Optimization of a Green Extraction Technique for Kratom (Ketum) Leaf Extract via Accelerated Solvent Extraction: Phytochemical Profiles, Cytotoxicity, and Antinociceptive Activity
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Bibliographic record
Abstract
Kratom ( Mitragyna speciosa Korth.) has gained increasing scientific interest for its potential in pain management and addiction treatment. This study employs a green chemistry approach to optimize the extraction of kratom leaves by using Accelerated Solvent Extraction (ASE) with an ethanol–water binary solvent system. The goal was to improve the yield and potency of key bioactive compounds, especially mitragynine. Optimization was performed using One-Factor-at-a-Time (OFAT) analysis and Response Surface Methodology (RSM) employing a Box-Behnken Design (BBD). The optimal extraction conditions were determined to be an 8 min extraction time, 60 °C temperature, and 40% ethanol concentration, which resulted in mitragynine content of 4.66%, total phenolic content of 212.69 GAE mg/g, and total flavonoid content of 126.13 QE mg/g. The safety profile of the optimized ASE kratom leaf extract was evaluated using MTT cytotoxicity assay, which revealed selective cytotoxicity against HepG2 liver cancer cells (IC 50 = 7.69 μg/mL), while showing no cytotoxicity toward HL-7702 normal liver cells (IC 50 > 200 μg/mL). Antinociceptive activity was tested in BALB/c albino mice using the hot-plate test, where the optimized ASE kratom leaf extract demonstrated analgesic effects at dosages of 100, 200, and 500 mg/kg. Phytochemical profiling combining NMR and UPLC-ESI-QTOF-MS/MS identified several known kratom constituents, including mitragynine and its congeners as well as bioactive flavonoids such as isoquercitrin and rutin. The optimized ASE method using a green ethanol–water system produces kratom extracts with promising safety and therapeutic potential, though further work is needed to refine and scale the approach for broader phytopharmaceutical use.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it