Target identification of small molecules based on chemical biology approaches

2013 ◽  
Vol 9 (5) ◽  
pp. 897 ◽  
Author(s):  
Yushi Futamura ◽  
Makoto Muroi ◽  
Hiroyuki Osada
Keyword(s):  
Small Molecules ◽  
Chemical Biology ◽  
Target Identification

scholarly journals From Phenotypic Hit to Chemical Probe: Chemical Biology Approaches to Elucidate Small Molecule Action in Complex Biological Systems

Molecules ◽  
2020 ◽  
Vol 25 (23) ◽  
pp. 5702
Author(s):  
Quentin T. L. Pasquer ◽  
Ioannis A. Tsakoumagkos ◽  
Sascha Hoogendoorn
Keyword(s):  
Small Molecules ◽  
Chemical Biology ◽  
Target Identification ◽  
Biologically Active ◽  
Phenotypic Screening ◽  
Biological Processes ◽  
Chemical Probes ◽  
Target Deconvolution ◽  
Cellular Target ◽  
Screening Approaches

Biologically active small molecules have a central role in drug development, and as chemical probes and tool compounds to perturb and elucidate biological processes. Small molecules can be rationally designed for a given target, or a library of molecules can be screened against a target or phenotype of interest. Especially in the case of phenotypic screening approaches, a major challenge is to translate the compound-induced phenotype into a well-defined cellular target and mode of action of the hit compound. There is no “one size fits all” approach, and recent years have seen an increase in available target deconvolution strategies, rooted in organic chemistry, proteomics, and genetics. This review provides an overview of advances in target identification and mechanism of action studies, describes the strengths and weaknesses of the different approaches, and illustrates the need for chemical biologists to integrate and expand the existing tools to increase the probability of evolving screen hits to robust chemical probes.

scholarly journals Development of a chemogenomics library for phenotypic screening

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Bryan Dafniet ◽  
Natacha Cerisier ◽  
Batiste Boezio ◽  
Anaelle Clary ◽  
Pierre Ducrot ◽  
...  
Keyword(s):  
Small Molecules ◽  
Drug Target ◽  
Drug Targets ◽  
Chemical Biology ◽  
Target Identification ◽  
Biological Effects ◽  
Phenotypic Screening ◽  
High Content Imaging ◽  
Advanced Technologies ◽  
System Pharmacology

AbstractWith the development of advanced technologies in cell-based phenotypic screening, phenotypic drug discovery (PDD) strategies have re-emerged as promising approaches in the identification and development of novel and safe drugs. However, phenotypic screening does not rely on knowledge of specific drug targets and needs to be combined with chemical biology approaches to identify therapeutic targets and mechanisms of actions induced by drugs and associated with an observable phenotype. In this study, we developed a system pharmacology network integrating drug-target-pathway-disease relationships as well as morphological profile from an existing high content imaging-based high-throughput phenotypic profiling assay known as “Cell Painting”. Furthermore, from this network, a chemogenomic library of 5000 small molecules that represent a large and diverse panel of drug targets involved in diverse biological effects and diseases has been developed. Such a platform and a chemogenomic library could assist in the target identification and mechanism deconvolution of some phenotypic assays. The usefulness of the platform is illustrated through examples.

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.

scholarly journals Small molecules with big roles in microRNA chemical biology and microRNA-targeted therapeutics

RNA Biology ◽  
2019 ◽  
Vol 16 (6) ◽  
pp. 707-718 ◽  
Author(s):  
Rengen Fan ◽  
Chaocheng Xiao ◽  
Xinqiang Wan ◽  
Wenzhang Cha ◽  
Yufeng Miao ◽  
...  
Keyword(s):  
Small Molecules ◽  
Chemical Biology ◽  
Targeted Therapeutics

Target engagement approaches for pharmacological evaluation in animal models

2019 ◽  
Vol 55 (63) ◽  
pp. 9241-9250 ◽  
Author(s):  
James E. Kath ◽  
Aleksandra Baranczak
Keyword(s):  
Animal Models ◽  
Small Molecules ◽  
Chemical Biology ◽  
Target Engagement ◽  
Pharmacological Evaluation

We highlight recent applications of chemical biology approaches to measure target engagement of small molecules in animal models to support the nomination of clinical candidates.

scholarly journals Extending the small molecule similarity principle to all levels of biology

2019 ◽  
Author(s):  
Miquel Duran-Frigola ◽  
Eduardo Pauls ◽  
Oriol Guitart-Pla ◽  
Martino Bertoni ◽  
Víctor Alcalde ◽  
...  
Keyword(s):  
Small Molecules ◽  
Biological Networks ◽  
Target Identification ◽  
Chemical Properties ◽  
Chemical Information ◽  
Chemical Similarity ◽  
Similarity Principle ◽  
Genetic Perturbations ◽  
Chemical Descriptors ◽  
Library Characterization

AbstractWe present the Chemical Checker (CC), a resource that provides processed, harmonized and integrated bioactivity data on 800,000 small molecules. The CC divides data into five levels of increasing complexity, ranging from the chemical properties of compounds to their clinical outcomes. In between, it considers targets, off-targets, perturbed biological networks and several cell-based assays such as gene expression, growth inhibition and morphological profilings. In the CC, bioactivity data are expressed in a vector format, which naturally extends the notion of chemical similarity between compounds to similarities between bioactivity signatures of different kinds. We show how CC signatures can boost the performance of drug discovery tasks that typically capitalize on chemical descriptors, including target identification and library characterization. Moreover, we demonstrate and experimentally validate that CC signatures can be used to reverse and mimic biological signatures of disease models and genetic perturbations, options that are otherwise impossible using chemical information alone.

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