Identification and Evaluation of Multi-Targeted Lead Compounds for Breast Cancer Therapy Using In-Silico Approaches

Uncontrolled cell proliferation inside breast tissue is a hallmark of breast cancer and is linked to increased cell division and overexpression of particular genes. Notably, human epidermal growth factor receptors EGFR and HER2/neu, as well as the co-chaperone HSP90, are often upregulated in breast cancer cases, making them excellent candidates for in-silico investigation. Drug-likeness filters use pharmacoinformatics to minimize energy and assess potential lead compounds. DB08512_CID_135566329, DB06925_CID_15991573 and DB02354_CID_445756 have strong binding energies, which may indicate multi-targeted hits. These chemicals show promise as possible anticancer therapies due to their compelling physiochemical interactions and favourable absorption, distribution, metabolism, excretion, and toxicity profiles. This research highlights the compounds potential usefulness in the treatment of breast cancer and outlines directions for future research.

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Published
2024-12-20
DOI
https://doi.org/10.1109/ariia63345.2024.11051501
Primary Topic
Computational Drug Discovery Methods
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article
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article

Identification and Evaluation of Multi-Targeted Lead Compounds for Breast Cancer Therapy Using In-Silico Approaches

Nilesh P. Sable, Sonopant Joshi, Vijay Nema, Bela Khiratkar et al.
1 links
Computational Drug Discovery Methods
article

Identification and Evaluation of Multi-Targeted Lead Compounds for Breast Cancer Therapy Using In-Silico Approaches

Nilesh P. Sable, Sonopant Joshi, Vijay Nema, Bela Khiratkar, Ranjit Kumar, Nikita Yadav
article en

Abstract

Uncontrolled cell proliferation inside breast tissue is a hallmark of breast cancer and is linked to increased cell division and overexpression of particular genes. Notably, human epidermal growth factor receptors EGFR and HER2/neu, as well as the co-chaperone HSP90, are often upregulated in breast cancer cases, making them excellent candidates for in-silico investigation. Drug-likeness filters use pharmacoinformatics to minimize energy and assess potential lead compounds. DB08512_CID_135566329, DB06925_CID_15991573 and DB02354_CID_445756 have strong binding energies, which may indicate multi-targeted hits. These chemicals show promise as possible anticancer therapies due to their compelling physiochemical interactions and favourable absorption, distribution, metabolism, excretion, and toxicity profiles. This research highlights the compounds potential usefulness in the treatment of breast cancer and outlines directions for future research.

International Institute of Information Technology (IN), Symbiosis International University (IN), D.Y. Patil University (IN), National AIDS Research Institute (IN)
Openalex Percentile: Top 67%
Computational Drug Discovery Methods
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