Phenotypic chemical biology for predicting safety and efficacy.

Phenotypic assays using in vitro cell cultures to forecast compound effects in people are transforming pharmaceutical research and contribute to alternative methods for chemical safety testing. How these assays are validated for human disease relevance is a critical factor for developing more predictive assays. Chemical biology, using drugs as well as target-selective chemical probes, is a direct and efficient approach for establishing disease relevance. Chemical probes can connect information across assays and associate targets to clinical effects. When applied at scale, phenotypic chemical biology advances our understanding of drug and toxicity mechanisms enabling construction of disease outcome pathways. To improve the physiological relevance of phenotypic assays, standardized testing of a curated set of phenotypic pathway probes can provide a higher level of validation for phenotypic assay best practices.

Elucidating mechanisms of toxicity using phenotypic data from primary human cell systems--a chemical biology approach for thrombosis-related side effects.

Here we describe a chemical biology approach for elucidating potential toxicity mechanisms for thrombosis-related side effects. This work takes advantage of a large chemical biology data set comprising the effects of known, well-characterized reference agents on the cell surface levels of tissue factor (TF) in a primary human endothelial cell-based model of vascular inflammation, the BioMAP® 3C system. In previous work with the Environmental Protection Agency (EPA) for the ToxCast™ program, aryl hydrocarbon receptor (AhR) agonists and estrogen receptor (ER) antagonists were found to share an usual activity, that of increasing TF levels in this system. Since human exposure to compounds in both chemical classes is associated with increased incidence of thrombosis-related side effects, we expanded this analysis with a large number of well-characterized reference compounds in order to better understand the underlying mechanisms. As a result, mechanisms for increasing (AhR, histamine H1 receptor, histone deacetylase or HDAC, hsp90, nuclear factor kappa B or NFκB, MEK, oncostatin M receptor, Jak kinase, and p38 MAPK) and decreasing (vacuolar ATPase or V-ATPase) and mTOR) TF expression levels were uncovered. These data identify the nutrient, lipid, bacterial, and hypoxia sensing functions of autophagy as potential key regulatory points controlling cell surface TF levels in endothelial cells and support the mechanistic hypothesis that these functions are associated with thrombosis-related side effects in vivo.

Evaluation of 147 perfluoroalkyl substances for immunotoxic and other (patho)physiological activities through phenotypic screening of human primary cells.

A structurally diverse set of 147 per- and polyfluoroalkyl substances (PFAS) was screened in a panel of 12 human primary cell systems by measuring 148 biomarkers relevant to (patho)physiological pathways to inform hypotheses about potential mechanistic effects of data-poor PFAS in human model systems. This analysis focused on immunosuppressive activity, which was previously reported as an in vivo effect of perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS), by comparing PFAS responses to four pharmacological immunosuppressants. The PFOS response profile had little correlation with reference immunosuppressants, suggesting in vivo activity does not occur by similar mechanisms. The PFOA response profile did share features with the profile of dexamethasone, although some distinct features were lacking. Other PFAS, including 2,2,3,3-tetrafluoropropyl acrylate, demonstrated more similarity to the reference immunosuppressants but with additional activities not found in the reference immunosuppressive drugs. Correlation of PFAS profiles with a database of environmental chemical responses and pharmacological probes identified potential mechanisms of bioactivity for some PFAS, including responses similar to ubiquitin ligase inhibitors, deubiquitylating enzyme (DUB) inhibitors, and thioredoxin reductase inhibitors. Approximately 21% of the 147 PFAS with confirmed sample quality were bioactive at nominal testing concentrations in the 1-60 micromolar range in these human primary cell systems. These data provide new hypotheses for mechanisms of action for a subset of PFAS and may further aid in development of a PFAS categorization strategy useful in safety assessment.

The future of phenotypic drug discovery.

Phenotypic drug discovery (PDD) uses biological systems directly for new drug screening. While PDD has proved effective in the discovery of drugs with novel mechanisms, for broader adoption, key challenges need resolution: progression of poorly qualified leads and overloaded pipelines due to lack of effective tools to process and prioritize hits; and advancement of leads with undesirable mechanisms that fail at more expensive stages of discovery. Here I discuss how human-based phenotypic platforms are being applied throughout the discovery process for hit triage and prioritization, for elimination of hits with unsuitable mechanisms, and for supporting clinical strategies through pathway-based decision frameworks. Harnessing the data generated in these platforms can also fuel a deeper understanding of drug efficacy and toxicity mechanisms. As these approaches increase in use, they will gain in power for driving better decisions, generating better leads faster and in turn promoting greater adoption of PDD.

Use of a rapid human primary cell-based disease screening model, to compare next generation products to combustible cigarettes.

A growing number of public health bodies, regulators and governments around the world consider electronic vapor products a lower risk alternative to conventional cigarettes. Of critical importance are rapid new approach methodologies to enable the screening of next generation products (NGPs) also known as next generation tobacco and nicotine products. In this study, the activity of conventional cigarette (3R4F) smoke and a range of NGP aerosols (heated tobacco product, hybrid product and electronic vapor product) captured in phosphate buffered saline, were screened by exposing a panel of human cell-based model systems using Biologically Multiplexed Activity Profiling (BioMAP® Diversity PLUS® Panel, Eurofins Discovery). Following exposure, the biological activity for a wide range of biomarkers in the BioMAP panel were compared to determine the presence of toxicity signatures that are associated with specific clinical findings. NGP aerosols were found to be weakly active in the BioMAP Diversity PLUS Panel (≤3/148 biomarkers) whereas significant activity was observed for 3R4F (22/148 biomarkers). Toxicity associated biomarker signatures for 3R4F included immunosuppression, skin irritation and thrombosis, with no toxicity signatures seen for the NGPs. BioMAP profiling could effectively be used to differentiate between complex mixtures of cigarette smoke or NGP aerosol extracts in a panel of human primary cell-based assays. Clinical validation of these results will be critical for confirming the utility of BioMAP for screening NGPs for potential adverse human effects.

The activities of drug inactive ingredients on biological targets.

Excipients, considered "inactive ingredients," are a major component of formulated drugs and play key roles in their pharmacokinetics. Despite their pervasiveness, whether they are active on any targets has not been systematically explored. We computed the likelihood that approved excipients would bind to molecular targets. Testing in vitro revealed 25 excipient activities, ranging from low-nanomolar to high-micromolar concentration. Another 109 activities were identified by testing against clinical safety targets. In cellular models, five excipients had fingerprints predictive of system-level toxicity. Exposures of seven excipients were investigated, and in certain populations, two of these may reach levels of in vitro target potency, including brain and gut exposure of thimerosal and its major metabolite, which had dopamine D3 receptor dissociation constant K d values of 320 and 210 nM, respectively. Although most excipients deserve their status as inert, many approved excipients may directly modulate physiologically relevant targets.

Human Cell-Based in vitro Phenotypic Profiling for Drug Safety-Related Attrition.

Ensuring the safety of new drugs is critically important to regulators, pharmaceutical researchers and patients alike. Even so, unexpected toxicities still account for 20-30% of clinical trial failures, in part due to the persistence of animal testing as the primary approach for de-risking new drugs. Clearly, improved methods for safety attrition that incorporate human-relevant biology are needed. This recognition has spurred interest in non-animal alternatives or new approach methodologies (NAMs) including in vitro models that utilize advances in the culture of human cell types to provide greater clinical relevance for assessing risk. These phenotypic assay systems use human primary and induced pluripotent stem cell-derived cells in various formats, including co-cultures and advanced cellular systems such as organoids, bioprinted tissues, and organs-on-a-chip. Despite the promise of these human-based phenotypic approaches, adoption of these platforms into drug discovery programs for reducing safety-related attrition has been slow. Here we discuss the value of large-scale human cell-based phenotypic profiling for incorporating human-specific biology into the de-risking process. We describe learnings from our experiences with human primary cell-based assays and analysis of clinically relevant reference datasets in developing in vitro-based toxicity signatures. We also describe how Adverse Outcome Pathway (AOP) frameworks can be used to integrate results from diverse platforms congruent with weight-of-evidence approaches from risk assessment to improve safety-related decisions in early discovery.

Advancing nonclinical innovation and safety in pharmaceutical testing.

Nonclinical tests are considered crucial for understanding the safety of investigational medicines. However, the effective translation from nonclinical to human application is limited and must be improved. Drug development stakeholders are working to advance human-based in vitro and in silico methods that may be more predictive of human efficacy and safety in vivo because they enable scientists to model the direct interaction of drugs with human cells, tissues, and biological processes. Here, we recommend test-neutral regulations; increased funding for development and integration of human-based approaches; support for existing initiatives that advance human-based approaches; evaluation of new approaches using human data; establishment of guidelines for procuring human cells and tissues for research; and additional training and educational opportunities in human-based approaches.

Assessing bioactivity-exposure profiles of fruit and vegetable extracts in the BioMAP profiling system.

The ToxCast program has generated in vitro screening data on over a thousand chemicals to assess potential disruption of important biological processes and assist in hazard identification and chemical testing prioritization. Few results have been reported for complex mixtures. To extend these ToxCast efforts to mixtures, we tested extracts from 30 organically grown fruits and vegetables in concentration-response in the BioMAP® assays. BioMAP systems use human primary cells primed with endogenous pathway activators to identify phenotypic perturbations related to proliferation, inflammation, immunomodulation, and tissue remodeling. Clustering of bioactivity profiles revealed separation of these produce extracts and ToxCast chemicals. Produce extracts elicited 87 assay endpoint responses per item compared to 20 per item for ToxCast chemicals. On a molar basis, the produce extracts were 10 to 50-fold less potent and when constrained to the maximum testing concentration of the ToxCast chemicals, the produce extracts did not show activity in as many assay endpoints. Using intake adjusted measures of dose, the bioactivity potential was higher for produce extracts than for agrichemicals, as expected based on the comparatively small amounts of agrichemical residues present on conventionally grown produce. The evaluation of BioMAP readouts and the dose responses for produce extracts showed qualitative and quantitative differences from results with single chemicals, highlighting challenges in the interpretation of bioactivity data and dose-response from complex mixtures.

Systems biology in drug discovery.

The hope of the rapid translation of 'genes to drugs' has foundered on the reality that disease biology is complex, and that drug development must be driven by insights into biological responses. Systems biology aims to describe and to understand the operation of complex biological systems and ultimately to develop predictive models of human disease. Although meaningful molecular level models of human cell and tissue function are a distant goal, systems biology efforts are already influencing drug discovery. Large-scale gene, protein and metabolite measurements ('omics') dramatically accelerate hypothesis generation and testing in disease models. Computer simulations integrating knowledge of organ and system-level responses help prioritize targets and design clinical trials. Automation of complex primary human cell-based assay systems designed to capture emergent properties can now integrate a broad range of disease-relevant human biology into the drug discovery process, informing target and compound validation, lead optimization, and clinical indication selection. These systems biology approaches promise to improve decision making in pharmaceutical development.

Mechanisms of Skin Toxicity Associated with Metabotropic Glutamate Receptor 5 Negative Allosteric Modulators.

Cutaneous reactions represent one of the most common adverse drug effects observed in clinical trials leading to substantial compound attrition. Three negative allosteric modulators (NAMs) of metabotropic glutamate receptors (mGluRs), which represent an important target for neurological diseases, developed by Pfizer, were recently failed in preclinical development due to delayed type IV skin hypersensitivity observed in non-human primates (NHPs). Here we employed large-scale phenotypic profiling in standardized panels of human primary cell/co-culture systems to characterize the skin toxicity mechanism(s) of mGluR5 NAMs from two different series. Investigation of a database of chemicals tested in these systems and transcriptional profiling suggested that the mechanism of toxicity may involve modulation of nuclear receptor targets RAR/RXR, and/or VDR with AhR antagonism. The studies reported here demonstrate how phenotypic profiling of preclinical drug candidates using human primary cells can provide insights into the mechanisms of toxicity and inform early drug discovery and development campaigns.

Characterization of compound mechanisms and secondary activities by BioMAP analysis.

INTRODUCTION: Unexpected drug activities account for many of the failures of new chemical entities in clinical trials. These activities can be target-dependent, resulting from feedback mechanisms downstream of the primary target, or they can occur as a result of unanticipated secondary target(s). Methods that would provide rapid and efficient characterization of compounds with respect to a broad range of biological pathways and mechanisms relevant to human disease have the potential to improve preclinical and clinical success rates. METHODS: BioMAP assays containing primary human cells (endothelial cells and co-cultures with peripheral blood leukocytes) were stimulated in complex formats (specific combinations of inflammatory mediators) for 24 h in the presence or absence of test agents (drugs, experimental compounds, etc.). The levels of selected protein readouts (adhesion receptors, cytokines, enzymes, etc.) were measured and activity profiles (normalized data sets comprising BioMAP profiles) were generated for each test agent. The resulting profiles were compared by statistical methods to identify similarities and mechanistic insights. RESULTS: Compounds with known mechanisms including inhibitors of histamine H1 receptor, angiotensin converting enzyme, IkappaB kinase-2, beta2 adrenergic receptor and others were shown to generate reproducible and distinguishable BioMAP activity profiles. Similarities were observed between compounds targeting components within the same signal transduction pathway (e.g. NFkappaB), and also between compounds that share secondary targets (e.g. ibuprofen and FMOC-L-leucine, a PPARgamma agonist). DISCUSSION: Complex primary cell-based assays can be applied for detecting and distinguishing unexpected activities that may be of relevance to drug action in vivo. The ability to rapidly test compounds prior to animal or clinical studies may reduce the number of compounds that unexpectedly fail in preclinical or clinical studies.

Approaches to the analysis of cell signaling networks and their application in drug discovery.

The ability to predict the safety and efficacy of novel drugs prior to clinical testing is a key goal in pharmaceutical drug discovery. Gaining a mechanistic understanding of the complex cell signaling networks (CSNs) underlying disease processes promises to help reduce the number of clinical failures by identifying points of intervention as well as redundancies and feedback mechanisms that contribute to toxicities, lack of efficacy and unexpected biological activities. Experimental and computational approaches to analyzing and modeling CSNs are currently being validated using simple organisms and cell lines. In vitro cell systems of sufficient complexity to resemble human disease physiology, but which are also amenable to chemical and genetic perturbations on a large scale, are now required for deciphering the signaling networks operating in human disease. In this review, experimental and computational methods for modeling complex CSNs and the applications of these approaches to pharmaceutical drug discovery are discussed.

An integrative biology approach for analysis of drug action in models of human vascular inflammation.

Unexpected drug activities discovered during clinical testing establish the need for better characterization of compounds in human disease-relevant conditions early in the discovery process. Here, we describe an approach to characterize drug function based on statistical analysis of protein expression datasets from multiple primary human cell-based models of inflammatory disease. This approach, termed Biologically Multiplexed Activity Profiling (BioMAP), provides rapid characterization of drug function, including mechanism of action, secondary or off-target activities, and insights into clinical phenomena. Using three model systems containing primary human endothelial cells and peripheral blood mononuclear cells in different environments relevant to vascular inflammation and immune activation, we show that BioMAP profiles detect and discriminate multiple functional drug classes, including glucocorticoids; TNF-alpha antagonists; and inhibitors of HMG-CoA reductase, calcineurin, IMPDH, PDE4, PI-3 kinase, hsp90, and p38 MAPK, among others. The ability of cholesterol lowering HMG-CoA reductase inhibitors (statins) to improve outcomes in rheumatic disease patients correlates with the activities of these compounds in our BioMAP assays. In addition, the activity profiles identified for the immunosuppressants mycophenolic acid, cyclosporin A, and FK-506 provide a potential explanation for a reduced incidence of posttransplant cardiovascular disease in patients receiving mycophenolic acid. BioMAP profiling can allow integration of meaningful human biology into drug development programs.

Rapid structure-activity and selectivity analysis of kinase inhibitors by BioMAP analysis in complex human primary cell-based models.

Rapid, quantitative methods for characterizing the biological activities of kinase inhibitors in complex human cell systems could allow the biological consequences of differential target selectivity to be monitored early in development, improving the selection of drug candidates. We have previously shown that Biologically Multiplexed Activity Profiling (BioMAP) permits rapid characterization of drug function based on statistical analysis of protein expression data sets from complex primary human cellbased models of disease biology. Here, using four such model systems containing primary human endothelial cells and peripheral blood mononuclear cells in which multiple signaling pathways relevant to inflammation and immune responses are simultaneously activated, we demonstrate that BioMAP analysis can detect and distinguish a wide range of inhibitors directed against different kinase targets. Using a panel of p38 mitogen-activated protein kinase antagonists as a test set, we show further that related compounds can be distinguished by unique features of the biological responses they induce in complex systems, and can be classified according to their induction of shared (on-target) and secondary activities. Statistical comparisons of quantitative BioMAP profiles and analysis of profile features allow correlation of induced biological effects with chemical structure and mapping of biological responses to chemical series or substituents on a common scaffold. Integration of automated BioMAP analysis for prioritization of hits and for structure-activity relationship studies may improve and accelerate the design and selection of optimal therapeutic candidates.

Systems biology in drug discovery and development.

The complexity of human biology makes it challenging to develop safe and effective new medicines. Systems biology omics-based efforts have led to an explosion of high-throughput data and focus is now shifting to the integration of diverse data types to connect molecular and pathway information to predict disease outcomes. Better models of human disease biology, including more integrated network-based models that can accommodate multiple omics data types, as well as more relevant experimental systems, will help predict drug effects in patients, enabling personalized medicine, improvement of the success rate of new drugs in the clinic, and the finding of new uses for existing drugs.

Phenotypic screening of the ToxCast chemical library to classify toxic and therapeutic mechanisms.

Addressing the safety aspects of drugs and environmental chemicals has historically been undertaken through animal testing. However, the quantity of chemicals in need of assessment and the challenges of species extrapolation require the development of alternative approaches. Our approach, the US Environmental Protection Agency's ToxCast program, utilizes a large suite of in vitro and model organism assays to interrogate important chemical libraries and computationally analyze bioactivity profiles. Here we evaluated one component of the ToxCast program, the use of primary human cell systems, by screening for chemicals that disrupt physiologically important pathways. Chemical-response signatures for 87 endpoints covering molecular functions relevant to toxic and therapeutic pathways were generated in eight cell systems for 641 environmental chemicals and 135 reference pharmaceuticals and failed drugs. Computational clustering of the profiling data provided insights into the polypharmacology and potential off-target effects for many chemicals that have limited or no toxicity information. The endpoints measured can be closely linked to in vivo outcomes, such as the upregulation of tissue factor in endothelial cell systems by compounds linked to the risk of thrombosis in vivo. Our results demonstrate that assaying complex biological pathways in primary human cells can identify potential chemical targets, toxicological liabilities and mechanisms useful for elucidating adverse outcome pathways.

Neoclassic drug discovery: the case for lead generation using phenotypic and functional approaches.

Innovation and new molecular entity production by the pharmaceutical industry has been below expectations. Surprisingly, more first-in-class small-molecule drugs approved by the U.S. Food and Drug Administration (FDA) between 1999 and 2008 were identified by functional phenotypic lead generation strategies reminiscent of pre-genomics pharmacology than contemporary molecular targeted strategies that encompass the vast majority of lead generation efforts. This observation, in conjunction with the difficulty in validating molecular targets for drug discovery, has diminished the impact of the "genomics revolution" and has led to a growing grassroots movement and now broader trend in pharma to reconsider the use of modern physiology-based or phenotypic drug discovery (PDD) strategies. This "From the Guest Editors" column provides an introduction and overview of the two-part special issues of Journal of Biomolecular Screening on PDD. Terminology and the business case for use of PDD are defined. Key issues such as assay performance, chemical optimization, target identification, and challenges to the organization and implementation of PDD are discussed. Possible solutions for these challenges and a new neoclassic vision for PDD that combines phenotypic and functional approaches with technology innovations resulting from the genomics-driven era of target-based drug discovery (TDD) are also described. Finally, an overview of the manuscripts in this special edition is provided.

Building predictive models for mechanism-of-action classification from phenotypic assay data sets.

Compound mechanism-of-action information can be critical for drug development decisions but is often challenging for phenotypic drug discovery programs. One concern is that compounds selected by phenotypic screening will have a previously known but undesirable target mechanism. Here we describe a useful method for assigning mechanism class to compounds and bioactive agents using an 84-feature signature from a panel of primary human cell systems (BioMAP systems). For this approach, a reference data set of well-characterized compounds was used to develop predictive models for 28 mechanism classes using support vector machines. These mechanism classes encompass safety and efficacy-related mechanisms, include both target-specific and pathway-based classes, and cover the most common mechanisms identified in phenotypic screens, such as inhibitors of mitochondrial and microtubule function, histone deacetylase, and cAMP elevators. Here we describe the performance and the application of these predictive models in a decision scheme for triaging phenotypic screening hits using a previously published data set of 309 environmental chemicals tested as part of the Environmental Protection Agency's ToxCast program. By providing quantified membership in specific mechanism classes, this approach is suitable for identification of off-target toxicity mechanisms as well as enabling target deconvolution of phenotypic drug discovery hits.