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Volume 18, Issue 3
  • ISSN: 1874-4710
  • E-ISSN: 1874-4729

Abstract

Introduction

A promising material used in radiation synovectomy of small joints is hydroxyapatite, labeled with 177Lu. During the design and production of radiopharmaceuticals, the condition of the radiolabeling process directly influences the radiochemical yield and consequently the quality of the final product so this process necessitates precise optimization.

Methods

In this investigation, a central composite design based on response surface methodology and artificial neural networks modeling coupled with genetic algorithm technique is applied to build predictive models and explore key parameters' effect in hydroxyapatite's radiolabeling process with 177Lu radionuclide. The variables that directly affected the labeling reaction were the initial 177Lu radioactivity, pH, radiolabeling reaction time, and temperature.

Results

Based on the validation data set, the statistical values demonstrate that the artificial neural networks model performs better than the response surface methodology model. The artificial neural networks model has a small mean squared error (9.08 artificial neural networks < 12.36 response surface methodology) and a high coefficient of determination (R2: 0.99 artificial neural networks > 0.93 response surface methodology). The optimum conditions to achieve maximum radiochemical yield based on response surface methodology using artificial neural networks modeling coupled with genetic algorithm were at the initial radioactivity of 177Lu radionuclide = 0.082 Gigabecquerel (GB), pH = 6.75, time= 22 (min), and temperature = 37.8 (℃).

Conclusion

The ability to generate more data with fewer experiments for optimization and improved production is a pertinent advantage of multivariate optimization methods over traditional methods in radiation-related activities. The central composite design and artificial neural network-genetic algorithm optimization approaches are successfully utilized to create prediction models and investigate the impact of critical variables in the radiolabeling of hydroxyapatite with 177Lu radionuclide.

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2025-06-21

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References

  1. KampenW.U. Boddenberg-PätzoldB. FischerM. GabrielM. KlettR. KonijnenbergM. KresnikE. LelloucheH. PaychaF. TerslevL. TurkmenC. van der ZantF. AntunovicL. PanagiotidisE. GnanasegaranG. KuwertT. Van den WyngaertT. The EANM guideline for radiosynoviorthesis.Eur. J. Nucl. Med. Mol. Imaging202249268170810.1007/s00259‑021‑05541‑734671820
     [Google Scholar]
  2. LepareurN. RaméeB. Mougin-DegraefM. BourgeoisM. Clinical advances and perspectives in targeted radionuclide therapy.Pharmaceutics2023156173310.3390/pharmaceutics1506173337376181
     [Google Scholar]
  3. Rodriguez-MerchanE.C. De la Corte-RodriguezH. Alvarez-RomanM.T. Gomez-CarderoP. Jimenez-YusteV. Radiosynovectomy for the treatment of chronic hemophilic synovitis: An old technique, but still very effective.J. Clin. Med.20221124747510.3390/jcm1124747536556091
     [Google Scholar]
  4. GhiasiB. SefidbakhtY. Mozaffari-JovinS. GharehchelooB. MehraryaM. KhodadadiA. RezaeiM. Ranaei SiadatS.O. UskokovićV. Hydroxyapatite as a biomaterial – A gift that keeps on giving.Drug Dev. Ind. Pharm.20204671035106210.1080/03639045.2020.177632132476496
     [Google Scholar]
  5. Attar NosratiS. AlizadehR. AhmadiS.J. ErfaniM. Optimized precipitation process for efficient and size-controlled synthesis of hydroxyapatite–chitosan nanocomposite.J. Korean Ceram. Soc.202057663264410.1007/s43207‑020‑00064‑7
     [Google Scholar]
  6. RezaeiR DarziSJ YazdaniM Synthesis and evaluation of 198Au/PAMAM-MPEG-FA against cancer cells.Anticancer Agents Med. Chem.2020201012501265
     [Google Scholar]
  7. CawthrayJ.F. CreaghA.L. HaynesC.A. OrvigC. Ion exchange in hydroxyapatite with lanthanides.Inorg. Chem.20155441440144510.1021/ic502425e25594577
     [Google Scholar]
  8. BartoliF. ElsingaP. NazarioL.R. ZanaA. GalbiatiA. MillulJ. MiglioriniF. CazzamalliS. NeriD. SlartR.H.J.A. ErbaP.A. Automated radiosynthesis, preliminary in vitro/in vivo characterization of OncoFAP-based radiopharmaceuticals for cancer imaging and therapy.Pharmaceuticals202215895810.3390/ph1508095836015106
     [Google Scholar]
  9. BowdenG.D. PichlerB.J. MaurerA. A design of experiments (DoE) approach accelerates the optimization of copper-mediated 18F-Fluorination reactions of arylstannanes.Sci. Rep.2019911137010.1038/s41598‑019‑47846‑631388076
     [Google Scholar]
  10. MurrayPM BellanyF BenhamouL BučarD-K TaborAB SheppardTDJO The application of design of experiments (DoE) reaction optimisation and solvent selection in the development of new synthetic chemistry.Org. Biomol. Chem.2016142373238410.1039/C5OB01892G
     [Google Scholar]
  11. KhuriA.I. MukhopadhyayS. Response surface methodology.Wiley Interdiscip. Rev. Comput. Stat.20102212814910.1002/wics.73
     [Google Scholar]
  12. SalahinejadM. AflakiF. Optimization and determination of Cd (II) in different environmental water samples with dispersive liquid–liquid microextraction preconcentration combined with inductively coupled plasma optical emission spectrometry.Environ. Monit. Assess.20111771-411512510.1007/s10661‑010‑1622‑120652739
     [Google Scholar]
  13. TamijiZ. SalahinejadM. NiaziA. Optimized vortex-assisted dispersive liquid–liquid microextraction coupled with spectrofluorimetry for determination of aspirin in human urine: Response surface methodology.Curr. Pharm. Anal.202016220120910.2174/1573412914666181031115209
     [Google Scholar]
  14. BowdenG.D. ChailanggarN. PichlerB.J. MaurerA. Scalable 18 F processing conditions for copper-mediated radiofluorination chemistry facilitate DoE optimization studies and afford an improved synthesis of [ 18 F]olaparib.Org. Biomol. Chem.202119326995700010.1039/D1OB00903F34351339
     [Google Scholar]
  15. BowdenG.D. StotzS. DunkelG. HaasS. KimmerleE. SchallerM. WeigelinB. HerfertK. PichlerB.J. MaurerA. [ 18 F] p FBC, a covalent CLIP-Tag radiotracer for detection of viral reporter gene transfer in the murine brain.Bioconjug. Chem.202435225426410.1021/acs.bioconjchem.3c0055138308817
     [Google Scholar]
  16. FarragN.S. Abdel-HalimH.A. Abdel MoamenO.A. Facile radiolabeling optimization process via design of experiments and an intelligent optimization algorithm: Application for omeprazole radioiodination.J. Label. Comp. Radiopharm.201962628028710.1002/jlcr.373430970164
     [Google Scholar]
  17. JaynesJ. DingX. XuH. WongW.K. HoC.M. Application of fractional factorial designs to study drug combinations.Stat. Med.201332230731810.1002/sim.552622859316
     [Google Scholar]
  18. AouanB. El AlouaniM. AlehyenS. FadilM. SaufiH. LaghzizilA. TaibiM. NunziJ-M. Application of central composite design for optimisation of the development of metakaolin based geopolymer as adsorbent for water treatment.Int. J. Environ. Anal. Chem.2024104112623264110.1080/03067319.2022.2070010
     [Google Scholar]
  19. KatochS. ChauhanS.S. KumarV. A review on genetic algorithm: Past, present, and future.Multimedia Tools Appl.20218058091812610.1007/s11042‑020‑10139‑633162782
     [Google Scholar]
  20. GuptaA.K. GuntukuS.C. DesuR.K. BaluA. Optimisation of turning parameters by integrating genetic algorithm with support vector regression and artificial neural networks.Int. J. Adv. Manuf. Technol.2015771-433133910.1007/s00170‑014‑6282‑9
     [Google Scholar]
  21. AbdolrasolM.G.M. HussainS.M.S. UstunT.S. SarkerM.R. HannanM.A. MohamedR. AliJ.A. MekhilefS. MiladA. Artificial neural networks based optimization techniques: A review.Electronics20211021268910.3390/electronics10212689
     [Google Scholar]
  22. DavarpanahM.R. KhoshhosnH.A. HaratiM. Attar NosratiS. ZoghiM. MazidiM. Ghannadi MaraghehM. Optimization of fundamental parameters in routine production of 90Y-hydroxyapatite for radiosynovectomy.J. Radioanal. Nucl. Chem.20143021697710.1007/s10967‑014‑3326‑4
     [Google Scholar]
  23. BeheraS.K. MeenaH. ChakrabortyS. MeikapB.C. Application of response surface methodology (RSM) for optimization of leaching parameters for ash reduction from low-grade coal.Int. J. Min. Sci. Technol.201828462162910.1016/j.ijmst.2018.04.014
     [Google Scholar]
  24. de OliveiraL.G. de PaivaA.P. BalestrassiP.P. FerreiraJ.R. da CostaS.C. da Silva CamposP.H. Response surface methodology for advanced manufacturing technology optimization: Theoretical fundamentals, practical guidelines, and survey literature review.Int. J. Adv. Manuf. Technol.20191045-81785183710.1007/s00170‑019‑03809‑9
     [Google Scholar]
  25. SalahinejadM. AflakiF. Screening and optimization of microextraction of Pb(II) by inductively coupled plasma-atomic emission using response surface methodology.J. Appl. Chem. Res.201481324
     [Google Scholar]
  26. Agatonovic-KustrinS. BeresfordR. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research.J. Pharm. Biomed. Anal.200022571772710.1016/S0731‑7085(99)00272‑110815714
     [Google Scholar]
  27. WinklerD.A. SalahinejadM. ShiriF. Discovery and design of radiopharmaceuticals by in silico methods.Curr. Radiopharm.202215427131910.2174/187447101566622083109140336045539
     [Google Scholar]
  28. JacksonI.M. WebbE.W. ScottP.J.H. JamesM.L. In silico approaches for addressing challenges in CNS radiopharmaceutical design.ACS Chem. Neurosci.202213121675168310.1021/acschemneuro.2c0026935606334
     [Google Scholar]
  29. HouhouR. BocklitzT. Trends in artificial intelligence, machine learning, and chemometrics applied to chemical data.Anal. Sci. Adv.202123-412814110.1002/ansa.20200016238716450
     [Google Scholar]
  30. GuptaT.K. RazaK. NilanjanD. AmiraS.A. SurekhaB. FuqianS. Optimization of ANN architecture: A review on nature-inspired techniques.Machine Learning in Bio-Signal Analysis and Diagnostic Imaging.Academic Press Cambridge, Massachusetts201915918210.1016/B978‑0‑12‑816086‑2.00007‑2
     [Google Scholar]
  31. SohailA. Genetic algorithms in the fields of artificial intelligence and data sciences.Ann. Data Sci.20231041007101810.1007/s40745‑021‑00354‑9
     [Google Scholar]
  32. BreckE. PolyzotisN. RoyS. WhangS. ZinkevichM. Data Validation for Machine Learning.2019Available from: https://mlsys.org/Conferences/2019/doc/2019/167.pdf
  33. MauludD. AbdulazeezA.M. A review on linear regression comprehensive in machine learning.J Appl Sci Technol Trends.20201214014710.38094/jastt1457
     [Google Scholar]
  34. BatesS. HastieT. TibshiraniR. Cross-validation: what does it estimate and how well does it do it?J. Am. Stat. Assoc.20241195461434144510.1080/01621459.2023.219768639308484
     [Google Scholar]
  35. DeP. KarS. AmbureP. RoyK. Prediction reliability of QSAR models: An overview of various validation tools.Arch. Toxicol.20229651279129510.1007/s00204‑022‑03252‑y35267067
     [Google Scholar]
  36. AlexanderD.L.J. TropshaA. WinklerD.A. Beware of R 2 : Simple, unambiguous assessment of the prediction accuracy of QSAR and QSPR models.J. Chem. Inf. Model.20155571316132210.1021/acs.jcim.5b0020626099013
     [Google Scholar]
  37. GhasemiJ.B. SalahinejadM. RofoueiM.K. Review of the quantitative structure–activity relationship modelling methods on estimation of formation constants of macrocyclic compounds with different guest molecules.Supramol. Chem.201123961462910.1080/10610278.2011.581281
     [Google Scholar]
  38. TalipZ. FavarettoC. GeistlichS. MeulenN.P. A step-by-step guide for the novel radiometal production for medical applications: Case studies with 68Ga, 44Sc, 177Lu and 161Tb.Molecules202025496610.3390/molecules2504096632093425
     [Google Scholar]
  39. AleksandarV. DrinaJ. MagdalenaR. ZoranaM. MarijaM. DraganaS. SanjaV.Đ. Optimization of the radiolabelling method for improved in vitro and in vivo stability of 90Y-albumin microspheres.Appl. Radiat. Isot.202015610898410.1016/j.apradiso.2019.10898431760344
     [Google Scholar]
  40. GundogduE. DemirE.S. ÖzgençE. YeğenG. AksuB. Applying quality by design principles in the development and preparation of a new radiopharmaceutical: Technetium-99m-Imatinib Mesylate.ACS Omega20205105297530510.1021/acsomega.9b0432732201818
     [Google Scholar]
  41. MolavipordanjaniS. HosseinimehrS.J. Fundamental concepts of radiopharmaceuticals quality controls.Pharm. Biomed. Res.2019431710.18502/pbr.v4i3.538
     [Google Scholar]
  42. VermeulenK. VandammeM. BormansG. CleerenF. Design and challenges of radiopharmaceuticals.Semin. Nucl. Med.201949533935610.1053/j.semnuclmed.2019.07.00131470930
     [Google Scholar]
  43. de BloisE. Radiochemical and analytical aspects of inter-institutional quality control measurements on radiopharmaceutical.J. Nucl. Med.20175825910.1186/s41181‑018‑0052‑1
     [Google Scholar]
  44. CoenenH.H. GeeA.D. AdamM. AntoniG. CutlerC.S. FujibayashiY. JeongJ.M. MachR.H. MindtT.L. PikeV.W. WindhorstA.D. Consensus nomenclature rules for radiopharmaceutical chemistry — Setting the record straight.Nucl. Med. Biol.201755vxi10.1016/j.nucmedbio.2017.09.00429074076
     [Google Scholar]
  45. ChochevskaM. VelichkovskaM. Atanasova LazarevaM. KolevskaK. JolevskiF. RazmoskaJ. FilipovskiZ. NikolovskiS. Zdraveska KocovskaM. UgrinskaA. Evaluation of factors with potential influence on [18F]FDG radiochemical synthesis yield.Appl. Radiat. Isot.202319911090010.1016/j.apradiso.2023.11090037348257
     [Google Scholar]
  46. BaudhuinH. CousaertJ. VanwolleghemP. RaesG. CaveliersV. KeyaertsM. LahoutteT. XavierC. 68Ga-Labeling: Laying the foundation for an anti-radiolytic formulation for NOTA-sdAb PET tracers.Pharmaceuticals202114544810.3390/ph1405044834068666
     [Google Scholar]
  47. EppardE Pèrez-MaloM RöschF. Improved radiolabeling of DOTATOC with trivalent radiometals for clinical application by addition of ethanol.EJNMMI Radiopharm. Chem.201711610.1186/s41181‑016‑0010‑8
     [Google Scholar]
  48. LarenkovA. MitrofanovI. PavlenkoE. RakhimovM. Radiolysis-associated decrease in radiochemical purity of 177Lu-Radiopharmaceuticals and comparison of the effectiveness of selected quenchers against this process.Molecules2023284188410.3390/molecules2804188436838872
     [Google Scholar]
  49. LuurtsemaG PichlerV BongarzoneS SeimbilleY ElsingaP GeeA EANM guideline for harmonisation on molar activity or specific activity of radiopharmaceuticals: Impact on safety and imaging quality.EJNMMI Radiopharm. Chem.2021613410.1186/s41181‑021‑00149‑6
     [Google Scholar]
  50. López-GonzálezH. Jiménez-ReyesM. Solache-RíosM. Rojas-HernándezA. Solubility and hydrolysis of lutetium at different [Lu3+]initial.J. Radioanal. Nucl. Chem.2007274110310810.1007/s10967‑006‑6910‑4
     [Google Scholar]
  51. Swiatla-WojcikD. BuxtonG.V. On the possible role of the reaction in the radiolysis of water at high temperatures.Radiat. Phys. Chem.2005743-421021910.1016/j.radphyschem.2005.04.014
     [Google Scholar]
  52. KlasenB. MoonE.S. RöschF. AAZTA5-squaramide ester competing with DOTA-, DTPA- and CHX-A″-DTPA-analogues: Promising tool for 177Lu-labeling of monoclonal antibodies under mild conditions.Nucl. Med. Biol.202196-97809310.1016/j.nucmedbio.2021.03.00733839678
     [Google Scholar]
  53. Di LeoG. SardanelliF. Statistical significance: p value, 0.05 threshold, and applications to radiomics—reasons for a conservative approach.Eur. Radiol. Exp.2020411810.1186/s41747‑020‑0145‑y32157489
     [Google Scholar]
  54. PellicoJ. GawneP.J. T M de RosalesR. Radiolabelling of nanomaterials for medical imaging and therapy.Chem. Soc. Rev.20215053355342310.1039/D0CS00384K33491714
     [Google Scholar]
  55. OkoyeN.C. BaumeisterJ.E. Najafi KhosroshahiF. HennkensH.M. JurissonS.S. Chelators and metal complex stability for radiopharmaceutical applications.Radiochim. Acta20191079-111087112010.1515/ract‑2018‑3090
     [Google Scholar]
  56. Attar NosratiS AlizadehR AhmadiSJ ErfaniM Design, synthesis and characterization of hydroxyapatite-chitosan nanocomposite radiolabelled with 153Sm as radiopharmaceutical for use in radiosynovectomy.Radiochimica Acta20201081576510.1515/ract‑2018‑3038
     [Google Scholar]
  57. HolikH.A. IbrahimF.M. ElaineA.A. PutraB.D. AchmadA. KartamihardjaA.H.S. The chemical scaffold of theranostic radiopharmaceuticals: Radionuclide, bifunctional chelator, and pharmacokinetics modifying linker.Molecules20222710306210.3390/molecules2710306235630536
     [Google Scholar]
  58. AertsM. ClaeskensG. HartJ. Testing lack of fit in multiple regression.Biometrika200087240542410.1093/biomet/87.2.405
     [Google Scholar]
  59. GuillodT. PapamanolisP. KolarJ.W. Artificial neural network (ANN) based fast and accurate inductor modeling and design.IEEE Open J. Power Electron.2020128429910.1109/OJPEL.2020.3012777
     [Google Scholar]
  60. NguyenH. MoayediH. FoongL.K. Al NajjarH.A.H. JusohW.A.W. RashidA.S.A. JamaliJ. Optimizing ANN models with PSO for predicting short building seismic response.Eng. Comput.202036382383710.1007/s00366‑019‑00733‑0
     [Google Scholar]
  61. SibiP. JonesS.A. SiddarthP. Analysis of different activation functions using back propagation neural networks.J. Theor. Appl. Inf. Technol.201347312641268
     [Google Scholar]
  62. WangL. YeW. ZhuY. YangF. ZhouY. Optimal parameters selection of back propagation algorithm in the feedforward neural network.Eng. Anal. Bound. Elem.202315157559610.1016/j.enganabound.2023.03.033
     [Google Scholar]
  63. ApicellaA. DonnarummaF. IsgròF. PreveteR. A survey on modern trainable activation functions.Neural Netw.2021138143210.1016/j.neunet.2021.01.02633611065
     [Google Scholar]
  64. ArthurC.K. TemengV.A. ZiggahY.Y. Performance evaluation of training algorithms in backpropagation neural network approach to blast-induced ground vibration prediction.Ghana Mining J.2020201203310.4314/gm.v20i1.3
     [Google Scholar]
  65. JabbarH.K. KhanR.Z. Methods to avoid over-fitting and under-fitting in supervised machine learning (comparative study).Comput Sci Commun Instrum Devices.20147016317210.3850/978‑981‑09‑5247‑1_017
     [Google Scholar]
  66. Mu’azuN.D. Insight into ANN and RSM models’ predictive performance for mechanistic aspects of Cr(VI) uptake by layered double hydroxide nanocomposites from water.Water20221410164410.3390/w14101644
     [Google Scholar]
  67. AhmadiH. GolianA. Response surface and neural network models for performance of broiler chicks fed diets varying in digestible protein and critical amino acids from 11 to 17 days of age.Poult. Sci.20119092085209610.3382/ps.2011‑0136721844277
     [Google Scholar]
  68. AvramovićJ.M. VeličkovićA.V. StamenkovićO.S. RajkovićK.M. MilićP.S. VeljkovićV.B. Optimization of sunflower oil ethanolysis catalyzed by calcium oxide: RSM versus ANN-GA.Energy Convers. Manage.20151051149115610.1016/j.enconman.2015.08.072
     [Google Scholar]
  69. BelloV.E. OlafadehanO.A. Comparative investigation of RSM and ANN for multi-response modeling and optimization studies of derived chitosan from Archachatina marginata shell.Alex. Eng. J.20216043869389910.1016/j.aej.2021.02.047
     [Google Scholar]
  70. Venkatesh PrabhuM. KarthikeyanR. Comparative studies on modelling and optimization of hydrodynamic parameters on inverse fluidized bed reactor using ANN-GA and RSM.Alex. Eng. J.20185743019303210.1016/j.aej.2018.05.002
     [Google Scholar]
  71. JhaA.K. SitN. Comparison of response surface methodology (RSM) and artificial neural network (ANN) modelling for supercritical fluid extraction of phytochemicals from Terminalia chebula pulp and optimization using RSM coupled with desirability function (DF) and genetic algorithm (GA) and ANN with GA.Ind. Crops Prod.202117011376910.1016/j.indcrop.2021.113769
     [Google Scholar]
  72. SalariM. AlahabadiA. Rahmani-SaniA. MiriM. Yazdani-AvalM. LotfiH. SaghiM.H. RastegarA. SepehrM.N. DarvishmotevalliM. A comparative study of response surface methodology and artificial neural network based algorithm genetic for modeling and optimization of EP/US/GAC oxidation process in dexamethasone degradation: Application for real wastewater, electrical energy consumption.Chemosphere202434914083210.1016/j.chemosphere.2023.14083238042425
     [Google Scholar]
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The Central Composite Design and Artificial Neural Network Coupled with Genetic Algorithm in Optimization and Modeling of the Radiolabeling Process of 177Lu-hydroxyapatite as a Potential Radiosynovectomy Agent
Current Radiopharmaceuticals 18, 201 (2025); https://doi.org/10.2174/0118744710336283250227020659
/content/journals/crp/10.2174/0118744710336283250227020659
/content/journals/crp/10.2174/0118744710336283250227020659
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  • Article Type:     Research Article
Keyword(s): 177Lu-hydroxyapatite; artificial neural networks (ANN); central composite design (CCD); genetic algorithm (GA); radiochemical yield; Radiolabeling; response surface methodology (RSM)

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