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Liu P, Gao C, Chen H, Vong CT, Wu X, Tang X, Wang S, Wang Y. Receptor-mediated targeted drug delivery systems for treatment of inflammatory bowel disease: Opportunities and emerging strategies. Acta Pharm Sin B 2021; 11:2798-2818. [PMID: 34589398 PMCID: PMC8463263 DOI: 10.1016/j.apsb.2020.11.003] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 10/01/2020] [Accepted: 10/14/2020] [Indexed: 02/08/2023] Open
Abstract
Inflammatory bowel disease (IBD) is a chronic intestinal disease with painful clinical manifestations and high risks of cancerization. With no curative therapy for IBD at present, the development of effective therapeutics is highly advocated. Drug delivery systems have been extensively studied to transmit therapeutics to inflamed colon sites through the enhanced permeability and retention (EPR) effect caused by the inflammation. However, the drug still could not achieve effective concentration value that merely utilized on EPR effect and display better therapeutic efficacy in the inflamed region because of nontargeted drug release. Substantial researches have shown that some specific receptors and cell adhesion molecules highly expresses on the surface of colonic endothelial and/or immune cells when IBD occurs, ligand-modified drug delivery systems targeting such receptors and cell adhesion molecules can specifically deliver drug into inflamed sites and obtain great curative effects. This review introduces the overexpressed receptors and cell adhesion molecules in inflamed colon sites and retrospects the drug delivery systems functionalized by related ligands. Finally, challenges and future directions in this field are presented to advance the development of the receptor-mediated targeted drug delivery systems for the therapy of IBD.
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Key Words
- ACQ, aggregation-caused quenching
- ADR, adverse drug reaction
- AIE, aggregation-induced emission
- Active target
- BSA, bovine serum albumin
- CAM, cell adhesion molecule
- CD, Crohn's disease
- CRD, cysteine-rich domain
- CS, chondroitin sulfate
- CT, computed tomography
- CTLD, c-type lectin-like domain
- Cell adhesion molecule
- Crohn's disease
- DCs, dendritic cells
- DSS, dextran sulfate sodium salt
- Drug delivery
- EGF, epidermal growth factor
- EPR, enhanced permeability and retention
- FNII, fibronectin type II domain
- FR, folate receptor
- FRET, fluorescence resonance energy transfer
- GIT, gastrointestinal tract
- HA, hyaluronic acid
- HUVEC, human umbilical vein endothelial cells
- IBD, inflammatory bowel disease
- ICAM, intercellular adhesion molecule
- Inflammatory bowel disease
- LMWC, low molecular weight chitosan
- LPS, lipopolysaccharide
- MAP4K4, mitogen-activated protein kinase kinase kinase kinase 4
- MGL, macrophage galactose lectin
- MPO, myeloperoxidase
- MPS, mononuclear phagocyte system
- MR, mannose receptor
- MRI, magnetic resonance imaging
- PAMAM, poly(amidoamine)
- PEI, polyethylenimine
- PSGL-1, P-selectin glycoprotein ligand-1
- PepT1, peptide transporter 1
- QDs, quantum dots
- RES, reticuloendothelial system
- Receptor-mediated target
- Targeted therapy
- TfR, transferrin receptor
- UC, ulcerative colitis
- Ulcerative colitis
- VCAM, vascular cell adhesion molecule
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Hassanzadeh P. The capabilities of nanoelectronic 2-D materials for bio-inspired computing and drug delivery indicate their significance in modern drug design. Life Sci 2021; 279:119272. [PMID: 33631171 DOI: 10.1016/j.lfs.2021.119272] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 02/10/2021] [Accepted: 02/19/2021] [Indexed: 12/13/2022]
Abstract
Remarkable advancements in the computational techniques and nanoelectronics have attracted considerable interests for development of highly-sophisticated materials (Ms) including the theranostics with optimal characteristics and innovative delivery systems. Analyzing the huge amounts of multivariate data and solving the newly-emerged complicated problems including the healthcare-related ones have created increasing demands for improving the computational speed and minimizing the consumption of energy. Shifting towards the non-von Neumann approaches enables performing specific computational tasks and optimizing the processing of signals. Besides usefulness for neuromorphic computing and increasing the efficiency of computation energy, 2-D electronic Ms are capable of optical sensing with ultra-fast and ultra-sensitive responses, mimicking the neurons, detection of pathogens or biomolecules, and prediction of the progression of diseases, assessment of the pharmacokinetics/pharmacodynamics of therapeutic candidates, mimicking the dynamics of the release of neurotransmitters or fluxes of ions that might provide a deeper knowledge about the computations and information flow in the brain, and development of more effective treatment protocols with improved outcomes. 2-D Ms appear as the major components of the next-generation electronically-enabled devices for highly-advanced computations, bio-imaging, diagnostics, tissue engineering, and designing smart systems for site-specific delivery of therapeutics that might result in the reduced adverse effects of drugs and improved patient compliance. This manuscript highlights the significance of 2-D Ms in the neuromorphic computing, optimizing the energy efficiency of the multi-step computations, providing novel architectures or multi-functional systems, improved performance of a variety of devices and bio-inspired functionalities, and delivery of theranostics.
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Affiliation(s)
- Parichehr Hassanzadeh
- Nanotechnology Research Center, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran 13169-43551, Iran.
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Gao M, Liu S, Chen J, Gordon KC, Tian F, McGoverin CM. Potential of Raman spectroscopy in facilitating pharmaceutical formulations development - An AI perspective. Int J Pharm 2021; 597:120334. [PMID: 33540015 DOI: 10.1016/j.ijpharm.2021.120334] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/26/2021] [Accepted: 01/27/2021] [Indexed: 01/17/2023]
Abstract
Drug development is time-consuming and inherently possesses a high failure rate. Pharmaceutical formulation development is the bridge that links a new chemical entity (NCE) to pre-clinical and clinical trials, and has a high impact on the efficacy and safety of the final drug product. Further, the time required for this process is escalating as formulation techniques are becoming more complicated due to the rising demands for drug products with better efficacy and patient compliance, as well as the inherent difficulties of addressing the unfavorable properties of NCEs such as low water solubility. The advent of artificial intelligence (AI) provides possibilities to accelerate the drug development process. In this review, we first examine applications of AI methods in different types of pharmaceutical formulations and formulation techniques. Moreover, as availability of data is the engine for the advancement of AI, we then suggest a potential way (i.e. applying Raman spectroscopy) for faster high-quality data gathering from formulations. Raman techniques have the capability of analyzing the composition and distribution of components and the physicochemical properties thereof within formulations, which are prominent factors governing drug dissolution profiles and subsequently bioavailability. Thus, useful information can be obtained bridging formulation development to the final product quality.
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Affiliation(s)
- Ming Gao
- Nycrist Pharmtech Limited, 2/2D, A3, Science and Technology Park, 3009 Guanguang Rd, Guangming, Shenzhen, Guangdong 518107, China
| | - Sibo Liu
- Nycrist Pharmtech Limited, 2/2D, A3, Science and Technology Park, 3009 Guanguang Rd, Guangming, Shenzhen, Guangdong 518107, China
| | - Jianan Chen
- Department of Medical Biophysics, University of Toronto, Princess Margaret Cancer Research Tower, MaRS Centre, 101 College Street, Toronto, Ontario M5G 1L7, Canada
| | - Keith C Gordon
- Dodd-Walls Centre, Department of Chemistry, University of Otago, PO Box 56, Dunedin 9054, New Zealand
| | - Fang Tian
- Nycrist Pharmtech Limited, 2/2D, A3, Science and Technology Park, 3009 Guanguang Rd, Guangming, Shenzhen, Guangdong 518107, China
| | - Cushla M McGoverin
- Nycrist Pharmtech Limited, 2/2D, A3, Science and Technology Park, 3009 Guanguang Rd, Guangming, Shenzhen, Guangdong 518107, China.
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Hassanzadeh P, Atyabi F, Dinarvand R. The significance of artificial intelligence in drug delivery system design. Adv Drug Deliv Rev 2019; 151-152:169-190. [PMID: 31071378 DOI: 10.1016/j.addr.2019.05.001] [Citation(s) in RCA: 93] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 04/14/2019] [Accepted: 05/02/2019] [Indexed: 02/07/2023]
Abstract
Over the last decade, increasing interest has been attracted towards the application of artificial intelligence (AI) technology for analyzing and interpreting the biological or genetic information, accelerated drug discovery, and identification of the selective small-molecule modulators or rare molecules and prediction of their behavior. Application of the automated workflows and databases for rapid analysis of the huge amounts of data and artificial neural networks (ANNs) for development of the novel hypotheses and treatment strategies, prediction of disease progression, and evaluation of the pharmacological profiles of drug candidates may significantly improve treatment outcomes. Target fishing (TF) by rapid prediction or identification of the biological targets might be of great help for linking targets to the novel compounds. AI and TF methods in association with human expertise may indeed revolutionize the current theranostic strategies, meanwhile, validation approaches are necessary to overcome the potential challenges and ensure higher accuracy. In this review, the significance of AI and TF in the development of drugs and delivery systems and the potential challenging issues have been highlighted.
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Affiliation(s)
- Parichehr Hassanzadeh
- Nanotechnology Research Center, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran 13169-43551, Iran.
| | - Fatemeh Atyabi
- Nanotechnology Research Center, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran 13169-43551, Iran.
| | - Rassoul Dinarvand
- Nanotechnology Research Center, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran 13169-43551, Iran.
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McKinley D, Patel SK, Regev G, Rohan LC, Akil A. Delineating the effects of hot-melt extrusion on the performance of a polymeric film using artificial neural networks and an evolutionary algorithm. Int J Pharm 2019; 571:118715. [PMID: 31560958 DOI: 10.1016/j.ijpharm.2019.118715] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 08/05/2019] [Accepted: 08/12/2019] [Indexed: 12/26/2022]
Abstract
The aim of this study was to utilize an artificial neural network (ANN) in conjunction with an evolutionary algorithm to investigate the relationship between hot melt extrusion (HME) process parameters and vaginal film performance. Investigated HME process parameters were: barrel temperature, screw speed, and feed rate. Investigated film performance attributes were: percent dissolution at 30 min, puncture strength, and drug content. An ANN model was successfully developed and validated with a root mean squared error of 0.043 and 0.098 for training and validation, respectively. Of all three assessed process parameters, the model revealed that barrel temperature has a significant impact on film performance. An increase in barrel temperature resulted in increased dissolution and punctures strength and decreased drug content. Additionally, a successful implementation of an evolutionary algorithm was carried out in order to demonstrate the potential applicability of the developed ANN model in film formulation optimization. In this analysis, the values predicted of film performance attributes were within 1% error of the experimental data. The findings of this study provide a quantitative framework to understand the relationship between HME parameters and film performance. This quantitative framework has the potential to be used for film formulation development and optimization.
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Affiliation(s)
- DeAngelo McKinley
- Department of Pharmaceutical Sciences, College of Pharmacy, Mercer University, Atlanta, GA, 30341, USA
| | - Sravan Kumar Patel
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, 15213, USA; Magee-Womens Research Institute, Pittsburgh, PA, 15213, USA
| | - Galit Regev
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, 15213, USA; Magee-Womens Research Institute, Pittsburgh, PA, 15213, USA
| | - Lisa C Rohan
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, 15213, USA; Department of Obstetrics, Gynecology & Reproductive Sciences, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA; Magee-Womens Research Institute, Pittsburgh, PA, 15213, USA
| | - Ayman Akil
- Department of Pharmaceutical Sciences, College of Pharmacy, Mercer University, Atlanta, GA, 30341, USA.
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McKinley D, Kumar Patel S, Regev G, Rohan LC, Akil A. WITHDRAWN: Delineating the Effects of Hot-Melt Extrusion on the Performance of a Polymeric Film using Artificial Neural Networks and an Evolutionary Algorithm. Int J Pharm X 2019. [DOI: 10.1016/j.ijpx.2019.100031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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Caccavo D. An overview on the mathematical modeling of hydrogels' behavior for drug delivery systems. Int J Pharm 2019; 560:175-190. [PMID: 30763681 DOI: 10.1016/j.ijpharm.2019.01.076] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 01/28/2019] [Accepted: 01/31/2019] [Indexed: 02/07/2023]
Abstract
Hydrogels-based systems (HBSs) for drug delivery are nowadays extensively used and the interest in modeling their behavior is dramatically increasing. In this review a critical overview on the modeling approaches is given, quantitatively and qualitatively analyzing the publications on the subject, the trend of the publications per year and the type of modeling approaches. It was found that, despite the drug release fitting models (i.e. Higuchi's equation) are the most abundant, their use for HBSs is decreasing in the last years and luckily, considering the limiting assumption on which they were built, they will be confined to simple mathematical fitting equations. Within the mechanistic models the "multi-component" with the swelling approximation (mass transport only) and with the mechanics (fully coupled) are experiencing the highest growth rate, with much more interest toward the last one that, in the next years could be able to provide a first principles model. Statistical models, especially based on the response surface methodology, are rapidly spreading in the scientific community mainly thanks to their ability to be predictive, regardless of the phenomenology, in the analyzed design space with very low efforts. Neural Networks models for HBSs, in countertrend with their use in the pharmaceutical industry, have never take off preferring less data demanding statistical models.
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Affiliation(s)
- Diego Caccavo
- Department of Industrial Engineering, University of Salerno, 84084 Fisciano, SA, Italy.
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Nemati P, Imani M, Farahmandghavi F, Mirzadeh H, Marzban-Rad E, Nasrabadi AM. Dexamethasone-releasing cochlear implant coatings: application of artificial neural networks for modelling of formulation parameters and drug release profile. J Pharm Pharmacol 2013; 65:1145-57. [DOI: 10.1111/jphp.12086] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 04/24/2013] [Indexed: 11/29/2022]
Abstract
Abstract
Objectives
Over the past few decades, mathematical modelling and simulation of drug delivery systems has been steadily gained interest as a focus for academic and industrial attention. Here, simulation of dexamethasone (DEX, a corticosteroid anti-inflammatory agent) release profile from drug-eluting cochlear implant coatings is reported using artificial neural networks.
Methods
The devices were fabricated as monolithic dispersions of the pharmaceutically active ingredient in a silicone rubber matrix. A two-phase exponential model was fitted on the experimentally obtained DEX release profiles. An artificial neural network (ANN) was trained to determine formulation parameters (i.e. DEX loading percentage, the devices surface area and their geometry) for a specific experimentally obtained drug release profile. In a reverse strategy, an ANN was trained for determining expected drug release profiles for the same set of formulation parameters.
Key findings
An algorithm was developed by combining the two previously developed ANNs in a serial manner, and this was successfully used for simulating the developed drug-eluting cochlear implant coatings. The models were validated by a leave-one-out method and performing new experiments.
Conclusions
The developed ANN algorithms were capable to bilaterally predict drug release profile for a known set of formulation parameters or find out the levels for input formulation parameters to obtain a desired DEX release profile.
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Affiliation(s)
- Pedram Nemati
- Novel Drug Delivery Systems Department, Iran Polymer and Petrochemical Institute, Tehran, Iran
| | - Mohammad Imani
- Novel Drug Delivery Systems Department, Iran Polymer and Petrochemical Institute, Tehran, Iran
| | - Farhid Farahmandghavi
- Novel Drug Delivery Systems Department, Iran Polymer and Petrochemical Institute, Tehran, Iran
| | - Hamid Mirzadeh
- Department of Polymer Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Ehsan Marzban-Rad
- Ceramics Department, Materials and Energy Research Center, Tehran, Iran
| | - Ali Motie Nasrabadi
- Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
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Lou H, Liu M, Qu W, Hu Z, Brunson E, Johnson J, Almoazen H. Evaluation of Chlorpheniramine Maleate microparticles in orally disintegrating film and orally disintegrating tablet for pediatrics. Drug Dev Ind Pharm 2013; 40:910-8. [DOI: 10.3109/03639045.2013.789907] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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10
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Singh B, Kapil R, Nandi M, Ahuja N. Developing oral drug delivery systems using formulation by design: vital precepts, retrospect and prospects. Expert Opin Drug Deliv 2011; 8:1341-60. [DOI: 10.1517/17425247.2011.605120] [Citation(s) in RCA: 108] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Al-Shyoukh I, Yu F, Feng J, Yan K, Dubinett S, Ho CM, Shamma JS, Sun R. Systematic quantitative characterization of cellular responses induced by multiple signals. BMC SYSTEMS BIOLOGY 2011; 5:88. [PMID: 21624115 PMCID: PMC3138445 DOI: 10.1186/1752-0509-5-88] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2010] [Accepted: 05/30/2011] [Indexed: 11/15/2022]
Abstract
Background Cells constantly sense many internal and environmental signals and respond through their complex signaling network, leading to particular biological outcomes. However, a systematic characterization and optimization of multi-signal responses remains a pressing challenge to traditional experimental approaches due to the arising complexity associated with the increasing number of signals and their intensities. Results We established and validated a data-driven mathematical approach to systematically characterize signal-response relationships. Our results demonstrate how mathematical learning algorithms can enable systematic characterization of multi-signal induced biological activities. The proposed approach enables identification of input combinations that can result in desired biological responses. In retrospect, the results show that, unlike a single drug, a properly chosen combination of drugs can lead to a significant difference in the responses of different cell types, increasing the differential targeting of certain combinations. The successful validation of identified combinations demonstrates the power of this approach. Moreover, the approach enables examining the efficacy of all lower order mixtures of the tested signals. The approach also enables identification of system-level signaling interactions between the applied signals. Many of the signaling interactions identified were consistent with the literature, and other unknown interactions emerged. Conclusions This approach can facilitate development of systems biology and optimal drug combination therapies for cancer and other diseases and for understanding key interactions within the cellular network upon treatment with multiple signals.
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Affiliation(s)
- Ibrahim Al-Shyoukh
- Department of Molecular and Medical Pharmacology, University of California at Los Angeles, Los Angeles, CA 90095, USA
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Yin LF, Huang SJ, Jiang SG, Zhao CJ, Pei ZQ, Zhang Q. In vitro and in vivo evaluation of levofloxacin sustained-release capsules. Drug Dev Ind Pharm 2010; 37:33-40. [DOI: 10.3109/03639045.2010.489562] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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