Opportunities and Challenges in Phenotypic ... - ACS Publications

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Cite This: J. Med. Chem. XXXX, XXX, XXX−XXX

Opportunities and Challenges in Phenotypic Screening for Neurodegenerative Disease Research Dean G. Brown*,† and Heike J. Wobst*,‡ †

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Hit Discovery, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, 35 Gatehouse Drive, Waltham, Massachusetts 02451, United States ‡ Neuroscience, BioPharmaceuticals R&D, AstraZeneca, 35 Gatehouse Drive, Waltham, Massachusetts 02451, United States ABSTRACT: Toxic misfolded proteins potentially underly many neurodegenerative diseases, but individual targets which regulate these proteins and their downstream detrimental effects are often unknown. Phenotypic screening is an unbiased method to screen for novel targets and therapeutic molecules and span the range from primitive model organisms such as Sacchaomyces cerevisiae, which allow for high-throughput screening to patient-derived cell-lines that have a close connection to the disease biology but are limited in screening capacity. This perspective will review current phenotypic models, as well as the chemical screening strategies most often employed. Advances in in 3D cell cultures, high-content screens, robotic microscopy, CRISPR screening, and use of machine learning methods to process the enormous amount of data generated by these screens are certain to change the paradigm for phenotypic screening and will be discussed.

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INTRODUCTION Historically, phenotypic screening was the origin for many important classes of drugs.1 Even though molecular targetbased methods were widely adopted beginning in the late 1980s, phenotypic screening continues to provide productive routes for the discovery of new drugs. An analysis of first-inclass (FIC) drugs approved from 1998 to 2008 demonstrated that ∼62% (28 drugs) could be traced back to phenotypic screening approaches.2 Of these 28 first-in-class drugs, seven were approved for infectious diseases. This trend has continued in the past decade, with several recent examples of new drugs discovered by phenotypic screening for bacterial and viral infections,3,4 including the first novel drug for tuberculosis in more than 40 years.5 In addition, a significant number of drugs for central nervous system (CNS) indications have been discovered using phenotypic screening approaches. In the 1998−2008 analysis of FIC drugs, seven were approved for CNS indications. These include aripiprazole (schizophrenia), varenicline (smoking cessation), levetiracetam (antiepileptic), and refinamide (antiepileptic). A large number of CNS drugs (12) were discovered as part of an effort to identify novel antiepileptic drugs through the National Institutes of Health (NIH) drug-screening program Anticonvulsant Drug Development Program, by screening in rodent models of seizure. Examples from this effort include lamotrigine, gabapentin, and pregabalin.6 Phenotypic screens are attractive because, if properly designed, they may represent a more holistic model to the biology of interest as compared to a biochemical screen (Figure 1).7 Furthermore, phenotypic screens are often the © XXXX American Chemical Society

gateway to novel target-based drug discovery. Examples in this category include HDAC inhibitors such a vorinostat,8 mTOR inhibitors such as rapamycin,8 BET bromodomain inhibitors,9 and inhibitors of the cholesterol transporter Niemann−Pick C1-Like 1 (NPC1L1).1 These targets were all discovered using phenotypic screens and subsequently molecular target-based approaches were enabled. Neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, and frontotemporal dementia (FTD) are debilitating disorders, and there are currently no disease-modifying therapies available to patients. The World Health Organization has predicted that neurodegenerative diseases will become the second leading cause of death in 20 years’ time, after cardiovascular diseases.10 Consequently, there is an urgent need to find novel drugs which can slow down or arrest the course of these disorders. Many of these diseases share common mechanisms and phenotypes (e.g., aberrant protein aggregation, impaired autophagic flux, excitotoxicity, mitochondrial dysfunction).11−13 Phenotypic screens are used to interrogate these and other disease mechanisms in a variety of model systems. Here we review current screening strategies in neurodegeneration, along with specific examples of compounds and novel targets discovered with these methods. We also discuss best practices for developing a robust screening cascade, along with methods for hit confirmation and target validation. Special Issue: Women in Medicinal Chemistry Received: May 15, 2019 Published: July 3, 2019 A

DOI: 10.1021/acs.jmedchem.9b00797 J. Med. Chem. XXXX, XXX, XXX−XXX

Journal of Medicinal Chemistry

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Figure 1. Schematic of phenotypic screening approaches. An appropriate cellular screening model is chosen according to the type of screening library and the phenotype of interest. Screening libraries can be divided into three classes: small molecule compound libraries, knockdown or deletion libraries, or overexpression libraries. Knockdown or deletion libraries contain either siRNA, shRNA, or gRNA for CRISPR screens. For CRISPR screens, the chosen cell type requires expression of Cas9 endonuclease. Cells can also express a disease gene that causes the phenotype of interest (e.g., overexpression of tau, α-synuclein, or Htt fragments) and/or a reporter gene (e.g., GFP-labeled LC3 to visualize autophagic flux, a minigene to assess splicing, a luciferase-labeled transgene to visualize up-or downregulation of protein levels).

Nonmammalian Model Organisms for Phenotypic Small Molecule Screening. Disease models have been generated using nonmammalian organisms such as Saccharomyces cerevisiae (yeast),14−18 Caenorhabditis elegans (nematode worm),19−24 Drosophila melanogaster (fruit fly),25,26 and Danio rerio (zebrafish)27,28 for a number of neurodegenerative diseases including Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, and amyotrophic lateral sclerosis (ALS) (Table 1). Some of these model organisms offer the advantage of high screening capacity and well-studied genomes. Yeast, for

example, have a simple and compact genome of about 6K genes with few and often nonessential introns.14,29 However, only about a third of yeast genes have a human orthologue.30 Furthermore, sequence identity between human-yeast orthologues varies greatly depending on the gene in question, ranging from 9−92% and averaging 32% across the genome.31 Despite the limited overlap between human and yeast genome, important yeast genes can be connected to human disease, a fact that has been exploited in a number of small molecule screens. In one recent example, yeast were engineered to overexpress the Parkinson’s disease gene α-synuclein, which causes a toxicity phenotype, and a compound library was screened for suppressors of this toxic phenotype.32 Using spontaneous drug-resistant mutants and genetics, one of the chemical series found in the yeast screen allowed the research team to identify the human orthologue target stearoyl-CoA desaturase. Potent compounds to the human target were synthesized (Table 2, 1, YTZ-465) and were shown to protect against α-synuclein toxicity in induced pluripotent stem cells (iPSCs). Another example from this screen provided an unknown mechanism which reverses endoplasmic reticulum (ER)-to-Golgi trafficking defects and mitochondrial dysfunction (Table 2, 2).33

Table 1. Model Organisms Used in Phenotypic Screening species S. cerevisiae (yeast) C. elegans (nematode) D. melanogaster (fruit fly) D. rerio (zebrafish) M. musculus (mouse)

estimated number of protein-coding genes

protein-coding genes with orthologues in H. sapiens (%)

∼620035

∼3730

∼2025036

∼3836

∼1400037

∼4638

∼2600039

∼7039

∼2500040

∼9940

B



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Table 2. Summary of Phenotypic Screens in Neurodegenerationa

C



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Table 2. continued

D



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Table 2. continued

E



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Table 2. continued

a

(1) Background pharmacology of compound is the known pharmacology of the hit at the time of the screen. (2) MOA in phenotypic assay is the mechanism elucidated in the publication. (3) A set of 1K compounds was selected based on complexity and drug-like properties.

The nematode C. elegans has been used to model several neurodegenerative diseases such as Alzheimer’s disease, FTD, ALS, and Parkinson’s disease.24 Key advantages of C. elegans are its transparent body, simple neuronal circuit, wellunderstood genetics, and rapid reproductive cycle. Although widely used, very few examples are reported in the literature of chemical screens and hits and subsequent deconvolution of targets. One example from Mondal et al. reported on the screen of 1000 compounds from a library of Food and Drug Administration (FDA)-approved drugs in a C. elegans model of poly glutamine (polyQ) aggregation.21 PolyQ diseases such as

Huntington’s disease and several forms of spinocerebellar ataxias are a class of neurodegenerative disorders characterized by expansion of a CAG trinucleotide repeat sequence encoding a polyQ repeat stretch. While in healthy individuals these CAG repeats are usually 40 or less (dependent on the gene and the individual), in the disease state the repeat length can reach >100. In the case of Huntingtin, the gene associated with Huntington’s disease, the expanded polyQ stretch makes the resulting protein aggregation-prone. In this screen dronedarone 3 (Table 2), an antiarrhythmic, was found to reduce polyQ aggregation by 46% at a 50 μM concentration. In another F



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Figure 2. Overview of cell models used in phenotypic screening for neurodegeneration.

example, Yao et al. used a C. elegans model of Parkinson’s disease to demonstrate that the leucine-rich repeat kinase 2 (LRRK2) inhibitor LRRK2-IN1 4 (Table 2) rescues R1441Cand G2019S-LRRK2-induced dopaminergic deficits and neurodegeneration.19 This example serves to illustrate that the C. elegans model can recapitulate known human target biology using a well-known Parkinson’s disease target. In another recent paper, Halliday et al. screened 1040 compounds from the NINDS (National Institute for Neurological Disorders and Stroke) library containing 75% FDAapproved drugs in a C. elegans model of tunicamycin-induced developmental delay.34 Tunicamycin is a nucleoside antibiotic that activates the unfolded protein response (UPR). Two compounds were identified, trazadone and dibenzoylmethane, which inhibit UPR-induced eIF2α-P signaling, resulting in normalization of protein synthesis rates (Table 2, 5 and 6). Both compounds also demonstrated neuroprotection in prioninfected mice as well as a P301L-tau model of FTD, thus highlighting the translatability of certain pathways and genes in C. elegans into mammalian models of neurodegeneration. Zebrafish (D. rerio) is another attractive model organism to study neurodegenerative disorders. Zebrafish are easy to maintain and have a relatively short generation time but crucially are vertebrates. Furthermore, zebrafish larvae are transparent, allowing for live imaging using fluorescent labels.41 A recent review summarized more than 65 chemical screens in zebrafish, but none were aimed at identifying drugs for neurodegenerative diseases.28 However, zebrafish behavioral screens have been used to identify novel neuroactive drugs based on clustering of elicited responses. In two published papers, stereotypical quantitative behavioral features were extracted from two distinct locomotor paradigms and thousands of compounds were then tested for their effects on these behavioral features.27 Compounds were clustered according to their “fingerprint” or “barcode”, the multidimensional impact of the compounds on these behavioral features. In these examples, fingerprints of behavioral activity can be ascribed to known drugs and then used to match compounds from a drug screen to infer the mechanism of action. While not widely used as a screening model for neurodegeneration, zebrafish have been used to validate compounds identified in cellular screens. For example, Corman et al. demonstrated that the BET bromodomain inhibitor PFI-1 (7), identified in a cellular toxicity screen, increases survival in developing

zebrafish exposed to PR20 peptide, a frequently used stressor to elicit toxicity associated with C9ORF72-ALS.42 The fruit fly D. melanogaster has become a useful translational model for neurological diseases and in particular for neurodegenerative diseases. The advantages of this model organism are its short life span, inexpensive maintenance, and ready access to transgenic and knockout lines for the use of genetic modifier screens.25 In one recent study, McGurk et al. performed a genetic modifier screen in flies expressing the ALS-associated human transactive response DNA binding protein 43 kDa (TDP-43), which results in an eye degeneration phenotype.43 They identified tankyrase (a member of the Poly-ADP ribose polymerization or “PARP” family), which attenuated TDP-43 associated toxicity in the TDP-43 fly model. The team further established that tankyrase-1/2 inhibitors such as XAV939 reduced the formation of arsenite-induced cytoplasmic TDP-43 foci in mammalian cells (Table 2, 8). While Drosophila is a widely used model organism for genetic screens in neurodegenerative disease paradigms (for an overview, see Lenz et al.44), compound screens have not been frequently reported. One example was published by Lawal et al., where a set of ∼1K known drugs were screened against a mutant vesicular monoamine transporter (VMAT) strain to identify potentiators of neurotransmitter release as potential therapeutic agents for the treatment of Parkinson’s disease and depression. The screen yielded several hits, including the chemotherapy agent dacarbazine 9 (Table 2).45 In another study, a Drosophila model of fragile X-associated tremor/ataxia syndrome (FXTAS) was used to test 2000 FDA-approved drugs and natural products (Spectrum Collection) to identify compounds that rescue the lethality phenotype induced by expression of CGG repeats, a mutation found in the FMR1 gene in affected patients.46 Among the identified hits were xylazine 10 and fluocinolone acetonide 11, a phospholipase A2 inhibitor (Table 2). Although rare, mouse screening models are also sometimes employed. One recent example was reported by Pieper et al., who identified small molecules capable of enhancing neuron formation in the hippocampus of adult mice in vivo. A set of 1K molecules were screened, and eight were found to enhance neuron formation in the subgranular zone of the dentate gyrus, with the most promising candidate P7C3 12 (Table 2).47 Furthermore, advances in rodent behavioral profiling platforms allow for test compounds to be compared to known drugs and G



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types of assays, (2) confounding off-target activity at the higher concentrations such as cytotoxicity, or (3) compound-related issues such as poor solubility. Only dipyridamole 13 (Table 2), a nonspecific phosphodiesterase (PDE) inhibitor, was active at both concentrations after triplicate experiments. Pathway-specific phenotypic screens can likewise be done in immortalized cell lines, where one is able to understand and modulate a specific pathway. One of the more interesting tool molecules to originate from a pathway-specific phenotypic screen is ISRIB 14 (“integrated stress response inhibitor)”, which was discovered by screening ∼100K molecules in modified HEK293T cells treated with the ER stressor thapsigargin to trigger a luciferase end-point of PERK activation (Table 2).51 ISRIB was identified as a potent inhibitor of the PERK activation pathway.51 Deconvolution studies identified ISRIB as a stabilizer of activated eIF2b dimers with the potential to improve cognitive deficits as was demonstrated by improved memory consolidation in rodents.51,71 ISRIB has become one of the leading tool molecules in the study of UPR and stress response in neurological disease; however, very poor solubility may confound interpretation of results and limit its use as a tool compound.72 Another very notable example of a major advance in the field using immortalized cell lines was the discovery of small molecules which could correct mis-splicing in survival motor neuron 2 (SMN2) such as RG7800 15 (Table 2) as a treatment for spinal muscular atrophy (SMA).52−54 This was discovered in a high-throughput chemical screen in HEK293 cells expressing an SMN2 minigene. The mechanism of action was ultimately elucidated and determined to be stabilization of the SMN2 pre-mRNA interaction with U1 small nuclear ribonucleic protein, resulting in enhanced production of full length SMN.73 Because misfolding and aggregation of disease-associated proteins is thought to be causative of, or significantly contribute to, neurodegenerative processes in diseases such as ALS, Parkinson’s disease, and FTD, correcting or preventing these misfolding events is a widely investigated therapeutic approach. In one such instance, Pickhardt et al. tested a chemogenomic library of 1649 molecules in N2a cells expressing an aggregation-prone version of the repeat domain of tau.55 Aggregated tau protein is the common hallmark of a number of neurodegenerative diseases termed tauopathies, including Alzheimer’s disease, FTD-tau, progressive supranuclear palsy, and corticobasal degeneration.74 The screen identified 18 inhibitors of tau aggregation, including several HDAC inhibitors and p38 MAPK inhibitors such as LY2228820 16 (Table 2).55 Primary Cell Assays. Primary neuronal or other CNS cell types can provide a more physiological setting for compound screens. For example, primary neurons do not divide, differentiate, and mature over time in culture and form synaptic connections. However, large-scale small molecule screens are hampered by several factors. Primary cultures require live animals as well as training to obtain the cells. It is also more difficult to produce sufficient cell numbers as the cultures require differentiation over many days, and reagents are more expensive (Figure 2). Furthermore, primary CNS cells are obtained from mice or rats, which can introduce species bias or result in species-specific effects. However, despite these limitations, a number of small-scale screens have yielded interesting chemical matter. In one such screen, a NINDS library of ∼1K known bioactive molecules and FDA-

tool compounds but has been mostly applied to identifying novel psychiatric drugs such as the recent SEP-363856 for schizophrenia.48,49 These profiling models have also been proposed for neurodegenerative diseases such as Huntington’s disease, where behavioral phenotypes such as locomotor deficits, cognitive impairment, or circadian rhythm phenotypes can be monitored in high capacity.48 Immortalized Cell Lines. Perhaps the historical mainstay of neuroscience-driven phenotypic screens is based on the use of immortalized cell lines. Immortalized cells are often considered a starting point for high-throughput phenotypic screens, as cell lines are widely commercially available, do not typically require special skills to culture and are inexpensive to maintain (Figure 2). Furthermore, many cell lines are of human origin, thus ruling out any species effects that could impede translation of the results from the cellular screen. Published screening approaches can be divided into several classes: (1) screens for molecules that rescue a toxicity phenotype, (2) screens for modulators of a specific pathway, and (3) screens for modulators of a specific target gene or protein. Screens for compounds which rescue a toxic phenotype are an attractive model in neurodegeneration research. In neurodegenerative diseases such as Parkinson’s disease, ALS, and FTD, disease-associated proteins misfold and aggregate, causing toxicity through either gain-of-function or loss-of-function mechanisms or a combination of both. In one recent paper, Corman et al. tested >4K compounds to identify suppressors of PR20 toxicity in U-2 OS cells.42 PR20 is a 20mer repeat of proline and arginine amino acids and is frequently used to elicit toxicity in C9ORF72-ALS and -FTD models. Hexanucleotide repeat expansions in C9ORF72 are associated with ALS and FTD. These expansions lead to the non-ATG-dependent translation of five dipeptide repeats (GP, GA, PA, GR, and PR), of which the PR dipeptide repeat is toxic in vitro.66 The screen identified the histone deacetylase (HDAC) inhibitor sodium phenylbutyrate as well as the BET bromodomain inhibitors PFI-1 7 (Table 2) and bromosporine.42 Of these, both PFI-1 and sodium phenylbutyrate also improved survival in zebrafish treated with PR20 peptide. The authors were able to demonstrate that the BET inhibitors were responsible for rescue of nucleolar stress caused by PR20 as well as actinomycin D, suggesting a very general effect of these inhibitors. It should be noted that BET bromodomain inhibitors, and PFI-1 in particular, are active in a variety of phenotypic screens such as in assays measuring reactivation of HIV-1 latency,67 promotion of HSV infection,68 reduction of human collagen Col2a1 gene expression,69 and reduction of inflammation as measured by etoposide release of IL-6 and IL8.70 This background information may help underscore that compounds which interfere with BET domains may cause gross general pharmacological effects that may confound the interpretation of a variety of phenotypic screens. To screen for compounds that rescue toxicity induced by viral overexpression of wild-type α-synuclein in a cellular model of Parkinson’s disease, Höllerhage et al. utilized Lund human mesencephalic (LUHMES) cells, which can be differentiated into dopamine-like cells.50 A set of 1600 FDAapproved drugs were screened at concentrations of 3 and 10 μM in this model, and cell death was quantified by high content imaging. Of the identified 53 hits, 22 were active at the lower concentration and nine at both concentrations. This disconnect between screening concentrations and activity can be due to the following factors: (1) natural variability in these H



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comes with a number of caveats. The process of differentiation can be time-consuming, expensive, and produce variable results. Furthermore, in the case of neuronal differentiation, cells may resemble neurons by morphology but not have the electrophysiological or electrochemical characteristics of a mature neuron. Finally, the observed phenotype may arise as a result of the specific genetic background of the donor, thus making assay optimization and validation in multiple lines from different donors necessary if modeling sporadic disease or require the generation of isogenic control lines when modeling familial disease. To date, a number of small molecule screens have been performed in iPSCs to identify novel drugs for neurodegenerative diseases. In one such instance, Fujimori et al. performed a drug screen of >1200 FDA-approved compounds using Fused In Sarcoma (FUS)- and TDP-43-iPSC-derived motor neurons by assessing multiple phenotypes with high content imaging including neurite length, stress granule formation, FUS/TDP-43 aggregation, and cell death.60 The screen identified the D2 agonist ropinirole 21 (Table 2) as a top modulator of these phenotypes. The efficacy of ropinirole was further demonstrated in 24 sporadic ALS-derived iPSC lines. Ropinirole was shown to reduce apoptosis, reactive oxygen species, neuronal atrophy, and protein aggregation in several of the sporadic lines. The authors demonstrated that ropinirole may be working in these models through both a dopamine and a dopamine-independent pathway, as evidenced by lack of complete reversal of effects with dopamine antagonists. In a screen to identify compounds that lower levels of tau, Wang et al. tested the “Library of Pharmacologically Active Compounds” (LOPAC), a commercial set of 1280 bioactive molecules, in iPSC-derived glutamatergic neurons.61 The adrenergic receptor agonists moxonidine 22 (Table 2) and metaproterenol 23 (Table 2) were shown to lower tau in a dose-dependent manner. In another paper, familial Alzheimer’s disease (FAD)-derived iPSCs with the PSEN1 G384A mutation were differentiated into cortical neurons and >1200 bioactive molecules were tested to identify Aβ-lowering compounds.62 Of the 27 hits, six leading compounds were identified and tested in all possible combinations. A cocktail of bromocriptine 24, cromolyn 25, and topiramate 26 (Table 2) was further validated in four additional FAD lines, four sporadic Alzheimer’s lines, and six control lines. The authors did not attempt to dissect and understand the basis of the synergistic effect. Although this result is quite interesting, it will be a challenge to carry this combination forward in more advanced models because the efficacy will greatly depend on the pharmacokinetics, exposure, and target engagement of each compound within every model. 3D Cultures. Currently, most cell-based high throughput compound screens are carried out using a single cell type grown in monolayers. However, these nonphysiological conditions do not reproduce the highly complex architecture, cell-to-cell interactions and microenvironment cells share within the context of a tissue. Thus, complex 3D culture systems, organoids, organs-on-chips, and hydrogel scaffolds are being developed and refined to more accurately allow cells to “behave” as they would in vivo (for a recent review on 3D cultures in drug discovery, see Fang and Eglen75). In the future, complex 3D culture systems and organoids may be more widely adopted in phenotypic screening. However, to the best of our knowledge, no low or high throughput small

approved drugs was tested to identify inhibitors of Aβ-induced neurite degeneration in mouse cortical neurons.56 Of the 36 identified hits, several belonged to the class of nonsteroidal anti-inflammatory drugs (NSAID) including metacetamol 17 (Table 2). In another instance, Schmidt et al. synthesized a small collection of quinoline-derived compounds and tested these for potential neuroprotective and neuritogenic effects.57 The compounds were designed based on lembehyne A, a compound known for its neurotrophic activity, and were screened in both spontaneous and -methyl-4-phenylpyridinium (MPP+)-induced Parkinson’s disease models of mesencephalic dopaminergic neuron degeneration. Neuroprotection was assessed by quantifying the number of tyrosine hydroxylase (TH)-positive dopaminergic neurons, while neuritogenesis was assessed by quantifying neurite length per dopaminergic neuron. An example hit from this screen 18 is exemplified in Table 2. Huang et al. sought to identify compounds for the possible treatment of Angelman syndrome, a neurodevelopmental disorder.58 Angelman syndrome is caused by mutations or deletion of the maternal allele for ubiquitin protein ligase E3A (UBE3A). In neurons, the paternal allele is epigenetically silenced, while the maternal allele is expressed. A screen was conducted using 2306 compounds sourced from several libraries for hits that “unsilence” the expression of the paternal UBE3A allele. For this approach, cortical neurons were cultured from mice harboring a paternal UBE3A-YFP reporter and upregulation of this reporter was measured using high content imaging. The initial screen yielded only one hit, the FDA-approved topoisomerase type I inhibitor irinotecan 19 (Table 2). Further structure−activity relationship (SAR) analysis and testing of chemically distinct topoisomerase type I and II inhibitors led to the identification of 11 additional type I and 4 type II inhibitors. The irinotecan analogue topotecan was also found to unsilence UBE3A in several brain regions of Ube3am+/pYFP mice. Rubinstein et al. have reported on a screen of L-type calcium channel blockers in primary neurons expressing the autophagy marker construct mRFP-GFP-LC3.59 The screening hit felodipine 20 (Table 2) also demonstrated activity in a tau zebrafish model as well as in A53T α-synuclein-expressing iPSC-derived neurons and a transgenic mouse model with the same mutation. iPSC Lines. Given the limitations of scalability with primary neuronal cells, most phenotypic screens to date are carried out in immortalized cell lines. Immortalized cells are a replenishable and scalable resource, generally easy to maintain, and widely available (Figure 2). However, neuronal cell lines such as SH-SY5Y cells, N2a cells, or NSC-34 cells do not fully recapitulate the features of neuronal cells, such as generation of spontaneous action potentials, formation of mature synaptic connections, or expression of relevant neurotransmitter receptors. Furthermore, immortalized cell models usually rely on expression of a transgene or addition of a stressor and are thus not suitable to mimic the pathobiology of sporadic disease. In this regard, induced pluripotent stem cells (iPSCs) have revolutionized the field. iPSCs, once generated by reprogramming somatic cells, can be indefinitely replenished and can be differentiated into many different cell types while retaining the genetic background of the donor or patient, thus making them a valuable resource for high content screening. However, like every other type of cell or model organism amenable to high throughput screening, iPSC technology I



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automatically moved from incubator to microscope and back, and images of the same cell can be taken over days without human intervention.83,84 Because of the volume and complexity of the data generated in these experiments, manual annotation is often impossible. As a result, machine-learning methods have been developed to track individual cells and identify meaningful patterns and cellular features. For example, Finkbeiner et al. have described a deep neural network model to process and interpret low-quality and out-of-focus images which historically would require human intervention, enabling full automation of image analysis.85,86 Data is then analyzed with the same statistical methods used in clinical trials, as each cell is viewed as an “individual”.84,87 In one example of such an approach to study small molecule pharmacological effects, Tsetkov et al. demonstrated that the Akt inhibitor 10-NCP 30 (Table 2) induces autophagy. Robotic microscopy and longitudinal tracking of individual neurons revealed that the compound improved survival in a neuronal model of Huntington’s disease and increased the fraction of neurons that clear inclusion bodies formed from the mutant Huntingtin exon 1 fragment.65 In another example, using a method termed “in silico labeling”, Christiansen et al. were able to predict fluorescent labels, including cell type and cell death, from transmitted light images of fixed as well as live cells.88 With such an approach, expensive staining or labeling techniques with limited multiplexing possibilities and the potential to introduce labeling artifacts may in the future be eschewed. Automated platforms of the kind described above, that can perform phenotypic screens, process the raw data and analyze it using deep neural network algorithms, are becoming ever more sophisticated, and will increase both the capacity of the screens as well as the quality and size of the data collected. Genetic Screens. Unbiased phenotypic screens using small molecule libraries can be used to uncover new targets. This approach, however, can be fraught with difficulties. For example, chemical libraries may contain a large fraction of molecules without a known target annotation or with multiple annotated targets, thus making for complex deconvolution. They also do not cover the classically “undruggable” part of the genome. Off-target effects, rather than the effect on the annotated target, can be responsible for rescuing the observed phenotype, which has to be ascertained by genetic studies. Furthermore, small molecules might lack potency in the species of the cell line or organism chosen. These pitfalls can be avoided by utilizing genetic screens to identify suppressors or enhancers of the observed phenotype. In such screens, a fraction or the entirety of the annotated genome is either overexpressed or knocked down/out using approaches such as siRNA, yeast deletion strains, or genome-editing strategies. Using an siRNA-based assay to knock down the kinome of 719 kinases, Mason et al. identified 24 kinases that regulate levels of the growth factor Progranulin (PGRN).89 Mutations in the gene encoding progranulin, GRN, are a cause of FTD as a result of haploinsufficiency. Thus, upregulation of progranulin could be of potential therapeutic benefit. Among the top gene hits were the Parkinson’s disease-associated LRRK2 and the necroptosis regulator receptor-interacting serine/ threonine-protein kinase 1 (RIPK1). Genetic modifiers of TDP-43 toxicity have been discovered in plasmid overexpression as well as deletion screens in yeast.90−93 In one study, 5500 yeast genes were individually transformed into a yeast strain that overexpresses TDP-43 under the control of a galactose-inducible promoter.92 Of the

molecule screens to investigate neurodegenerative phenotypes using 3D culture systems have been performed, reflecting the novelty of the approach, the technical challenges, and the high costs associated with 3D cultures. However, several publications detail the effects of specifically selected compounds on neurodegeneration-associated phenotypes in 3D culture systems. For example, a recently developed “ALS-on-a-chip” platform was developed to test the effects of two drugs, the mammalian target of rapamycin (mTOR) inhibitor rapamycin 27 (Table 2) and the Src/c-Abl kinase inhibitor bosutinib 28 (Table 2). The system consists of a three-dimensional iPSCderived muscle fiber bundle as well as iPSC-derived motor neurons obtained from a sporadic ALS (sALS) patient in a microfluidic device.63 Neural activity was optogenetically induced to elicit controlled muscle fiber contractions. Cotreatment with rapamycin 27 and bosutinib 28 increased the force of muscle contractions and decreased levels of caspase 3/7. In a 3D triculture system of neurons, astrocytes, and microglia, Parker et al. modeled neurodegeneration and neuroinflammation associated with Alzheimer’s disease.76 Cells were derived from neural progenitor cells (NPCs) overexpressing amyloid precursor protein (APP) with the Swedish and London mutations. These neuron/astrocyte cocultures secreted more Aβ40 and Aβ42, phospho-tau, and pro-inflammatory cytokines compared to control cultures. Microglia added to the microfluidic platform were recruited to form 3D cocultures and showed increased activation, leading to neuronal death. A number of publications have described protocols to generate brain and spinal organoids.64,77−80 Brain organoids are derived from pluripotent or induced pluripotent stem cells that self-assemble into 3D neuronal structures similar to certain brain regions. Organoids can be used to study a variety of diseases such as neurodevelopmental disorders, epilepsy, or neurodegenerative diseases. For example, a recent study showed that 110-day old brain organoids grown from iPSCs derived from a patient with familial Alzheimer’s disease recapitulated features of the disease such as increased Aβ42/ Aβ40 production, hyperphosphorylated tau, and apoptotic cell death.81 In another report, Ng et al. described a spinal organoid model of SMA using differentiated patient derived pluripotent stem cells.64 These organoids demonstrated a reduction in motor neuron survival at day 35 compared to wild-type cells. Also, of note, the cells expressed high levels of cell cycle-dependent kinases (CDK) and cyclins, which led them to hypothesize that CDK inhibitors would be effective in rescue of the neurodegeneration. By screening a focused set of CDK inhibitors, they subsequently identified the CDK4/6 inhibitor PD 0332991 29 (Table 2). However, current limitations of organoids include the lack of vasculature, which is indispensable to overcome limits in the size of the assemblies as well as cell death due to lack of oxygen and nutrients, difficulties in establishing mature synaptic connections and a lack of diversity in the number of different cell types that can be incorporated into current models.82 High-Content Imaging Methods. Many phenotypic screens rely on high content imaging technologies. Fluorescent protein tags as well as fluorescent dyes and probes allow diverse cellular processes to be visualized, such as autophagy, mitochondrial function, neuronal activity, proteasomal degradation, or protein translation. Robotic microscopy platforms have further enabled the monitoring of phenotypes in a longitudinal manner. In this setup, 96- or 384-well plates are J



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reverse the disease. However, the output from these screens is simply a list of genes, most of which will not translate into a viable therapeutic target. For example, the observed modulatory effect may be specific to the cell type used in the screen or in the case of knockdown or knockout screens may result from off-target effects rather than from the intended target. Furthermore, a genetic knockdown or knockout of a classically druggable target may have detrimental effects that a small molecule inhibitor may not. It is, for example, conceivable that inhibiting a kinase chemically may have a protective effect in the chosen cell assay while knocking out the same kinase may have a detrimental effect due to possible additional scaffolding functions of the enzyme. Last, many of the identified genes may encode classically undruggable targets. New therapeutic modalities such as CRISPR, siRNAs, ASOs, or anticalins may overcome this particular barrier in theory and represent a rapidly growing area of drug discovery and development. For example, one ASO (inotersen98) and one siRNA (patisiran99) were both approved by the FDA in 2018, with many more in the pipeline.100 Connecting key genes in pathways and understanding cellular and disease context are critical steps in the field, which will lead to insight into cellular responses to disease, important biomarkers, and hopefully novel therapeutic approaches. Screening Collections. Drug and Drug-Like Libraries. There are two key decisions that a project team will face when deciding to construct a phenotypic screen. One of those decisions is the selection of a primary screening model and subsequent orthogonal models to confirm the results. The second decision is selecting the chemical library to screen. Given that the primary screening model may dictate the size of the collection screened, these two decisions are often linked together. For example, primary cells and iPSC lines are not typically amenable to a large high-throughput screen, and typically sets on the order of 1000−5000 compounds or less are used. In these circumstances where a only small set of compounds can be screened, a popular approach is to screen drug or drug-like collections, of which many examples are cited in Table 2. There are at least two arguments underlying the rationale for this approach. Opportunistically, if a hit is found, it could potentially move very quickly into the clinic, a topic referred to as “drug repurposing”. Drug repurposing is an attractive opportunity because it will save significant time and costs for regulatory approval. However, examples of drug repurposing are rare and are often either found as an observation in a clinical trial, or the new indication is an obvious connection to the original indication.101 Drug repurposing of a known marketed drug to a completely unexpected and unrelated disease indication is a low-chance endeavor. The second rationale for using these decks is one of finding unknown polypharmacology. These are drugs which are known to have in vivo efficacy against a human disease. If one is to search for unknown polypharmacological effects, a known drug seems a logical place to start because these compounds have some effect (e.g., as opposed to a random set of compounds which might not have any effect). Annotated Decks. Annotated screening decks harness the power of legacy data which has been collected on an individual compound and can help identify the key target pharmacology. Drug and drug-like libraries are an example of a class of annotated decks. However, the coffers of many large pharmaceutical companies contain large historical data sets from HTS screens on millions of compounds. This data can be

40 hit genes identified, 13 suppressed and 27 enhanced TDP43 toxicity when overexpressed. One enhancer of toxicity was Pab1-binding protein 1 (PBP1), an orthologue of the human Ataxin-2 (ATXN2) gene. A subsequently published paper showed that genetic or antisense oligonucleotide (ASO)-based suppression of ataxin-2 effectively improved motor function and extended lifespan in a TDP-43 mouse model.94 Employing the opposite approach, Armarkola et al. mated a galactoseinducible TDP-43 strain to the yeast haploid deletion collection of nonessential genes to screen for genes that suppress or enhance toxicity of TDP-43 when deleted.90 This screen identified six enhancers and eight suppressors of toxicity. Among the suppressors were pbp1, confirming the results from Elden et al., as well as RNA lariat debranching enzyme 1 (dbr1), which also rescued TDP-43 toxicity in a primary neuronal assay assessed by robotic microscopy. Genome-wide loss of function screens utilize either RNA interference (RNAi) or genome editing strategies. While an invaluable technology to this date, RNAi-based approaches have a number of caveats: (1) genes are post-transcriptionally knocked down rather than knocked out, thus, there can still be residual gene expression, (2) the RNAi machinery works predominantly in the cytoplasm, making it difficult to target nuclear RNA species such as long noncoding RNA (lncRNA), and (3) RNA transcripts with limited complementarity can be recognized by the RNAi probe, causing off-target knockdown.95 Genome editing strategies can overcome some of these limitations, and the advent of clustered regularly interspaced palindromic repeat (CRISPR)/Cas9 technology served as a watershed moment for the field. In this genome editing method, the endonuclease Cas9 binds to a guide RNA (gRNA) encoding a sequence complementary to a genomic sequence of interest. Having been “guided” by the gRNA to the genomic target sequence, Cas9 induces the formation of a double-strand break, which is repaired by nonhomologous end joining, resulting in insertions or deletions which cause a loss of gene function.95,96 CRISPR knockout screens are now routinely used to identify modifiers of a given phenotype of interest genome-wide. In one recent example, Kraemer et al. performed a genome-wide knockout screen of ∼20500 genes with a lentiviral sgRNA library covering 10 sgRNAs per gene to identify modifiers of C9ORF72 dipeptide repeat (DPR) toxicity.97 In the screen, human K562 cells stably expressing Cas9 were infected with a pooled genome-wide lentiviral sgRNA library. After infection, the cell population was split and half the cells were either left untreated or treated with either toxic PR20 or GR20 at concentrations required to kill 50% of cells. Deep sequencing was used to identify sgRNAs that were enriched (i.e., the knockout was protective) or depleted (i.e., the knockout sensitized cells to the toxic effects of the DPRs) in the pooled cell population. The screens revealed 215 and 387 modifiers of PR20 and GR20 toxicity, respectively. The top ∼200 modifiers of PR20 toxicity in K562 cells were subsequently tested in primary mouse cortical neurons. The two strongest knockout suppressors of toxicity were Rab7 and Tmx2.97 Genetic screens for suppressors or enhancers of phenotypic screens in neurodegeneration hold promising potential to identify pathways and genes which would be difficult or impossible to identify using annotated chemical screening decks. The most ambitious goal of these screens is to identify new targets which can be modified with genetic or small molecule therapeutic approaches and slow down, halt, or K



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Figure 3. Generic hit-validation cascade for phenotypic screening.

associated background pharmacology. However, random screening libraries offer chemical diversity. If hits are found, these are more likely to represent novel chemical space in comparison to annotated decks of known compounds. If the random set also has many related analogues, one can get a sense of immediate SAR and begin to form hypotheses around what substituents are critical to activity. However, screening a large set of compounds produced primarily based on chemical diversity may result in the need for significant target deconvolution campaigns. There is an ongoing debate on whether target identification of a phenotypic hit is necessary for progression of a compound through development,104 as it is not required for FDA approval. However, identification of a target may allow for greater understanding of clinical outcomes (e.g., toxicity, target engagement or lack thereof to explain a clinical finding). An understanding of the target biology may also facilitate more focused SAR campaigns. Selection of Appropriate Screening Library. Selection of a screening library puts in motion the fate of finding quality hits and leads for the project. A “bigger is better” screening approach comes with the cost of having to invest significant resources in hit validation and deconvolution. If the model is not amenable to screening large numbers of compounds, then a project team must have a hypothesis on the types of molecules which could potentially work or must take a risk with a small random screen. Screening of FDA-approved drug libraries has proven to be a very popular approach and represents 7 of the 30 screens outlined in Table 2. Another strategy often employed is to screen “bioactive molecules”, (11/30 screens) which have significant overlap with FDAapproved drug libraries but also have pharmacologically active molecules which are not marketed drugs (e.g., NINDS set, LOPAC set). Another approach if the model is low-throughput is to focus the screen around a pathway or target (e.g., Table 2, entry 4 = PARP, entry 7 = LRRK2, entry 29 = CDK, and entry 30 = Akt). These focused approaches lend themselves to providing an immediate hypothesis regarding a potential mechanism of action. Building a Screening Cascade. In addition to the selection of a compound library, the choice of screening models and downstream cascade to identify and validate hits is the cornerstone of a successful phenotypic screen. In our experience, a phenotypic screen in neurodegeneration has a

useful in several aspects. For example, a large number of hits annotated to the same target can support a hypothesis of the MOA, particularly if the hits include multiple and diverse scaffolds. The other utility of this data is that the level of prior activity can be understood and taken into context, for example, identification of pan-assay interference compounds (PAINS).102 This can be a major advantage as significant time and money can be saved if such compounds can be eliminated early in the cascade. One important aspect to consider when screening an annotated set is the potency of the primary pharmacology in comparison to the concentration used in the phenotypic assay. For example, a single-digit nanomolar kinase inhibitor which is active at 50 μM in a mammalian cell line may be cause for skepticism because most kinase inhibitors will likely have off-target kinase activity at such high concentrations. However, if the kinase inhibitor is active in a nonmammalian model with low homology to the human target (e.g., yeast), this could explain the weak activity. There is no database to help the medicinal chemist understand the loss or gain in potency when moving between different species, especially if these species are only distantly related. A chemist must rely on intuition and other contextual data to help make decisions on what series to advance. In these examples, it is best to treat the phenotypic data as qualitative and keep all the possible hits alive until they can be confirmed in an appropriate and definitive mammalian cell model. Although the preliminary goal of these phenotypic screens is to identify a hit compound, a longer-term view of a chemistry strategy should take into consideration the CNS-like properties of hit compounds. If a hit chemical series has constraints that limit blood−brain barrier (BBB) penetration, it may be very difficult to validate the target in vivo and further to develop such a drug. In silico tools such as the Pfizer MPO model103 could help prioritize compounds for screening with better CNS-like properties. From our perspective, however, if the goal is to find a tool compound or a novel target, then the initial strategy should focus on well-annotated sets that cover as much diverse biology as possible. If the goal is to try and repurpose a known compound, then it is more appropriate to bias that set toward compounds which have, or are predicted to have, CNS penetration. Random Screening Libraries. In contrast to annotated sets, large and diverse random screening libraries often do not have L



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presents significant risk because neurodegenerative in vivo models are expensive and time-consuming. Target Deconvolution. Once hits are confirmed in the cascade, target deconvolution efforts can be undertaken to understand a possible mechanism of action (MOA). If compounds have known annotation, there are several strategies that a medicinal chemist can employ to help bolster confidence in the target. For example, other compounds with the same background pharmacology could be tested, ideally from different chemical series. If multiple chemical series with the same pharmacology show activity, this will increase the confidence in the hit. Another strategy would be to test compounds which work on the same target or in the same pathway but have the opposite effect. For example, if the hit is an agonist at a certain receptor, one can test an antagonist to block the effect, if such a molecule exists. Genetic knockdown or overexpression experiments can also be used to validate the target. However, if the target is unknown, there are a variety of methods which could be deployed at this stage. While not required for regulatory approval, elucidation and understanding of the target will greatly simplify the development process. In silico and public databases are available to help the medicinal chemist to form hypotheses about potential MOAs. These include public databases, where one can search for similar compounds and their associated bioactivity, and ligandbased methods which attempt to predict target class. A comprehensive list of these tools has been reported by Koutsoukas et al. and is a valuable resource for these exercises.105 In vitro screens and panels can also be helpful to provide a hypothesis around the MOA. Large panels of known targets, including enzymes, G-protein-coupled receptors (GPCRs), ion channels, and kinases are commercially available. 106 These can either be screened at single concentration or in dose−response, but the expenses of the exercise increase with increasing amount of data quality and target lists, and there is a significant chance that the target of the hit is not included in the panel. If the team has formed a specific hypothesis based on the scaffold (e.g., suspected kinase inhibitor), these panel screens are more informative because they can help to bolster support of the hypothesis. Gene expression profiling can also be used to elucidate new targets and mechanisms. In this approach, genes and gene clusters which are up- or downregulated in the presence of a compound are compared to expression profiles generated by compounds with known pharmacology.107,108 Advances in single cell transcriptional profiling are likely to make this a more impactful technology for neurodegenerative phenotypic screening and may allow for direct interrogation of individual cells which exhibit the phenotype.109,110 Cell-based phenotypic panels can also be used in a similar fashion to compare phenotypic responses of test compounds. The signatures of known compounds (i.e., the aggregated response in many cell lines and assay types) are used as a reference to identify potential pathways that the unknown hit could modulate.111 All of these methods require some prior knowledge of known targets, known pathways or known gene expression profiles and are thus referred to as biased approaches. There is also a risk that the results of these approaches may “cement” a false hypothesis that may be hard to break. For example, a positive hit against any particular target in a panel, even if the hit is very potent, does not necessarily mean that the compound is working through this mechanism in the phenotypic screen. On

cascade such as that outlined in Figure 3. As discussed, a variety of primary screening models can be employed at the top of the cascade, each with their own intrinsic advantages and liabilities. Ideally, a disease model which is as close to the human disease state as possible is the first choice for a primary screen, bearing in mind that such models may be limited in scope by capacity and technical feasibility. A key challenge in establishing a primary screening model is to validate the assay. Without a benchmark or internal standard this can prove difficult. Furthermore, even if an internal standard does exist, it is not necessarily obvious how much efficacy is required for a clinical candidate. For example, how much reduction of a toxic protein is needed for a drug to be effective? Is this accurately reflected in the cell model? Is it sufficient to reduce levels of a toxic protein but not have a profound effect on cell death? An important step is thus the identification of a tool compound that is validated throughout the screening cascade and that the researchers can use to address these questions. An important consideration for the primary screen is the screening concentration. Ideally one should screen at least at two concentrations in the initial primary screen (e.g., 1 and 10 μM). If a compound is toxic at the higher concentration, it could be missed and judged to be inactive if only that concentration was screened. Screening at two concentrations (or more) also creates greater confidence in hits if they are active at both concentrations. Furthermore, if the activity at the higher concentration is due to multiple target pharmacology, the lower concentrations may help to differentiate the involvement of various targets. The hits from this initial screen should then be run in full dose−response mode. From a chemistry perspective, the confidence around the hit will increase if close structural analogues of the hit also show activity. However, if the assay is particularly noisy it may not provide meaningful SAR, or worse, it may provide misleading information to the chemist. Multiple repeats of hits in the assay should be performed to provide a level of confidence to the medicinal chemist to inform analogue synthesis. In addition, a counter-screen should be employed to mitigate false positives. For example, if the initial screen is designed to identify compounds which reduce the levels of a fluorescently labeled toxic protein, using a nontoxic protein with the same fluorescent label would be an appropriate counter-screen. It is important to conduct the counter-screen in a dose−response mode as single concentration screening is not sensitive enough to allow a chemist to definitively distinguish between a real hit and a false positive in the primary screen. Following the counter-screen, the remaining hits should be tested for cytotoxicity to rule out confounding effects. Confirmation of hits from phenotypic screens in an orthogonal assay is a critical next step to prioritize hits and identify true lead series. For example, if an immortalized cell line was used in the initial screen, then primary cells or an iPSC line would be appropriate for confirmation screens. Finally, confirmation of a lead molecule in an animal model of disease could be attempted if the lead compound has acceptable pharmacokinetic properties for in vivo testing. If this is not the case, a chemistry optimization campaign may be required. Furthermore, in order to be effective, the animal model must express the target, the target must have sufficient homology to the human target, and it must retain enough affinity for the compound to be efficacious. If the target is unknown, this cannot be ascertained and subsequently M



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screening methods, and innovative target deconvolution technologies.

the basis of our experience, potent hits using these methods can be seductive but can also mislead the project team. In contrast to biased approaches, unbiased approaches such as chemoproteomic-based methods can provide direct detection of the target proteins in cells or cell lysates and are thus especially useful for identifying unknown targets.112,113 For these methods, the original hit compound needs to be derivatized with a label (e.g., photoaffinity), which can covalently tag the target proteins. This requires a chemistry campaign to explore the various vectors of the hit molecule and confirm that addition of a label does not result in a loss of the phenotypic activity. Unlabeled methods do exist, such as multiplexed quantitative mass-spectrometry-based cellular thermal shift assays (MS-CETSA), which measures thermal stability of the entire proteome in the presence and absence of compound.105,114,115 A recent example of this technology was the discovery of a novel target (purine nucleoside phosphorylase) in Plasmodium falciparum (the causative parasite of malaria). While a powerful technique for target deconvolution, chemoproteomics techniques require expensive investments in equipment, skilled MS expertise, and synthetic chemistry. Even then, the outcome is not guaranteed to produce a novel target, as a large number of hits may be pulled out, none of which may ultimately account for the phenotypic response. Follow-up genetic and pathway-probing experiments are necessary to confirm these hits. Building a screening cascade, accessing screening libraries and target deconvolution exercise require significant resources (e.g., computational chemistry, computational biology, chemical biology), which may be especially hard for small biotech companies and academic laboratories, many of whom are developing these innovative screens. One solution to this dilemma is to work within a precompetitive model, where large pharmaceutical companies share their resources in collaboration with smaller institutions. Open innovation models such as these have been described previously116,117 and, in conjunction with funding agencies as a third partner, may benefit all parties involved by sharing resources and forming collaborative working relationships.

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AUTHOR INFORMATION

Corresponding Authors

For D.G.B.: phone, +1-484-639-4153; E-mail, dean.brown@ astrazeneca.com. For H.J.W.: phone, 1-617-892-1824; E-mail, Heike.Wobsts@ azneuro.com. ORCID

Dean G. Brown: 0000-0002-7130-3928 Notes

The authors declare no competing financial interest. Biographies Dean Brown is Director of Chemistry at AstraZeneca within the Hit Discovery Department, Discovery Science, R&D BioPharmaceuticals Unit. He obtained a B.Sc. in chemistry at Abilene Christian University (Abilene, TX) and a Ph.D at the University of Minnesota (Minneapolis) in organic chemistry. He has over 20 years of experience in industry with AstraZeneca. Dean has been responsible for building many new scientific programs in both neuroscience and infection, several which have resulted in successful transition to clinical trials. His scientific interests are in lead generation, library design, DNA-encoded library screening, synthetic chemistry, and applications of computational chemistry. Dean is listed as an author or coauthor on more than 50 publications and patent applications in medicinal chemistry and drug design, including granted patents on clinical candidates. Heike Wobst is a Principal Scientist at AstraZeneca within the Neuroscience Department, R&D BioPharmaceuticals Unit. She obtained a B.Sc. in Molecular Biotechnology from Heidelberg University, an M.Sc. in Molecular Medicine from Charité Universitätsmedizin Berlin, and a D.Phil. in Physiology, Anatomy, and Genetics from the University of Oxford. Her scientific interests are focused on molecular mechanisms of neurodegeneration and therapeutic intervention strategies targeting these mechanisms.

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ACKNOWLEDGMENTS We thank Nick Brandon and Damian Crowther for their helpful advice.

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SUMMARY In conclusion, phenotypic screens in neurodegeneration are being facilitated by advances in screening models all the way from primitive organisms such as yeast to complex 3D cell models. The outcome of a screen is critically dependent on the design of the assay, the nature of the screening collection, the concentrations of the test molecules and an appropriate triage effort to confirm the hits. Recent examples in ALS, Huntington’s disease, Parkinson’s disease, and Alzheimer’s disease illustrate that a diverse range of techniques can be used to identify hits and lead chemical series that mitigate neurodegeneration-associated phenotypes. In this review, we have discussed 30 recent screens, several of which have resulted in novel targets such as the discovery of stearoyl-CoA desaturase for Parkinson’s disease,32 eIF2b for cognitive defects,72 and SMN2 splicing for SMA.73 In addition to small molecule screens, genetic screens have also yielded new targets, for example, Ataxin-2 for the treatment of ALS. These discoveries will hopefully advance the field toward new diseasemodifying therapies. More novel targets are likely to be discovered with wider access to patient cell lines, automated

ABBREVIATIONS USED ALS, amyotrophic lateral sclerosis; APP, amyloid precursor protein; ASO, antisense oligonucleotide; ATXN2, ataxin-2; BBB, blood−brain barrier; BET, bromodomain and extraterminal motif; C9ORF72, chromosome 9 open reading frame 72; CAG, cytosine−adenine−guanine trinucleotide repeat; CDK, cyclin-dependent kinase; CGG, cytosine−guanine− guanine trinucleotide repeat; CNS, central nervous system; CRISPR, clustered regularly interspaced short palindromic repeats; DPR, dipeptide repeat; ER, endoplasmic reticulum; FAD, familial Alzheimer’s disease; FDA, Food and Drug Administration; FIC, first-in-class; FTD, frontotemporal dementia; FUS, fused in sarcoma; FXTAS, fragile X-associated tremor/ataxia syndrome; GFP, green fluorescent protein; GPCR, G-protein-coupled receptor; gRNA, guide RNA; HDAC, histone deacetylase; iPSC, induced pluripotent stem cells; ISRIB, integrated stress response inhibitor; lncRNA, long noncoding RNA; LOPAC, Library of Pharmacologically Active Compounds; LRRK2, leucine-rich repeat kinase 2; LUHMES, Lund human mesencephalic; MAPK, mitogen activated N



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(17) Outeiro, T. F.; Lindquist, S. Yeast Cells Provide Insight into Alpha-Synuclein Biology and Pathobiology. Science 2003, 302, 1772− 1775. (18) Tenreiro, S.; Munder, M. C.; Alberti, S.; Outeiro, T. F. Harnessing the Power of Yeast to Unravel the Molecular Basis of Neurodegeneration. J. Neurochem. 2013, 127, 438−452. (19) Yao, C.; Johnson, W. M.; Gao, Y.; Wang, W.; Zhang, J.; Deak, M.; Alessi, D. R.; Zhu, X.; Mieyal, J. J.; Roder, H.; Wilson-Delfosse, A. L.; Chen, S. G. Kinase Inhibitors Arrest Neurodegeneration in Cell and C. Elegans Models of Lrrk2 Toxicity. Hum. Mol. Genet. 2013, 22, 328−344. (20) Chen, X.; Barclay, J. W.; Burgoyne, R. D.; Morgan, A. Using C. Elegans to Discover Therapeutic Compounds for Ageing-Associated Neurodegenerative Diseases. Chem. Cent J. 2015, 9, 65. (21) Mondal, S.; Hegarty, E.; Martin, C.; Gokce, S. K.; Ghorashian, N.; Ben-Yakar, A. Large-Scale Microfluidics Providing HighResolution and High-Throughput Screening of Caenorhabditis Elegans Poly-Glutamine Aggregation Model. Nat. Commun. 2016, 7, 13023. (22) O’Reilly, L. P.; Luke, C. J.; Perlmutter, D. H.; Silverman, G. A.; Pak, S. C. C. Elegans in High-Throughput Drug Discovery. Adv. Drug Delivery Rev. 2014, 69−70, 247−253. (23) Shaye, D. D.; Greenwald, I. Ortholist: A Compendium of C. Elegans Genes with Human Orthologs. PLoS One 2011, 6, No. e20085. (24) Alexander, A. G.; Marfil, V.; Li, C. Use of Caenorhabditis Elegans as a Model to Study Alzheimer’s Disease and Other Neurodegenerative Diseases. Front. Genet. 2014, 5, 279. (25) McGurk, L.; Berson, A.; Bonini, N. M. Drosophila as an in Vivo Model for Human Neurodegenerative Disease. Genetics 2015, 201, 377−402. (26) Reiter, L. T.; Potocki, L.; Chien, S.; Gribskov, M.; Bier, E. A Systematic Analysis of Human Disease-Associated Gene Sequences in Drosophila Melanogaster. Genome Res. 2001, 11, 1114−1125. (27) Rihel, J.; Prober, D. A.; Arvanites, A.; Lam, K.; Zimmerman, S.; Jang, S.; Haggarty, S. J.; Kokel, D.; Rubin, L. L.; Peterson, R. T.; Schier, A. F. Zebrafish Behavioral Profiling Links Drugs to Biological Targets and Rest/Wake Regulation. Science 2010, 327, 348−351. (28) MacRae, C. A.; Peterson, R. T. Zebrafish as Tools for Drug Discovery. Nat. Rev. Drug Discovery 2015, 14, 721−731. (29) Parenteau, J.; Durand, M.; Veronneau, S.; Lacombe, A. A.; Morin, G.; Guerin, V.; Cecez, B.; Gervais-Bird, J.; Koh, C. S.; Brunelle, D.; Wellinger, R. J.; Chabot, B.; Abou Elela, S. Deletion of Many Yeast Introns Reveals a Minority of Genes That Require Splicing for Function. Mol. Biol. Cell 2008, 19, 1932−1941. (30) O’Brien, K. P.; Remm, M.; Sonnhammer, E. L. Inparanoid: A Comprehensive Database of Eukaryotic Orthologs. Nucleic Acids Res. 2004, 33, D476−D480. (31) Kachroo, A. H.; Laurent, J. M.; Yellman, C. M.; Meyer, A. G.; Wilke, C. O.; Marcotte, E. M. Evolution. Systematic Humanization of Yeast Genes Reveals Conserved Functions and Genetic Modularity. Science 2015, 348, 921−925. (32) Vincent, B. M.; Tardiff, D. F.; Piotrowski, J. S.; Aron, R.; Lucas, M. C.; Chung, C. Y.; Bacherman, H.; Chen, Y.; Pires, M.; Subramaniam, R.; Doshi, D. B.; Sadlish, H.; Raja, W. K.; Solis, E. J.; Khurana, V.; Le Bourdonnec, B.; Scannevin, R. H.; Rhodes, K. J. Inhibiting Stearoyl-Coa Desaturase Ameliorates Alpha-Synuclein Cytotoxicity. Cell Rep. 2018, 25, 2742−2754 e2731 . (33) Su, L. J.; Auluck, P. K.; Outeiro, T. F.; Yeger-Lotem, E.; Kritzer, J. A.; Tardiff, D. F.; Strathearn, K. E.; Liu, F.; Cao, S.; Hamamichi, S.; Hill, K. J.; Caldwell, K. A.; Bell, G. W.; Fraenkel, E.; Cooper, A. A.; Caldwell, G. A.; McCaffery, J. M.; Rochet, J. C.; Lindquist, S. Compounds from an Unbiased Chemical Screen Reverse Both Er-toGolgi Trafficking Defects and Mitochondrial Dysfunction in Parkinson’s Disease Models. Dis. Models & Mech. 2010, 3, 194−208. (34) Halliday, M.; Radford, H.; Zents, K. A. M.; Molloy, C.; Moreno, J. A.; Verity, N. C.; Smith, E.; Ortori, C. A.; Barrett, D. A.; Bushell, M.; Mallucci, G. R. Repurposed Drugs Targeting

protein kinase; MOA, mechanism of action; MPP, methyl-4phenylpyridinium; MS, mass spectroscopy; mTOR, mammalian target of rapamycin; NIH, National Institutes of Health; NINDS, National Institute for Neurological Disorders and Stroke; NPC, neural progenitor cell; NSAID, nonsteroidal antiinflammatory drug; PAINS, pan-assay interference compounds; PAR, poly(ADP ribose); PARP, poly(ADP ribose) polymerase; PBP1, Pab1-binding protein; PDE, phosphodiesterase; PGRN, progranulin; polyQ, polyglutamine repeat; RIPK1, receptor-interacting serine/threonine-protein kinase 1; RNAi, RNA interference; sALS, sporadic amyotrophic lateral sclerosis; SAR, structure−activity relationships; SMA, spinal muscular atrophy; snRNP, small nuclear ribonucleic protein; TDP-43, transactive response DNA binding protein 43 kDa; TH, tyrosine hydroxylase; UBE3A, ubiquitin protein ligase E3A; UPR, unfolded protein response; VMAT, vesicular monoamine transporter; WT, wild-type

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