AGE-RELATED MACULAR DEGENERATION CURRENT INVESTIGATIONS AND TREATMENTS

agerelatedmaculardeg774enerationkapak

CURRENT INVESTIGATIONS IN WET AGE-RELATED MACULAR DEGENERATION

İnci Elif Erbahçeci Timur

Ankara Bilkent City Hospital, Department of Ophthalmology, Ankara, Türkiye

Erbahçeci Timur İE. Current Investigations in Wet Age-Related Macular Degeneration. Çıtırık M, Şekeryapan Gediz B, eds. Age-Related Macular Degeneration: Current Investigations and Treatments. 1st ed. Ankara: Türkiye Klinikleri; 2025. p.145-156.

ABSTRACT

Wet age-related macular degeneration (AMD) is one of the common leading causes of irreversible visual loss. Current treatment options include agents that inhibit the vascular endothelial growth factor (VEGF) and complement pathways. Despite the proven efficacy of anti-VEGF agents in the treatment of wet AMD, frequent injections, high treatment costs, sustainability of treatment, and injection-related risks are the limitations of anti-VEGF therapy. Tyrosine kinase inhibitors are novel therapeutic agents in the management of wet AMD. Sustained release intravitreal forms or suprachoroidal implants of tyrosine kinase inhibitors showed efficacy, safety, and tolerability with less frequent required anti-VEGF injections. Promising results were reported in phase trials. Predicting treatment response and optimizing treatment regimen in patients with wet AMD remains challenging. Artificial intelligence (AI) has driven recent advancements in diagnosing, monitoring treatment, and predicting outcomes for AMD. Artificial intelligence-based models have facilitated the analysis of optical coherence tomography (OCT) images, enabling automatic qualitative and quantitative assessment of image biomarkers detected on OCT. Insufficient and unbalanced data, lack of interpretability in models, dependence on data quality, and limited generality are the limitations of AI-based models.

Keywords: Age-related macular degeneration (AMD); Wet-AMD; Vascular endothelial growth factor (VEGF) receptors; Anti-VEGF; Tyrosine kinase inhibitors; Tyrosine kinase receptors; Artificial intelligenc; Deep learning

Referanslar

  1. Klaver CC, Assink JJ, van Leeuwen R, Wolfs RC, Vingerling JR, Stijnen T, et al. Incidence and progression rates of age-related maculopathy: the Rotterdam Study. Invest Ophthalmol Vis Sci. 2001 Sep;42(10):2237-41. [PubMed]
  2. Wong TY, Chakravarthy U, Klein R, Mitchell P, Zlateva G, Buggage R, et al. The natural history and prognosis of neovascular age-related macular degeneration: a systematic review of the literature and meta-analysis. Ophthalmology. 2008 Jan;115(1):116-26. [Crossref]  [PubMed]
  3. Wong WL, Su X, Li X, Cheung CM, Klein R, Cheng CY, et al. Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. Lancet Glob Health. 2014 Feb;2(2):e106-16. [Crossref]  [PubMed]
  4. Jonas JB, Cheung CMG, Panda-Jonas S. Updates on the epidemiology of age-related macular degeneration. Asia Pac J Ophthalmol. 2017;6 (6):493-497. [Crossref]  [PubMed]
  5. Smith W, Assink J, Klein R, Mitchell P, Klaver CC, Klein BE, et al. Risk factors for age-related macular degeneration:Pooled findings from three continents. Ophthalmology. 2001 Apr;108(4):697-704. [Crossref]  [PubMed]
  6. Laser photocoagulation of subfoveal neovascular lesions of age-related macular degeneration. Updated findings from two clinical trials. Macular Photocoagulation Study Group. Arch Ophthalmol. 1993;111(9):1200-9. [Crossref]  [PubMed]
  7. Schmidt-Erfurth U, Miller J, Sickenberg M, Bunse A, Laqua H, Gragoudas E, et al. Photodynamic therapy of subfoveal choroidal neovascularization: clinical and angiographic examples. Graefes Arch Clin Exp Ophthalmol.1998;236(5):365-74. [Crossref]  [PubMed]
  8. Gragoudas ES, Adamis AP, Cunningham ET Jr, Feinsod M, Guyer DR; VEGF Inhibition Study in Ocular Neovascularization Clinical Trial Group. Pegaptanib for neovascular age-related macular degeneration. N Engl J Med. 2004;351(27):2805-16. [Crossref]  [PubMed]
  9. Martin DF, Maguire MG, Ying GS, Grunwald JE, Fine SL, Jaffe GJ. CATT Research Group. Ranibizumab and bevacizumab for neovascular age-related macular degeneration. N Engl J Med. 2011;364(20):1897-908. [Crossref]  [PubMed]  [PMC]
  10. Rosenfeld PJ, Brown DM, Heier JS, Boyer DS, Kaiser PK, Chung CY, Kim RY. MARINA Study Group. Ranibizumab for neovascular age-related macular degeneration. N Engl J Med. 2006;355(14):1419-31. [Crossref]  [PubMed]
  11. Busbee BG, Ho AC, Brown DM, Heier JS, Suñer IJ, Li Z, et al. HARBOR Study Group. Twelve-month efficacy and safety of 0.5 mg or 2.0 mg ranibizumab in patients with subfoveal neovascular age-related macular degeneration. Ophthalmology. 2013;120(5):1046-56. [Crossref]  [PubMed]
  12. Heier JS, Brown DM, Chong V, Korobelnik JF, Kaiser PK, Nguyen QD, et al. VIEW 1 and VIEW 2 Study Groups. Intravitreal aflibercept (VEGF trap-eye) in wet age-related macular degeneration. Ophthalmology. 2012;119(12):2537-48. [Crossref]  [PubMed]
  13. Baek SU, Park IW, Suh W. Long-term intraocular pressure changes after intravitreal injection of bevacizumab. Cutan Ocul Toxicol. 2016;35(4):310-4. [Crossref]  [PubMed]
  14. Holz FG, Tadayoni R, Beatty S, Berger A, Cereda MG, Cortez R, et al. Multi-country real-life experience of anti-vascular endothelial growth factor therapy for wet age-related macular degeneration. Br J Ophthalmol. 2015;99(2):220-6. [Crossref]  [PubMed]  [PMC]
  15. Smith AG, Kaiser PK. Emerging treatments for wet age-related macular degeneration. Expert Opin Emerg Drugs.2014;19(1):157-64. [Crossref]  [PubMed]
  16. Falavarjani KG, Nguyen QD. Adverse events and complications associated with intravitreal injection of anti-VEGF agents: a review of literature. Eye (Lond). 2013 Jul;27(7):787-94. [Crossref]  [PubMed]  [PMC]
  17. Raica M, Cimpean AM. Platelet-Derived Growth Factor (PDGF)/PDGF Receptors (PDGFR) Axis as Target for Antitumor and Antiangiogenic Therapy. Pharmaceuticals (Basel). 2010;3(3):572-599. [Crossref]  [PubMed]  [PMC]
  18. Yamaoka T, Kusumoto S, Ando K, Ohba M, Ohmori T. Receptor Tyrosine Kinase-Targeted Cancer Therapy. Int J Mol Sci. 2018;19(11):3491. [Crossref]  [PubMed]  [PMC]
  19. Lieu CH, Tran H, Jiang ZQ, Mao M, Overman MJ, Lin E, et al. The association of alternate VEGF ligands with resistance to anti-VEGF therapy in metastatic colorectal cancer. PLoS One. 2013 15;8(10):e77117. [Crossref]  [PubMed]  [PMC]
  20. Papaetis GS, Syrigos KN. Sunitinib: a multitargeted receptor tyrosine kinase inhibitor in the era of molecular cancer therapies. BioDrugs. 2009;23(6):377-89. [Crossref]  [PubMed]
  21. Graybug Vision Inc. A Phase 2b Multicenter Dose-Ranging Study Evaluating the Safety and Efficacy of SunitinibMalate Depot Formulation (GB-102) Compared to Aflibercept in Subjects With Neovascular (Wet) Age-related Macular Degeneration (ALTISSIMO Study). ClinicalTrials.gov NCT03953079. 2022. [Link]
  22. Liang C, Yuan X, Shen Z, Wang Y, Ding L. Vorolanib, a novel tyrosine receptor kinase receptor inhibitor with potent preclinical anti-angiogenic and anti-tumor activity. Mol Ther Oncolytics. 2022;24:577-584. [Crossref]  [PubMed]  [PMC]
  23. Cohen MN, O'Shaughnessy D, Fisher K, Cerami J, AwhCC, Salazar DE, et al. APEX: a phase II randomised clinical trial evaluating the safety and preliminary efficacy of oral X-82 to treat exudative age-related macular degeneration. Br J Ophthalmol. 2021;105(5):716-722. [Crossref]  [PubMed]
  24. Gao Y, Lu F, Li X, Dai H, Liu K, Liu X, et al. Safety and tolerability of oral vorolanib for neovascular (wet) age-related macular degeneration: a phase I, open-label study. Eye (Lond). 2023;37(15):3228-3233. [Crossref]  [PubMed]  [PMC]
  25. Patel S, Storey PP, Barakat MR, Hershberger V, Bridges WZ Jr, Eichenbaum DA, et al. Phase I DAVIO Trial: EYP-1901 Bioerodible, Sustained-Delivery Vorolanib Insert in Patients With Wet Age-Related Macular Degeneration. Ophthalmol Sci. 2024;4(5):100527. [Crossref]  [PubMed]  [PMC]
  26. EyePoint Pharmaceuticals Announces Positive Topline Data from the Phase 2 DAVIO 2 Trial of EYP-1901 in Wet AMD Achieving All Primary and Secondary Endpoints. EYPT Stock News. 2023. Available from: [Link]
  27. Avery RLWJ, Chang A, Guymer R, Wickremasinghe S, Bell N, Vantipalli S, Metzinger JLet al. Preliminary Findings from a Phase 1 Trial Evaluating the Safety, Tolerability and Biological Activity of OTX-TKI, a Hydrogel-Based, SustainedRelease Intravitreal Axitinib Implant, in Subjects with Neovascular Age-Related Macular Degeneration. Retina Society Annual Scientific Meeting; 2020. [Link]
  28. Avery LR. OTX-TKI, Sustained-Release Axitinib Hydrogel Implant, for Neovascular AgeRelated Macular Degeneration. In: 56th Annual Scientific Meeting of the Retina Society. New York, NY; 2023. [Link]
  29. Kuppermann BD. Safety and Tolerability Study of Suprachoroidal Injections of CLS-AX in Neovascular AMD Patients with Persistent Activity Following Anti-VEGF Therapy: OASIS Phase 1/2a Clinical Trial 6-Month Extension Study Results. In: The Macula Society 46th Annual Meeting;2023; Miami Beach, Florida. Available from: [Link]
  30. Biomedical C Clearside Biomedical Announces Positive Safety Results from Cohort 1 of OASIS Phase 1/2a Clinical Trial of CLS-AX (axitinib injectable suspension) for the Treatment of Wet AMD. Published 2021. [Link]
  31. Gomi F, Iida T, Mori R, Horita S, Nakamura H, Nakajima Y, et al. Phase I Study of Tivozanib Eye Drops in Healthy Volunteers and Patients with Neovascular Age-Related Macular Degeneration. Ophthalmol Sci. 2024;4(6):100553. [Crossref]  [PubMed]  [PMC]
  32. Shirian JD, Shukla P, Singh RP. Exploring new horizons in neovascular age-related macular degeneration: novel mechanisms of action and future therapeutic avenues. Eye (Lond).2025;39(1):40-44. [Crossref]  [PubMed]  [PMC]
  33. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks in Advances in Neural Information Processing Systems 25 (NIPS). Communication of the ACM. 2012: 84-90. [Crossref]
  34. van Grinsven MJ, Lechanteur YT, van de Ven JP, van Ginneken B, Hoyng CB, Theelen T, et al. Automatic drusen quantification and risk assessment of age-related macular degeneration on color fundus images. Invest Ophthalmol Vis Sci. 2013;54(4):3019-27. [Crossref]  [PubMed]
  35. Feeny AK, Tadarati M, Freund DE, Bressler NM, Burlina P. Automated segmentation of geographic atrophy of the retinal epithelium via random forests in AREDS color fundus images. Comput Biol Med. 2015;65:124-36. [Crossref]  [PubMed]  [PMC]
  36. Grassmann F, Mengelkamp J, Brandl C, Harsch S, Zimmermann ME, Linkohr B, et al. A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography. Ophthalmology. 2018;125(9):1410-1420. [Crossref]  [PubMed]
  37. Xu K, Huang S, Yang Z, Zhang Y, Fang Y, Zheng G, et al. Automatic detection and differential diagnosis of age-related macular degeneration from color fundus photographs using deep learning with hierarchical vision transformer. Comput Biol Med. 2023;167:107616. [Crossref]  [PubMed]
  38. Schlegl T, Waldstein SM, Vogl WD, Schmidt-Erfurth U, Langs G. Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks. Inf Process Med Imaging. 2015;24:437-48. [Crossref]  [PubMed]
  39. Fang L, Cunefare D, Wang C, Guymer RH, Li S, Farsiu S. Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. Biomed Opt Express. 2017;8(5):2732-2744. [Crossref]  [PubMed]  [PMC]
  40. Schlegl T, Waldstein SM, Bogunovic H, Endstraßer F, Sadeghipour A, Philip AM, et al. Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning. Ophthalmology. 2018;125(4):549-558. [Crossref]  [PubMed]
  41. Venhuizen FG, van Ginneken B, Liefers B, van Asten F, Schreur V, Fauser S, et al. Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography. Biomed Opt Express. 2018;9(4):1545-1569. [Crossref]  [PubMed]  [PMC]
  42. De Fauw J, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med.2018;24(9):1342-1350. [Crossref]  [PubMed]
  43. Fu DJ, Faes L, Wagner SK, Moraes G, Chopra R, Patel PJ, et al. Predicting Incremental and Future Visual Change in Neovascular Age-Related Macular Degeneration Using Deep Learning. Ophthalmol Retina. 2021;5(11):1074-1084. [Crossref]  [PubMed]
  44. Moraes G, Fu DJ, Wilson M, Khalid H, Wagner SK, Korot E, et al. Quantitative Analysis of OCT for Neovascular Age-Related Macular Degeneration Using Deep Learning. Ophthalmology. 2021;128(5):693-705. [Crossref]  [PubMed]  [PMC]
  45. Liefers B, Taylor P, Alsaedi A, Bailey C, Balaskas K, Dhingra N,et al. Quantification of Key Retinal Features in Early and Late Age-Related Macular Degeneration Using Deep Learning. Am J Ophthalmol. 2021;226:1-12. [Crossref]  [PubMed]
  46. Chakravarthy U, Goldenberg D, Young G, Havilio M, Rafaeli O, Benyamini G, Loewenstein A. Automated Identification of Lesion Activity in Neovascular Age-Related Macular Degeneration. Ophthalmology. 2016;123(8):1731-1736. [Crossref]  [PubMed]
  47. Keenan TDL, Clemons TE, Domalpally A, Elman MJ, Havilio M, Agrón E, et al. Retinal Specialist versus Artificial Intelligence Detection of Retinal Fluid from OCT: Age-Related Eye Disease Study 2: 10-Year Follow-On Study. Ophthalmology. 2021;128(1):100-109. [Crossref]  [PubMed]  [PMC]
  48. Chakravarthy U, Havilio M, Syntosi A, Pillai N, Wilkes E, Benyamini G, et al. Impact of macular fluid volume fluctuations on visual acuity during anti-VEGF therapy in eyes with nAMD. Eye (Lond). 2021;35(11):2983-2990. [Crossref]  [PubMed]  [PMC]
  49. Schmidt-Erfurth U, Vogl WD, Jampol LM, Bogunović H. Application of Automated Quantification of Fluid Volumes to Anti-VEGF Therapy of Neovascular Age-Related Macular Degeneration. Ophthalmology. 2020;127(9):1211-1219. [Crossref]  [PubMed]
  50. Reiter GS, Grechenig C, Vogl WD, Guymer RH, Arnold JJ, Bogunovic H, et al. Analyiıs of Fluid Volume and its Impact on Visual acuity in the Fluid Study as Quantified with Deep Learning. Retina. 2021 Jun 1;41(6):1318-1328. [Crossref]  [PubMed]
  51. Fasler K, Moraes G, Wagner S, Kortuem KU, Chopra R, Faes L, et al. One- and two-year visual outcomes from the Moorfields age-related macular degeneration database: a retrospective cohort study and an open science resource. BMJ Open.2019;9(6):e027441. [Crossref]  [PubMed]  [PMC]
  52. Lee AY, Campbell JP, Hwang TS, Lum F, Chew EY; American Academy of Ophthalmology. Recommendations for Standardization of Images in Ophthalmology. Ophthalmology. 2021;128(7):969-970. [Crossref]  [PubMed]  [PMC]
  53. Yu S, Jones IL, Maunz A, Bachmeier I, Albrecht T, Ebneter A, et al. Artificial intelligence-based analysis of retinal fluid volume dynamics in neovascular age-related macular degeneration and association with vision and atrophy. Eye (Lond).2025;39(1):154-161. [Crossref]  [PubMed]  [PMC]
  54. Riedl S, Vogl WD, Waldstein SM, Schmidt-Erfurth U, Bogunović H. Impact of Intra- and Subretinal Fluid on Vision Based on Volume Quantification in the HARBOR Trial. Ophthalmol Retina. 2022;6(4):291-297. [Crossref]  [PubMed]
  55. Romo-Bucheli D, Erfurth US, Bogunovic H. End-to-End Deep Learning Model for Predicting Treatment Requirements in Neovascular AMD From Longitudinal Retinal OCT Imaging. IEEE J Biomed Health Inform. 2020;24(12):3456-3465. [Crossref]  [PubMed]
  56. Bogunović H, Mares V, Reiter GS, Schmidt-Erfurth U. Predicting treat-and-extend outcomes and treatment intervals in neovascular age-related macular degeneration from retinal optical coherence tomography using artificial intelligence. Front Med (Lausanne). 2022;9:958469. [Crossref]  [PubMed]  [PMC]
  57. Zhao X, Zhang X, Lv B, Meng L, Zhang C, Liu Y, et al. Optical coherence tomography-based short-term effect prediction of anti-vascular endothelial growth factor treatment in neovascular age-related macular degeneration using sensitive structure guided network. Graefes Arch Clin Exp Ophthalmol. 2021;259(11):3261-3269. [Crossref]  [PubMed]
  58. Thakoor KA, Yao J, Bordbar D, Moussa O, Lin W, et al. A multimodal deep learning system to distinguish late stages of AMD and to compare expert vs. AI ocular biomarkers. Sci Rep. 2022;12(1):2585. [Crossref]  [PubMed]  [PMC]