Deep Learning for the Accurate Prediction of Triggered Drug Delivery
The need to mitigate the adverse effects of chemotherapy has driven the exploration of innovative drug delivery approaches. One emerging trend in cancer treatment is the utilization of Drug Delivery Systems (DDSs), facilitated by nanotechnology. Nanoparticles, ranging from 1 nm to 1000 nm, act as ca...
محفوظ في:
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| مؤلفون آخرون: | , , |
| التنسيق: | article |
| منشور في: |
2024
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/11073/25709 |
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| _version_ | 1864513440728481792 |
|---|---|
| author | Husseini, Ghaleb |
| author2 | Sabouni, Rana Puzyrev, Vladimir Ghommem, Mehdi |
| author2_role | author author author |
| author_facet | Husseini, Ghaleb Sabouni, Rana Puzyrev, Vladimir Ghommem, Mehdi |
| author_role | author |
| dc.creator.none.fl_str_mv | Husseini, Ghaleb Sabouni, Rana Puzyrev, Vladimir Ghommem, Mehdi |
| dc.date.none.fl_str_mv | 2024-11-13T12:05:25Z 2024-11-13T12:05:25Z 2024 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Husseini, G. A., Sabouni, R., Puzyrev, V., & Ghommem, M. (2024). Deep Learning for the Accurate Prediction of Triggered Drug Delivery. In IEEE Transactions on NanoBioscience (pp. 1–1). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/tnb.2024.3426291 1536-1241 https://hdl.handle.net/11073/25709 10.1109/TNB.2024.3426291 2-s2.0-85199061767 SCOPUS_ID:85199061767 IEEE Transactions on Nanobioscience 15582639 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | https://doi.org/10.1109/tnb.2024.3426291 |
| dc.rights.none.fl_str_mv | Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
| dc.source.none.fl_str_mv | IEEE Transactions on Nanobioscience |
| dc.subject.none.fl_str_mv | active targeting Cancer cancer treatment chemotherapy deep learning Drug delivery Drug delivery systems Drugs Fluorescence liposomes metal organic frameworks microwave Microwave theory and techniques Nanocarriers nanoparticles pH-triggering Ultrasonic imaging ultrasound |
| dc.title.none.fl_str_mv | Deep Learning for the Accurate Prediction of Triggered Drug Delivery |
| dc.type.none.fl_str_mv | Peer-Reviewed Postprint info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | The need to mitigate the adverse effects of chemotherapy has driven the exploration of innovative drug delivery approaches. One emerging trend in cancer treatment is the utilization of Drug Delivery Systems (DDSs), facilitated by nanotechnology. Nanoparticles, ranging from 1 nm to 1000 nm, act as carriers for chemotherapeutic agents, enabling precise drug delivery. The triggered release of these agents is vital for advancing this novel drug delivery system. Our research investigated this multifaceted delivery capability using liposomes and metal organic frameworks as nanocarriers and utilizing all three targeting techniques: passive, active, and triggered. Liposomes are modified using targeting ligands to render them more targeted toward certain cancers. Moieties are conjugated to the surfaces of these nanocarriers to allow for their binding to receptors overexpressed on cancer cells, thus increasing the accumulation of the agent at the tumor site. A novel class of nanocarriers, namely metal organic frameworks, has emerged, showing promise in cancer treatment. Triggering techniques (both intrinsic and extrinsic) can be used to release therapeutic agents from nanoparticles, thus enhancing the efficacy of drug delivery. In this study, we develop a predictive model combining experimental measurements with deep learning techniques. The model accurately predicts drug release from liposomes and MOFs under various conditions, including low- and high-frequency ultrasound (extrinsic triggering), microwave exposure (extrinsic triggering), ultraviolet light exposure (extrinsic triggering), and different pH levels (intrinsic triggering). The deep learning-based predictions significantly outperform linear predictions, proving the utility of advanced computational methods in drug delivery. Our findings demonstrate the potential of these nanocarriers and highlight the efficacy of deep learning models in predicting drug release behavior, paving the way for enhanced cancer treatment strategies. |
| format | article |
| id | aus_e2b8aa9ff3f151d22936780558f2e097 |
| identifier_str_mv | Husseini, G. A., Sabouni, R., Puzyrev, V., & Ghommem, M. (2024). Deep Learning for the Accurate Prediction of Triggered Drug Delivery. In IEEE Transactions on NanoBioscience (pp. 1–1). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/tnb.2024.3426291 1536-1241 10.1109/TNB.2024.3426291 2-s2.0-85199061767 SCOPUS_ID:85199061767 IEEE Transactions on Nanobioscience 15582639 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/25709 |
| publishDate | 2024 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
| spelling | Deep Learning for the Accurate Prediction of Triggered Drug DeliveryHusseini, GhalebSabouni, RanaPuzyrev, VladimirGhommem, Mehdiactive targetingCancercancer treatmentchemotherapydeep learningDrug deliveryDrug delivery systemsDrugsFluorescenceliposomesmetal organic frameworksmicrowaveMicrowave theory and techniquesNanocarriersnanoparticlespH-triggeringUltrasonic imagingultrasoundThe need to mitigate the adverse effects of chemotherapy has driven the exploration of innovative drug delivery approaches. One emerging trend in cancer treatment is the utilization of Drug Delivery Systems (DDSs), facilitated by nanotechnology. Nanoparticles, ranging from 1 nm to 1000 nm, act as carriers for chemotherapeutic agents, enabling precise drug delivery. The triggered release of these agents is vital for advancing this novel drug delivery system. Our research investigated this multifaceted delivery capability using liposomes and metal organic frameworks as nanocarriers and utilizing all three targeting techniques: passive, active, and triggered. Liposomes are modified using targeting ligands to render them more targeted toward certain cancers. Moieties are conjugated to the surfaces of these nanocarriers to allow for their binding to receptors overexpressed on cancer cells, thus increasing the accumulation of the agent at the tumor site. A novel class of nanocarriers, namely metal organic frameworks, has emerged, showing promise in cancer treatment. Triggering techniques (both intrinsic and extrinsic) can be used to release therapeutic agents from nanoparticles, thus enhancing the efficacy of drug delivery. In this study, we develop a predictive model combining experimental measurements with deep learning techniques. The model accurately predicts drug release from liposomes and MOFs under various conditions, including low- and high-frequency ultrasound (extrinsic triggering), microwave exposure (extrinsic triggering), ultraviolet light exposure (extrinsic triggering), and different pH levels (intrinsic triggering). The deep learning-based predictions significantly outperform linear predictions, proving the utility of advanced computational methods in drug delivery. Our findings demonstrate the potential of these nanocarriers and highlight the efficacy of deep learning models in predicting drug release behavior, paving the way for enhanced cancer treatment strategies.American University of SharjahAl-Jalila FoundationAl Qasimi FoundationPatient’s Friends Committee-SharjahBiosciences and Bioengineering Research InstituteGulf Cooperation Council (GCC) Co-Fund ProgramTakamul ProgramTechnology Innovation Pioneer (TIP) Healthcare AwardsSheikh Hamdan Awards for Medical SciencesFriends of Cancer Patients (FoCP)Dana Gas Endowed Chair for Chemical EngineeringIEEE2024-11-13T12:05:25Z2024-11-13T12:05:25Z2024Peer-ReviewedPostprintinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfHusseini, G. A., Sabouni, R., Puzyrev, V., & Ghommem, M. (2024). Deep Learning for the Accurate Prediction of Triggered Drug Delivery. In IEEE Transactions on NanoBioscience (pp. 1–1). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/tnb.2024.34262911536-1241https://hdl.handle.net/11073/2570910.1109/TNB.2024.34262912-s2.0-85199061767SCOPUS_ID:85199061767IEEE Transactions on Nanobioscience15582639IEEE Transactions on Nanobioscienceen_UShttps://doi.org/10.1109/tnb.2024.3426291Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/oai:repository.aus.edu:11073/257092024-11-14T14:25:02Z |
| spellingShingle | Deep Learning for the Accurate Prediction of Triggered Drug Delivery Husseini, Ghaleb active targeting Cancer cancer treatment chemotherapy deep learning Drug delivery Drug delivery systems Drugs Fluorescence liposomes metal organic frameworks microwave Microwave theory and techniques Nanocarriers nanoparticles pH-triggering Ultrasonic imaging ultrasound |
| status_str | publishedVersion |
| title | Deep Learning for the Accurate Prediction of Triggered Drug Delivery |
| title_full | Deep Learning for the Accurate Prediction of Triggered Drug Delivery |
| title_fullStr | Deep Learning for the Accurate Prediction of Triggered Drug Delivery |
| title_full_unstemmed | Deep Learning for the Accurate Prediction of Triggered Drug Delivery |
| title_short | Deep Learning for the Accurate Prediction of Triggered Drug Delivery |
| title_sort | Deep Learning for the Accurate Prediction of Triggered Drug Delivery |
| topic | active targeting Cancer cancer treatment chemotherapy deep learning Drug delivery Drug delivery systems Drugs Fluorescence liposomes metal organic frameworks microwave Microwave theory and techniques Nanocarriers nanoparticles pH-triggering Ultrasonic imaging ultrasound |
| url | https://hdl.handle.net/11073/25709 |