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Exploring EZH2-Proteasome Dual-Targeting Drug Discovery through a Computational Strategy to Fight Multiple Myeloma
Filipe G A Estrada1,2,3, Silvia Miccoli1,4, Natália Aniceto1,2
1Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, Universidade de Lisboa, 1649-003 Lisbon, Portugal.
Abstract:
Multiple myeloma is an incurable plasma cell neoplastic disease representing about 10-15% of all haematological malignancies diagnosed in developed countries. Proteasome is a key player in multiple myeloma and proteasome inhibitors are the current first-line of treatment. However, these are associated with limited clinical efficacy due to acquired resistance. One of the solutions to overcome this problem is a polypharmacology approach, namely combination therapy and multitargeting drugs. Several polypharmacology avenues are currently being explored. The simultaneous inhibition of EZH2 and Proteasome 20S remains to be investigated, despite the encouraging evidence of therapeutic synergy between the two. Therefore, we sought to bridge this gap by proposing a holistic in silico strategy to find new dual-target inhibitors. First, we assessed the characteristics of both pockets and compared the chemical space of EZH2 and Proteasome 20S inhibitors, to establish the feasibility of dual targeting. This was followed by molecular docking calculations performed on EZH2 and Proteasome 20S inhibitors from ChEMBL 25, from which we derived a predictive model to propose new EZH2 inhibitors among Proteasome 20S compounds, and vice versa, which yielded two dual-inhibitor hits. Complementarily, we built a machine learning QSAR model for each target but realised their application to our data is very limited as each dataset occupies a different region of chemical space. We finally proceeded with molecular dynamics simulations of the two docking hits against the two targets. Overall, we concluded that one of the hit compounds is particularly promising as a dual-inhibitor candidate exhibiting extensive hydrogen bonding with both targets. Furthermore, this work serves as a framework for how to rationally approach a dual-targeting drug discovery project, from the selection of the targets to the prediction of new hit compounds.
Insights
This study introduces a computational approach to discover dual-target inhibitors for multiple myeloma by simultaneously targeting EZH2 and Proteasome 20S. One promising compound was identified, offering a new strategy for drug discovery.
Area of Science:
- Oncology
- Pharmacology
- Computational Chemistry
Background:
- Multiple myeloma is an incurable cancer with limited treatment efficacy due to resistance to proteasome inhibitors.
- Polypharmacology, combining therapies or multitargeting drugs, offers a potential solution to overcome resistance.
- Simultaneous inhibition of EZH2 and Proteasome 20S is a promising but unexplored therapeutic strategy.
Purpose of the Study:
- To develop an in silico strategy for identifying novel dual-target inhibitors of EZH2 and Proteasome 20S.
- To assess the feasibility of targeting both EZH2 and Proteasome 20S simultaneously.
- To propose new drug candidates for multiple myeloma treatment.
Main Methods:
- Comparative analysis of EZH2 and Proteasome 20S binding pockets and chemical spaces.
- Molecular docking of inhibitors from ChEMBL 25 against both targets.
- Development of predictive models and machine learning QSAR models.
- Molecular dynamics simulations of identified dual-inhibitor hits.
Main Results:
- Identified two dual-inhibitor hits through molecular docking and predictive modeling.
- One hit compound demonstrated significant potential as a dual-inhibitor, showing extensive hydrogen bonding with both EZH2 and Proteasome 20S.
- Machine learning QSAR models had limited applicability due to distinct chemical spaces of the datasets.
Conclusions:
- The study presents a successful in silico framework for rational dual-targeting drug discovery.
- One identified compound is a promising candidate for further development as a multiple myeloma therapeutic.
- This approach provides a roadmap for future polypharmacology drug discovery projects.
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