Optimizing Cell Density and Unveiling Cytotoxic Profiles of DMSO and Ethanol in Six Cancer Cell Lines: Experimental and In Silico Insights

<b>Background:</b> Accurate assessment of drug cytotoxicity in vitro is essential for preclinical evaluation of anticancer agents. Methodological parameters such as cell density and solvent concentrations can significantly influence the reproducibility and reliability of cell-based assay results. <b>Objective:</b> This study aims to optimize cell seeding density and evaluate the cytotoxic effects of common solvents (DMSO and ethanol) on different cancer cell lines, complemented by in silico analysis to elucidate underlying mechanisms. <b>Materials and Methods:</b> Six cancer cell lines (HepG2, Huh7, HT29, SW480, MCF-7, and MDA-MB-231) were seeded at different densities to determine the optimal cell seeding number ideal for cell viability assay at 24, 48, and 72 h. The cytotoxicity of DMSO and ethanol was assessed in these cell lines using an MTT assay at multiple time points. In silico docking studies were conducted to investigate the interactions between solvents and key proteins involved in apoptosis, membrane function, and metabolism. <b>Results:</b> A cell density of 2000 cells per well yielded consistent linear viability across cell lines and time points. DMSO at 0.3125% showed minimal cytotoxicity across all cell lines (except MCF-7) and time points; the cytotoxic effect at higher concentrations is variable depending on cell type and exposure duration. Ethanol exhibited rapid and concentration-dependent cytotoxicity, reducing viability by more than 30% at as low as 0.3125% concentration after 24 h. Docking analyses revealed that DMSO binds specifically to apoptotic and membrane proteins, suggesting a role in inducing apoptosis. In contrast, ethanol primarily interacts with metabolic proteins, consistent with its effect on membrane disruption and rapid cell death. <b>Conclusion:</b> DMSO at 0.3125% is a good choice as a solvent since it has low toxicity in most tested cell lines; however, the safe concentration limit is dependent on cell type and exposure duration. Ethanol exhibited higher cytotoxicity, necessitating careful concentration management. The in silico analysis supports these findings, indicating that DMSO interacts with apoptosis-related proteins, whereas ethanol primarily affects metabolic processes. These results highlight the importance of precise cell density optimization and solvents for reliable cytotoxicity assessment in cell-based assays.

Authors

Institutions

Publication Details

Journal
Methods and Protocols
Published
2025-08-10
DOI
https://doi.org/10.3390/mps8040093
Citations
5
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
7.52

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimizing Cell Density and Unveiling Cytotoxic Profiles of DMSO and Ethanol in Six Cancer Cell Lines: Experimental and In Silico Insights

Munazzah Tasleem, Ahmed Bakillah, Abdulaziz Asiri, Madhukar S. Said et al.
5 citations
Methods and Protocols
Computational Drug Discovery Methods
7.52
article

Optimizing Cell Density and Unveiling Cytotoxic Profiles of DMSO and Ethanol in Six Cancer Cell Lines: Experimental and In Silico Insights

Munazzah Tasleem, Ahmed Bakillah, Abdulaziz Asiri, Madhukar S. Said, Abutaleb Asiri, Ali Al Qarni
article en
5 citations

Abstract

<b>Background:</b> Accurate assessment of drug cytotoxicity in vitro is essential for preclinical evaluation of anticancer agents. Methodological parameters such as cell density and solvent concentrations can significantly influence the reproducibility and reliability of cell-based assay results. <b>Objective:</b> This study aims to optimize cell seeding density and evaluate the cytotoxic effects of common solvents (DMSO and ethanol) on different cancer cell lines, complemented by in silico analysis to elucidate underlying mechanisms. <b>Materials and Methods:</b> Six cancer cell lines (HepG2, Huh7, HT29, SW480, MCF-7, and MDA-MB-231) were seeded at different densities to determine the optimal cell seeding number ideal for cell viability assay at 24, 48, and 72 h. The cytotoxicity of DMSO and ethanol was assessed in these cell lines using an MTT assay at multiple time points. In silico docking studies were conducted to investigate the interactions between solvents and key proteins involved in apoptosis, membrane function, and metabolism. <b>Results:</b> A cell density of 2000 cells per well yielded consistent linear viability across cell lines and time points. DMSO at 0.3125% showed minimal cytotoxicity across all cell lines (except MCF-7) and time points; the cytotoxic effect at higher concentrations is variable depending on cell type and exposure duration. Ethanol exhibited rapid and concentration-dependent cytotoxicity, reducing viability by more than 30% at as low as 0.3125% concentration after 24 h. Docking analyses revealed that DMSO binds specifically to apoptotic and membrane proteins, suggesting a role in inducing apoptosis. In contrast, ethanol primarily interacts with metabolic proteins, consistent with its effect on membrane disruption and rapid cell death. <b>Conclusion:</b> DMSO at 0.3125% is a good choice as a solvent since it has low toxicity in most tested cell lines; however, the safe concentration limit is dependent on cell type and exposure duration. Ethanol exhibited higher cytotoxicity, necessitating careful concentration management. The in silico analysis supports these findings, indicating that DMSO interacts with apoptosis-related proteins, whereas ethanol primarily affects metabolic processes. These results highlight the importance of precise cell density optimization and solvents for reliable cytotoxicity assessment in cell-based assays.

Methods and ProtocolsVol. 8(4)
King Saud bin Abdulaziz University for Health Sciences (SA), King Abdullah International Medical Research Center (SA), King Abdulaziz Hospital (SA), University of Bisha (SA), National Guard Health Affairs (SA)
University of Bisha, King Abdullah International Medical Research Center
Good health and well-being
Openalex Percentile: Top 3%
Computational Drug Discovery Methods
7.52
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.