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<p>Allergic rhinitis (AR) is a common immunological disorder marked by nasal inflammation, sneezing, and congestion, typically triggered by allergens such as pollen, dust or pet dander. The increasing prevalence of AR underscores the need for safer and more effective antihistamines. Histamine, a key biogenic amine, plays crucial roles in neurotransmission. gastric acid secretion, and immune response; however, its dysregulation is closely associated with allergic reactions and inflammatory conditions. Although second-generation antihistamines such as bilastine and bepotastine offer improved safety profiles, they still face limitations in potency, selectivity, and metabolic stability. In this study, originally 50 novel derivatives of bilastine and bepotastine were designed using Artificial intelligence (AI) to enhance therapeutic potential against the H1-receptor. Following a comprehensive virtual screening workflow, four top analogs (<b>Bep-1</b>, <b>Bep-2</b>, <b>Bil-1</b>, and <b>Bil-2</b>) were selected for detailed investigation. Molecular docking revealed strong binding affinities of -8.2 to -8.7&#xa0;kcal/mol, with key interactions involving active site residues such as TRP 158, LEU 154, and PHE 116. Notably, <b>Bil-1</b> and <b>Bep-2</b> exhibited higher hydrogen bond interactions, indicating greater stability of the ligand-protein complex. Molecular dynamics (MD) simulation over 120 ns confirmed the structural stability of complexes through RMSD, RMSF, SASA, and radius of gyration (Rg) profiles. The MD simulations were carried out in an explicit liquid environment employing the TIP3P water model, which accurately represents biomolecular solvation and interaction. Furthermore, MM/PBSA calculations supported the docking and MD results by explaining binding free energy of all studied compounds. Frontier molecular orbital analysis showed HOMO-LUMO energy gaps at 3.62 eV (<b>Bil-2</b>) and 4.47 eV (<b>Bep-2</b>), classifying them as soft molecules. UV-visible analysis of <b>Bep-2</b> displayed a π → π* transition at 277&#xa0;nm, and vibrational analysis of <b>Bep-1</b> identified a distinct C = C bending peak at 1095&#xa0;cm<sup>-1</sup>. Topological descriptions such as ELF and MEP identified potential reactive sites, while NBO analysis confirmed intramolecular charge transfer. ADMET predictions revealed acceptable pharmacokinetic and non-toxic profiles, with <b>Bep-2</b> and <b>Bil-1</b> showing optimal solubility and drug likeness. Collectively, these AI-generated compounds present promising candidates for the development of next-generation antihistamines for allergic rhinitis.</p>

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Artificial Intelligence-assisted Discovery of Novel Bilastine and Bepotastine Analogs Against the Histamine H1 Receptor: Integrated Molecular Dynamics and MM/PBSA Evaluation

  • 期刊论文

  • 发表日期：

    2026年3月19日

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  • Muhammad Husnain,
  • Shabbir Muhammad,
  • Shamsa Bibi,
  • Shafiq ur Rehman,
  • Abdullah G. Al-Sehemi,
  • Shimmaa Mansour Moustafa Mohamed,
  • Saleh S. Alarfaji

摘要

Allergic rhinitis (AR) is a common immunological disorder marked by nasal inflammation, sneezing, and congestion, typically triggered by allergens such as pollen, dust or pet dander. The increasing prevalence of AR underscores the need for safer and more effective antihistamines. Histamine, a key biogenic amine, plays crucial roles in neurotransmission. gastric acid secretion, and immune response; however, its dysregulation is closely associated with allergic reactions and inflammatory conditions. Although second-generation antihistamines such as bilastine and bepotastine offer improved safety profiles, they still face limitations in potency, selectivity, and metabolic stability. In this study, originally 50 novel derivatives of bilastine and bepotastine were designed using Artificial intelligence (AI) to enhance therapeutic potential against the H1-receptor. Following a comprehensive virtual screening workflow, four top analogs (Bep-1, Bep-2, Bil-1, and Bil-2) were selected for detailed investigation. Molecular docking revealed strong binding affinities of -8.2 to -8.7 kcal/mol, with key interactions involving active site residues such as TRP 158, LEU 154, and PHE 116. Notably, Bil-1 and Bep-2 exhibited higher hydrogen bond interactions, indicating greater stability of the ligand-protein complex. Molecular dynamics (MD) simulation over 120 ns confirmed the structural stability of complexes through RMSD, RMSF, SASA, and radius of gyration (Rg) profiles. The MD simulations were carried out in an explicit liquid environment employing the TIP3P water model, which accurately represents biomolecular solvation and interaction. Furthermore, MM/PBSA calculations supported the docking and MD results by explaining binding free energy of all studied compounds. Frontier molecular orbital analysis showed HOMO-LUMO energy gaps at 3.62 eV (Bil-2) and 4.47 eV (Bep-2), classifying them as soft molecules. UV-visible analysis of Bep-2 displayed a π → π* transition at 277 nm, and vibrational analysis of Bep-1 identified a distinct C = C bending peak at 1095 cm-1. Topological descriptions such as ELF and MEP identified potential reactive sites, while NBO analysis confirmed intramolecular charge transfer. ADMET predictions revealed acceptable pharmacokinetic and non-toxic profiles, with Bep-2 and Bil-1 showing optimal solubility and drug likeness. Collectively, these AI-generated compounds present promising candidates for the development of next-generation antihistamines for allergic rhinitis.