A systems biology approach towards understanding and treating non-neovascular age-related macular degeneration

• Published in: Nature communications Vol. 10; no. 1; pp. 3347 - 11
• Main Authors: Handa, James T., Bowes Rickman, Cathy, Dick, Andrew D., Gorin, Michael B., Miller, Joan W., Toth, Cynthia A., Ueffing, Marius, Zarbin, Marco, Farrer, Lindsay A.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 26.07.2019; Nature Publishing Group; Nature Portfolio
• Subjects: 631/553; 692/53; 692/698/1688/512/2613; 692/699/3161/1626; Age; Age related diseases; Animals; Biomarkers; Blindness; Geriatrics; Humanities and Social Sciences; Humans; Macular degeneration; Macular Degeneration - immunology; Macular Degeneration - metabolism; Macular Degeneration - pathology; Macular Degeneration - therapy; Mathematical models; multidisciplinary; Oxidative Stress; Perspective; Pharmacology; Science; Science (multidisciplinary); Systems Biology; Therapy
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-019-11262-1

Age-related macular degeneration (AMD) is the most common cause of blindness among the elderly in the developed world. While treatment is effective for the neovascular or “wet” form of AMD, no therapy is successful for the non-neovascular or “dry” form. Here we discuss the current knowledge on dry AMD pathobiology and propose future research directions that would expedite the development of new treatments. In our view, these should emphasize system biology approaches that integrate omic, pharmacological, and clinical data into mathematical models that can predict disease onset and progression, identify biomarkers, establish disease causing mechanisms, and monitor response to therapy. No effective therapies exist for dry age-related macular degeneration. In this perspective, the authors propose that research should emphasize system biology approaches that integrate various ‘omics’ data into mathematical models to establish pathogenic mechanisms on which to design novel treatments, and identify biomarkers that predict disease progression and therapeutic response.