Daniil Rusanov

Mr. Daniil Rusanov

orcid

Research Focus & Metrics

Academic Career Length

6Years (2021-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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6 April 2022 in Pharmaceuticals
Verified
Journal Article
Open access
PDF file
DRDaniil Rusanov
Daniil Rusanov
JZJiaying Zou
Jiaying Zou
MBMaria Babak
Maria Babak
Metformin is a widely prescribed medication for the treatment and management of type 2 diabetes. It belongs to a class of biguanides, which are characterized by a wide range of diverse biological properties, including anticancer, antimicrobial, antimalarial, cardioprotective and other activities. It is known that biguanides serve as excellent N-donor bidentate ligands and readily form complexes with virtually all transition metals. Recent evidence suggests that the mechanism of action of metformin and its analogues is linked to their metal-binding properties. These findings prompted us to summarize the existing data on the synthetic strategies and biological properties of various metal complexes with metformin and its analogues. We demonstrated that coordination of biologically active biguanides to various metal centers often resulted in an improved pharmacological profile, including reduced drug resistance as well as a wider spectrum of activity. In addition, coordination to the redox-active metal centers, such as Au(III), allowed for various activatable strategies, leading to the selective activation of the prodrugs and reduced off-target toxicity.

3 May 2023 in ChemBioChem
Verified
Journal Article
Closed access
DRDaniil Rusanov
Daniil Rusanov
SASamah Mutasim Alfadul
Samah Mutasim Alfadul
EPEkaterina Yu. Portnyagina
Ekaterina Yu. Portnyagina
ESEugenia A. Silyanova
Eugenia A. Silyanova
NKNikita A. Kuznetsov
Nikita A. Kuznetsov
MBMaria Babak
Maria Babak

28 October 2025 in Proceedings of the National Academy of Sciences
Verified
Journal Article
Open access
JAJemma Arakelyan
Jemma Arakelyan
HCHo-Jung Choe
Ho-Jung Choe
CWChengnan Wu
Chengnan Wu
DRDaniil Rusanov
Daniil Rusanov
EBElena E. Bardina
Elena E. Bardina
MBMaria Babak
Maria Babak
Recent studies have suggested that the commensal microbiome positively affects cancer prognosis and treatment outcomes. However, only a few strategies for regulating the microbiome composition have been reported. In this study, we identified gold(I) complexes that selectively inhibited nonbeneficial bacterial strains in vitro without affecting commensal Lactobacillus strains. In contrast, clinically used drugs demonstrated comparable effects on both commensal and noncommensal strains. Consistent with the in vitro results, the selected gold(I) complex induced favorable changes in the intratumoral and gastrointestinal microbiomes in vivo. Furthermore, its anticancer efficacy was found to be dependent on the composition of microbiome and correlated with the production of short-chain fatty acid bacterial metabolites. Structure–activity relationship studies have dissected the contribution of each structural component in both the in vivo efficacy and microbiome-modulating properties. Transcriptomic analysis of tumors revealed microbiome-associated gene signaling pathways. These findings provide valuable insights for future research on microbiome-modulating anticancer drugs, presenting potential avenues to optimize cancer treatment outcomes and mitigate side effects such as gastrointestinal dysbiosis. Furthermore, our study provides insights into the involvement of microbiome in the mechanism of action of metal-based chemotherapeutics.

30 September 2021 in Chemistry of Heterocyclic Compounds
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Journal Article
Closed access
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DRDaniil Rusanov
Daniil Rusanov
ASAlexander V. Samet
Alexander V. Samet
VRVyacheslav V. Rusak
Vyacheslav V. Rusak
VSVictor V. Semenov
Victor V. Semenov

2 March 2026 in ChemRxiv
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Preprint
Open access
DRDaniil Rusanov
Daniil Rusanov
EMEgor M. Matnurov
Egor M. Matnurov
DDDmitry Dolgov
Dmitry Dolgov
DBDmitrii Yu. Brezgunov
Dmitrii Yu. Brezgunov
PRPavel A. Ivanov Rostovtsev
Pavel A. Ivanov Rostovtsev
MBMaria Babak
Maria Babak
Cisplatin resistance poses a significant challenge in cancer treatment, particularly when no alternative broad-spectrum drugs are available. While artificial intelligence has emerged as a powerful tool for drug discovery in organic chemistry, its application to metallodrugs remains largely underdeveloped. We hereby report a deep learning model for the screening and de novo design of platinum anticancer drugs. This model integrates a curated dataset of 3,725 platinum complexes with chemoinformatic representations supporting metal-ligand bonds and charges. After screening a large library of 214,373 known complexes, we selected 19 structurally diverse platinum complexes predicted by the AI model to be either active or inactive in different cancer cell lines. The experimental synthesis and testing of these complexes showed up to 72% agreement with the predictions. Leveraging AI-driven metal-ligand combinatorics for de novo design, we developed the PlatinAI complex, which overcame cisplatin resistance in both in vitro and in vivo experiments through a protein signaling network distinct from DNA damage. Additionally, we integrated our manually curated comprehensive platinum database of 17,732 IC50 values with the metal-supporting deep learning model to create MB Finder (www.mb-finder.org), a free public platform that enables users to search for platinum complexes with reported cytotoxicity and predict the anticancer activity of their desired molecules. This open-access resource provides a foundational tool for next-generation platinum-based drug discovery.

20 June 2025 in ChemRxiv
Verified
Preprint
Open access
PDF file
DRDaniil Rusanov
Daniil Rusanov
DBDmitrii Yu. Brezgunov
Dmitrii Yu. Brezgunov
EMEgor Matnurov
Egor Matnurov
BPBekir L. Pashaliev
Bekir L. Pashaliev
VVVarvara Vetrova
Varvara Vetrova
MBMaria Babak
Maria Babak
Resistance to cisplatin poses a significant challenge in cancer treatment for patients globally. Despite the continuous efforts of both the pharmaceutical industry and academia, no new platinum drugs have been approved for the treatment of cancers that have developed resistance to cisplatin-based chemotherapy regimens. Even in the new era of AI, the potential to accelerate the discovery of metal-based anticancer therapeutics has remained largely unexplored. Here, we present the first implementation of deep learning models for activity prediction of de novo designed platinum anticancer agents. Our approach overcomes historic limitations through a manually curated dataset of 4,078 platinum complexes with 18,995 IC50 values, as well as specialized algorithms resolving metal-specific representation challenges. For experimental validation, we predicted the likelihood of potential anticancer activity for 214,373 previously published platinum complexes in MCF-7, A549, A2780 and A2780cis cancer cell lines, synthesized and tested 20 of them, demonstrating strong agreement between predicted and observed activity. Additionally, using de novo fragment assembly approach we designed and successfully synthesized a novel bis-carbene platinum(II) complex, PlatinAI. The experimentally determined cytotoxicity of PlatinAI in MCF-7, A549, A2780 and A2780cis cancer cell lines was in a perfect agreement with the model prediction. PlatinAI demonstrated superior activity compared to cisplatin in cisplatin-resistant cell lines and exhibited a distinct mechanism of action, not reliant on the formation of DNA adducts. This research sets the stage for the development of AI-assisted platforms that enable data-driven design of next-generation platinum therapies, offering improved efficacy and the ability to overcome resistance to current anticancer treatments.