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Updated: Jun 27, 2026
Utilizing Functional Genomics Screening to Identify Potentially Novel Drug Targets in Cancer Cell Spheroid Cultures
Published on: December 26, 2016
An integrative machine learning, explainable AI, molecular simulation, and cytotoxicity validation framework for the
Deepak Sharma1, Madhan Subramaniam2, Madhu Anabala1
1School of Bio-Sciences and Technology, Vellore Institute of Technology, Vellore, India.
Background:
Triple negative breast cancer (TNBC) is an aggressive disease characterized by a poor prognosis, high decline rates, lack of hormonal receptors for targeted therapy, limited effectiveness of existing treatments, and the emergence of chemoresistance. Sirtuin1 (SIRT1) is an epigenetic modifier and nicotinamide adenine dinucleotide (NAD+) dependent class III histone deacetylase (HDACs) protein. It promotes the modulation of several tumour suppressors and oncogenes. The evidence also suggests that inhibiting SIRT1 activity using selective SIRT1 inhibitors could restore E-cadherin expression and suppress EMT-mediated metastasis in TNBC cells. Though we have many SIRT1 protein inhibitors, they exhibited off-target effects, low isoform selectivity, and inefficiency at the clinical trial stage.
Methods:
In this study, we aim to identify SIRT1 isoform inhibitors by utilizing an integrated computational approach. Three stages of ML modelling were performed to find the best model from the SIRT1-based dataset. The QDA + ROS and XGBClassifier + ROS models were identified as the most robust, and they were subjected to the SHAP framework (XAI approach) to address the "black box" nature of the developed ML models. The NPASS natural compound dataset was first screened with the applicability domain of the developed models, followed by a two-step virtual screening with UniDock and AutoDock GPU in Scientiflow. Finally, the selected compounds were taken for molecular dynamics simulation, with rigorous trajectory analysis, and preliminary experimental validation was done.
Results:
The compounds with NPASS IDs: NPC216682, NPC480509, NPC210910, and NPC247082 were identified as the most promising hits. Among these hits, Praziquantel (NPC480509), the only available test compound with reported anticancer properties, revealed the cytotoxic nature on MDA-MD-231 and MCF7 breast cancer cell lines, whereas non-cytotoxic on normal breast cell line (MCF10A).
Conclusion:
This study is exploratory. Exact SIRT1 selective inhibition by Praziquantel, along with other hits, may be further studies thorugh in-vitro and in vivo evaluation to understand the exact mechanism of action of these hits. The integrated in silico, and preliminary in-vitro approach proposed in this study could lead to innovative outcomes when applied in a pharmaceutical framework distinct from traditional methods.
Insights
This study identifies potential SIRT1 isoform inhibitors for triple-negative breast cancer (TNBC) using computational methods. Praziquantel showed promising cytotoxic effects against TNBC cells, offering a new avenue for targeted therapy development.
Area of Science:
- Computational chemistry and cheminformatics
- Oncology and cancer research
- Epigenetics and molecular biology
Background:
- Triple-negative breast cancer (TNBC) is aggressive, lacking targeted therapy options and often developing chemoresistance.
- Sirtuin1 (SIRT1) is an epigenetic modifier implicated in cancer progression.
- Existing SIRT1 inhibitors lack selectivity and show limited clinical efficacy.
Purpose of the Study:
- To identify selective SIRT1 isoform inhibitors for TNBC using an integrated computational approach.
- To address the limitations of current SIRT1 inhibitors through advanced modeling and screening.
- To discover novel therapeutic strategies for TNBC by targeting SIRT1.
Main Methods:
- Employed machine learning (ML) models (QDA + ROS, XGBClassifier + ROS) for SIRT1 inhibitor identification.
- Utilized Explainable AI (XAI) SHAP framework to interpret ML models.
- Performed virtual screening of natural compounds (NPASS dataset) followed by molecular docking and dynamics simulations.
Main Results:
- Identified four promising SIRT1 inhibitor candidates: NPC216682, NPC480509, NPC210910, and NPC247082.
- Praziquantel (NPC480509) demonstrated significant cytotoxicity against TNBC cell lines (MDA-MD-231, MCF7) while sparing normal cells (MCF10A).
Conclusions:
- The integrated in silico and preliminary in vitro approach offers a novel strategy for drug discovery in TNBC.
- Praziquantel and other identified hits warrant further in vitro and in vivo investigation for their precise mechanism of action and therapeutic potential.
- This study paves the way for developing innovative pharmaceutical frameworks distinct from traditional methods for TNBC treatment.
