نشریه شیمی و مهندسی شیمی ایران

نشریه شیمی و مهندسی شیمی ایران

چارچوب های فلز-آلی و بررسی کاربرد آنها در داروهای ضدسرطانی به روش یادگیری ماشین

نوع مقاله : علمی-پژوهشی

نویسندگان
مینوش لعلی نیا 1
ناهید حسن زاده نعمتی 1
جواد کریمی ثابت 2
سید خطیب الاسلام صدرنژاد 3
1 گروه مهندسی پزشکی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران،ایران
2 پژوهشکده چرخه سوخت هسته ای،پژوهشگاه علوم و فنون هسته‌ای،تهران،ایران
3 دانشکده مهندسی و علم مواد، دانشگاه صنعتی شریف، تهران،ایران
چکیده
در این تحقیق، چارچوب‌های فلز-آلی (MOFs) به‌عنوان حامل‌های دارویی به دلیل ساختارهای متخلخل و ظرفیت بارگذاری بالای خود مورد بررسی قرار گرفتند. هدف این مطالعه، استفاده از الگوریتم‌های یادگیری ماشین برای پیش‌بینی دقیق درصد رهایش دارو از MOF ها در شرایط مختلف بود. برای این منظور، داده‌های حاصل از آزمایش‌های آزمایشگاهی و مقالات علمی، شامل ویژگی‌هایی مانند اندازه منافذ، مساحت سطح، چگالی، حجم منافذ، pH ، بارگذاری دارو، زمان، آب گریزی، نوع دارو، نوع MOF  به‌عنوان ورودی مدل‌های یادگیری ماشین استفاده شدند و درصد رهایش دارو به عنوان خروجی در نظر گرفته شد. همچنین، برای استخراج داده‌های آزمایشگاهی از روش‌های مختلف آنالیز شامل میکروسکوپ الکترونی روبشی (SEM)، پراش پرتو ایکس (XRD) و جذب سطحی نیتروژن (BET) استفاده شد. سپس، چهار الگوریتم مختلف شامل رگرسیون بردار پشتیبان (SVR)، Gradient Boosting، جنگل تصادفی (Random Forest) و درخت تصمیم (Decision Tree) برای پیش‌بینی درصد رهایش دارو به کار گرفته شدند. نتایج نشان داد که الگوریتم Gradient Boosting با 85/0 R² = بهترین عملکرد را در پیش‌بینی‌ها داشت، در حالی که جنگل تصادفی 81/0 R² = و درخت تصمیم 72/0 R² = نیز نتایج قابل قبولی ارائه دادند. مدل SVR  نیز توانست 64/0 R² = را به‌دست آورد. در نهایت، تحلیل اهمیت ویژگی‌ها نشان داد که که اندازه منافذ مهم‌ترین عامل در تعیین کارایی رهایش دارو است. مساحت سطحی MOFs نیز از اهمیت بالایی برخوردار است. این نتایج نشان‌دهنده توانایی بالای الگوریتم‌های یادگیری ماشین در پیش‌بینی رفتار رهایش دارو و بهینه‌سازی طراحی MOFها برای کاربردهای دارورسانی است.
کلیدواژه‌ها
چارچوب‌های فلز-آلی (MOFs)
یادگیری ماشین
پیش‌بینی رهایش دارو
اهمیت ویژگی

موضوعات

[1] Lestari W.W., Arvinawati M., Martien R., Kusumaningsih T., Green and Facile Synthesis of MOF and Nano MOF Containing Zinc(II) and Benzen 1,3,5-Tri Carboxylate and Its Study in Ibuprofen Slow-Release. Mater. Chem. PHys., 204: 141-146 (2018).
[2] Jiang K., Ni W., Cao X., Zhang L., Lin S., A Nanosized Anionic MOF with Rich Thiadiazole Groups for Controlled Oral Drug Delivery. Mater. Today Bio., 13: 100180 (2022).
[3] Ma P., Zhang J., Liu P., Wang Q., Zhang Y., Song K., Li R., Shen L. Computer-Assisted Design for Stable and Porous Metal-Organic Framework (MOF) as a Carrier for Curcumin Delivery. LWT, 120: 108949 (2020).
[4] PHatharapeetranun N., Ksapabutr B., Marani D., Bowen J.R., Esposito V. 3D-Printed Barium Titanate/Poly-(Vinylidene Fluoride) Nano-Hybrids with Anisotropic Dielectric Properties. J. Mater. Chem. C, 5: 12430-12440 (2017).
[5] Wang G., Huang X., Jiang P., Tailoring Dielectric Properties and Energy Density of Ferroelectric Polymer Nanocomposites by High-k Nanowires. ACS Appl. Mater. Interface., 7: 18017-18027 (2015).
[6] Yang Y., Li L., Lin R., Ye Y., Yao Z., Yang L., Xiang F., Chen S., Zhang Z., Xiang S., Chen B., Ethylene/Ethane Separation in a Stable Hydrogen-Bonded Organic Framework Through a Gating Mechanism. Nat. Chem, 13(10): 933-939 (2021).
[7] Zhang L., Li L., Hu E., Yang L., Shao K., Yao L., Jiang K., Cui Y., Yang Y., Li B.,Chen B.,Qian G, Boosting Ethylene/Ethane Separation within Copper(I)-Chelated Metal–Organic Frameworks through Tailor-Made Aperture and Specific -Complexation. Adv. Sci, 7: 1901918 (2020).
[8] Cui Y., Zhang J., He H., Qian G., PHotonic Functional Metal-Organic Frameworks. Chem. Soc. Rev, 47: 5740–5785 (2018).
[9] Dhakshinamoorthy A., Asiri A.M., Garcia H., 2D Metal–Organic Frameworks as Multifunctional Materials in Heterogeneous Catalysis and Electro/PHotocatalysis. Adv. Mater, 31: 1900617 (2019).
[10] Rivera-Torrente M., Mandemaker L.D.B., Filez M., Delen G., Seoane B., Meirer F., Weckhuysen B.M., Spectroscopy, Microscopy, Diffraction and Scattering of Archetypal MOFs: Formation, Metal Sites in Catalysis and Thin Films. Chem. Soc. Rev, 49: 6694-6732 (2020).
] 11[ اسدی، مهدی؛ آزرده، سهیلا؛ حذف یون فلزهای سنگین Cd2+ و Pb2+ از آب با نانو مواد متخلخل، نشریه شیمی و مهندسی شیمی ایران، (4)39: 13 تا 23 (1399).
[12] Robison L., Zhang L., Drout R.J., Li P., Haney C.R., Brikha A., Noh H., Mehdi B.L., Browning N.D., Dravid V.P., Cui Q., Islamoglu T., Farha O, A Bismuth Metal–Organic Framework as a Contrast Agent for X-ray Computed TomograpHy. ACS Appl. Bio Mater. 2: 1197–1203 (2019).
[13] Wang S., Chen Y., Wang S., Li P., Mirkin C.A., Farha O.K., DNA-Functionalized Metal–Organic Framework Nanoparticles for Intracellular Delivery of Proteins. J. Am. Chem. Soc, 141: 2215–2219 (2019).
[14] Zhao H., Hou S., Zhao X., Liu D., Adsorption and pH-Responsive Release of Tinidazole on Metal–Organic Framework CAU-1. J. Chem. Eng, 64: 1851–1858 (2019).
[15] Baino F., Kargozar S., Regulation of the Ocular Cell/Tissue Response by Implantable Biomaterials and Drug Delivery Systems. Bioengineering, 7: 65 (2020).
[16] Horcajada P., Serre C., Vallet-Regí M., Sebban M., Taulelle F., Férey G., Metal–Organic Frameworks as Efficient Materials for Drug Delivery. Angew. Chem. Int. Ed, 45: 5974–5978 (2006).
[17] Suwardi A., Wang F., Xue K., Han M.Y., Teo P., Wang P., Wang S., Liu Y., Ye E., Li Z., Loh X.J, Machine Learning-Driven Biomaterials Evolution. Adv. Mater, 34: 2102703 (2022).
[18] Wang Y., Yan J., Wen N., Xiong H., Cai S., He Q., Hu Y., Peng D., Liu Z., Liu Y., Metal-Organic Frameworks for Stimuli Responsive Drug Delivery. Biomaterials, 230: 119619 (2020).
[19] Wu M., Yang Y., Metal-Organic Framework (MOF)-Based Drug/Cargo Delivery and Cancer Therapy. Adv. Mater, 29: 1606134 (2017).
[20] Harrison D., Lawson S. Walton P., Chan C., Metal–Organic Frameworks for Drug Delivery: A Design Perspective. ACS Appl. Mater. Interfaces, 13, 6: 7004–7020 (2021).
[21] Hashemzadeh A., Drummen G.P.C., Avan A., Darroudi M., Khazaei M., Khajavian R., Rangrazi A., Mirzaei M., When Metal–Organic Framework Mediated Smart Drug Delivery Meets Gastrointestinal Cancers .J. Mater. Chem. B, 9: 3967-3982 (2021).
[22] Peng X., Tang S., Tang D., Zhou D., Li Y., Chen Q., Wan F., Lukas H., Han H., Zhang X., Gao W., Wu S., Autonomous Metal-Organic Framework Nanorobots for Active Mitochondria-Targeted Cancer Therapy. Sci Adv, 9(23): (2023).
[23] Cai M.,  Qin L.,  You L.,  Yao Y.,  Wu H.,  Zhang Z.,  Zhang L.,Yin X., Ni J, Functionalization of MOF-5 With Mono-Substituents: Effects on Drug Delivery Behavior. RSC Adv, 10: 36862-36872 (2020).
[24] Sun Y., Zheng L., Yang Y., Metal–Organic Framework Nanocarriers for Drug Delivery in Biomedical Applications. Nano-Micro Lett, 12: 103 (2020).
[25] Syah R., Al-Khowarizmi A., Elveny M., Khan A., Machine Learning Based Simulation of Water Treatment Using LDH/MOF Nanocomposites, Environmental Technology & Innovation, 23: 101805 (2021).
[26] Daglar H., Keskin S., Combining Machine Learning and Molecular Simulations to Unlock Gas Separation Potentials of MOF Membranes and MOF/Polymer MMMs. ACS Applied Materials & Interfaces, 14(28): 32134–32148 (2022).
[27] Daglar H., Gulbalkan H.C., Habib N., Integrating Molecular Simulations with Machine Learning Guides in the Design and Synthesis of [BMIM][BF4]/MOF Composites for CO2/N2 Separation. ACS Appl. Mater. Interfaces, 15(13): 17421–17431 (2023).
[28] Chong S., Lee S., Kim B., Kim J., Applications of Machine Learning in Metal-Organic Frameworks. Coordination Chemistry Reviews, 423: 213487 (2020).
[29] Liu X., Wang Y., Yuan J., Li X., Wu S., Bao Y., Feng Z., Ou F., He Y., Prediction of the Ibuprofen Loading Capacity of Mofs by Machine Learning. Bioengineering, Bioengineering, 9(10): 517 (2022).
[30] Pouyanfar N., Ahmadi M., Ayyoubzadeh S.M., Ghorbani-Bidkorpeh F., Drug Delivery System Tailoring Via Metal-Organic Framework Property Prediction Using Machine Learning: A Disregarded Approach. Materials Today Communications, 38: 107938 (2023).
[31] Guido R., Ferrisi S., Lofaro D., Conforti D., An Overview on the Advancements of Support Vector Machine Models in Healthcare Applications: A Review. Information, 15: 235 (2024).
[32] Sun Z., Wang G., Li P., Wang H., Zhang M., Liang X., An Improved Random Forest Based on the Classification Accuracy and Correlation Measurement of Decision Trees. Expert Systems with Applications, 237: 121549 (2024).
[33] Deepa S., Booba B., Predict Diabetes Healthcare Analytics Using Hybrid Gradient Boosting Machine Learning Model. Educational Administration: Theory and Practice, 30(5): 2928–2945 (2024).
[34] Venugopal Reddy M., Sivakumar P., Comparative Analysis of Machine Learning Algorithms for Liver Disease Prediction: SVM, Logistic Regression, and Decision Tree. Asian Journal of Research in Computer Science, 17(6): 188-201 (2024).
[35] Sud D., Kaur G., A Comprehensive Review on Synthetic Approaches for Metal-Organic Frameworks: From Traditional Solvothermal to Greener Protocols. Polyhedron,193: 114897 (2021).
[36] Martí-Ruja J., Structural Elucidation of Microcrystalline MOFs from Powder X-Ray Diffraction. Dalton transaction, 49: 13897-13916 (2020).
[37] Yao L., Tang Y., Cao W., Cui Y., Qian G., Highly Efficient Encapsulation of Doxorubicin Hydrochloride in Metal–Organic Frameworks for Synergistic Chemotherapy and Chemodynamic Therapy. ACS Biomaterials Science & Engineering,  7(10): 4999-5006 (2021).
[38] Zhao H., Gong L., Wu H., Liu C., Liu Y., Xiao C., Liu C., Chen L., Jin M., Gao Z., Guan Y., Huang W., Development of Novel Paclitaxel-Loaded ZIF-8 Metal-Organic Framework Nanoparticles Modified with Peptide Dimers and an Evaluation of Its Inhibitory Effect against Prostate Cancer Cells. Pharmaceutics, 15: 1874 (2023).
[39] Molavi H., Moghimi H., Taheri R.A., Zr-Based MOFs with High Drug Loading for Adsorption Removal of Anti-Cancer Drugs: A Potential Drug Storage, Applied Organometallic Chemistry,  34: 4 (2020).
[40] Al Haydar M, Abid HR, Sunderland B, Wang S., Metal Organic Frameworks as a Drug Delivery System for Flurbiprofen. Drug Des Devel Ther. 11: 2685-2695 (2017).
[41] Bhattacharjee A., Purkait M.K. Gumma S., Doxorubicin Loading Capacity of MIL-100(Fe): Effect of Synthesis Conditions. J Inorg Organomet Polym, 30: 2366–2375 (2020).
[42] Chun-Yi S., Chao Q., Xin-Long W., Guang-Sheng Y., Kui-Zhan S., Zeolitic imidazolate Framework-8 as Efficient pH-Sensitive Drug Delivery Vehicle, Dalton Transaction, 41: 6906-6909  (2012).
[43] Ibrahim M., Sabouni R., Husseini G.A., Anti-Cancer Drug Delivery Using Metal Organic Frameworks (MOFs), Current Medicinal Chemistry, 24(2): 193 – 214 (2017).
[44] Chen F., Li C., Zhu Y-J., Zhao X-Y., Lu B-Q., Wu J., Magnetic Nanocomposite of Hydroxyapatite Ultrathin Nanosheets/Fe3O4 Nanoparticles: Microwave-Assisted Rapid Synthesis and Application in pH-Responsive Drug Release. Biomater, 1: 1074-1081 (2013).
[45] Xie C., Guo B., You H., Wang Z., Leng Q., Ding L., Wang Q., Retracted: Synthesis and Surface Modification of Mesoporous Metal-Organic Framework (UiO-66) for Efficient pH-Responsive Drug Delivery and Lung Cancer Treatment. Nanotechnology, 32(29): (2021).
[46] Gordon J., Kazemian H., Rohani S., MIL-53(Fe), MIL-101, and SBA-15 Porous Materials: Potential platforms for Drug Delivery. Materials Science and Engineering: C, 47: 172-179 (2015).
[47] Adhikari C., Das A., Chakraborty A., Zeolitic Imidazole Framework (ZIF) Nanospheres for Easy Encapsulation and Controlled Release of an Anticancer Drug Doxorubicin under Different External Stimuli: A Way toward Smart Drug Delivery System. Mol. Pharmaceutics, 12(9): 3158–3166 (2015).
[48] Cabello-Solorzano K., Ortigosa de Araujo I., Peña M., Correia L., Tallón-Ballesteros J.A., The Impact of Data Normalization on the Accuracy of Machine Learning Algorithms: A Comparative Analysis. 18th International Conference on Soft Computing Models in Industrial and Environmental Applications, 344–353 (2023).
[49] Avuçlu E., Elen A., Evaluation of Train and Test Performance of Machine Learning Algorithms and Parkinson Diagnosis with Statistical Measurements. Med Biol Eng Comput, 58: 2775–2788 (2020).
[50] Botchkarev A., Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology. Interdisciplinary Journal of Information, Knowledge, and Management, (2019).
[51] Chicco D, Warrens MJ, Jurman G., The Coefficient of Determination R-Squared is More Informative than SMAPE, MAE, MAPE, MSE and RMSE in Regression Analysis Evaluation. PeerJ Computer Science, 5(7): e623 (2021).
[52] Lalinia M., Hassanzadeh Nemati N., Karimi Sabet J., Sadrnezhaad S.K., Synthesis and Comparative Study of zif8 and zif7 Metal Organic Frameworks as Carrier for Controlled Release of Doxorubicin in Cancer Treatment. Iranian Journal of Chemistry and Chemical Engineering, 43(11): 3863-3878 (2024).
[53] Hashemian S., Sedrpoushan A. Eshbala F.H., Co-Zeolite Imidazolate Frameworks (ZIF-9@Zeolite) as Heterogen Catalyst for Alcohols Oxidation. Catal Lett, 147: 196–203 (2017).
[54] Rahmawati I.D., Ediati R., Prasetyoko D., Synthesis of UiO-66 Using Solvothermal Method at High Temperature. Journal of proceeding series, 1: 2354-6026 (2014).
[55] Taherzade S.D., Soleimannejad J., Tarlani A., Application of Metal-Organic Framework Nano-MIL-100(Fe) for Sustainable Release of Doxycycline and Tetracycline. Nanomaterials, 7: 215 (2017).
[56] Zhang Z, Zhao Y, Canes A, Steinberg D, Lyashevska O., Predictive Analytics with Gradient Boosting in Clinical Medicine. Ann Transl Med.,7(7): 152 (2019).
[57] Zhang F., Lauren J. O'Donnell., Chapter 7 - Support Vector Regression. Machine Learning Methods and Applications to Brain Disorders., 123-140 (2020).
[58] Jehad A., Rehanullah K., Nasir A., Imran M., Random Forests and Decision Trees. IJCSI International Journal of Computer Science Issues, 5(3): 1694-0814 (2012).
[59] Altmann A., Toloşi L., Sander O., Lengauer T., Permutation Importance: A Corrected Feature Importance Measure, Bioinformatics, 26(10): 1340–1347 (2010).