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Identification of novel selective estrogen receptor degraders (SERD) via physics-based and deep-learning-based
Mengyu Chen1, Hao Zhang1, Shiyun Chen1
1Guangdong Provincial Key Laboratory of New Drug Screening, NMPA Key Laboratory for Research and Evaluation of Drug Metabolism and Guangdong-Hong KongMacao Joint Laboratory for New Drug Screening, School of Pharmaceutical Science, Southern Medical University, Guangzhou, China.
Abstract:
Breast cancer is the most common malignant tumor among women, most of which are ERα(Estrogen Receptor alpha) positive. SERDs(Selective Estrogen Receptor Degraders), such as Fulvestrant(the first SERD), can induce degradation of this receptor, leading to overcome the acquired endocrine resistance. However, only two SERDs (Fulvestrant and Elacestrant) are currently clinically approved, highlighting an urgent demand for novel, oral, and more potent alternatives. In this study we developed a multi-tiered virtual screening combing physics-based docking methods (Glide) with deep-learning-based docking methods (Karmadock and Carsidock) to identify SERDs with novel scaffold. After ADMET and MM-GBSA screening, four purchasable candidate compounds were selected for biological evaluation in three cell lines. Among them, two compounds exhibited significant anti-proliferation activity aganist ER-positive cells. The fingerprint analysis also revealed their structural novelty, which are distinct from the known SERDs. Further study indicated F0840-0093 could directly bound to ERα and induced its proteasomal degradation (mimicking Fulvestrant). In summary, our work not only provided a feasible virtual screening approach in drug discovery but also identified some compounds, particularly F0840-0093, which can be a promising lead with new chemical scaffold for further optimization and development as SERDs.
Insights
Researchers identified novel Selective Estrogen Receptor Degraders (SERDs) for breast cancer treatment. Compound F0840-0093 shows promise as a new lead for developing more effective ERα-positive breast cancer therapies.
Area of Science:
- Oncology
- Medicinal Chemistry
- Computational Drug Discovery
Background:
- Breast cancer, predominantly ERα-positive, necessitates treatments like Selective Estrogen Receptor Degraders (SERDs) to combat endocrine resistance.
- Current clinical options are limited, with only two approved SERDs, creating an urgent need for novel therapeutic agents.
- Existing SERDs face challenges, driving the search for new scaffolds with improved potency and oral bioavailability.
Purpose of the Study:
- To discover novel SERDs with unique chemical scaffolds using a multi-tiered virtual screening approach.
- To identify potent drug candidates for ERα-positive breast cancer by combining physics-based and deep-learning docking methods.
- To evaluate promising compounds for anti-proliferative activity and ERα binding.
Main Methods:
- Employed a virtual screening strategy integrating Glide, Karmadock, and Carsidock for docking simulations.
- Utilized ADMET and MM-GBSA for compound filtering and selection.
- Conducted biological evaluations, including anti-proliferation assays and fingerprint analysis, on selected candidates.
Main Results:
- Two novel compounds demonstrated significant anti-proliferation activity against ER-positive breast cancer cells.
- Fingerprint analysis confirmed the structural novelty of the identified compounds, distinct from existing SERDs.
- Compound F0840-0093 was found to directly bind ERα and induce its proteasomal degradation, similar to Fulvestrant.
Conclusions:
- The study presents a successful virtual screening methodology for identifying novel drug candidates.
- Compound F0840-0093 emerges as a promising lead compound with a novel scaffold for SERD development.
- This research offers a potential new therapeutic avenue for overcoming endocrine resistance in ERα-positive breast cancer.
