Type-II diabetes mellitus (T2DM) results from a combination of genetic and lifestyle factors, and the prevalence of T2DM is increasing worldwide. Clinically, both α-glucosidase and α-amylase enzymes inhibitors can suppress peaks of postprandial glucose with surplus adverse effects, leading to efforts devoted to urgently seeking new anti-diabetes drugs from natural sources for delayed starch digestion. This review attempts to explore 10 families e.g., Bignoniaceae, Ericaceae, Dryopteridaceae, Campanulaceae, Geraniaceae, Euphorbiaceae, Rubiaceae, Acanthaceae, Rutaceae, and Moraceae as medicinal plants, and folk and herb medicines for lowering blood glucose level, or alternative anti-diabetic natural products. Many natural products have been studied in silico, in vitro, and in vivo assays to restrain hyperglycemia. In addition, natural products, and particularly polyphenols, possess diverse structures for exploring them as inhibitors of α-glucosidase and α-amylase. Interestingly, an in silico discovery approach using natural compounds via virtual screening could directly target α-glucosidase and α-amylase enzymes through Monte Carto molecular modeling. Autodock, MOE-Dock, Biovia Discovery Studio, PyMOL, and Accelrys have been used to discover new candidates as inhibitors or activators. While docking score, binding energy (Kcal/mol), the number of hydrogen bonds, or interactions with critical amino acid residues have been taken into concerning the reliability of software for validation of enzymatic analysis, in vitro cell assay and in vivo animal tests are required to obtain leads, hits, and candidates in drug discovery and development.

Oral administration is the preferred and predominant route of choice for medication. As such, drug absorption is one of critical drug metabolism and pharmacokinetics (DM/PK) parameters that should be taken into consideration in the process of drug discovery and development. The cell-free in vitro parallel artificial membrane permeability assay (PAMPA) has been adopted as the primary screening to assess the passive diffusion of compounds in the practical applications. A classical quantitative structure–activity relationship (QSAR) model and a machine learning (ML)-based QSAR model were derived using the partial least square (PLS) scheme and hierarchical support vector regression (HSVR) scheme to elucidate the underlying passive diffusion mechanism and to predict the PAMPA effective permeability, respectively, in this study. It was observed that HSVR executed better than PLS as manifested by the predictions of the samples in the training set, test set, and outlier set as well as various statistical assessments. When applied to the mock test, which was designated to mimic real challenges, HSVR also showed better predictive performance. PLS, conversely, cannot cover some mechanistically interpretable relationships between descriptors and permeability. Accordingly, the synergy of predictive HSVR and interpretable PLS models can be greatly useful in facilitating drug discovery and development by predicting passive diffusion.

P-glycoprotein (P-gp), a membrane-bound transporter, can eliminate xenobiotics by transporting them out of the cells or blood–brain barrier (BBB) at the expense of ATP hydrolysis. Thus, P-gp mediated efflux plays a pivotal role in altering the absorption and disposition of a wide range of substrates. Nevertheless, the mechanism of P-gp substrate efflux is rather complex since it can take place through active transport and passive permeability in addition to multiple P-gp substrate binding sites. A nonlinear quantitative structure–activity relationship (QSAR) model was developed in this study using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to explore the perplexing relationships between descriptors and efflux ratio. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 50, r² = 0.96, $q_{\mathrm{CV}}^2$ = 0.94, RMSE = 0.10, s = 0.10) and test set (n = 13, q² = 0.80–0.87, RMSE = 0.21, s = 0.22). When subjected to a variety of statistical validations, the developed HSVR model consistently met the most stringent criteria. A mock test also asserted the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.

The inhibition of α-glucosidase and α-amylase is a clinical strategy for the treatment of type II diabetes, and herbal medicines have been reported to credibly alleviate hyperglycemia. Our previous study has reported some constituents from plant or herbal sources targeted to α-glucosidase and α-amylase via molecular docking and enzymatic measurement, but the hypoglycemic potencies in cell system and mice have not been validated yet. This study was aimed to elucidate the hypoglycemic efficacy of docking selected compounds in cell assay and oral glucose and starch tolerance tests of mice. All test compounds showed the inhibition of α-glucosidase activity in Caco-2 cells. The decrease of blood sugar levels of test compounds in 30 min and 60 min of mice after OGTT and OSTT, respectively and the decreased glucose levels of test compounds were significantly varied in acarbose. Taken altogether, in vitro and in vivo experiments suggest that selected natural compounds (curcumin, antroquinonol, HCD, docosanol, tetracosanol, rutin, and actinodaphnine) via molecular docking were confirmed as potential candidates of α-glucosidase and α-amylase inhibitors for treating diabetes.

Skin direct contact with chemical or physical substances is predisposed to allergic contact dermatitis (ACD), producing various allergic reactions, namely rash, blister, or itchy, in the contacted skin area. ACD can be triggered by various extremely complicated adverse outcome pathways (AOPs) remains to be causal for biosafety warrant. As such, commercial products such as ointments or cosmetics can fulfill the topically safe requirements in animal and non-animal models including allergy. Europe, nevertheless, has banned animal tests for the safety evaluations of cosmetic ingredients since 2013, followed by other countries. A variety of non-animal in vitro tests addressing different key events of the AOP, the direct peptide reactivity assay (DPRA), KeratinoSens™, LuSens and human cell line activation test h-CLAT and U-SENS™ have been developed and were adopted in OECD test guideline to identify the skin sensitizers. Other methods, such as the SENS-IS are not yet fully validated and regulatorily accepted. A broad spectrum of in silico models, alternatively, to predict skin sensitization have emerged based on various animal and non-animal data using assorted modeling schemes. In this article, we extensively summarize a number of skin sensitization predictive models that can be used in the biopharmaceutics and cosmeceuticals industries as well as their future perspectives, and the underlined challenges are also discussed.

Diabetes mellitus (DM) is concomitant with significant morbidity and mortality and its prevalence is accumulative in worldwide. The conventional antidiabetic agents are known to mitigate the symptoms of diabetes; however, they may also cause side and adverse effects. There is an imperative necessity to conduct preclinical and clinical trials for the discovery of alternative therapeutic agents that can overcome the drawbacks of current synthetic antidiabetic drugs. This study aimed to investigate the efficacy of lowering blood glucose and underlined mechanism of γ-mangostin, mangosteen (Garcinia mangostana) xanthones. The results showed γ-Mangostin had a antihyperglycemic ability in short (2 h)- and long-term (28 days) administrations to diet-induced diabetic mice. The long-term administration of γ-mangostin attenuated fasting blood glucose of diabetic mice and exhibited no hepatotoxicity and nephrotoxicity. Moreover, AMPK, PPARγ, α-amylase, and α-glucosidase were found to be the potential targets for simulating binds with γ-mangostin after molecular docking. To validate the docking results, the inhibitory potency of γ-mangostin againstα-amylase/α-glucosidase was higher than Acarbose via enzymatic assay. Interestingly, an allosteric relationship between γ-mangostin and insulin was also found in the glucose uptake of VSMC, FL83B, C2C12, and 3T3-L1 cells. Taken together, the results showed that γ-mangostin exerts anti-hyperglycemic activity through promoting glucose uptake and reducing saccharide digestion by inhibition of α-amylase/α-glucosidase with insulin sensitization, suggesting that γ-mangostin could be a new clue for drug discovery and development to treat diabetes.