Archives

  • 2018-07
  • 2019-04
  • 2019-05
  • 2019-06
  • 2019-07
  • 2019-08
  • 2019-09
  • 2019-10
  • 2019-11
  • 2019-12
  • 2020-01
  • 2020-02
  • 2020-03
  • 2020-04
  • 2020-05
  • 2020-06
  • 2020-07
  • 2020-08
  • 2020-09
  • 2020-10
  • 2020-11
  • 2020-12
  • 2021-01
  • 2021-02
  • 2021-03
  • 2021-04
  • 2021-05
  • 2021-06
  • 2021-07
  • 2021-08
  • 2021-09
  • 2021-10
  • 2021-11
  • 2021-12
  • 2022-01
  • 2022-02
  • 2022-03
  • 2022-04
  • 2022-05
  • 2022-06
  • 2022-07
  • 2022-08
  • 2022-09
  • 2022-10
  • 2022-11
  • 2022-12
  • 2023-01
  • 2023-02
  • 2023-03
  • 2023-04
  • 2023-05
  • 2023-06
  • 2023-07
  • 2023-08
  • 2023-09
  • 2023-10
  • 2023-11
  • 2023-12
  • 2024-01
  • 2024-02
  • 2024-03
  • Based on pharmacophore modeling a good HDAC inhibitor

    2021-11-25

    Based on pharmacophore modeling, a good HDAC inhibitor has at least three sides/regions: the attachment side of the Zn2+ cofactor/HDAC active site (Zn2+ binding group/ZBG), hydrophobic cap (CAP) and linker containing connecting unit (CU) with electronegative groups (Mohan et al., 2011, Rossi et al., 2011). In this study, the novel HDAC inhibitors (HDACIs) were designed from 2-oxo-1,3-thiazolidine as Zn2+ binding group/ZBG, para-amino benzoic Quinidine (PABA) as linker and acetamide as connecting unit (CU) on hydrophobic cap (CAP) (Marek et al., 2013). Furthermore, functional group variation of these compounds was conducted on hydrophobic cap region and inhibition studies of each varied compound were employed in silico through molecular docking and interaction analysis. In addition, pharmacology analysis was also done for lead molecules which have better binding affinity against HDAC than standards (Vilar et al., 2008). This analysis includes pharmacological properties based on Lipinski’s rule of five, bioactivity, drug likeness and drug score (Tambunan et al., 2012). Furthermore, in silico preclinical trials including absorption, distribution, metabolism, excretion, and toxicity (ADMET) test, health effect probability and maximum recommended daily dose (MRDD) prediction were also performed in this study (Moroy et al., 2012, Van de Waterbeemd and Gifford, 2003). The synthetic accessibility and novelty of the lead compounds were also predicted. This research is expected to obtain novel potential inhibitors of Homo sapiens class II HDAC which can be used as drugs for cervical cancer treatment.
    Methods
    Results and discussion
    Conclusion In this study, novel potential inhibitors for cervical cancer targeting H. sapiens class II histone deacetylase (HDAC) were designed by de novo approach. Based on pharmacophore model, HDAC inhibitors (HDACIs) consist of three main parts: zinc binding group (ZBG), linker and hydrophobic cap (CAP). From this model, the compound series based on 4-[(2-oxo-1,3-thiazolidin-3-yl)carbonyl]aniline has been designed and modified. In silico studies have been performed for testing pharmacological properties and bioactivities of designed ligands. The best ligands selection from molecular docking simulation was based on free energy of binding (ΔGbinding) and molecular interaction with enzyme active site. The selection yielded eight best ligands which have better binding affinity (lower free energy of binding) than SAHA, TSA, and VPA standards. Through interaction analysis of enzyme–ligand complex, all best ligands have interactions which followed hypothesis. Fragment 2-oxo-1,3-thiazolidine as zinc binding group (ZBG) forms a covalent coordination bond with the Zn2+ cofactor in HDAC charge-relay system with interaction that reached 100.0%. In silico preclinical trial was also conducted to evaluate the ADMET properties of the ligands including toxicological properties, passive absorption, health effect probability, MRDD prediction and oral bioavailability prediction. Based on the results, on average, all best ligands have a low toxicity and health effect probability on blood, cardiovascular system, gastrointestinal system, kidney, liver and lugs. They also have a high passive absorption and good oral bioavailability based on Veber’s and Egan’s rules. From the drug screening based on docking, dynamics and drug scan analysis, the best inhibitor ligands for HDAC4, HDAC5, HDAC6, HDAC7, HDAC9, and HDAC10 are F, Ib14, O38, Kb17, Gd40, and Aa50 respectively. The best ligands Quinidine from drug screening then could be developed as novel potent drugs for cervical cancer targeting H. sapiens class II histone deacetylase (HDAC). It can be deduced that the combination of in silico drug design based on de novo lead molecules design approach, molecular docking, pharmacology analysis, virtual screening, and manual interpretation can be more efficient to discover new effective inhibitors and drug leads that may have a great impact for future experimental studies. Computational or in silico methods play the important roles in drug discovery and development and can improve the efficiency of the novel HDACIs. This method may limit the use of chemicals for synthesis and biological testing, thus greatly reducing resource requirements than conventional methods. This study is expected to produce more potent HDAC inhibitors as drugs for cervical cancer treatment.