An integrated strategy for identifying new targets and inferring the mechanism of action: taking rhein as an example

Target Identification-Based Analysis of Mechanism of Betulinic Acid-Induced Cells Apoptosis of Cervical Cancer SiHa

Cervical cancer is the fourth most common female malignancy with high morbidity and mortality, which urgently needs novel anti-cancer drugs. Accumulating investigations have focused on the antitumor activity of betulinic acid (BA), which is a natural compound with low toxicity and high efficiency. Although the effect of BA on SiHa cells is obvious, the specific mechanism is seldom studied. Target identification is an important part of research on the internal mechanism of action. In this current study, an integrated method based on literature collection, target prediction, enrichment analysis, network analysis, and western blotting experiments was performed to identify the potential key targets of BA-induced apoptosis. Then, combined with the identified potential key targets, the specific mechanism of BA-induced cervical cancer SiHa cells apoptosis was elucidated. Our present study demonstrated that BA significantly reduces the viability of cervical cancer SiHa cells in a dose- and time-dependent manner. In addition, 8 potential key targets (AKT1, CASP8, LMNA, TNF, BCL2, CASP3, PARP1, and XIAP) were obtained through our integrated target identification method. Meanwhile, western blotting showed that within a certain concentration range, the expression of cleaved-caspase 3, cleaved-PARP, and cytochrome c increased with the BA concentration, while XIAP was almost unchanged. Therefore, the effect of BA on cervical cancer is noticeable. BA-induced SiHa cells apoptosis is a multi-molecule coordinated process. In this process, BA is not only a participant in either the extrinsic or intrinsic pathways, but also a regulator of apoptosis effector molecules of the CASP3/PARP1 axis.

What are the challenges with multi-targeted drug design for complex diseases?

INTRODUCTION

Current findings on multifactorial diseases with a complex pathomechanism confirm that multi-target drugs are more efficient ways in treating them as opposed to single-target drugs. However, to design multi-target ligands, a number of factors and challenges must be taken into account.

AREAS COVERED

In this perspective, we summarize the concept of application of multi-target drugs for the treatment of complex diseases such as neurodegenerative diseases, schizophrenia, diabetes, and cancer. We discuss the aspects of target selection for multifunctional ligands and the application of in silico methods in their design and optimization. Furthermore, we highlight other challenges such as balancing affinities to different targets and drug-likeness of obtained compounds. Finally, we present success stories in the design of multi-target ligands for the treatment of common complex diseases.

EXPERT OPINION

Despite numerous challenges resulting from the design of multi-target ligands, these efforts are worth making. Appropriate target selection, activity balancing, and ligand drug-likeness belong to key aspects in the design of ligands acting on multiple targets. It should be emphasized that in silico methods, in particular inverse docking, pharmacophore modeling, machine learning methods and approaches derived from network pharmacology are valuable tools for the design of multi-target drugs.

Severe acute respiratory syndrome coronavirus-2: An era of struggle and discovery leading to the emergency use authorization of treatment and prevention measures based on computational analysis

Severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2), a novel betacoronavirus, has surprised the world with its disease spread and mortality rate. SARS-CoV-2 is a positive-sense, enveloped RNA virus that can infect various organs of the body, potentially leading to multiple organ dysfunction and eventual death. While various medications have received emergency use authorizations (EUAs) for the treatment of Coronavirus disease-2019 (COVID-19), as of April 30, 2021, only one drug has been Food and Drug Administration (FDA)-approved: remdesivir. Currently, three vaccines have received EUAs in the United States, but none are FDA-approved. This shortage of treatments and prevention measures is extremely problematic. Thus computational approaches would provide important data about drug resistance and variants. Such data will be useful for the development of drugs and vaccines. This chapter is a synopsis of SARS-CoV-2 clinical presentation, COVID-19 symptomology, treatment, prevention mechanisms, and SARS-CoV-2 variants using computational analysis.

Aloin induced apoptosis by enhancing autophagic flux through the PI3K/AKT axis in osteosarcoma

Background

Osteosarcoma is a malignant tumor of bone and soft tissue in adolescents. Due to its tumor biological behavior pattern, osteosarcoma usually generates poor prognosis. Autophagy is an important self-defense mechanism in osteosarcoma.

Methods

Cell viability in IC₅₀ testing and reverse assays was examined by the MTT assay. Cell apoptosis conditions were examined by flow cytometry, Hoechst 33,342 staining and apoptosis-related protein immunoblotting. Autophagy conditions were tested by autophagy-related protein immunoblotting, transmission electron microscopic observation and dual fluorescence autophagy flux detection. The possible targets of aloin were screened out by network pharmacology and bioinformatic methods. Osteosarcoma xenografts in nude BALB/c mice were the model for in vivo research on tumor suppression, autophagy induction, pathway signaling and toxicity tests. In vivo bioluminescence imaging systems, immunohistochemical assays, and gross tumor volume comparisons were applied as the main research methods in vivo.

Results

Aloin induced osteosarcoma apoptosis in a dose-dependent manner. Its possible effects on the PI3K/AKT pathway were screened out by network pharmacology methods. Aloin increased autophagic flux in osteosarcoma by downregulating the PI3K/AKT pathway. Aloin promoted autophagic flux in the osteosarcoma cell lines HOS and MG63 in a dose-dependent manner by promoting autophagosome formation. Chloroquine reversed the apoptosis-promoting and autophagy-enhancing effects of aloin. Autophagy induced by starvation and rapamycin significantly enhanced the autophagic flux and apoptosis induced by aloin, which verified the role of the PI3K/AKT axis in the pharmacological action of aloin. Therapeutic effects, autophagy enhancement and regulatory effects on the PI3K/AKT/mTOR pathway were demonstrated in a nude mouse xenogeneic osteosarcoma transplantation model.

Conclusions

Aloin inhibited the proliferation of osteosarcoma by inhibiting the PI3K/AKT/mTOR pathway, increasing autophagic flux and promoting the apoptosis of osteosarcoma cells.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13020-021-00520-4.

The European Federation for Medicinal Chemistry and Chemical Biology (EFMC) Best Practice Initiative: Phenotypic Drug Discovery

Phenotypic drug discovery has a long track record of delivering innovative drugs and has received renewed attention in the last few years. The promise of this approach, however, comes with several challenges that should be addressed to avoid wasting time and resources on drugs with undesired modes of action or, worse, false-positive hits. In this set of best practices, we go over the essential steps of phenotypic drug discovery and provide guidance on how to increase the chance of success in identifying validated and relevant chemical starting points for optimization: selecting the right assay, selecting the right compound screening library and developing appropriate hit validation assays. Then, we highlight the importance of initiating studies to determine the mode of action of the identified hits early and present the current state of the art.

SPRD: a surface plasmon resonance database of common factors for better experimental planning

Background

Surface plasmon resonance is a label-free biophysical technique that is widely used in investigating biomolecular interactions, including protein-protein, protein-DNA, and protein-small molecule binding. Surface plasmon resonance is a very powerful tool in different stages of small molecule drug development and antibody characterization. Both academic institutions and pharmaceutical industry extensively utilize this method for screening and validation studies involving direct molecular interactions. In most applications of the surface plasmon resonance technology, one of the studied molecules is immobilized on a microchip, while the second molecule is delivered through a microfluidic system over the immobilized molecules. Changes in total mass on the chip surface is recorded in real time as an indicator of the molecular interactions.

Main body

Quality and accuracy of the surface plasmon resonance data depend on experimental variables, including buffer composition, type of sensor chip, coupling chemistry of molecules on the sensor surface, and surface regeneration conditions. These technical details are generally included in materials and methods sections of published manuscripts and are not easily accessible using the common internet browser search engines or PubMed. Herein, we introduce a surface plasmon resonance database, www.sprdatabase.info that contains technical details extracted from 5140 publications with surface plasmon resonance data. We also provide an analysis of experimental conditions preferred by different laboratories. These experimental variables can be searched within the database and help future users of this technology to design better experiments.

Conclusion

Amine coupling and CM5 chips were the most common methods used for immobilizing proteins in surface plasmon resonance experiments. However, number of different chips, capture methods and buffer conditions were used by multiple investigators. We predict that the database will significantly help the scientific community using this technology and hope that users will provide feedback to improve and expand the database indefinitely. Publicly available information in the database can save a great amount of time and resources by assisting initial optimization and troubleshooting of surface plasmon resonance experiments.

Quantitative and network pharmacology: A case study of rhein alleviating pathological progress of renal interstitial fibrosis

ETHNOPHARMACOLOGICAL RELEVANCE

The current network pharmacology model focuses mainly on static and qualitative characterisation between drugs and targets or molecular pathway networks, but it does not reflect the multi-scale, dynamic and quantitative process of drug action.

AIM OF THE STUDY

In this study, we developed a new model known as quantitative and network pharmacology (QNP) to characterise the dynamic and quantitative interventions of drugs within a multi-scale biological network.

MATERIALS AND METHODS

Firstly, we used a systems biology method to construct a molecule-cell dynamic network model to simulate the pathological processes of diseases. Secondly, according to the principles of enzymatic kinetics, we generated a multi-scale drug intervention model to simulate the intervention of drugs in multi-scale networks at different concentrations and pathological stages. Finally, we took rhein treatment of renal interstitial fibrosis (RIF) as an example to illustrate the QNP model.

RESULTS

We successfully constructed the a QNP model that includes both a multi-scale dynamic network disease model and drug intervention model. The QNP model accurately simulated the pathological process of RIF, and the simulation results were validated by a series of cell and animal experiments. Meanwhile, the QNP model demonstrated that rhein can delay the pathological process at the studied concentrations of 5 nM, 10 nM, and 20 nM, and can also exert a better therapeutic effect on fibrosis before the proliferation stage of RIF. Furthermore, through uncertainty and sensitivity analysis, we identified that FAK and Smad3 may be potential targets for RIF.

CONCLUSION

Our QNP model provides a molecular-cellular understanding of the pathological mechanisms of RIF, serving as a new approach and strategy for the construction of dynamic multi-scale network model of diseases and drug intervention.

An overview of COVID-19 with an emphasis on computational approach for its preventive intervention

SARS-CoV-2, a novel Betacoronavirus, has attracted global attention because of its extremely high infection rate and large number of human deaths. It possesses a positive-sense, single-stranded RNA of ~ 30 kb nucleotides as its genetic material. It is responsible for COVID-19 which has been declared a pandemic by WHO. Having reported for the first time in Wuhan, China, the virus infected over 21.48 million people and caused over 0.77 million deaths till mid-august 2020. SARS-CoV-2 contains the spike protein site that gets activated by an enzyme furin which is found in the lung, liver, and small intestine of humans. It shows the potentiality of virus for attacking multiple organs and their failures. Due to the absence of vaccines, the cure is restricted to supportive care including repurposed drugs. In silico approaches may offer an alternative screening to optimize hits to lead stages. It can provide important related to drug resistance, their lineages and evolution. This approach may also help to find an effective vaccine against COVID-19. This review focuses on the in silico aspects of genomics, proteomics, pathogenesis, phylogenetic analysis and viral receptor binding analysis in Betacoronavirus.