Literature DB >> 35337101

Exploring the Prominent and Concealed Inhibitory Features for Cytoplasmic Isoforms of Hsp90 Using QSAR Analysis.

Magdi E A Zaki1, Sami A Al-Hussain1, Syed Nasir Abbas Bukhari2, Vijay H Masand3, Mithilesh M Rathore3, Sumer D Thakur4, Vaishali M Patil5.   

Abstract

Cancer is a major life-threatening disease with a high mortality rate in many countries. Even though different therapies and options are available, patients generally prefer chemotherapy. However, serious side effects of anti-cancer drugs compel us to search for a safer drug. To achieve this target, Hsp90 (heat shock protein 90), which is responsible for stabilization of many oncoproteins in cancer cells, is a promising target for developing an anti-cancer drug. The QSAR (Quantitative Structure-Activity Relationship) could be useful to identify crucial pharmacophoric features to develop a Hsp90 inhibitor. Therefore, in the present work, a larger dataset encompassing 1141 diverse compounds was used to develop a multi-linear QSAR model with a balance of acceptable predictive ability (Predictive QSAR) and mechanistic interpretation (Mechanistic QSAR). The new developed six-parameter model satisfies the recommended values for a good number of validation parameters such as R2tr = 0.78, Q2LMO = 0.77, R2ex = 0.78, and CCCex = 0.88. The present analysis reveals that the Hsp90 inhibitory activity is correlated with different types of nitrogen atoms and other hidden structural features such as the presence of hydrophobic ring/aromatic carbon atoms within a specific distance from the center of mass of the molecule, etc. Thus, the model successfully identified a variety of reported as well as novel pharmacophoric features. The results of QSAR analysis are further vindicated by reported crystal structures of compounds with Hsp90.

Entities:  

Keywords:  Hsp90; QSAR; cancer; machine learning; pharmacophores

Year:  2022        PMID: 35337101      PMCID: PMC8953649          DOI: 10.3390/ph15030303

Source DB:  PubMed          Journal:  Pharmaceuticals (Basel)        ISSN: 1424-8247


  36 in total

1.  Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection.

Authors:  Nicola Chirico; Paola Gramatica
Journal:  J Chem Inf Model       Date:  2012-07-13       Impact factor: 4.956

Review 2.  QSAR without borders.

Authors:  Eugene N Muratov; Jürgen Bajorath; Robert P Sheridan; Igor V Tetko; Dmitry Filimonov; Vladimir Poroikov; Tudor I Oprea; Igor I Baskin; Alexandre Varnek; Adrian Roitberg; Olexandr Isayev; Stefano Curtarolo; Denis Fourches; Yoram Cohen; Alan Aspuru-Guzik; David A Winkler; Dimitris Agrafiotis; Artem Cherkasov; Alexander Tropsha
Journal:  Chem Soc Rev       Date:  2020-05-01       Impact factor: 54.564

3.  Heat shock protein 90: inhibitors in clinical trials.

Authors:  Marco A Biamonte; Ryan Van de Water; Joseph W Arndt; Robert H Scannevin; Daniel Perret; Wen-Cherng Lee
Journal:  J Med Chem       Date:  2010-01-14       Impact factor: 7.446

4.  The importance of molecular structures, endpoints' values, and predictivity parameters in QSAR research: QSAR analysis of a series of estrogen receptor binders.

Authors:  Jiazhong Li; Paola Gramatica
Journal:  Mol Divers       Date:  2009-11-17       Impact factor: 2.943

5.  Applicability Domain: A Step Toward Confident Predictions and Decidability for QSAR Modeling.

Authors:  Supratik Kar; Kunal Roy; Jerzy Leszczynski
Journal:  Methods Mol Biol       Date:  2018

Review 6.  Progress in the discovery and development of heat shock protein 90 (Hsp90) inhibitors.

Authors:  Rohit Bhat; Sreedhar R Tummalapalli; David P Rotella
Journal:  J Med Chem       Date:  2014-08-29       Impact factor: 7.446

7.  External Evaluation of QSAR Models, in Addition to Cross-Validation: Verification of Predictive Capability on Totally New Chemicals.

Authors:  Paola Gramatica
Journal:  Mol Inform       Date:  2014-04-15       Impact factor: 3.353

8.  QSAR Studies to Predict Activity of HSP90 Inhibitors.

Authors:  Vaishali M Patil; Neeraj Masand; Satya P Gupta; Brian S J Blagg
Journal:  Curr Top Med Chem       Date:  2021       Impact factor: 3.295

9.  Approaches for externally validated QSAR modelling of Nitrated Polycyclic Aromatic Hydrocarbon mutagenicity.

Authors:  P Gramatica; P Pilutti; E Papa
Journal:  SAR QSAR Environ Res       Date:  2007 Jan-Mar       Impact factor: 3.000

10.  Reply to the comment of S. Rayne on "QSAR model reproducibility and applicability: A case study of rate constants of hydroxyl radical reaction models applied to polybrominated diphenyl ethers and (benzo-)triazoles".

Authors:  Paola Gramatica; Simona Kovarich; Partha Pratim Roy
Journal:  J Comput Chem       Date:  2013-05-21       Impact factor: 3.376

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  1 in total

1.  Mechanistic Analysis of Chemically Diverse Bromodomain-4 Inhibitors Using Balanced QSAR Analysis and Supported by X-ray Resolved Crystal Structures.

Authors:  Magdi E A Zaki; Sami A Al-Hussain; Aamal A Al-Mutairi; Vijay H Masand; Abdul Samad; Rahul D Jawarkar
Journal:  Pharmaceuticals (Basel)       Date:  2022-06-14
  1 in total

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