Screening Strategies for Drug Discovery and Target Identification

2015 ◽  
pp. 135-166 ◽  
Author(s):  
Fatma Al-­Awadhi ◽  
Lilibeth Salvador ◽  
Hendrik Luesch
Keyword(s):  
Drug Discovery ◽  
Target Identification ◽  
Screening Strategies

The applications of next-generation sequencing (NGS) in drug development for cancer therapy

2020 ◽  
Vol 16 ◽  
Author(s):  
Pelin Telkoparan-Akillilar ◽  
Dilek Cevik
Keyword(s):  
Next Generation Sequencing ◽  
Drug Discovery ◽  
Cancer Therapy ◽  
Target Identification ◽  
Rapid Development ◽  
Next Generation ◽  
Biomedical Sciences ◽  
Dna And Rna ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

Background: Numerous sequencing techniques have been progressed since the 1960s with the rapid development of molecular biology studies focusing on DNA and RNA. Methods: a great number of articles, book chapters, websites are reviewed, and the studies covering NGS history, technology and applications to cancer therapy are included in the present article. Results: High throughput next-generation sequencing (NGS) technologies offer many advantages over classical Sanger sequencing with decreasing cost per base and increasing sequencing efficiency. NGS technologies are combined with bioinformatics software to sequence genomes to be used in diagnostics, transcriptomics, epidemiologic and clinical trials in biomedical sciences. The NGS technology has also been successfully used in drug discovery for the treatment of different cancer types. Conclusion: This review focuses on current and potential applications of NGS in various stages of drug discovery process, from target identification through to personalized medicine.

scholarly journals A New Big-Data Paradigm for Target Identification and Drug Discovery

2017 ◽  
Author(s):  
Neel S. Madhukar ◽  
Prashant K. Khade ◽  
Linda Huang ◽  
Kaitlyn Gayvert ◽  
Giuseppe Galletti ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
Clinical Application ◽  
Small Molecule ◽  
Target Identification ◽  
Clinical Development ◽  
Data Types ◽  
Public Data ◽  
Drug Target Identification ◽  
Approved Drugs

AbstractDrug target identification is one of the most important aspects of pre-clinical development yet it is also among the most complex, labor-intensive, and costly. This represents a major issue, as lack of proper target identification can be detrimental in determining the clinical application of a bioactive small molecule. To improve target identification, we developed BANDIT, a novel paradigm that integrates multiple data types within a Bayesian machine-learning framework to predict the targets and mechanisms for small molecules with unprecedented accuracy and versatility. Using only public data BANDIT achieved an accuracy of approximately 90% over 2000 different small molecules – substantially better than any other published target identification platform. We applied BANDIT to a library of small molecules with no known targets and generated ∼4,000 novel molecule-target predictions. From this set we identified and experimentally validated a set of novel microtubule inhibitors, including three with activity on cancer cells resistant to clinically used anti-microtubule therapies. We next applied BANDIT to ONC201 – an active anti- cancer small molecule in clinical development – whose target has remained elusive since its discovery in 2009. BANDIT identified dopamine receptor 2 as the unexpected target of ONC201, a prediction that we experimentally validated. Not only does this open the door for clinical trials focused on target-based selection of patient populations, but it also represents a novel way to target GPCRs in cancer. Additionally, BANDIT identified previously undocumented connections between approved drugs with disparate indications, shedding light onto previously unexplained clinical observations and suggesting new uses of marketed drugs. Overall, BANDIT represents an efficient and highly accurate platform that can be used as a resource to accelerate drug discovery and direct the clinical application of small molecule therapeutics with improved precision.

TB Drug Discovery from Target Identification to Proof of Concept Studies

2017 ◽  
pp. 143-163
Author(s):  
Thomas Dick ◽  
Véronique Dartois ◽  
Thomas H. Keller ◽  
Alex Matter
Keyword(s):  
Drug Discovery ◽  
Target Identification ◽  
Proof Of Concept

Modern Paradigm Towards Potential Target Identification for Antiviral (SARS-nCoV-2) and Anticancer Lipopeptides: A Pharmacophore-Based Approach

2022 ◽  
Author(s):  
Manisha Yadav ◽  
J. Satya Eswari
Keyword(s):  
Drug Discovery ◽  
Target Identification ◽  
Target Prediction ◽  
Cost Effective ◽  
Time Saving ◽  
Web Based ◽  
Target Proteins ◽  
Lead Compounds ◽  
Potential Target ◽  
Biopharmaceutical Properties

Background: Lipopeptides are potential microbial metabolites that are abandoned with broad spectrum biopharmaceutical properties ranging from antimicrobial, antiviral and anticancer, etc. Clinical studies are not much explored beyond the experimental methods to understand drug mechanisms on target proteins at the molecular level for large molecules. Due to the less available studies on potential target proteins of lipopeptide based drugs, their potential inhibitory role for more obvious treatment on disease have not been explored in the direction of lead optimization. However, Computational approaches need to be utilized to explore drug discovery aspects on lipopeptide based drugs, which are time saving and cost-effective techniques. Methods: Here a ligand-based drug discovery approach is coupled with reverse pharmacophore-mapping for the prediction of potential targets for antiviral (SARS-nCoV-2) and anticancer lipopeptides. Web-based servers PharmMapper and Swiss Target Prediction are used for the identification of target proteins for lipopeptides surfactin and iturin produced by Bacillus subtilis. Results: The studies have given the insight to treat the diseases with next-generation large molecule therapeutics. Results also indicate the affinity for Angiotensin-Converting Enzymes (ACE) and proteases as the potential viral targets for these categories of peptide therapeutics. A target protein for the Human Papilloma Virus (HPV) has also been mapped. Conclusion: The work will further help in exploring computer-aided drug designing of novel compounds with greater efficiency where the structure of the target proteins and lead compounds are known.  

Industry–Academic Relationship in a New Era of Drug Discovery

2016 ◽  
Vol 34 (29) ◽  
pp. 3570-3575 ◽  
Author(s):  
Patrick Vallance
Keyword(s):  
Drug Discovery ◽  
Career Development ◽  
Pharmaceutical Industry ◽  
Target Identification ◽  
Human Biology ◽  
Experimental Medicine ◽  
Clinical Testing ◽  
New Era ◽  
Academic Partnerships ◽  
The Relationship

The pharmaceutical industry is entering a renewed period of productivity as a result of advances in the understanding of human biology, particularly in the areas of genetics and immunology. The relationship between industry and academia needs to evolve to maximize the opportunity. In four areas—target identification, the molecule itself, experimental medicine, and larger-scale clinical testing—there are specific needs for academic partnerships that should be open and transparent and include talent, skills, and career development.

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