scholarly journals Uncovering drug repurposing candidates for head and neck cancers: insights from systematic pharmacogenomics data analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Annie Wai Yeeng Chai ◽  
Aik Choon Tan ◽  
Sok Ching Cheong
Keyword(s):  
Head And Neck ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Treatment Options ◽  
Hdac Inhibitors ◽  
Drug Repurposing ◽  
Genomic Data ◽  
Reference Database ◽  
Drug Candidates ◽  
Egfr Ligands

AbstractEffective treatment options for head and neck squamous cell carcinoma (HNSCC) are currently lacking. We exploited the drug response and genomic data of the 28 HNSCC cell lines, screened with 4,518 compounds, from the PRISM repurposing dataset to uncover repurposing drug candidates for HNSCC. A total of 886 active compounds, comprising of 418 targeted cancer, 404 non-oncology, and 64 chemotherapy compounds were identified for HNSCC. Top classes of mechanism of action amongst targeted cancer compounds included PI3K/AKT/MTOR, EGFR, and HDAC inhibitors. We have shortlisted 36 compounds with enriched killing activities for repurposing in HNSCC. The integrative analysis confirmed that the average expression of EGFR ligands (AREG, EREG, HBEGF, TGFA, and EPGN) is associated with osimertinib sensitivity. Novel putative biomarkers of response including those involved in immune signalling and cell cycle were found to be associated with sensitivity and resistance to MEK inhibitors respectively. We have also developed an RShiny webpage facilitating interactive visualization to fuel further hypothesis generation for drug repurposing in HNSCC. Our study provides a rich reference database of HNSCC drug sensitivity profiles, affording an opportunity to explore potential biomarkers of response in prioritized drug candidates. Our approach could also reveal insights for drug repurposing in other cancers.

scholarly journals Drug repurposing using transcriptome sequencing and virtual drug screening in a patient with glioblastoma

2020 ◽  
Author(s):  
Mohamed E. M. Saeed ◽  
Onat Kadioglu ◽  
Henry Johannes Greten ◽  
Adem Yildirim ◽  
Katharina Mayr ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Treatment Options ◽  
Drug Repurposing ◽  
Sequencing Data ◽  
Survival Times ◽  
Chemical Library ◽  
Ki 67 ◽  
Drug Candidates ◽  
Egfr Expression ◽  
Approved Drugs

SummaryBackground Precision medicine and drug repurposing are attractive strategies, especially for tumors with worse prognosis. Glioblastoma is a highly malignant brain tumor with limited treatment options and short survival times. We identified novel BRAF (47-438del) and PIK3R1 (G376R) mutations in a glioblastoma patient by RNA-sequencing. Methods The protein expression of BRAF and PIK3R1 as well as the lack of EGFR expression as analyzed by immunohistochemistry corroborated RNA-sequencing data. The expression of additional markers (AKT, SRC, mTOR, NF-κB, Ki-67) emphasized the aggressiveness of the tumor. Then, we screened a chemical library of > 1500 FDA-approved drugs and > 25,000 novel compounds in the ZINC database to find established drugs targeting BRAF47-438del and PIK3R1-G376R mutated proteins. Results Several compounds (including anthracyclines) bound with higher affinities than the control drugs (sorafenib and vemurafenib for BRAF and PI-103 and LY-294,002 for PIK3R1). Subsequent cytotoxicity analyses showed that anthracyclines might be suitable drug candidates. Aclarubicin revealed higher cytotoxicity than both sorafenib and vemurafenib, whereas idarubicin and daunorubicin revealed higher cytotoxicity than LY-294,002. Liposomal formulations of anthracyclines may be suitable to cross the blood brain barrier. Conclusions In conclusion, we identified novel small molecules via a drug repurposing approach that could be effectively used for personalized glioblastoma therapy especially for patients carrying BRAF47-438del and PIK3R1-G376R mutations.

scholarly journals EGFR and PI3K Pathway Activities Might Guide Drug Repurposing in HPV-Negative Head and Neck Cancers

2021 ◽  
Vol 11 ◽  
Author(s):  
Andreas Mock ◽  
Michaela Plath ◽  
Julius Moratin ◽  
Maria Johanna Tapken ◽  
Dirk Jäger ◽  
...  
Keyword(s):  
Head And Neck ◽  
Hdac Inhibitors ◽  
Drug Repurposing ◽  
B Cell Lymphoma ◽  
Pi3k Pathway ◽  
Genetic Alterations ◽  
The Cancer Genome Atlas ◽  
Cancer Drug ◽  
Oncogenic Signaling

While genetic alterations in Epidermal growth factor receptor (EGFR) and PI3K are common in head and neck squamous cell carcinomas (HNSCC), their impact on oncogenic signaling and cancer drug sensitivities remains elusive. To determine their consequences on the transcriptional network, pathway activities of EGFR, PI3K, and 12 additional oncogenic pathways were inferred in 498 HNSCC samples of The Cancer Genome Atlas using PROGENy. More than half of HPV-negative HNSCC showed a pathway activation in EGFR or PI3K. An amplification in EGFR and a mutation in PI3KCA resulted in a significantly higher activity of the respective pathway (p = 0.017 and p = 0.007). Interestingly, both pathway activations could only be explained by genetic alterations in less than 25% of cases indicating additional molecular events involved in the downstream signaling. Suitable in vitro pathway models could be identified in a published drug screen of 45 HPV-negative HNSCC cell lines. An active EGFR pathway was predictive for the response to the PI3K inhibitor buparlisib (p = 6.36E-03) and an inactive EGFR and PI3K pathway was associated with efficacy of the B-cell lymphoma (BCL) inhibitor navitoclax (p = 9.26E-03). In addition, an inactive PI3K pathway correlated with a response to multiple Histone deacetylase inhibitor (HDAC) inhibitors. These findings require validation in preclinical models and clinical studies.

scholarly journals Tree-Based QSAR Model for Drug Repurposing in the Discovery of New Antibacterial Compounds against Escherichia coli

2020 ◽  
Vol 13 (12) ◽  
pp. 431
Author(s):  
Beatriz Suay-Garcia ◽  
Antonio Falcó ◽  
J. Ignacio Bueso-Bordils ◽  
Gerardo M. Anton-Fos ◽  
M. Teresa Pérez-Gracia ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Antibacterial Activity ◽  
Prediction Models ◽  
Treatment Options ◽  
Drug Repurposing ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Linear Discriminant ◽  
Drug Candidates ◽  
Close Relationship

Drug repurposing appears as an increasing popular tool in the search of new treatment options against bacteria. In this paper, a tree-based classification method using Linear Discriminant Analysis (LDA) and discrete indexes was used to create a QSAR (Quantitative Structure-Activity Relationship) model to predict antibacterial activity against Escherichia coli. The model consists on a hierarchical decision tree in which a discrete index is used to divide compounds into groups according to their values for said index in order to construct probability spaces. The second step consists in the calculation of a discriminant function which determines the prediction of the model. The model was used to screen the DrugBank database, identifying 134 drugs as possible antibacterial candidates. Out of these 134 drugs, 8 were antibacterial drugs, 67 were drugs approved for different pathologies and 55 were drugs in experimental stages. This methodology has proven to be a viable alternative to the traditional methods used to obtain prediction models based on LDA and its application provides interesting new drug candidates to be studied as repurposed antibacterial treatments. Furthermore, the topological indexes Nclass and Numhba have proven to have the ability to group active compounds effectively, which suggests a close relationship between them and the antibacterial activity of compounds against E. coli.

Molecular profiling of advanced refractory prostate cancer.

2014 ◽  
Vol 32 (4_suppl) ◽  
pp. 107-107
Author(s):  
Rebecca A. Feldman ◽  
Gargi Dan Basu ◽  
Joanne Xiu ◽  
David Arguello ◽  
Sherri Z. Millis ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Drug Response ◽  
Treatment Options ◽  
Hdac Inhibitors ◽  
Gene Copy Number ◽  
Gene Copy ◽  
Molecular Profile ◽  
Sequencing Data ◽  
Pathway Activation

107 Background: Prostate cancer is the second leading cause of cancer-related death among men in the U.S. Forty percent of men diagnosed will develop metastatic disease, which has few treatment options. We aim to describe the molecular profile of prostate cancer tumors and potential for novel therapeutic options. Methods: We reviewed profiling data of over 330 patients from a large referral laboratory (Caris Life Sciences, Phoenix, AZ) for biomarkers of drug response. Multiple methodologies were employed: sequencing (NGS, Sanger, pyrosequencing), in-situ hybridization (fluorescent and chromogenic), and immunohistochemistry. Results: High expression was observed for AR, MRP1, TOPO1, TLE3 and EGFR, with positivity rates of 89%, 87%, 63%, 48%, and 47%, respectively. Low expression was observed for TS, PGP, TUBB3, RRM1, PTEN and MGMT, with negativity rates of 94%, 87%, 75%, 69%, 54%, and 45%, respectively. Gene copy number increases for EGFR and cMYC were observed in 13% of patients. Sequencing data showed a 48% mutation rate for TP53, 18% for PTEN, 9% for CTNNB1, 8% for PIK3CA, 5% for RB1, ATM, and cMET, and approximately 2% for K/HRAS, ERBB4, ALK, BRAF, and cKIT. Targeted therapy options include imatinib for patients with high cKIT or PDGFRA (9 to 10%) and cetuximab for patients with EGFR positivity (13 to 47%). Promising agents may be considered, including cabozantinib, based on 4% of cohort with cMET aberrations or PAM pathway inhibitors (BEZ234, everolimus) based on approximately 30% of cohort with PIK3CA pathway activation. Lastly, HDAC inhibitors have recently been linked to cMYC driven cancers (13% amplified). 5-FU, gemcitabine, and temozolomide chemotherapies may be options, as approximately 70% of cohort with low TS, RRM1, or MGMT. Biomarker guidance for common prostate cancer drugs such as cabazitaxel is also provided, based on approximately 70% of cohort with low TUBB3 or PGP, or high TLE3. Also, continued dependence on androgen signaling is exhibited by 89% of cohort with high AR, indicating potential utility of anti-androgen agents like enzalutamide. Conclusions: Tumor profiling identified small subsets of patients that may benefit from targeted agents approved for other solid tumors (imatinib, cetuximab), promising therapies in clinical trials (cabozantinib) or agents not routinely used for prostate cancer (gemcitabine).

scholarly journals Using predictive machine learning models for drug response simulation by calibrating patient-specific pathway signatures

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Sepehr Golriz Khatami ◽  
Sarah Mubeen ◽  
Vinay Srinivas Bharadhwaj ◽  
Alpha Tom Kodamullil ◽  
Martin Hofmann-Apitius ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Response ◽  
Clinical Decision Making ◽  
Biological Process ◽  
Drug Repurposing ◽  
Clinical Decision ◽  
Patient Specific ◽  
Drug Candidates ◽  
Scoring Algorithm ◽  
Specific Pathway

AbstractThe utility of pathway signatures lies in their capability to determine whether a specific pathway or biological process is dysregulated in a given patient. These signatures have been widely used in machine learning (ML) methods for a variety of applications including precision medicine, drug repurposing, and drug discovery. In this work, we leverage highly predictive ML models for drug response simulation in individual patients by calibrating the pathway activity scores of disease samples. Using these ML models and an intuitive scoring algorithm to modify the signatures of patients, we evaluate whether a given sample that was formerly classified as diseased, could be predicted as normal following drug treatment simulation. We then use this technique as a proxy for the identification of potential drug candidates. Furthermore, we demonstrate the ability of our methodology to successfully identify approved and clinically investigated drugs for four different cancers, outperforming six comparable state-of-the-art methods. We also show how this approach can deconvolute a drugs’ mechanism of action and propose combination therapies. Taken together, our methodology could be promising to support clinical decision-making in personalized medicine by simulating a drugs’ effect on a given patient.

scholarly journals Knowing and combating the enemy: a brief review on SARS-CoV-2 and computational approaches applied to the discovery of drug candidates

2021 ◽  
Author(s):  
Mateus S.M. Serafim ◽  
Jadson C. Gertrudes ◽  
Débora M. A. Costa ◽  
Patricia R. Oliveira ◽  
Vinicius G. Maltarollo ◽  
...  
Keyword(s):  
Current Knowledge ◽  
Treatment Options ◽  
Drug Repurposing ◽  
Critical Issue ◽  
Economic Systems ◽  
Computational Approaches ◽  
Research Teams ◽  
Drug Candidates ◽  
Current Scenario ◽  
Governmental Institutions

Since the emergence of the new severe acute respiratory syndrome-related coronaviruses 2 (SARS-CoV-2) at the end of December 2019 in China, and with the urge of the coronavirus disease 2019 (COVID-19) pandemic, there have been a huge effort of many research teams and governmental institutions worldwide to mitigate the current scenario. Reaching more than 1,377,000 deaths in the world and still with a growing number of infections, SARS-CoV-2 remains a critical issue for global health and economic systems, with an urgency for available therapeutic options. In this scenario, as drug repurposing and discovery remains a challenge, computer-aided drug design (CADD) approaches, including machine learning (ML) techniques, can be useful tools to the design and discovery of novel potential antiviral inhibitors against SARS-CoV-2. In this work, we describe and review the current knowledge on this virus and the pandemic, the latest strategies and computational approaches applied to search for treatment options, as well as the challenges to overcome COVID-19.

scholarly journals Identification of 37 Heterogeneous Drug Candidates for Treatment of COVID-19 via a Rational Transcriptomics-Based Drug Repurposing Approach

2021 ◽  
Vol 14 (2) ◽  
pp. 87
Author(s):  
Andrea Gelemanović ◽  
Tinka Vidović ◽  
Višnja Stepanić ◽  
Katarina Trajković
Keyword(s):  
Treatment Options ◽  
Drug Repurposing ◽  
Enrichment Analysis ◽  
Current Treatment ◽  
Host Cells ◽  
Biological Knowledge ◽  
Pathway Enrichment Analysis ◽  
Biological Processes ◽  
Pathway Enrichment ◽  
Drug Candidates

A year after the initial outbreak, the COVID-19 pandemic caused by SARS-CoV-2 virus remains a serious threat to global health, while current treatment options are insufficient to bring major improvements. The aim of this study is to identify repurposable drug candidates with a potential to reverse transcriptomic alterations in the host cells infected by SARS-CoV-2. We have developed a rational computational pipeline to filter publicly available transcriptomic datasets of SARS-CoV-2-infected biosamples based on their responsiveness to the virus, to generate a list of relevant differentially expressed genes, and to identify drug candidates for repurposing using LINCS connectivity map. Pathway enrichment analysis was performed to place the results into biological context. We identified 37 structurally heterogeneous drug candidates and revealed several biological processes as druggable pathways. These pathways include metabolic and biosynthetic processes, cellular developmental processes, immune response and signaling pathways, with steroid metabolic process being targeted by half of the drug candidates. The pipeline developed in this study integrates biological knowledge with rational study design and can be adapted for future more comprehensive studies. Our findings support further investigations of some drugs currently in clinical trials, such as itraconazole and imatinib, and suggest 31 previously unexplored drugs as treatment options for COVID-19.

scholarly journals Drug Repurposing for COVID-19 Treatment by Integrating Network Pharmacology and Transcriptomics

Pharmaceutics ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 545
Author(s):  
Dan-Yang Liu ◽  
Jia-Chen Liu ◽  
Shuang Liang ◽  
Xiang-He Meng ◽  
Jonathan Greenbaum ◽  
...  
Keyword(s):  
Immune Cell ◽  
Treatment Plan ◽  
Treatment Options ◽  
Drug Repurposing ◽  
Gene Set Enrichment Analysis ◽  
Exposure Model ◽  
Network Pharmacology ◽  
Integrated Network ◽  
Health Crisis ◽  
Drug Candidates

Since coronavirus disease 2019 (COVID-19) is a serious new worldwide public health crisis with significant morbidity and mortality, effective therapeutic treatments are urgently needed. Drug repurposing is an efficient and cost-effective strategy with minimum risk for identifying novel potential treatment options by repositioning therapies that were previously approved for other clinical outcomes. Here, we used an integrated network-based pharmacologic and transcriptomic approach to screen drug candidates novel for COVID-19 treatment. Network-based proximity scores were calculated to identify the drug–disease pharmacological effect between drug–target relationship modules and COVID-19 related genes. Gene set enrichment analysis (GSEA) was then performed to determine whether drug candidates influence the expression of COVID-19 related genes and examine the sensitivity of the repurposing drug treatment to peripheral immune cell types. Moreover, we used the complementary exposure model to recommend potential synergistic drug combinations. We identified 18 individual drug candidates including nicardipine, orantinib, tipifarnib and promethazine which have not previously been proposed as possible treatments for COVID-19. Additionally, 30 synergistic drug pairs were ultimately recommended including fostamatinib plus tretinoin and orantinib plus valproic acid. Differential expression genes of most repurposing drugs were enriched significantly in B cells. The findings may potentially accelerate the discovery and establishment of an effective therapeutic treatment plan for COVID-19 patients.

scholarly journals Assessment of pharmacogenomic agreement

2016 ◽  
Author(s):  
Zhaleh Safikhani ◽  
Nehme El-Hachem ◽  
Rene Quevedo ◽  
Petr Smirnov ◽  
Anna Goldenberg ◽  
...  
Keyword(s):  
Cell Line ◽  
Reasonable Agreement ◽  
Large Scale ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Cancer Cell Line ◽  
Genomic Data ◽  
Clinical Settings ◽  
Response Data ◽  
Pharmacological Data

AbstractIn 2013 we published an analysis demonstrating that drug response data and gene-drug associations reported in two independent large-scale pharmacogenomic screens, Genomics of Drug Sensitivity in Cancer1(GDSC) and Cancer Cell Line Encyclopedia2(CCLE), were inconsistent3. The GDSC and CCLE investigators recently reported that their respective studies exhibit reasonable agreement and yield similar molecular predictors of drug response4, seemingly contradicting our previous findings3. Reanalyzing the authors’ published methods and results, we found that their analysis failed to account for variability in the genomic data and more importantly compared different drug sensitivity measures from each study, which substantially deviate from our more stringent consistency assessment. Our comparison of the most updated genomic and pharmacological data from the GDSC and CCLE confirms our published findings that the measures of drug response reported by these two groups are not consistent5. We believe that a principled approach to assess the reproducibility of drug sensitivity predictors is necessary before envisioning their translation into clinical settings.

scholarly journals Considerations and Challenges for Sex-Aware Drug Repurposing

2021 ◽  
Author(s):  
Jennifer Fisher ◽  
Emma Jones ◽  
Victoria Flanary ◽  
Avery Williams ◽  
Elizabeth Ramsey ◽  
...  
Keyword(s):  
Sex Differences ◽  
Drug Discovery ◽  
Study Design ◽  
Drug Response ◽  
Drug Repurposing ◽  
Multiple Hypothesis Testing ◽  
Sex Bias ◽  
Therapeutic Effects ◽  
Drug Candidates ◽  
Traditional Drug

Sex differences are essential factors in disease etiology and manifestation in many diseases such as cardiovascular disease, cancer, and neurodegeneration (1). The biological influence of sex differences (including genomic, epigenetic, hormonal, immunological, and metabolic differences between males and females) and the lack of biomedical studies considering sex differences in their study design has led to several policies. For example, the National Institute of Health’s (NIH) sex as a biological variable (SABV) and Sex and Gender Equity in Research (SAGER)) policies to motivate researchers to consider sex differences (2). However, drug repurposing, a promising alternative to traditional drug discovery by identifying novel uses for FDA-approved drugs, lacks sex-aware methods that can improve the identification of drugs that have sex-specific responses (1,3–5). Sex-aware drug repurposing methods either select drug candidates that are more efficacious in one sex or deprioritize drug candidates based on if they are predicted to cause a sex-bias adverse event (SBAE), unintended therapeutic effects that are more likely to occur in one sex. Computational drug repurposing methods are encouraging approaches to develop for sex-aware drug repurposing because they can prioritize sex-specific drug candidates or SBAEs at lower cost and time than traditional drug discovery. Sex-aware methods currently exist for clinical, genomic, and transcriptomic information (3,6,7). They have not expanded to other data types, such as DNA variation, which has been beneficial in other drug repurposing methods that do not consider sex (8). Additionally, some sex-aware methods suffer from poorer performance because a disproportionate number of male and female samples are available to train computational methods (3). However, there is development potential for several different categories (i.e., data mining, ligand binding predictions, molecular associations, and networks). Low-dimensional representations of molecular association and network approaches are also especially promising candidates for future sex-aware drug repurposing methodologies because they reduce the multiple hypothesis testing burden and capture sex-specific variation better than the other methods (9,10). Here we review how sex influences drug response, the current state of drug repurposing including with respect to sex-bias drug response, and how model organism study design choices influence drug repurposing validation.

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