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Classifying compound mechanism of action for linking whole cell phenotypes to molecular targets.

ABSTRACT: Drug development programs have proven successful when performed at a whole cell level, thus incorporating solubility and permeability into the primary screen. However, linking those results to the target within the cell has been a major setback. The Phenotype Microarray system, marketed and sold by Biolog, seeks to address this need by assessing the phenotype in combination with a variety of chemicals with known mechanism of action (MOA). We have evaluated this system for usefulness in deducing the MOA for three test compounds. To achieve this, we constructed a database with 21 known antimicrobials, which served as a comparison for grouping our unknown MOA compounds. Pearson correlation and Ward linkage calculations were used to generate a dendrogram that produced clustering largely by known MOA, although there were exceptions. Of the three unknown compounds, one was definitively placed as an antifolate. The second and third compounds' MOA were not clearly identified, likely because the unique MOA was not represented within the database. The availability of the database generated in this report for Staphylococcus aureus ATCC 29213 will increase the accessibility of this technique to other investigators. From our analysis, the Phenotype Microarray system can group compounds with clear MOA, but the distinction of unique or broadly acting MOA at this time is less clear.

SUBMITTER: Bourne CR

PROVIDER: S-EPMC3703735 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

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Classifying compound mechanism of action for linking whole cell phenotypes to molecular targets.

Bourne Christina R CR Wakeham Nancy N Bunce Richard A RA Nammalwar Baskar B Berlin K Darrell KD Barrow William W WW

Journal of molecular recognition : JMR 20120401 4

Drug development programs have proven successful when performed at a whole cell level, thus incorporating solubility and permeability into the primary screen. However, linking those results to the target within the cell has been a major setback. The Phenotype Microarray system, marketed and sold by Biolog, seeks to address this need by assessing the phenotype in combination with a variety of chemicals with known mechanism of action (MOA). We have evaluated this system for usefulness in deducing ...[more]

PMID: 22434711

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Project description:Pharmaceutical or phytopharmaceutical molecules rely on the interaction with one or more specific molecular targets to induce their anticipated biological responses. Nonetheless, these compounds are also prone to interact with many other non-intended biological targets, also known as off-targets. Unfortunately, off-target identification is difficult and expensive. Consequently, QSAR models predicting the activity on a target have gained importance in drug discovery or in the de-risking of chemicals. However, a restricted number of targets are well characterized and hold enough data to build such in silico models. A good alternative to individual target evaluations is to use integrative evaluations such as transcriptomics obtained from compound-induced gene expression measurements derived from cell cultures. The advantage of these particular experiments is to capture the consequences of the interaction of compounds on many possible molecular targets and biological pathways, without having any constraints concerning the chemical space. In this work, we assessed the value of a large public dataset of compound-induced transcriptomic data, to predict compound activity on a selection of 69 molecular targets. We compared such descriptors with other QSAR descriptors, namely the Morgan fingerprints (similar to extended-connectivity fingerprints). Depending on the target, active compounds could show similar signatures in one or multiple cell lines, whether these active compounds shared similar or different chemical structures. Random forest models using gene expression signatures were able to perform similarly or better than counterpart models built with Morgan fingerprints for 25% of the target prediction tasks. These performances occurred mostly using signatures produced in cell lines showing similar signatures for active compounds toward the considered target. We show that compound-induced transcriptomic data could represent a great opportunity for target prediction, allowing to overcome the chemical space limitation of QSAR models.

Dataset Information

Classifying compound mechanism of action for linking whole cell phenotypes to molecular targets.

Publications

Classifying compound mechanism of action for linking whole cell phenotypes to molecular targets.

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