Screening Of Large Compound Libraries Research Articles

Identification of novel and potent inhibitors of SARS-CoV-2 main protease from DNA-encoded chemical libraries.

In vitro screening of large compound libraries with automated high-throughput screening is expensive and time-consuming and requires dedicated infrastructures. Conversely, the selection of DNA-encoded chemical libraries (DECLs) can be rapidly performed with routine equipment available in most laboratories. In this study, we identified novel inhibitors of SARS-CoV-2 main protease (Mpro) through the affinity-based selection of the DELopen library (open access for academics), containing 4.2 billion compounds. The identified inhibitors were peptide-like compounds containing an N-terminal electrophilic group able to form a covalent bond with the nucleophilic Cys145 of Mpro, as confirmed by x-ray crystallography. This DECL selection campaign enabled the discovery of the unoptimized compound SLL11 (IC50 = 30 nM), proving that the rapid exploration of large chemical spaces enabled by DECL technology allows for the direct identification of potent inhibitors avoiding several rounds of iterative medicinal chemistry. As demonstrated further by x-ray crystallography, SLL11 was found to adopt a highly unique U-shaped binding conformation, which allows the N-terminal electrophilic group to loop back to the S1' subsite while the C-terminal amino acid sits in the S1 subsite. MP1, a close analog of SLL11, showed antiviral activity against SARS-CoV-2 in the low micromolar range when tested in Caco-2 and Calu-3 (EC50 = 2.3 µM) cell lines. As peptide-like compounds can suffer from low cell permeability and metabolic stability, the cyclization of the compounds will be explored in the future to improve their antiviral activity.

An adaptable, fit-for-purpose screening approach with high-throughput capability to determine speed of action and stage specificity of anti-malarial compounds.

A revamped in vitro compound identification and activity profiling approach is required to meet the large unmet need for new anti-malarial drugs to combat parasite drug resistance. Although compound hit identification utilizing high-throughput screening of large compound libraries is well established, the ability to rapidly prioritize such large numbers for further development is limited. Determining the speed of action of anti-malarial drug candidates is a vital component of malaria drug discovery, which currently occurs predominantly in lead optimization and development. This is due in part to the capacity of current methods which have low throughput due to the complexity and labor intensity of the approaches. Here, we provide an adaptable screening paradigm utilizing automated high content imaging, including the development of an automated schizont maturation assay, which collectively can identify anti-malarial compounds, classify activity into fast and slow acting, and provide an indication of the parasite stage specificity, with high-throughput capability. By frontloading these critical biological parameters much earlier in the drug discovery pipeline, it has the potential to reduce lead compound attrition rates later in the development process. The capability of the approach in its alternative formats is demonstrated using three Medicines for Malaria Venture open access compound "boxes," namely Pathogen Box (malaria set-125 compounds), Global Health Priority Box [Malaria Box 2 (80 compounds) and zoonotic neglected diseases (80 compounds)], and the Pandemic Response Box (400 compounds). From a total of 685 compounds tested, 79 were identified as having fast ring-stage-specific activity comparable to that of artemisinin and therefore of high priority for further consideration and development.

Large-Scale Pretraining Improves Sample Efficiency of Active Learning-Based Virtual Screening.

Virtual screening of large compound libraries to identify potential hit candidates is one of the earliest steps in drug discovery. As the size of commercially available compound collections grows exponentially to the scale of billions, active learning and Bayesian optimization have recently been proven as effective methods of narrowing down the search space. An essential component of those methods is a surrogate machine learning model that predicts the desired properties of compounds. An accurate model can achieve high sample efficiency by finding hits with only a fraction of the entire library being virtually screened. In this study, we examined the performance of a pretrained transformer-based language model and graph neural network in a Bayesian optimization active learning framework. The best pretrained model identifies 58.97% of the top-50,000 compounds after screening only 0.6% of an ultralarge library containing 99.5 million compounds, improving 8% over the previous state-of-the-art baseline. Through extensive benchmarks, we show that the superior performance of pretrained models persists in both structure-based and ligand-based drug discovery. Pretrained models can serve as a boost to the accuracy and sample efficiency of active learning-based virtual screening.

CRISPR-Cas9-Mediated Bioluminescent Tagging of Endogenous Proteins by Fluorescent Protein-Assisted Cell Sorting.

Oncogenic fusion genes are attractive therapeutic targets because of their tumor-specific expression and central "driver" roles in various human cancers. However, oncogenic fusions involving transcription factors such as PAX3-FOXO1 in alveolar fusion gene-positive rhabdomyosarcoma (FP-RMS) have been difficult to inhibit due to the apparent lack of tractable drug-like binding sites comparable to that recognized by Gleevec (imatinib mesylate) on the BCR-ABL1 tyrosine kinase fusion protein. Toward the identification of novel small molecules that selectively target PAX3-FOXO1, we used CRISPR-Cas9-mediated knock-in to append the pro-luminescent HiBiT tag onto the carboxy terminus of the endogenous PAX3-FOXO1 fusion protein in two human FP-RMS cell lines (RH4 and SCMC). HiBiT is an 11-amino acid peptide derived from the NanoLuc luciferase that produces a luminescence signal which is ~100-fold brighter than firefly or Renilla luciferases through high-affinity binding to a complementary NanoLuc peptide fragment called LgBiT. To facilitate single-cell clonal isolation of knock-ins, the homology-directed repair template encoding HiBiT was followed by a P2A self-cleaving peptide for coexpression of an mCherry fluorescent protein as a fluorescence-activated cell sorter (FACS)-selectable marker. HiBiT tagging thus allows highly sensitive luminescence detection of endogenous PAX3-FOXO1 levels permitting quantitative high-throughput screening of large compound libraries for the discovery of PAX3-FOXO1 inhibitors and degraders.

A Robust Phenotypic High-Throughput Antiviral Assay for the Discovery of Rabies Virus Inhibitors.

Rabies virus (RABV) causes severe neurological symptoms in mammals. The disease is almost inevitably lethal as soon as clinical symptoms appear. The use of rabies immunoglobulins (RIG) and vaccination in post-exposure prophylaxis (PEP) can provide efficient protection, but many people do not receive this treatment due to its high cost and/or limited availability. Highly potent small molecule antivirals are urgently needed to treat patients once symptoms develop. In this paper, we report on the development of a high-throughput phenotypic antiviral screening assay based on the infection of BHK-21 cells with a fluorescent reporter virus and high content imaging readout. The assay was used to screen a repurposing library of 3681 drugs (all had been studied in phase 1 clinical trials). From this series, salinomycin was found to selectively inhibit viral replication by blocking infection at the entry stage. This shows that a high-throughput assay enables the screening of large compound libraries for the purposes of identifying inhibitors of RABV replication. These can then be optimized through medicinal chemistry efforts and further developed into urgently needed drugs for the treatment of symptomatic rabies.

Cheminformatics-based discovery of new organoselenium compounds with potential for the treatment of cutaneous and visceral leishmaniasis

Leishmaniasis affects more than 12 million humans globally and a further 1 billion people are at risk in leishmaniasis endemic areas. The lack of a vaccine for leishmaniasis coupled with the limitations of existing anti-leishmanial therapies prompted this study. Cheminformatic techniques are widely used in screening large libraries of compounds, studying protein-ligand interactions, analysing pharmacokinetic properties, and designing new drug molecules with great speed, accuracy, and precision. This study was undertaken to evaluate the anti-leishmanial potential of some organoselenium compounds by quantitative structure-activity relationship (QSAR) modeling, molecular docking, pharmacokinetic analysis, and molecular dynamic (MD) simulation. The built QSAR model was validated (R2 train = 0.8646, R2 test = 0.8864, Q2 = 0.5773) and the predicted inhibitory activity (pIC50) values of the newly designed compounds were higher than that of the template (Compound 6). The new analogues (6a, 6b, and 6c) showed good binding interactions with the target protein (Pyridoxal kinase, PdxK) while also presenting excellent drug-likeness and pharmacokinetic profiles. The results of density functional theory, MD simulation, and molecular mechanics generalized Born surface area (MM/GBSA) analyses suggest the favourability and stability of protein-ligand interactions of the new analogues with PdxK, comparing favourably well with the reference drug (Pentamidine). Conclusively, the newly designed compounds could be synthesized and tested experimentally as potential anti-leishmanial drug molecules. Communicated by Ramaswamy H. Sarma

AI-Powered Virtual Screening of Large Compound Libraries Leads to the Discovery of Novel Inhibitors of Sirtuin-1.

The discovery of new scaffolds and chemotypes via high-throughput screening is tedious and resource intensive. Yet, there are millions of small molecules commercially available, rendering comprehensive in vitro tests intractable. We show how smart algorithms reduce large screening collections to target-specific sets of just a few hundred small molecules, allowing for a much faster and more cost-effective hit discovery process. We showcase the application of this virtual screening strategy by preselecting 434 compounds for Sirtuin-1 inhibition from a library of 2.6 million compounds, corresponding to 0.02% of the original library. Multistage in vitro validation ultimately confirmed nine chemically novel inhibitors. When compared to a competitive benchmark study for Sirtuin-1, our method shows a 12-fold higher hit rate. The results demonstrate how AI-driven preselection from large screening libraries allows for a massive reduction in the number of small molecules to be tested in vitro while still retaining a large number of hits.

A high-throughput screening system for SARS-CoV-2 entry inhibition, syncytia formation and cell toxicity

BackgroundThe entry of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into the host cell is mediated through the binding of the SARS-CoV-2 Spike protein via the receptor binding domain (RBD) to human angiotensin-converting enzyme 2 (hACE2). Identifying compounds that inhibit Spike-ACE2 binding would be a promising and safe antiviral approach against COVID-19.MethodsIn this study, we used a BSL-2 compatible replication-competent vesicular stomatitis virus (VSV) expressing Spike protein of SARS-CoV-2 with eGFP reporter system (VSV-eGFP-SARS-CoV-2) in a recombinant permissive cell system for high-throughput screening of viral entry blockers. The SARS-CoV-2 permissive reporter system encompasses cells that stably express hACE2-tagged cerulean and H2B tagged with mCherry, as a marker of nuclear condensation, which also enables imaging of fused cells among infected EGFP positive cells and could provide real-time information on syncytia formation.ResultsA limited high-throughput screening identified six natural products that markedly inhibited VSV-eGFP-SARS-CoV-2 with minimum toxicity. Further studies of Spike-S1 binding using the permissive cells showed Scillaren A and 17-Aminodemethoxygeldanamycin could inhibit S1 binding to ACE2 among the six leads. A real-time imaging revealed delayed inhibition of syncytia by Scillaren A, Proscillaridin, Acetoxycycloheximide and complete inhibition by Didemnin B indicating that the assay is a reliable platform for any image-based drug screening.ConclusionA BSL-2 compatible assay system that is equivalent to the infectious SARS-CoV-2 is a promising tool for high-throughput screening of large compound libraries for viral entry inhibitors against SARS-CoV-2 along with toxicity and effects on syncytia. Studies using clinical isolates of SARS-CoV-2 are warranted to confirm the antiviral potency of the leads and the utility of the screening system.

Machine learning-based drug design for identification of thymidylate kinase inhibitors as a potential anti-Mycobacterium tuberculosis

The rise of antibiotic-resistant Mycobacterium tuberculosis (Mtb) has reduced the availability of medications for tuberculosis therapy, resulting in increased morbidity and mortality globally. Tuberculosis spreads from the lungs to other parts of the body, including the brain and spine. Developing a single drug can take several decades, making drug discovery costly and time-consuming. Machine learning algorithms like support vector machines (SVM), k-nearest neighbor (k-NN), random forest (RF) and Gaussian naive base (GNB) are fast and effective and are commonly used in drug discovery. These algorithms are ideal for the virtual screening of large compound libraries to classify molecules as active or inactive. For the training of the models, a dataset of 307 was downloaded from BindingDB. Among 307 compounds, 85 compounds were labeled as active, having an IC50 below 58 mM, while 222 compounds were labeled inactive against thymidylate kinase, with 87.2% accuracy. The developed models were subjected to an external ZINC dataset of 136,564 compounds. Furthermore, we performed the 100-ns dynamic simulation and post trajectories analysis of compounds having good interaction and score in molecular docking. As compared to the standard reference compound, the top three hits revealed greater stability and compactness. In conclusion, our predicted hits can inhibit thymidylate kinase overexpression to combat Mycobacterium tuberculosis. Communicated by Ramaswamy H. Sarma

Early-phase drug discovery of β-III-spectrin actin-binding modulators for treatment of spinocerebellar ataxia type 5

β-III-Spectrin is a key cytoskeletal protein that localizes to the soma and dendrites of cerebellar Purkinje cells and is required for dendritic arborization and signaling. A spinocerebellar ataxia type 5 L253P mutation in the cytoskeletal protein β-III-spectrin causes high-affinity actin binding. Previously we reported a cell-based fluorescence assay for identification of small-molecule actin-binding modulators of the L253P mutant β-III-spectrin. Here we describe a complementary, invitro, fluorescence resonance energy transfer (FRET) assay that uses purified L253P β-III-spectrin actin-binding domain (ABD) and F-actin. To validate the assay for high-throughput compatibility, we first confirmed that our 50% FRET signal was responsive to swinholide A, an actin-severing compound, and that this yielded excellent assay quality with a Z' value > 0.77. Second, we screened a 2684-compound library of US Food and Drug Administration-approved drugs. Importantly, the screening identified numerous compounds that decreased FRET between fluorescently labeled L253P ABD and F-actin. The activity and target of multiple Hit compounds were confirmed in orthologous cosedimentation actin-binding assays. Through future medicinal chemistry, the Hit compounds can potentially be developed into a spinocerebellar ataxia type 5-specific therapeutic. Furthermore, our validated FRET-based invitro high-throughput screening platform is poised for screening large compound libraries for β-III-spectrin ABD modulators.

The Impact of Software Used and the Type of Target Protein on Molecular Docking Accuracy.

The modern development of computer technology and different in silico methods have had an increasing impact on the discovery and development of new drugs. Different molecular docking techniques most widely used in silico methods in drug discovery. Currently, the time and financial costs for the initial hit identification can be significantly reduced due to the ability to perform high-throughput virtual screening of large compound libraries in a short time. However, the selection of potential hit compounds still remains more of a random process, because there is still no consensus on what the binding energy and ligand efficiency (LE) of a potentially active compound should be. In the best cases, only 20-30% of compounds identified by molecular docking are active in biological tests. In this work, we evaluated the impact of the docking software used as well as the type of the target protein on the molecular docking results and their accuracy using an example of the three most popular programs and five target proteins related to neurodegenerative diseases. In addition, we attempted to determine the "reliable range" of the binding energy and LE that would allow selecting compounds with biological activity in the desired concentration range.

Machine Learning-based Virtual Screening for STAT3 Anticancer Drug Target.

Signal transducers and activators of the transcription (STAT) family is composed of seven structurally similar and highly conserved members, including STAT1, STAT2, STAT3, STAT4, STAT5a, STAT5b, and STAT6. The STAT3 signaling cascade is activated by upstream kinase signals and undergoes phosphorylation, homo-dimerization, nuclear translocation, and DNA binding, resulting in the expression of target genes involved in tumor cell proliferation, metastasis, angiogenesis, and immune editing. STAT3 hyperactivation has been documented in a number of tumors, including head and neck, breast, lung, liver, kidney, prostate, pancreas cancer, multiple myeloma, and acute myeloid leukemia. Drug discovery is a timeconsuming and costly process; it may take ten to fifteen years to bring a single drug to the market. Machine learning algorithms are very fast and effective and commonly used in the field, such as drug discovery. These algorithms are ideal for the virtual screening of large compound libraries to classify molecules as active or inactive. The present work aims to perform machine learning-based virtual screening for the STAT3 drug target. Machine learning models, such as k-nearest neighbor, support vector machine, Gaussian naïve Bayes, and random forest for classifying the active and inactive inhibitors against a STAT3 drug target, were developed. Ten-fold cross-validation was used for model validation. Then the test dataset prepared from the zinc database was screened using the random forest model. A total of 20 compounds with 88% accuracy was predicted as active against STAT3. Furthermore, these twenty compounds were docked into the active site of STAT3. The two complexes with good docking scores as well as the reference compound were subjected to MD simulation. A total of 100ns MD simulation was performed. Compared to all other models, the random forest model revealed better results. Compared to the standard reference compound, the top two hits revealed greater stability and compactness. In conclusion, our predicted hits have the ability to inhibit STAT3 overexpression to combat STAT3-associated diseases.

Insights into performance evaluation of compound-protein interaction prediction methods.

Machine-learning-based prediction of compound-protein interactions (CPIs) is important for drug design, screening and repurposing. Despite numerous recent publication with increasing methodological sophistication claiming consistent improvements in predictive accuracy, we have observed a number of fundamental issues in experiment design that produce overoptimistic estimates of model performance. We systematically analyze the impact of several factors affecting generalization performance of CPI predictors that are overlooked in existing work: (i) similarity between training and test examples in cross-validation; (ii) synthesizing negative examples in absence of experimentally verified negative examples and (iii) alignment of evaluation protocol and performance metrics with real-world use of CPI predictors in screening large compound libraries. Using both state-of-the-art approaches by other researchers as well as a simple kernel-based baseline, we have found that effective assessment of generalization performance of CPI predictors requires careful control over similarity between training and test examples. We show that, under stringent performance assessment protocols, a simple kernel-based approach can exceed the predictive performance of existing state-of-the-art methods. We also show that random pairing for generating synthetic negative examples for training and performance evaluation results in models with better generalization in comparison to more sophisticated strategies used in existing studies. Our analyses indicate that using proposed experiment design strategies can offer significant improvements for CPI prediction leading to effective target compound screening for drug repurposing and discovery of putative chemical ligands of SARS-CoV-2-Spike and Human-ACE2 proteins. Code and supplementary material available at https://github.com/adibayaseen/HKRCPI. Supplementary data are available at Bioinformatics online.

Applications of the Novel Quantitative Pharmacophore Activity Relationship Method QPhAR in Virtual Screening and Lead-Optimisation.

Pharmacophores are an established concept for the modelling of ligand–receptor interactions based on the abstract representations of stereoelectronic molecular features. They became widely popular as filters for the fast virtual screening of large compound libraries. A lot of effort has been put into the development of sophisticated algorithms and strategies to increase the computational efficiency of the screening process. However, hardly any focus has been put on the development of automated procedures that optimise pharmacophores towards higher discriminatory power, which still has to be done manually by a human expert. In the age of machine learning, the researcher has become the decision-maker at the top level, outsourcing analysis tasks and recurrent work to advanced algorithms and automation workflows. Here, we propose an algorithm for the automated selection of features driving pharmacophore model quality using SAR information extracted from validated QPhAR models. By integrating the developed method into an end-to-end workflow, we present a fully automated method that is able to derive best-quality pharmacophores from a given input dataset. Finally, we show how the QPhAR-generated models can be used to guide the researcher with insights regarding (un-)favourable interactions for compounds of interest.

Next-Generation Molecular Discovery: From Bottom-Up In Vivo and In Vitro Approaches to In Silico Top-Down Approaches for Therapeutics Neogenesis.

Protein and drug engineering comprises a major part of the medical and research industries, and yet approaches to discovering and understanding therapeutic molecular interactions in biological systems rely on trial and error. The general approach to molecular discovery involves screening large libraries of compounds, proteins, or antibodies, or in vivo antibody generation, which could be considered “bottom-up” approaches to therapeutic discovery. In these bottom-up approaches, a minimal amount is known about the therapeutics at the start of the process, but through meticulous and exhaustive laboratory work, the molecule is characterised in detail. In contrast, the advent of “big data” and access to extensive online databases and machine learning technologies offers promising new avenues to understanding molecular interactions. Artificial intelligence (AI) now has the potential to predict protein structure at an unprecedented accuracy using only the genetic sequence. This predictive approach to characterising molecular structure—when accompanied by high-quality experimental data for model training—has the capacity to invert the process of molecular discovery and characterisation. The process has potential to be transformed into a top-down approach, where new molecules can be designed directly based on the structure of a target and the desired function, rather than performing screening of large libraries of molecular variants. This paper will provide a brief evaluation of bottom-up approaches to discovering and characterising biological molecules and will discuss recent advances towards developing top-down approaches and the prospects of this.

Stable Cell Clones Harboring Self-Replicating SARS-CoV-2 RNAs for Drug Screen.

ABSTRACTThe development of antivirals against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been hampered by the lack of efficient cell-based replication systems that are amenable to high-throughput screens in biosafety level 2 laboratories. Here we report that stable cell clones harboring autonomously replicating SARS-CoV-2 RNAs without spike (S), membrane (M), and envelope (E) genes can be efficiently derived from the baby hamster kidney (BHK-21) cell line when a pair of mutations were introduced into the non-structural protein 1 (Nsp1) of SARS-CoV-2 to ameliorate cellular toxicity associated with virus replication. In a proof-of-concept experiment we screened a 273-compound library using replicon cells and identified three compounds as novel inhibitors of SARS-CoV-2 replication. Altogether, this work establishes a robust, cell-based system for genetic and functional analyses of SARS-CoV-2 replication and for the development of antiviral drugs.IMPORTANCE SARS-CoV-2 replicon systems that have been reported up to date were unsuccessful in deriving stable cell lines harboring non-cytopathic replicons. The transient expression of viral sgmRNA or a reporter gene makes it impractical for industry-scale screening of large compound libraries using these systems. Here, for the first time, we derived stable cell clones harboring the SARS-CoV-2 replicon. These clones may now be conveniently cultured in a standard BSL-2 laboratory for high throughput screen of compound libraries. Additionally, our stable replicon cells represent a new model system to study SARS-CoV-2 replication.

Discovery and Mechanism of Action of Small Molecule Inhibitors of Ceramidases.

Sphingolipid metabolism is tightly controlled by enzymes to regulate essential processes in human physiology. The central metabolite is ceramide, a pro-apoptotic lipid catabolized by ceramidase enzymes to produce pro-proliferative sphingosine-1-phosphate. Alkaline ceramidases are transmembrane enzymes that recently attracted attention for drug development in fatty liver diseases. However, due to their hydrophobic nature, no specific small molecule inhibitors have been reported. We present the discovery and mechanism of action of the first drug-like inhibitors of alkaline ceramidase 3 (ACER3). In particular, we chemically engineered novel fluorescent ceramide substrates enabling screening of large compound libraries and characterized enzyme:inhibitor interactions using mass spectrometry and MD simulations. In addition to revealing a new paradigm for inhibition of lipid metabolising enzymes with non-lipidic small molecules, our data lay the ground for targeting ACER3 in drug discovery efforts.

Discovery and Mechanism of Action of Small Molecule Inhibitors of Ceramidases**

AbstractSphingolipid metabolism is tightly controlled by enzymes to regulate essential processes in human physiology. The central metabolite is ceramide, a pro‐apoptotic lipid catabolized by ceramidase enzymes to produce pro‐proliferative sphingosine‐1‐phosphate. Alkaline ceramidases are transmembrane enzymes that recently attracted attention for drug development in fatty liver diseases. However, due to their hydrophobic nature, no specific small molecule inhibitors have been reported. We present the discovery and mechanism of action of the first drug‐like inhibitors of alkaline ceramidase 3 (ACER3). In particular, we chemically engineered novel fluorescent ceramide substrates enabling screening of large compound libraries and characterized enzyme:inhibitor interactions using mass spectrometry and MD simulations. In addition to revealing a new paradigm for inhibition of lipid metabolising enzymes with non‐lipidic small molecules, our data lay the ground for targeting ACER3 in drug discovery efforts.

Development of a Novel Pharmacophore Model Guided by the Ensemble of Waters and Small Molecule Fragments Bound to SARS-CoV-2 Main Protease.

Recent fragment‐based drug design efforts have generated huge amounts of information on water and small molecule fragment binding sites on SARS‐CoV‐2 Mpro and preference of the sites for various types of chemical moieties. However, this information has not been effectively utilized to develop automated tools for in silico drug discovery which are routinely used for screening large compound libraries. Utilization of this information in the development of pharmacophore models can help in bridging this gap. In this study, information on water and small molecule fragments bound to Mpro has been utilized to develop a novel Water Pharmacophore (Waterphore) model. The Waterphore model can also implicitly represent the conformational flexibilities of binding pockets in terms of pharmacophore features. The Waterphore model derived from 173 apo‐ or small molecule fragment‐bound structures of Mpro has been validated by using a dataset of 68 known bioactive inhibitors and 78 crystal structure bound inhibitors of SARS‐CoV‐2 Mpro. It is encouraging to note that, even though no inhibitor data has been used in developing the Waterphore model, it could successfully identify the known inhibitors from a library of decoys with a ROC‐AUC of 0.81 and active hit rate (AHR) of 70 %. The Waterphore model is also general enough for potential applications for other drug targets.

Single cell morphology distinguishes genotype and drug effect in Hereditary Spastic Paraplegia

A central need for neurodegenerative diseases is to find curative drugs for the many clinical subtypes, the causative gene for most cases being unknown. This requires the classification of disease cases at the genetic and cellular level, an understanding of disease aetiology in the subtypes and the development of phenotypic assays for high throughput screening of large compound libraries. Herein we describe a method that facilitates these requirements based on cell morphology that is being increasingly used as a readout defining cell state. In patient-derived fibroblasts we quantified 124 morphological features in 100,000 cells from 15 people with two genotypes (SPAST and SPG7) of Hereditary Spastic Paraplegia (HSP) and matched controls. Using machine learning analysis, we distinguished between each genotype and separated them from controls. Cell morphologies changed with treatment with noscapine, a tubulin-binding drug, in a genotype-dependent manner, revealing a novel effect on one of the genotypes (SPG7). These findings demonstrate a method for morphological profiling in fibroblasts, an accessible non-neural cell, to classify and distinguish between clinical subtypes of neurodegenerative diseases, for drug discovery, and potentially for biomarkers of disease severity and progression.