scholarly journals Biomedical Graph Visualizer for Identifying Drug Candidates

2020 ◽  
Author(s):  
Ashton Teng ◽  
Blanca Villanueva ◽  
Derek Jow ◽  
Shih-Cheng (Mars) Huang ◽  
Samantha N. Piekos ◽  
...  
Keyword(s):  
Drug Development ◽  
De Novo ◽  
Drug Candidate ◽  
Knowledge Graph ◽  
Major Barrier ◽  
Drug Candidates ◽  
Novel Treatments ◽  
Computational Discovery ◽  
Complex Relationships ◽  
User Friendly

1.AbstractMillions of Americans suffer from illnesses with non-existent or ineffective drug treatment. Identifying plausible drug candidates is a major barrier to drug development due to the large amount of time and resources required; approval can take years when people are suffering now. While computational tools can expedite drug candidate discovery, these tools typically require programming expertise that many biologists lack. Though biomedical databases continue to grow, they have proven difficult to integrate and maintain, and non-programming interfaces for these data sources are scarce and limited in capability. This creates an opportunity for us to present a suite of user-friendly software tools to aid computational discovery of novel treatments through de novo discovery or repurposing. Our tools eliminate the need for researchers to acquire computational expertise by integrating multiple databases and offering an intuitive graphical interface for analyzing these publicly available data. We built a computational knowledge graph focused on biomedical concepts related to drug discovery, designed visualization tools that allow users to explore complex relationships among entities in the graph, and served these tools through a free and user-friendly web interface. We show that users can conduct complex analyses with relative ease and that our knowledge graph and algorithms recover approved repurposed drugs. Our evaluation indicates that our method provides an intuitive, easy, and effective toolkit for discovering drug candidates. We show that our toolkit makes computational analysis for drug development more accessible and efficient and ultimately plays a role in bringing effective treatments to all patients.Our application is hosted at: https://biomedical-graph-visualizer.wl.r.appspot.com/

scholarly journals Drug Repurposing Opportunities in Pancreatic Ductal Adenocarcinoma

2021 ◽  
Vol 14 (3) ◽  
pp. 280
Author(s):  
Rita Rebelo ◽  
Bárbara Polónia ◽  
Lúcio Lara Santos ◽  
M. Helena Vasconcelos ◽  
Cristina P. R. Xavier
Keyword(s):  
Clinical Trials ◽  
Drug Development ◽  
Pancreatic Ductal Adenocarcinoma ◽  
De Novo ◽  
Drug Repurposing ◽  
Metastatic Tumor ◽  
Therapeutic Options ◽  
Ductal Adenocarcinoma ◽  
Clinical Indication ◽  
Novel Treatments

Pancreatic ductal adenocarcinoma (PDAC) is considered one of the deadliest tumors worldwide. The diagnosis is often possible only in the latter stages of the disease, with patients already presenting an advanced or metastatic tumor. It is also one of the cancers with poorest prognosis, presenting a five-year survival rate of around 5%. Treatment of PDAC is still a major challenge, with cytotoxic chemotherapy remaining the basis of systemic therapy. However, no major advances have been made recently, and therapeutic options are limited and highly toxic. Thus, novel therapeutic options are urgently needed. Drug repurposing is a strategy for the development of novel treatments using approved or investigational drugs outside the scope of the original clinical indication. Since repurposed drugs have already completed several stages of the drug development process, a broad range of data is already available. Thus, when compared with de novo drug development, drug repurposing is time-efficient, inexpensive and has less risk of failure in future clinical trials. Several repurposing candidates have been investigated in the past years for the treatment of PDAC, as single agents or in combination with conventional chemotherapy. This review gives an overview of the main drugs that have been investigated as repurposing candidates, for the potential treatment of PDAC, in preclinical studies and clinical trials.

Preclinical Assessment of a Novel CDC7 Inhibitor: Genomewide RNAi Screening Identifies Unique Synergetic and Resistance Genes,

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 3570-3570
Author(s):  
Nancy Liu-Sullivan ◽  
Bhavneet Bhinder ◽  
David Shum ◽  
Christina Ramirez ◽  
Constantin Radu ◽  
...  
Keyword(s):  
Kinase Inhibitor ◽  
De Novo ◽  
Conflicts Of Interest ◽  
Drug Candidate ◽  
Microscopy Imaging ◽  
Drug Candidates ◽  
Shrna Library ◽  
Ubiquitin Proteasome ◽  
Screening Platform ◽  
Drug Responsiveness

Abstract Abstract 3570 Despite extensive drug discovery efforts, drug-candidate failure and patients relapsing in the clinic remain as persistent problems. While insufficient drug-gene engagement leads to drug failure, de novo escape mutations give rise to patients relapsing, calling the need for systemic studies on how genes influence drug responsiveness. Towards this end, we have explored a functional short hairpin RNA (shRNA) based genomic screening platform aimed at interrogating drug-gene engagement and assessing its consequences on signaling pathways. We propose this concept as a novel way to evaluate drug candidates prior to clinical trials enabling liability assessment and predicting clinical outcome. We took advantage of the arrayed shRNA library produced in lentiviral particles and characterized by several obvious advantageous features including shRNA targeting one hairpin at a time and on the fly high content whole well microscopy imaging analysis. We carried out three parallel genomewide shRNA screens in the absence or presence of the novel CDC7 kinase inhibitor (MSK-777) at its IC20 and IC50 and have identified several gene candidates that influence MSK-777 sensitivity and resistance. These include synergizers that enhance MSK-777 sensitivity and rescuers that confer MSK-777 resistance. IPA analysis mapped clusters of these hits to multiple major pathways among them were the NF-kB pathway, the ubiquitin-proteasome pathway, DNA replication, and several epigenetic regulatory genes. We will present and discuss this concept together with the emerging pathways as a means to identify both key therapeutic targets and biomarkers of sensitivity and resistance. Thus, allowing for not only a broader applicability of assessing candidate genes that modulate specific drug agents, but also for the identification of a tailored and more efficacious therapeutic regimen to treat cancer. Disclosures: No relevant conflicts of interest to declare.

Antidepressants and antipsychotic agents as repurposable oncological drug candidates

2020 ◽  
Vol 27 ◽  
Author(s):  
Michał Antoszczak ◽  
Anna Markowska ◽  
Janina Markowska ◽  
Adam Huczyński
Keyword(s):  
Drug Development ◽  
De Novo ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Antipsychotic Agents ◽  
Therapeutic Action ◽  
Drug Candidates ◽  
New Strategy ◽  
The Central Nervous System ◽  
Medical Indications

: Drug repurposing, known also as drug repositioning/reprofiling, is a relatively new strategy for identification of alternative uses of well-known therapeutics that are outside the scope of their original medical indications. Such an approach might entail a number of advantages compared to standard de novo drug development, including less time needed to introduce the drug to the market, and lower costs. The group of compounds that could be considered as promising candidates for repurposing in oncology includes the central nervous system drugs, especially selected antidepressant and antipsychotic agents. In this article, we provide an overview of some antidepressants (citalopram, fluoxetine, paroxetine, sertraline) and antipsychotics (chlorpromazine, pimozide, thioridazine, trifluoperazine) that have the potential to be repurposed as novel chemotherapeutics in cancer treatment, as they have been found to exhibit preventive and/or therapeutic action in cancer patients. Nevertheless, although drug repurposing seems to be an attractive strategy to search for oncological drugs, we would like to clearly indicate that it should not replace the search for new lead structures, but only complement de novo drug development.

scholarly journals Use of Microdosing and Accelerator Mass Spectrometry To Evaluate the Pharmacokinetic Linearity of a Novel Tricyclic GyrB/ParE Inhibitor in Rats

2014 ◽  
Vol 58 (11) ◽  
pp. 6477-6483 ◽  
Author(s):  
Michael A. Malfatti ◽  
Victoria Lao ◽  
Courtney L. Ramos ◽  
Voon S. Ong ◽  
Kenneth W. Turteltaub
Keyword(s):  
Drug Development ◽  
Development Process ◽  
Dose Range ◽  
Area Under The Curve ◽  
Drug Candidate ◽  
Drug Development Process ◽  
Sprague Dawley ◽  
Drug Candidates ◽  
Biological Fate ◽  
Therapeutic Doses

ABSTRACTDetermining the pharmacokinetics (PKs) of drug candidates is essential for understanding their biological fate. The ability to obtain human PK information early in the drug development process can help determine if future development is warranted. Microdosing was developed to assess human PKs, at ultra-low doses, early in the drug development process. Microdosing has also been used in animals to confirm PK linearity across subpharmacological and pharmacological dose ranges. The current study assessed the PKs of a novel antimicrobial preclinical drug candidate (GP-4) in rats as a step toward human microdosing studies. Dose proportionality was determined at 3 proposed therapeutic doses (3, 10, and 30 mg/kg of body weight), and PK linearity between a microdose and a pharmacological dose was assessed in Sprague-Dawley rats. Plasma PKs over the 3 pharmacological doses were proportional. Over the 10-fold dose range, the maximum concentration in plasma and area under the curve (AUC) increased 9.5- and 15.8-fold, respectively. PKs from rats dosed with a14C-labeled microdose versus a14C-labeled pharmacological dose displayed dose linearity. In the animals receiving a microdose and the therapeutically dosed animals, the AUCs from time zero to infinity were 2.6 ng · h/ml and 1,336 ng · h/ml, respectively, and the terminal half-lives were 5.6 h and 1.4 h, respectively. When the AUC values were normalized to a dose of 1.0 mg/kg, the AUC values were 277.5 ng · h/ml for the microdose and 418.2 ng · h/ml for the pharmacological dose. This 1.5-fold difference in AUC following a 300-fold difference in dose is considered linear across the dose range. On the basis of the results, the PKs from the microdosed animals were considered to be predictive of the PKs from the therapeutically dosed animals.

scholarly journals Finding melanoma drugs through a probabilistic knowledge graph

2016 ◽  
Author(s):  
James P McCusker ◽  
Michel Dumontier ◽  
Rui Yan ◽  
Sylvia He ◽  
Jonathan S Dordick ◽  
...  
Keyword(s):  
Systems Biology ◽  
Probabilistic Methods ◽  
Drug Candidate ◽  
Knowledge Graph ◽  
Web Interface ◽  
Drug Candidates ◽  
Probabilistic Knowledge ◽  
Data Explosion

Metastatic cutaneous melanoma is an aggressive skin cancer with some progression-slowing treatments but no known cure. The omics data explosion has created many possible drug candidates, however filtering criteria remain challenging, and systems biology approaches have become fragmented with many disconnected databases. Using drug, protein, and disease interactions, we built an evidence-weighted knowledge graph of integrated interactions. Our knowledge graph-based system, ReDrugS, can be used via an API or web interface, and has generated 25 high quality melanoma drug candidates. We show that probabilistic analysis of systems biology graphs increases drug candidate quality compared to non-probabilistic methods. Four of the 25 candidates are novel therapies, three of which have been tested with other cancers. All other candidates have current or completed clinical trials, or have been studied in in vivo or in vitro. This approach can be used to identify candidate therapies for use in research or personalized medicine.

Prodrug Strategies for Critical Drug Developability Issues: Part I

2021 ◽  
Vol 21 (24) ◽  
pp. 2155-2156
Author(s):  
Xingyue Ji
Keyword(s):  
Drug Development ◽  
Chemical Entity ◽  
Attrition Rate ◽  
Drug Candidate ◽  
Structure Property ◽  
Drug Candidates ◽  
Fda Approval ◽  
Cns Drugs ◽  
Approved Drugs ◽  
Preclinical And Clinical Studies

Drug development is a very time, capital, and labor-intensive process. It was anticipated that bringing a novel chemical entity to market would take over a billion dollars and around 14 years [1]. In addition, drug development is characterized by a very high attrition rate both in preclinical and clinical studies. It was reported that only 40% of drug candidates with the most drug-like properties could make their way into clinical trials, and only 10% of these can eventually reach FDA approval [2]. After analyzing the data from seven UK‐based pharmaceutical companies from 1964 through 1985, Prentis et al. found that 39% of failure was attributed to poor pharmacokinetic (PK) profiles in humans, 29% was attributed to a lack of clinical efficacy, 21% was attributed to toxicity and adverse effects, and about 6% was attributed to commercial limitations [3]. When a drug candidate is identified with one of these issues (except the commercial limitations), normally, a new round of structureactivity or structure-property relationship (SAR/SPR) studies is carried out to generate a new chemical entity with improved profiles, and in most cases, such a process is time and labor-intensive. Alternatively, prodrug strategy can be leveraged to efficiently address associated drug developability issues without making enormous derivatives. Prodrug strategy has been demonstrated to be very successful and fruitful in drug development, with around 20% of approved drugs from 2008 through 2020 being clarified as prodrugs [4]. In recent years, prodrug strategy has also been leveraged to address the delivery issues associated with gasotransmitters, including NO, H2S, CO as well as SO2 [5-8]. In this thematic issue, six excellent reviews were included, focusing on varied prodrug strategies in addressing different drug developability issues associated with anticancer drugs, central nervous system (CNS) drugs, and gasotransmitters....

scholarly journals Finding melanoma drugs through a probabilistic knowledge graph

2017 ◽  
Vol 3 ◽  
pp. e106 ◽  
Author(s):  
James P. McCusker ◽  
Michel Dumontier ◽  
Rui Yan ◽  
Sylvia He ◽  
Jonathan S. Dordick ◽  
...  
Keyword(s):  
Systems Biology ◽  
Application Programming Interface ◽  
Probabilistic Methods ◽  
Drug Candidate ◽  
Knowledge Graph ◽  
Drug Candidates ◽  
Application Programming ◽  
Data Explosion

Metastatic cutaneous melanoma is an aggressive skin cancer with some progression-slowing treatments but no known cure. The omics data explosion has created many possible drug candidates; however, filtering criteria remain challenging, and systems biology approaches have become fragmented with many disconnected databases. Using drug, protein and disease interactions, we built an evidence-weighted knowledge graph of integrated interactions. Our knowledge graph-based system, ReDrugS, can be used via an application programming interface or web interface, and has generated 25 high-quality melanoma drug candidates. We show that probabilistic analysis of systems biology graphs increases drug candidate quality compared to non-probabilistic methods. Four of the 25 candidates are novel therapies, three of which have been tested with other cancers. All other candidates have current or completed clinical trials, or have been studied in in vivo or in vitro. This approach can be used to identify candidate therapies for use in research or personalized medicine.

scholarly journals Finding melanoma drugs through a probabilistic knowledge graph

2016 ◽  
Author(s):  
James P McCusker ◽  
Michel Dumontier ◽  
Rui Yan ◽  
Sylvia He ◽  
Jonathan S Dordick ◽  
...  
Keyword(s):  
Systems Biology ◽  
Probabilistic Methods ◽  
Drug Candidate ◽  
Knowledge Graph ◽  
Web Interface ◽  
Drug Candidates ◽  
Probabilistic Knowledge ◽  
Data Explosion

Metastatic cutaneous melanoma is an aggressive skin cancer with some progression-slowing treatments but no known cure. The omics data explosion has created many possible drug candidates, however filtering criteria remain challenging, and systems biology approaches have become fragmented with many disconnected databases. Using drug, protein, and disease interactions, we built an evidence-weighted knowledge graph of integrated interactions. Our knowledge graph-based system, ReDrugS, can be used via an API or web interface, and has generated 25 high quality melanoma drug candidates. We show that probabilistic analysis of systems biology graphs increases drug candidate quality compared to non-probabilistic methods. Four of the 25 candidates are novel therapies, three of which have been tested with other cancers. All other candidates have current or completed clinical trials, or have been studied in in vivo or in vitro. This approach can be used to identify candidate therapies for use in research or personalized medicine.

scholarly journals Finding melanoma drugs through a probabilistic knowledge graph

2016 ◽  
Author(s):  
James P McCusker ◽  
Michel Dumontier ◽  
Rui Yan ◽  
Sylvia He ◽  
Jonathan S Dordick ◽  
...  
Keyword(s):  
Systems Biology ◽  
Probabilistic Methods ◽  
Drug Candidate ◽  
Knowledge Graph ◽  
Web Interface ◽  
Drug Candidates ◽  
Probabilistic Knowledge ◽  
Data Explosion

Metastatic cutaneous melanoma is an aggressive skin cancer with some progression-slowing treatments but no known cure. The omics data explosion has created many possible drug candidates, however filtering criteria remain challenging, and systems biology approaches have become fragmented with many disconnected databases. Using drug, protein, and disease interactions, we built an evidence-weighted knowledge graph of integrated interactions. Our knowledge graph-based system, ReDrugS, can be used via an API or web interface, and has generated 25 high quality melanoma drug candidates. We show that probabilistic analysis of systems biology graphs increases drug candidate quality compared to non-probabilistic methods. Four of the 25 candidates are novel therapies, three of which have been tested with other cancers. All other candidates have current or completed clinical trials, or have been studied in in vivo or in vitro. This approach can be used to identify candidate therapies for use in research or personalized medicine.

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