Phytochemical Analysis, In Vitro Biological Activities, and Computer-Aided Analysis of Potentilla nepalensis Hook Compounds as Potential Melanoma Inhibitors Based on Molecular Docking, MD Simulations, and ADMET.

Potentilla nepalensis Hook is a perennial Himalayan medicinal herb of the Rosaceae family. The present study aimed to evaluate biological activities such as the antioxidant, antibacterial, and anticancer activities of roots and shoots of P. nepalensis and its synergistic antibacterial activity with antibacterial drugs. Folin-Ciocalteau and aluminium chloride methods were used for the calculation of total phenolic (TPC) and flavonoid content (TFC). A DPPH radical scavenging assay and broth dilution method were used for the determination of the antioxidant and antibacterial activity of the root and shoot extracts of P. nepalensis. Cytotoxic activity was determined using a colorimetric MTT assay. Further, phytochemical characterization of the root and shoot extracts was performed using the Gas chromatography-mass spectrophotometry (GC-MS) method. The TPC and TFC were found to be higher in the methanolic root extract of P. nepalensis. The methanolic shoot extract of P. nepalensis showed good antioxidant activity, while then-hexane root extract of P. nepalensis showed strong cytotoxic activity against tested SK-MEL-28 cells. Subsequently, in silico molecular docking studies of the identified bioactive compounds predicted potential anticancer properties. This study can lead to the production of new herbal medicines for various diseases employing P. nepalensis, leading to the creation of new medications.

In-Silico Drug Toxicity and Interaction Prediction for Plant Complexes Based on Virtual Screening and Text Mining.

Potential drug toxicities and drug interactions of redundant compounds of plant complexes may cause unexpected clinical responses or even severe adverse events. On the other hand, super-additivity of drug interactions between natural products and synthetic drugs may be utilized to gain better performance in disease management. Although without enough datasets for prediction model training, based on the SwissSimilarity and PubChem platforms, for the first time, a feasible workflow of prediction of both toxicity and drug interaction of plant complexes was built in this study. The optimal similarity score threshold for toxicity prediction of this system is 0.6171, based on an analysis of 20 different herbal medicines. From the PubChem database, 31 different sections of toxicity information such as "Acute Effects", "NIOSH Toxicity Data", "Interactions", "Hepatotoxicity", "Carcinogenicity", "Symptoms", and "Human Toxicity Values" sections have been retrieved, with dozens of active compounds predicted to exert potential toxicities. In Spatholobus suberectus Dunn (SSD), there are 9 out of 24 active compounds predicted to play synergistic effects on cancer management with various drugs or factors. The synergism between SSD, luteolin and docetaxel in the management of triple-negative breast cancer was proved by the combination index assay, synergy score detection assay, and xenograft model.

[Establishment of multiple evidence-integrated evaluation and prediction method for "toxic" Chinese medicines].

Traditional Chinese medicine(TCM) carries the experience and theoretical knowledge of the ancients, and the use of "toxic" Chinese medicines is a major feature and advantage of TCM. "Toxic" Chinese medicines have unique clinical value and certain medication risk under the guidance of TCM theories such as compatibility for detoxification and treatment based on syndrome differentiation. In recent years, the safety events of Chinese medicines have occurred frequently, which has made the safety of Chinese medicine a public concern in China and abroad. However, limited by conventional cognitive laws and technical methods, basic research on toxicity of Chinese medicines fails to be combined with the clinical application. As a result, it is difficult to identify the clinical characteristics of, predict toxic and side effects of, or form a universal precise medication regimen for "toxic" Chinese medicines, which restricts the clinical application of them. In view of the problem that the toxicity of "toxic" Chinese medicines is difficult to be predicted and restricts the clinical application, the evidence-based research concept will provide new ideas for safe applcation of them in clinical practice. The integrated development of multiple disciplines and techniques in the field of big data and artificial intelligence will also promote the renewal and development of the research models for "toxic" Chinese medicines. Our team tried to propose the academic concept of evidence-based Chinese medicine toxicology and establish the data-intelligence research mode for "toxic" Chinese medicines and the intelligent risk prediction method for medicinal combination in the early stage, which provided methodological supports for solving the above problem. Thus, on the basis of summarizing the research status and problems of the clinical medication regimen of "toxic" Chinese medicines, our team took the evidence-based toxicology of TCM as the core concept, and tried to construct the multiple-evidence integrated evaluation and prediction method for "toxic" Chinese medicine, so as to guide the establishment of the non-toxic medication regimen of "toxic" Chinese medicines. Specifically, through the analysis of multivariate data obtained from the basic research, the evidence-based toxicology database of Chinese medicines and the individualized "toxicity-effect" intelligent prediction platform were built based on the disease-syndrome virtual patients, so as to identify the clinical characteristics and risks of "toxic" Chinese medicines and develop individualized medication regime. This study is expected to provide a methodological reference for the establishment of medication regimen and risk prevention strategy for "toxic" Chinese medicines. The method established in this study will bridge clinical research and basic research, enhance the transformation of the scientific connotation of attenuated compatibility, promote the development of evidence-based Chinese medicine toxicology, and ensure the clinical safety of "toxic" Chinese medicines.

Establishment of multiple evidence-integrated evaluation and prediction method for "toxic" Chinese medicines / 中国中药杂志

Traditional Chinese medicine(TCM) carries the experience and theoretical knowledge of the ancients, and the use of "toxic" Chinese medicines is a major feature and advantage of TCM. "Toxic" Chinese medicines have unique clinical value and certain medication risk under the guidance of TCM theories such as compatibility for detoxification and treatment based on syndrome differentiation. In recent years, the safety events of Chinese medicines have occurred frequently, which has made the safety of Chinese medicine a public concern in China and abroad. However, limited by conventional cognitive laws and technical methods, basic research on toxicity of Chinese medicines fails to be combined with the clinical application. As a result, it is difficult to identify the clinical characteristics of, predict toxic and side effects of, or form a universal precise medication regimen for "toxic" Chinese medicines, which restricts the clinical application of them. In view of the problem that the toxicity of "toxic" Chinese medicines is difficult to be predicted and restricts the clinical application, the evidence-based research concept will provide new ideas for safe applcation of them in clinical practice. The integrated development of multiple disciplines and techniques in the field of big data and artificial intelligence will also promote the renewal and development of the research models for "toxic" Chinese medicines. Our team tried to propose the academic concept of evidence-based Chinese medicine toxicology and establish the data-intelligence research mode for "toxic" Chinese medicines and the intelligent risk prediction method for medicinal combination in the early stage, which provided methodological supports for solving the above problem. Thus, on the basis of summarizing the research status and problems of the clinical medication regimen of "toxic" Chinese medicines, our team took the evidence-based toxicology of TCM as the core concept, and tried to construct the multiple-evidence integrated evaluation and prediction method for "toxic" Chinese medicine, so as to guide the establishment of the non-toxic medication regimen of "toxic" Chinese medicines. Specifically, through the analysis of multivariate data obtained from the basic research, the evidence-based toxicology database of Chinese medicines and the individualized "toxicity-effect" intelligent prediction platform were built based on the disease-syndrome virtual patients, so as to identify the clinical characteristics and risks of "toxic" Chinese medicines and develop individualized medication regime. This study is expected to provide a methodological reference for the establishment of medication regimen and risk prevention strategy for "toxic" Chinese medicines. The method established in this study will bridge clinical research and basic research, enhance the transformation of the scientific connotation of attenuated compatibility, promote the development of evidence-based Chinese medicine toxicology, and ensure the clinical safety of "toxic" Chinese medicines.

[Discovery and activity verification of reniformin A as an anti-tumor leading compound].

Reverse prediction and molecular docking techniques were employed to evaluate the feasibility of reniformin A(RA) as an anti-tumor leading compound. Based on the reverse prediction, network pharmacology was used to construct a "disease-compound-target-pathway" network. Thirty-nine tumor-related targets of RA were predicted, which participated in the regulation of multiple cellular activities such as apoptosis, cell cycle, and tumor metastasis, and regulated estrogen signal transduction and inflammatory response. Discovery Studio 2020 was adopted for molecular docking and toxicity prediction(TOPKAT). As revealed by the results, the binding affinity of RA with the tumor-related targets ABL1, ESR1, SRC and BCL-XL was stronger than that of oridonin(OD), while its mutagenicity, rodent carcinogenesis, and oral LD_(50) in rats were all inferior to that of OD. Furthermore, in vitro experiments were performed to confirm the anti-tumor activity of RA, and the mechanism was preliminarily discussed. The results demonstrated that RA was superior to OD in cytotoxicity, inhibition of cell colony formation, and induction of apoptosis. RA, possessing potent anti-tumor activity, is expected to be a new anti-tumor leading compound.

Assessment of ecotoxicity of spent fluid catalytic cracking (FCC) refinery catalysts on Raphidocelis subcapitata and predictive models for toxicity.

The 17 spent fluid catalytic cracking refinery catalysts (SFCCCs) from different petroleum refineries were collected and the leachates of SFCCCs were prepared. The ecotoxicity of SFCCC leachates to Raphidocelis subcapitata was assayed. The results showed that the toxicity of the 17 SFCCCs differ greatly. Ji SFCCC was the most toxic to R. subcapitata with a 96 h EC50 value of 1.38%, while Ha SFCCC was the least toxic, with the EC50 value was >100%. The relationships between the toxicity of SFCCCs and the metal concentrations in leachates were analyzed. The concentration of Ni (p = 0.001), La (p = 0.001), Mn (p = 0.014), Ce (p = 0.017), Co (p = 0.018), and Ca (p = 0.031) in leachates showed significant correlation with EC50 values. The predictive model for the ecotoxicity of SFCCCs were established with the concentrations of Ni and La in leachates as: ln(EC50) = 0.817 + exp(1.356 - 1.736 × CNi - 0.262 × CLa) (R2 = 0.926). The main toxic ingredients of SFCCC to microalgae were identified for the first time in this work. The results and predictive model of this study are significance for toxicity determination and management of SFCCCs.

Chemo-informatics guided study of natural inhibitors targeting rho GTPase: a lead for treatment of glaucoma.

Glaucoma, the most perilous disease leading to blindness is a result of optical neuropathy. Accumulation of aqueous humor in the posterior chamber due to a large difference in the rate of formation and its drainage in the anterior chamber causes an increase in intraocular pressure (IOP) leading to damage of nerve cells. A literature survey has revealed that inhibition of the Rho guanosine triphosphatases (rho GTPase) pathway by specific inhibitors leads to the relaxation of contractile cells involved in the aqueous outflow pathway. Relaxation of the strained contractile cells results in increased outflow thereby releasing IOP. In the present study molecular docking has been used to screen twenty seven bioactive (17 natural compounds and 10 conventional drugs) compounds that may play a significant role in relaxing contractile cells by inhibiting rho-GTPase protein. Docking results showed that among all-natural bioactive compounds Cyanidin and Delphinidine have a good binding affinity (- 8.4 kcal/mol) than the top screened conventional drug molecule Mitomycin, (- 6.3 kcal/mol) when docked with rho-GTPase protein. Cyanidin and Delphinidin belong to anthocyanidin, a glycoside form of anthocyanins from Vaccinium myrtillus L. and Punica granatum. The resembling potential of Cyanidin and Delphinidin concerning the drug Mitomycin was confirmed through simulation analysis. Molecular dynamics study (MDS) for 100 ns, showed that the rho GTPase-Delphinidine complex structure was energetically more stable than rho GTPase-Cyaniding complex in comparison to rho GTPase-Mitomycin complex. The comparative study of both the selected hits (Cyanidin and Delphinidin) was assessed by RMSD, RMSF, Rg, SASA, H-bond, PCA MM/PBSA analysis. The analysis revealed that Delphinidine is more potent to inhibit the rho GTPase as compare to Cyaniding and available conventional drugs in terms of stability and binding free energy. Based on the results, these molecules have good pharmacokinetic and pharmacodynamics properties and will prove to be a promising lead compound as a future drug for Glaucoma.

Saponin fraction from Sapindus mukorossi Gaertn as a novel cosmetic additive: Extraction, biological evaluation, analysis of anti-acne mechanism and toxicity prediction.

ETHNOPHARMACOLOGICAL RELEVANCE: Sapindus mukorossi Gaertn. (S. mukorossi), known as 'mu huan zi' in Chinese folklore, belongs to the family Sapindaceae and it has been traditionally used for treating coughing and excessive salivation, removing freckle, whitening skin, etc. Evidence-based medicine also verified the antimicrobial, anti-tyrosinase and anti-acne activity of S. mukorossi extract, suggesting that it has the potential to be a pharmaceutical and cosmetic additive. AIM OF THE STUDY: The present study was intended to evaluate the freckle-removing and skin-whitening activities of S. mukorossi extracts, and further analyzing the potential anti-acne mechanism. METHODS: Saponin fractions were purified by using the semi-preparative high-performance liquid chromatography, and their antibacterial activity was detected against Propionibacterium acnes (P. acnes), which was the leading cause of inflamed lesions in acne vulgaris. The anti-lipase and anti-tyrosinase activities were assayed using a commercial kit, while the potential anti-acne mechanism was predicted on the basis of the network pharmacology. Active components of saponin fraction were identified by HPLC-MS analysis. Furthermore, the different toxicity level of compounds was predicted according to the quantitative structure-activity relationship, and the first application of crude extract and saponin fraction to facial masks was analyzed based on the comprehensive evaluation method. RESULTS: The saponin fraction (F4) purified from the fermentation liquid-based water extract (SWF) showed the best antibacterial activity against P. acnes ATCC 6919 with the MIC of 0.06 mg/mL, which was 33-fold of its parent SWF (with the MIC of 2.0 mg/mL). Compared with SWF, the application of F4 caused greater inhibition rates on lipase and tyrosinase. Chemical constituents of F4 were evaluated, from which four oleanane-type triterpenoid saponins were detected to contribute to the above biological activities of F4. The mechanism of the four compounds on anti-acne was predicted, and seven targets such as PTGS2 and F2RL1 were obtained to be important for the treatment of acne. The four compounds were also predicted to have different levels of toxicity to various species, and they were not harmful to rats. Besides, F4 and SWF were applied to facial masks and there was no significant influence on the physicochemical properties including pH, stability, and sensory characteristics. CONCLUSION: This work demonstrated that oleanane-type triterpenoid saponins were speculated to contribute to the skin-whitening, freckle-removing, and anti-acne activities of F4. These findings will facilitate the development of the S. mukorossi extract and the allied products as the new and natural anti-acne agent and cosmetic additives.

Stem Cell Based Preclinical Drug Development and Toxicity Prediction.

Stem cell based toxicity prediction plays a very important role in the development of the drug. Unexpected adverse effects of the drugs during clinical trials are a major reason for the termination or withdrawal of drugs. Methods for predicting toxicity employ in vitro as well as in vivo models; however, the major drawback seen in the data derived from these animal models is the lack of extrapolation, owing to interspecies variations. Due to these limitations, researchers have been striving to develop more robust drug screening platforms based on stem cells. The application of stem cells based toxicity testing has opened up robust methods to study the impact of new chemical entities on not only specific cell types, but also organs. Pluripotent stem cells, as well as cells derived from them, can be evaluated for modulation of cell function in response to drugs. Moreover, the combination of state-of-the -art techniques such as tissue engineering and microfluidics to fabricate organ- on-a-chip, has led to assays which are amenable to high throughput screening to understand the adverse and toxic effects of chemicals and drugs. This review summarizes the important aspects of the establishment of the embryonic stem cell test (EST), use of stem cells, pluripotent, induced pluripotent stem cells and organoids for toxicity prediction and drug development.

Discovery and activity verification of reniformin A as an anti-tumor leading compound / 中国中药杂志

Reverse prediction and molecular docking techniques were employed to evaluate the feasibility of reniformin A(RA) as an anti-tumor leading compound. Based on the reverse prediction, network pharmacology was used to construct a "disease-compound-target-pathway" network. Thirty-nine tumor-related targets of RA were predicted, which participated in the regulation of multiple cellular activities such as apoptosis, cell cycle, and tumor metastasis, and regulated estrogen signal transduction and inflammatory response. Discovery Studio 2020 was adopted for molecular docking and toxicity prediction(TOPKAT). As revealed by the results, the binding affinity of RA with the tumor-related targets ABL1, ESR1, SRC and BCL-XL was stronger than that of oridonin(OD), while its mutagenicity, rodent carcinogenesis, and oral LD_(50) in rats were all inferior to that of OD. Furthermore, in vitro experiments were performed to confirm the anti-tumor activity of RA, and the mechanism was preliminarily discussed. The results demonstrated that RA was superior to OD in cytotoxicity, inhibition of cell colony formation, and induction of apoptosis. RA, possessing potent anti-tumor activity, is expected to be a new anti-tumor leading compound.

Toxicological Screening of Four Bioactive Citroflavonoids: In Vitro, In Vivo, and In Silico Approaches.

Many studies describe different pharmacological effects of flavonoids on experimental animals and humans. Nevertheless, few ones are confirming the safety of these compounds for therapeutic purposes. This study aimed to investigate the preclinical safety of naringenin, naringin, hesperidin, and quercetin by in vivo, in vitro, and in silico approaches. For this, an MTT-based cytotoxicity assay in VERO and MDCK cell lines was performed. In addition, acute toxicity was evaluated on Wistar rats by OECD Guidelines for the Testing of Chemicals (Test No. 423: Acute Oral Toxicity-Class Method). Furthermore, we used the ACD/Tox Suite to predict toxicological parameters such as hERG channel blockade, CYP450 inhibition, and acute toxicity in animals. The results showed that quercetin was slightly more cytotoxic on cell lines (IC50 of 219.44 ± 7.22 mM and 465.41 ± 7.44 mM, respectively) than the other citroflavonoids. All flavonoids exhibited an LD50 value > 2000 mg/kg, which classifies them as low-risk substances as OECD guidelines established. Similarly, predicted LD50 was LD50 > 300 to 2000 mg/kg for all flavonoids as acute toxicity assay estimated. Data suggests that all these flavonoids did not show significant toxicological effects, and they were classified as low-risk, useful substances for drug development.

The integrated use of in silico methods for the hepatotoxicity potential of Piper methysticum.

Herbal products as supplements and therapeutic intervention have been used for centuries. However, their toxicities are not completely evaluated and the mechanisms are not clearly understood. Dried rhizome of the plant kava (Piper methysticum) is used for its anxiolytic, and sedative effects. The drug is also known for its hepatotoxicity potential. Major constituents of the plant were identified as kavalactones, alkaloids and chalcones in previous studies. Kava hepatotoxicity mechanism and the constituent that causes the toxicity have been debated for decades. In this paper, we illustrated the use of computational tools for the hepatotoxicity of kava constituents. The proposed mechanisms and major constituents that are most probably responsible for the toxicity have been scrutinized. According to the experimental and prediction results, the kava constituents play a substantial role in hepatotoxicity by some means or other via glutathione depletion, CYP inhibition, reactive metabolite formation, mitochondrial toxicity and cyclooxygenase activity. Some of the constituents, which have not been tested yet, were predicted to involve mitochondrial membrane potential, caspase-3 stimulation, and AhR activity. Since Nrf2 activation could be favorable for prevention of hepatotoxicity, we also suggest that these compounds should undergo testing given that they were predicted not to be activating Nrf2. Among the major constituents, alkaloids appear to be the least studied and the least toxic group in general. The outcomes of the study could help to appreciate the mechanisms and to prioritize the kava constituents for further testing.

Real-time toxicity prediction of Aconitum stewing system using extractive electrospray ionization mass spectrometry.

Due to numerous obstacles such as complex matrices, real-time monitoring of complex reaction systems (e.g., medicinal herb stewing system) has always been a challenge though great values for safe and rational use of drugs. Herein, facilitated by the potential ability on the tolerance of complex matrices of extractive electrospray ionization mass spectrometry, a device was established to realize continuous sampling and real-time quantitative analysis of herb stewing system for the first time. A complete analytical strategy, including data acquisition, data mining, and data evaluation was proposed and implemented with overcoming the usual difficulties in real-time mass spectrometry quantification. The complex Fuzi (the lateral root of Aconitum)-meat stewing systems were real-timely monitored in 150 min by qualitative and quantitative analysis of the nine key alkaloids accurately. The results showed that the strategy worked perfectly and the toxicity of the systems were evaluated and predicated accordingly. Stewing with trotters effectively accelerated the detoxification of Fuzi soup and reduced the overall toxicity to 68%, which was recommended to be used practically for treating rheumatic arthritis and enhancing immunity. The established strategy was versatile, simple, and accurate, which would have a wide application prospect in real-time analysis and evaluation of various complex reaction systems.

Artificial intelligence models to predict acute phytotoxicity in petroleum contaminated soils.

Environment pollutants, especially those from total petroleum hydrocarbons (TPH), have a highly complex chemical, biological and physical impact on soils. Here we study this influence via modelling the TPH acute phytotoxicity effects on eleven samples of soils from Sakhalin island in greenhouse conditions. The soils were contaminated with crude oil in different doses ranging from the 3.0-100.0 g kg-1. Measuring the Hordeum vulgare root elongation, the crucial ecotoxicity parameter, we have estimated. We have also investigated the contrast effect in different soils. To predict TPH phytotoxicity different machine learning models were used, namely artificial neural network (ANN) and support vector machine (SVM). The models under discussion were proved to be valid using the mean absolute error method (MAE), the root mean square error method (RMSE), and the coefficient of determination (R2). We have shown that ANN and SVR can successfully predict barley response based on soil chemical properties (pH, LOI, N, P, K, clay, TPH). The best achieved accuracy was as following: MAE - 8.44, RMSE -11.05, and R2 -0.80.

Real-time toxicity prediction of stewing system using extractive electrospray ionization mass spectrometry

Due to numerous obstacles such as complex matrices, real-time monitoring of complex reaction systems (, medicinal herb stewing system) has always been a challenge though great values for safe and rational use of drugs. Herein, facilitated by the potential ability on the tolerance of complex matrices of extractive electrospray ionization mass spectrometry, a device was established to realize continuous sampling and real-time quantitative analysis of herb stewing system for the first time. A complete analytical strategy, including data acquisition, data mining, and data evaluation was proposed and implemented with overcoming the usual difficulties in real-time mass spectrometry quantification. The complex Fuzi (the lateral root of )-meat stewing systems were real-timely monitored in 150 min by qualitative and quantitative analysis of the nine key alkaloids accurately. The results showed that the strategy worked perfectly and the toxicity of the systems were evaluated and predicated accordingly. Stewing with trotters effectively accelerated the detoxification of Fuzi soup and reduced the overall toxicity to 68%, which was recommended to be used practically for treating rheumatic arthritis and enhancing immunity. The established strategy was versatile, simple, and accurate, which would have a wide application prospect in real-time analysis and evaluation of various complex reaction systems.

Technical basis for using passive sampling as a biomimetic extraction procedure to assess bioavailability and predict toxicity of petroleum substances.

Solid-phase microextraction fibers coated with polydimethylsiloxane (PDMS) provide a convenient passive sampling format to characterize bioavailability of petroleum substances. Hydrocarbons absorb onto PDMS in proportion to both freely dissolved concentrations and partitioning properties of the individual constituents, which parallels the mechanistic basis used to predict aquatic toxicity in the PETROTOX model. When deployed in a non-depletive manner, combining SPME with thermal desorption and quantification using gas chromatography-flame ionization creates a biomimetic extraction (BE) procedure that has the potential to simplify aquatic hazard assessments of petroleum substances since the total moles of all hydrocarbons sorbed to the fiber can be related to toxic thresholds in target lipid of aquatic organisms. The objective of this work is to describe the technical basis for applying BE measurements to predict toxicity of petroleum substances. Critical BE-based PDMS concentrations corresponding to adverse effects were empirically derived from toxicity tests on different petroleum substances with multiple test species. The resulting species sensitivity distribution (SSD) of PDMS effect concentrations was then compared and found consistent with the previously reported target lipid-based SSD. Further, BE data collected on samples of aqueous media dosed with a wide range of petroleum substances were highly correlated to predicted toxic units derived using the PETROTOX model. These findings provide justification for applying BE in environmental hazard and risk evaluations of petroleum substances and related mixtures.

Toxic Constituents Index: A Toxicity-Calibrated Quantitative Evaluation Approach for the Precise Toxicity Prediction of the Hypertoxic Phytomedicine-Aconite.

Complex chemical composition is an important reason for restricting herbal quality evaluation. Despite the multi-components determination method significantly promoted the progress of herbal quality evaluation, however, which mainly concerned the total amount of multiple components and ignored the activity variation between each one, and did not accurately reflect the biological activity of botanical medicines. In this manuscript, we proposed a toxicity calibrated contents determination method for hyper toxic aconite, called toxic constituents index (TCI). Initially, we determined the minimum lethal dose value of mesaconitine (MA), aconitine (AC), and hypaconitine (HA), and established the equation TCI = 100 × (0.3387 ×X MA + 0.4778 ×X AC + 0.1835 ×X HA). Then, 10 batches of aconite were selected and their evaluation results of toxic potency (TP), diester diterpenoid alkaloids (DDAs), and TCI were compared. Linear regression analysis result suggested that the relevance between TCI and TP was the highest and the correlation coefficient R was 0.954. Prediction error values study also indicated that the evaluation results of TCI was highly consistent with that of TP. Moreover, TCI and DDAs were both applied to evaluate 14 batches of aconite samples oriented different origins; from the different evaluation results, we found when the proportion of HA was reached 25% in DDAs, the pharmacopeia method could generate false positive results. All these results testified the accuracy and universality of TCI method. We believe that this study method is rather accurate, simple, and easy operation and it will be of great utility in studies of other foods and herbs.

VirtualToxLab: Exploring the Toxic Potential of Rejuvenating Substances Found in Traditional Medicines.

Docking and quantifying the binding of small molecules to the 3D structure of a macromolecular bioregulator by computational techniques is a typical task in R&D aimed at the design and optimization of medically or otherwise active compounds. Much less known is the fact that these methods can be successfully applied for the purpose of toxicity prediction-for example, detecting a compound's potential binding to so-called "off-targets" already at the preclinical stage. In this chapter, we provide an overview of such a computational approach, discuss its strengths and weaknesses, and include a case study-focused on natural compounds present in traditional medicines.

High-throughput imaging-based nephrotoxicity prediction for xenobiotics with diverse chemical structures.

The kidney is a major target for xenobiotics, which include drugs, industrial chemicals, environmental toxicants and other compounds. Accurate methods for screening large numbers of potentially nephrotoxic xenobiotics with diverse chemical structures are currently not available. Here, we describe an approach for nephrotoxicity prediction that combines high-throughput imaging of cultured human renal proximal tubular cells (PTCs), quantitative phenotypic profiling, and machine learning methods. We automatically quantified 129 image-based phenotypic features, and identified chromatin and cytoskeletal features that can predict the human in vivo PTC toxicity of 44 reference compounds with ~82 % (primary PTCs) or 89 % (immortalized PTCs) test balanced accuracies. Surprisingly, our results also revealed that a DNA damage response is commonly induced by different PTC toxicants that have diverse chemical structures and injury mechanisms. Together, our results show that human nephrotoxicity can be predicted with high efficiency and accuracy by combining cell-based and computational methods that are suitable for automation.

Sida tuberculata (Malvaceae): a study based on development of extractive system and in silico and in vitro properties

Sida tuberculata (Malvaceae) is a medicinal plant traditionally used in Brazil as an antimicrobial and anti-inflammatory agent. Here, we aimed to investigate the different extractive techniques on phytochemical parameters, as well as to evaluate the toxicity and antioxidant capacity of S. tuberculata extracts using in silico and in vitro models. Therefore, in order to determine the dry residue content and the main compound 20-hydroxyecdysone (20E) concentration, extracts from leaves and roots were prepared testing ethanol and water in different proportions. Extracts were then assessed by Artemia salina lethality test, and toxicity prediction of 20E was estimated. Antioxidant activity was performed by DPPH and ABTS radical scavenger assays, ferric reducing power assay, nitrogen derivative scavenger, deoxyribose degradation, and TBARS assays. HPLC evaluation detected 20E as main compound in leaves and roots. Percolation method showed the highest concentrations of 20E (0.134 and 0.096 mg/mL of extract for leaves and roots, respectively). All crude extracts presented low toxic potential on A. salina (LD50 >1000 µg/mL). The computational evaluation of 20E showed a low toxicity prediction. For in vitro antioxidant tests, hydroethanolic extracts of leaves were most effective compared to roots. In addition, hydroethanolic extracts presented a higher IC50 antioxidant than aqueous extracts. TBARS formation was prevented by leaves hydroethanolic extract from 0.015 and 0.03 mg/mL and for roots from 0.03 and 0.3 mg/mL on egg yolk and rat tissue, respectively (P<0.05). These findings suggest that S. tuberculata extracts are a considerable source of ecdysteroids and possesses a significant antioxidant property with low toxic potential.