Identification of Potent hDHODH Inhibitors for Lung Cancer via Virtual Screening of a Rationally Designed Small Combinatorial Library

Nada, Hossam; Kim, Sung-Do; Park, Suin; Lee, Moo Yeol; Lee, Kyeong

doi:10.1021/acsomega.3c01323

Cited by 4 publications

(1 citation statement)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unfortunately, the development of novel medicine is typically time-consuming and expensive and requires a significant number of resources . The drug development process can be significantly speeded up by using cheminformatics. , Cheminformatics is a multidisciplinary field that uses computational and information technologies to find solutions to a wide range of problems in chemistry .…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Approach to Developing Potent EGFR Inhibitors for Breast Cancer─Design, Synthesis, and In Vitro Evaluation

Nada,

Gul,

Elkamhawy

et al. 2023

ACS Omega

Self Cite

View full text Add to dashboard Cite

The epidermal growth factor receptor (EGFR) is vital for regulating cellular functions, including cell division, migration, survival, apoptosis, angiogenesis, and cancer. EGFR overexpression is an ideal target for anticancer drug development as it is absent from normal tissues, marking it as tumor-specific. Unfortunately, the development of medication resistance limits the therapeutic efficacy of the currently approved EGFR inhibitors, indicating the need for further development. Herein, a machine learning-based application that predicts the bioactivity of novel EGFR inhibitors is presented. Clustering of the EGFR smallmolecule inhibitor (∼9000 compounds) library showed that Nsubstituted quinazolin-4-amine-based compounds made up the largest cluster of EGFR inhibitors (∼2500 compounds). Taking advantage of this finding, rational drug design was used to design a novel series of 4-anilinoquinazoline-based EGFR inhibitors, which were first tested by the developed artificial intelligence application, and only the compounds which were predicted to be active were then chosen to be synthesized. This led to the synthesis of 18 novel compounds, which were subsequently evaluated for cytotoxicity and EGFR inhibitory activity. Among the tested compounds, compound 9 demonstrated the most potent antiproliferative activity, with 2.50 and 1.96 μM activity over MCF-7 and MDA-MB-231 cancer cell lines, respectively. Moreover, compound 9 displayed an EGFR inhibitory activity of 2.53 nM and promising apoptotic results, marking it a potential candidate for breast cancer therapy.

show abstract

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Approach to Developing Potent EGFR Inhibitors for Breast Cancer─Design, Synthesis, and In Vitro Evaluation

Nada,

Gul,

Elkamhawy

et al. 2023

ACS Omega

Self Cite

View full text Add to dashboard Cite

show abstract

Chalcone‐based benzenesulfonamides as potent and selective inhibitors for human carbonic anhydrase II: Design, synthesis, in vitro, and in silico studies

Lee,

Elkamhawy,

Al‐Karmalawy

et al. 2024

Archiv der Pharmazie

View full text Add to dashboard Cite

Sulfonamides are promising classical carbonic anhydrase (CA; EC 4.2.1.1) inhibitors, being used for several medical purposes such as diuretics, anticonvulsants, topically acting antiglaucoma agents, for antiobesity and anticancer therapies. Herein, a series of chalcone‐based benzenesulfonamides (3a‒m) was synthesized and assessed for its inhibitory activity against a panel of four human carbonic anhydrases (hCA isoforms I, II, IX, and XII). Most compounds displayed single‐ to double‐digit nanomolar inhibition constants (Kis), with some derivatives being more potent and/or selective than the standard drug acetazolamide (AAZ). Among the synthesized compounds, 3g compound demonstrated the highest inhibitory activity against the hCA II isoform (Ki = 2.5 nM) with 30‐, 9‐, and 11‐fold selectivity for hCA II over the I, IX, and XII isoforms, respectively. Structure–activity relationships for different substitution patterns were analyzed. Additionally, a molecular docking study showed that compound 3g bound to hCA II by coordinating with the zinc ion through the deprotonated benzenesulfonamide moiety, in addition to a hydrogen bond formed between an oxygen of the sulfonamide moiety and Thr199. Moreover, the chalcone core participated in van der Waals interactions with some active site residues, such as Ile91, Val121, and Leu198. Consequently, this report introduces a successful approach toward identifying compound 3g as a highly potent and selective chalcone‐based benzenesulfonamide inhibitor of hCA II worthy of further investigation.

show abstract

Development of a machine learning‐based target‐specific scoring function for structure‐based binding affinity prediction for human dihydroorotate dehydrogenase inhibitors

Meng,

Zhang,

et al. 2024

J Comput Chem

View full text Add to dashboard Cite

Human dihydroorotate dehydrogenase (hDHODH) is a flavin mononucleotide‐dependent enzyme that can limit de novo pyrimidine synthesis, making it a therapeutic target for diseases such as autoimmune disorders and cancer. In this study, using the docking structures of complexes generated by AutoDock Vina, we integrate interaction features and ligand features, and employ support vector regression to develop a target‐specific scoring function for hDHODH (TSSF‐hDHODH). The Pearson correlation coefficient values of TSSF‐hDHODH in the cross‐validation and external validation are 0.86 and 0.74, respectively, both of which are far superior to those of classic scoring function AutoDock Vina and random forest (RF) based generic scoring function RF‐Score. TSSF‐hDHODH is further used for the virtual screening of potential inhibitors in the FDA‐Approved & Pharmacopeia Drug Library. In conjunction with the results from molecular dynamics simulations, crizotinib is identified as a candidate for subsequent structural optimization. This study can be useful for the discovery of hDHODH inhibitors and the development of scoring functions for additional targets.

show abstract

Identification of Potent hDHODH Inhibitors for Lung Cancer via Virtual Screening of a Rationally Designed Small Combinatorial Library

Cited by 4 publications

References 44 publications

Machine Learning-Based Approach to Developing Potent EGFR Inhibitors for Breast Cancer─Design, Synthesis, and In Vitro Evaluation

Machine Learning-Based Approach to Developing Potent EGFR Inhibitors for Breast Cancer─Design, Synthesis, and In Vitro Evaluation

Chalcone‐based benzenesulfonamides as potent and selective inhibitors for human carbonic anhydrase II: Design, synthesis, in vitro, and in silico studies

Development of a machine learning‐based target‐specific scoring function for structure‐based binding affinity prediction for human dihydroorotate dehydrogenase inhibitors

Contact Info

Product

Resources

About