- Email: [email protected]
Turning the Light On in the Phenotypic Drug Discovery Black Box
Cell Chemical Biology
Previews
wide a variety of cancers. Overexpressed HSP60 promotes tumor growth, angiogenesis, and metastasis in more than 15 types of cancers (Wu et al., 2017). Similarly, Lon overexpression in various cancers has been shown to support cancer cell growth (Bernstein et al., 2012; Quiro´s et al., 2014). Inhibiting the UPRMT pathway at multiple levels and blocking the cancer cell adaptive responses to stress is a strategy that may hold promise in treating cancers. Precedents for such a strategy have been demonstrated by drugs and drug trials of inhibitors of the endoplasmic UPR (UPRER). The combined or synergetic effect of inhibiting both HSP60 and Lon by MC may be a more effective strategy to the treatment of cancer than inhibiting HSP60 or Lon
alone. Therefore, the discovery of drugs inhibiting UPRMT mediators is likely to be a high yield endeavor, and the identification of MC is an HSP60 inhibitor contributes to accomplishing that aim.
REFERENCES Bernstein, S.H., Venkatesh, S., Li, M., Lee, J., Lu, B., Hilchey, S.P., Morse, K.M., Metcalfe, H.M., Skalska, J., Andreeff, M., et al. (2012). Blood 119, 3321–3329. Itoh, H., Komatsuda, A., Wakui, H., Miura, A.B., and Tashima, Y. (1999). J. Biol. Chem. 274, 35147–35151. Liu, L., Sanosaka, M., Lei, S., Bestwick, M.L., Frey, J.H., Jr., Surovtseva, Y.V., Shadel, G.S., and Cooper, M.P. (2011). J. Biol. Chem. 286, 41253–41264.
Luo, J., Solimini, N.L., and Elledge, S.J. (2009). Cell 136, 823–837. Nagumo, Y., Kakeya, H., Shoji, M., Hayashi, Y., Dohmae, N., and Osada, H. (2005). Biochem. J. 387, 835–840. Quiro´s, P.M., Espan˜ol, Y., Acı´n-Pe´rez, R., Rodrı´guez, F., Ba´rcena, C., Watanabe, K., Calvo, E., Loureiro, M., Ferna´ndez-Garcı´a, M.S., Fueyo, A., et al. (2014). Cell Rep. 8, 542–556. Tretiakova, I., Blaesius, D., Maxia, L., Wesselborg, S., Schulze-Osthoff, K., Cinatl, J., Jr., Michaelis, M., and Werz, O. (2008). Apoptosis 13, 119–131. Venkatesh, S., Lee, J., Singh, K., Lee, I., and Suzuki, C.K. (2012). Biochim. Biophys. Acta 1823, 56–66. €ller, H., Ko¨nig, S., Wielsch, N., Wiechmann, K., Mu Svatos, A., Jauch, J., and Werz, O. (2017). Cell Chem. Biol. 24, this issue, 614–623. Wu, J., Liu, T., Rios, Z., Mei, Q., Lin, X., and Cao, S. (2017). Trends Pharmacol. Sci. 38, 226–256.
Turning the Light On in the Phenotypic Drug Discovery Black Box John G. Moffat1,* 1Department of Biochemical and Cellular Pharmacology, Genentech Research and Early Development, South San Francisco, CA 94112, USA *Correspondence: [email protected] http://dx.doi.org/10.1016/j.chembiol.2017.05.005
In this issue, Drawnel et al. (2017) introduce the concept of a ‘‘molecular phenotype’’ and demonstrate how ‘‘big data’’ coming from gene expression profiling, combined with signaling pathway information, smallmolecule chemical information, preclinical animal models, and clinical samples can empower phenotypic discovery at several critical levels. Whenever two or more practitioners of phenotypic drug discovery (PDD) are gathered together, a debate can be easily initiated about the practical definition of ‘‘phenotype.’’ A phenotypic assay is a black-box system, an experimental model, usually (but not always) cellular, that reads out in an endpoint that, to the best of one’s knowledge and technology, resembles a disease biomarker or pathology. Compounds are tested empirically for their ability to modify the assay endpoint that resembles the desired therapeutic effect, but the direct molecular target of the sought-for drug is left as an unknown variable. The degree and nature of the resemblance between disease and assay model, and between therapeutic
and assay endpoints, varies widely. At one extreme are whole-animal models of diseases, in which multiple tissues and cell types interact; at the other are highly targeted assays, such as a specific pathway where everything is known except for the existence of a causal druggable target. In each case, the difference between a normal and a perturbed cell, tissue, or organism can be viewed as a difference in the state of the system. At a cellular level, this state—what’s going on inside the black box—can be equated to the regulatory network embodied in changes in gene expression and activities of nodes of signaling pathways. The degree of coherence between the model and dis-
ease states (Figure 1) determines what Scannell and Bosley described as the predictive validity of the model (Scannell and Bosley, 2016). Predictive validity can be improved by understanding and replicating the disease-causing perturbation, be it cell-intrinsic or exogenous perturbation, and using cellular systems that have as much in common as possible in terms of origin and growth conditions as the disease tissue (Horvath et al., 2016; Vincent et al., 2015). Ultimately, it would be desirable to generate a ‘‘snapshot’’ of the state determining the assay phenotype and hold it up against a similarly obtained picture of the disease state. In this way, this snapshot could help determine whether the model is valid, to the extent that the
Cell Chemical Biology 24, May 18, 2017 ª 2017 Published by Elsevier Ltd. 545
Cell Chemical Biology
Previews
Figure 1. Transcriptome-Based Molecular Phenotyping Provides Insight into the Validity of Phenotypic Screening Models and Phenotypically Active Compounds Top: empirical screening models assume that if the perturbation (e.g., mutation, external stimulus) is similar for the disease and the model and that the assay readout (overt phenotype) is empirically equivalent to disease pathology, then there is a probability that the effects of test compounds on the mechanisms inside the assay black box will translate to the disease mechanisms. By applying molecular phenotyping, the states of the regulatory networks and mechanisms in the disease and model can be directly compared. On the lower left, although the overt phenotype of the model resembles the disease, it is a consequence of mechanistically unrelated processes, as evidenced by dissimilarity in the molecular phenotype (orange nodes). On the lower right, high concordance between the molecular phenotypes of disease and model increases confidence that a compound that affects the state of the will translates to in vivo and clinical efficacy.
assay readout is driven by the same molecular mechanisms as the clinical endpoint, and whether the compounds that alter the assay readout act via a therapeutically beneficial mechanism. In a key proof-of-concept study in this issue, Drawnel et al. (2017) present a practical solution to sampling the ‘‘internal’’ states of both the disease and the discovery model and describe this as ‘‘molecular phenotyping.’’ The system chosen was diabetic cardiomyopathy. Cultured human cardiomyocytes treated with a cocktail of physiologically relevant diabetic stressors (glucose, endothelin, and cortisol) show the same morphological defect as in myopathic heart tissue, namely decreased subcellular organization (striatedness) and contractility. This overt phenotype, quantitated by highcontent imaging, represents a classical phenotypic assay. However, the authors chose to look much deeper into the mechanistic state underlying the cellular and disease phenotypes. The molecular phenotype (MP) is in this instance quantitative mRNA sequencing (transcriptomic analysis) of a panel of 546 Cell Chemical Biology 24, May 18, 2017
917 ‘‘reporter’’ genes that are representative of the state of a broad range of cellular pathways. This approach was established by Jitao Zhang and his colleagues (Zhang et al., 2015), and the human genes included in the panel were enriched for genes that are representative of a broad range of key regulatory and diseaseassociated pathways. Gene expression assays are nothing new, and depending on how well-characterized the mechanism driving specific gene expression is, these assays may or may not fall into the category of phenotypic assays. However, unlike an assay for regulation of a specific gene, differential expression of genes in this reporter panel provides the requisite snapshot of global changes in cellular state. This reporter gene panel was designed to be simultaneously broad enough to provide an unbiased classification of multiple different mechanisms of action (MoA) and sufficiently pathway-enriched enough to support gene set analysis, indicating pathway-specific effects of compounds. Differential gene expression data was thus used to assess whether treatment
with compounds had the desired effect not only at the level of an overt phenotype, cardiomyocyte function, but also at the pathways associated with diabetic cardiomyopathy. Three key issues that can bedevil PDD were addressed by this application of the molecular phenotype platform. First, compounds that induce the desired overt phenotype could be compared at an MoA level with a control system, and the degree of similarity or dissimilarity determined. Second, false-positive compounds that induced the desired overt phenotype through a disease-irrelevant or undesirable MoA could be identified and rapidly eliminated from consideration. Taken together, the ability to mechanistically classify types of true and false positives is critical for following up on HTS hits by establishing a structure-activity relationship for analogs of a given hit compound. A frequent concern in phenotypic hit-to-lead compound optimization is ensuring that mechanistic fidelity is retained as potency and chemical properties change. Third, the degree of similarity between the assay model and the disease could be determined at a molecular mechanistic level. The differences in gene expression induced by the diabetic stressors were compared with gene expression profiles from patient-derived tissues and mouse models of cardiomyopathy and were shown to be significantly correlated at the pathway level. In other words, use of the molecular phenotype greatly increased confidence in the predictive validity of the model. An important differentiation between this reporter gene panel and many other gene-expression-based assays lies in the discriminatory power. A gene signature is a set of genes for which changes in expression are associated with a specific phenotype or response. For a diagnostic gene signature, minimizing type II errors is at a premium, whereas drug discovery efforts can be greatly hampered if there is an inability to avoid type I ‘‘false positives.’’ There is a general agreement among practitioners of PDD, and many individual examples, that identifying false positives due to undesirable MoA from phenotypic screens is one of the major scientific and technical challenges. Even establishing a definition for positive hits is a challenge if a compound shows the desired effect in the phenotypic assay
Cell Chemical Biology
Previews
but there is no understanding of the molecular MoA responsible for the phenotypic response and disease. There is, therefore, tremendous power in a broad and unbiased gene expression panel to detect unexpected and undesirable cellular responses to compounds, even some that may result in an apparently desirable overt phenotype. In the current study, the ability to identify false positives was shown for the case of topoisomerase inhibitors, which appeared to be protective in the cardiomyocyte morphology model. In applying the MP approach, these compounds were readily identified as biological false positives in that the effect on molecular phenotype was unrelated to the desired effect of reversing the perturbed state. Commonalities with the L1000 gene signature platform (Liu et al., 2015) and other transcriptional profiling efforts are evident. However, the design, intent, and interpretation of the data as a molecular phenotype differ from broad MoA profiling. A key difference is that the set of genes for MP were selected to represent biologically interpretable effects on important pathways in a disease-specific context. Although differential gene expression was used to define molecular phenotypes in this study, it is only one way in which an unbiased snapshot of the cell state could be captured. At this point in technological development, next-generation sequencing holds an advantage over proteomics in terms of sample requirements, accessibility to non-specialist users, and
equipment costs. However, there are arguments to be made that the proteome, including post-translational modifications, is more dynamic and informative of the cellular state than mRNA levels, and in the future these may become routine MP screening tools as well. This approach also shares some things in common with the ‘‘Cell Painting’’ strategy developed and disseminated by Carpenter and her group at the Broad Institute (Gustafsdottir et al., 2013). While these profiling approaches, as with the L1000 platform, are likely to prove extremely powerful for clustering compounds by MoA, they are not designed to interrogate disease-specific effects. However, there is no reason why features for different multiparametric analysis methods cannot be combined into a single unbiased signature. How else might this sort of molecular phenotyping enhance the productivity of PDD? One pertinent observation is the sensitivity of the method: effects were detected at earlier times and lower doses than the morphological phenotype. Typically, phenotypic assay hits that show a mixture of desired and undesired phenotypes are best discarded as unsalvageable; trying to empirically ‘‘rescue’’ the desirable activity of a promiscuous compound is likely to end in tears. However, the sensitivity and granularity of MP profiling in dose-response assays may enhance the ability to tease diseaserelated and unrelated effects apart. If less is known about what the desirable change in state is, then the same MP platform provides a means of clus-
tering compounds in different mechanistic classes with greater precision and confidence than single-readout screening and counter-screening phenotypic assays typically employed. Molecular phenotyping adds significant value to PDD at multiple key points: validation of a discovery model, validation of hits as having a relevant molecular MoA, and mechanistic clustering and filtering of hits with unexpected and undesirable MoA. It seems likely that such unbiased pathway-informed profiling techniques will become a central part of PDD efforts. REFERENCES €ng, E., Aoyama, N., Drawnel, F.M., Zhang, J.D., Ku Benmansour, F., Del Rosario, A.A., Zoffmann, S.J., Delobel, F., Prummer, M., Weibel, F., et al. (2017). Cell Chem. Biol. 24, this issue, 624–634. Gustafsdottir, S.M., Ljosa, V., Sokolnicki, K.L., Anthony Wilson, J., Walpita, D., Kemp, M.M., Petri Seiler, K., Carrel, H.A., Golub, T.R., Schreiber, S.L., et al. (2013). PLoS One 8, e80999. Horvath, P., Aulner, N., Bickle, M., Davies, A.M., Nery, E.D., Ebner, D., Montoya, M.C., O¨stling, P., €inen, V., Price, L.S., et al. (2016). Nat. Rev. Pietia Drug Discov. 15, 751–769. Liu, C., Su, J., Yang, F., Wei, K., Ma, J., and Zhou, X. (2015). Mol. Biosyst. 11, 714–722. Scannell, J.W., and Bosley, J. (2016). PLoS One 11, e0147215. Vincent, F., Loria, P., Pregel, M., Stanton, R., Kitching, L., Nocka, K., Doyonnas, R., Steppan, C., Gilbert, A., Schroeter, T., and Peakman, M.C. (2015). Sci. Transl. Med. 7, 293ps15. €ng, E., Boess, F., Certa, U., and Zhang, J.D., Ku Ebeling, M. (2015). BMC Genomics 16, 342.
Cell Chemical Biology 24, May 18, 2017 547
Previews
wide a variety of cancers. Overexpressed HSP60 promotes tumor growth, angiogenesis, and metastasis in more than 15 types of cancers (Wu et al., 2017). Similarly, Lon overexpression in various cancers has been shown to support cancer cell growth (Bernstein et al., 2012; Quiro´s et al., 2014). Inhibiting the UPRMT pathway at multiple levels and blocking the cancer cell adaptive responses to stress is a strategy that may hold promise in treating cancers. Precedents for such a strategy have been demonstrated by drugs and drug trials of inhibitors of the endoplasmic UPR (UPRER). The combined or synergetic effect of inhibiting both HSP60 and Lon by MC may be a more effective strategy to the treatment of cancer than inhibiting HSP60 or Lon
alone. Therefore, the discovery of drugs inhibiting UPRMT mediators is likely to be a high yield endeavor, and the identification of MC is an HSP60 inhibitor contributes to accomplishing that aim.
REFERENCES Bernstein, S.H., Venkatesh, S., Li, M., Lee, J., Lu, B., Hilchey, S.P., Morse, K.M., Metcalfe, H.M., Skalska, J., Andreeff, M., et al. (2012). Blood 119, 3321–3329. Itoh, H., Komatsuda, A., Wakui, H., Miura, A.B., and Tashima, Y. (1999). J. Biol. Chem. 274, 35147–35151. Liu, L., Sanosaka, M., Lei, S., Bestwick, M.L., Frey, J.H., Jr., Surovtseva, Y.V., Shadel, G.S., and Cooper, M.P. (2011). J. Biol. Chem. 286, 41253–41264.
Luo, J., Solimini, N.L., and Elledge, S.J. (2009). Cell 136, 823–837. Nagumo, Y., Kakeya, H., Shoji, M., Hayashi, Y., Dohmae, N., and Osada, H. (2005). Biochem. J. 387, 835–840. Quiro´s, P.M., Espan˜ol, Y., Acı´n-Pe´rez, R., Rodrı´guez, F., Ba´rcena, C., Watanabe, K., Calvo, E., Loureiro, M., Ferna´ndez-Garcı´a, M.S., Fueyo, A., et al. (2014). Cell Rep. 8, 542–556. Tretiakova, I., Blaesius, D., Maxia, L., Wesselborg, S., Schulze-Osthoff, K., Cinatl, J., Jr., Michaelis, M., and Werz, O. (2008). Apoptosis 13, 119–131. Venkatesh, S., Lee, J., Singh, K., Lee, I., and Suzuki, C.K. (2012). Biochim. Biophys. Acta 1823, 56–66. €ller, H., Ko¨nig, S., Wielsch, N., Wiechmann, K., Mu Svatos, A., Jauch, J., and Werz, O. (2017). Cell Chem. Biol. 24, this issue, 614–623. Wu, J., Liu, T., Rios, Z., Mei, Q., Lin, X., and Cao, S. (2017). Trends Pharmacol. Sci. 38, 226–256.
Turning the Light On in the Phenotypic Drug Discovery Black Box John G. Moffat1,* 1Department of Biochemical and Cellular Pharmacology, Genentech Research and Early Development, South San Francisco, CA 94112, USA *Correspondence: [email protected] http://dx.doi.org/10.1016/j.chembiol.2017.05.005
In this issue, Drawnel et al. (2017) introduce the concept of a ‘‘molecular phenotype’’ and demonstrate how ‘‘big data’’ coming from gene expression profiling, combined with signaling pathway information, smallmolecule chemical information, preclinical animal models, and clinical samples can empower phenotypic discovery at several critical levels. Whenever two or more practitioners of phenotypic drug discovery (PDD) are gathered together, a debate can be easily initiated about the practical definition of ‘‘phenotype.’’ A phenotypic assay is a black-box system, an experimental model, usually (but not always) cellular, that reads out in an endpoint that, to the best of one’s knowledge and technology, resembles a disease biomarker or pathology. Compounds are tested empirically for their ability to modify the assay endpoint that resembles the desired therapeutic effect, but the direct molecular target of the sought-for drug is left as an unknown variable. The degree and nature of the resemblance between disease and assay model, and between therapeutic
and assay endpoints, varies widely. At one extreme are whole-animal models of diseases, in which multiple tissues and cell types interact; at the other are highly targeted assays, such as a specific pathway where everything is known except for the existence of a causal druggable target. In each case, the difference between a normal and a perturbed cell, tissue, or organism can be viewed as a difference in the state of the system. At a cellular level, this state—what’s going on inside the black box—can be equated to the regulatory network embodied in changes in gene expression and activities of nodes of signaling pathways. The degree of coherence between the model and dis-
ease states (Figure 1) determines what Scannell and Bosley described as the predictive validity of the model (Scannell and Bosley, 2016). Predictive validity can be improved by understanding and replicating the disease-causing perturbation, be it cell-intrinsic or exogenous perturbation, and using cellular systems that have as much in common as possible in terms of origin and growth conditions as the disease tissue (Horvath et al., 2016; Vincent et al., 2015). Ultimately, it would be desirable to generate a ‘‘snapshot’’ of the state determining the assay phenotype and hold it up against a similarly obtained picture of the disease state. In this way, this snapshot could help determine whether the model is valid, to the extent that the
Cell Chemical Biology 24, May 18, 2017 ª 2017 Published by Elsevier Ltd. 545
Cell Chemical Biology
Previews
Figure 1. Transcriptome-Based Molecular Phenotyping Provides Insight into the Validity of Phenotypic Screening Models and Phenotypically Active Compounds Top: empirical screening models assume that if the perturbation (e.g., mutation, external stimulus) is similar for the disease and the model and that the assay readout (overt phenotype) is empirically equivalent to disease pathology, then there is a probability that the effects of test compounds on the mechanisms inside the assay black box will translate to the disease mechanisms. By applying molecular phenotyping, the states of the regulatory networks and mechanisms in the disease and model can be directly compared. On the lower left, although the overt phenotype of the model resembles the disease, it is a consequence of mechanistically unrelated processes, as evidenced by dissimilarity in the molecular phenotype (orange nodes). On the lower right, high concordance between the molecular phenotypes of disease and model increases confidence that a compound that affects the state of the will translates to in vivo and clinical efficacy.
assay readout is driven by the same molecular mechanisms as the clinical endpoint, and whether the compounds that alter the assay readout act via a therapeutically beneficial mechanism. In a key proof-of-concept study in this issue, Drawnel et al. (2017) present a practical solution to sampling the ‘‘internal’’ states of both the disease and the discovery model and describe this as ‘‘molecular phenotyping.’’ The system chosen was diabetic cardiomyopathy. Cultured human cardiomyocytes treated with a cocktail of physiologically relevant diabetic stressors (glucose, endothelin, and cortisol) show the same morphological defect as in myopathic heart tissue, namely decreased subcellular organization (striatedness) and contractility. This overt phenotype, quantitated by highcontent imaging, represents a classical phenotypic assay. However, the authors chose to look much deeper into the mechanistic state underlying the cellular and disease phenotypes. The molecular phenotype (MP) is in this instance quantitative mRNA sequencing (transcriptomic analysis) of a panel of 546 Cell Chemical Biology 24, May 18, 2017
917 ‘‘reporter’’ genes that are representative of the state of a broad range of cellular pathways. This approach was established by Jitao Zhang and his colleagues (Zhang et al., 2015), and the human genes included in the panel were enriched for genes that are representative of a broad range of key regulatory and diseaseassociated pathways. Gene expression assays are nothing new, and depending on how well-characterized the mechanism driving specific gene expression is, these assays may or may not fall into the category of phenotypic assays. However, unlike an assay for regulation of a specific gene, differential expression of genes in this reporter panel provides the requisite snapshot of global changes in cellular state. This reporter gene panel was designed to be simultaneously broad enough to provide an unbiased classification of multiple different mechanisms of action (MoA) and sufficiently pathway-enriched enough to support gene set analysis, indicating pathway-specific effects of compounds. Differential gene expression data was thus used to assess whether treatment
with compounds had the desired effect not only at the level of an overt phenotype, cardiomyocyte function, but also at the pathways associated with diabetic cardiomyopathy. Three key issues that can bedevil PDD were addressed by this application of the molecular phenotype platform. First, compounds that induce the desired overt phenotype could be compared at an MoA level with a control system, and the degree of similarity or dissimilarity determined. Second, false-positive compounds that induced the desired overt phenotype through a disease-irrelevant or undesirable MoA could be identified and rapidly eliminated from consideration. Taken together, the ability to mechanistically classify types of true and false positives is critical for following up on HTS hits by establishing a structure-activity relationship for analogs of a given hit compound. A frequent concern in phenotypic hit-to-lead compound optimization is ensuring that mechanistic fidelity is retained as potency and chemical properties change. Third, the degree of similarity between the assay model and the disease could be determined at a molecular mechanistic level. The differences in gene expression induced by the diabetic stressors were compared with gene expression profiles from patient-derived tissues and mouse models of cardiomyopathy and were shown to be significantly correlated at the pathway level. In other words, use of the molecular phenotype greatly increased confidence in the predictive validity of the model. An important differentiation between this reporter gene panel and many other gene-expression-based assays lies in the discriminatory power. A gene signature is a set of genes for which changes in expression are associated with a specific phenotype or response. For a diagnostic gene signature, minimizing type II errors is at a premium, whereas drug discovery efforts can be greatly hampered if there is an inability to avoid type I ‘‘false positives.’’ There is a general agreement among practitioners of PDD, and many individual examples, that identifying false positives due to undesirable MoA from phenotypic screens is one of the major scientific and technical challenges. Even establishing a definition for positive hits is a challenge if a compound shows the desired effect in the phenotypic assay
Cell Chemical Biology
Previews
but there is no understanding of the molecular MoA responsible for the phenotypic response and disease. There is, therefore, tremendous power in a broad and unbiased gene expression panel to detect unexpected and undesirable cellular responses to compounds, even some that may result in an apparently desirable overt phenotype. In the current study, the ability to identify false positives was shown for the case of topoisomerase inhibitors, which appeared to be protective in the cardiomyocyte morphology model. In applying the MP approach, these compounds were readily identified as biological false positives in that the effect on molecular phenotype was unrelated to the desired effect of reversing the perturbed state. Commonalities with the L1000 gene signature platform (Liu et al., 2015) and other transcriptional profiling efforts are evident. However, the design, intent, and interpretation of the data as a molecular phenotype differ from broad MoA profiling. A key difference is that the set of genes for MP were selected to represent biologically interpretable effects on important pathways in a disease-specific context. Although differential gene expression was used to define molecular phenotypes in this study, it is only one way in which an unbiased snapshot of the cell state could be captured. At this point in technological development, next-generation sequencing holds an advantage over proteomics in terms of sample requirements, accessibility to non-specialist users, and
equipment costs. However, there are arguments to be made that the proteome, including post-translational modifications, is more dynamic and informative of the cellular state than mRNA levels, and in the future these may become routine MP screening tools as well. This approach also shares some things in common with the ‘‘Cell Painting’’ strategy developed and disseminated by Carpenter and her group at the Broad Institute (Gustafsdottir et al., 2013). While these profiling approaches, as with the L1000 platform, are likely to prove extremely powerful for clustering compounds by MoA, they are not designed to interrogate disease-specific effects. However, there is no reason why features for different multiparametric analysis methods cannot be combined into a single unbiased signature. How else might this sort of molecular phenotyping enhance the productivity of PDD? One pertinent observation is the sensitivity of the method: effects were detected at earlier times and lower doses than the morphological phenotype. Typically, phenotypic assay hits that show a mixture of desired and undesired phenotypes are best discarded as unsalvageable; trying to empirically ‘‘rescue’’ the desirable activity of a promiscuous compound is likely to end in tears. However, the sensitivity and granularity of MP profiling in dose-response assays may enhance the ability to tease diseaserelated and unrelated effects apart. If less is known about what the desirable change in state is, then the same MP platform provides a means of clus-
tering compounds in different mechanistic classes with greater precision and confidence than single-readout screening and counter-screening phenotypic assays typically employed. Molecular phenotyping adds significant value to PDD at multiple key points: validation of a discovery model, validation of hits as having a relevant molecular MoA, and mechanistic clustering and filtering of hits with unexpected and undesirable MoA. It seems likely that such unbiased pathway-informed profiling techniques will become a central part of PDD efforts. REFERENCES €ng, E., Aoyama, N., Drawnel, F.M., Zhang, J.D., Ku Benmansour, F., Del Rosario, A.A., Zoffmann, S.J., Delobel, F., Prummer, M., Weibel, F., et al. (2017). Cell Chem. Biol. 24, this issue, 624–634. Gustafsdottir, S.M., Ljosa, V., Sokolnicki, K.L., Anthony Wilson, J., Walpita, D., Kemp, M.M., Petri Seiler, K., Carrel, H.A., Golub, T.R., Schreiber, S.L., et al. (2013). PLoS One 8, e80999. Horvath, P., Aulner, N., Bickle, M., Davies, A.M., Nery, E.D., Ebner, D., Montoya, M.C., O¨stling, P., €inen, V., Price, L.S., et al. (2016). Nat. Rev. Pietia Drug Discov. 15, 751–769. Liu, C., Su, J., Yang, F., Wei, K., Ma, J., and Zhou, X. (2015). Mol. Biosyst. 11, 714–722. Scannell, J.W., and Bosley, J. (2016). PLoS One 11, e0147215. Vincent, F., Loria, P., Pregel, M., Stanton, R., Kitching, L., Nocka, K., Doyonnas, R., Steppan, C., Gilbert, A., Schroeter, T., and Peakman, M.C. (2015). Sci. Transl. Med. 7, 293ps15. €ng, E., Boess, F., Certa, U., and Zhang, J.D., Ku Ebeling, M. (2015). BMC Genomics 16, 342.
Cell Chemical Biology 24, May 18, 2017 547