Chemistry and Biochemistry: Research Progress Vol. 12

Preclinical Safety Profiling of Curcumin, Atractylodin, α-Mangostin, Ethyl-p-Methoxycinnamate, Ligustilide, and β-Eudesmol: Hepatic, Neuronal, and Cardiotoxic Assessments

Yosita Kasemnitichok — 2026-09-26

Preclinical safety profiling refers to the assessment of the potential toxic effects of compounds in vitro before progression to clinical trials. This evaluation typically includes parameters such as cytotoxicity, neurotoxicity, and cardiotoxicity. Curcumin (CUR), atractylodin (ATD), α-mangostin (αMG), ethyl-p-methoxycinnamate (EPMC), ligustilide (LIG), and β-eudesmol (BEU) are prominent bioactive compounds frequently used in Thai traditional medicine formulations. This study evaluated the cytotoxic profiles of these natural compounds in HepG2 hepatocellular carcinoma cells and ReNcell VM neural progenitor cells using the resazurin reduction assay. In addition, their potential to induce cardiotoxicity through hERG channel inhibition was assessed in hERG-overexpressing HEK293 cells using automated patch-clamp electrophysiology. The results indicated that αMG and CUR significantly reduced HepG2 cell viability (IC50 values of 5.5 µM and 21 µM, respectively), with reductions of 75% and 50% in total cell viability, respectively. In undifferentiated ReNcell VM cells, αMG emerged as the most potent inhibitor of viability (IC₅₀ = 2.1 µM), followed by CUR (IC₅₀ = 21.1 µM), resulting in decreases in viability of approximately 80% and 50%, respectively. However, in differentiated ReNcell VM populations, only αMG demonstrated significant neurotoxicity (IC₅₀ = 6.0 µM). The remaining compounds exerted no substantial cytotoxic effects on these cell lines. Regarding cardiotoxicity, ATD, BEU, LIG, and EPMC exhibited low inhibition of hERG channels (IC₅₀ = 26.4, 33.4, 37.3, and 53 µM, respectively), whereas CUR and αMG displayed negligible inhibitory effects (IC₅₀ > 100 µM). These findings suggest that, while αMG may exert cytotoxic effects on hepatocytes and neurons at concentrations far exceeding standard dietary or medicinal intake, compounds such as ATD, BEU, EPMC, LIG, and CUR are unlikely to cause significant adverse effects at typical clinical doses. Nonetheless, if these phytochemicals are advanced in drug development, their hERG interaction profiles warrant careful monitoring to mitigate cardiotoxic risks. Comprehensive preclinical and clinical pharmacokinetic evaluations are essential to elucidate the relationships between the in vivo plasma concentration profiles of compounds such as EPMC and their respective thresholds for hepatotoxicity, neurotoxicity, cardiotoxicity, and drug-drug interactions.

Abstract Curcumin (CUR), atractylodin (ATD), α-mangostin (αMG), ethyl-p-methoxycinnamate (EPMC), ligustilide (LIG), and β-eudesmol (BEU) are prominent bioactive compounds frequently used in Thai traditional medicine formulations. This study evaluated the cytotoxic profiles of these natural compounds in HepG2 hepatocellular carcinoma cells and ReNcell VM neural progenitor cells using the resazurin reduction assay. In addition, their potential to induce cardiotoxicity through hERG channel inhibition was assessed in hERG-overexpressing HEK293 cells using automated patch-clamp electrophysiology. The results indicated that αMG and CUR significantly reduced HepG2 cell viability (IC₅₀ values of 5.5 µM and 21 µM, respectively), with reductions of 75% and 50% in total cell viability, respectively. In undifferentiated ReNcell VM cells, αMG emerged as the most potent inhibitor of viability (IC₅₀ = 2.1 µM), followed by CUR (IC₅₀ = 21.1 µM), resulting in decreases in viability of approximately 80% and 50%, respectively. However, in differentiated ReNcell VM populations, only αMG demonstrated significant neurotoxicity (IC₅₀ = 6.0 µM). The remaining compounds exerted no substantial cytotoxic effects on these cell lines. Regarding cardiotoxicity, ATD, BEU, LIG, and EPMC exhibited low inhibition of hERG channels (IC₅₀= 26.4, 33.4, 37.3, and 53 µM, respectively), whereas CUR and αMG displayed negligible inhibitory effects (IC₅₀ > 100 µM). These findings suggest that, while αMG may exert cytotoxic effects on hepatocytes and neurons at concentrations far exceeding standard dietary or medicinal intake, compounds such as ATD, BEU, EPMC, LIG, and CUR are unlikely to cause significant adverse effects at typical clinical doses. Nonetheless, if these phytochemicals are advanced in drug development, their hERG interaction profiles warrant careful monitoring to mitigate cardiotoxic risks. Comprehensive preclinical and clinical pharmacokinetic evaluations are essential to elucidate the relationships between the in vivo plasma concentration profiles of compounds such as EPMC and their respective thresholds for hepatotoxicity, neurotoxicity, cardiotoxicity, and drug-drug interactions.

Green Chemistry as a Design Strategy for Sustainable Development: A Critical Narrative Review

Reena Singh — 2026-09-26

Green chemistry is commonly presented as a route to sustainable development because it seeks to prevent pollution and hazard at the molecular and process-design stages rather than manage them after formation. Yet the relationship between a greener reaction and a genuinely sustainable chemical system is not automatic. This critical narrative review evaluates how far green chemistry contributes to sustainable development when its principles are interpreted through resource efficiency, hazard reduction, life-cycle thinking, circularity, enabling technologies, and implementation governance. Literature eligible for thematic inclusion was considered from 1990 to 15 June 2026, supplemented by foundational works where necessary. The strongest evidence supports green chemistry as a design discipline that can reduce material waste, hazardous inputs, solvent burden, and process intensity, particularly when catalysis, biocatalysis, solvent selection, continuous processing, electrochemistry, and mechanochemistry are deployed against explicit baselines. Confidence weakens when local reaction metrics are treated as proxies for system-wide sustainability. Mass-based indicators can fail to track life-cycle impacts; renewable feedstocks can transfer burdens to land, water, or energy systems; recycling can perpetuate hazardous substances; and apparently benign solvents or technologies may create upstream or separation burdens. Recent safe-and-sustainable-by-design approaches therefore extend green chemistry by integrating hazard, exposure, life-cycle environmental impacts, circularity, and, increasingly, socioeconomic considerations. The review argues that green chemistry contributes most credibly to sustainable development when it functions as an upstream design infrastructure embedded within life-cycle assessment, absolute sustainability benchmarks, transparent trade-off analysis, and value-chain governance. Priority research needs include early-stage predictive sustainability assessment, industrial-scale validation of emerging technologies, harmonised metrics, non-toxic circularity, and stronger measurement of social outcomes. Green chemistry is thus necessary for sustainable chemical innovation, but it is insufficient when practised as an isolated set of reaction-level rules.

Thermodynamic Study of the Thermal Stability of Acid Phosphatase from Artocarpus communis Seeds

Kambiré Sobamfou Marius — 2026-09-26

Thermal stability is a pivotal parameter in evaluating the biotechnological potential of enzymes. The objective of this study is to characterise, from a thermodynamic perspective, the thermal behaviour of an acid phosphatase extracted from the seeds of Artocarpus communis. The experimental data on thermal inactivation, obtained in the presence of p-nitrophenyl phosphate (pNPP), were subjected to re-analysis using an equilibrium model (EQM) describing the reversible transition between an active and an inactive form of the enzyme, followed by irreversible thermal inactivation. This methodological approach enabled the quantitative description of the evolution of enzymatic activity as a function of temperature and time, and the estimation of the thermodynamic parameters associated with the catalytic and inactivation processes. The values obtained for the free energies of activation for catalysis and inactivation were 83.37 ± 0.02 kJ mol⁻¹ and 101.9 ± 0.2 kJ mol⁻¹, respectively. The equilibrium enthalpy and equilibrium temperature were estimated at 185 ± 2 kJ mol^-1 and 326.90 ± 0.16 K, respectively. The relatively high value of the free energy associated with inactivation indicates the enzyme's good resistance to thermal destabilisation. The maximum level of activity was observed within a temperature range of approximately 315 to 330 K, while a sustained increase in temperature resulted in a gradual decline in activity. The findings demonstrate that the equilibrium model provides a pertinent framework for the thermodynamic analysis of the thermal stability of the acid phosphatase from A. communis and for the identification of parameters that are useful for evaluating its potential in biotechnology.

Artificial Intelligence for Organic Synthesis: From Retrosynthetic Planning to Reaction Prediction and Experimental Decision-Making

S. Farook Basha — 2026-09-26

Artificial intelligence (AI) is increasingly embedded across organic synthesis, from choosing disconnections to predicting products, conditions and yields, and from selecting experiments to controlling automated platforms. Yet performance at one computational layer does not necessarily translate into reliable synthetic practice. This critical narrative review evaluates AI for organic synthesis as an integrated decision stack rather than as a collection of isolated benchmark tasks. Literature published from 1 January 2010 to 10 July 2026 was searched across multidisciplinary, biomedical, engineering and scholarly indexing sources, with seminal earlier work retained when needed for context. The evidence indicates that AI has matured most convincingly in bounded inference problems where large reaction corpora provide close precedents: single-step retrosynthesis, major-product prediction, reaction classification and local optimisation. Multi-step planning has also progressed through combinations of learned policies and graph or tree search. However, benchmark success remains only an imperfect proxy for chemical utility because commonly used datasets over-represent successful reactions, incompletely encode conditions and work-up, contain duplicated or closely related chemistry, and often favour random splits that reward interpolation. Yield and selectivity prediction are particularly sensitive to dataset design and out-of-distribution shifts. Closed-loop optimisation and robotic synthesis provide stronger evidence of practical value because predictions are tested experimentally, but published demonstrations remain narrower in reaction scope, hardware compatibility and scale than the breadth implied by general AI benchmarks. Large language models and tool-using agents improve orchestration and natural-language access, while introducing additional risks of overconfidence, hidden tool failures and poorly calibrated reasoning. The most defensible near-term model is therefore augmented synthesis: machine-generated options, uncertainty-aware prioritisation, structured experimental execution and expert oversight. Progress towards trustworthy autonomy will depend less on marginal benchmark accuracy than on provenance-rich reaction data, realistic prospective evaluation, uncertainty propagation across the full synthesis stack, machine-readable procedures, and explicit validation of route feasibility, robustness, safety and reproducibility.