1
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Souri M, Kiani Shahvandi M, Chiani M, Moradi Kashkooli F, Farhangi A, Mehrabi MR, Rahmim A, Savage VM, Soltani M. Stimuli-sensitive nano-drug delivery with programmable size changes to enhance accumulation of therapeutic agents in tumors. Drug Deliv 2023; 30:2186312. [PMID: 36895188 PMCID: PMC10013474 DOI: 10.1080/10717544.2023.2186312] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023] Open
Abstract
Nano-based drug delivery systems hold significant promise for cancer therapies. Presently, the poor accumulation of drug-carrying nanoparticles in tumors has limited their success. In this study, based on a combination of the paradigms of intravascular and extravascular drug release, an efficient nanosized drug delivery system with programmable size changes is introduced. Drug-loaded smaller nanoparticles (secondary nanoparticles), which are loaded inside larger nanoparticles (primary nanoparticles), are released within the microvascular network due to temperature field resulting from focused ultrasound. This leads to the scale of the drug delivery system decreasing by 7.5 to 150 times. Subsequently, smaller nanoparticles enter the tissue at high transvascular rates and achieve higher accumulation, leading to higher penetration depths. In response to the acidic pH of tumor microenvironment (according to the distribution of oxygen), they begin to release the drug doxorubicin at very slow rates (i.e., sustained release). To predict the performance and distribution of therapeutic agents, a semi-realistic microvascular network is first generated based on a sprouting angiogenesis model and the transport of therapeutic agents is then investigated based on a developed multi-compartment model. The results show that reducing the size of the primary and secondary nanoparticles can lead to higher cell death rate. In addition, tumor growth can be inhibited for a longer time by enhancing the bioavailability of the drug in the extracellular space. The proposed drug delivery system can be very promising in clinical applications. Furthermore, the proposed mathematical model is applicable to broader applications to predict the performance of drug delivery systems.
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Affiliation(s)
- Mohammad Souri
- Department of NanoBiotechnology, Pasteur Institute of Iran, Tehran, Iran
| | | | - Mohsen Chiani
- Department of NanoBiotechnology, Pasteur Institute of Iran, Tehran, Iran
| | | | - Ali Farhangi
- Department of NanoBiotechnology, Pasteur Institute of Iran, Tehran, Iran
| | | | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, British Columbia, Canada.,Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, British Columbia, Canada
| | - Van M Savage
- Department of Ecology and Evolutionary Biology, University of California Los Angeles, Los Angeles, California, USA.,Department of Computational Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.,Santa Fe Institute, Santa Fe, New Mexico, USA
| | - M Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran.,Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada.,Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, Canada.,Advanced Bioengineering Initiative Center, Multidisciplinary International Complex, K. N. Toosi University of Technology, Tehran, Iran
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2
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Kiani Shahvandi M, Souri M, Tavasoli S, Moradi Kashkooli F, Kar S, Soltani M. A comparative study between conventional chemotherapy and photothermal activated nano-sized targeted drug delivery to solid tumor. Comput Biol Med 2023; 166:107574. [PMID: 37839220 DOI: 10.1016/j.compbiomed.2023.107574] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 09/05/2023] [Accepted: 10/11/2023] [Indexed: 10/17/2023]
Abstract
Delivery of chemotherapeutic medicines to solid tumors is critical for optimal therapeutic success and minimal adverse effects. We mathematically developed a delivery method using thermosensitive nanocarriers activated by light irradiation. To assess its efficacy and identify critical events and parameters affecting therapeutic response, we compared this method to bolus and continuous infusions of doxorubicin for both single and multiple administrations. A hybrid sprouting angiogenesis approach generates a semi-realistic microvascular network to evaluate therapeutic drug distribution and microvascular heterogeneity. A pharmacodynamics model evaluates treatment success based on tumor survival cell percentage. The study found that whereas bolus injection boosted extracellular drug concentration levels by 90%, continuous infusion improved therapeutic response due to improved bioavailability. Cancer cell death increases by 6% with several injections compared to single injections due to prolonged chemotherapeutic medication exposure. However, responsive nanocarriers supply more than 2.1 times more drug than traditional chemotherapy in extracellular space, suppressing tumor development longer. Also, controlled drug release decreases systemic side effects substantial through diminishing the concentration of free drug in the circulation. The primary finding of this work highlights the significance of high bioavailability in treatment response. The results indicate that responsive nanocarriers contribute to increased bioavailability, leading to improved therapeutic benefits. By including drug delivery features in a semi-realistic model, this numerical study sought to improve drug-bio interaction comprehension. The model provides a good framework for understanding preclinical and clinical targeted oncology study outcomes.
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Affiliation(s)
| | - Mohammad Souri
- Department of NanoBiotechnology, Pasteur Institute of Iran, Tehran, Iran
| | - Shaghayegh Tavasoli
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | | | - Saptarshi Kar
- College of Engineering and Technology, American University of the Middle East, Kuwait
| | - M Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran; Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada; Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, Canada; Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, Canada; Centre for Sustainable Business, International Business University, Toronto, Canada.
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3
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Mishra S, Bhatt T, Kumar H, Jain R, Shilpi S, Jain V. Nanoconstructs for theranostic application in cancer: Challenges and strategies to enhance the delivery. Front Pharmacol 2023; 14:1101320. [PMID: 37007005 PMCID: PMC10050349 DOI: 10.3389/fphar.2023.1101320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 03/06/2023] [Indexed: 03/17/2023] Open
Abstract
Nanoconstructs are made up of nanoparticles and ligands, which can deliver the loaded cargo at the desired site of action. Various nanoparticulate platforms have been utilized for the preparation of nanoconstructs, which may serve both diagnostic as well as therapeutic purposes. Nanoconstructs are mostly used to overcome the limitations of cancer therapies, such as toxicity, nonspecific distribution of the drug, and uncontrolled release rate. The strategies employed during the design of nanoconstructs help improve the efficiency and specificity of loaded theranostic agents and make them a successful approach for cancer therapy. Nanoconstructs are designed with a sole purpose of targeting the requisite site, overcoming the barriers which hinders its right placement for desired benefit. Therefore, instead of classifying modes for delivery of nanoconstructs as actively or passively targeted systems, they are suitably classified as autonomous and nonautonomous types. At large, nanoconstructs offer numerous benefits, however they suffer from multiple challenges, too. Hence, to overcome such challenges computational modelling methods and artificial intelligence/machine learning processes are being explored. The current review provides an overview on attributes and applications offered by nanoconstructs as theranostic agent in cancer.
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Affiliation(s)
- Shivani Mishra
- Department of Pharmaceutics, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Mysuru, India
| | - Tanvi Bhatt
- Department of Pharmaceutics, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Mysuru, India
| | - Hitesh Kumar
- Department of Pharmaceutics, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Mysuru, India
| | - Rupshee Jain
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Mysuru, India
| | - Satish Shilpi
- Department of Pharmaceutics, School of Pharmaceutical and Populations Health Informatics, DIT University, Dehradun, India
| | - Vikas Jain
- Department of Pharmaceutics, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Mysuru, India
- *Correspondence: Vikas Jain,
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4
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Kour S, Biswas I, Sheoran S, Arora S, Sheela P, Duppala SK, Murthy DK, Pawar SC, Singh H, Kumar D, Prabhu D, Vuree S, Kumar R. Artificial intelligence and nanotechnology for cervical cancer treatment: Current status and future perspectives. J Drug Deliv Sci Technol 2023. [DOI: 10.1016/j.jddst.2023.104392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
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5
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Bakrania A, Joshi N, Zhao X, Zheng G, Bhat M. Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases. Pharmacol Res 2023; 189:106706. [PMID: 36813095 DOI: 10.1016/j.phrs.2023.106706] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 02/17/2023] [Accepted: 02/19/2023] [Indexed: 02/22/2023]
Abstract
Liver cancers are the fourth leading cause of cancer-related mortality worldwide. In the past decade, breakthroughs in the field of artificial intelligence (AI) have inspired development of algorithms in the cancer setting. A growing body of recent studies have evaluated machine learning (ML) and deep learning (DL) algorithms for pre-screening, diagnosis and management of liver cancer patients through diagnostic image analysis, biomarker discovery and predicting personalized clinical outcomes. Despite the promise of these early AI tools, there is a significant need to explain the 'black box' of AI and work towards deployment to enable ultimate clinical translatability. Certain emerging fields such as RNA nanomedicine for targeted liver cancer therapy may also benefit from application of AI, specifically in nano-formulation research and development given that they are still largely reliant on lengthy trial-and-error experiments. In this paper, we put forward the current landscape of AI in liver cancers along with the challenges of AI in liver cancer diagnosis and management. Finally, we have discussed the future perspectives of AI application in liver cancer and how a multidisciplinary approach using AI in nanomedicine could accelerate the transition of personalized liver cancer medicine from bench side to the clinic.
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Affiliation(s)
- Anita Bakrania
- Toronto General Hospital Research Institute, Toronto, ON, Canada; Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
| | | | - Xun Zhao
- Toronto General Hospital Research Institute, Toronto, ON, Canada; Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Gang Zheng
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Mamatha Bhat
- Toronto General Hospital Research Institute, Toronto, ON, Canada; Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Division of Gastroenterology, Department of Medicine, University Health Network and University of Toronto, Toronto, ON, Canada; Department of Medical Sciences, Toronto, ON, Canada.
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6
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Abbasi R, Shineh G, Mobaraki M, Doughty S, Tayebi L. Structural parameters of nanoparticles affecting their toxicity for biomedical applications: a review. JOURNAL OF NANOPARTICLE RESEARCH : AN INTERDISCIPLINARY FORUM FOR NANOSCALE SCIENCE AND TECHNOLOGY 2023; 25:43. [PMID: 36875184 PMCID: PMC9970140 DOI: 10.1007/s11051-023-05690-w] [Citation(s) in RCA: 48] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 02/13/2023] [Indexed: 06/18/2023]
Abstract
Rapidly growing interest in using nanoparticles (NPs) for biomedical applications has increased concerns about their safety and toxicity. In comparison with bulk materials, NPs are more chemically active and toxic due to the greater surface area and small size. Understanding the NPs' mechanism of toxicity, together with the factors influencing their behavior in biological environments, can help researchers to design NPs with reduced side effects and improved performance. After overviewing the classification and properties of NPs, this review article discusses their biomedical applications in molecular imaging and cell therapy, gene transfer, tissue engineering, targeted drug delivery, Anti-SARS-CoV-2 vaccines, cancer treatment, wound healing, and anti-bacterial applications. There are different mechanisms of toxicity of NPs, and their toxicity and behaviors depend on various factors, which are elaborated on in this article. More specifically, the mechanism of toxicity and their interactions with living components are discussed by considering the impact of different physiochemical parameters such as size, shape, structure, agglomeration state, surface charge, wettability, dose, and substance type. The toxicity of polymeric, silica-based, carbon-based, and metallic-based NPs (including plasmonic alloy NPs) have been considered separately.
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Affiliation(s)
- Reza Abbasi
- Department of Bioengineering, McGill University, Montreal, QC Canada
| | - Ghazal Shineh
- Biomaterial Group, Faculty of Biomedical Engineering (Center of Excellence), Amirkabir University of Technology, Tehran, 15916-34311 Iran
| | - Mohammadmahdi Mobaraki
- Biomaterial Group, Faculty of Biomedical Engineering (Center of Excellence), Amirkabir University of Technology, Tehran, 15916-34311 Iran
| | - Sarah Doughty
- Marquette University School of Dentistry, Milwaukee, WI USA
| | - Lobat Tayebi
- Marquette University School of Dentistry, Milwaukee, WI USA
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7
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Mehrabi MR, Soltani M, Chiani M, Raahemifar K, Farhangi A. Nanomedicine: New Frontiers in Fighting Microbial Infections. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:483. [PMID: 36770443 PMCID: PMC9920255 DOI: 10.3390/nano13030483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 01/21/2023] [Accepted: 01/22/2023] [Indexed: 06/18/2023]
Abstract
Microbes have dominated life on Earth for the past two billion years, despite facing a variety of obstacles. In the 20th century, antibiotics and immunizations brought about these changes. Since then, microorganisms have acquired resistance, and various infectious diseases have been able to avoid being treated with traditionally developed vaccines. Antibiotic resistance and pathogenicity have surpassed antibiotic discovery in terms of importance over the course of the past few decades. These shifts have resulted in tremendous economic and health repercussions across the board for all socioeconomic levels; thus, we require ground-breaking innovations to effectively manage microbial infections and to provide long-term solutions. The pharmaceutical and biotechnology sectors have been radically altered as a result of nanomedicine, and this trend is now spreading to the antibacterial research community. Here, we examine the role that nanomedicine plays in the prevention of microbial infections, including topics such as diagnosis, antimicrobial therapy, pharmaceutical administration, and immunizations, as well as the opportunities and challenges that lie ahead.
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Affiliation(s)
- Mohammad Reza Mehrabi
- Department of NanoBiotechnology, Pasteur Institute of Iran, Tehran 13169-43551, Iran
| | - Madjid Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
- Advanced Bioengineering Initiative Center, Multidisciplinary International Complex, K. N. Toosi University of Technology, Tehran 14176-14411, Iran
- Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON N2L 3G1, Canada
- Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Mohsen Chiani
- Department of NanoBiotechnology, Pasteur Institute of Iran, Tehran 13169-43551, Iran
| | - Kaamran Raahemifar
- Data Science and Artificial Intelligence Program, College of Information Sciences and Technology (IST), Penn State University, State College, PA 16801, USA
- Department of Chemical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
- School of Optometry and Vision Science, Faculty of Science, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
| | - Ali Farhangi
- Department of NanoBiotechnology, Pasteur Institute of Iran, Tehran 13169-43551, Iran
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8
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The Future of Nanomedicine. Nanomedicine (Lond) 2023. [DOI: 10.1007/978-981-16-8984-0_24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
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9
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Zare Harofte S, Soltani M, Siavashy S, Raahemifar K. Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2203169. [PMID: 36026569 DOI: 10.1002/smll.202203169] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 07/16/2022] [Indexed: 05/14/2023]
Abstract
Nowadays, artificial intelligence (AI) creates numerous promising opportunities in the life sciences. AI methods can be significantly advantageous for analyzing the massive datasets provided by biotechnology systems for biological and biomedical applications. Microfluidics, with the developments in controlled reaction chambers, high-throughput arrays, and positioning systems, generate big data that is not necessarily analyzed successfully. Integrating AI and microfluidics can pave the way for both experimental and analytical throughputs in biotechnology research. Microfluidics enhances the experimental methods and reduces the cost and scale, while AI methods significantly improve the analysis of huge datasets obtained from high-throughput and multiplexed microfluidics. This review briefly presents a survey of the role of AI and microfluidics in biotechnology. Also, the incorporation of AI with microfluidics is comprehensively investigated. Specifically, recent studies that perform flow cytometry cell classification, cell isolation, and a combination of them by gaining from both AI methods and microfluidic techniques are covered. Despite all current challenges, various fields of biotechnology can be remarkably affected by the combination of AI and microfluidic technologies. Some of these fields include point-of-care systems, precision, personalized medicine, regenerative medicine, prognostics, diagnostics, and treatment of oncology and non-oncology-related diseases.
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Affiliation(s)
- Samaneh Zare Harofte
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, 19967-15433, Iran
| | - Madjid Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, 19967-15433, Iran
- Department of Electrical and Computer Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
- Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, N2L 3G1, Canada
- Advanced Bioengineering Initiative Center, Multidisciplinary International Complex, K. N. Toosi University of Technology, Tehran, 14176-14411, Iran
- Cancer Biology Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, 14197-33141, Iran
| | - Saeed Siavashy
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, 19967-15433, Iran
| | - Kaamran Raahemifar
- Data Science and Artificial Intelligence Program, College of Information Sciences and Technology (IST), Penn State University, State College, PA, 16801, USA
- School of Optometry and Vision Science, Faculty of Science, University of Waterloo, 200 University Ave. W, Waterloo, ON, N2L 3G1, Canada
- Department of Chemical Engineering, Faculty of Engineering, University of Waterloo, 200 University Ave. W, Waterloo, ON, N2L 3G1, Canada
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10
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Sahu M, Gupta R, Ambasta RK, Kumar P. Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2022; 190:57-100. [PMID: 36008002 DOI: 10.1016/bs.pmbts.2022.03.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that translational research exploring the convergence between artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine. Here, we discuss different aspects of artificial intelligence in precision medicine that improve healthcare delivery. First, we discuss how artificial intelligence changes the landscape of precision medicine and the evolution of artificial intelligence in precision medicine. Second, we highlight the synergies between artificial intelligence and precision medicine and promises of artificial intelligence and precision medicine in healthcare delivery. Third, we briefly explain the promise of big data analytics and the integration of nanomaterials in precision medicine. Last, we highlight the challenges and opportunities of artificial intelligence in precision medicine.
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Affiliation(s)
- Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Shahbad Daulatpur, Delhi, India
| | - Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Shahbad Daulatpur, Delhi, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Shahbad Daulatpur, Delhi, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Shahbad Daulatpur, Delhi, India.
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11
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Synthetic 18F-FDG PET Image Generation Using a Combination of Biomathematical Modeling and Machine Learning. Cancers (Basel) 2022; 14:cancers14112786. [PMID: 35681767 PMCID: PMC9179454 DOI: 10.3390/cancers14112786] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 05/21/2022] [Accepted: 06/01/2022] [Indexed: 12/10/2022] Open
Abstract
Simple Summary Training computer-assisted algorithms on medical images, particularly 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) due to its excellent diagnostic accuracy, is difficult, considering small/fragmented samples or privacy concerns. In computer-vision, deep learning-based models, unlike the conventional data augmentation methods, are highly sought after for creating massive medical samples. For this reason, we developed a multi-scale computational framework to generate synthetic 18F-FDG PET images similar to the real ones in different stages of solid tumor growth and angiogenesis. The framework is developed based on the bio-physiological phenomena of FDG radiotracer uptake in solid tumors using a biomathematical model and a generative adversarial network (GAN)-based architecture. The non-invasive augmented 18F-FDG PET images can be used in clinical practice without the need to manage the patient data. In addition, our spatiotemporal mathematical model can calculate the distribution of various radiopharmaceuticals in different tumor-associated vasculatures. Abstract No previous works have attempted to combine generative adversarial network (GAN) architectures and the biomathematical modeling of positron emission tomography (PET) radiotracer uptake in tumors to generate extra training samples. Here, we developed a novel computational model to produce synthetic 18F-fluorodeoxyglucose (18F-FDG) PET images of solid tumors in different stages of progression and angiogenesis. First, a comprehensive biomathematical model is employed for creating tumor-induced angiogenesis, intravascular and extravascular fluid flow, as well as modeling of the transport phenomena and reaction processes of 18F-FDG in a tumor microenvironment. Then, a deep convolutional GAN (DCGAN) model is employed for producing synthetic PET images using 170 input images of 18F-FDG uptake in each of 10 different tumor microvascular networks. The interstitial fluid parameters and spatiotemporal distribution of 18F-FDG uptake in tumor and healthy tissues have been compared against previously published numerical and experimental studies, indicating the accuracy of the model. The structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) of the generated PET sample and the experimental one are 0.72 and 28.53, respectively. Our results demonstrate that a combination of biomathematical modeling and GAN-based augmentation models provides a robust framework for the non-invasive and accurate generation of synthetic PET images of solid tumors in different stages.
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12
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Kashkooli FM, Rezaeian M, Soltani M. Drug delivery through nanoparticles in solid tumors: a mechanistic understanding. Nanomedicine (Lond) 2022; 17:695-716. [PMID: 35451315 DOI: 10.2217/nnm-2021-0126] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Aim: In this study, the main goal was to apply a multi-scale computational model in evaluating nano-sized drug-delivery systems, following extracellular drug release, into solid tumors in order to predict treatment efficacy. Methods: The impact of several parameters related to tumor (size, shape, vessel-wall pore size, and necrotic core size) and therapeutic agents (size of nanoparticles, binding affinity of drug, drug release rate from nanoparticles) are examined in detail. Results: This study illustrates that achieving a higher treatment efficacy requires smaller nanoparticles (NPs) or a low binding affinity and drug release rate. Long-term analysis finds that a slow release rate in extracellular space does not always improve treatment efficacy compared with a rapid release rate; NP size as well as binding affinity of drug are also highly influential. Conclusions: The presented methodology can be used as a step forward towards optimization of patient-specific nanomedicine plans.
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Affiliation(s)
| | - Mohsen Rezaeian
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - M Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran.,Department of Electrical & Computer Engineering, University of Waterloo, Waterloo, Canada.,Centre for Biotechnology & Bioengineering (CBB), University of Waterloo, Waterloo, Canada.,Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran
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13
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Analysis of Magneto-Hyperthermia Duration in Nano-sized Drug Delivery System to Solid Tumors Using Intravascular-Triggered Thermosensitive-Liposome. Pharm Res 2022; 39:753-765. [DOI: 10.1007/s11095-022-03255-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 04/05/2022] [Indexed: 12/11/2022]
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14
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Souri M, Chiani M, Farhangi A, Mehrabi MR, Nourouzian D, Raahemifar K, Soltani M. Anti-COVID-19 Nanomaterials: Directions to Improve Prevention, Diagnosis, and Treatment. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:783. [PMID: 35269270 PMCID: PMC8912597 DOI: 10.3390/nano12050783] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 02/18/2022] [Accepted: 02/23/2022] [Indexed: 02/04/2023]
Abstract
Following the announcement of the outbreak of COVID-19 by the World Health Organization, unprecedented efforts were made by researchers around the world to combat the disease. So far, various methods have been developed to combat this "virus" nano enemy, in close collaboration with the clinical and scientific communities. Nanotechnology based on modifiable engineering materials and useful physicochemical properties has demonstrated several methods in the fight against SARS-CoV-2. Here, based on what has been clarified so far from the life cycle of SARS-CoV-2, through an interdisciplinary perspective based on computational science, engineering, pharmacology, medicine, biology, and virology, the role of nano-tools in the trio of prevention, diagnosis, and treatment is highlighted. The special properties of different nanomaterials have led to their widespread use in the development of personal protective equipment, anti-viral nano-coats, and disinfectants in the fight against SARS-CoV-2 out-body. The development of nano-based vaccines acts as a strong shield in-body. In addition, fast detection with high efficiency of SARS-CoV-2 by nanomaterial-based point-of-care devices is another nanotechnology capability. Finally, nanotechnology can play an effective role as an agents carrier, such as agents for blocking angiotensin-converting enzyme 2 (ACE2) receptors, gene editing agents, and therapeutic agents. As a general conclusion, it can be said that nanoparticles can be widely used in disinfection applications outside in vivo. However, in in vivo applications, although it has provided promising results, it still needs to be evaluated for possible unintended immunotoxicity. Reviews like these can be important documents for future unwanted pandemics.
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Affiliation(s)
- Mohammad Souri
- Department of NanoBiotechnology, Pasteur Institute of Iran, Tehran 13169-43551, Iran; (M.S.); (M.C.); (A.F.)
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
| | - Mohsen Chiani
- Department of NanoBiotechnology, Pasteur Institute of Iran, Tehran 13169-43551, Iran; (M.S.); (M.C.); (A.F.)
| | - Ali Farhangi
- Department of NanoBiotechnology, Pasteur Institute of Iran, Tehran 13169-43551, Iran; (M.S.); (M.C.); (A.F.)
| | - Mohammad Reza Mehrabi
- Department of NanoBiotechnology, Pasteur Institute of Iran, Tehran 13169-43551, Iran; (M.S.); (M.C.); (A.F.)
| | - Dariush Nourouzian
- Department of NanoBiotechnology, Pasteur Institute of Iran, Tehran 13169-43551, Iran; (M.S.); (M.C.); (A.F.)
| | - Kaamran Raahemifar
- Data Science and Artificial Intelligence Program, College of Information Sciences and Technology (IST), Penn State University, State College, PA 16801, USA;
- Department of Chemical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
- School of Optometry and Vision Science, Faculty of Science, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
| | - M. Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
- Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
- Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON N2L 3G1, Canada
- Advanced Bioengineering Initiative Center, Multidisciplinary International Complex, K. N. Toosi University of Technology, Tehran 14176-14411, Iran
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15
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Villa Nova M, Lin TP, Shanehsazzadeh S, Jain K, Ng SCY, Wacker R, Chichakly K, Wacker MG. Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence. Front Digit Health 2022; 4:799341. [PMID: 35252958 PMCID: PMC8894322 DOI: 10.3389/fdgth.2022.799341] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 01/26/2022] [Indexed: 12/12/2022] Open
Abstract
Today, a growing number of computational aids and simulations are shaping model-informed drug development. Artificial intelligence, a family of self-learning algorithms, is only the latest emerging trend applied by academic researchers and the pharmaceutical industry. Nanomedicine successfully conquered several niche markets and offers a wide variety of innovative drug delivery strategies. Still, only a small number of patients benefit from these advanced treatments, and the number of data sources is very limited. As a consequence, “big data” approaches are not always feasible and smart combinations of human and artificial intelligence define the research landscape. These methodologies will potentially transform the future of nanomedicine and define new challenges and limitations of machine learning in their development. In our review, we present an overview of modeling and artificial intelligence applications in the development and manufacture of nanomedicines. Also, we elucidate the role of each method as a facilitator of breakthroughs and highlight important limitations.
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Affiliation(s)
- Mônica Villa Nova
- Department of Pharmacy, State University of Maringá, Maringá, Brazil
| | - Tzu Ping Lin
- Wacker Research Lab, Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore, Singapore
| | - Saeed Shanehsazzadeh
- Biological Resources Imaging Laboratory, Mark Wainwright Analytical Centre, UNSW Sydney, Sydney, NSW, Australia
| | - Kinjal Jain
- Wacker Research Lab, Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore, Singapore
| | - Samuel Cheng Yong Ng
- Wacker Research Lab, Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore, Singapore
| | | | | | - Matthias G. Wacker
- Wacker Research Lab, Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore, Singapore
- *Correspondence: Matthias G. Wacker
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16
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The Future of Nanomedicine. Nanomedicine (Lond) 2022. [DOI: 10.1007/978-981-13-9374-7_24-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022] Open
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17
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Souri M, Soltani M, Moradi Kashkooli F, Kiani Shahvandi M, Chiani M, Shariati FS, Mehrabi MR, Munn LL. Towards principled design of cancer nanomedicine to accelerate clinical translation. Mater Today Bio 2022; 13:100208. [PMID: 35198957 PMCID: PMC8841842 DOI: 10.1016/j.mtbio.2022.100208] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/24/2022] [Accepted: 01/25/2022] [Indexed: 02/08/2023] Open
Abstract
Nanotechnology in medical applications, especially in oncology as drug delivery systems, has recently shown promising results. However, although these advances have been promising in the pre-clinical stages, the clinical translation of this technology is challenging. To create drug delivery systems with increased treatment efficacy for clinical translation, the physicochemical characteristics of nanoparticles such as size, shape, elasticity (flexibility/rigidity), surface chemistry, and surface charge can be specified to optimize efficiency for a given application. Consequently, interdisciplinary researchers have focused on producing biocompatible materials, production technologies, or new formulations for efficient loading, and high stability. The effects of design parameters can be studied in vitro, in vivo, or using computational models, with the goal of understanding how they affect nanoparticle biophysics and their interactions with cells. The present review summarizes the advances and technologies in the production and design of cancer nanomedicines to achieve clinical translation and commercialization. We also highlight existing challenges and opportunities in the field.
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Key Words
- CFL, Cell-free layer
- CGMD, Coarse-grained molecular dynamic
- Clinical translation
- DPD, Dissipative particle dynamic
- Drug delivery
- Drug loading
- ECM, Extracellular matrix
- EPR, Permeability and retention
- IFP, Interstitial fluid pressure
- MD, Molecular dynamic
- MDR, Multidrug resistance
- MEC, Minimum effective concentration
- MMPs, Matrix metalloproteinases
- MPS, Mononuclear phagocyte system
- MTA, Multi-tadpole assemblies
- MTC, Minimum toxic concentration
- Nanomedicine
- Nanoparticle design
- RBC, Red blood cell
- TAF, Tumor-associated fibroblast
- TAM, Tumor-associated macrophage
- TIMPs, Tissue inhibitor of metalloproteinases
- TME, Tumor microenvironment
- Tumor microenvironment
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Affiliation(s)
- Mohammad Souri
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
- Department of Nanobiotechnology, Pasteur Institute of Iran, Tehran, Iran
| | - M. Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
- Department of Electrical and Computer Engineering, University of Waterloo, ON, Canada
- Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, Canada
- Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran
| | | | | | - Mohsen Chiani
- Department of Nanobiotechnology, Pasteur Institute of Iran, Tehran, Iran
| | | | | | - Lance L. Munn
- Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
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18
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Fasaeiyan N, Soltani M, Moradi Kashkooli F, Taatizadeh E, Rahmim A. Computational modeling of PET tracer distribution in solid tumors integrating microvasculature. BMC Biotechnol 2021; 21:67. [PMID: 34823506 PMCID: PMC8620574 DOI: 10.1186/s12896-021-00725-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 11/05/2021] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND We present computational modeling of positron emission tomography radiotracer uptake with consideration of blood flow and interstitial fluid flow, performing spatiotemporally-coupled modeling of uptake and integrating the microvasculature. In our mathematical modeling, the uptake of fluorodeoxyglucose F-18 (FDG) was simulated based on the Convection-Diffusion-Reaction equation given its high accuracy and reliability in modeling of transport phenomena. In the proposed model, blood flow and interstitial flow are solved simultaneously to calculate interstitial pressure and velocity distribution inside cancer and normal tissues. As a result, the spatiotemporal distribution of the FDG tracer is calculated based on velocity and pressure distributions in both kinds of tissues. RESULTS Interstitial pressure has maximum value in the tumor region compared to surrounding tissue. In addition, interstitial fluid velocity is extremely low in the entire computational domain indicating that convection can be neglected without effecting results noticeably. Furthermore, our results illustrate that the total concentration of FDG in the tumor region is an order of magnitude larger than in surrounding normal tissue, due to lack of functional lymphatic drainage system and also highly-permeable microvessels in tumors. The magnitude of the free tracer and metabolized (phosphorylated) radiotracer concentrations followed very different trends over the entire time period, regardless of tissue type (tumor vs. normal). CONCLUSION Our spatiotemporally-coupled modeling provides helpful tools towards improved understanding and quantification of in vivo preclinical and clinical studies.
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Affiliation(s)
- Niloofar Fasaeiyan
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Tehran Province, Iran
- Department of Civil Engineering, Polytechnique University, Montreal, QC, Canada
| | - M Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Tehran Province, Iran.
- Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada.
- Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, Canada.
- Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Tehran Province, Iran.
| | - Farshad Moradi Kashkooli
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Tehran Province, Iran
| | - Erfan Taatizadeh
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Tehran Province, Iran
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Arman Rahmim
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
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19
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Tinajero-Díaz E, Salado-Leza D, Gonzalez C, Martínez Velázquez M, López Z, Bravo-Madrigal J, Knauth P, Flores-Hernández FY, Herrera-Rodríguez SE, Navarro RE, Cabrera-Wrooman A, Krötzsch E, Carvajal ZYG, Hernández-Gutiérrez R. Green Metallic Nanoparticles for Cancer Therapy: Evaluation Models and Cancer Applications. Pharmaceutics 2021; 13:1719. [PMID: 34684012 PMCID: PMC8537602 DOI: 10.3390/pharmaceutics13101719] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 09/29/2021] [Accepted: 10/05/2021] [Indexed: 12/15/2022] Open
Abstract
Metal-based nanoparticles are widely used to deliver bioactive molecules and drugs to improve cancer therapy. Several research works have highlighted the synthesis of gold and silver nanoparticles by green chemistry, using biological entities to minimize the use of solvents and control their physicochemical and biological properties. Recent advances in evaluating the anticancer effect of green biogenic Au and Ag nanoparticles are mainly focused on the use of conventional 2D cell culture and in vivo murine models that allow determination of the half-maximal inhibitory concentration, a critical parameter to move forward clinical trials. However, the interaction between nanoparticles and the tumor microenvironment is not yet fully understood. Therefore, it is necessary to develop more human-like evaluation models or to improve the existing ones for a better understanding of the molecular bases of cancer. This review provides recent advances in biosynthesized Au and Ag nanoparticles for seven of the most common and relevant cancers and their biological assessment. In addition, it provides a general idea of the in silico, in vitro, ex vivo, and in vivo models used for the anticancer evaluation of green biogenic metal-based nanoparticles.
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Affiliation(s)
- Ernesto Tinajero-Díaz
- Departament d’Enginyeria Química, Universitat Politècnica de Catalunya, ETSEIB, Diagonal 647, 08028 Barcelona, Spain;
- Biotecnología Médica y Farmacéutica, Centro de Investigación y Asistencia en Tecnología y Diseño del Estado de Jalisco, A.C., Av. Normalistas 800, Col. Colinas de La Normal, Guadalajara 44270, Mexico; (M.M.V.); (J.B.-M.); (F.Y.F.-H.); (S.E.H.-R.)
| | - Daniela Salado-Leza
- Facultad de Ciencias Químicas, Universidad Autónoma de San Luis Potosí, Av. Dr. Manuel Nava, Zona Universitaria, San Luis Potosí 78210, Mexico; (D.S.-L.); (C.G.)
- Cátedras CONACyT, México City 03940, Mexico
| | - Carmen Gonzalez
- Facultad de Ciencias Químicas, Universidad Autónoma de San Luis Potosí, Av. Dr. Manuel Nava, Zona Universitaria, San Luis Potosí 78210, Mexico; (D.S.-L.); (C.G.)
| | - Moisés Martínez Velázquez
- Biotecnología Médica y Farmacéutica, Centro de Investigación y Asistencia en Tecnología y Diseño del Estado de Jalisco, A.C., Av. Normalistas 800, Col. Colinas de La Normal, Guadalajara 44270, Mexico; (M.M.V.); (J.B.-M.); (F.Y.F.-H.); (S.E.H.-R.)
| | - Zaira López
- Centro Universitario de la Ciénega, Cell Biology Laboratory, Universidad de Guadalajara, Av. Universidad 1115, Ocotlán 47810, Mexico; (Z.L.); (P.K.)
| | - Jorge Bravo-Madrigal
- Biotecnología Médica y Farmacéutica, Centro de Investigación y Asistencia en Tecnología y Diseño del Estado de Jalisco, A.C., Av. Normalistas 800, Col. Colinas de La Normal, Guadalajara 44270, Mexico; (M.M.V.); (J.B.-M.); (F.Y.F.-H.); (S.E.H.-R.)
| | - Peter Knauth
- Centro Universitario de la Ciénega, Cell Biology Laboratory, Universidad de Guadalajara, Av. Universidad 1115, Ocotlán 47810, Mexico; (Z.L.); (P.K.)
| | - Flor Y. Flores-Hernández
- Biotecnología Médica y Farmacéutica, Centro de Investigación y Asistencia en Tecnología y Diseño del Estado de Jalisco, A.C., Av. Normalistas 800, Col. Colinas de La Normal, Guadalajara 44270, Mexico; (M.M.V.); (J.B.-M.); (F.Y.F.-H.); (S.E.H.-R.)
| | - Sara Elisa Herrera-Rodríguez
- Biotecnología Médica y Farmacéutica, Centro de Investigación y Asistencia en Tecnología y Diseño del Estado de Jalisco, A.C., Av. Normalistas 800, Col. Colinas de La Normal, Guadalajara 44270, Mexico; (M.M.V.); (J.B.-M.); (F.Y.F.-H.); (S.E.H.-R.)
| | - Rosa E. Navarro
- Departamento de Biología Celular y Desarrollo, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, Circuito Exterior s/n, Ciudad Universitaria, México City 04510, Mexico;
| | - Alejandro Cabrera-Wrooman
- Centro Nacional de Investigación y Atención de Quemados, Laboratory of Connective Tissue, Instituto Nacional de Rehabilitación “Luis Guillermo Ibarra Ibarra”, México City 14389, Mexico; (A.C.-W.); (E.K.)
| | - Edgar Krötzsch
- Centro Nacional de Investigación y Atención de Quemados, Laboratory of Connective Tissue, Instituto Nacional de Rehabilitación “Luis Guillermo Ibarra Ibarra”, México City 14389, Mexico; (A.C.-W.); (E.K.)
| | - Zaira Y. García Carvajal
- Biotecnología Médica y Farmacéutica, Centro de Investigación y Asistencia en Tecnología y Diseño del Estado de Jalisco, A.C., Av. Normalistas 800, Col. Colinas de La Normal, Guadalajara 44270, Mexico; (M.M.V.); (J.B.-M.); (F.Y.F.-H.); (S.E.H.-R.)
| | - Rodolfo Hernández-Gutiérrez
- Biotecnología Médica y Farmacéutica, Centro de Investigación y Asistencia en Tecnología y Diseño del Estado de Jalisco, A.C., Av. Normalistas 800, Col. Colinas de La Normal, Guadalajara 44270, Mexico; (M.M.V.); (J.B.-M.); (F.Y.F.-H.); (S.E.H.-R.)
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20
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Computational modeling of thermal combination therapies by magneto-ultrasonic heating to enhance drug delivery to solid tumors. Sci Rep 2021; 11:19539. [PMID: 34599207 PMCID: PMC8486865 DOI: 10.1038/s41598-021-98554-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 08/12/2021] [Indexed: 02/02/2023] Open
Abstract
For the first time, inspired by magnetic resonance imaging-guidance high intensity focused ultrasound (MR-HIFU) technology, i.e., medication therapy and thermal ablation in one session, in a preclinical setting based on a developed mathematical model, the performance of doxorubicin (Dox) and its encapsulation have been investigated in this study. Five different treatment methods, that combine medication therapy with mild hyperthermia by MRI contrast (\documentclass[12pt]{minimal}
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\begin{document}$$\gamma -{Fe}_{2}{O}_{3}$$\end{document}γ-Fe2O3) and thermal ablation via HIFU, are investigated in detail. A comparison between classical chemotherapy and thermochemistry shows that temperature can improve the therapeutic outcome by stimulating biological properties. On the other hand, the intravascular release of ThermoDox increases the concentration of free drug by 2.6 times compared to classical chemotherapy. The transport of drug in interstitium relies mainly on the diffusion mechanism to be able to penetrate deeper and reach the cancer cells in the inner regions of the tumor. Due to the low drug penetration into the tumor center, thermal ablation has been used for necrosis of the central areas before thermochemotherapy and ThermoDox therapy. Perfusion of the region around the necrotic zone is found to be damaged, while cells in the region are alive and not affected by medication therapy; so, there is a risk of tumor recurrence. Therefore, it is recommended that ablation be performed after the medication therapy. Our model describes a comprehensive assessment of MR-HIFU technology, taking into account many effective details, which can be a reliable guide towards the optimal use of drug delivery systems.
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