A novel adaptive ensemble classification framework for ADME prediction

The Efficacy of Ganoderma lucidum Extracts on Treating Endometrial Cancer: A Network Pharmacology Approach

Ganoderma lucidum (GL) is a prominent medicinal mushroom in traditional Chinese medicine, known for its potent antitumor properties. This study aimed to illustrate the efficacy of GL extracts (GLE) on treating endometrial cancer (EC) and explore the underlying mechanisms via network pharmacology and experimental validation. Network pharmacological analysis was conducted to explore the therapeutic efficacy and mechanisms of GL on EC. In vitro experimental validation was performed on human endometrial cancer cell lines HEC-1-A and KLE. Network pharmacology revealed that key targets of GL against EC were primarily associated with the Rap1 signaling pathway. In in vitro experiments, GLE or GGTI-298 (a GTPase inhibitor) treatment inhibited cell proliferation and migration, promoted cell apoptosis, increased caspase-3 level, and arrested cell cycle in G1 phase in HEC-1-A and KLE cells. GLE increased the protein expression of Rap1-GTP, p-AKT, and p-ERK2 in HEC-1-A and KLE cells. Moreover, GGTI-298 enhanced the effects of GLE on suppressing the malignant progression of EC cells and on activating Rap1 signaling pathway. GLE inhibited the malignant progression of EC cells probably via activating the Rap1 signaling pathway.

TCMNPAS: a comprehensive analysis platform integrating network formulaology and network pharmacology for exploring traditional Chinese medicine

The application of network formulaology and network pharmacology has significantly advanced the scientific understanding of traditional Chinese medicine (TCM) treatment mechanisms in disease. The field of herbal biology is experiencing a surge in data generation. However, researchers are encountering challenges due to the fragmented nature of the data and the reliance on programming tools for data analysis. We have developed TCMNPAS, a comprehensive analysis platform that integrates network formularology and network pharmacology. This platform is designed to investigate in-depth the compatibility characteristics of TCM formulas and their potential molecular mechanisms. TCMNPAS incorporates multiple resources and offers a range of functions designed for automated analysis implementation, including prescription mining, molecular docking, network pharmacology analysis, and visualization. These functions enable researchers to analyze and obtain core herbs and core formulas from herbal prescription data through prescription mining. Additionally, TCMNPAS facilitates virtual screening of active compounds in TCM and its formulas through batch molecular docking, allowing for the rapid construction and analysis of networks associated with "herb-compound-target-pathway" and disease targets. Built upon the integrated analysis concept of network formulaology and network pharmacology, TCMNPAS enables quick point-and-click completion of network-based association analysis, spanning from core formula mining from clinical data to the exploration of therapeutic targets for disease treatment. TCMNPAS serves as a powerful platform for uncovering the combinatorial rules and mechanism of TCM formulas holistically. We distribute TCMNPAS within an open-source R package at GitHub ( https://github.com/yangpluszhu/tcmnpas ), and the project is freely available at http://54.223.75.62:3838/ .

In Silico prediction of inhibitors for multiple transporters via machine learning methods

Transporters play an indispensable role in facilitating the transport of nutrients, signaling molecules and the elimination of metabolites and toxins in human cells. Contemporary computational methods have been employed in the prediction of transporter inhibitors. However, these methods often focus on isolated endpoints, overlooking the interactions between transporters and lacking good interpretation. In this study, we integrated a comprehensive dataset and constructed models to assess the inhibitory effects on seven transporters. Both conventional machine learning and multi-task deep learning methods were employed. The results demonstrated that the MLT-GAT model achieved superior performance with an average AUC value of 0.882. It is noteworthy that our model excels not only in prediction performance but also in achieving robust interpretability, aided by GNN-Explainer. It provided valuable insights into transporter inhibition. The reliability of our model's predictions positioned it as a promising and valuable tool in the field of transporter inhibition research. Related data and code are available at https://gitee.com/wutiantian99/transporter_code.git.

Caged Polyprenylated Xanthones in Garcinia hanburyi and the Biological Activities of Them

The previous phytochemical analyses of Garcinia hanburyi revealed that the main structural characteristic associated with its biological activity is the caged polyprenylated xanthones with a unique 4-oxatricyclo [4.3.1.03,7] dec-2-one scaffold, which contains a highly substituted tetrahydrofuran ring with three quaternary carbons. Based on the progress in research of the chemical constituents, pharmacological effects and modification methods of the caged polyprenylated xanthones, this paper presents a preliminary predictive analysis of their drug-like properties based on the absorption, distribution, metabolism, excretion and toxicity (ADME/T) properties. It was found out that these compounds have very similar pharmacokinetic properties because they possess the same caged xanthone structure, the 9,10-double bond in a,b-unsaturated ketones are critical for the antitumor activity. The author believes that there is an urgent need to seek new breakthroughs in the study of these caged polyprenylated xanthones. Thus, the research on the route of administration, therapeutic effect, structural modification and development of such active ingredients is of great interest. It is hoped that this paper will provide ideas for researchers to develop and utilize the active ingredients derived from natural products.

ADMET property prediction via multi-task graph learning under adaptive auxiliary task selection

It is a critical step in lead optimization to evaluate the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of drug-like compounds. Classical single-task learning (STL) has effectively predicted individual ADMET endpoints with abundant labels. Conversely, multi-task learning (MTL) can predict multiple ADMET endpoints with fewer labels, but ensuring task synergy and highlighting key molecular substructures remain challenges. To tackle these issues, this work elaborates a multi-task graph learning framework for predicting multiple ADMET properties of drug-like small molecules (MTGL-ADMET) by holding a new paradigm of MTL, "one primary, multiple auxiliaries." It first adeptly combines status theory with maximum flow for auxiliary task selection. The subsequent phase introduces a primary-task-centric MTL model with integrated modules. MTGL-ADMET not only outstrips existing STL and MTL methods but also offers a transparent lens into crucial molecular substructures. It is anticipated that this work can promote lead compound finding and optimization in drug discovery.

In Silico Tools and Software to Predict ADMET of New Drug Candidates

Implication of computational techniques and in silico tools promote not only reduction of animal experimentations but also save time and money followed by rational designing of drugs as well as controlled synthesis of those "Hits" which show drug-likeness and possess suitable absorption, distribution, metabolism, excretion, and toxicity (ADMET) profile. With globalization of diseases, resistance of drugs over the time and modification of viruses and microorganisms, computational tools, and artificial intelligence are the future of drug design and one of the important areas where the principles of sustainability and green chemistry (GC) perfectly fit. Most of the new drug entities fail in the clinical trials over the issue of drug-associated human toxicity. Although ecotoxicity related to new drugs is rarely considered, but this is the high time when ecotoxicity prediction should get equal importance along with human-associated drug toxicity. Thus, the present book chapter discusses the available in silico tools and software for the fast and preliminary prediction of a series of human-associated toxicity and ecotoxicity of new drug entities to screen possibly safer drugs before going into preclinical and clinical trials.

An in-silico approach to identify potential drug molecules for Alzheimer’s disease: a case with four therapeutic targets

Background:

Computational Methods in the ‘omics’ era has been a boon in the drug discovery field. Bioinformatics and cheminformatics databases and tools complement the successful discovery of promising lead compounds in the treatment of several disease conditions including neurodegenerative diseases such as Alzheimer’s Disease (AD). However, commercially available drugs in the market to alleviate the disease progression in AD patients is sparse. The current research aims to apply an in-silico approach on multi-therapeutic agents against multi-therapeutic targets through docking studies to explore potential lead compounds for AD clinical trials.

Method:

In the proposed research, virtual screening was performed with four US FDA-approved control drugs (Donepezil (DON), Galantamine (GAL), Rivastigmine (RIV), and Tacrine (TAC)) for mild-moderate-severe stages of AD treatment. The panel of compounds identified through virtual screening was subjected to chemical absorption, distribution, metabolism, excretion, and toxicity (ADMET) and Pharmacokinetics (PK). The compound with good ADMET and PK score was investigated further with molecular docking against the four therapeutic targets involved in AD. Ligands showing the highest binding affinity against cholinesterase inhibitors (AChE, BuChE), receptor antagonist (NMDA), and β-amyloid peptide (Aβ) were computed.

Result:

It was observed that the compounds Quinazolidinone analogue, 2b, Isoquinoline-pyridine, 1, Benzylmorphine and Coelenteramide are the best lead candidates with the least side effects and better efficacy.

Conclusion:

The predicted lead candidates are suitable for further investigation in the drug discovery pipeline.

Pharmacokinetic Tools and Applications

Drug toxicity, as well as therapeutic activity, is contingent upon the parent drug, or a derivative thereof, reaching the relevant site of action in the body, at sufficient concentration, over a given period of time. Thus, the potential to truly elicit an effect is governed by both the intrinsic activity/toxicity of the drug (or its transformation products) and its pharmacokinetic profile. As the pharmaceutical industry has become increasingly aware of the role of pharmacokinetics in determining drug activity and toxicity, the range of software, both freely available and commercial, to predict relevant properties has proliferated. Such tools can be considered on three different levels, applicable at different stages within the drug development process and providing increasing detail and relevance of information. Level (i) is the prediction of fundamental physicochemical properties that can be used to screen vast virtual libraries of potential candidates. Level (ii), predicting the individual absorption, distribution, metabolism, and excretion (ADME) characteristics of potential drugs, can also be applied to many compounds simultaneously. Level (iii), predicting the concentration-time profile of a drug in blood or specific tissues/organs for individuals or a population, is the most sophisticated level of prediction, applied to fewer candidates. In this chapter, in silico tools for predicting ADME-relevant properties, across these three levels, and the applications of this information, are described using exemplar, freely available resources. Further resources are signposted but not all are considered in detail as the purpose here is more to provide an introduction to the capabilities and practicalities of the tools, rather than to provide an exhaustive review of all the tools available.

Anti-Autophagy Mechanism of Zhi Gan Prescription Based on Network Pharmacology in Nonalcoholic Steatohepatitis Rats

Background and Aims: Zhi Gan prescription (ZGP) has been clinically proven to exert a favorable therapeutic effect on nonalcoholic steatohepatitis (NASH). This study purpose to reveal the underlying molecular mechanisms of ZGP action in NASH. Methods: Systematic network pharmacology was used to identify bioactive components, potential targets, and the underlying mechanism of ZGP action in NASH. High fat (HF)-induced NASH model rats were used to assess the effect of ZGP against NASH, and to verify the possible molecular mechanisms as predicted by network pharmacology. Results: A total of 138 active components and 366 potential targets were acquired in ZGP. In addition, 823 targets of NASH were also screened. In vivo experiments showed that ZGP significantly improved the symptoms in HF-induced NASH rats. qRT-PCR and western blot analyses showed that ZGP could regulate the hub genes, PTEN, IL-6 and TNF in NASH model rats. In addition, ZGP suppressed mitochondrial autophagy through mitochondrial fusion and fission via the PINK/Parkin pathway. Conclusion: ZGP exerts its effects on NASH through mitochondrial autophagy. These findings provide novel insights into the mechanisms of ZGP in NASH.

A Study on the Therapeutic Efficacy of San Zi Yang Qin Decoction for Non-Alcoholic Fatty Liver Disease and the Underlying Mechanism Based on Network Pharmacology

OBJECTIVE

This study aims to explore the therapeutic efficacy of San Zi Yang Qin Decoction (SZ) and its potential mechanism in the treatment of non-alcoholic fatty liver disease (NAFLD) based on network pharmacology and in vivo experiments.

METHODS

Effective chemicals and targets of SZ were searched in online databases, according to the drug-likeness of compounds and the binomial distribution of targets. A disease-target-chemical network was established using NAFLD-associated genes screened through GeneCards database, Gene Ontology (GO) terms, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Furthermore, animal experiments were conducted to verify the efficacy and mechanism of SZ predicted by network pharmacology. The NAFLD mouse model was established with C57BL/6J mice fed with a high-fat diet for 22 weeks. The mice in the control group were fed with a chow diet. From the 23rd week, the NAFLD mice were treated with intragastric SZ or normal saline for 8 weeks. After the glucose tolerance was measured, the mice were sacrificed, followed by the collection of serum and liver tissues. Pathological changes in liver tissues were examined by H&E staining. Additionally, alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum fast blood glucose, and insulin levels were detected. Expression levels of TNF-α of serum and liver tissues were determined by ELISA and qRT-PCR, respectively. Western blot was used to detect the activation of AKT in liver tissues.

RESULTS

A total of 27 effective compounds and 20 targets of SZ were screened. GO analysis uncovered a significant correlation between the targets of SZ and those of NAFLD. KEGG analysis presented the signaling pathways enriched in SZ and NAFLD, including NAFLD, TNF-α, and apoptosis pathways. The area under the curve of major GO and KEGG pathways indicated the potential role of SZ in improving NAFLD. In vivo experiments demonstrated that SZ significantly alleviated hepatosteatosis and inflammatory cell infiltration in liver tissues, reduced serum transaminases, and improved insulin resistance and glucose tolerance of NAFLD mice. The protein level of phospho-AKT was upregulated by SZ. Additionally, SZ treatment obviously impaired the TNF-α level in the serum and liver tissue of NAFLD mice.

CONCLUSIONS

According to the network pharmacology analysis and in vivo experiments, SZ could have therapeutic efficacy for NALFD. The mechanism mainly involves pathways relative to insulin resistance, TNF-α, and apoptosis. Our results provide a scientific basis for SZ in the clinical treatment of NAFLD.

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

As expenditure on drug development increases exponentially, the overall drug discovery process requires a sustainable revolution. Since artificial intelligence (AI) is leading the fourth industrial revolution, AI can be considered as a viable solution for unstable drug research and development. Generally, AI is applied to fields with sufficient data such as computer vision and natural language processing, but there are many efforts to revolutionize the existing drug discovery process by applying AI. This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization, and drug repositioning. The main data sources in each field are also summarized in this review. In addition, an in-depth analysis of the remaining challenges and limitations will be provided, and proposals for promising future directions in each of the aforementioned areas.

Current status and future directions of high-throughput ADME screening in drug discovery

During the last decade high-throughput in vitro absorption, distribution, metabolism and excretion (HT-ADME) screening has become an essential part of any drug discovery effort of synthetic molecules. The conduct of HT-ADME screening has been "industrialized" due to the extensive development of software and automation tools in cell culture, assay incubation, sample analysis and data analysis. The HT-ADME assay portfolio continues to expand in emerging areas such as drug-transporter interactions, early soft spot identification, and ADME screening of peptide drug candidates. Additionally, thanks to the very large and high-quality HT-ADME data sets available in many biopharma companies, in silico prediction of ADME properties using machine learning has also gained much momentum in recent years. In this review, we discuss the current state-of-the-art practices in HT-ADME screening including assay portfolio, assay automation, sample analysis, data processing, and prediction model building. In addition, we also offer perspectives in future development of this exciting field.

Prediction of P-glycoprotein inhibitors with machine learning classification models and 3D-RISM-KH theory based solvation energy descriptors

Development of novel in silico methods for questing novel PgP inhibitors is crucial for the reversal of multi-drug resistance in cancer therapy. Here, we report machine learning based binary classification schemes to identify the PgP inhibitors from non-inhibitors using molecular solvation theory with excellent accuracy and precision. The excess chemical potential and partial molar volume in various solvents are calculated for PgP± (PgP inhibitors and non-inhibitors) compounds with the statistical-mechanical based three-dimensional reference interaction site model with the Kovalenko-Hirata closure approximation (3D-RISM-KH molecular theory of solvation). The statistical importance analysis of descriptors identified the 3D-RISM-KH based descriptors as top molecular descriptors for classification. Among the constructed classification models, the support vector machine predicted the test set of Pgp± compounds with highest accuracy and precision of ~ 97% for test set. The validation of models confirms the robustness of state-of-the-art molecular solvation theory based descriptors in identification of the Pgp± compounds.

In Silico Assessment of ADME Properties: Advances in Caco-2 Cell Monolayer Permeability Modeling

One of the main goals of in silico Caco-2 cell permeability models is to identify those drug substances with high intestinal absorption in human (HIA). For more than a decade, several in silico Caco-2 models have been made, applying a wide range of modeling techniques; nevertheless, their capacity for intestinal absorption extrapolation is still doubtful. There are three main problems related to the modest capacity of obtained models, including the existence of inter- and/or intra-laboratory variability of recollected data, the influence of the metabolism mechanism, and the inconsistent in vitro-in vivo correlation (IVIVC) of Caco-2 cell permeability. This review paper intends to sum up the recent advances and limitations of current modeling approaches, and revealed some possible solutions to improve the applicability of in silico Caco-2 permeability models for absorption property profiling, taking into account the above-mentioned issues.

A Network Pharmacology Approach to Uncover the Molecular Mechanisms of Herbal Formula Ban-Xia-Xie-Xin-Tang

Ban-Xia-Xie-Xin-Tang (BXXXT) is a classical formula from Shang-Han-Lun which is one of the earliest books of TCM clinical practice. In this work, we investigated the therapeutic mechanisms of BXXXT for the treatment of multiple diseases using a network pharmacology approach. Here three BXXXT representative diseases (colitis, diabetes mellitus, and gastric cancer) were discussed, and we focus on in silico methods that integrate drug-likeness screening, target prioritizing, and multilayer network extending. A total of 140 core targets and 72 representative compounds were finally identified to elucidate the pharmacology of BXXXT formula. After constructing multilayer networks, a good overlap between BXXXT nodes and disease nodes was observed at each level, and the network-based proximity analysis shows that the relevance between the formula targets and disease genes was significant according to the shortest path distance (SPD) and a random walk with restart (RWR) based scores for each disease. We found that there were 22 key pathways significantly associated with BXXXT, and the therapeutic effects of BXXXT were likely addressed by regulating a combination of targets in a modular pattern. Furthermore, the synergistic effects among BXXXT herbs were highlighted by elucidating the molecular mechanisms of individual herbs, and the traditional theory of "Jun-Chen-Zuo-Shi" of TCM formula was effectively interpreted from a network perspective. The proposed approach provides an effective strategy to uncover the mechanisms of action and combinatorial rules of BXXXT formula in a holistic manner.