Optimizing kinase and PARP inhibitor combinations through machine learning and in silico approaches for targeted
1Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran. alireza110_p@yahoo.com.
Abstract:
The drug combination is an attractive approach for cancer treatment. PARP and kinase inhibitors have recently been explored against cancer cells, but their combination has not been investigated comprehensively. In this study, we used various drug combination databases to build ML models for drug combinations against brain cancer cells. Some decision tree-based models were used for this purpose. The results were further evaluated using molecular docking and molecular dynamics (MD) simulation. The possibility of the hit drug combinations for crossing the Blood-brain barrier (BBB) was also examined. Based on the obtained results, the combination of niraparib, as the PARP inhibitor, and lapatinib, as the kinase inhibitor, exhibited more considerable outcomes with a remarkable model performance (accuracy of 0.915) and prediction confidence of 0.92. The protein tweety homolog 3 and BTB/POZ domain-containing protein 2 are the main targets of niraparib and lapatinib with - 10.2 and - 8.5 scores, respectively. Due to the outcomes, this drug combination can use the CAT1 transporter on the BBB surface and effectively cross the BBB. Based on the obtained results, niraparib-lapatinib can be a promising drug combination candidate for brain cancer treatment. This combination is worth to be examined by experimental investigation in vitro and in vivo.
Insights
This study explored drug combinations for brain cancer, finding that niraparib (PARP inhibitor) and lapatinib (kinase inhibitor) show promise. This combination effectively crosses the blood-brain barrier, suggesting potential for brain cancer treatment.
Area of Science:
- Oncology
- Computational Biology
- Pharmacology
Background:
- Drug combinations offer a promising strategy for cancer therapy.
- The combined use of PARP and kinase inhibitors has not been extensively studied for brain cancers.
- Machine learning (ML) models can predict effective drug combinations.
Purpose of the Study:
- To build and evaluate ML models for predicting effective drug combinations against brain cancer cells.
- To investigate the synergistic potential of combining PARP inhibitors and kinase inhibitors.
- To assess the blood-brain barrier (BBB) penetration of potential drug combinations.
Main Methods:
- Utilized drug combination databases to construct ML models, specifically decision tree-based algorithms.
- Performed molecular docking and molecular dynamics (MD) simulations to validate predicted drug interactions.
- Examined the mechanism of BBB crossing, including transporter interactions.
Main Results:
- The combination of niraparib (PARP inhibitor) and lapatinib (kinase inhibitor) demonstrated high model performance (accuracy 0.915, confidence 0.92).
- Identified key protein targets for niraparib (tweety homolog 3) and lapatinib (BTB/POZ domain-containing protein 2).
- Predicted that the niraparib-lapatinib combination can utilize the CAT1 transporter to cross the BBB.
Conclusions:
- The niraparib-lapatinib drug combination shows significant potential as a therapeutic strategy for brain cancer.
- This combination exhibits favorable properties for BBB penetration, crucial for treating brain tumors.
- Further in vitro and in vivo experimental validation is warranted for this promising drug combination.
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