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Heydari S, Masoumi N, Esmaeeli E, Ayyoubzadeh SM, Ghorbani-Bidkorpeh F, Ahmadi M. Artificial intelligence in nanotechnology for treatment of diseases. J Drug Target 2024:1-20. [PMID: 39155708 DOI: 10.1080/1061186x.2024.2393417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 07/06/2024] [Accepted: 08/11/2024] [Indexed: 08/20/2024]
Abstract
Nano-based drug delivery systems (DDSs) have demonstrated the ability to address challenges posed by therapeutic agents, enhancing drug efficiency and reducing side effects. Various nanoparticles (NPs) are utilised as DDSs with unique characteristics, leading to diverse applications across different diseases. However, the complexity, cost and time-consuming nature of laboratory processes, the large volume of data, and the challenges in data analysis have prompted the integration of artificial intelligence (AI) tools. AI has been employed in designing, characterising and manufacturing drug delivery nanosystems, as well as in predicting treatment efficiency. AI's potential to personalise drug delivery based on individual patient factors, optimise formulation design and predict drug properties has been highlighted. By leveraging AI and large datasets, developing safe and effective DDSs can be accelerated, ultimately improving patient outcomes and advancing pharmaceutical sciences. This review article investigates the role of AI in the development of nano-DDSs, with a focus on their therapeutic applications. The use of AI in DDSs has the potential to revolutionise treatment optimisation and improve patient care.
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Affiliation(s)
- Soroush Heydari
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Niloofar Masoumi
- Department of Pharmaceutics and Pharmaceutical Nanotechnology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Erfan Esmaeeli
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyed Mohammad Ayyoubzadeh
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
- Health Information Management Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Fatemeh Ghorbani-Bidkorpeh
- Department of Pharmaceutics and Pharmaceutical Nanotechnology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mahnaz Ahmadi
- Department of Tissue Engineering and Applied Cell Sciences, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Medical Nanotechnology and Tissue Engineering Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Kleandrova VV, Ds Cordeiro MN, Speck-Planche A. Current in silico methods for multi-target drug discovery in early anticancer research: the rise of the perturbation-theory machine learning approach. Future Med Chem 2023; 15:1647-1650. [PMID: 37728008 DOI: 10.4155/fmc-2023-0241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/21/2023] Open
Affiliation(s)
- Valeria V Kleandrova
- LAQV@REQUIMTE/Department of Chemistry & Biochemistry, University of Porto, Porto, 4169-007, Portugal
| | - Maria Natália Ds Cordeiro
- LAQV@REQUIMTE/Department of Chemistry & Biochemistry, University of Porto, Porto, 4169-007, Portugal
| | - Alejandro Speck-Planche
- LAQV@REQUIMTE/Department of Chemistry & Biochemistry, University of Porto, Porto, 4169-007, Portugal
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Kleandrova VV, Cordeiro MNDS, Speck-Planche A. Optimizing drug discovery using multitasking models for quantitative structure-biological effect relationships: an update of the literature. Expert Opin Drug Discov 2023; 18:1231-1243. [PMID: 37639708 DOI: 10.1080/17460441.2023.2251385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023]
Abstract
INTRODUCTION Drug discovery has provided modern societies with the means to fight against many diseases. In this sense, computational methods have been at the forefront, playing an important role in rationalizing the search for novel drugs. Yet, tackling phenomena such as the multi-genic nature of diseases and drug resistance are limitations of the current computational methods. Multi-tasking models for quantitative structure-biological effect relationships (mtk-QSBER) have emerged to overcome such limitations. AREAS COVERED The present review describes an update on the fundamentals and applications of the mtk-QSBER models as tools to accelerate multiple stages/substages of the drug discovery process. EXPERT OPINION Computational approaches are extremely important for the rationalization of the search for novel and efficacious therapeutic agents. However, they need to focus more on the multi-target drug discovery paradigm. In this sense, mtk-QSBER models are particularly suited for multi-target drug discovery, offering encouraging opportunities across multiple therapeutic areas and scientific disciplines associated with drug discovery.
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Affiliation(s)
- Valeria V Kleandrova
- Laboratory of Fundamental and Applied Research of Quality and Technology of Food Production, Russian Biotechnological University, Moscow, Russian Federation
| | - M Natália D S Cordeiro
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal
| | - Alejandro Speck-Planche
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal
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Palai D, Tahara H, Chikami S, Latag GV, Maeda S, Komura C, Kurioka H, Hayashi T. Prediction of Serum Adsorption onto Polymer Brush Films by Machine Learning. ACS Biomater Sci Eng 2022; 8:3765-3772. [PMID: 35905395 DOI: 10.1021/acsbiomaterials.2c00441] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Using machine learning based on a random forest (RF) regression algorithm, we attempted to predict the amount of adsorbed serum protein on polymer brush films from the films' physicochemical information and the monomers' chemical structures constituting the films using a RF model. After the training of the RF model using the data of polymer brush films synthesized from five different types of monomers, the model became capable of predicting the amount of adsorbed protein from the chemical structure, physicochemical properties of monomer molecules, and structural parameters (density and thickness of the films). The analysis of the trained RF quantitatively provided the importance of each structural parameter and physicochemical properties of monomers toward serum protein adsorption (SPA). The ranking for the significance of the parameters agrees with our general understanding and perception. Based on the results, we discuss the correlation between brush film's physical properties (such as thickness and density) and SPA and attempt to provide a guideline for the design of antibiofouling polymer brush films.
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Affiliation(s)
- Debabrata Palai
- Department of Materials Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, 4259 Nagatsuta-Cho Midori-Ku, Yokohama, Kanagawa 226-8502, Japan
| | - Hiroyuki Tahara
- Department of Materials Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, 4259 Nagatsuta-Cho Midori-Ku, Yokohama, Kanagawa 226-8502, Japan
| | - Shunta Chikami
- Department of Materials Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, 4259 Nagatsuta-Cho Midori-Ku, Yokohama, Kanagawa 226-8502, Japan
| | - Glenn Villena Latag
- Department of Materials Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, 4259 Nagatsuta-Cho Midori-Ku, Yokohama, Kanagawa 226-8502, Japan
| | - Shoichi Maeda
- Department of Materials Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, 4259 Nagatsuta-Cho Midori-Ku, Yokohama, Kanagawa 226-8502, Japan
| | - Chisato Komura
- Research Institute for Advanced Materials and Devices, Kyocera Corporation, 3-5-3 Hikaridai, Seika-Cho, Soraku-gun, Kyoto 619-0237, Japan
| | - Hideharu Kurioka
- Research Institute for Advanced Materials and Devices, Kyocera Corporation, 3-5-3 Hikaridai, Seika-Cho, Soraku-gun, Kyoto 619-0237, Japan
| | - Tomohiro Hayashi
- Department of Materials Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, 4259 Nagatsuta-Cho Midori-Ku, Yokohama, Kanagawa 226-8502, Japan.,The Institute for Solid State Physics, the University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, Chiba, 277-0882, Japan
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