Synthesis, extracorporeal nephrotoxicity, and 3D-QSAR of andrographolide derivatives

Andrographolide derivative Andro-III modulates neuroinflammation and attenuates neuropathological changes of Alzheimer's disease via GSK-3β/NF-κB/CREB pathway

Andrographolide has anti-inflammatory and neuroprotective effects, making it a potential therapeutic option for Alzheimer's disease (AD). Our research group optimized its structure in a previous study to minimize the risk of renal toxicity, which would beneficial for future clinical research. This study aims to examine the impact of Andro-III on enhancing cognitive learning ability in 3xTg-AD mice, as well as the mechanisms involved. Andro-III improved spatial learning ability, prevented the loss of Nysted's vesicles, reduced the accumulation of β-amyloid (Aβ) and tau proteins, and suppressed microglial activation. Further research found that the expression of nuclear factor kappa-B RelA (NF-κB p65) expression and glycogen synthase kinase-3β (GSK-3β) activity were inhibited, while CREB was upregulated in brain tissue treated with Andro-III. Moreover, Andro-III downregulated the expression of IBA1 and inflammatory factors in microglial cells of mice induced by Aβ. The regulation of the GSK-3β/NF-κB/CREB pathway was similar to that observed in 3xTg-AD mice. Therefore, Andro-III modulates neuroinflammation and attenuates neuropathological changes of AD via the GSK-3β/NF-κB/CREB pathway.

Application of Machine Learning Methods to Predict the Air Half-Lives of Persistent Organic Pollutants

Persistent organic pollutants (POPs) are ubiquitous and bioaccumulative, posing potential and long-term threats to human health and the ecological environment. Quantitative structure-activity relationship (QSAR) studies play a guiding role in analyzing the toxicity and environmental fate of different organic pollutants. In the current work, five molecular descriptors are utilized to construct QSAR models for predicting the mean and maximum air half-lives of POPs, including specifically the energy of the highest occupied molecular orbital (HOMO_Energy_DMol3), a component of the dipole moment along the z-axis (Dipole_Z), fragment contribution to SAscore (SAscore_Fragments), subgraph counts (SC_3_P), and structural information content (SIC). The QSAR models were achieved through the application of three machine learning methods: partial least squares (PLS), multiple linear regression (MLR), and genetic function approximation (GFA). The determination coefficients (R2) and relative errors (RE) for the mean air half-life of each model are 0.916 and 3.489% (PLS), 0.939 and 5.048% (MLR), 0.938 and 5.131% (GFA), respectively. Similarly, the determination coefficients (R2) and RE for the maximum air half-life of each model are 0.915 and 5.629% (PLS), 0.940 and 10.090% (MLR), 0.939 and 11.172% (GFA), respectively. Furthermore, the mechanisms that elucidate the significant factors impacting the air half-lives of POPs have been explored. The three regression models show good predictive and extrapolation abilities for POPs within the application domain.

CORAL Models for Drug-Induced Nephrotoxicity

Drug-induced nephrotoxicity is a major cause of kidney dysfunction with potentially fatal consequences. The poor prediction of clinical responses based on preclinical research hampers the development of new pharmaceuticals. This emphasises the need for new methods for earlier and more accurate diagnosis to avoid drug-induced kidney injuries. Computational predictions of drug-induced nephrotoxicity are an attractive approach to facilitate such an assessment and such models could serve as robust and reliable replacements for animal testing. To provide the chemical information for computational prediction, we used the convenient and common SMILES format. We examined several versions of so-called optimal SMILES-based descriptors. We obtained the highest statistical values, considering the specificity, sensitivity and accuracy of the prediction, by applying recently suggested atoms pairs proportions vectors and the index of ideality of correlation, which is a special statistical measure of the predictive potential. Implementation of this tool in the drug development process might lead to safer drugs in the future.

Computer especially AI-assisted drug virtual screening and design in traditional Chinese medicine

BACKGROUND

Traditional Chinese medicine (TCM), as a significant part of the global pharmaceutical science, the abundant molecular compounds it contains is a valuable potential source of designing and screening new drugs. However, due to the un-estimated quantity of the natural molecular compounds and diversity of the related problems drug discovery such as precise screening of molecular compounds or the evaluation of efficacy, physicochemical properties and pharmacokinetics, it is arduous for researchers to design or screen applicable compounds through old methods. With the rapid development of computer technology recently, especially artificial intelligence (AI), its innovation in the field of virtual screening contributes to an increasing efficiency and accuracy in the process of discovering new drugs.

PURPOSE

This study systematically reviewed the application of computational approaches and artificial intelligence in drug virtual filtering and devising of TCM and presented the potential perspective of computer-aided TCM development.

STUDY DESIGN

We made a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Then screening the most typical articles for our research.

METHODS

The systematic review was performed by following the PRISMA guidelines. The databases PubMed, EMBASE, Web of Science, CNKI were used to search for publications that focused on computer-aided drug virtual screening and design in TCM.

RESULT

Totally, 42 corresponding articles were included in literature reviewing. Aforementioned studies were of great significance to the treatment and cost control of many challenging diseases such as COVID-19, diabetes, Alzheimer's Disease (AD), etc. Computational approaches and AI were widely used in virtual screening in the process of TCM advancing, which include structure-based virtual screening (SBVS) and ligand-based virtual screening (LBVS). Besides, computational technologies were also extensively applied in absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction of candidate drugs and new drug design in crucial course of drug discovery.

CONCLUSIONS

The applications of computer and AI play an important role in the drug virtual screening and design in the field of TCM, with huge application prospects.

Andrographolide, a natural anti-inflammatory agent: An Update

Botanicals have attracted much attention in the field of anti-inflammatory due to their good pharmacological activity and efficacy. Andrographis paniculata is a natural plant ingredient that is widely used around the world. Andrographolide is the main active ingredient derived from Andrographis paniculata, which has a good effect on the treatment of inflammatory diseases. This article reviews the application, anti-inflammatory mechanism and molecular targets of andrographolide in different inflammatory diseases, including respiratory, digestive, immune, nervous, cardiovascular, skeletal, and tumor system diseases. And describe its toxicity and explain its safety. Studies have shown that andrographolide can be used to treat inflammatory lesions of various systemic diseases. In particular, it acts on many inflammation-related signalling pathways. The future direction of andrographolide research is also introduced, as is the recent research that indicates its potential clinical application as an anti-inflammatory agent.

Isoandrographolide from Andrographis paniculata ameliorates tubulointerstitial fibrosis in ureteral obstruction-induced mice, associated with negatively regulating AKT/GSK-3β/β-cat signaling pathway

Tubulointerstitial fibrosis (TIF) is a prominent pathological manifestation for the progression of almost all chronic kidney diseases (CKDs) to end-stage renal failure. However, there exist few efficient therapies to cure TIF. Our recent results showed that (8R, 12S)-isoandrographolide (ISA), a diterpenoid lactone ingredient of traditional Chinese herbal Andrographis paniculata (Burm.f.) Nees, exhibited anti-pulmonary fibrosis in silica-induced mice. Herein, we investigated the therapeutic effect of ISA on TIF, using mice subjected to unilateral ureteral obstruction (UUO) and human kidney proximal tubular epithelial (HK-2) cells treated with transforming growth factor-β1 (TGF-β1) or tumor necrosis factor-α (TNF-α). The pathological changes and collagen deposition results displayed that ISA administration significantly attenuated inflammatory response, ameliorated TIF, and protected the kidney injury. Interestingly, ISA revealed much lower cytotoxicity on HK-2 cells, but exhibited stronger inhibitory effect on tubular epithelial mesenchymal transformation (EMT) and inflammation, as compared to andrographolide (AD), the major ingredient of A. paniculata extract that has been reported to ameliorate TIF in diabetic nephropathy mice. It was further clarified that the amelioration of TIF by ISA was associated with suppressing the aberrant activation of AKT/GSK-3β/β-catenin pathway through network pharmacology analysis and experimental validation. Taken together, these findings indicate that ISA is a promising lead compound for development of anti-TIF, and even broad-spectrum anti-fibrotic drugs.

Investigation of potential descriptors of chemical compounds on prevention of nephrotoxicity via QSAR approach

Drug-induced nephrotoxicity remains a common problem after exposure to medications and diagnostic agents, which may be heightened in the kidney microenvironment and deteriorate kidney function. In this study, the toxic effects of fourteen marked drugs with the individual chemical structure were evaluated in kidney cells. The quantitative structure-activity relationship (QSAR) approach was employed to investigate the potential structural descriptors of each drug-related to their toxic effects. The most reasonable equation of the QSAR model displayed that the estimated regression coefficients such as the number of ring assemblies, three-membered rings, and six-membered rings were strongly related to toxic effects on renal cells. Meanwhile, the chemical properties of the tested compounds including carbon atoms, bridge bonds, H-bond donors, negative atoms, and rotatable bonds were favored properties and promote the toxic effects on renal cells. Particularly, more numbers of rotatable bonds were positively correlated with strong toxic effects that displayed on the most toxic compound. The useful information discovered from our regression QSAR models may help to identify potential hazardous moiety to avoid nephrotoxicity in renal preventive medicine.