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 (avtor), Goričan, Tjaša (avtor), Strašek Benedik, Nika (avtor), Golič Grdadolnik, Simona (avtor), Sosič, Izidor (avtor), Perdih, Andrej (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: molecular design, molecular simulations, deep learning, human DNA, topoisomerase IIα, catalytic inhibitors, anticancer agents
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FFA - Fakulteta za farmacijo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2024
• Št. strani: str. 2995-3018
• Številčenje: Vol. 23
• PID: 20.500.12556/RUL-160545
• UDK: 615.4:54:616-006
• ISSN pri članku: 2001-0370
• DOI: 10.1016/j.csbj.2024.06.037
• COBISS.SI-ID: 200950531
• Datum objave v RUL: 30.08.2024

Gradivo je del revije

• Naslov: Computational and Structural Biotechnology Journal
• Založnik: Chalmers University of Technology
• ISSN: 2001-0370
• COBISS.SI-ID: 5068826

Licence

• Licenca: CC BY-NC-ND 4.0, Creative Commons Priznanje avtorstva-Nekomercialno-Brez predelav 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by-nc-nd/4.0/deed.sl
• Opis: Najbolj omejujoča licenca Creative Commons. Uporabniki lahko prenesejo in delijo delo v nekomercialne namene in ga ne smejo uporabiti za nobene druge namene.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: molekularna zasnova, molekularne simulacije, globoko učenje, človeška DNA, topoizomeraza IIα, katalitični inhibitorji, sredstva proti raku, rak (medicina), farmacevtska kemija