Andrew Yale

Saloni Dash

Karan Bhanot

Isabelle Guyon

John S Erickson

Kristin P Bennett

Synthesizing Quality Open Data Assets from Private Health Research Studies

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The inability to share private health data can stifle research and education activities. For example, studies based on unpublished electronic medical record (EMR) data cannot be reproduced, thus future researchers are not able to use them to develop and compare new research. This contributes to the reproduciblity crisis in biomedical research [START_REF] Begley | Reproducibility in science: improving the standard for basic and preclinical research[END_REF]. Making open data available for research can spur innovation and research. The public Medical Information Mart for Intensive Care datasets, MIMIC-II and MIMIC-III, are widely used with over 2000 citations reported in Google Scholar in March 2020 [START_REF] Johnson | Mimic-iii, a freely accessible critical care database[END_REF][START_REF] Saeed | Multiparameter intelligent monitoring in intensive care II (MIMIC-II): a public-access intensive care unit database[END_REF]. But since MIMIC-II and MIMIC-III focus on Intensive Care Unit patients in Boston hospitals, the resulting research may be biased and have limited generalization. Also since MIMIC requires users to undergo a training/approval process, it is not well suited for classroom use. The cost and time required, along with re-identification risk concerns make de-identification only a partial solution to this problem.

Recent synthetic data generation methods provide an attractive alternative for making data available for research and education purposes without violating privacy. Deep learning approaches for synthetic data specifically show significant promise [START_REF] Alzantot | Sensegen: A deep learning architecture for synthetic sensor data generation[END_REF][START_REF] Gupta | Synthetic data for text localisation in natural images[END_REF][START_REF] Krishnan | Generating synthetic data for text recognition[END_REF] In the future, synthetic data generation methods combined with automatic machine learning methods could enable synthetic versions of data to be released when research papers are published. Results could be reproduced and novel methods and analysis could be developed without compromising patient privacy. Figure 1 illustrates one scenario for use of synthetic data. To accomplish this, synthetic data assets must have 1) privacy: how well does the synthetic generation data method preserve anonymity, 2) resemblance : whether the distribution of synthetic data is indistinguishable from the distribution of real data.

3) utility: can research studies be reproduced successfully with synthetic data and 4) efficiency: how practical is the training and generation pipeline.

In this paper, we report our experience of generating synthetic data using the process in Figure 1 for two published research studies [START_REF] Vargason | Clustering of co-occurring conditions in autism spectrum disorder during early childhood: A retrospective analysis of medical claims data[END_REF][START_REF] Vargason | Gastrointestinal symptoms and oral antibiotic use in children with autism spectrum disorder: Retrospective analysis of a privately insured us population[END_REF] performed in the OptumLabs Data Warehouse (OLDW). The studies focus on the same cohort of children with ASD. The first focuses on the difference between gastrointestinal symptoms and oral antibiotic use in children with ASD and children without ASD. The second study investigates how different groups of children with ASD can be clustered based on their comorbid medical conditions (CMCs), and what that means about the different clusters. We utilize novel enhancements of the prior HealthGAN [START_REF] Dash | Synthetic event time series health data generation[END_REF][START_REF] Yale | Assessing privacy and quality of synthetic health data[END_REF][START_REF] Yale | Privacy preserving synthetic health data[END_REF][START_REF] Yale | Generation and evaluation of privacy preserving synthetic health data[END_REF] synthetic data approach, and then evaluate the privacy, resemblance, efficiency, and utility of the synthetic data.

Fig. 1: Workflow used to generate synthetic data securely. The data is gathered in the same way as the studies did, processed, and used to train the generator model inside the secure environment. The synthetic data is then validated for privacy, resemblance, and utility. After being certified as private by the OptumLabs staff, the model is exported. Finally, data is generated using the model and used for many types of applications.

This paper focuses on documenting and assessing the HealthGAN synthetic data generation method on real medical datasets in a secure environment and exporting the models outside of the environment. We used the OptumLabs Data Warehouse as the source of the medical data. We report on the process of ex-porting the data from the OLDW 5 . We demonstrate how to verify privacy and resemblance using recently published metric. We make the tools for generating and evaluating synthetic readily available in a Python package. 6 The next section goes over synthetic data generation and evaluation methods. Following that the selected generation and evaluation methods are used to reproduce two studies in OLDW. The synthetic data for each of these datasets are evaluated for privacy, resemblance, and utility. Finally, we conclude and discuss future work.

Methods

Generation

We use HealthGAN [START_REF] Dash | Synthetic event time series health data generation[END_REF][START_REF] Yale | Assessing privacy and quality of synthetic health data[END_REF][START_REF] Yale | Privacy preserving synthetic health data[END_REF][START_REF] Yale | Generation and evaluation of privacy preserving synthetic health data[END_REF], a generative adversarial network (GAN) method we developed to generate synthetic data for datasets containing categorical, continuous, binary, and time series data. This method is based on the Wasserstein Generative Adversarial Network [START_REF] Arjovsky | Wasserstein gan[END_REF][START_REF] Gulrajani | Improved training of wasserstein gans[END_REF] and uses a novel variant of the categorical encoding method from "The Synthetic data vault" (SDV) [START_REF] Patki | The synthetic data vault[END_REF]. We added the ability to encode ordinal data to the original HealthGAN. By creating the SDV mapping using the inherent ordinal order and including values that might not exist in the original dataset, we improved resemblance of ordinal variables.

Part of the constraints for our generative model is the ability to export the model from the secure environment instead of synthetic data. To fulfill this requirement we needed to ensure that the model itself did not contain or require any real data to generate synthetic data. In "Privacy Preserving Synthetic Health Data" [START_REF] Yale | Privacy preserving synthetic health data[END_REF], after comparing multiple methods, we found that Health-GAN method best satisfies these constraints due to the fact that a neural network model does not store real data or require real data to run. For HealthGAN specifically, we export just the generator network and never export any actual data.

Evaluation

To test the synthetic data generated by HealthGAN we investigate privacy, resemblance, and utility. In "Privacy Preserving Synthetic Health Data" [START_REF] Yale | Privacy preserving synthetic health data[END_REF], we developed the concept of nearest neighbor adversarial accuracy and privacy loss. Nearest neighbor adversarial accuracy, shown in Equation 1, compares the distance from one point in a target distribution T , to the nearest point in a source distribution S, defined as d T S (i) = min j x i T -x j S , to the distance to the next nearest point in the target distribution, defined as d T T (i) = min j,j =i ||x i T -x j T ||. By comparing this across all points, it gives us the adversarial accuracy. This metric can be interpreted much like balanced accuracy where the value is an average of the accuracy for each class. Therefore we are striving for a value of 0.5 where the synthetic and real data cannot be distinguished. If that is achieved then we can say that the synthetic data and real data have high resemblance.

AA T S = 1 2 1 n n i=1 1 (d T S (i) > d T T (i)) + 1 n n i=1 1 (d ST (i) > d SS (i)) (1)
Privacy loss is defined in Equation 2 as the difference between the adversarial accuracy on the test set and the adversarial accuracy on the training set. As the ideal value for both of these is 0.5, the privacy loss should be 0.0 when privacy is completely conserved. In the case where the model is exposing data, the value of the training adversarial accuracy will be lower than 0.5, and therefore even if the test adversarial accuracy is 0.5, the loss will increase.

TrResemblLoss (T rain Adversarial Acc.) = E[AA RtrA1 ] TrResemblLoss (T est Adversarial Acc.) = E[AA RteA2 ] PrivacyLoss = T est AA -T rain AA (2)
Beyond nearest neighbor adversarial accuracy, we test the privacy of the synthetic data using a membership inference attack scenario. In this scenario an attacker attempts to determine whether a given record was used to train a model [START_REF] Truex | Demystifying membership inference attacks in machine learning as a service[END_REF]. The attacker has black-box access to the model, meaning they have the ability to feed data into the model and observe the output of the model [START_REF] Salem | Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models[END_REF][START_REF] Shokri | Membership inference attacks against machine learning models[END_REF]. The original scenario doesn't exactly match what would happen with HealthGAN because the input to HealthGAN generator network is random noise, rather than real data. In HealthGAN setting the model the attacker has access to is just the generator and cannot train the model, only feed it random noise in order to generate data. Therefore, instead we show how using the synthetic data generated from the network and a variant of nearest neighbor accuracy can be used to assess vulnerability to this kind of attack.

In the attack scenario we are considering, an attacker has access to some real data R with incomplete records for each patient. We assume, without loss of generality, that the attacker has access to columns [c 1 . . . c k ], but not to columns [c k+1 . . . c N ]. Simultaneously, the attacker has access to a synthetic (artificial) dataset A for which all columns [c 1 . . . c N ] are given, which allows him/her to create a predictor of columns [c k+1 . . . c N ] from columns [c 1 . . . c k ]. Subsequently, this could allow him/her to predict the missing columns in real data, which could constitute a breach of privacy. This violation of privacy can be quantified in the membership attack scenario context by evaluating the fraction of real data records that can be identified after completing the missing data in R.

In the worst case scenario, the attacker has available a large fraction of the columns in R, making the attack simpler. We consider the limit case in which all the columns are available, and determine how easy it is to identify which real data records were used for training our data generative model. We construct R to be a random shuffle of the training and non-training data, and attempt to sort out if each point is from training or not, using a nearest neighbor classifier.

We compute the distance from a sample in R to its nearest neighbor in A, then measure the AUC of prediction {training vs. non-training sample} using the measured nearest neighbor distance as a ranking measure. If the AUC is greater than 0.5 (chance level), then the model may be exposing private data by allowing the attacker to know which records are in training.

Finally, once the privacy and resemblance have been tested the utility of the synthetic data must be evaluated. The method for this varies based on what the real dataset was intended for, but in the case where we are replicating a published study we reproduce the analysis done on the original data on the synthetic data. For the two studies being reproduced in this paper we will use Cox regression and k-means clustering to analyze the performance of the synthetic data.

Research Studies Reproduced

We examine creating synthetic open-data for two research papers, including the synthetic generation method, and the approaches for validating the quality of the two generated datasets. Both of the original studies were performed in the OLDW. OptumLabs is an open, collaborative research and innovation center founded in 2013 as a partnership between Optum and Mayo Clinic with its core linked data assets in the OLDW. The database contains de-identified, longitudinal health information on enrollees and patients, representing a diverse mixture of ages, ethnicities and geographical regions across the United States. The claims data in OLDW includes medical and pharmacy claims, laboratory results and enrollment records for commercial and Medicare Advantage enrollees. The EMR-derived data includes a subset of EMR data that has been normalized and standardized into a single database. Access to the OptumLabs database is, justifiably, tightly controlled. Access is limited to partner researchers who are granted access to certified de-identified research data views. All work is completed in the secure environment in which OLDW is hosted. Any release of data or derived products like graphs and tables must be reviewed and approved by OptumLabs on a case by case basis. Data for the subjects in the studies are typically not released, which means the OptumLabs datasets used in published studies are typically not available.

Study 1: Gastrointestinal Symptoms and Oral Antibiotic Use in

Children with Autism Spectrum Disorder

In "Gastrointestinal Symptoms and Oral Antibiotic Use in Children with Autism Spectrum Disorder: Retrospective Analysis of a Privately Insured U.S. Population" [START_REF] Vargason | Gastrointestinal symptoms and oral antibiotic use in children with autism spectrum disorder: Retrospective analysis of a privately insured us population[END_REF] the authors look at the relationship between taking oral antibiotics in early childhood and occurrences of later gastrointestinal (GI) diagnosis in children diagnosed with autism spectrum disorder (ASD). Previous studies have shown different rates of GI symptoms in children with ASD, but at least one study claims that the estimated odds of having a general GI complaint are 4.4 times greater for children with ASD than for children without ASD. One confounding factor in this comparison is the presence of oral antibiotics. Oral antibiotics in early childhood may cause long-term disruption in the gut microbiome's composition, leading to an increased number of GI symptoms in early childhood. Several studies have shown that on average children diagnosed with ASD consume more oral antibiotics than children without ASD. Therefore, this work looks at the hazard ratios of demographics, oral antibiotics, and ASD diagnoses. The authors found children with ASD had a greater rate of GI diagnoses than children without ASD. Examining hazard ratios, greater numbers of oral antibiotics significantly increased risks GI-related diagnoses for both groups.

EMR Data

The study looks at OptumLabs claims data from 1/1/2000 to 9/30/2015. The patients that are used in the dataset must have at least five years of un-interrupted data called continuous enrollment to be included. From there they are included in the ASD cohort if the patient has at least two different ASD related diagnoses in the time period. In order to measure the effect of oral antibiotics, the study defines two periods for observation: early enrollment and late enrollment. Early enrollment is the first three years of the five year period, while late enrollment is the last two years. The number of GI related diagnoses are counted for each period and examined. This type of analysis separates separate short term GI symptoms related to oral antibiotics from longer term conditions.

The ASD cohort collected has 3,278 patients while the non-ASD cohort has 279,428 patients. Within that cohort 37% of the ASD cohort has GI related diagnoses and 20% of the non-ASD cohort has GI related diagnoses. The final demographics of the study population are tabulated in Table 2 of their paper [START_REF] Vargason | Gastrointestinal symptoms and oral antibiotic use in children with autism spectrum disorder: Retrospective analysis of a privately insured us population[END_REF]. Due to slight variations in the view we are using in OptumLabs, we are not able to get the exact same data. Specifically, our view is a zoomed out version of the census divisions, which means our data has four different regions instead of the nine in this study. However, the data is almost exactly the same despite this change. The nine regions in the original study can be cleanly aggregated into the four regions in our data, reflected in the similar cumulative counts for the four regions across both datasets. Our dataset contains a total of 283,462 patients, which is very similar to the overall total of 281,623 patients in the original study.

Synthetic Data

The first measure of quality for the synthetic data is our nearest neighbor adversarial accuracy metric [START_REF] Yale | Privacy preserving synthetic health data[END_REF]. The values for the TrainRe-semblanceLoss and the TestResemblanceLoss should be close to 0.5 to ensure that the resemblance is good, but most importantly the privacy loss, which is defined as PrivacyLoss = TestResemblanceLoss -TrainResemblanceLoss, should be as close to 0 as possible [START_REF] Yale | Privacy preserving synthetic health data[END_REF]. On our candidate model the TrainResemblanceLoss is 0.5236 and the TestResemblanceLoss is 0.5272. For the PrivacyLoss, we get 0.0036. Overall, these metrics indicate we are preserving privacy with a low privacy loss, but also maintaining resemblance with resemblance loss values close to 0.5. Another measure of privacy is robustness against a membership inference attack [START_REF] Yale | Assessing privacy and quality of synthetic health data[END_REF]. This measured uses the area under the curve (AUC). Any AUC value above 0.50 in this test indicates a potential loss of privacy in a membership inference attack. To verify the baseline for this metric, we run the membership inference attack using the real data and obtain an AUC of 1.00 as expected. When computing this measure for the synthetic data, we get an optimal value of 0.50. This result further affirms previous privacy metrics, indicating privacy is also preserved in the membership inference attack scenario.

Finally, we assess utility by reproducing the study on the synthetic data. In the original study, the analysis used Cox regression to estimate adjusted and unadjusted hazard ratios (HRs). The original results from the Cox regression model show that GI diagnoses in late enrollment are more likely to happen if oral antibiotics are taken in early enrollment, regardless of an ASD diagnosis. In addition, the similarity in the HR of the two groups indicates that the effect is not exclusive to the ASD population. Another covariate was a diagnosis for GI symptoms in early enrollment. Understandably, this variable has a high HR as it likely indicates systemic issues with the GI tract.

We run the same Cox regression analysis on the synthetic data in order to calculate the hazard ratios of the same covariates. In Figure 2 the ASD Cohort is on top and the Control Cohort is on the bottom, the real 95% confidence intervals are shown in black compared to the synthetic confidence intervals (CI) in red. If the CI overlap, then there is no statistically significant difference between the real and significant results. In the top of Figure 2, the only variable that does not overlap in confidence intervals is whether the patient had any late enrollment prescription fills. In the bottom of Figure 2, all the variables have overlapping confidence intervals, thus display no significant differences. In addition, the findings of the original paper state that the ASD and POP cohorts have similar hazard ratios for 7-9 fills. Our results verify this claim in both our real data with an ASD value of 1.25 CI (0.98, 1.61), and a POP value of 1.24 CI (1.11, 1.36), and in our synthetic data with an ASD value of 1.48 CI (0.92, 2.41), and a POP value of 1.24 CI (1.04, 1.49). These metrics indicate the synthetic data has high utility in terms of providing the same relationships demonstrated in prior work. Overall, the metrics computed demonstrate that the synthetic data created using our end-to-process result in data that retains privacy while maintaining high levels of resemblance and utility.

Study 2: Clustering of co-occuring conditions in ASD

In the paper "Clustering of co-occurring conditions in autism spectrum disorder during early childhood: A retrospective analysis of medical claims data" [START_REF] Vargason | Clustering of co-occurring conditions in autism spectrum disorder during early childhood: A retrospective analysis of medical claims data[END_REF] the authors analyze patterns in diagnoses of comorbid medical conditions (CMCs) in patients with ASD. The study uses the same cohort of patients as the previous study. Based on the data, they are able to separate the patients with ASD diagnoses into three different clusters. The first cluster was 23.7% (n = 776) of the patients and encompassed a high rate of CMCs. The second cluster was 26.5% (n = 870) and contained patients with a higher rate of developmental delays. The third cluster, making up 49.8% (n = 1,632) of the data, contained low numbers of CMCs. Evaluating the data over time shows that the same patterns persist within these three clusters. The goal of this work was to help inform future treatment protocols for patients with ASD related to CMCs.

EMR Data Membership in the cohort of patients ASD was determined with the same criteria as the previous paper, but with the addition of data regarding the CMCs. Seven different categories of CMCs were identified: auditory disorders, development delays, gastrointestinal symptoms, immune related conditions, psychiatric disorders, seizure disorders, and sleep disorders. These conditions were measured over time in six-month windows. For each patient, ten six-month windows were constructed for each of the categories, resulting in a vector of length 70 per individual with binary values indicating whether there was a diagnosis in the time window.

To analyze the CMC dataset the authors used principal component analysis (PCA) on the 70 CMC columns to transform the data to a continuous dataset, and then clustered the transformed data. This analysis was only done on the sub-cohort of patients with ASD diagnoses. Through k-means clustering they found the three different clusters of children with ASD defined previously.

Synthetic Data Following the same process as the first paper, we train a new HealthGAN model on this dataset. We compute the privacy methods for the data and we evaluate the nearest neighbor adversarial accuracy metric. For our candidate model the performance on TrainResmeblanceLoss is 0.5416 and our TestResemblanceLoss is 0.5430. This value results in a PrivacyLoss of 0.0014. These metrics demonstrate that this synthetic data retains privacy as well as maintaining resemblance values close to 0.5. We also examine membership inference attacks using AUC. After verifying the baseline for the real data, the synthetic data had an AUC of 0.50, meaning no privacy loss in a membership inference attack scenario as well. Next we examine resemblance of the synthetic data compared to the real data. For Study 2, we look at the population cohort, ASD cohort, and each of the three specific clusters. In addition to the demographic data, we look at the average number of CMC categories diagnosed per cohort. Visualizing the first 2 components of PCA for the real data and the synthetic data can further help understand any differences. In Figure 3, PCA plots of the real and synthetic data (colored by cluster see below) show very similar distributions. We see that we have a good level of coverage over the real data. This is shown by the fact that we do not have many outlier values being generated in the synthetic data as well as the synthetic data not missing that many portions where the real data exists.

After measuring privacy and resemblance, we examine the utility of the synthetic data. Instead of Cox regression, the analysis of the original dataset is done with k-means clustering. To verify the utility of the synthetic data, this cohort is put through the same clustering method as the one used on the real data. The three emerging clusters are compared using characteristics of the CMCs for patients in each cluster. Using the PCA plot that was created to check the resemblance of the data, we can also check the utility by looking at how well the k-means clustering method works on the synthetic data. In Figure 4, we have a PCA of the real data on the left and colored according to the clusters. On the right we have the PCA of the synthetic data colored by clusters found using the same k-means model found on real data. Visually we see that the clusters are very similar in both the real and synthetic plots which indicates a high level of resemblance and therefore utility.

In addition to the pure size and shape of the clusters, we can also look at the average number of CMCs over time in the clusters. In Figure 5, we can see a comparison of the average number of CMCs in each group over the time, in years, of the study. We see that there is a similar, if less smooth, trend in the groups. The high and mid clusters have a very similar relationship where they cross each other around the three year mark. The ASD population as a whole stays consistent in the middle of the graph, and the low and control populations have

Conclusions and Future Work

We demonstrate how to create synthetic datasets from real and assess their resemblance, privacy, and utility using a process that obtained approval from a commercial health EMR data provider. HealthGAN creates a GAN model in a secure environment, which is then exported to generate synthetic data outside the secure environment Recreating the analysis of two studies show that the method produces high quality for health informatics education. New research models and algorithms can be created on the synthetic data to create and evaluate new approaches and evaluate without compromising patient privacy. Synthetic data generation provides an approach to make private data assets public. We recommend that empirical metrics for assessing privacy, resemblance and utility be utilized whenever synthetic data are generated, even if the underlying algorithms have theoretical guarantees of privacy. If synthetic data generated by automatic machine learning methods became a routine part of the publication process, scientific discovery and reproducibility would be accelerated and improved. Approaches could be developed on the synthetic data, and then evaluation on the real data in the secure environment. Since HealthGAN is designed to duplicate the underlying multivariate distributions while preserving individual privacy, it may not not necessarily protect proprietary information represented in the data, such as business process and patterns.

Fig. 3 :

 3 Fig. 3: PCA Plot of Real vs Synthetic Data for Study 2

Fig. 4 :

 4 Fig. 4: PCA Plot of Real Data and Synthetic Data with Marked Clusters

  Study 2: CMC Over Time by Different Clusters, Real and Synthetic similar numbers of CMCs over time. Overall by replicating these various results from the original paper we can see that we achieve a high level of resemblance and utility while maintaining a high level of privacy 7 .

	1.6 1.8 2.0	Average CMC Count Over Time by Cluster: Real ASD High Cluster Mid Cluster Low Cluster POP	1.6 1.8 2.0	Average CMC Count Over Time by Cluster: Synthetic ASD High Cluster Mid Cluster Low Cluster POP
	Avg CMC		1.0 1.2 1.4 Avg CMC
			0.8
			0.6
			0.4
				0	1	2 Time in Years	3	4
	Fig. 5:

As of 4/27, we successfully exported the model of the first dataset but approval on the second dataset is ongoing

github.com/TheRensselaerIDEA/synthetic data

Further analysis of the synthetic data for both studies is included in the supplemental material https://git.io/Jf3mK


Aline Bamia

Maha Sinane

Rima Naït-Saïdi

Jamila Dhiab

Phu Marc Keruzoré

Hai Nguyen

Agathe Bertho

Flavie Soubigou

Sophie S Halliez

Marc Blondel

Phu Hai Nguyen

Capucine Trollet

Martine Simonelig

Gaëlle Friocourt

Vincent Béringue

Frédéric Bihel
email: frederic.bihel@unistra.fr

Cécile Voisset
email: c.voisset@univ-brest.fr

Anti-prion Drugs Targeting the Protein Folding Activity of the Ribosome Reduce PABPN1 Aggregation

Keywords: Prions, PrP Sc, drug repurposing, PFAR, OPMD, PABPN1

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Although the concept of infectious proteins was first established for the prion protein PrP in mammals suffering from transmissible spongiform encephalopathy [START_REF] Prusiner | Novel proteinaceous infectious particles cause scrapie[END_REF], prions are broadly found in other model organisms including the budding yeast S. cerevisiae. Over the last years, we have taken advantage of the existence of prions in yeast to identify new anti-prion compounds by screening drugs able to cure [PSI+] and [URE3] yeast prions [START_REF] Bach | Isolation of drugs active against mammalian prions using a yeast-based screening assay[END_REF][START_REF] Nguyen | Evaluation of the antiprion activity of 6aminophenanthridines and related heterocycles[END_REF][START_REF] Nguyen | Structure-activity relationship study around guanabenz identifies two derivatives retaining antiprion activity but having lost alpha2-adrenergic receptor agonistic activity[END_REF][START_REF] Oumata | The toll-like receptor agonist imiquimod is active against prions[END_REF][START_REF] Tribouillard-Tanvier | Antihypertensive drug guanabenz is active in vivo against both yeast and mammalian prions[END_REF][START_REF] Voisset | Procedure for identification and characterization of drugs efficient against mammalian prion: from a yeast-based antiprion drug screening assay to in vivo mouse models[END_REF]. Some of the compounds we have identified are active both in vitro and in vivo against PrP Sc mammalian prions, demonstrating that some of the molecular mechanisms controlling prion onset and propagation are similar in yeast and mammals [2-6, 8, 9]. In particular, we identified two FDA-approved drugs, guanabenz (GA) and imiquimod (IQ), and 6-aminophenanthridine (6AP), with anti-prion activity [4-6, 9, 10]. A reverse screening approach has allowed us to identify one of the cellular targets of these compounds: the domain V of the large ribosomal RNA (rRNA) of the large 60S ribosome subunit [4-6, 9, 10]. The domain V of large rRNA (23S in E. coli, 25S in S. cerevisiae, 28S in metazoans) is a ribozyme carrying 2 enzymatic activities: (i) the peptidyl transferase activity and (ii) the protein folding activity, PFAR (protein folding activity of the ribosome), which can refold denatured proteins back to their functionally active forms [START_REF] Chattopadhyay | Protein folding in Escherichia coli: role of 23S ribosomal RNA[END_REF][START_REF] Das | Role of the ribosome in protein folding[END_REF]. Ribosomal RNA from all three kingdoms of life exhibit a protein folding activity [START_REF] Das | Role of the ribosome in protein folding[END_REF], which is in good agreement with the fact that both sequence and secondary structure of rRNA domain V are highly conserved [START_REF] Ben-Shem | The structure of the eukaryotic ribosome at 3.0 A resolution[END_REF]. 6AP, GA and IQ were shown to be the first described competitive inhibitors of PFAR that do not affect protein translation [START_REF] Oumata | The toll-like receptor agonist imiquimod is active against prions[END_REF][START_REF] Tribouillard-Tanvier | Antihypertensive drug guanabenz is active in vivo against both yeast and mammalian prions[END_REF][START_REF] Tribouillard-Tanvier | Protein folding activity of ribosomal RNA is a selective target of two unrelated antiprion drugs[END_REF]. The fact that these three anti-prion compounds are also PFAR inhibitors led us to explore the link between PFAR and prion propagation. We found that PFAR is involved in the de novo formation and propagation of [PSI+] prion in yeast [START_REF] Blondel | Protein Folding Activity of the Ribosome is involved in Yeast Prion Propagation[END_REF], strongly suggesting that PFAR is a promising cellular target for the treatment of prion diseases.

There is growing evidence that other proteinopathies share key biophysical and biochemical characteristics with prionopathies [START_REF] Scheckel | Prions, prionoids and protein misfolding disorders[END_REF]. In this type of disease, affected proteins are for example A and Tau for Alzheimer's disease, and α-synuclein for Parkinson's disease. Oculopharyngeal muscular dystrophy (OPMD) is also classified as a proteinopathy, in which poly(A) binding protein nuclear 1 protein (PABPN1) forms aggregates within the nuclei of patients' muscle cells [START_REF] Gidaro | Atrophy, fibrosis, and increased PAX7-positive cells in pharyngeal muscles of oculopharyngeal muscular dystrophy patients[END_REF][START_REF] Tome | Nuclear inclusions in oculopharyngeal dystrophy[END_REF]. OPMD is a late-onset, autosomal dominant genetic neuromuscular disease caused by a short expansion of a GCG repeat encoding a polyalanine tract located at the N-terminus of PABPN1 [START_REF] Brais | Short GCG expansions in the PABP2 gene cause oculopharyngeal muscular dystrophy[END_REF]. Ten alanine residues are present in the normal protein and are expanded from 11 to 18 alanine residues in the mutant forms of the protein [START_REF] Brais | Short GCG expansions in the PABP2 gene cause oculopharyngeal muscular dystrophy[END_REF][START_REF] Jouan | An 18 alanine repeat in a severe form of oculopharyngeal muscular dystrophy[END_REF][START_REF] Richard | PABPN1 (GCN)11 as a Dominant Allele in Oculopharyngeal Muscular Dystrophy -Consequences in Clinical Diagnosis and Genetic Counselling[END_REF]. Similarly to polyglutamine expansion diseases, the triplet-expanded mutant PABPN1 protein aggregates and thid is the pathological hallmark of OPMD. More important, we have previously shown that the anti-PFAR drug GA was active in OPMD cells as well as Drosophila and mouse models [START_REF] Barbezier | Antiprion drugs 6-aminophenanthridine and guanabenz reduce PABPN1 toxicity and aggregation in oculopharyngeal muscular dystrophy[END_REF][START_REF] Malerba | Pharmacological modulation of the ER stress response ameliorates oculopharyngeal muscular dystrophy[END_REF], suggesting that PFAR plays a role in PABPN1 aggregation in OPMD. These data highlight the fact that PFAR inhibitors may be beneficial for various proteinopathies, including prion diseases and OPMD.

As the prion field desperately lacks therapeutic solutions for affected patients, and since GA (hypotensive drug) as well as IQ (immune system activator used in epicutaneous administration) are not compatible with a chronic treatment targeting the CNS, we sought to identify other antiprion compounds which are also approved drugs. Here, we show that flunarizine, an approved drug used to treat migraine, is also a potent anti-prion drug in cell culture. Flunarizine also shows a significant effect on the survival of intracerebrally PrP Sc infected mice. Following this observation, we employed an in silico approach based on similarities with the flunarizine chemical structure to identify other FDA-approved drugs that may also possess anti-prion activity and allow their therapeutic repositioning according to SOSA (Selective Optimization of Side Activities of drug molecules [START_REF] Wermuth | Selective optimization of side activities: another way for drug discovery[END_REF][START_REF] Wermuth | Multitargeted drugs: the end of the "one-target-one-disease" philosophy?[END_REF]) approach. This strategy is based on the screening of drugs that are already in the clinic or in clinical trials to determine if they could have biological activities -in our case the inhibition of prion propagationother than those initially described during their therapeutic development. This approach is particularly popular for rare or neglected diseases, as their therapeutic targets are not present in the safety or selectivity profiles in pharmaceutical companies.

As their toxicity, safety and bioavailability in humans have already been tested, the drugs identified in these SOSA-based libraries may reach patients faster and may also be directly administered as potential compassionate therapeutics to people presenting symptoms of prion diseases. Using this approach, we have been able to identify 17 drugs approved by the FDA and/or other drug agencies, which are, to our knowledge, newly described anti-PrP Sc drugs, and provide a new perspective for their therapeutic repositioning as anti-prion compounds in the clinic. Among a selection of the most potent anti-prion drugs identified, we found that, like flunarizine, azelastine, duloxetine, ebastine, loperamide and metixene can inhibit PrP Sc propagation in organotypic mouse cerebellum slice cultures. From a mechanistic point of view, flunarizine and its six structural analogs were also found to be potent PFAR inhibitors, confirming our previous findings that PFAR is a relevant therapeutic target to curb prion propagation. As PFAR has also been shown to play a role in OPMD [START_REF] Barbezier | Antiprion drugs 6-aminophenanthridine and guanabenz reduce PABPN1 toxicity and aggregation in oculopharyngeal muscular dystrophy[END_REF] and since there is no pharmacological treatment currently available for this disease, flunarizine and some of its structural analogs were also challenged for their capacity to reduce the phenotypes of OPMD in both cellular and Drosophila models. These new anti-PFAR molecules were able to reduce not only PABPN1 aggregation in cell culture but also muscular defects in OPMD Drosophila.

Altogether, we report here the identification of six new anti-PFAR FDA-approved drugs, namely azelastine, duloxetine, ebastine, flunarizine, loperamide and metixene as potent drug candidates for prion diseases as well as for OPMD. In addition, these data confirm our previous observations that PFAR plays a major role in prion propagation and toxicity of PABPN1 aggregation [START_REF] Barbezier | Antiprion drugs 6-aminophenanthridine and guanabenz reduce PABPN1 toxicity and aggregation in oculopharyngeal muscular dystrophy[END_REF].

Methods

Selection of anti-prion drug candidates

ROCS shape-based virtual screening: The latest release of DrugBank (version 5.1.0, released 2018-04-02) was downloaded directly from https://www.drugbank.ca and filtered to only conserve FDA-approved or withdrawn drugs. Multiconformer files were generated by OMEGA v2.5.1.4 from OpenEye Scientific Software (http://www.eyesopen.com/), leading to 1887 approved or withdrawn drugs saved in oeb.gz format. These generated multiconformational files were used as the input database for performing similarity search with Rapid Overlay of Chemical Structures (vROCS 3.2.2.2 from OpenEye Scientific Software (http://www.eyesopen.com/)). ROCS is designed to carry out large-scale 3D database searches. It performs similarity searches by using a shape-and/or pharmacophore-based superposition method that finds the similar but non-intuitive compounds. It uses only the heavy atoms of a ligand ignoring the hydrogens. The output files of the similarity search were then ranked according to their ComboScore, which is based on a combination of shape and pharmacophore similarity.

The structure of Flunarizine used as query was extracted from experimentally crystallized structure [START_REF] Kavitha | Flunarizinium isonicotinate[END_REF] obtained from The Cambridge Crystallographic Data Centre via www.ccdc.cam.ac.uk/structures (CCDC identifier: JOBSIE).

Azelastine, biperiden, cinnarizine, diphenhydramine, epinastine, ethosuximide, flunarizine, imipramine, loperamide, orphenadrine, paramethadione, thioridazine, triflupromazine, zonisamide and GuHCl were purchased from Sigma Aldrich. Astemizole, clemastine, duloxetine, ebastine and metixene were purchased from CarboSynth. Benzydamine and nefopam were purchased from

LGC. Antazoline, atomoxetine, cetirizine, citalopram, diazepam, ketotifen and zimelidine were purchased from Tocris. Alimemazine, chloropyramine, dicyclomine, diphenidol, mirtazapine and prenylamine were purchased from Prestwick.

PrP Sc clearance assay in cultured MovS6 cells

These experiments were performed as previously described [START_REF] Nguyen | Structure-activity relationship study around guanabenz identifies two derivatives retaining antiprion activity but having lost alpha2-adrenergic receptor agonistic activity[END_REF][START_REF] Oumata | The toll-like receptor agonist imiquimod is active against prions[END_REF][START_REF] Archer | Cultured peripheral neuroglial cells are highly permissive to sheep prion infection[END_REF]. Briefly, MovS6 cells chronically infected with ovine 127S prion strain were plated in 6 well plates (Starlab, France), treated for six days with the indicated concentrations of candidate drugs and then lysed in a buffer containing 0.5 % sodium deoxycholate, 0.5 % Triton-X100, 5 mM Tris-HCl pH 7.5. Cell lysates (250 μg) were digested by 20 µg/mL of proteinase K (PK, Thermo Scientific, Waltham, MA) to identify PrP Sc proteins which are resistant to proteolytic degradation. PK-treated homogenates were loaded on 10% Bis-Tris SDS-polyacrylamide gels (NuPAGE, Invitrogen/Thermo Scientific), and subjected to western blotting onto nitrocellulose membranes (GE Healthcare, Barrington, Illinois). For normalization purposes, 25 µg of non-PK-treated homogenates were also loaded on 10% Bis-Tris SDS-polyacrylamide gels, and subjected to western blotting onto nitrocellulose membranes. PrP proteins were immunolabelled on PK-treated and non-PK treated samples using an anti-PrP antibody (Sha31, 1/40,000, Bertin pharma, [START_REF] Feraudet | Screening of 145 anti-PrP monoclonal antibodies for their capacity to inhibit PrPSc replication in infected cells[END_REF]). Loading accuracy was controlled by tubulin detection on non-PK treated samples (YOL1/34, 1/3,000, Abcam, Cambridge, United Kingdom).

Mouse lines and ethical Statement

We used mice overexpressing ovine PrP (tg338 line previously described [START_REF] Vilotte | Markedly increased susceptibility to natural sheep scrapie of transgenic mice expressing ovine prp[END_REF]). All animal experiments were carried out in strict accordance with EU directive 2010/63 and were approved by the author's institution local ethics committee (Comethea, INRA Agroparitech ethics committee, permit number 12/034) and by the French Ministry of Education, higher education and research (authorization n°4292). All efforts were made to minimize suffering.

Drug treatment of a mouse model for prion disease

Experiments were performed as previously described [START_REF] Oumata | The toll-like receptor agonist imiquimod is active against prions[END_REF][START_REF] Dur | A newly identified type of scrapie agent can naturally infect sheep with resistant PrP genotypes[END_REF]. Briefly, thirty 8-week-old female mice overexpressing ovine PrP (tg338 line [START_REF] Vilotte | Markedly increased susceptibility to natural sheep scrapie of transgenic mice expressing ovine prp[END_REF]) were infected intracerebrally with 20 μL of the 127S scrapie strain at the 0.01% (w/v) dose, corresponding to 2 x 10 3.2 LD50 (the infectious titer of 127S in tg338 mouse brain is 10 9.2 LD50/g of brain, [START_REF] Langevin | Marked influence of the route of infection on prion strain apparent phenotype in a scrapie transgenic mouse model[END_REF]). Forty days post-infection, 9 mice were intraperitoneally injected 3 times per week with a dose of 100 µL at 4 mg/mL (20 mg/kg) of flunarizine solubilized in 8% DMSO. As negative controls, 11 mice were intraperitoneally injected 3 times per week with 100 µL of 8% DMSO. To avoid unethical suffering of prion-infected mice, the treatment was stopped around day 70 post-infection, when the first symptoms appeared in the control group of mice. Mice were euthanized at the terminal stage according to ethics rules.

Spleens and brains were collected from 4 mice euthanized at the terminal stage of the disease and analysed for their PrP Sc content, as previously described [START_REF] Oumata | The toll-like receptor agonist imiquimod is active against prions[END_REF][START_REF] Tribouillard-Tanvier | Antihypertensive drug guanabenz is active in vivo against both yeast and mammalian prions[END_REF][START_REF] Langevin | Marked influence of the route of infection on prion strain apparent phenotype in a scrapie transgenic mouse model[END_REF].

Luciferase refolding assay.

[psi-] ltv1Δ/hsp104Δ 74-D694 S. cerevisiae yeast strain (ΔLΔH, [START_REF] Blondel | Protein Folding Activity of the Ribosome is involved in Yeast Prion Propagation[END_REF]) was transformed with pDCM90 plasmid [START_REF] Hasin | Global transcript and phenotypic analysis of yeast cells expressing Ssa1, Ssa2, Ssa3 or Ssa4 as sole source of cytosolic Hsp70-Ssa chaperone activity[END_REF][START_REF] Parsell | Saccharomyces cerevisiae Hsp104 protein. Purification and characterization of ATP-induced structural changes[END_REF], allowing constitutive expression of temperature-sensitive luciferase polypeptide (LuxAB). Transformants were exponentially grown at 29°C. Cells were treated with the indicated concentrations of drugs or DMSO 2 hours before the heat-shock. The 100% reference corresponds to luciferase activity measured just before the heat-shock. Luciferase was then heatinactivated by incubation at 43.5°C for 60 min. Cycloheximide (10 μg/mL; Sigma Aldrich, St.

Louis, MO) was added after 45 min at 43.5°C to prevent luciferase synthesis during recovery.

Luciferase activity was immediately measured after 60 min post-heat-shock (0 min) and cells were then left to recover at 29°C for indicated times, luciferase activity being assessed at 90 and 150 min by adding 10 μL n-decylaldehyde (Decanal, Sigma Aldrich) to 120 μL yeast culture.

Luminescence was quantified using a Varioscan microplate reader (Thermofisher). Luciferase activity at 0, 90 and 150 min recovery was then expressed as a percentage of the activity before the heat treatment for each strain (100%).

Drug treatment of prion-infected cultured organotypic cerebellar slices.

The anti-prion activity of drugs was evaluated using cultured organotypic cerebellar slices (OCSs), as described in [START_REF] Nguyen | Structure-activity relationship study around guanabenz identifies two derivatives retaining antiprion activity but having lost alpha2-adrenergic receptor agonistic activity[END_REF] using tg338 transgenic mice overexpressing the VRQ allele of the ovine prion protein [START_REF] Dur | A newly identified type of scrapie agent can naturally infect sheep with resistant PrP genotypes[END_REF]. The preparation and culture of slices were performed as described in [START_REF] Nguyen | Structure-activity relationship study around guanabenz identifies two derivatives retaining antiprion activity but having lost alpha2-adrenergic receptor agonistic activity[END_REF][START_REF] Falsig | The prion organotypic slice culture assay--POSCA[END_REF] using insert (Millipore), except for prion infection which was performed the day of cerebellum slicing for 1h on ice in 24-well plates (Starlab) with 50 µg/mL of a brain stock prepared from terminally ill tg338 mice experimentally infected with 127S prion strain. Seven days after infection, the drugs were added to the cerebellum slices at indicated concentrations. As a negative control, vehicle only (DMSO) was added. The slices were then cultured for an additional 21 days before harvesting.

Fresh drugs were added at each medium change, 3 times per week, at indicated concentrations.

Pools of slices (at least seven slices/condition) were harvested by scrapping and homogenized in 350 µL of lysis buffer (0.5 % sodium deoxycholate, 0.5 % Triton-X100, 5 mM Tris-HCl pH 7.5)

with the Beadbug homogenizer (Benchmark Scientific, Sayreville, NJ) as previously described [START_REF] Nguyen | Structure-activity relationship study around guanabenz identifies two derivatives retaining antiprion activity but having lost alpha2-adrenergic receptor agonistic activity[END_REF]. Slice lysates (100 µg) were treated by 20 μg/mL proteinase K (PK, Thermo Scientific) for 30 min at 37°C. For the analysis, PK-treated slice homogenates and 25 µg non-PK-treated slice homogenates were denatured for 10 min at 95°C and then loaded on 10% SDS-polyacrylamide gels (NuPAGE, Invitrogen) and electrotransferred onto nitrocellulose membranes (GE Healthcare). PK-treated and non-PK treated samples were immunoblotted with anti-PrP antibody (Sha31, 1/40,000, Bertin Pharma, Montigny-le-Bretonneux, France). Loading accuracy was controlled by immunoblotting 25 µg of non-PK-treated homogenates with anti-tubulin antibody (YOL1/34, 1/3 000, Abcam).

Assessment of the action of drugs on nuclear aggregate number in an OPMD cell line

The OPMD mutated PABPN1 (Ala17) stable cell line [START_REF] Raz | Modeling oculopharyngeal muscular dystrophy in myotube cultures reveals reduced accumulation of soluble mutant PABPN1 protein[END_REF] was cultivated on a matrigel-coated surface (µ-slide 8 wells ibidis, Biovalley) in DMEM medium supplemented with 20% of fetal calf serum (Invitrogen), 0.5% of chick embryo extract (MP Biomedicals, Irvine, CA), 100 U/mL penicillin-streptomycin antibiotic (Thermofisher), 500 μg/mL geneticin (G418, Life Technologies, Carlsbad, CA) and 10 U/mL interferon gamma (Millipore/Merck, Darmstadt, Germany) at 33°C in a humidified 5% CO2 air atmosphere. At 80% confluence, Ala17 cells were differentiated in DMEM supplemented with 10% horse serum and 1% penicillin-streptomycin at 37°C in a 5% CO2, humidified atmosphere.

At 2 days of differentiation, half of the differentiation medium was replaced with fresh differentiation medium containing the drugs solubilized in DMSO at indicated concentrations.

Two days later, cells were washed once in phosphate buffer saline (PBS) and fixed for 15 min with 4% paraformaldehyde for PABPN1 immunostaining. Cells were then incubated for 15 min in PBS-0.1 M glycine and permeabilized in blocking buffer containing 3% BSA, 5% Goat serum in PBS-0.2 % Triton X-100. Anti-PABPN1 primary antibody (1/200, ab75855, Abcam) was incubated 1h at room temperature in blocking buffer. Cells were washed five times with PBS-0.1% Triton X-100 prior incubation with goat anti-rabbit secondary antibody conjugated to Alexa Fluo 488 (Life Technologies) together with Phalloidin 555 (Interchim, Montluçon, France). Cells were then washed five times with PBS-0.1% Triton X-100, and stained with Hoechst for 10 min at room temperature. Pictures were taken with the 40X objective and five to seven-hundred nuclei were counted for each condition.

OPMD Drosophila treatment

The Drosophila OPMD model contains an alanine-expanded mammalian PABPN1 cDNA (PABPN1-17ala) that is specifically and constitutively expressed in adult indirect flight muscles from the Act88F-PABPN1-17ala transgene [START_REF] Chartier | Mitochondrial dysfunction reveals the role of mRNA poly(A) tail regulation in oculopharyngeal muscular dystrophy pathogenesis[END_REF]. Drug-supplemented food was prepared as follows: instant Drosophila medium (Carolina Biological Supply Company, Whitsett, NC) was reconstituted in each vial with a solution of 1% yeast in water, supplemented with indicated concentrations of either drug solubilized in DMSO or DMSO alone. Each vial contained 2 mL of reconstituted medium for twenty flies per vial. The flies were put on freshly prepared medium every day. Abnormal wing posture was quantified every day by pooling males by batches of five in an empty vial and scoring abnormal wing position by direct observation of the flies through the vials, without anaesthesia.

Results

Flunarizine is a potent anti-prion drug

Flunarizine (Fig. 1a) was identified in our laboratory through a yeast-based screening strategy and was shown to reduce the amount of PrP Sc in chronically prion-infected MovS6 cells, with an IC50 of 3.9 µM (Fig. 1b). Flunarizine is a selective calcium entry blocker as well as an antagonist of histamine H1 receptor that readily crosses the blood-brain barrier [START_REF] Amery | Brain hypoxia in migraine: pathophysiologic and therapeutic implications[END_REF]. This drug is used in many developed countries (except USA, UK and Japan) to treat migraine headaches, occlusive peripheral vascular disease, vertigo of central and peripheral origin, and as an adjuvant in the therapy of epilepsy [START_REF] Amery | Brain hypoxia in migraine: pathophysiologic and therapeutic implications[END_REF][START_REF]Flunarizine, a calcium channel blocker: a new prophylactic drug in migraine[END_REF][START_REF] Binnie | Open dose-ranging trial of flunarizine as add-on therapy in epilepsy[END_REF][START_REF] Holmes | Flunarizine. A review of its pharmacodynamic and pharmacokinetic properties and therapeutic use[END_REF][START_REF] Santi | Differential inhibition of T-type calcium channels by neuroleptics[END_REF].

We next challenged the effect of flunarizine using an in vivo model of prion infection. We increased by approximately 6% compared to the control group (n=9 for flunarizine and n=11 for DMSO, Fig. 1c). These results confirm that flunarizine is a potential anti-prion drug.

Identification of new approved anti-prion drugs based on flunarizine structure

In order to broaden the range of new anti-prion drugs that could potentially be used to treat patients with prion diseases, and considering that flunarizine might not be the most optimized anti-prion drug, we performed an in silico screening, ranking the 1887 FDA-approved or withdrawn drugs present in the Drugbank database (https://www.drugbank.ca/) for their structural similarities with flunarizine. In terms of pharmacophoric pattern, flunarizine is composed of three aromatic moieties, as well as two protonable nitrogen atoms, which can interact with specific biological targets through electrostatic or H-bond acceptor interactions (Fig. 2a). We chose to use the ROCS method from OpenEye Scientific, using a shape-and/or pharmacophore-based superposition method that finds similar but non-intuitive compounds. FDA-approved drugs exhibiting similarities with flunarizine were then ranked according to the scoring function ComboScore, which is based on a combination of shape and pharmacophore similarities. The resulting ranking was a manner of classifying the FDA-approved drugs according to their similarities with flunarizine (Table S1). As flunarizine is known to be a non-selective calcium channel blocker with antihistamine activity as well as other actions including serotonin receptor and dopamine D2 blocking activities, it was not surprising to find in the top-100 several compounds targeting histamine, muscarinic, dopamine, as well as both serotonin receptors and transporters.

From the ranking shown in Table S1, a series of thirty molecules was selected throughout the classification. According their ROCS ranks, we chose compounds exhibiting a high or a medium level of similarity with flunarizine, short-listing compounds sharing targets with flunarizine, such as H1 receptor, as well as compounds targeting cellular pathways common or not with flunarizine (Table 1 and Table S2). In other words, we consider the ROCS ranking as a guideline to classify approved drugs exhibiting more or less structural similarities with flunarizine, and combine that with a diversity of addressed biological targets in order to select these thirty compounds. To evaluate the potential involvement of calcium channels in PrP Sc propagation, three calcium antagonists were selected, although they are not closely related to the calcium antagonist flunarizine (ranking > 500) (ethosuximide, paramethadione, zonisamide, Table S2). Altogether, the thirty selected FDA-approved drugs represent thirteen different cellular targets (Table S2). In addition to the thirty selected drugs resulting from our similarity screening approach, we also selected three reference compounds, namely astemizole [START_REF] Karapetyan | Unique drug screening approach for prion diseases identifies tacrolimus and astemizole as antiprion agents[END_REF][START_REF] Kocisko | New inhibitors of scrapie-associated prion protein formation in a library of 2000 drugs and natural products[END_REF][START_REF] Srivastava | Prion protein dynamics before aggregation[END_REF], clemastine [START_REF] Kocisko | New inhibitors of scrapie-associated prion protein formation in a library of 2000 drugs and natural products[END_REF] and thioridazine [START_REF] Kocisko | New inhibitors of scrapie-associated prion protein formation in a library of 2000 drugs and natural products[END_REF][START_REF] Kocisko | Evaluation of new cell culture inhibitors of protease-resistant prion protein against scrapie infection in mice[END_REF] which have previously been shown to inhibit prion propagation (Table 1, labelled # and

Table S1).

We first evaluated the anti-PrP Sc activity of the thirty selected structural analogs of flunarizine using the MovS6 cell line infected by the 127S scrapie prion strain [START_REF] Vilotte | Markedly increased susceptibility to natural sheep scrapie of transgenic mice expressing ovine prp[END_REF]. We showed that seventeen out of the thirty tested drugs showed anti-PrP Sc activity, ten of which with an IC50 ≤ 4 µM (Fig. 2b) and 7 with an IC50 between 4 and 20 µM (Fig. S2), without affecting PrP tot level (Fig. 2b and Fig. S2). Thirteen out of the thirty selected drugs were inactive (IC50 ≥20 µM; Fig. S3). Overall, this approach allowed us to identify seventeen new potential anti-prion FDA-approved drugs.

Flunarizine and some of its structural analogs are active against PrP Sc in organotypic cerebellar slice culture

We next evaluated the anti-prion activity of the 7 most active newly identified drugs (azelastine, duloxetine, ebastine, flunarizine, loperamide, metixene and triflupromazine) on organotypic cerebellum slice culture (OSC), a relevant alternative to animal experimentation which has been shown to closely mimic prion replication and propagation on a structured organ in which cohesion between cells and tissues is maintained [START_REF] Falsig | The prion organotypic slice culture assay--POSCA[END_REF]. The selected FDA-approved drugs are marketed to target different biological targets (GCPRs: H1, µ-opioid, 5-HT, D2, M1; Transporters: SERT and NET; Channels: VSCC). Cerebellum slices derived from ovine PrP transgenic mice (tg338 line)

and infected with 127S prions [START_REF] Halliez | White blood cell-based detection of asymptomatic scrapie infection by ex vivo assays[END_REF], were treated for 21 days by the indicated concentrations of the drugs, 7 days post-infection. Tested at doses ranging from 20 to 35 µM, azelastine, duloxetine, ebastine, flunarizine, loperamide, metixene and the reference drug astemizole showed anti-PrP Sc activity in OSC (Fig. 3), whereas triflupromazine and the reference drugs clemastine and thioridazine showed no activity. None of the active drugs except loperamide affected the level of PrP tot . Altogether, these data indicate the potency of azelastine, duloxetine, ebastine, flunarizine, loperamide and metixene to reduce PrP Sc burden and highlight their therapeutic potential.

Flunarizine and its structural analogs are PFAR inhibitors

We next wondered if the known targets of our selected active FDA-approved drugs could be involved in their anti-PrP Sc activity. Despite the fact that some of them are not used for their H1

receptor antagonist property, seven out of the seventeen active drugs identified were able to antagonize histamine H1 receptor, suggesting that histamine H1 receptor could be involved in PrP Sc clearance. We challenged this hypothesis by comparing the binding to H1 (H1 binding IC50 from the literature) with the anti-PrP Sc IC50 of flunarizine and its 30 selected structural analogs.

The three reference drugs astemizole, clemastine and thioridazine were also included. We found no correlation between H1 affinity and anti-PrP Sc activity (Fig. S4). The fact that flunarizine was identified in an assay based on S. cerevisiae yeast cells that lack known homologues of histamine receptors [START_REF] Papamichael | A subset of histamine receptor ligands improve thermotolerance of the yeast Saccharomyces cerevisiae[END_REF], is an additional argument indicating that the H1 receptor is not involved in the antiprion effect of the drug tested. Finally, since 5 out of the 13 inactive compounds tested also bind to H1 receptor, we concluded that H1 receptor is unlikely to be involved in PrP Sc clearance.

Altogether, this set of thirty-three anti-prion compounds binds to sixteen different cellular targets (Fig. S5), suggesting that no common mechanisms can explain their capacity to clear PrP Sc .

As we recently identified PFAR as being involved in [PSI+] prion propagation in yeast [START_REF] Blondel | Protein Folding Activity of the Ribosome is involved in Yeast Prion Propagation[END_REF], we sought to determine whether flunarizine and its structural analogs could also inhibit PFAR. To this aim, we monitored PFAR inhibition using a luciferase refolding assay in yeast, which measures renaturation of a heat-denatured luciferase over time [START_REF] Hasin | Global transcript and phenotypic analysis of yeast cells expressing Ssa1, Ssa2, Ssa3 or Ssa4 as sole source of cytosolic Hsp70-Ssa chaperone activity[END_REF]. We performed this assay in PFARenriched yeast cells that were deleted for the protein chaperon Hsp104, and that have previously been shown to be particularly suitable to monitor PFAR inhibition [START_REF] Blondel | Protein Folding Activity of the Ribosome is involved in Yeast Prion Propagation[END_REF]. The PFAR assay is based on S. cerevisiae cells which are much less permeable to drugs than mammalian cells. However, as yeast cell permeability is different for each drug, the tested concentrations had to be individually adapted to each drug [START_REF] Nguyen | Structure-activity relationship study around guanabenz identifies two derivatives retaining antiprion activity but having lost alpha2-adrenergic receptor agonistic activity[END_REF][START_REF] Oumata | The toll-like receptor agonist imiquimod is active against prions[END_REF][START_REF] Tribouillard-Tanvier | Protein folding activity of ribosomal RNA is a selective target of two unrelated antiprion drugs[END_REF]. Yeast cells expressing luciferase were heat-shocked to unfold luciferase, and cycloheximide was added to inhibit translation and avoid de novo luciferase synthesis, in order to only monitor luciferase refolding by PFAR over time (Fig. S6). For each drug, a range of concentrations was tested. Luciferase activity remained basal in the presence of 45 µM ebastine, 100 µM metixene, 150 µM flunarizine, loperamide, duloxetine and azelastine, as well as 50 µM of the reference compound astemizole, indicating that these molecules are potent PFAR inhibitors (Fig. 4). On the contrary, antazoline and diazepam, which showed no anti-prion activity in cultured MovS6 infected cells (Fig. S3), were unable to inhibit PFAR (Fig. 4).

Altogether, these data indicate that flunarizine and six of its anti-prion structural analogs astemizole, azelastine, duloxetine, ebastine, loperamide and metixene are PFAR inhibitors and share a common mechanism explaining their anti-prion activity.

Flunarizine is active in OPMD cellular and Drosophila in vivo models

We previously showed that anti-PFAR and anti-prion drugs 6AP and GA efficiently decreased muscle degeneration in an OPMD Drosophila model, and had a synergistic effect with partial deletions of the DNA locus encoding rRNA that bear PFAR [START_REF] Barbezier | Antiprion drugs 6-aminophenanthridine and guanabenz reduce PABPN1 toxicity and aggregation in oculopharyngeal muscular dystrophy[END_REF]. In addition, GA was also shown to reduce the size and number of PABPN1 nuclear aggregates in an OPMD Ala17 muscle cell model and improve muscle force in vivo in an OPMD mouse model [START_REF] Malerba | Pharmacological modulation of the ER stress response ameliorates oculopharyngeal muscular dystrophy[END_REF]. Together, these data suggest that PFAR might also play a role in PABPN1 aggregation in OPMD. We therefore tested the efficiency of 10 of the newly identified anti-PFAR drugs in the OPMD Ala17 muscle cell model. When differentiated, this myogenic cellular model expresses PABPN1-Ala17, resulting in the formation of a large number of nuclear aggregates [START_REF] Raz | Modeling oculopharyngeal muscular dystrophy in myotube cultures reveals reduced accumulation of soluble mutant PABPN1 protein[END_REF][START_REF] Klein | Nuclear poly(A)-binding protein aggregates misplace a pre-mRNA outside of SC35 speckle causing its abnormal splicing[END_REF]. Drugs were added to the cells at day 2 of differentiation and maintained in the medium for 48 hours. As 10 µM of GA was previously shown to significantly reduce the number and size of PABPN1-Ala17 aggregates in Ala17 differentiated cells [START_REF] Malerba | Pharmacological modulation of the ER stress response ameliorates oculopharyngeal muscular dystrophy[END_REF], GA was used as a positive control. Similarly, anti-PFAR drugs were tested at 10 µM. In these conditions, treatment with 10 µM of flunarizine, metixene, thioridazine, astemizole, loperamide, duloxetine, azelastine and ebastine led to a significant reduction in the number of PABPN1-Ala17 aggregates (Fig. 5a), without altering cell differentiation. Moreover, flunarizine, metixene, thioridazine, loperamide and ebastine showed a better efficiency than GA (Fig. 5a) [START_REF] Malerba | Pharmacological modulation of the ER stress response ameliorates oculopharyngeal muscular dystrophy[END_REF]. However, clemastine and triflupromazine, which showed no anti-PrP Sc activity in OSC, also showed no significant reduction in the number of PABPN1-Ala17 aggregates, similarly to the negative control diazepam which showed neither anti-PFAR nor anti-prion activity (Fig. 5a).

As flunarizine, metixene and ebastine induced a strong reduction in PABPN1 aggregates in OPMD cells and were more efficient than GA (Fig. 5a), we next tested their effect in the OPMD Drosophila model. This model recapitulates the disease characteristics, namely progressive muscle weakness and degeneration, and formation of PABPN1 nuclear aggregates [START_REF] Chartier | Mitochondrial dysfunction reveals the role of mRNA poly(A) tail regulation in oculopharyngeal muscular dystrophy pathogenesis[END_REF][START_REF] Chartier | A Drosophila model of oculopharyngeal muscular dystrophy reveals intrinsic toxicity of PABPN1[END_REF]. Specifically, PABPN1-17ala expression in muscles leads to abnormal wing posture (Fig. S7a) resulting from affected indirect flight muscle function and muscle degeneration [START_REF] Barbezier | Antiprion drugs 6-aminophenanthridine and guanabenz reduce PABPN1 toxicity and aggregation in oculopharyngeal muscular dystrophy[END_REF][START_REF] Chartier | Mitochondrial dysfunction reveals the role of mRNA poly(A) tail regulation in oculopharyngeal muscular dystrophy pathogenesis[END_REF]. The relevance of the Drosophila model has been validated by the fact that the molecular mechanisms identified as participating in OPMD in Drosophila have been confirmed in OPMD patient biopsies [START_REF] Chartier | Mitochondrial dysfunction reveals the role of mRNA poly(A) tail regulation in oculopharyngeal muscular dystrophy pathogenesis[END_REF][START_REF] Anvar | Deregulation of the ubiquitin-proteasome system is the predominant molecular pathology in OPMD animal models and patients[END_REF]. As 2 mM GA was previously shown to significantly reduce the wing position defects of OPMD Drosophila when provided in the food from day 2 of adulthood [START_REF] Barbezier | Antiprion drugs 6-aminophenanthridine and guanabenz reduce PABPN1 toxicity and aggregation in oculopharyngeal muscular dystrophy[END_REF], anti-PFAR drugs were also tested at 2 mM. However, at this concentration, flunarizine and ebastine showed toxicity. These two drugs were thus further used at 1.5 mM, concentration at which both drugs shown no toxicity (Fig. S7b). Drugs were administered to adult flies orally in the food at the indicated concentrations from day 2 to day 5, and fresh food with drug was provided every day. Wing posture defects were recorded from day 3 to day 6 of adulthood. The effect of drugs was quantified by recording the number of PABPN1-17ala-expressing flies with abnormal wing posture. Flunarizine, metixene and ebastine showed a beneficial effect with a significant decrease in the number of flies with abnormal wing posture compared to DMSO alone (Fig. 5b), without affecting Drosophila survival (Fig. S7b). Altogether, these data demonstrate that these new anti-PFAR drugs are active in alleviating defects in OPMD cell and Drosophila models. This indicates that these drugs might have a therapeutic potential for OPMD patients.

Discussion

We started this study by identifying flunarizine as an anti-prion drug in yeast and mammalian cells.

We next established a proof of principle that flunarizine exerts an anti-prion activity ex vivo and in vivo. The mice survival was significantly improved, despite the use of a very stringent transgenic mouse model, which overexpresses 8-fold ovine PrP and was inoculated by the intracerebral route with a relatively high-dose of prion. Furthermore, the treatment was initiated at the late stage of the disease and was administered by the intraperitoneal route.

Based on this promising result, we decided to look for other FDA-approved drugs and used a strategy mixing structural similarities with flunarizine (ROCS ranking) and diversity in the addressed biological targets, leading to the selection of thirty drugs. The anti-prion activity of the selected molecules was mainly correlated with their ROCS ranking, as 76% of the selected drugs showing an anti-prion activity have a rank ≤ 200 (Fig. S8). These observations indicate that our implication of a chaperone such as PFAR in propagating protein prion conformation is intuitive, since amyloid proteins exist in several conformations and their replication corresponds to the propagation of these particular conformations. This is illustrated by the role of Hsp104, a protozoan-specific protein chaperone that, together with Hsp70 and Hsp40, is the only cellular factor known to date to be essential for prion propagation [PSI+] in yeast [START_REF] Winkler | Chaperone networks in protein disaggregation and prion propagation[END_REF]. Using [PSI+] as a prion model, we showed that, similarly to Hsp104, PFAR is involved in prion propagation [START_REF] Blondel | Protein Folding Activity of the Ribosome is involved in Yeast Prion Propagation[END_REF].

Therefore, since PFAR fosters the propagation of pathologic protein conformation, inhibiting PFAR will allow ones to reduce the burden of aggregation of these pathologic proteins. Our present data confirm the therapeutic potential of anti-PFAR drugs and highlight this second enzymatic activity of the ribosome as an innovative therapeutic target for prion diseases.

OPMD is a dominant hereditary neuromuscular disease caused by the extension of a polyalanine repeat in the PABPN1 protein. Wild-type PABPN1 is localized in nuclear speckles, whereas polyalanine expanded PABPN1 forms nuclear aggregates in skeletal muscle fibers, which is the pathological hallmark of OPMD. We thus evaluated the potential of anti-prion drugs on OPMD cellular and animal models. We previously showed that oral treatment with the anti-PFAR drugs 6AP and GA reduces OPMD-associated phenotypes, including defective wing posture due to thoracic muscle defects and the size of PABPN1 nuclear aggregates [START_REF] Barbezier | Antiprion drugs 6-aminophenanthridine and guanabenz reduce PABPN1 toxicity and aggregation in oculopharyngeal muscular dystrophy[END_REF]. It should be noted that the OPMD Drosophila model previously allowed us to demonstrate a direct link between PFAR and the aggregation of mutated PABPN1 protein responsible for OPMD phenotypes [START_REF] Barbezier | Antiprion drugs 6-aminophenanthridine and guanabenz reduce PABPN1 toxicity and aggregation in oculopharyngeal muscular dystrophy[END_REF]. Eight out of the ten new anti-PFAR drugs tested were also able to reduce the number of PABPN1

aggregates in a cell model of OPMD. Moreover, flunarizine, metixene and ebastine, which were more active than GA, were also able to alleviate muscle degeneration in the Drosophila OPMD model. Together, these data strengthen the role of PFAR in PABPN1 aggregation and highlight PFAR as a therapeutic target to treat OPMD. It is interesting to note that the activity spectrum of the drugs is remarkably similar for both diseases. Loperamide, which poorly crosses the blood brain barrier, is a better candidate for OPMD than for prion diseases, but might be considered in prion diseases, to limit the peripheral propagation of PrP Sc in acquired forms of the diseases.

Flunarizine and some of its anti-prion structural analogs identified in this study have been previously assessed in other neurodegenerative disorders, some of which were also proteinopathies. Furthermore, flunarizine has recently been shown to increase impedance in learning and memory in a murine pharmacological model of Alzheimer's disease [START_REF] Kamili | Evaluation of memory-enhancing effect of flunarizine on active avoidance in experimental model of Alzheimer's disease through calcium homeostasis[END_REF]. Flunarizine synthase is known to abnormally accumulate and which is characterized by progressive vision loss, seizures, loss of cognitive and motor function, and early death [START_REF] Kwon | Flunarizine rescues reduced lifespan in CLN3 triple knock-out Caenorhabditis elegans model of batten disease[END_REF]. Loperamide has been shown to be able to clear A30P α-synuclein aggregates in a Parkinson's disease cellular model [START_REF] Williams | Novel targets for Huntington's disease in an mTORindependent autophagy pathway[END_REF], and reduce the accumulation of polyQ misfolded proteins in a Huntington's disease cellular model [START_REF] Zhang | Small molecule regulators of autophagy identified by an image-based high-throughput screen[END_REF] by promoting autophagic degradation. We cannot exclude that the anti-prion activity of some drugs VSCC inhibitors [START_REF] Tome | Nuclear inclusions in oculopharyngeal dystrophy[END_REF][START_REF] Brais | Short GCG expansions in the PABP2 gene cause oculopharyngeal muscular dystrophy[END_REF][START_REF] Brais | Short GCG expansions in the PABP2 gene cause oculopharyngeal muscular dystrophy[END_REF][START_REF] Kavitha | Flunarizinium isonicotinate[END_REF][START_REF] Dur | A newly identified type of scrapie agent can naturally infect sheep with resistant PrP genotypes[END_REF]) Opioid receptors [START_REF] Richard | PABPN1 (GCN)11 as a Dominant Allele in Oculopharyngeal Muscular Dystrophy -Consequences in Clinical Diagnosis and Genetic Counselling[END_REF] H1 receptor antagonists [START_REF] Prusiner | Novel proteinaceous infectious particles cause scrapie[END_REF][START_REF] Bach | Isolation of drugs active against mammalian prions using a yeast-based screening assay[END_REF][START_REF] Nguyen | Structure-activity relationship study around guanabenz identifies two derivatives retaining antiprion activity but having lost alpha2-adrenergic receptor agonistic activity[END_REF][START_REF] Voisset | Procedure for identification and characterization of drugs efficient against mammalian prion: from a yeast-based antiprion drug screening assay to in vivo mouse models[END_REF][START_REF] Voisset | A yeast-based assay identifies drugs that interfere with immune evasion of the Epstein-Barr virus[END_REF][START_REF] Voisset | Tools for the study of ribosome-borne protein folding activity[END_REF][START_REF] Ben-Shem | The structure of the eukaryotic ribosome at 3.0 A resolution[END_REF][START_REF] Scheckel | Prions, prionoids and protein misfolding disorders[END_REF][START_REF] Gidaro | Atrophy, fibrosis, and increased PAX7-positive cells in pharyngeal muscles of oculopharyngeal muscular dystrophy patients[END_REF][START_REF] Brais | Short GCG expansions in the PABP2 gene cause oculopharyngeal muscular dystrophy[END_REF][START_REF] Jouan | An 18 alanine repeat in a severe form of oculopharyngeal muscular dystrophy[END_REF][START_REF] Wermuth | Multitargeted drugs: the end of the "one-target-one-disease" philosophy?[END_REF][START_REF] Langevin | Marked influence of the route of infection on prion strain apparent phenotype in a scrapie transgenic mouse model[END_REF][START_REF] Hasin | Global transcript and phenotypic analysis of yeast cells expressing Ssa1, Ssa2, Ssa3 or Ssa4 as sole source of cytosolic Hsp70-Ssa chaperone activity[END_REF]) α2 adrenergic receptor antagonist [START_REF] Malerba | Pharmacological modulation of the ER stress response ameliorates oculopharyngeal muscular dystrophy[END_REF] SERT inhibitors [START_REF] Voisset | The double life of the ribosome: When its protein folding activity supports prion propagation[END_REF][START_REF] Blondel | Protein Folding Activity of the Ribosome is involved in Yeast Prion Propagation[END_REF] NET inhibitors (3, 15, 26) Voltage-gated L-type calcium channel [START_REF] Voisset | Tools for the study of ribosome-borne protein folding activity[END_REF] Muscarinic acetylcholine receptor [START_REF] Tribouillard-Tanvier | Antihypertensive drug guanabenz is active in vivo against both yeast and mammalian prions[END_REF][START_REF] Das | Role of the ribosome in protein folding[END_REF][START_REF] Tribouillard-Tanvier | Protein folding activity of ribosomal RNA is a selective target of two unrelated antiprion drugs[END_REF][START_REF] Barbezier | Antiprion drugs 6-aminophenanthridine and guanabenz reduce PABPN1 toxicity and aggregation in oculopharyngeal muscular dystrophy[END_REF] D2 dopamine receptors antagonists (29, 34) GABA-A [START_REF] Chattopadhyay | Protein folding in Escherichia coli: role of 23S ribosomal RNA[END_REF] 5-HT receptor antagonists (23, 24, 34) NSAID ( 5) Calmodulin [START_REF] Archer | Cultured peripheral neuroglial cells are highly permissive to sheep prion infection[END_REF] Serotonin reuptake [START_REF] Wermuth | Selective optimization of side activities: another way for drug discovery[END_REF][START_REF] Vilotte | Markedly increased susceptibility to natural sheep scrapie of transgenic mice expressing ovine prp[END_REF] Sodium channels (26, 28) NMDA receptor antagonist (26)

  monitored the survival of tg338 mice intracerebrally infected with a relatively high dose of 127S prion at day 0, and treated by 20 mg/kg flunarizine three times per week from 40 days postinfection onward. Mice treated by flunarizine survived significantly longer than DMSO-treated mice (p<0.0146, Kruskal-Wallis test) with a mean survival time of the flunarizine group mice

  has also been shown to have a beneficial effect in spinal muscular atrophy (SMA) models[START_REF] Sapaly | Small-molecule flunarizine increases SMN protein in nuclear Cajal bodies and motor function in a mouse model of spinal muscular atrophy[END_REF], a hereditary neurodegenerative disease characterized by the death of spinal cord motor neurons and skeletal muscle atrophy. Survival motor neuron (SMN) protein forms nuclear aggregates associated to Cajal bodies. Altered Cajal body localization of mutant SMN protein is a hallmark of SMA. Interestingly, flunarizine restores SMN localisation into the Cajal body of SMA-derived fibroblasts and spinal cord motor neurons in SMA mice, reduces the synaptic alterations of spinal cord motor neurons, improves motor function, and expands survival of SMA mice[START_REF] Sapaly | Small-molecule flunarizine increases SMN protein in nuclear Cajal bodies and motor function in a mouse model of spinal muscular atrophy[END_REF]. Finally, flunarizine has also been shown to rescue the short lifespan of a C. elegans model of CLN3 disease (Spielmeyer-Vogt-Sjogren-Batten disease), a pediatric-onset progressive neurodegenerative disease caused by recessive mutations in CLN3, in which the C subunit of mitochondrial ATP

6 7 8 Fig. 1 Fig. 2 Fig. 1 .Fig. 3 Fig. 4 PFARFig. 5

 68121345 Fig. 1 Flunarizine is a potent anti-prion drug. Flunarizine was identified through a yeast-based

Fig. 1 Fig

 1 Fig. 1

  Fig. S1

  Fig. S5

  Fig. S6

Table 1 . Anti-PrP Sc IC50 and ROCS ranks of flunarizine, the 30 selected flunarizine 1 structural analogs, and the three anti-prion references.

 1 For each drug, IC50s against PrP Sc in 2 cell culture are indicated, together with ROCS ranks. # indicates drugs previously described as

	3
	4	anti-PrP Sc , * indicates withdrawn drugs.
	5
		Drugs	IC50	ROCS ranks	Drugs	IC50	ROCS ranks
		1 Alimemazine	11.4	197	18 Ethosuximide	neg	1471
		2 Antazoline	neg	129	19 Flunarizine	3.9	1
		3 Atomoxetine	3.9	41	20 Isradipine	neg	327
		4 Azelastine	3.1	140	21 Ketotifen	neg	78
		5 Benzydamine	2.7	368	22 Loperamide	2.3	25
		6 Biperiden	9.2	33	23 Metixene	1.3	101
		7 Cetirizine	neg	10	24 Mirtazapine	neg	225
		8 Chloropyramine	18.5	133	25 Nefopam	neg	471
		9 Cinnarizine	neg	2	26 Orphenadrine	9.1	16
		10 Citalopram	10.4	23	27 Paramethadione	neg	1475
		11 Diazepam	neg	595	28 Prenylamine *	3.8	6
		12 Dicyclomine	3.6	112	29 Triflupromazine	2	256
		13 Diphenhydramine	14.8	21	30 Zimelidine *	14.3	149
		14 Diphenidol	neg	24	31 Zonisamide	neg	1216
		15 Duloxetine	1.8	69	32 Astemizole * #	0,5	36
		16 Ebastine	2.1	14	33 Clemastine #	2	27
		17 Epinastine	neg	683	34 Thioridazine #	1.7	159

Acknowledgments

We thank G. Butler-Browne for careful review of the manuscript, A. Nasir for critical reading of the manuscript, Séverine Loisel, Marie-Françoise Scoazec and Manuel Feillant for expert animal care, Hélène Simon and Johanna Mazé for their excellent technical assistance, Laetitia Guedeville, Laetitia Herzog, Fabienne Reine, Emilie Jaumain for expert animal care and excellent technical assistance, and Fanny Roth for technical assistance. This work was supported by Institut de France -Fondation NRJ (CV), MESR (AB, PHN), Inserm (CV and CT), UBO (CV), CNRS and University of Montpellier (MSg), AFM-Téléthon eOPMD project 17110 (CT and MSg), Sorbonne Université and Association Institut de Myologie (CT), DIM MALINF, INRAE and Fondation pour la Recherche Médicale (Equipe FRM DEQ20150331689) (VB and SH), Association Défi Organisation (CV). RNS held a PhD grant from AFM-Téléthon. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

screening strategy was relevant to select molecules active against PrP Sc and it is possible that there are still some undescribed anti-prion drugs present in the top two-hundred drugs shown in Table S1.

Thanks to this approach, we report here the identification of seventeen new anti-prion drugs that are FDA-approved for other pathologies and that could be readily usable to treat patients with prion diseases for whom no treatment is currently available. Notably, flunarizine and ten of these newly identified anti-prion drugs, namely atomoxetine, azelastine, benzydamine, dicyclomine, duloxetine, ebastine, loperamide, metixene, prenylamine and triflupromazine, have an IC50 ≤ 4 µM in mammalian cell culture. We further showed that azelastine, duloxetine, ebastine, flunarizine, loperamide and metixene are active against PrP Sc in organotypic mouse cerebellum slice culture, thus emphasizing the therapeutic potential of these drugs for the treatment of prion diseases.

While we have identified anti-prion drugs exhibiting structural similarities, we may also have found a common mechanism explaining their anti-prion activity. Indeed, flunarizine and six of its most potent anti-prion structural analogs (astemizole, azelastine, duloxetine, ebastine, loperamide and metixene) are potent inhibitors of PFAR, a protein folding activity born by rRNA which has previously been shown to be involved in [PSI+] prion propagation [START_REF] Blondel | Protein Folding Activity of the Ribosome is involved in Yeast Prion Propagation[END_REF]. PFAR has been described for the first time in vitro by the group of C. Das Gupta who showed that any denatured protein can recover its functional conformation thanks to domain V of 23S, 25S or 28S rRNA [START_REF] Das | Role of the ribosome in protein folding[END_REF]. Ribosomeassisted folding was then described for ribosomes from all living kingdoms and for all classes and protein sources tested [START_REF] Das | Role of the ribosome in protein folding[END_REF][START_REF] Barbezier | Antiprion drugs 6-aminophenanthridine and guanabenz reduce PABPN1 toxicity and aggregation in oculopharyngeal muscular dystrophy[END_REF], which is consistent with the high conservation of the sequence and the secondary structure of rRNA domain V [START_REF] Ben-Shem | The structure of the eukaryotic ribosome at 3.0 A resolution[END_REF]. We previously identified anti-prion drugs 6AP and GA which also target PFAR in vitro [START_REF] Voisset | Tools for the study of ribosome-borne protein folding activity[END_REF] as well as in living bacteria [START_REF] Voisset | Tools for the study of ribosome-borne protein folding activity[END_REF] and yeast [START_REF] Blondel | Protein Folding Activity of the Ribosome is involved in Yeast Prion Propagation[END_REF].

Although the main role of protein chaperones is to prevent protein misfolding and aggregation, the like loperamide, which is known to induce cell autophagy, could also be due to their capacity to activate autophagy. Altogether these data reinforce the concept that prion diseases share common mechanisms of pathogenic protein conformation propagation with other proteinopathies. They also suggest that PFAR may be a pertinent therapeutic target common to several of these diseases. It seems increasingly obvious that the success of the fight against these pathologies will come from complementary and synergistic therapies. Together, these data show that, despite being fundamentally different diseases, the common protein aggregation hallmark of protein misfolding diseases is a potent therapeutic target that could be used in combination with drugs targeting specific hallmarks of each disease. In this respect, anti-PFAR drugs like astemizole, azelastine, duloxetine, ebastine, flunarizine, loperamide and metixene appear to be of particular interest.

Competing interests

The authors declare that they have no competing interests.

Zonisamide