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<p>The newly described CIRDD and IROD clusters of Type 2 Diabetes represent clinically meaningful phenotypes, yet no subtype-specific molecular or omics datasets currently exist to define their mechanisms. This study introduces a predictive extrapolation framework to infer how phytocompounds from Andrographis paniculata may interact with these subtypes by mapping established pathways of insulin resistance, β-cell dysfunction, and obesity-driven inflammation onto CIRDD- and IROD-relevant axes. From the broader T2DM network, ten hub proteins (INS, AKT1, TNF, IL6, MMP9, and others) were prioritized based on functional importance and subsequently assigned to each subtype according to their documented physiological roles. Docking analysis, supported by redocking validation against high-resolution crystallographic complexes, enabled assessment of theoretical ligand–protein interactions. The model predicts that CIRDD may be primarily modulated through β-cell regulatory hubs (INS, AKT1), whereas IROD appears more strongly influenced through inflammatory–metabolic hubs (TNF, TLR4, MMP9). Apigenin and andrographolide displayed the strongest predicted affinities (binding energies ≤ –8.5&#xa0;kcal/mol), and redocking yielded RMSD values below 2.0&#xa0;Å, supporting the reliability of the docking protocol. Overall, this work proposes a theoretical, biologically anchored framework for predicting subtype-specific phytochemical mechanisms in the absence of direct molecular datasets. While experimental validation is required, the approach offers a rational basis for prioritizing plant-derived candidates for CIRDD and IROD.</p>

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A theoretical framework for extrapolating Andrographis paniculata mechanisms to the novel CIRDD and IROD diabetes clusters

  • 期刊论文

  • 发表日期：

    2026年1月11日

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  • Maniratnam Puli,
  • Sonesh Bachu,
  • Veeresh Bantal,
  • Venkata Ramana Singamaneni

摘要

The newly described CIRDD and IROD clusters of Type 2 Diabetes represent clinically meaningful phenotypes, yet no subtype-specific molecular or omics datasets currently exist to define their mechanisms. This study introduces a predictive extrapolation framework to infer how phytocompounds from Andrographis paniculata may interact with these subtypes by mapping established pathways of insulin resistance, β-cell dysfunction, and obesity-driven inflammation onto CIRDD- and IROD-relevant axes. From the broader T2DM network, ten hub proteins (INS, AKT1, TNF, IL6, MMP9, and others) were prioritized based on functional importance and subsequently assigned to each subtype according to their documented physiological roles. Docking analysis, supported by redocking validation against high-resolution crystallographic complexes, enabled assessment of theoretical ligand–protein interactions. The model predicts that CIRDD may be primarily modulated through β-cell regulatory hubs (INS, AKT1), whereas IROD appears more strongly influenced through inflammatory–metabolic hubs (TNF, TLR4, MMP9). Apigenin and andrographolide displayed the strongest predicted affinities (binding energies ≤ –8.5 kcal/mol), and redocking yielded RMSD values below 2.0 Å, supporting the reliability of the docking protocol. Overall, this work proposes a theoretical, biologically anchored framework for predicting subtype-specific phytochemical mechanisms in the absence of direct molecular datasets. While experimental validation is required, the approach offers a rational basis for prioritizing plant-derived candidates for CIRDD and IROD.