Simulation- and AI-directed optimization of 4,6-substituted 1,3,5-triazin-2(1H)-ones as inhibitors of human DNA topoisomerase IIα

Herlah, Barbara (author), Goričan, Tjaša (author), Strašek Benedik, Nika (author), Golič Grdadolnik, Simona (author), Sosič, Izidor (author), Perdih, Andrej (author)

The 4,6-substituted-1,3,5-triazin-2(1H)-ones are promising inhibitors of human DNA topoisomerase IIα. To further develop this chemical class targeting the enzyme´s ATP binding site, the triazin-2(1H)-one substitution position 6 was optimized. Inspired by binding of preclinical substituted 9H-purine derivative, bicyclic substituents were incorporated at position 6 and the utility of this modification was validated by a combination of molecular simulations, dynamic pharmacophores, and free energy calculations. Considering also predictions of Deepfrag, a software developed for structure-based lead optimization based on deep learning, compounds with both bicyclic and monocyclic substitutions were synthesized and investigated for their inhibitory activity. The SAR data showed that the bicyclic substituted compounds exhibited good inhibition of topo IIα, comparable to their mono-substituted counterparts. Further evaluation on a panel of human protein kinases showed selectivity for the inhibition of topo IIα. Mechanistic studies indicated that the compounds acted predominantly as catalytic inhibitors, with some exhibiting topo IIα poison effects at higher concentrations. Integration of STD NMR experiments and molecular simulations, provided insights into the binding model and highlighted the importance of the Asn120 interaction and hydrophobic interactions with substituents at positions 4 and 6. In addition, NCI-60 screening demonstrated cytotoxicity of the compounds with bicyclic substituents and identified sensitive human cancer cell lines, underlining the translational relevance of our findings for further preclinical development of this class of compounds. The study highlights the synergy between simulation and AI-based approaches in efficiently guiding molecular design for drug optimization, which has implications for further preclinical development of this class of compounds.

• Language: English
• Keywords: molecular design, molecular simulations, deep learning, human DNA, topoisomerase IIα, catalytic inhibitors, anticancer agents
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FFA - Faculty of Pharmacy
• Publication status: Published
• Publication version: Version of Record
• Year: 2024
• Number of pages: str. 2995-3018
• Numbering: Vol. 23
• PID: 20.500.12556/RUL-160545
• UDC: 615.4:54:616-006
• ISSN on article: 2001-0370
• DOI: 10.1016/j.csbj.2024.06.037
• COBISS.SI-ID: 200950531
• Publication date in RUL: 30.08.2024

Record is a part of a journal

• Title: Computational and structural biotechnology journal
• Publisher: Elsevier, Research Network of Computational and Structural Biotechnology
• ISSN: 2001-0370
• COBISS.SI-ID: 5068826

Licences

• License: CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
• Link: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Description: The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.

Secondary language

• Language: Slovenian
• Keywords: molekularna zasnova, molekularne simulacije, globoko učenje, človeška DNA, topoizomeraza IIα, katalitični inhibitorji, sredstva proti raku, rak (medicina), farmacevtska kemija