Probing the stability and quality of the cellulose-based Pickering emulsion containing sesamolin-enriched sesame oil by chemometrics-assisted ATR-FTIR spectroscopy.

This study presents the employment of Fourier transform infrared (FTIR) spectroscopy with attenuated total reflection and principal component analysis (PCA) to analyze the stability of a Pickering emulsion stabilized by carboxylated-cellulose nanocrystal (cCNC) comprising sesame oil phases with or without sesamolin. FTIR measurements identified an intermolecular hydrogen bond between the ester group of the triglyceride and the carboxyl group of the cCNC to create the emulsion droplet. The spectral bands from the hydroxyl group vibration (3700-3050 cm-1), carbonyl (1744 cm-1), CO groups of the ester triglyceride and cCNC (1160-998 cm-1) markedly discriminated between stabilized and destabilized emulsions. The PCA of FTIR spectra detected the change of molecular interaction during storage according to creaming, aggregation, and coalescence and changes in physicochemical parameters such as droplet size, refractive index, and zeta potential. Hence, PCA enabled the observation of the destabilization of emulsion in real-time.

Insights into the structure-activity relationship of pyrimidine-sulfonamide analogues for targeting BRAF V600E protein.

B-rapidly accelerated fibrosarcoma (BRAF) V600E plays a crucial role in the progression of cutaneous melanoma. Core structures of BRAF V600E inhibitors are based on pyrimidine-sulfonamide scaffolds. Exploring the QSAR of these structures can improve our understanding of BRAF V600E inhibitor drug design. This study utilized machine learning-based QSAR to elucidate chemical substructures of pyrimidine-sulfonamide analogues that correlated to the BRAF V600E inhibitory activity. The findings indicate that the support vector regression (SVR) combined with 15 fingerprints achieved the highest statistical performances in terms of goodness-of-fit, robustness, and predictability. Nine key fingerprints from pyrimidine-sulfonamide analogues were identified to exert the BRAF V600E inhibitory activity. These key fingerprints were validated using network-based activity cliff landscape and molecular docking. Together, the developed algorithm can serve as a screening tool for designing BRAF V600E inhibitors. To further utilize this model, we deployed our developed algorithm at https://qsarlabs.com/#braf.

Prediction of Spheroid Cell Death Using Fluorescence Staining and Convolutional Neural Networks.

Three-dimensional (3D) cell culture is emerging for drug design and drug screening. Skin toxicity is one of the most important assays for determining the toxicity of a compound before being used in skin application. Much work has been done to find an alternative assay without animal experiments. 3D cell culture is one of the methods that provides clinically relevant models with superior clinical translation compared to that of 2D cell culture. In this study, we developed a spheroid toxicity assay using keratinocyte HaCaT cells with propidium iodide and calcein AM. We also applied the transfer learning-containing convolutional neural network (CNN) to further determine spheroid cell death with fluorescence labeling. Our result shows that the morphologies of the spheroid are the key features in determining the apoptosis cell death of the HaCaT spheroid. Our CNN model provided good statistical measurement in terms of accuracy, precision, and recall in both validation and external test data sets. One can predict keratinocyte spheroid cell death if that spheroid image contains the fluorescence signals from propidium iodide and calcein AM. The CNN model can be accessed in the web application at https://qsarlabs.com/#spheroiddeath.

Ensemble Quantitative Read-Across Structure-Activity Relationship Algorithm for Predicting Skin Cytotoxicity.

Read-across (RA) and quantitative structure-activity relationship (QSAR) are two alternative methods commonly used to fill data gaps in chemical registrations. These approaches use physicochemical properties or molecular fingerprints of source substances to predict the properties of unknown substances that have similar chemical structures or physicochemical properties. Research on RA and QSAR is essential to minimize the time, money, and animal testing needed to determine biological properties that are not currently known. This study developed a stacked ensemble quantitative read-across structure-activity relationship algorithm (enQRASAR) for predicting skin irritation toxicity based on negative log cell viability inhibition concentration at 50% (pIC50) against skin keratinocytes as the end point. The goodness-of-fit and predictability of this algorithm were validated using leave-one-out cross-validation and external test data sets. The results obtained were statistically reliable in terms of goodness-of-fit, robustness, and predictability metrics. Additionally, the developed model demonstrated a low prediction error when predicting FDA-approved drugs. These results confirm that the enQRASAR algorithm can be used to predict skin cytotoxicity of chemicals. Therefore, this model was publicly available to further facilitate toxicity predictions of unknown compounds in chemical registrations.

Stacked ensemble learning on HaCaT cytotoxicity for skin irritation prediction: A case study on dipterocarpol.

Skin irritation is an adverse effect associated with various substances, including chemicals, drugs, or natural products. Dipterocarpol, extracted from Dipterocarpus alatus, contains several skin benefits notably anticancer, wound healing, and antibacterial properties. However, the skin irritation of dipterocarpol remains unassessed. Quantitative structure-activity relationship (QSAR) is a recommended tool for toxicity assessment involving less time, money, and animal testing to access unavailable acute toxicity data. Therefore, our study aimed to develop a highly accurate machine learning-based QSAR model for predicting skin irritation. We utilized a stacked ensemble learning model with 1064 chemicals. We also adhered to the recommendations from the OECD for QSAR validation. Subsequently, we used the proposed model to explore the cytotoxicity of dipterocarpol on keratinocytes. Our findings indicate that the model displayed promising statistical quality in terms of accuracy, precision, and recall in both 10-fold cross-validation and test datasets. Moreover, the model predicted that dipterocarpol does not have skin irritation, which was confirmed by the cell-based assay. In conclusion, our proposed model can be applied for the risk assessment of skin irritation in untested compounds that fall within its applicability domain. The web application of this model is available at https://qsarlabs.com/#stackhacat.

Phytochemical Screening on Phenolic, Flavonoid Contents, and Antioxidant Activities of Six Indigenous Plants Used in Traditional Thai Medicine.

The antioxidant activity of a traditional Thai formula has been studied and compared to each plant. The formula comprised the roots of Caesalpinia digyna Rottler, Huberantha cerasoides (Roxb.) Benth), Oxyceros horridus Lour, Antidesma ghaesembilla Gaerth, Combretum quadrangulare Kurz, and Ziziphus cambodiana Pierre. The stem was also studied in comparison. The ethanolic extract from each plant part and the mixed plants mimicking the traditional formula were prepared and investigated for antioxidant capability in vitro via DPPH radical scavenging and ferric-reducing antioxidant power assays. The phytochemical constituents were determined by chemical screening, total phenolic (TPC) and flavonoid contents (TFC), and high-performance liquid chromatography. The relationship between antioxidant activity and the contributed phytochemicals was determined using correlation analysis and principal component analysis (PCA). Results showed that extracts from both parts of the plant formula showed the highest antioxidant activity compared to a single plant extract. Among the six plants, C. digyna exhibited the highest TPC and antioxidant activity. TPC had a strong positive correlation with antioxidant activity. PCA revealed that gallic acid contributed to the antioxidant activity. In conclusion, the ethanolic extracts of the traditional formula and C. digyna have the potential for further chemical characterization and study related to antioxidant activity.

StackBRAF: A Large-Scale Stacking Ensemble Learning for BRAF Affinity Prediction.

The B-rapidly accelerated fibrosarcoma (BRAF) is a proto-oncogene that plays a vital role in cell signaling and growth regulation. Identifying a potent BRAF inhibitor can enhance therapeutic success in high-stage cancers, particularly metastatic melanoma. In this study, we proposed a stacking ensemble learning framework for the accurate prediction of BRAF inhibitors. We obtained 3857 curated molecules with BRAF inhibitory activity expressed as a predicted half-maximal inhibitory concentration value (pIC50) from the ChEMBL database. Twelve molecular fingerprints from PaDeL-Descriptor were calculated for model training. Three machine learning algorithms including extreme gradient boosting, support vector regression, and multilayer perceptron were utilized for constructing new predictive features (PFs). The meta-ensemble random forest regression, called StackBRAF, was created based on the 36 PFs. The StackBRAF model achieves lower mean absolute error (MAE) and higher coefficient of determination (R2 and Q2) than the individual baseline models. The stacking ensemble learning model provides good y-randomization results, indicating a strong correlation between molecular features and pIC50. An applicability domain of the model with an acceptable Tanimoto similarity score was also defined. Moreover, a large-scale high-throughput screening of 2123 FDA-approved drugs against the BRAF protein was successfully demonstrated using the StackBRAF algorithm. Thus, the StackBRAF model proved beneficial as a drug design algorithm for BRAF inhibitor drug discovery and drug development.

Prediction of KRASG12C inhibitors using conjoint fingerprint and machine learning-based QSAR models.

Kirsten rat sarcoma virus G12C (KRASG12C) is the major protein mutation associated with non-small cell lung cancer (NSCLC) severity. Inhibiting KRASG12C is therefore one of the key therapeutic strategies for NSCLC patients. In this paper, a cost-effective data driven drug design employing machine learning-based quantitative structure-activity relationship (QSAR) analysis was built for predicting ligand affinities against KRASG12C protein. A curated and non-redundant dataset of 1033 compounds with KRASG12C inhibitory activity (pIC50) was used to build and test the models. The PubChem fingerprint, Substructure fingerprint, Substructure fingerprint count, and the conjoint fingerprint-a combination of PubChem fingerprint and Substructure fingerprint count-were used to train the models. Using comprehensive validation methods and various machine learning algorithms, the results clearly showed that the XGBoost regression (XGBoost) achieved the highest performance in term of goodness of fit, predictivity, generalizability and model robustness (R2 = 0.81, Q2CV = 0.60, Q2Ext = 0.62, R2 - Q2Ext = 0.19, R2Y-Random = 0.31 ± 0.03, Q2Y-Random = -0.09 ± 0.04). The top 13 molecular fingerprints that correlated with the predicted pIC50 values were SubFPC274 (aromatic atoms), SubFPC307 (number of chiral-centers), PubChemFP37 (≥1 Chlorine), SubFPC18 (Number of alkylarylethers), SubFPC1 (number of primary carbons), SubFPC300 (number of 1,3-tautomerizables), PubChemFP621 (N-C:C:C:N structure), PubChemFP23 (≥1 Fluorine), SubFPC2 (number of secondary carbons), SubFPC295 (number of C-ONS bonds), PubChemFP199 (≥4 6-membered rings), PubChemFP180 (≥1 nitrogen-containing 6-membered ring), and SubFPC180 (number of tertiary amine). These molecular fingerprints were virtualized and validated using molecular docking experiments. In conclusion, this conjoint fingerprint and XGBoost-QSAR model demonstrated to be useful as a high-throughput screening tool for KRASG12C inhibitor identification and drug design.

Bioactive Compounds, Antioxidant Activities, and HPLC Analysis of Nine Edible Sprouts in Cambodia.

The non-nutritional health benefits of sprouts are unconfirmed. Thus, nine sprout methanolic extracts were tested for phytoconstituents and antioxidant activity. The TPC, TCC, TFC, TAC, and TALC were measured. ABTS and DPPH radical scavenging and ferric-reducing antioxidant power assays were used to assess the antioxidant activity. HPLC detected gallic acid, vanillin, syringic acid, chlorogenic acid, caffeic acid, and rutin in the extracts. The sprout extracts contained six compounds, with caffeic acid being the most abundant. Gallic acid, syringic acid, chlorogenic acid, caffeic acid, vanillin, and rutin were highest in soybean, black sesame, mustard, sunflower, white radish, and black sesame sprouts, respectively. Sunflower sprouts had the highest level of TCC while soybean sprouts had the highest level of TFC, Taiwanese morning glory had the highest level of TPC, mustard sprouts had the highest level of TALC, and black sesame sprouts had the highest level of TAC. Taiwanese morning glories scavenged the most DPPH and ABTS radicals. Colored and white radish sprouts had similar ferric-reducing antioxidant power. Antioxidation mechanisms varied by compound. Our findings demonstrated that sprouts have biological effects, and their short time for mass production offers an alternative food source for health benefits, and that they are useful for future research development of natural products and dietary supplements.

Polyphenol and Tryptophan Contents of Purple Corn (Zea mays L.) Variety KND and Butterfly Pea (Clitoria ternatea) Aqueous Extracts: Insights into Phytochemical Profiles with Antioxidant Activities and PCA Analysis.

Plants are a rich source of phytochemical compounds with antioxidant activity. Several studies have revealed that the consumption of plant polyphenols reduces the risk of diseases. Purple corn (Zea mays L. variety KND) and butterfly pea (Clitoria ternatea; CT) were selected to be investigated as alternative natural polyphenol sources to increase the value of these plants. Phytochemical profiles and antioxidant activities of KND cob, silk, husk and CT extracts alone and in combination were investigated in this study. The results revealed that purple corn cob (C) extract had the highest tryptophan, melatonin, total anthocyanin (TAC) and delphinidin content, while the purple corn silk (S) extract showed the highest total phenolic content (TPC) and antioxidant activities. Serotonin was found only in purple corn husk (H) extract and C extract. High contents of tryptophan and sinapic acid were found in CT extract. Principal component analysis (PCA) revealed that strong antioxidant activities were strongly correlated with protocatechuic acid and p-hydroxybenzoic acid contents, moderate antioxidant activities were strongly correlated with melatonin, and low antioxidant activities were strongly correlated with sinapic acid content. Therefore, the purple corn variety KND waste cobs, silk and husks are a potentially rich source of health-promoting phytochemical compounds.

Effect of Germination and Illumination on Melatonin and Its Metabolites, Phenolic Content, and Antioxidant Activity in Mung Bean Sprouts.

Mung bean (Vigna radiata L.) sprouts are increasingly consumed and have become part of a healthy diet. The sprouts are composed of proteins, carbohydrates, and biochemical compounds. During germination, the phytochemical compounds are significantly elevated, especially under stress conditions such as salinity, drought, extreme temperature, and illumination. The present study examined the effects of light and germination time on the bioactive compounds in mung bean sprout extracts. Mung bean seeds were sprouted under different light exposure conditions, and the phytochemical composition and antioxidant activity of sprout extracts were determined compared to seeds. The results show that tryptophan sharply decreased during germination. On the contrary, melatonin, polyphenols, and total phenolic content (TPC) were elevated with increased germination time, correlated with increased antioxidant activity. Sprouts germinated in the dark presented higher levels of melatonin and TPC compared with those germinated under 12 h light exposure (3.6- and 1.5-fold, respectively). In conclusion, germination can enhance valuable phytochemicals and antioxidant activity of mung bean sprouts. Mung bean sprouts may be a good alternative functional food for promoting human health.

Development of Sesamol Carbamate-L-Phenylalanine Prodrug Targeting L-Type Amino Acid Transporter1 (LAT1) as a Potential Antiproliferative Agent against Melanoma.

Sesamol is a compound reported to have anti-melanogenesis and anti-melanoma actions. Sesamol, however, has low intracellular drug concentration and fast excretion, which can limit its benefits in the clinic. To overcome this drawback and increase intracellular delivery of sesamol into the target melanoma, research has focused on L-type amino acid transporter 1 (LAT1)-mediated prodrug delivery into melanoma cells. The sesamol prodrug was designed by conjugating sesamol with L-phenylalanine at the para position with a carbamate bond. LAT1 targeting was evaluated vis-à-vis a competitive [14C]-leucine uptake inhibition. The sesamol prodrug has a higher [14C]-leucine uptake inhibition than sesamol in human LAT1-transfected HEK293 cells. Moreover, the sesamol prodrug was taken up by LAT1-mediated transport into SK-MEL-2 cells more effectively than sesamol. The sesamol prodrug underwent complete hydrolysis, releasing the active sesamol at 72 h, which significantly exerted its cytotoxicity (IC50 of 29.3 µM) against SK-MEL-cells more than sesamol alone. Taken together, the strategy for LAT1-mediated prodrug delivery has utility for the selective uptake of sesamol, thereby increasing its intracellular concentration and antiproliferation activity, targeting melanoma SK-MEL-2 cells that overexpress the LAT1 protein. The sesamol prodrug thus warrants further evaluation in an in vivo model.

Improvement in the binding specificity of anti-isomiroestrol antibodies by expression as fragments under oxidizing conditions inside the SHuffle T7 E. coli cytoplasm.

Sensitive and specific analysis of isomiroestrol (Iso) is required for the quality control of Pueraria candollei, a herb used to treat menopausal disorders. The anti-isomiroestrol monoclonal antibody (Iso-mAb) exhibits cross-reactivity with miroestrol and deoxymiroestrol, which impacts the analytical results. Here, the active and soluble forms of the single-chain variable fragment (Iso-scFv) and fragment antigen-binding (Iso-Fab) against Iso were expressed using Escherichia coli SHuffle® T7 to alter the binding specificity. The Iso-scFv format exhibited a higher binding activity than the Iso-Fab format. The reactivity of Iso-scFv towards Iso was comparable with that of the parental Iso-mAb. Remarkably, the binding specificity of the scFv structure was improved and cross-reactivity against analogs was reduced from 13.3-21.0% to ˂ 1%. The structure of recombinant antibodies affects the binding characteristics. Therefore, the immunoassays should improve specificity; these findings can be useful in agricultural processes and for quality monitoring of P. candollei-related materials.

Machine Learning and In Vitro Chemical Screening of Potential α-Amylase and α-Glucosidase Inhibitors from Thai Indigenous Plants.

Diabetes mellitus is a major predisposing factor for cardiovascular disease and mortality. α-Amylase and α-glucosidase enzymes are the rate-limiting steps for carbohydrate digestion. The inhibition of these two enzymes is clinically used for the treatment of diabetes mellitus. Here, in vitro study and machine learning models were employed for the chemical screening of inhibiting the activity of 31 plant samples on α-amylase and α-glucosidase enzymes. The results showed that the ethanolic twig extract of Pinus kesiya had the highest inhibitory activity against the α-amylase enzyme. The respective ethanolic extract of Croton oblongifolius stem, Parinari anamense twig, and Polyalthia evecta leaf showed high inhibitory activity against the α-glucosidase enzyme. The classification analysis revealed that the α-glucosidase inhibitory activity of Thai indigenous plants was more predictive based on phytochemical constituents, compared with the α-amylase inhibitory activity (1.00 versus 0.97 accuracy score). The correlation loading plot revealed that flavonoids and alkaloids contributed to the α-amylase inhibitory activity, while flavonoids, tannins, and reducing sugars contributed to the α-glucosidase inhibitory activity. In conclusion, the ethanolic extracts of P. kesiya, C. oblongifolius, P. anamense, and P. evecta have the potential for further chemical characterization and the development of anti-diabetic recipes.

Drug Repurposing against KRAS Mutant G12C: A Machine Learning, Molecular Docking, and Molecular Dynamics Study.

The Kirsten rat sarcoma viral G12C (KRASG12C) protein is one of the most common mutations in non-small-cell lung cancer (NSCLC). KRASG12C inhibitors are promising for NSCLC treatment, but their weaker activity in resistant tumors is their drawback. This study aims to identify new KRASG12C inhibitors from among the FDA-approved covalent drugs by taking advantage of artificial intelligence. The machine learning models were constructed using an extreme gradient boosting (XGBoost) algorithm. The models can predict KRASG12C inhibitors well, with an accuracy score of validation = 0.85 and Q2Ext = 0.76. From 67 FDA-covalent drugs, afatinib, dacomitinib, acalabrutinib, neratinib, zanubrutinib, dutasteride, and finasteride were predicted to be active inhibitors. Afatinib obtained the highest predictive log-inhibitory concentration at 50% (pIC50) value against KRASG12C protein close to the KRASG12C inhibitors. Only afatinib, neratinib, and zanubrutinib covalently bond at the active site like the KRASG12C inhibitors in the KRASG12C protein (PDB ID: 6OIM). Moreover, afatinib, neratinib, and zanubrutinib exhibited a distance deviation between the KRASG2C protein-ligand complex similar to the KRASG12C inhibitors. Therefore, afatinib, neratinib, and zanubrutinib could be used as drug candidates against the KRASG12C protein. This finding unfolds the benefit of artificial intelligence in drug repurposing against KRASG12C protein.

Route of intracellular uptake and cytotoxicity of sesamol, sesamin, and sesamolin in human melanoma SK-MEL-2 cells.

The intracellular uptake concentration determines drug absorption, drug activity, and toxicity. Sesamol, sesamin, and sesamolin are promising bioactive components from Sesame indicum L. Their respective intracellular uptake pathway and cytotoxicity were evaluated using melanoma and non-cancerous cells. Quantitative structure-activity relationship (QSAR) models were built to identify the molecular features affecting drug uptake in cells. The respective intracellular uptake pathway for sesamol vs. sesamin and sesamolin was carrier-mediated vs. passive transport. Topological polar surface area (PSA) and 2D autocorrections increase the intracellular concentration (C/M ratio) of these compounds. Sesamol has the lowest C/M ratio compared to sesamin and sesamolin, but only sesamol inhibits the cell viability of melanoma and provides an inhibition concentration at 50% (IC50) against melanoma cells. The slightly aqueous solubility of sesamin and sesamolin, therefore, limits testing of their cytotoxicity. In conclusion, sesamol has the potential to inhibit melanoma cell growth, but requires improvement of the C/M ratio to increase its physicochemical properties. Thus, in order to investigate the cytotoxicity of sesamin and sesamolin against melanoma cells a solubility enhancer is needed.

Evaluation of Melanoma (SK-MEL-2) Cell Growth between Three-Dimensional (3D) and Two-Dimensional (2D) Cell Cultures with Fourier Transform Infrared (FTIR) Microspectroscopy.

Fourier transform infrared (FTIR) microspectroscopy was used to evaluate the growth of human melanoma cells (SK-MEL-2) in two-dimensional (2D) versus three-dimensional (3D) spheroid culture systems. FTIR microspectroscopy, coupled with multivariate analysis, could be used to monitor the variability of spheroid morphologies prepared from different cell densities. The characteristic shift in absorbance bands of the 2D cells were different from the spectra of cells from 3D spheroids. FTIR microspectroscopy can also be used to monitor cell death similar to fluorescence cell staining in 3D spheroids. A change in the secondary structure of protein was observed in cells from the 3D spheroid versus the 2D culture system. FTIR microspectroscopy can detect specific alterations in the biological components inside the spheroid, which cannot be detected using fluorescence cell death staining. In the cells from 3D spheroids, the respective lipid, DNA, and RNA region content represent specific markers directly proportional to the spheroid size and central area of necrotic cell death, which can be confirmed using unsupervised PCA and hierarchical cluster analysis. FTIR microspectroscopy could be used as an alternative tool for spheroid cell culture discrimination, and validation of the usual biochemical technique.

Role of L-Type Amino Acid Transporter 1 (LAT1) for the Selective Cytotoxicity of Sesamol in Human Melanoma Cells.

Sesamol is effective against melanoma cells with less damage to normal cells. The underlying selective cytotoxicity of sesamol in melanoma vs. non-cancerous cells is undefined. Melanoma cells differ from normal cells by over-expression of the L-type amino acid transporter 1 (LAT1). We sought to clarify the transport mechanism on selective cytotoxicity of sesamol in melanoma cells. A human melanoma cell line (SK-MEL-2) and African monkey epithelial cell line (Vero) were used to study the cellular uptake and cytotoxicity of sesamol. The intracellular concentration of sesamol was quantified by UV-HPLC. The cytotoxicity was determined by neutral red uptake assay. Sesamol showed a higher distribution volume and uptake clearance in SK-MEL-2 than Vero cells. Sesamol was distributed by both carrier-mediated and passive transport by having greater carrier-mediated transport into SK-MEL-2 cells than Vero cells. Higher mRNA expression and function of LAT1 over LAT2 were evident in SK-MEL-2 cells compared to Vero cells. Sesamol uptake and sesamol cytotoxicity were inhibited by the LAT1 inhibitor, suggesting LAT1 had a role in sesamol transport and its bioactivity in melanoma. The LAT1-mediated transport of sesamol is indicative of how it engages cytotoxicity in melanoma cells with promising therapeutic benefits.

Validation of Cell-Based Assay for Quantification of Sesamol Uptake and Its Application for Measuring Target Exposure.

The intracellular drug concentration is needed for determined target exposure at the site of action regarding its pharmacological action and adverse effects. Sesamol is an antiproliferative molecule from Sesamum indicum with promising health benefits. We present a method for measuring the intracellular sesamol content using reverse-phase HPLC with a UV diode array in melanoma cells. Sesamol was completely resolved by isocratic elution (4.152 ± 0.008 min) with methanol/water (70%, v/v) through a 30 °C, 5-µm C-18 column and detection at 297 nm. The present assay offers high sensitivity, fast elution, and an accurate and linear nominal concentration range of 10-1000 ng/mL (R2 = 0.9972). The % accuracy of the sesamol quality control sample was -3.36% to 1.50% (bias) with a 0.84% to 5.28% relative standard deviation (RSD), representing high repeatability and high reproducibility. The % recovery was 94.80% to 99.29%, which determined that there was no loss of sesamol content during the sample preparation. The validated method was applied to monitor intracellular sesamol concentration after treatment from 5 min to 24 h. The remaining intracellular sesamol content was correlated with its antiproliferative effect (R2 = 0.9483). In conclusion, this assay demonstrated low manipulation, quick elution, and high sensitivity, precision, accuracy, and recovery, and it was successfully applied to the quantification of sesamol in target cells.