Quantitative Estimate Index for Early-Stage Screening of Compounds Targeting Protein-Protein Interactions

The quantification of drug-likeness is very useful for screening drug candidates. The quantitative estimate of drug-likeness (QED) is the most commonly used quantitative drug efficacy assessment method proposed by Bickerton et al. However, QED is not considered suitable for screening compounds that target protein-protein interactions (PPI), which have garnered significant interest in recent years. Therefore, we developed a method called the quantitative estimate of protein-protein interaction targeting drug-likeness (QEPPI), specifically for early-stage screening of PPI-targeting compounds. QEPPI is an extension of the QED method for PPI-targeting drugs and developed using the QED concept, involving modeling physicochemical properties based on the information available on the drug. QEPPI models the physicochemical properties of compounds that have been reported in the literature to act on PPIs. Compounds in iPPI-DB, which comprises PPI inhibitors and stabilizers, and FDA-approved drugs were evaluated using QEPPI. The results showed that QEPPI is more suitable for the early screening of PPI-targeting compounds than QED. QEPPI was also considered an extended concept of "Rules of Four" (RO4), a PPI inhibitor index proposed by Morelli et al. To compare the discriminatory performance of QEPPI and RO4, we evaluated their discriminatory performance using the datasets of PPI-target compounds and FDA-approved drugs using F-score and other indices. Results of the F-score of RO4 and QEPPI were 0.446 and 0.499, respectively. QEPPI demonstrated better performance and enabled quantification of drug-likeness for early-stage PPI drug discovery. Hence, it could be used as an initial filter for efficient screening of PPI-targeting compounds, which has been difficult in the past.

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Quantitative Estimate of Protein-Protein Interaction Targeting Drug-likeness

10.26434/chemrxiv.14489769.v1 ◽

2021 ◽

Author(s):

Takatsugu Kosugi ◽

Masahito Ohue

Keyword(s):

Physicochemical Properties ◽

Protein Interaction ◽

Quantitative Estimate ◽

Early Stage ◽

Assessment Method ◽

Early Screening ◽

Protein Protein Interaction ◽

Approved Drugs ◽

Fda Approved Drugs ◽

Discriminatory Performance

The quantification of drug-likeness is very useful for screening drug candidates. The quantitative estimate of drug-likeness (QED) is the most commonly used quantitative drug efficacy assessment method proposed by Bickerton et al. However, QED is not considered suitable for screening compounds that target protein-protein interactions (PPI), which have garnered significant interest in recent years. Therefore, we developed a method called the quantitative estimate of protein-protein interaction targeting drug-likeness (QEPPI), specifically for early-stage screening of PPI-targeting compounds. QEPPI is an extension of the QED method for PPI-targeting drugs and developed using the QED concept, involving modeling physicochemical properties based on the information available on the drug. QEPPI models the physicochemical properties of compounds that have been reported in the literature to act on PPIs. Compounds in iPPI-DB, which comprises PPI inhibitors and stabilizers, and FDA-approved drugs were evaluated using QEPPI. The results showed that QEPPI is more suitable for the early screening of PPI-targeting compounds than QED. QEPPI was also considered an extended concept of "Rules of Four" (RO4), a PPI inhibitor index proposed by Morelli et al. To compare the discriminatory performance of QEPPI and RO4, we evaluated their discriminatory performance using the datasets of PPI-target compounds and FDA-approved drugs using F-score and other indices. Results of the F-score of RO4 and QEPPI were 0.446 and 0.499, respectively. QEPPI demonstrated better performance and enabled quantification of drug-likeness for early-stage PPI drug discovery. Hence, it could be used as an initial filter for efficient screening of PPI-targeting compounds, which has been difficult in the past.

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Ways into Understanding HIF Inhibition

Cancers ◽

10.3390/cancers13010159 ◽

2021 ◽

Vol 13 (1) ◽

pp. 159

Author(s):

Tina Schönberger ◽

Joachim Fandrey ◽

Katrin Prost-Fingerle

Keyword(s):

Protein Interactions ◽

Target Genes ◽

Protein Protein Interactions ◽

Low Oxygen ◽

Drug Candidates ◽

Open Questions ◽

Wide Range ◽

Hif Pathway

Hypoxia is a key characteristic of tumor tissue. Cancer cells adapt to low oxygen by activating hypoxia-inducible factors (HIFs), ensuring their survival and continued growth despite this hostile environment. Therefore, the inhibition of HIFs and their target genes is a promising and emerging field of cancer research. Several drug candidates target protein–protein interactions or transcription mechanisms of the HIF pathway in order to interfere with activation of this pathway, which is deregulated in a wide range of solid and liquid cancers. Although some inhibitors are already in clinical trials, open questions remain with respect to their modes of action. New imaging technologies using luminescent and fluorescent methods or nanobodies to complement widely used approaches such as chromatin immunoprecipitation may help to answer some of these questions. In this review, we aim to summarize current inhibitor classes targeting the HIF pathway and to provide an overview of in vitro and in vivo techniques that could improve the understanding of inhibitor mechanisms. Unravelling the distinct principles regarding how inhibitors work is an indispensable step for efficient clinical applications and safety of anticancer compounds.

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PS4DR: A multimodal workflow for identification and prioritization of drugs based on pathway signatures

10.21203/rs.2.12791/v4 ◽

2020 ◽

Author(s):

Mhammad Asif Emon ◽

Daniel Domingo-Fernández ◽

Charles Tapley Hoyt ◽

Martin Hofmann-Apitius

Keyword(s):

Gene Expression ◽

Genome Wide Association Study ◽

Drug Repositioning ◽

Heterogeneous Data ◽

New Drugs ◽

Gwas Data ◽

Drug Candidates ◽

Gene Expression Signatures ◽

Approved Drugs ◽

Fda Approved Drugs

Abstract Background: During the last decade, there has been a surge towards computational drug repositioning owing to constantly increasing -omics data in the biomedical research field. While numerous existing methods focus on the integration of heterogeneous data to propose candidate drugs, it is still challenging to substantiate their results with mechanistic insights of these candidate drugs. Therefore, there is a need for more innovative and efficient methods which can enable better integration of data and knowledge for drug repositioning. Results: Here, we present a customizable workflow ( PS4DR) which not only integrates high-throughput data such as genome-wide association study (GWAS) data and gene expression signatures from disease and drug perturbations but also takes pathway knowledge into consideration to predict drug candidates for repositioning. We have collected and integrated publicly available GWAS data and gene expression signatures for several diseases and hundreds of FDA-approved drugs or those under clinical trial in this study. Additionally, different pathway databases were used for mechanistic knowledge integration in the workflow. Using this systematic consolidation of data and knowledge, the workflow computes pathway signatures that assist in the prediction of new indications for approved and investigational drugs. Conclusion: We showcase PS4DR with applications demonstrating how this tool can be used for repositioning and identifying new drugs as well as proposing drugs that can simulate disease dysregulations. We were able to validate our workflow by demonstrating its capability to predict FDA-approved drugs for their known indications for several diseases. Further, PS4DR returned many potential drug candidates for repositioning that were backed up by epidemiological evidence extracted from scientific literature. Source code is freely available at https://github.com/ps4dr/ps4dr .

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Discovery, development and application of drugs targeting BCL-2 pro-survival proteins in cancer

Biochemical Society Transactions ◽

10.1042/bst20210749 ◽

2021 ◽

Author(s):

Erinna F. Lee ◽

W. Douglas Fairlie

Keyword(s):

Structural Biology ◽

Protein Interactions ◽

Basic Science ◽

Great Success ◽

Protein Protein Interactions ◽

New Class ◽

Success Stories ◽

History Of ◽

Approved Drugs ◽

Close Relatives

The discovery of a new class of small molecule compounds that target the BCL-2 family of anti-apoptotic proteins is one of the great success stories of basic science leading to translational outcomes in the last 30 years. The eponymous BCL-2 protein was identified over 30 years ago due to its association with cancer. However, it was the unveiling of the biochemistry and structural biology behind it and its close relatives’ mechanism(s)-of-action that provided the inspiration for what are now known as ‘BH3-mimetics’, the first clinically approved drugs designed to specifically inhibit protein–protein interactions. Herein, we chart the history of how these drugs were discovered, their evolution and application in cancer treatment.

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Screening of FDA Approved Drugs Against COVID-19 Main Protease: Coronavirus Disease

10.20944/preprints202004.0062.v1 ◽

2020 ◽

Cited By ~ 4

Author(s):

Vijayakumar Balakrishnan ◽

Karthik Lakshminarayanan

Keyword(s):

Vaccine Development ◽

New Drugs ◽

World Health ◽

Wuhan City ◽

Drug Candidates ◽

Human Contact ◽

Main Protease ◽

Potent Drug ◽

Approved Drugs ◽

Fda Approved Drugs

In the end of December 2019, a new strain of coronavirus was identified in the Wuhan city of Hubei province in China. Within a shorter period of time, an unprecedented outbreak of this strain was witnessed over the entire Wuhan city. This novel coronavirus strain was later officially renamed as COVID-19 (Coronavirus disease 2019) by the World Health Organization. The mode of transmission had been found to be human-to-human contact and hence resulted in a rapid surge across the globe where more than 1,100,000 people have been infected with COVID-19. In the current scenario, finding potent drug candidates for the treatment of COVID-19 has emerged as the most challenging task for clinicians and researchers worldwide. Identification of new drugs and vaccine development may take from a few months to years based on the clinical trial processes. To overcome the several limitations involved in identifying and bringing out potent drug candidates for treating COVID-19, in the present study attempts were made to screen the FDA approved drugs using High Throughput Virtual Screening (HTVS). The COVID-19 main protease (COVID-19 Mpro) was chosen as the drug target for which the FDA approved drugs were initially screened with HTVS. The drug candidates that exhibited favorable docking score, energy and emodel calculations were further taken for performing Induced Fit Docking (IFD) using Schrodinger’s GLIDE. From the flexible docking results, the following four FDA approved drugs Sincalide, Pentagastrin, Ritonavir and Phytonadione were identified. In particular, Sincalide and Pentagastrin can be considered potential key players for the treatment of COVID-19 disease.

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Factors in Oncogenesis: Viral Infections in Ovarian Cancer

Cancers ◽

10.3390/cancers12030561 ◽

2020 ◽

Vol 12 (3) ◽

pp. 561 ◽

Cited By ~ 2

Author(s):

Sudipta Pathak ◽

Jacek R. Wilczyński ◽

Edyta Paradowska

Keyword(s):

Ovarian Cancer ◽

Molecular Mechanisms ◽

Viral Infections ◽

Early Stage ◽

Drug Efficacy ◽

Microbial Infection ◽

Early Screening ◽

High Fatality Rate ◽

Microbiome Research ◽

Potential Risk Factors

Ovarian cancer (OC) is one of the leading causes of cancer death in women, with high-grade serous ovarian cancer (HGSOC) being the most lethal gynecologic malignancy among women. This high fatality rate is the result of diagnosis of a high number of new cases when cancer implants have already spread. The poor prognosis is due to our inadequate understanding of the molecular mechanisms preceding ovarian malignancy. Knowledge about the site of origination has been improved recently by the discovery of tube intraepithelial cancer (TIC), but the potential risk factors are still obscure. Due to high tumoral heterogeneity in OC, the establishment of early stage biomarkers is still underway. Microbial infection may induce or result in chronic inflammatory infection and in the pathogenesis of cancers. Microbiome research has shed light on the relationships between the host and microbiota, as well as the direct roles of host pathogens in cancer development, progression, and drug efficacy. While controversial, the detection of viruses within ovarian malignancies and fallopian tube tissues suggests that these pathogens may play a role in the development of OC. Genomic and proteomic approaches have enhanced the methods for identifying candidates in early screening. This article summarizes the existing knowledge related to the molecular mechanisms that lead to tumorigenesis in the ovary, as well as the viruses detected in OC cases and how they may elevate this process.

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Signaling Protein Networks as Targets of New Antineoplastic Drugs

The International Journal of Biological Markers ◽

10.1177/172460080301800110 ◽

2003 ◽

Vol 18 (1) ◽

pp. 57-61 ◽

Cited By ~ 2

Author(s):

S. Alberti ◽

S. Parodi

Keyword(s):

Protein Interactions ◽

High Throughput Screening ◽

Regulatory Networks ◽

Signaling Proteins ◽

Protein Protein Interactions ◽

Therapeutic Approaches ◽

Chemical Libraries ◽

Drug Candidates ◽

Microbial Extracts ◽

Depth Analysis

In-depth analysis of molecular regulatory networks in cancer holds the promise of improved knowledge of the pathophysiology of tumor cells so that it will become possible to design a detailed molecular tumor taxonomy. This knowledge will also offer new opportunities for the identification and validation of key molecular tumor targets to be exploited for novel therapeutic approaches. Some signaling proteins have already been identified as such, e.g. c-Myc, Cyclin D1, Bcl-XL, kinases and some nuclear receptors. This has led to the successful development of a few function-modulatory drugs (Glivec, SERM, Iressa), providing proof-of-principle of the validity of this approach. Further developments are likely to derive from “-omic” approaches, aimed at the understanding of signaling networks and of the mechanism of action of newfound lead molecules. High-throughput screening of small drug-like molecules from combinatorial chemical libraries or from microbial extracts will identify novel, “intelligent” drug candidates. An additional medicinal chemistry strategy (via 40–50 unit rosary-bead chains) has the potential to be much more effective than small molecules in interfering with protein-protein interactions. This may lead to considerably higher selectivity and effectiveness compared with historical approaches in drug discovery.

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High-throughput behavioral screen in C. elegans reveals novel Parkinson’s disease drug candidates

10.1101/2020.02.20.958751 ◽

2020 ◽

Cited By ~ 1

Author(s):

Salman Sohrabi ◽

Danielle E. Mor ◽

Rachel Kaletsky ◽

William Keyes ◽

Coleen T. Murphy

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

High Throughput ◽

C Elegans ◽

Drug Candidates ◽

Branched Chain Amino Acid ◽

Branched Chain ◽

Approved Drugs ◽

Fda Approved Drugs ◽

Behavioral Screen

AbstractWe recently linked branched-chain amino acid transferase 1 (BCAT1) with the movement disorder Parkinson’s disease (PD), and found that reduction of C. elegans bcat-1 causes abnormal spasm-like ‘curling’ behavior with age. Here, we report the development of a high-throughput automated curling assay and its application to the discovery of new potential PD therapeutics. Four FDA-approved drugs were identified as candidates for late-in-life intervention, with metformin showing the greatest promise for repurposing to PD.

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Identification of Candidate COVID-19 Therapeutics using hPSC-derived Lung Organoids

10.1101/2020.05.05.079095 ◽

2020 ◽

Cited By ~ 19

Author(s):

Yuling Han ◽

Liuliu Yang ◽

Xiaohua Duan ◽

Fuyu Duan ◽

Benjamin E. Nilsson-Payant ◽

...

Keyword(s):

Pluripotent Stem Cells ◽

Lung Cells ◽

Type I ◽

Type Ii Cells ◽

Type Ii ◽

Interferon Signaling ◽

Pulmonary Infections ◽

Drug Candidates ◽

Approved Drugs ◽

Fda Approved Drugs

Summary ParagraphThe SARS-CoV-2 virus has caused already over 3.5 million COVID-19 cases and 250,000 deaths globally. There is an urgent need to create novel models to study SARS-CoV-2 using human disease-relevant cells to understand key features of virus biology and facilitate drug screening. As primary SARS-CoV-2 infection is respiratory-based, we developed a lung organoid model using human pluripotent stem cells (hPSCs) that could be adapted for drug screens. The lung organoids, particularly aveolar type II cells, express ACE2 and are permissive to SARS-CoV-2 infection. Transcriptomic analysis following SARS-CoV-2 infection revealed a robust induction of chemokines and cytokines with little type I/III interferon signaling, similar to that observed amongst human COVID-19 pulmonary infections. We performed a high throughput screen using hPSC-derived lung organoids and identified FDA-approved drug candidates, including imatinib and mycophenolic acid, as inhibitors of SARS-CoV-2 entry. Pre- or post-treatment with these drugs at physiologically relevant levels decreased SARS-CoV-2 infection of hPSC-derived lung organoids. Together, these data demonstrate that hPSC-derived lung cells infected by SARS-CoV-2 can model human COVID-19 disease and provide a valuable resource to screen for FDA-approved drugs that might be repurposed and should be considered for COVID-19 clinical trials.

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