Notch3 as a novel therapeutic target for the treatment of ADPKD by regulating cell proliferation and renal cyst development.

Autosomal dominant polycystic kidney disease (ADPKD) is a common monogenic kidney disease. Emerging research indicates that the Notch signaling pathway plays an indispensable role in the pathogenesis of numerous kidney diseases, including ADPKD. Herein, we identified that Notch3 but not other Notch receptors was overexpressed in renal tissues from mice with ADPKD and ADPKD patients. Inhibiting Notch3 with γ-secretase inhibitors, which block a proteolytic cleavage required for Notch3 activation, or shRNA knockdown of Notch3 significantly delayed renal cyst growth in vitro and in vivo. Subsequent mechanistic study elucidated that the cleaved intracellular domain of Notch3 (N3ICD) and Hes1 could bind to the PTEN promoter, leading to transcriptional inhibition of PTEN. This further activated the downstream PI3K-AKT-mTOR pathway and promoted renal epithelial cell proliferation. Overall, Notch3 was identified as a novel contributor to renal epithelial cell proliferation and cystogenesis in ADPKD. We envision that Notch3 represents a promising target for ADPKD treatment.

MCD-LightGBM System for Intelligent Analyzing Heterogeneous Clinical Drug Therapeutic Effects.

Causal effect estimation of individual heterogeneity is a core issue in the field of causal inference, and its application in medicine poses an active and challenging problem. In high-risk decision-making domain such as healthcare, inappropriate treatments can have serious negative impacts on patients. Recently, machine learning-based methods have been proposed to improve the accuracy of causal effect estimation results. However, many of these methods concentrate on estimating causal effects of continuous outcome variables under binary intervention conditions, and give less consideration to multivariate intervention conditions or discrete outcome variables, thus limiting their scope of application. To tackle this issue, we combine the double machine learning framework with Light Gradient Boosting Machine (LightGBM) and propose a double LightGBM model. This model can estimate binary causal effects more accurately and in less time. Two cyclic structures were added to the model. Data correction method was introduced and improved to transform discrete outcome variables into continuous outcome variables. Multivariate Cyclic Double LightGBM model (MCD-LightGBM) was proposed to intelligently estimate multivariate treatment effects. A visual human-computer interaction system for heterogeneous causal effect estimation was designed, which can be applied to different types of data. This paper reports that the system improved the Logarithm of the Minimum Angle of Resolution (LogMAR) of visual acuity change after Vascular Endothelial Growth Factor (anti-VEGF) treatment in patients with diabetic macular degeneration. The improvement was observed in two clinical problems, from 0.05 to 0.33, and the readmission rate of diabetic patients after cure was reduced from 48.4% to 10.5%. The results above demonstrate the potential of the proposed system in predicting heterogeneous clinical drug treatment effects.

Large Dataset-Based Regression Model of Chemical Toxicity to Vibrio fischeri.

For the first time, a global regression quantitative structure-toxicity/activity relationship (QSTR/QSAR) model was developed for the toxicity of a large data set including 1236 chemicals towards Vibrio fischeri, by using random forest (RF) regression algorithm. The optimal RF model with RF parameters of mtry = 3, ntree = 150 and nodesize = 5 was based on 13 molecular descriptors. It can achieve accurate prediction for the toxicity of 99.1% of 1236 chemicals, and yield coefficients of determination R2 of 0.893 for 930 log(Mw/IBC50) in the training set, 0.723 for 306 log(Mw/IBC50) in the test se, and 0.865 for 1236 toxicity log(Mw/IBC50) in the total set. The optimal RF global model proposed in this work is comparable to other published local QSTR models on small datasets of the toxicity to Vibrio fischeri.

Photochemical behavior and photo-induced toxicity of chiral pesticides and their chiral monomers in aqueous environment.

The photochemical behaviors of chiral pollutants in aqueous solutions are rarely studied using chiral monomers, which may hamper their precise risk assessment and lead to suspicious conclusions. In this study, we systematically investigated the phototransformation behavior and toxicity evolution of two widely used chiral pesticides (triadimefon (TF) and triadimenol (TN)) at enantiomer and diastereomer levels, and proposed a calculation method of total photolysis rate constants of chiral mixture. Results show that TF and TN could be photodegraded faster in pure water than in natural waters, and the observed photolysis rate constants (kobs) of TN with two chiral centers exhibit enantioselectivity, i.e., kobs(TN-RS) = kobs(TN-SR) > kobs(TN-RR) = kobs(TN-SS). The photolysis of TF and TN mainly occurs through their excited singlet and triplet states, respectively. Their photodegradation pathways mainly include dechlorination and elimination of triazole ring. TF could also undergo ether bond cleavage. It is also found that, both TF and TN exhibit photo-induced toxicity to V. fischeri, due to the generation of more toxic products than parent compounds. Furthermore, TN exhibits enantioselective photo-induced toxicity after 240-min irradiation, which could be ascribed to the formation of chiral products. These results could benefit the understanding of enantioselective environmental behavior of chiral pollutants.

Photolysis mechanism of eleven insecticides under simulated sunlight irradiation: Kinetics, pathway and QSAR.

Insecticides are widely used in crop protection against insects and frequently detected in aquatic environment. Photolysis kinetics are directly related with exposure assessment and risk assessment. However, the photolysis mechanism of neonicotinoid insecticides with different structures has not been studied and compared systematically in the literature. In this paper, the photolysis rate constants in water were determined for eleven insecticides under irradiation of simulated sunlight. At the same time, the photolysis mechanism and effect of dissolved organic matter (DOM) on their photolysis were studied. The results showed that photolysis rates of eleven insecticides vary in a large range. The photolysis rates of nitro-substituted neonicotinoids and butenolide insecticide are much faster than that of cyanoimino-substituted neonicotinoids and sulfoximine insecticide. The ROS scavenging activity assays reveal that direct photolysis dominates the degradation of seven insecticides and, on the other hand, self-sensitized photolysis dominates four insecticides. The shading-effect from DOM can reduce the direct photolysis rates, on the other hand, ROSs generated by triplet-state DOM (3DOM*) can also accelerate photolysis of insecticides. According to the photolytic products identified from HPLC-MS, these eleven insecticides have different photolysis pathways. Six insecticides are degraded from the removal of nitro group from their parent compounds and four insecticides are degraded through ·OH reaction or singlet oxygen (1O2) reaction. QSAR (quantitative structure-activity relationship) analysis showed that photolysis rate was directly related to the energy gap between the highest occupied molecular orbital to the lowest unfilled molecular orbital (Egap = ELUMO-EHOMO) and dipole moment (δ). These two descriptors reflect the chemical stability and reactivity of insecticides. The pathways developed from identified products and the molecular descriptors of QSAR models can well verify the photolysis mechanisms of eleven insecticides.

Structure-Affinity and Structure-Kinetic Relationship Studies of Benzodiazepine Derivatives for the Development of Efficacious Vasopressin V2 Receptor Antagonists.

Vasopressin V2 receptors (V2R) are a promising drug target for autosomal dominant polycystic kidney disease (ADPKD). As previous research demonstrated that the residence time of V2R antagonists is critical to their efficacy in both ex vivo and in vivo models of ADPKD, we performed extensive structure-kinetic relationship (SKR) analyses on a series of benzodiazepine derivatives. We found that subtle structural modifications of the benzodiazepine derivatives dramatically changed their binding kinetics but not their affinity. Compound 18 exhibited a residence time of 77 min, which was 7.7-fold longer than that of the reference compound tolvaptan (TVP). Accordingly, compound 18 exhibited higher efficacy compared to TVP in an in vivo model of ADPKD. Overall, our study exemplifies a kinetics-directed medicinal chemistry effort for the development of efficacious V2R antagonists. We envision that this strategy may also have general applicability in other therapeutic areas.

Identification of active or inactive agonists of tumor suppressor protein based on Tox21 library.

Exposure of cells to xenobiotic human-made products can lead to genotoxicity and cause DNA damage. It is an urgent need to quickly identify the chemicals that cause DNA damage, and their toxicity should be predicted. In this study, recursive partitioning (RP), binary logistic regression, and one machine learning approach, namely, random forest (RF) classifier, were used to predict the active and inactive compounds of a total 5036 data based on the assay conducted by a ß-lactamase reporter gene under control of the p53 response element (p53RE) from Tox21 library. Results show that the binary logistic regression model with a threshold of 0.5 has a high accuracy rate (83%) to distinguish active and inactive compounds. The RF classifier method has satisfactory results, with an accuracy rate (84.38%) approximately higher than that of binary logistic regression. The models established can identify compounds that induce DNA damage and activate p53, and provide a scientific basis for the risk assessment of organic chemicals in the environment.

PF-06409577 inhibits renal cyst progression by concurrently inhibiting the mTOR pathway and CFTR channel activity.

Renal cyst development and expansion in autosomal dominant polycystic kidney disease (ADPKD) involves over-proliferation of cyst-lining epithelial cells and excessive cystic fluid secretion. While metformin effectively inhibits renal cyst growth in mouse models of ADPKD it exhibits low potency, and thus an adenosine monophosphate-activated protein kinase (AMPK) activator with higher potency is required. Herein, we adopted a drug repurposing strategy to explore the potential of PF-06409577, an AMPK activator for diabetic nephropathy, in cellular, ex vivo and in vivo models of ADPKD. Our results demonstrated that PF-06409577 effectively down-regulated mammalian target of rapamycin pathway-mediated proliferation of cyst-lining epithelial cells and reduced cystic fibrosis transmembrane conductance regulator-regulated cystic fluid secretion. Overall, our data suggest that PF-06409577 holds therapeutic potential for ADPKD treatment.

Benzodiazepine Derivatives as Potent Vasopressin V2 Receptor Antagonists for the Treatment of Autosomal Dominant Kidney Disease.

Cyst formation and enlargement in autosomal dominant kidney disease (ADPKD) is mainly driven by aberrantly increased cytosolic cAMP in renal tubule epithelial cells. Because the vasopressin V2 receptor (V2R) regulates intracellular cAMP levels in kidneys, a series of benzodiazepine derivatives were developed targeting the V2R. Among these derivatives, compound 25 exhibited potent binding affinity to the V2R (Ki = 9.0 ± 1.5 nM) and efficacious cAMP inhibition (IC50 = 9.2 ± 3.0 nM). This led to the suppression of cyst formation and growth in both an MDCK cell model and an embryonic kidney cyst model. Further advancing compound 25 in a murine model of ADPKD demonstrated a significantly improved in vivo efficacy compared with the reference compound tolvaptan. Overall, compound 25 holds therapeutic potential for the treatment of ADPKD.

Long Residence Time at the Vasopressin V2 Receptor Translates into Superior Inhibitory Effects in Ex Vivo and In Vivo Models of Autosomal Dominant Polycystic Kidney Disease.

Prevailing strategies directing early-phase drug discovery heavily rely on equilibrium-based metrics such as affinity, which overlooks the kinetic process of a drug molecule interacting with its target. Herein, we developed a number of vasopressin V2 receptor (V2R) antagonists with divergent binding affinities and kinetics for autosomal dominant polycystic kidney disease (ADPKD). Surprisingly, the residence time of the V2R antagonists, but not their affinity, was correlated with the efficacy in both ex vivo and in vivo models of ADPKD. We envision that the kinetics-directed drug candidate selection and development may have general applicability for ADPKD and other therapeutic areas as well.

Comparison of modes of toxic action between Rana chensinensis tadpoles and Limnodrilus hoffmeisteri worms based on interspecies correlation, excess toxicity and QSAR for class-based compounds.

Insecticides, fungicides, dinitrobenzenes, resorcinols, phenols and anilines are widely used in agricultural and industrial productions. However, their modes of toxic action are unclear in some nontarget organisms, such as worms and tadpoles. In this study, acute toxicity data was experimentally collected for Limnodrilus hoffmeisteri worms and Rana chensinensis tadpoles, respectively. Interspecies correlation and excess toxicity were calculated to determine modes of action (MOAs) between the two species for class-based compounds. The result showed that, although the interspecies correlation of toxicity between the tadpoles and worms is significant with a coefficient of determination (R2) of 0.83, tadpoles are more sensitive than the worms and toxicity values between these two species are not identical with an overall 0.43 log unit difference. Regression analysis revealed that the toxicity of nonpolar narcotics or baseline compounds is linearly related to hydrophobicity for both the tadpoles and worms and the two baseline models are parallel, suggesting that these nonpolar narcotics share the same MOA between the two species. The difference of baseline toxicities between the two species is attributed to differences in bioconcentration factors. Analysis of the excess toxicity calculated from the toxicity ratio (TR) suggested that phenols and anilines can be classified as polar narcotics, not only to fish, but also to the tadpoles and worms. These compounds are more toxic than the baseline compounds and quantitative structure-activity relationship (QSAR) models show that their toxicity is linearly related to chemical hydrophobicity and polarity. Analysis of the excess toxicity reveals that aminophenols and resorcinols can be classified as reactive compounds, and insecticides and fungicides can be classified as specifically-acting compounds for both species. These compounds exhibited significantly greater toxic effect to both the tadpoles and worms. QSAR models have been developed to describe the toxic mechanisms for nonpolar narcotics, polar narcotics, reactive chemicals and specifically-acting compounds, and a theoretical equation has been derived to explain the effect of bio-uptake and interaction of the chemical with target receptors for both tadpole and worm toxicity. Our study reveals that tadpole toxicity can be estimated from worm toxicity data and the two species can serve as surrogates for each other in the safety evaluation of organic pollutants.

Application of machine learning to predict the inhibitory activity of organic chemicals on thyroid stimulating hormone receptor.

With the promotion of carbon neutrality, it is also important to synchronously promote the assessment and sustainable management of chemicals so as to protect public health. Humans and animals are possibly exposed to endocrine disruptors that have inhibitory effects on thyroid stimulating hormone receptor (TSHR). As such, it is important to identify chemicals that inhibit TSHR and to develop models to predict their inhibitory activity. In this study, 5952 compounds derived from a cyclic adenosine monophosphate (cAMP) analysis, a key signaling pathway in thyrocytes, were used to establish a binary classification model comparing methods that included random forest (RF), extreme gradient boosting (XGB), and logistic regression (LR). The prediction model based on RF showed the highest identification accuracy for revealing chemicals that may inhibit TSHR. For the RF model, recall was calculated at 0.89, balance accuracy was 0.85, and its receiver operating characteristic (ROC) curve-area under (AUC) was 0.92, indicating that the model had very high predictive capacity. The lowest CDocker energy (CE) and CDocker interaction energy (CIE) for chemicals and TSHR were determined and were subsequently introduced into the predictive model as descriptors. A regression model, extreme gradient boosting-Regression (XGBR), was successfully established yielding an R2 = 0.65 to predict inhibitory activity for active compounds. Parameters that included dissociation characteristics, molecular structure, and binding energy were all key factors in the predictive model. We demonstrate that QSAR models are useful approaches, not only for identifying chemicals that inhibit TSHR, but for predicting inhibitory activity of active compounds.

Comparison of modes of action between fish, cell and mitochondrial toxicity based on toxicity correlation, excess toxicity and QSAR for class-based compounds.

Mitochondria are significant targets in cells for many environmental chemicals. Mitochondrial damage and dysfunction can lead to apoptosis and death of fish. The objectives of this study were to compare the modes of action (MOAs) between fish, cell and mitochondrial toxicity. To achieve the goal, toxicity correlation, excess toxicity and quantitative structure-activity relationship (QSAR) were investigated between these three toxicity endpoints for a wide range of compounds. Results showed that fish toxicity is well correlated to cytotoxicity, but overall fish toxicity is relatively greater than the cytotoxicity. On the other hand, fish or cell toxicity is poorly related to mitochondrial toxicity, suggesting some compounds share same toxic mechanism but some not. The excess toxicity calculated from toxicity ratio (TR) shows that specifically-acting compounds in cytotoxicity, such as insecticides, fungicides, herbicides, dyes and medications used to treat cancer, depression, heart failure and blood pressure, are active compounds in mitochondrial toxicity. However, the less inert compounds identified in fish and cell toxicity exhibit greatly mitochondrial toxicity. QSAR models reveal that fish or cell toxicity is closely related to the chemical hydrophobicity, ionization, energy of lowest unoccupied molecular orbital, hydrogen bonding potential and stability. These descriptors reflect chemical bio-uptake, reactivity and interaction with target receptors. On the other hand, binomial model reveals that mitochondrial toxicity is closely related to the chemical hydrophobicity and polarizability/dipolarity, indicating bio-uptake and Van der Waals interaction play key roles in mitochondrial toxicity. Theoretical equations have been used to explain the toxicity correlation, excess toxicity and QSAR for fish, cell and mitochondrial toxicity. Above results suggest that cytotoxicity can serve as a surrogate for fish toxicity and be used in the safety evaluation of organic pollutants in aqueous environment, but not mitochondrial toxicity, although some compounds share same modes of action between fish or cell toxicity and mitochondrial toxicity.

Bio-uptake, tissue distribution and metabolism of a neonicotinoid insecticide clothianidin in zebrafish.

Neonicotinoids have been often detected in aquatic environment with high concentrations; however, little is known about their risk and fate to/in fish. This study systematically investigated the bio-uptake, tissue distribution and metabolism of neonicotinoids in zebrafish, taking clothianidin (CLO) as an example. The results revealed the uptake and elimination kinetics of CLO in whole fish and different tissues was very similar, and its bioconcentration factor (<1) indicates the low bioaccumulation potential in zebrafish. The highest accumulative tissues for CLO were found to be intestine and liver. Eight biotransformation products were identified in intestine and liver, and the metabolic pathways were found to be N-demethylation and nitro-reduction. The metabolic kinetics of two products (desmethyl clothianidin and clothianidin urea) revealed the metabolism of CLO mainly occurred in liver and intestine. This suggested that the hepatobiliary system played an important role in the metabolism and elimination of CLO. This study provides a comprehensive evaluation of the toxicokinetics of CLO in zebrafish, and these results can contribute to its ecological risk assessment.

Comparative analysis on the photolysis kinetics of four neonicotinoid pesticides and their photo-induced toxicity to Vibrio Fischeri: Pathway and toxic mechanism.

Neonicotinoids are widely used pesticides all over the world and pose severe water pollution. Although they can be degraded via absorbing sunlight, few attentions have been paid to the environmental risks of their photolysis products. In this paper, the photo-toxicity was investigated for four neonicotinoids (dinotefuran, nitenpyram, thiamethoxam and clothianidin) based on a series of experiments (i.e., photolysis kinetics, radical scavenging, bioluminescent inhibition test to Vibrio Fischeri and intermediate identification) and in-silico calculation of photolysis pathway. The results show that direct photolysis dominates the photolysis of the four neonicotinoids under simulated sunlight radiation. The bioluminescent inhibition kinetics shows that all four neonicotinoids have photo-induced toxicity to V. fischeri, but with different light-induced responses. Scavenging radicals (·OH and 1O2) will decrease the photo-induced toxicity of all the four neonicotinoids, indicating radicals play important roles to the photo-chemical reactions of intermediates. Dissolved organic matters exhibit slightly shading effect to the photolysis rates of four parent compounds. However, the ROSs generated by DOM can accelerate the photo-chemical reactions of intermediates, leading to different photo-induced toxicity in present of DOM. According to the detected intermediates and Gaussian calculations, there are different photolysis pathways and mechanisms for the four neonicotinoids. The calculation for photo-sensitization reactions with 3O2 indicates that both energy transfer reactions and electron transfer reactions can be produced under simulated sunlight radiation, which further consolidate that reactive oxygen species are involved in the photolysis process. A theoretical model has been developed to explain the toxicity variations of four neonicotinoids in different aqueous conditions.

Prediction of organic compounds adsorbed by polyethylene and chlorinated polyethylene microplastics in freshwater using QSAR.

Microplastics (MPs), a growing class of emerging pollutants in the environment, have attracted widespread attention due to their adsorption properties. Recent research on MPs has mainly concentrated on seawater, and little work has been conducted on freshwater. Investigating and predicting the adsorption behavior of organic pollutants by MPs are necessary in freshwater. In this study, the adsorption behavior of 13 organic chemicals by polyethylene (PE) and chlorinated polyethylene (CPE) MPs was determined under freshwater conditions. Results shows the majority of the organic chemicals exhibit no distinctive differences in their adsorption on two MPs. However, the adsorption of polycyclic aromatic hydrocarbons and chlorobenzene on CPE is obviously stronger than that on PE, and the result is a counter for two pesticides. Quantitative structure activity relationship (QSAR) analysis was performed for the prediction of adsorption capacity. A QSAR model with acceptable performance (R2 = 0.8586) was built to predict the adsorptive affinity (expressed as logKd) of organic compounds on the PE MPs via multivariable linear regression (MLR) on forty-nine determined and collected data. The octanol/water partition coefficient (logKow) and excess molar refractive index (E) play dominant roles in the model. A QSAR model with satisfactory performance (R2 = 0.9302) was also established for logKd values from CPE MPs in freshwater by using 13 adsorption data determined. The logKow and most negative charge on Cl atom (Q-max,cl) play decisive roles in the adsorption. The findings can provide a scientific basis for the risk assessment of waters contaminated by MPs and organic pollutants.

Quantitative source apportionment of heavy metals in cultivated soil and associated model uncertainty.

To estimate spatial distribution, source analysis and uncertainty of heavy metals (Pb, Cd, Cr, Hg, As, Cu, Zn, and Ni) based on geographic information system (GIS), positive matrix factorization model (PMF) and bootstrap (BS) using 382 soil samples collected from cultivated soils in Lanzhou. The mean contents of Cd, Hg, Cu, Zn and Ni were high as 1.7,1.7, 2.1, 1.5 and 1.3 times local background values, mean contents of Pb, Cr and As were lower than local background values. However, the mean contents of eight heavy metals were lower environmental quality risk control standard for soil contamination of agricultural soil. Proportions of four sources were identified: Cr was predominantly contributed by natural sources (29.14%), Cu, Zn and Ni was primarily from industrial sources (25.26%), Hg and As were mainly of agricultural sources (27.49%), Pb and Cd mainly came from traffic source and smelting-related activities (18.09%). Uncertainties analysis contained three aspects: bootstrap runs, factor contributions in the PMF solution, and coefficient of variation (CV) values. By combining the four pollution source factors with bootstrap runs, the accuracy of the four pollution source factors were reliable based on PMF model. The median values in the BS runs was considered the most true factor contribution, and the 5th-95th quartile interval represents the variability of each factor, Factor 4 (traffic source) R2 was 0.70 and lower variability. The highest CV value usually means a significantly deviation degree. In this study, the CV values of Cr in Factor 1, Cu, Zn, and Ni in Factor 2, Hg, and As in Factor 3, Pb, and Cd in Factor 4 were lower, indicates a lower deviation degree. and with the lowest content among heavy metals usually was also with the greatest uncertainties. In this study improves understanding of the reduction of heavy metal pollution in cultivated soil, and also serves as reference for pollution source apportionment in other regions.

Deep Residual Correction Network for Partial Domain Adaptation.

Deep domain adaptation methods have achieved appealing performance by learning transferable representations from a well-labeled source domain to a different but related unlabeled target domain. Most existing works assume source and target data share the identical label space, which is often difficult to be satisfied in many real-world applications. With the emergence of big data, there is a more practical scenario called partial domain adaptation, where we are always accessible to a more large-scale source domain while working on a relative small-scale target domain. In this case, the conventional domain adaptation assumption should be relaxed, and the target label space tends to be a subset of the source label space. Intuitively, reinforcing the positive effects of the most relevant source subclasses and reducing the negative impacts of irrelevant source subclasses are of vital importance to address partial domain adaptation challenge. This paper proposes an efficiently-implemented Deep Residual Correction Network (DRCN) by plugging one residual block into the source network along with the task-specific feature layer, which effectively enhances the adaptation from source to target and explicitly weakens the influence from the irrelevant source classes. Specifically, the plugged residual block, which consists of several fully-connected layers, could deepen basic network and boost its feature representation capability correspondingly. Moreover, we design a weighted class-wise domain alignment loss to couple two domains by matching the feature distributions of shared classes between source and target. Comprehensive experiments on partial, traditional and fine-grained cross-domain visual recognition demonstrate that DRCN is superior to the competitive deep domain adaptation approaches.

Predicting oxidative stress induced by organic chemicals by using quantitative Structure-Activity relationship methods.

Cellular exposure to xenobiotic human-made products will lead to oxidative stress that gives rise to DNA damage, as well as chemical or mechanical damage. Distinguishing the chemicals that will induce oxidative stress and predicting their toxicity is necessary. In the present study, 4270 compounds in the ARE-bla assay were investigated to predict active and inactive compounds by using simple algorithms, namely, recursive partitioning (RP) and binomial logistic regression, and to develop the quantitative structure-activity relationship (QSAR) models of chemicals that activate the ARE pathway to induce oxidative stress and exert toxic effects on cells. A decision tree based on scaffold-based fragments obtained through RP analysis showed the best identification accuracy. However, the overall identification accuracy of this model for active compounds was unsatisfactory due to limited fragments. Furthermore, a binomial logistic regression model was developed from 638 active compounds and 3632 inactive chemicals. The model with a cutoff of 0.15 could predict chemicals that were active or inactive with the prediction accuracy of 69.1%. Its area under the receiver operating characteristic (ROC) curve metric (AUROC) was 0.762, which indicated the acceptable predictive ability of this model. The parameters nBM (number of multiple bonds) and H% (percentage of H atom) played dominant roles in the prediction of the activity (inactive or active) of chemicals. A global QSAR model was developed to predict the toxicity of active chemicals. However, the model displayed an unsatisfactory result with R2 = 0.316 and R2ext = 0.090. Active chemicals were then classified on the basis of structure. A total of 79 compounds with carbon chains could be predicted with acceptable performance by using a QSAR model with six descriptors (R2 = 0.722, R2ext = 0.798, Q2Loo = 0.654, Q2Boot = 0.755, Q2ext = 0.721). The simple models established here contribute to efforts on identification compounds inducing oxidative stress and provide the scientific basis for risk assessment to organisms in the environment.

Preventive and therapeutic effect of Ganoderma lucidum on kidney injuries and diseases.

Ganoderma lucidum (G. lucidum, Lingzhi) is a well-known Chinese traditional medicine to improve health and to treat numerous diseases for over 2000 years in Asian countries. G. lucidum has the abundant chemical components such as triterpenes and polysaccharides, which have various biological activities including anti-oxidation, anti-inflammation, anti-liver disorders, anti-tumor growth and metastasis, etc. Recently, many lines of studies have elucidated the therapeutic effects of G. lucidum and its extractions on various acute kidney injury (AKI) and chronic kidney disease (CKD) pathogenesis, including autosomal dominant polycystic kidney disease, diabetic nephropathy, renal proximal tubular cell oxidative damage and fibrotic process, renal ischemia reperfusion injury, cisplatin-induced renal injury, adriamycin-induced nephropathy, chronic proteinuric renal diseases, etc. Clinical researches also showed potent anti-renal disease bioactivities of G. lucidum. In this chapter, we review experimental and clinical researches and provide comprehensive insights into the renoprotective effects of G. lucidum. In recent years, renal diseases have gradually aroused attention on account of their booming prevalence worldwide and lack of effective therapies. Although the complicated pathogenesis of kidney diseases, such as acute kidney injury (AKI) and chronic kidney diseases (CKD) have been intensively studied. The morbidity and mortality of AKI and CKD still rise continuously. Thanks to the conventional experience and the multi-target characteristics, natural products have been increasingly recognized as an alternative source for treating renal diseases.