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Martinez-Borrajo R, Diaz-Rodriguez P, Landin M. Engineering mannose-functionalized nanostructured lipid carriers by sequential design using hybrid artificial intelligence tools. Drug Deliv Transl Res 2024:10.1007/s13346-024-01603-z. [PMID: 38811464 DOI: 10.1007/s13346-024-01603-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/10/2024] [Indexed: 05/31/2024]
Abstract
Nanostructured lipid carriers (NLCs) hold significant promise as drug delivery systems (DDS) owing to their small size and efficient drug-loading capabilities. Surface functionalization of NLCs can facilitate interaction with specific cell receptors, enabling targeted cell delivery. Mannosylation has emerged as a valuable tool for increasing the ability of nanoparticles to be recognized and internalized by macrophages. Nevertheless, the design and development of functionalized NLC is a complex task that entails the optimization of numerous variables and steps, making the process challenging and time-consuming. Moreover, no previous studies have been focused on evaluating the functionalization efficiency. In this work, hybrid Artificial Intelligence technologies are used to help in the design of mannosylated drug loaded NLCs. Artificial neural networks combined with fuzzy logic or genetic algorithms were employed to understand the particle formation processes and optimize the combinations of variables for the different steps in the functionalization process. Mannose was chemically modified to allow, for the first time, functionalization efficiency quantification and optimization. The proposed sequential methodology has enabled the design of a robust procedure for obtaining stable mannosylated NLCs with a uniform particle size distribution, small particle size (< 100 nm), and a substantial positive zeta potential (> 20mV). The incorporation of mannose on the surfaces of these DDS following the established protocols achieved > 85% of functionalization efficiency. This high effectiveness should enhance NLC recognition and internalization by macrophages, thereby facilitating the treatment of chronic inflammatory diseases.
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Affiliation(s)
- Rebeca Martinez-Borrajo
- Departamento de Farmacología, Farmacia y Tecnología Farmacéutica, Grupo I+D Farma (GI-1645), Facultad de Farmacia, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Instituto de Materiais da Universidade de Santiago de Compostela (iMATUS), Universidade de Santiago de Compostela, 15782, Santiago de Compostela, Spain
| | - Patricia Diaz-Rodriguez
- Departamento de Farmacología, Farmacia y Tecnología Farmacéutica, Grupo I+D Farma (GI-1645), Facultad de Farmacia, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Instituto de Materiais da Universidade de Santiago de Compostela (iMATUS), Universidade de Santiago de Compostela, 15782, Santiago de Compostela, Spain.
| | - Mariana Landin
- Departamento de Farmacología, Farmacia y Tecnología Farmacéutica, Grupo I+D Farma (GI-1645), Facultad de Farmacia, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Instituto de Materiais da Universidade de Santiago de Compostela (iMATUS), Universidade de Santiago de Compostela, 15782, Santiago de Compostela, Spain
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Malekjani N, Jafari SM. Intelligent and Probabilistic Models for Evaluating the Release of Food Bioactive Ingredients from Carriers/Nanocarriers. FOOD BIOPROCESS TECH 2022. [DOI: 10.1007/s11947-022-02791-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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3
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Lou H, Lian B, Hageman MJ. Applications of Machine Learning in Solid Oral Dosage Form Development. J Pharm Sci 2021; 110:3150-3165. [PMID: 33951418 DOI: 10.1016/j.xphs.2021.04.013] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 04/18/2021] [Accepted: 04/19/2021] [Indexed: 02/07/2023]
Abstract
This review comprehensively summarizes the application of machine learning in solid oral dosage form development over the past three decades. In both academia and industry, machine learning is increasingly applied for multiple preformulation/formulation and process development studies. Further, this review provides the authors' perspectives on how pharmaceutical scientists can use machine learning for right projects and in right ways; some key ingredients include (1) the determination of inputs, outputs, and objectives; (2) the generation of a database containing high-quality data; (3) the development of machine learning models based on dataset training and model optimization; (4) the application of trained models in making predictions for new samples. It is expected by the authors and others that machine learning will promisingly play a more important role in tomorrow's projects for solid oral dosage form development.
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Affiliation(s)
- Hao Lou
- Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, KS 66047, United States; Biopharmaceutical Innovation and Optimization Center, University of Kansas, Lawrence, KS 66047, United States.
| | - Bo Lian
- College of Pharmacy, University of Arizona, Tucson, AZ 85721, United States
| | - Michael J Hageman
- Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, KS 66047, United States; Biopharmaceutical Innovation and Optimization Center, University of Kansas, Lawrence, KS 66047, United States
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Ahmadi M, Taghizadeh R. A gene expression programming model for economy growth using knowledge-based economy indicators. JOURNAL OF MODELLING IN MANAGEMENT 2019. [DOI: 10.1108/jm2-12-2017-0130] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
The purpose of this paper is to focus on modeling economy growth with indicators of knowledge-based economy (KBE) introduced by World Bank for a case study in Iran during 1993-2013.
Design/methodology/approach
First, for grouping and reducing the number of variables, Tukey method and the principal component analysis are used. Also for modeling, 67 per cent of data is used for training in the two approaches of ARDL bounds testing and gene expression programming (GEP) and 33 per cent of them for testing the models. Then, the result models are compared with fitness function and Akaike information criteria (AIC).
Findings
The GEP model with fitness 945.7461 for training data and 954.8403 for testing data from 1000 is better than ARDL bounds testing model with fitness 335.5479 from 1000. In addition, according to model comparison tools (AIC), the GEP model has an extremely larger weight in comparison with ARDL bounds model. Therefore, the GEP model is introduced for future use in academia.
Practical implications
Knowledge and information is one of the most basic sources of wealth in economists’ sight. Thus, using KBE indicators appears essential in economic growth regarding daily progress in knowledge processes and its different theories. It is also extremely important to determine an appropriate model for KBE indicators which play a highly important role in the allocation of the economic resources of the country in an optimal manner.
Originality/value
This paper introduced a novel expression for economy growth using KBE indicators. All the data and the indicators are extracted from Word Bank service between 1993 and 2013.
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Nguyen CN, Tran BN, Do TT, Nguyen H, Nguyen TN. D-Optimal Optimization and Data-Analysis Comparison Between a DoE Software and Artificial Neural Networks of a Chitosan Coating Process onto PLGA Nanoparticles for Lung and Cervical Cancer Treatment. J Pharm Innov 2018. [DOI: 10.1007/s12247-018-9345-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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6
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Zhang L, Chen J, Gao C, Liu C, Xu K. An efficient model for auxiliary diagnosis of hepatocellular carcinoma based on gene expression programming. Med Biol Eng Comput 2018; 56:1771-1779. [DOI: 10.1007/s11517-018-1811-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Accepted: 02/27/2018] [Indexed: 02/06/2023]
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7
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Artificial Intelligence Tools for Scaling Up of High Shear Wet Granulation Process. J Pharm Sci 2017; 106:273-277. [DOI: 10.1016/j.xphs.2016.09.022] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Revised: 09/20/2016] [Accepted: 09/21/2016] [Indexed: 11/19/2022]
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8
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Meng F, Dave V, Chauhan H. Qualitative and quantitative methods to determine miscibility in amorphous drug–polymer systems. Eur J Pharm Sci 2015; 77:106-11. [DOI: 10.1016/j.ejps.2015.05.018] [Citation(s) in RCA: 73] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2015] [Revised: 05/15/2015] [Accepted: 05/17/2015] [Indexed: 11/25/2022]
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9
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Modeling, Optimization, and In Vitro Corneal Permeation of Chitosan-Lomefloxacin HCl Nanosuspension Intended for Ophthalmic Delivery. J Pharm Innov 2015. [DOI: 10.1007/s12247-015-9224-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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10
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Liu JYC, Hsieh JC. A hybrid selection algorithm for time series modeling. Soft comput 2015. [DOI: 10.1007/s00500-014-1236-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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11
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Alhalaweh A, Alzghoul A, Kaialy W. Data mining of solubility parameters for computational prediction of drug–excipient miscibility. Drug Dev Ind Pharm 2013; 40:904-9. [DOI: 10.3109/03639045.2013.789906] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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12
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Aksu B, Paradkar A, de Matas M, Ozer O, Güneri T, York P. Quality by design approach: application of artificial intelligence techniques of tablets manufactured by direct compression. AAPS PharmSciTech 2012; 13:1138-46. [PMID: 22956056 PMCID: PMC3513460 DOI: 10.1208/s12249-012-9836-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2012] [Accepted: 08/06/2012] [Indexed: 01/17/2023] Open
Abstract
The publication of the International Conference of Harmonization (ICH) Q8, Q9, and Q10 guidelines paved the way for the standardization of quality after the Food and Drug Administration issued current Good Manufacturing Practices guidelines in 2003. "Quality by Design", mentioned in the ICH Q8 guideline, offers a better scientific understanding of critical process and product qualities using knowledge obtained during the life cycle of a product. In this scope, the "knowledge space" is a summary of all process knowledge obtained during product development, and the "design space" is the area in which a product can be manufactured within acceptable limits. To create the spaces, artificial neural networks (ANNs) can be used to emphasize the multidimensional interactions of input variables and to closely bind these variables to a design space. This helps guide the experimental design process to include interactions among the input variables, along with modeling and optimization of pharmaceutical formulations. The objective of this study was to develop an integrated multivariate approach to obtain a quality product based on an understanding of the cause-effect relationships between formulation ingredients and product properties with ANNs and genetic programming on the ramipril tablets prepared by the direct compression method. In this study, the data are generated through the systematic application of the design of experiments (DoE) principles and optimization studies using artificial neural networks and neurofuzzy logic programs.
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Affiliation(s)
- Buket Aksu
- Santa Farma Pharmaceuticals, Borucicegi sok. No: 16 Sisli, Istanbul, Okmeydani, Turkey.
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Artificial neural networks in evaluation and optimization of modified release solid dosage forms. Pharmaceutics 2012; 4:531-50. [PMID: 24300369 PMCID: PMC3834927 DOI: 10.3390/pharmaceutics4040531] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2012] [Revised: 09/24/2012] [Accepted: 10/05/2012] [Indexed: 11/16/2022] Open
Abstract
Implementation of the Quality by Design (QbD) approach in pharmaceutical development has compelled researchers in the pharmaceutical industry to employ Design of Experiments (DoE) as a statistical tool, in product development. Among all DoE techniques, response surface methodology (RSM) is the one most frequently used. Progress of computer science has had an impact on pharmaceutical development as well. Simultaneous with the implementation of statistical methods, machine learning tools took an important place in drug formulation. Twenty years ago, the first papers describing application of artificial neural networks in optimization of modified release products appeared. Since then, a lot of work has been done towards implementation of new techniques, especially Artificial Neural Networks (ANN) in modeling of production, drug release and drug stability of modified release solid dosage forms. The aim of this paper is to review artificial neural networks in evaluation and optimization of modified release solid dosage forms.
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Application of artificial neural networks (ANNs) and genetic programming (GP) for prediction of drug release from solid lipid matrices. Int J Pharm 2012; 436:877-9. [DOI: 10.1016/j.ijpharm.2012.05.021] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2012] [Revised: 05/08/2012] [Accepted: 05/11/2012] [Indexed: 11/21/2022]
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15
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Establishing and analyzing the design space in the development of direct compression formulations by gene expression programming. Int J Pharm 2012; 434:35-42. [DOI: 10.1016/j.ijpharm.2012.04.078] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2011] [Accepted: 04/26/2012] [Indexed: 11/18/2022]
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16
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Aksu B, Paradkar A, de Matas M, Özer Ö, Güneri T, York P. A quality by design approach using artificial intelligence techniques to control the critical quality attributes of ramipril tablets manufactured by wet granulation. Pharm Dev Technol 2012; 18:236-45. [DOI: 10.3109/10837450.2012.705294] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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