Computational modeling of anti-VEGFA drug interactions with VEGF-A: Insights into therapeutic strategies for neovascular AMD

Vascular endothelial growth factor A (VEGF-A) is the main driver of pathological angiogenesis and represents the primary therapeutic target in neovascular age-related macular degeneration (nAMD). Several anti-VEGF agents are currently available, but systematic comparisons of their molecular interactions with VEGF-A remain limited. In this study, we performed the first integrated in silico evaluation of six clinically relevant anti-VEGF drugs: abicipar, aflibercept, bevacizumab, brolucizumab, faricimab, and ranibizumab. Structural modeling, residue conservation analysis, molecular docking (HADDOCK), and binding affinity prediction (PRODIGY) were employed to assess drug-VEGF-A interactions in terms of stability, energetic contributions, and key conserved residues. Our results showed that bevacizumab exhibited the most stable interaction profile, dominated by van der Waals forces and supported by highly conserved residues. Aflibercept displayed strong electrostatic stabilization, while ranibizumab and brolucizumab achieved robust interactions through mixed forces. Faricimab presented weaker binding stability but its dual-target mechanism, involving VEGF-A and angiopoietin-2, may extend its therapeutic efficacy beyond VEGF-A inhibition. Abicipar demonstrated favorable stability despite structural differences from conventional antibodies. Notably, hydrophobic interactions were consistently identified as a central determinant of complex stability across all drugs, whereas an excess of charged residues correlated with reduced affinity. This systematic analysis highlights the coexistence of evolutionary conservation and rational molecular engineering as complementary strategies shaping anti-VEGF drug performance. Overall, our findings demonstrate that computational modeling offers a cost-effective and predictive tool to guide the optimization of current therapies and the rational design of next-generation anti-VEGF agents for nAMD and other retinal vascular disorders.

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Publication Details

Journal
Computational Biology and Chemistry
Published
2025-11-20
DOI
https://doi.org/10.1016/j.compbiolchem.2025.108788
Citations
1
Primary Topic
Retinal Diseases and Treatments
Type
article
Field-Weighted Citation Impact
1.71
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article

Computational modeling of anti-VEGFA drug interactions with VEGF-A: Insights into therapeutic strategies for neovascular AMD

Eduardo Fernández, A Parra, Gema Martı́nez, Alba Román-Vallina
1 citations
Computational Biology and Chemistry
Retinal Diseases and Treatments
1.71
article

Computational modeling of anti-VEGFA drug interactions with VEGF-A: Insights into therapeutic strategies for neovascular AMD

Eduardo Fernández, A Parra, Gema Martı́nez, Alba Román-Vallina
article en
1 citations

Abstract

Vascular endothelial growth factor A (VEGF-A) is the main driver of pathological angiogenesis and represents the primary therapeutic target in neovascular age-related macular degeneration (nAMD). Several anti-VEGF agents are currently available, but systematic comparisons of their molecular interactions with VEGF-A remain limited. In this study, we performed the first integrated in silico evaluation of six clinically relevant anti-VEGF drugs: abicipar, aflibercept, bevacizumab, brolucizumab, faricimab, and ranibizumab. Structural modeling, residue conservation analysis, molecular docking (HADDOCK), and binding affinity prediction (PRODIGY) were employed to assess drug-VEGF-A interactions in terms of stability, energetic contributions, and key conserved residues. Our results showed that bevacizumab exhibited the most stable interaction profile, dominated by van der Waals forces and supported by highly conserved residues. Aflibercept displayed strong electrostatic stabilization, while ranibizumab and brolucizumab achieved robust interactions through mixed forces. Faricimab presented weaker binding stability but its dual-target mechanism, involving VEGF-A and angiopoietin-2, may extend its therapeutic efficacy beyond VEGF-A inhibition. Abicipar demonstrated favorable stability despite structural differences from conventional antibodies. Notably, hydrophobic interactions were consistently identified as a central determinant of complex stability across all drugs, whereas an excess of charged residues correlated with reduced affinity. This systematic analysis highlights the coexistence of evolutionary conservation and rational molecular engineering as complementary strategies shaping anti-VEGF drug performance. Overall, our findings demonstrate that computational modeling offers a cost-effective and predictive tool to guide the optimization of current therapies and the rational design of next-generation anti-VEGF agents for nAMD and other retinal vascular disorders.

Computational Biology and ChemistryVol. 120(Pt 1)
Universitat de Miguel Hernández d'Elx (ES)
Openalex Percentile: Top 12%
Retinal Diseases and Treatments
1.71
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