Journals / Turkish Journal of Pharmaceutical Sciences / 2021 / Cilt: 18 - Sayı: 6

In silico Modeling and Toxicity Profiling of a Set of Quinoline Derivatives as c-MET Inhibitors in the treatment of Human Tumors

İnsan Tümörlerinin Tedavisinde c-MET İnhibitörü Olarak Kullanılan Bir Dizi Kinolin Türevinin İn silico Modellemesi ve Toksisite Profili

Pages
738–743
Publication Date
2022-11-05
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The developed model is useful in the virtual screening and in the design of new anti-tumor compounds.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Objectives: 4-(2-fluorophenoxy) quinoline derivatives constitute one of the chemical classes of hepatocyte growth factor receptor (c-MET) inhibitors, a promising treatment against various human tumors.
📊
Key Findings & Data:There are three aims of the present study: (1) To develop a robust and validated quantitative structure-activity relationship model to predict the c-Met kinase inhibition; (2) to examine the toxicity profiles of these compounds; (3) to design new quinoline derivatives and apply the developed model on these compounds to observe its pertinence.
💡
Conclusion & Impact:The developed model is useful in the virtual screening and in the design of new anti-tumor compounds.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Objectives: 4-(2-fluorophenoxy) quinoline derivatives constitute one of the chemical classes of hepatocyte growth factor receptor (c-MET) inhibitors, a promising treatment against various human tumors. There are three aims of the present study: (1) To develop a robust and validated quantitative structure-activity relationship model to predict the c-Met kinase inhibition; (2) to examine the toxicity profiles of these compounds; (3) to design new quinoline derivatives and apply the developed model on these compounds to observe its pertinence. Materials and Methods: A multiple linear regression method was used to develop the model with calculated descriptors. State-of-the-art internal and external validation parameters were calculated. The in silico toxicity profiles including structural alerts and the lowest observed adverse effect level (LOAEL) values were evaluated using online tools. New derivatives were designed and tested on the developed model. Results: A series of 4-(2-fluorophenoxy) quinoline derivatives was linearly modeled and vigorously validated to predict the molecules’ c-MET kinase inhibition potential. Statistical metrics of the developed model showed that it was robust and able to make successful predictions for this chemical class. The mass, electronegativity, partial charges, and the structure of the molecules had an effect on the activity. Moreover, the toxicity profiles of the studied compounds were found to be adequate. Conclusion: Five of the synthesized compounds were observed to be auspicious for the toxicity/activity ratio. The developed model is useful in the virtual screening and in the design of new anti-tumor compounds.

Özet

Amaç: 4-(2-florofenoksi) kinolin türevleri, insan tümörlerinin tedavisinde kullanılan hepatosit büyüme faktörü reseptörü (c-MET) inhibitörleri kimyasal sınıflarından biridir. Bu çalışmanın üç amacı vardır: (1) c-MET kinaz inhibisyonunu tahmin etmek için sağlam ve doğrulanmış bir kantitatif yapı-aktivite ilişkisi modeli geliştirmek; (2) üzerinde çalışılan bileşiklerin toksisite profillerini incelemek; (3) yeni kinolin türevleri tasarlamak ve geliştirilen modeli bu bileşikler üzerine uygulayarak uygunluğunu incelemektir. Gereç ve Yöntemler: Bu çalışmada hesaplanmış tanımlayıcılarla modeli geliştirmek için çoklu doğrusal regresyon yöntemi kullanılmıştır. Güncel iç ve dış doğrulama parametreleri hesaplanmıştır. Böylelikle in silico toksisite profilleri yapısal uyarılar ve gözlemlenen en düşük advers etki düzeyi değerleri çevrimiçi araçlar kullanılarak değerlendirilmiştir. Yeni bileşikler tasarlanmış ve geliştirilen model üzerinde test edilmiştir. Bulgular: Bir dizi 4-(2-florofenoksi) kinolin türevi doğrusal olarak modellenmiş ve moleküllerin c-MET kinaz inhibisyon potansiyelini tahmin etmek için doğrulanmıştır. Geliştirilen modelin istatistiksel metrikleri, sağlam olduğunu ve bu kimyasal sınıf için başarılı tahminler yapabildiğini göstermiştir. Kütle, elektronegatiflik, kısmi yükler ve moleküllerin yapısının aktivite üzerinde etkili olduğu görülmüştür. Ek olarak, incelenen bileşiklerin toksisite profilinin kabul edilebilir olduğu görülmüştür. Sonuç: Sentezlenen bileşiklerden beşinin risk-yarar profili incelendiğinde, etkisinin risklerinden daha fazla olduğu görülmüştür. Geliştirilen model, sanal tarama ve yeni anti-tümör bileşiklerinin tasarlanmasında yararlı olduğu belirlenmiştir.

🔖

Cite This Article

SİPAHİ, H., TUĞCU, G., BAYRAM, F. E. Ö. (2022). In silico Modeling and Toxicity Profiling of a Set of Quinoline Derivatives as c-MET Inhibitors in the treatment of Human Tumors. Turkish Journal of Pharmaceutical Sciences, Cilt: 18 - Sayı: 6, 738–743. https://ajindex.com/articles/613666

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (14)

  1. WHO, 2018. Available from: https://www.who.int/health-topics/ cancer#tab=tab_1 2. Liu J, Gong Y, Shi J, Hao X, Wang Y, Zhou Y, Hou Y, Liu Y, Ding S, Chen Y. Design, synthesis and biological evaluation of novel N-[4-(2- fluorophenoxy) pyridin-2-yl] cyclopropanecarboxamide derivatives as potential c-Met kinase inhibitors. Eur J Med Chem. 2020
  2. 112244. 3. Muller P, Milton M. The determination and interpretation of the therapeutic index in drug development. Nat Rev Drug Discov. 2012
  3. Mo HN, Liu P. Targeting MET in cancer therapy. Chronic Dis Transl Med. 2017
  4. Puccini A, Marín-Ramos NI, Bergamo F, Schirripa M, Lonardi S, Lenz HJ, Loupakis F, Battaglin F. Safety and tolerability of c-MET inhibitors in cancer. Drug Saf. 2019
  5. Wang Q, Yang S, Wang K, Sun SY. MET inhibitors for targeted therapy of EGFR TKI-resistant lung cancer. J Hematol Oncol. 2019
  6. Lolkema MP, Bohets HH, Arkenau HT, Lampo A, Barale E, de Jonge MJA, van Doorn L, Hellemans P, de Bono JS, Eskens FALM. The c-Met tyrosine kinase inhibitor JNJ-38877605 causes renal toxicity through species-specific insoluble metabolite formation. Clin Cancer Res. 2015
  7. Bass AS, Cartwright ME, Mahon C, Morrison R, Snyder R, McNamara P, Bradley P, Zhou YY, Hunter J. Exploratory drug safety: a discovery strategy to reduce attrition in development. J Pharmacol Tox Met. 2009
  8. Nan X, Li HJ, Fang SB, Li QY, Wu YC. Structure-based discovery of novel 4-(2-fluorophenoxy) quinoline derivatives as c-Met inhibitors using isocyanide-involved multicomponent reactions. Eur J Med Chem. 2020
  9. 112241. 10. Stewart JJ. MOPAC: a semiempirical molecular orbital program. J Comput Aid Mol Des. 1990
  10. Dong J, Cao DS, Miao HY, Liu S, Deng BC, Yun YH, Wang NN, Lu AP, Zeng WB, Chen AF. ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation. J Cheminform. 2015
  11. Yap CW. PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem. 2011
  12. Gramatica P, Chirico N, Papa E, Kovarich S, Cassani S. QSARINS: A new software for the development, analysis, and validation of QSAR MLR models. J Comput Chem. 2013
  13. Gramatica P, Cassani S, Chirico N. QSARINS-Chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS. J Comput Chem. 2014
  14. Haupt RL, Haupt SE. Practical Genetic Algorithms (2nd ed