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Composition and applications of focus libraries to phenotypic assays.

Wassermann AM, Camargo LM, Auld DS - Front Pharmacol (2014)

Bottom Line: In this article we discuss the types of compounds in these annotated libraries composed of tools, probes, and drugs.As well, we provide rationale and a few examples for how such libraries can enable phenotypic/forward chemical genomic approaches.As with any approach, there are several pitfalls that need to be considered and we also outline some strategies to avoid these.

View Article: PubMed Central - PubMed

Affiliation: Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Cambridge, MA, USA.

ABSTRACT
The wealth of bioactivity information now available on low-molecular weight compounds has enabled a paradigm shift in chemical biology and early phase drug discovery efforts. Traditionally chemical libraries have been most commonly employed in screening approaches where a bioassay is used to characterize a chemical library in a random search for active samples. However, robust curating of bioassay data, establishment of ontologies enabling mining of large chemical biology datasets, and a wealth of public chemical biology information has made possible the establishment of highly annotated compound collections. Such annotated chemical libraries can now be used to build a pathway/target hypothesis and have led to a new view where chemical libraries are used to characterize a bioassay. In this article we discuss the types of compounds in these annotated libraries composed of tools, probes, and drugs. As well, we provide rationale and a few examples for how such libraries can enable phenotypic/forward chemical genomic approaches. As with any approach, there are several pitfalls that need to be considered and we also outline some strategies to avoid these.

No MeSH data available.


Shown are two different approaches developed at Novartis for the design of biodiverse compound libraries from a pre-plated screening deck. (A) For each compound in the Novartis screening collection, primary assay data from more than 200 HTS campaigns are collected, standardized in form of z-scores, and stored in a minable activity pattern (HTS-FP). This requires construction of a robust bioassay ontology and curation of compound and assay information. Then compounds are clustered by their activity patterns and screening plates are ranked in descending order of the number of different bioactivity clusters covered by the compounds contained in each plate. The more clusters are found on a plate, the more dissimilar the plated compounds are with respect to their bioactivity patterns and the higher the biodiversity of the plate. Therefore, plates from the top of the ranking are selected for screening (unless their cluster composition is redundant with higher ranked plates). (B) For each compound in the Novartis screening collection, concentration-response data is extracted from curated internal and external structure-activity databases. Compounds on a plate are annotated with their known targets and the biodiversity of a plate is measured by the number of different protein targets known to be modulated by the compounds on the plate. After ranking of the plates according to their biodiversity, the selection process is analogous to (A).
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Figure 2: Shown are two different approaches developed at Novartis for the design of biodiverse compound libraries from a pre-plated screening deck. (A) For each compound in the Novartis screening collection, primary assay data from more than 200 HTS campaigns are collected, standardized in form of z-scores, and stored in a minable activity pattern (HTS-FP). This requires construction of a robust bioassay ontology and curation of compound and assay information. Then compounds are clustered by their activity patterns and screening plates are ranked in descending order of the number of different bioactivity clusters covered by the compounds contained in each plate. The more clusters are found on a plate, the more dissimilar the plated compounds are with respect to their bioactivity patterns and the higher the biodiversity of the plate. Therefore, plates from the top of the ranking are selected for screening (unless their cluster composition is redundant with higher ranked plates). (B) For each compound in the Novartis screening collection, concentration-response data is extracted from curated internal and external structure-activity databases. Compounds on a plate are annotated with their known targets and the biodiversity of a plate is measured by the number of different protein targets known to be modulated by the compounds on the plate. After ranking of the plates according to their biodiversity, the selection process is analogous to (A).

Mentions: Historically, the majority of drugs have been discovered through phenotypic drug discovery approaches where one advanced in vivo activity in a manner agnostic of the molecular mechanism of action. Both target-based and phenotypic drug discovery approaches are practiced today with inherent advantages and disadvantages (Swinney and Anthony, 2011). Typical modern phenotypic assays include cell proliferation or selective growth inhibition assays oftentimes applied within oncology or infectious disease areas. Cell-based pathway–based approaches applied to chemical library screening efforts include the wide use of engineered cell lines in a reporter-gene assay (RGA), intracellular sensors, or high content screening approaches (Inglese et al., 2007; Inglese and Auld, 2009). In many of these cases, a screening library covering a broad spectrum of targets and molecular processes is generally most promising. For many years, the predominant paradigm for the assembly of such a library was to select a chemically maximally diverse compound set using diversity measures based on molecular scaffolds (Krier et al., 2006), physicochemical properties, 2D and 3D chemical structure (Matter, 1997), or pharmacophore descriptors (Mason et al., 2001). Irrespective of the specific diversity method used, all these library design strategies followed the belief that chemical diversity ultimately translates to biological diversity and that a chemically diverse screening library should hence be a suitable starting point for many different drug discovery projects. However, in many cases, these chemically diverse sets were not truly randomly chosen from chemical space given the widely accepted assumption in medicinal chemistry that not all parts of chemical space are biologically active or relevant. For example, Hert et al. (2009) have argued that, given the size of chemical space, the odds of finding a hit in a random diverse selection of ~1 million compounds seem rather negligible. They explain the success of HTS against these odds by the biogenic bias of screening libraries, i.e., their higher than average synthetic small molecule similarity to natural products and metabolites. By default, these naturally occurring molecules interact with biological systems and can therefore be viewed as representatives of biologically active chemical space. The idea of tailoring screening libraries toward biologically relevant chemical space is taken to the next level by strategies that directly integrate the known biology of compounds into screening set design by maximizing the known biodiversity instead of the chemical diversity of screening collections. These biodiversity methods have been made possible by the wealth of screening and small molecule bioactivity data that has been released over recent years in both corporate and public domains. For example, at Novartis, two different approaches for the selection of biodiverse screening sets have been developed (Figure 2).


Composition and applications of focus libraries to phenotypic assays.

Wassermann AM, Camargo LM, Auld DS - Front Pharmacol (2014)

Shown are two different approaches developed at Novartis for the design of biodiverse compound libraries from a pre-plated screening deck. (A) For each compound in the Novartis screening collection, primary assay data from more than 200 HTS campaigns are collected, standardized in form of z-scores, and stored in a minable activity pattern (HTS-FP). This requires construction of a robust bioassay ontology and curation of compound and assay information. Then compounds are clustered by their activity patterns and screening plates are ranked in descending order of the number of different bioactivity clusters covered by the compounds contained in each plate. The more clusters are found on a plate, the more dissimilar the plated compounds are with respect to their bioactivity patterns and the higher the biodiversity of the plate. Therefore, plates from the top of the ranking are selected for screening (unless their cluster composition is redundant with higher ranked plates). (B) For each compound in the Novartis screening collection, concentration-response data is extracted from curated internal and external structure-activity databases. Compounds on a plate are annotated with their known targets and the biodiversity of a plate is measured by the number of different protein targets known to be modulated by the compounds on the plate. After ranking of the plates according to their biodiversity, the selection process is analogous to (A).
© Copyright Policy - open-access
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4109565&req=5

Figure 2: Shown are two different approaches developed at Novartis for the design of biodiverse compound libraries from a pre-plated screening deck. (A) For each compound in the Novartis screening collection, primary assay data from more than 200 HTS campaigns are collected, standardized in form of z-scores, and stored in a minable activity pattern (HTS-FP). This requires construction of a robust bioassay ontology and curation of compound and assay information. Then compounds are clustered by their activity patterns and screening plates are ranked in descending order of the number of different bioactivity clusters covered by the compounds contained in each plate. The more clusters are found on a plate, the more dissimilar the plated compounds are with respect to their bioactivity patterns and the higher the biodiversity of the plate. Therefore, plates from the top of the ranking are selected for screening (unless their cluster composition is redundant with higher ranked plates). (B) For each compound in the Novartis screening collection, concentration-response data is extracted from curated internal and external structure-activity databases. Compounds on a plate are annotated with their known targets and the biodiversity of a plate is measured by the number of different protein targets known to be modulated by the compounds on the plate. After ranking of the plates according to their biodiversity, the selection process is analogous to (A).
Mentions: Historically, the majority of drugs have been discovered through phenotypic drug discovery approaches where one advanced in vivo activity in a manner agnostic of the molecular mechanism of action. Both target-based and phenotypic drug discovery approaches are practiced today with inherent advantages and disadvantages (Swinney and Anthony, 2011). Typical modern phenotypic assays include cell proliferation or selective growth inhibition assays oftentimes applied within oncology or infectious disease areas. Cell-based pathway–based approaches applied to chemical library screening efforts include the wide use of engineered cell lines in a reporter-gene assay (RGA), intracellular sensors, or high content screening approaches (Inglese et al., 2007; Inglese and Auld, 2009). In many of these cases, a screening library covering a broad spectrum of targets and molecular processes is generally most promising. For many years, the predominant paradigm for the assembly of such a library was to select a chemically maximally diverse compound set using diversity measures based on molecular scaffolds (Krier et al., 2006), physicochemical properties, 2D and 3D chemical structure (Matter, 1997), or pharmacophore descriptors (Mason et al., 2001). Irrespective of the specific diversity method used, all these library design strategies followed the belief that chemical diversity ultimately translates to biological diversity and that a chemically diverse screening library should hence be a suitable starting point for many different drug discovery projects. However, in many cases, these chemically diverse sets were not truly randomly chosen from chemical space given the widely accepted assumption in medicinal chemistry that not all parts of chemical space are biologically active or relevant. For example, Hert et al. (2009) have argued that, given the size of chemical space, the odds of finding a hit in a random diverse selection of ~1 million compounds seem rather negligible. They explain the success of HTS against these odds by the biogenic bias of screening libraries, i.e., their higher than average synthetic small molecule similarity to natural products and metabolites. By default, these naturally occurring molecules interact with biological systems and can therefore be viewed as representatives of biologically active chemical space. The idea of tailoring screening libraries toward biologically relevant chemical space is taken to the next level by strategies that directly integrate the known biology of compounds into screening set design by maximizing the known biodiversity instead of the chemical diversity of screening collections. These biodiversity methods have been made possible by the wealth of screening and small molecule bioactivity data that has been released over recent years in both corporate and public domains. For example, at Novartis, two different approaches for the selection of biodiverse screening sets have been developed (Figure 2).

Bottom Line: In this article we discuss the types of compounds in these annotated libraries composed of tools, probes, and drugs.As well, we provide rationale and a few examples for how such libraries can enable phenotypic/forward chemical genomic approaches.As with any approach, there are several pitfalls that need to be considered and we also outline some strategies to avoid these.

View Article: PubMed Central - PubMed

Affiliation: Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Cambridge, MA, USA.

ABSTRACT
The wealth of bioactivity information now available on low-molecular weight compounds has enabled a paradigm shift in chemical biology and early phase drug discovery efforts. Traditionally chemical libraries have been most commonly employed in screening approaches where a bioassay is used to characterize a chemical library in a random search for active samples. However, robust curating of bioassay data, establishment of ontologies enabling mining of large chemical biology datasets, and a wealth of public chemical biology information has made possible the establishment of highly annotated compound collections. Such annotated chemical libraries can now be used to build a pathway/target hypothesis and have led to a new view where chemical libraries are used to characterize a bioassay. In this article we discuss the types of compounds in these annotated libraries composed of tools, probes, and drugs. As well, we provide rationale and a few examples for how such libraries can enable phenotypic/forward chemical genomic approaches. As with any approach, there are several pitfalls that need to be considered and we also outline some strategies to avoid these.

No MeSH data available.