JOURNAL ARTICLE

Polymeric Membranes in Contact Lens Technology for Glaucoma Treatment: Breakthroughs, Obstacles, and Emerging Opportunities.

  • Published In: Polymers for Advanced Technologies, 2025, v. 36, n. 3. P. 1 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: Borgohain, Rajashree; Patel, Paresh N. 3 of 3

Abstract

Glaucoma, a leading cause of irreversible blindness and it requires innovative strategies for effective management. Among these, contact lenses are considered the promising therapeutic delivery approach for glaucoma treatment. This review explores recent advancements in polymer‐based contact lens technologies developed specifically for glaucoma treatment. Widely used materials such as polyhydroxyethylmethacrylate, dimethyl methacrylate, polydimethylsiloxane, and polyvinyl alcohol are highlighted as key polymers in contact lens fabrication. Additionally, it offers an in‐depth analysis of various polymers, their modifications, and their applications in advancing contact lens technologies. This review further examines the integration of fillers within polymer matrices to enhance drug delivery. Furthermore, the article addresses the challenges and future prospects of polymer‐based contact lenses in glaucoma therapy. This review discusses the transformative potential of polymers in advancing glaucoma management through innovative contact lens technologies, along with insights into material science, nanotechnology, and drug delivery. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Polymers for Advanced Technologies. 2025/03, Vol. 36, Issue 3, p1
  • Document Type:Literature Review
  • Subject Area:Health and Medicine
  • Publication Date:2025
  • ISSN:10427147
  • DOI:10.1002/pat.70135
  • Accession Number:184111299
  • Copyright Statement:Copyright of Polymers for Advanced Technologies is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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