| Title |
Cell and small animal models for phenotypic drug discovery
|
|---|---|
| Published in |
Drug Design, Development and Therapy, June 2017
|
| DOI | 10.2147/dddt.s129447 |
| Pubmed ID | |
| Authors |
Mihaly Szabo, Sara Svensson Akusjärvi, Ankur Saxena, Jianping Liu, Gayathri Chandrasekar, Satish S Kitambi |
| Abstract |
The phenotype-based drug discovery (PDD) approach is re-emerging as an alternative platform for drug discovery. This review provides an overview of the various model systems and technical advances in imaging and image analyses that strengthen the PDD platform. In PDD screens, compounds of therapeutic value are identified based on the phenotypic perturbations produced irrespective of target(s) or mechanism of action. In this article, examples of phenotypic changes that can be detected and quantified with relative ease in a cell-based setup are discussed. In addition, a higher order of PDD screening setup using small animal models is also explored. As PDD screens integrate physiology and multiple signaling mechanisms during the screening process, the identified hits have higher biomedical applicability. Taken together, this review highlights the advantages gained by adopting a PDD approach in drug discovery. Such a PDD platform can complement target-based systems that are currently in practice to accelerate drug discovery. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Finland | 1 | 25% |
| United States | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 50% |
| Members of the public | 2 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 74 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 20 | 27% |
| Student > Bachelor | 12 | 16% |
| Researcher | 9 | 12% |
| Student > Master | 7 | 9% |
| Professor > Associate Professor | 5 | 7% |
| Other | 11 | 15% |
| Unknown | 10 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 13 | 18% |
| Agricultural and Biological Sciences | 12 | 16% |
| Chemistry | 8 | 11% |
| Pharmacology, Toxicology and Pharmaceutical Science | 7 | 9% |
| Engineering | 6 | 8% |
| Other | 15 | 20% |
| Unknown | 13 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 July 2017.
All research outputs
#15,745,807
of 25,382,440 outputs
Outputs from Drug Design, Development and Therapy
#872
of 2,268 outputs
Outputs of similar age
#181,968
of 330,503 outputs
Outputs of similar age from Drug Design, Development and Therapy
#20
of 44 outputs
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We're also able to compare this research output to 44 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.