International Centre for Genetic Engineering and Biotechnology (ICGEB), Cape Town Component, Wernher and Beit Building (South), University of Cape Town Medical Campus, Anzio Road, Observatory, Cape Town 7925, South Africa.
- Division of Medical Biochemistry and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Anzio Road, Observatory, Cape Town 7925, South Africa.
| | - Arielle Rowe
- International Centre for Genetic Engineering and Biotechnology (ICGEB), Cape Town Component, Wernher and Beit Building (South), University of Cape Town Medical Campus, Anzio Road, Observatory, Cape Town 7925, South Africa.
| | - Daniella Munro
- Pharmacogenomics and Drug Metabolism Group, Division of Human Genetics, Department of Pathology and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Anzio Road, Observatory, Cape Town 7925, South Africa.
| | - Palesa Seele
- Division of Chemical and Systems Biology, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Anzio Road, Observatory, Cape Town 7925, South Africa.
| | - Alfred Maroyi
- Department of Botany, University of Fort Hare, Private Bag, Alice X1314, South Africa.
| | - Kevin Dzobo
- International Centre for Genetic Engineering and Biotechnology (ICGEB), Cape Town Component, Wernher and Beit Building (South), University of Cape Town Medical Campus, Anzio Road, Observatory, Cape Town 7925, South Africa.
- Division of Medical Biochemistry and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Anzio Road, Observatory, Cape Town 7925, South Africa.
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Chang J, Kim Y, Kwon HJ. Advances in identification and validation of protein targets of natural products without chemical modification. Nat Prod Rep 2016; 33:719-30. [DOI: 10.1039/c5np00107b] [Citation(s) in RCA: 79] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
This review focuses on and reports case studies of the latest advances in target protein identification methods for label-free natural products. The integration of newly developed technologies will provide new insights and highlight the value of natural products for use as biological probes and new drug candidates.
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Affiliation(s)
- J. Chang
- Department of Biotechnology
- Translational Research Center for Protein Function Control
- College of Life Science & Biotechnology
- Yonsei University
- Seoul 120-749
| | - Y. Kim
- Department of Biotechnology
- Translational Research Center for Protein Function Control
- College of Life Science & Biotechnology
- Yonsei University
- Seoul 120-749
| | - H. J. Kwon
- Department of Biotechnology
- Translational Research Center for Protein Function Control
- College of Life Science & Biotechnology
- Yonsei University
- Seoul 120-749
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Phatak SS, Stephan CC, Cavasotto CN. High-throughput and in silico screenings in drug discovery. Expert Opin Drug Discov 2013; 4:947-59. [PMID: 23480542 DOI: 10.1517/17460440903190961] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
BACKGROUND In the current situation of weak drug pipelines, impending patent expiration of several blockbuster drugs, industry consolidation and changing business models that target special diseases like cancer, diabetes, Alzheimer's and obesity, the pharmaceutical industry is under intense pressure to generate a strong drug pipeline distinguished by better productivity, diversity and cost effectiveness. The goal is discovering high-quality leads in the initial stages of the development cycle, to minimize the costs associated with failures at later ones. OBJECTIVE Thus, there is a great amount of interest in further developing and optimizing high-throughput screening and in silico screening, the two methods responsible for generating most of the lead compounds. Although high-throughput screening is the predominant starting point for discovery programs, in silico methods have gradually made inroads by their more rational approach, to expedite the drug discovery and development process. CONCLUSION Modern drug discovery strategies include both methods in tandem or in an iterative way. This review primarily provides a succinct overview and comparison of experimental and in silico screening techniques, selected case studies where both methods were used in concert to investigate their performance and complementary nature and a statement on the developments in experimental and in silico approaches in the near future.
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Affiliation(s)
- Sharangdhar S Phatak
- The University of Texas Health Science Center at Houston, School of Health Information Sciences, 7000 Fannin, Suite 860B, Houston, TX 77030, USA +1 713 500 3934 ; +1 713 500 3907 ;
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Huynh L, Neale C, Pomès R, Allen C. Computational approaches to the rational design of nanoemulsions, polymeric micelles, and dendrimers for drug delivery. NANOMEDICINE-NANOTECHNOLOGY BIOLOGY AND MEDICINE 2012; 8:20-36. [DOI: 10.1016/j.nano.2011.05.006] [Citation(s) in RCA: 86] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2011] [Revised: 05/09/2011] [Accepted: 05/14/2011] [Indexed: 12/20/2022]
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Chen YPP, Chen F. Identifying targets for drug discovery using bioinformatics. Expert Opin Ther Targets 2008; 12:383-9. [PMID: 18348676 DOI: 10.1517/14728222.12.4.383] [Citation(s) in RCA: 55] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
BACKGROUND Drug discovery is the process of discovering and designing drugs, which includes target identification, target validation, lead identification, lead optimization and introduction of the new drugs to the public. This process is very important, involving analyzing the causes of the diseases and finding ways to tackle them. OBJECTIVE The problems we must face include: i) that this process is so long and expensive that it might cost millions of dollars and take a dozen years; and ii) the accuracy of identification of targets is not good enough, which in turn delays the process. Introducing bioinformatics into the drug discovery process could contribute much to it. Bioinformatics is a booming subject combining biology with computer science. It can explore the causes of diseases at the molecular level, explain the phenomena of the diseases from the angle of the gene and make use of computer techniques, such as data mining, machine learning and so on, to decrease the scope of analysis and enhance the accuracy of the results so as to reduce the cost and time. METHODS Here we describe recent studies about how to apply bioinformatics techniques in the four phases of drug discovery, how these techniques improve the drug discovery process and some possible difficulties that should be dealt with. RESULTS We conclude that combining bioinformatics with drug discovery is a very promising method although it faces many problems currently.
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