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<p>Type 2 diabetes mellitus (T2DM) involves dysregulation of both insulin secretion and insulin resistance pathways. However, current therapies often target only one pathway, leading to limited effectiveness. We aimed to develop a computational framework to quantify synergistic ligand activity across both pathways, providing a foundation for multi-target therapeutic strategies. We built a synergy assessment framework targeting six key proteins: glucagon-like peptide-1 receptor (GLP1R) and kinesin family member 11 (KIF11) in the insulin secretion pathway; and insulin-like growth factor 1 receptor (IGF1R), insulin receptor (INSR), peroxisome proliferator-activated receptor gamma (PPARG), and fibroblast growth factor receptor 1 (FGFR1) in the insulin resistance pathway. Ligand data were compiled from ChEMBL, PubChem, and ZINC databases, with bioactivity standardized as pIC<sub>50</sub> values. Pathway activities were quantified as Secretion Pathway Activity (<i>A</i><sub><i>sec</i></sub>) and Resistance Pathway Activity (<i>A</i><sub><i>res</i></sub>). Synergistic effects were evaluated using expected effect (<i>E</i><sub><i>exp</i></sub>), synergy factor (SF = 0.5 × <i>A</i><sub><i>sec</i></sub> × <i>A</i><sub><i>res</i></sub>), actual effect (<i>E</i><sub><i>act</i></sub> = min (1.0, <i>E</i><sub><i>exp</i></sub> + <i>SF</i>)), and net synergy value (<i>Δ</i><sub><i>syn</i></sub> = <i>E</i><sub><i>act</i></sub> − <i>E</i><sub><i>exp</i></sub>). Molecular docking validation showed high-synergy compounds exhibited favorable binding energies (&lt; − 7&#xa0;kcal/mol), with docking scores correlating strongly with experimental pIC<sub>50</sub> values (<i>r</i> &gt; 0.7). Notably, CID_45271263 demonstrated potent binding affinity (&lt; − 10&#xa0;kcal/mol) for both GLP1R and KIF11. Network analysis identified INSR as a critical hub connecting both pathways. High-synergy ligands preferentially engaged both pathways simultaneously, with significant inter-pathway correlations observed between GLP1R-PPARG (<i>r</i> = 0.86) and KIF11-FGFR1 (<i>r</i> = 0.93). Three-dimensional modeling revealed non-linear synergistic surfaces with distinct high-efficacy zones (<i>Δ</i><sub><i>syn</i></sub> &gt; 0.12). In conclusion, this validated computational framework enables the systematic identification and design of multi-target drugs with enhanced synergistic effects, thereby improving the therapeutic efficacy of T2DM.</p>

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Computational framework to quantify synergistic ligand activity in insulin secretion and resistance pathways in type 2 diabetes

  • 期刊论文

  • 发表日期：

    2026年4月9日

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  • Junyu Zhou,
  • Xunbin Wei,
  • Meiling Lui,
  • Sunmin Park

摘要

Type 2 diabetes mellitus (T2DM) involves dysregulation of both insulin secretion and insulin resistance pathways. However, current therapies often target only one pathway, leading to limited effectiveness. We aimed to develop a computational framework to quantify synergistic ligand activity across both pathways, providing a foundation for multi-target therapeutic strategies. We built a synergy assessment framework targeting six key proteins: glucagon-like peptide-1 receptor (GLP1R) and kinesin family member 11 (KIF11) in the insulin secretion pathway; and insulin-like growth factor 1 receptor (IGF1R), insulin receptor (INSR), peroxisome proliferator-activated receptor gamma (PPARG), and fibroblast growth factor receptor 1 (FGFR1) in the insulin resistance pathway. Ligand data were compiled from ChEMBL, PubChem, and ZINC databases, with bioactivity standardized as pIC50 values. Pathway activities were quantified as Secretion Pathway Activity (Asec) and Resistance Pathway Activity (Ares). Synergistic effects were evaluated using expected effect (Eexp), synergy factor (SF = 0.5 × Asec × Ares), actual effect (Eact = min (1.0, Eexp + SF)), and net synergy value (Δsyn = Eact − Eexp). Molecular docking validation showed high-synergy compounds exhibited favorable binding energies (< − 7 kcal/mol), with docking scores correlating strongly with experimental pIC50 values (r > 0.7). Notably, CID_45271263 demonstrated potent binding affinity (< − 10 kcal/mol) for both GLP1R and KIF11. Network analysis identified INSR as a critical hub connecting both pathways. High-synergy ligands preferentially engaged both pathways simultaneously, with significant inter-pathway correlations observed between GLP1R-PPARG (r = 0.86) and KIF11-FGFR1 (r = 0.93). Three-dimensional modeling revealed non-linear synergistic surfaces with distinct high-efficacy zones (Δsyn > 0.12). In conclusion, this validated computational framework enables the systematic identification and design of multi-target drugs with enhanced synergistic effects, thereby improving the therapeutic efficacy of T2DM.