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<p>Prostate cancer remains one of the most prevalent and deadly malignancies worldwide, largely due to its resistance to conventional therapies and the urgent need for more effective treatment options. Targeting Pim1 kinase has emerged as a promising strategy in the quest for novel therapeutics. This study explored tetrahydropyrazolo-quinazoline and tetrahydropyrazolo-pyrimidocarbazole derivatives for their potential to inhibit Pim1 kinase activity. An integrated computational pipeline combining 2D-QSAR modeling, molecular docking, molecular dynamics (MD) simulations, and ADMET profiling was employed to guide the rational design and evaluation of new inhibitors. The constructed 2D-QSAR model, based on constitutional, physicochemical, and topological descriptors, demonstrated robust predictive performance (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\({R}^{2}\)</EquationSource> </InlineEquation>= 0.808, <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\({Q}_{CV}^{2}\)</EquationSource> </InlineEquation> = 0.748, <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\({R}_{test}^{2}\)</EquationSource> </InlineEquation> = 0.924), validated through rigorous applicability domain assessment using the Williams plot. Guided by QSAR predictions, three novel Pim1 kinase inhibitors were designed and subjected to molecular docking studies, revealing strong binding affinities (− 10.4 to − 11.5 kcal/mol) and key interactions within the active site. Compound P2 exhibited enhanced binding stability and predicted efficacy compared to the reference molecule 7q, aligning with previously reported structural determinants of Pim1 inhibition. MD simulations further confirmed the dynamic stability of the ligand–protein complexes, with compounds P2 and P3 maintaining optimal structural integrity throughout the simulation period. ADMET analysis indicated favorable pharmacokinetic and drug-likeness profiles for the proposed compounds. This work demonstrates the power of computational strategies in accelerating the discovery of new anticancer agents and identifies tetrahydropyrazolo derivatives as promising leads for further experimental validation.</p>

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In Silico Discovery of Pim-1 Kinase Inhibitors: Tetrahydropyrazolo-Quinazoline and Pyrimidocarbazole Derivatives for Prostate Cancer Therapy

  • 期刊论文

  • 发表日期：

    2026年7月1日

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  • Youssra Ech-Chahdi,
  • Marwa Alaqarbeh,
  • Yassine El Allouche,
  • Abdellah El Aissouq,
  • Lhoucine Naanaai,
  • Mohammed Bouachrine,
  • Hicham Zaitan,
  • Fouad Khalil

摘要

Prostate cancer remains one of the most prevalent and deadly malignancies worldwide, largely due to its resistance to conventional therapies and the urgent need for more effective treatment options. Targeting Pim1 kinase has emerged as a promising strategy in the quest for novel therapeutics. This study explored tetrahydropyrazolo-quinazoline and tetrahydropyrazolo-pyrimidocarbazole derivatives for their potential to inhibit Pim1 kinase activity. An integrated computational pipeline combining 2D-QSAR modeling, molecular docking, molecular dynamics (MD) simulations, and ADMET profiling was employed to guide the rational design and evaluation of new inhibitors. The constructed 2D-QSAR model, based on constitutional, physicochemical, and topological descriptors, demonstrated robust predictive performance ( \({R}^{2}\) = 0.808, \({Q}_{CV}^{2}\) = 0.748, \({R}_{test}^{2}\) = 0.924), validated through rigorous applicability domain assessment using the Williams plot. Guided by QSAR predictions, three novel Pim1 kinase inhibitors were designed and subjected to molecular docking studies, revealing strong binding affinities (− 10.4 to − 11.5 kcal/mol) and key interactions within the active site. Compound P2 exhibited enhanced binding stability and predicted efficacy compared to the reference molecule 7q, aligning with previously reported structural determinants of Pim1 inhibition. MD simulations further confirmed the dynamic stability of the ligand–protein complexes, with compounds P2 and P3 maintaining optimal structural integrity throughout the simulation period. ADMET analysis indicated favorable pharmacokinetic and drug-likeness profiles for the proposed compounds. This work demonstrates the power of computational strategies in accelerating the discovery of new anticancer agents and identifies tetrahydropyrazolo derivatives as promising leads for further experimental validation.