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Human esophageal squamous cell carcinoma (ESCC) is one of the most lethal cancers in human digestive system. It is necessary to discover novel antitumor agents for the treatment of esophageal cancers because of its poor prognosis. Indoline has been reported as an efficient anticancer fragment to design novel anticancer agents. In this work, indoline derivatives were designed, synthesized and explored their anticancer activity. Compound 9d, which exhibited potent antiproliferative activity with IC 50 values of 1.84 μM (MGC-803 cells), 6.82 μM (A549 cells), 1.61 μM (Kyse30 cells), 1.49 μM (Kyse450 cells), 2.08 μM (Kyse510 cells) and 2.24 μM (EC-109 cells), respectively. The most active compound 9d was identified as a tubulin inhibitor targeting colchicine binding site with an IC 50 value of 3.4 µM. Compound 9d could strongly suppress the tubulin polymerization in Kyse450 cells. The results of molecular docking also suggested compound 9d could tightly bind into the colchicine binding site of tubulin. Besides, compound 9d inhibited the growth of KYSE450 cells in a time and dose-dependent manner. All the results suggest that the indoline derivatives may be a class of novel tubulin inhibitors with potential anticancer activity, and which is worthy of further study., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Accumulated evolutionary knowledge not only benefits our understanding of the pathogenesis of diseases, but also help in the search for new drug targets. This is further supported by the recent finding that human accelerated regions (HARs) identified by comparative genomic studies are linked to human neural system evolution and are also associated with neurological disorders. Here, we analyze the associations between HARs and diseases and drugs. We found that 32.42% of approved drugs target at least one HAR gene, which is higher than the ratio of in-research drugs. More interestingly, HAR gene-targeted drugs are most significantly enriched with agents treating neurological disorders. Thus, HAR genes have important implications in medical genetics and drug discovery., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Due to synergistic effects, combinatorial drugs are widely used for treating complex diseases. However, combining drugs and making them synergetic remains a challenge. Genetic disease genes are considered a promising source of drug targets with important implications for navigating the drug space. Most diseases are not caused by a single pathogenic factor, but by multiple disease genes, in particular, interacting disease genes. Thus, it is reasonable to consider that targeting epistatic disease genes may enhance the therapeutic effects of combinatorial drugs. In this study, synthetic lethality gene pairs of tumors, similar to epistatic disease genes, were first targeted by combinatorial drugs, resulting in the enrichment of the combinatorial drugs with cancer treatment, which verified our hypothesis. Then, conventional epistasis detection software was used to identify epistatic disease genes from the genome wide association studies (GWAS) dataset. Furthermore, combinatorial drugs were predicted by targeting these epistatic disease genes, and five combinations were proven to have synergistic anti-cancer effects on MCF-7 cells through cell cytotoxicity assay. Combined with the three-dimensional (3D) genome-based method, the epistatic disease genes were filtered and were more closely related to disease. By targeting the filtered gene pairs, the efficiency of combinatorial drug discovery has been further improved., Competing Interests: We have received research/grant support from Wuhan Bio-Links Technology Co., Ltd. Huazhong Agricultural University and the developers of the methods for drug discovery and drug repositioning may benefit financially pursuant to the University’s Policy on Inventions, Patents and Technology Transfer, even if these methods are not used in the commercialized therapy.

Oxidative damage can lead to a wide range of diseases. Nrf2 is an important transcription factor that regulates many of the cytoprotective enzymes involved in the oxidative stress response. Therefore, targeting the regulation of Nrf2 activation is one logical and effective strategy to prevent or lower the risk of oxidative stress-related diseases. Until now, most research has focused on electrophilic indirect Nrf2 activators, but the risk of 'off-target' effects may be associated with these activators. To find novel small non-electrophilic modulators of Nrf2, we started from chemical agents derived from a connectivity map (cMap) and identified 22 non-electrophilic potential Nrf2-activating drugs through a drug repositioning tactic. By determining the expression changes of antioxidant genes in MCF7 cells that were treated with the potential Nrf2 activators using quantitative real-time polymerase chain reaction RT-PCR (real-time polymerase chain reaction) (qRT-PCR), astemizole was found to have a greater scale of upregulating antioxidant genes NQO1 , HO-1 , and GCLM than the positive control d,l-sulforaphane, although the testing concentration was lower than that of the control. Astemizole is a good potential redox regulator and deserves more pharmacodynamic experimentation to test and verify its feasibility for use as an Nrf2 activator., Competing Interests: The authors declare no conflict of interest.

Both recent studies and our calculation suggest that the physicochemical properties of launched drugs changed continuously over the past decades. Besides shifting of commonly used properties, the average biological relevance (BR) and similarity to natural products (NPs) of launched drugs decreased, reflecting the fact that current drug discovery deviated away from NPs. To change the current situation characterized by high investment but low productivity in drug discovery, efforts should be made to improve the BR of the screening library and hunt drugs more effectively in the biologically relevant chemical space. Additionally, a multiple dimensional molecular descriptor, named the biologically relevant spectrum (BRS) was proposed for quantitative structure-activity relationships (QSAR) study or screening library preparation. Prediction models for 43 biological activity categories were developed with BRS and support vector machine (SVM). In most cases, the overall prediction accuracies were around 95% and the Matthew's correlation coefficients (MCC) were over 0.8. Thirty-seven out of 48 drug-activity associations were successfully predicted for drugs that launched from 2006 to 2012, which were not included in the training data set. A web-server named BioRel ( http://ibi.hzau.edu.cn/biorel ) was developed to provide services including BR, BRS calculation, activity class, and pharmacokinetic property prediction.

The discovery and use of antibacterials represents a primary success of modern pharmaceutical industry. However, the pace of antibacterial discovery was heavily hindered by a series of technical difficulties and the unfavorable economics in recent years. The past decade has witnessed rapid progresses in omics and systems biology, which provided an unprecedented opportunity to accelerate the discovery of antibacterials. In this article, we first summarize the successful use of metabolic network analysis in antibacterial discovery. Then, we reveal that metabolite concentration serves as a useful criterion for selecting antimicrobial targets. The essential enzymes with low substrate concentrations (< 0.5 mM) are more druggable antibacterial targets. Besides, we find that the solubility of clinically used competitive antibacterials is at least 100 times higher than the concentrations of the competed substrates. By the new-proposed criterion, we not only identify some promising antibacterial targets but also explain some perplexing experimental observations as well.

New strategies for target identification are urgently needed to tackle the current productivity challenges in drug discovery. By examining successful human drug targets, it can be seen that approximately 50% are associated with genetic disorders. Further analysis shows that these successfully targeted genes share some common evolutionary features, which strongly suggests that evolutionary information can help identify drug targets with the greatest potential for therapeutic development., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

To evaluate the potential of natural products as novel structure suppliers, a historical analysis was performed on the structural novelty of a natural product library, viz., the Chapman & Hall/CRC Dictionary of Natural Products. The results show that although the unexplored natural product universe is still ample, it is more and more difficult to find novel agents from nature, with the discovery probability of novel structures and scaffolds being lower than 50% in the near future, which mainly results from the intrinsic redundancy of natural products and, thus, is unlikely to be reversed merely through technical progresses., (Copyright © 2011 Verlag Helvetica Chimica Acta AG, Zürich.)

Conceptual innovations are needed to address the challenge of 'more investments, fewer drugs' in the pharmaceutical industry. Since the publication of The Origin of Species by Charles Darwin 150 years ago, evolution has been a central concept in biology. In this article, we show that evolutionary concepts are also helpful to streamline the drug-discovery pipeline through facilitating the discovery of targets and drug candidates. Furthermore, the antioxidant paradox can be addressed by an evolutionary methodology. Through examining the evolved biological roles of natural polyphenols (which dominate current antioxidant drug discovery), we reveal that polyphenols (particularly flavonoids) are not evolved for scavenging free radicals. This finding provides new clues to understanding why the strong in vitro antioxidant activities of polyphenols cannot be translated into in vivo effects. Polyphenols have evolved a superior ability to bind various proteins, so we also argue that they are good starting points for multi-target drugs.

Traditional Chinese Medicine (TCM), which has been used for thousands of years to treat diseases, provides unique theoretical and practical methodologies for disease control. With the increasing accumulation of TCM data, it is imperative to study and analyze these resources with modern technologies and to elucidate the molecular mechanisms of TCM therapy. However, the philosophy, framework and technique of TCM are quite different from those of Western medicine, which causes complications when attempting to design modern drug treatments based on TCM. To meet this challenge, some basic chemoinformatics techniques, including molecular similarity searching, virtual screening and inverse docking, have been utilized in an attempt to gain a deeper understanding of TCM and to accelerate the TCM-based drug discovery. Recent progress on the use of chemoinformatics in TCM research will be discussed and an example of the preliminary application of chemoinformatics methods in anticancer drug design will be provided.

To prove the innate advantages of endogenous compounds/fragments for drug discovery and development, a novel index termed biological relevance (BR) is proposed. The results clearly indicate its ability to distinguish between synthetic chemicals, bioactive compounds, drug candidates, and launched drugs. Primarily, the average BR of the databases investigated decreases in the order DNP > CMC > ACD-3D > MDDR. Second, for compounds with the same bioactivity, drugs (CMC) possess higher average BR than their candidates (MDDR). These results suggest that compounds with higher BR have more chance to survive the drug development pipeline. Third, the above conclusion is supported by the fact that compounds in the later development phases possess higher BR than those in the earlier phases. Comparisons were made between BR and other indices, including toxicity, druglikeness, and natural productlikeness.

Despite the rapid technical progress in pharmaceutical industry in the past decade, it is still a great challenge to find new drugs and the situation seems more and more serious. However, the history of pharmaceutical industry clearly indicated that the significance of drug discovery went far beyond providing new drugs. For instance, drugs or candidates could be used as selective probes to reveal novel cellular mechanisms, which is a fundamental tenet of chemical biology. More interestingly, accumulating evidence indicates that drugs and candidates can find important use in stem cell biology. Not only approved drugs but also undeveloped pharmacological agents could serve as efficient agents to regulate stem cell fate. Moreover, the target and activity knowledge accumulated during the drug discovery process will help select the stem cell fate modulators in a rational manner. As the progress in stem cell biology will bring positive influence to drug discovery, it can be expected that the current drug discovery efforts will finally bear great fruits in the future.

For drug discovery, historical experience is always of significance. Through examining the history of traditional medicines, we find that these medicines have more implications for drug discovery than just providing new chemical entities. The history of traditional medicines indicates that they depended more on the combination of natural agents than on screening new agents to find new remedies. This phenomenon suggests that shifting the current drug discovery paradigm from 'finding new-entity drugs' to 'combining existing agents' may be helpful for overcoming the 'more investment, fewer drugs' challenge. We show that clues to finding antidementia combinatorial drugs can be derived from traditional medicine formulae. It seems that to create a brighter future of drug discovery, we would better go back to history.

Background: Alzheimer's disease (AD) is the leading cause of dementia, with its prevalence increasing as the global population ages. AD is a multifactorial and intricate neurodegenerative disease with pathological changes varying from person to person. Because the mechanism of AD is highly controversial, effective treatments remain a distant prospect. Currently, one of the most promising hypotheses posits mitochondrial dysfunction as an early event in AD diagnosis and a potential therapeutic target. Objective: Here, we adopted a systems medicine strategy to explore the mitochondria-related mechanisms of AD. Then, its implications for discovering nutrients combatting the disease were demonstrated. Methods: We employed conditional mutual information (CMI) to construct AD gene dependency networks. Furthermore, the GeneRank algorithm was applied to prioritize the gene importance of AD patients and identify potential anti-AD nutrients targeting crucial genes. Results: The results suggested that two highly interconnected networks of mitochondrial ribosomal proteins (MRPs) play an important role in the regulation of AD pathology. The close association between mitochondrial ribosome dysfunction and AD was identified. Additionally, we proposed seven nutrients with potential preventive and ameliorative effects on AD, five of which have been supported by experimental reports. Conclusions: Our study explored the important regulatory role of MRP genes in AD, which has significant implications for AD prevention and treatment. [ABSTRACT FROM AUTHOR]

Adenosine triphosphate (ATP) may be the most important biological small molecule. Since it was discovered in 1929, ATP has been regarded as life's energy reservoir. However, this compound means more to life. Its legend starts at the dawn of life and lasts to this day. ATP must be the basic component of ancient ribozymes and may facilitate the origin of structured proteins. In the existing organisms, ATP continues to construct ribonucleic acid (RNA) and work as a protein cofactor. ATP also functions as a biological hydrotrope, which may keep macromolecules soluble in the primitive environment and can regulate phase separation in modern cells. These functions are involved in the pathogenesis of aging-related diseases and breast cancer, providing clues to discovering anti-aging agents and precision medicine tactics for breast cancer. [ABSTRACT FROM AUTHOR]

Background: Genetics is best dedicated to interpreting pathogenesis and revealing gene functions. The past decade has witnessed unprecedented progress in genetics, particularly in genome‐wide identification of disorder variants through Genome‐Wide Association Studies (GWAS) and Phenome‐Wide Association Studies (PheWAS). However, it is still a great challenge to use GWAS/PheWAS‐derived data to elucidate pathogenesis. Methods: In this study, we used HotNet2, a heat diffusion‐based systems genetics algorithm, to calculate the networks for disease genes obtained from GWAS and PheWAS, with an attempt to get deeper insights into disease pathogenesis at a molecular level. Results: Through HotNet2 calculation, significant networks for 202 (for GWAS) and 167 (for PheWAS) types of diseases were identified and evaluated, respectively. The GWAS‐derived disease networks exhibit a stronger biomedical relevance than PheWAS counterparts. Therefore, the GWAS‐derived networks were used for pathogenesis interpretation by integrating the accumulated biomedical information. As a result, the pathogenesis for 64 diseases was elucidated in terms of mutation‐caused abnormal transcriptional regulation, and 47 diseases were preliminarily interpreted in terms of mutation‐caused varied protein‐protein interactions. In addition, 3,802 genes (including 46 function‐unknown genes) were assigned with new functions by disease network information, some of which were validated through mice gene knockout experiments. Conclusions: Systems genetics algorithm HotNet2 can efficiently establish genotype‐phenotype links at the level of biological networks. Compared with original GWAS/PheWAS results, HotNet2‐calculated disease‐gene associations have stronger biomedical significance, hence provide better interpretations for the pathogenesis of genome‐wide variants, and offer new insights into gene functions as well. These results are also helpful in drug development. [ABSTRACT FROM AUTHOR]

Genetic disease genes are considered a promising source of drug targets. Most diseases are caused by more than one pathogenic factor; thus, it is reasonable to consider that chemical agents targeting multiple disease genes are more likely to have desired activities. This is supported by a comprehensive analysis on the relationships between agent activity/druggability and target genetic characteristics. The therapeutic potential of agents increases steadily with increasing number of targeted disease genes, and can be further enhanced by strengthened genetic links between targets and diseases. By using the multi-label classification models for genetics-based drug activity prediction, we provide universal tools for prioritizing drug candidates. All of the documented data and the machine-learning prediction service are available at SCG-Drug (http://zhanglab.hzau.edu.cn/scgdrug). [ABSTRACT FROM AUTHOR]

Abstract: In the modern drug discovery pipeline, identification of novel drug targets is a critical step. Despite rapid progress in developing biomedical techniques, it is still a great challenge to find promising new targets from the ample space of human genes. This fact is partially responsible for the situation of “more investments, fewer drugs” in the pharmaceutical industry. A series of recent researches revealed that successfully targeted genes share some common evolutionary and genetic features, which means that the knowledge accumulated in modern evolutionary biology and genetics is very helpful to identify potential drug targets and to find new drugs as well. In this article, we comprehensively summarize the links between human drug targets and genetic diseases and their evolutionary origins, with an attempt to introduce these novel concepts and their medical implications to the biomedical community. [ABSTRACT FROM AUTHOR]