domain: info shs

text:
Anaïs Koptient
email: anais.koptient.etu@univ-lille.fr

Natalia Grabar
email: natalia.grabar@univ-lille.fr

Rated Lexicon for the Simplification of Medical Texts

Keywords: Medical and Health Text, Simplification, Lexicon, Paraphrases, Readability, Readability scores, Patients

The purpose of our work is to create a rated lexicon in French useful for automatic text simplification of medical texts. Currently, the lexicon contains 11,272 pairs {technical term; paraphrase} for 6,937 different terms. This lexicon is built automatically using different methods. It is validated manually. Then, the lexicon is rated with several readability formulas and models in order to appraise the readability of terms and paraphrases. The lexicon will be exploited and tested within automatic simplification systems, and will be made available for the research community.

I. INTRODUCTION

The purpose of Automatic Text Simplification (ATS) is to make a given text more understandable for a group of persons, like children, people with pathologies, foreigners, people with no training in a specialized domain, etc. The last few years have seen a growing interest for the ATS, with the main bulk of work done in English and on general-language texts. Very little work exists on simplification of specialized texts, like medical texts, and on languages other than English. Nevertheless, these recent works helped the field to gain in maturity.

Several levels of simplification are distinguished:

• Lexical simplification, in which difficult words are replaced by the corresponding easier words. This kind of simplification is performed on the basis of lexical knowledge, such as synonyms, hyperonyms, definitions, etc. An example from the SemEval 2012 challenge on English Lexical Simplification [START_REF] Specia | Semeval-2012 task 1: English lexical simplification[END_REF] is given in [START_REF] Specia | Semeval-2012 task 1: English lexical simplification[END_REF], in which the word atrocities is considered to be difficult to understand and is replaced by cruelties;

(1) Hitler committed terrible atrocities during the second World War. Hitler committed terrible cruelties during the second World War.

• Syntactic simplification is done at the level of syntactic trees and has the purpose to reduce the syntactic complexity of sentences. Syntactically complex sentences can be transformed into simpler syntactic structures by using to deletion, insertion, separation, merging, and reordering. In example [START_REF] Siddharthan | Syntactic simplification and text cohesion[END_REF], borrowed from [START_REF] Siddharthan | Syntactic simplification and text cohesion[END_REF], the subordinate clause is separated from the main clause;

(2) While the law generally supports clampers operating on private land, Mr Agar claims CCSs sign was not prominent enough to be a proper warning.

The law generally supports clampers operating on private land. But Mr Agar claims CCSs sign was not prominent enough to be a proper warning.

• Semantic simplification implies that information can be reorganized or added to make the understanding easier thanks to the context [START_REF] Brouwers | Syntactic sentence simplification for French[END_REF]. Hence, the word gabapentine becomes easier to understand in (3);

(3) Gabapentine should be prescribed with caution to pregnant women. Gabapentine medication should be prescribed with caution to pregnant women.

• Pragmatic simplification may imply that the structure of the text is modified [START_REF] Vettori | Supporting deaf children's reading skills: the many challenges of text simplification[END_REF], and its semantic cohesion becomes more global [START_REF] Caseli | Building a Brazilian Portuguese parallel corpus of original and simplified texts[END_REF] [START_REF] Brunato | Defining an annotation scheme with a view to automatic text simplification[END_REF].

Currently, the researchers have identified different ways to simplify texts automatically: [START_REF] Specia | Semeval-2012 task 1: English lexical simplification[END_REF] approaches based on distributional probabilities, such as word embeddings [START_REF] Glavas | Simplifying lexical simplification: Do we need simplified corpora?[END_REF] [START_REF] Kim | SimpleScience: Lexical simplification of scientific terminology[END_REF], which permit to propose simpler candidates for a given word considered as difficult to understand; (2) approaches based on automatic translation systems [START_REF] Zhao | Leveraging multiple MT engines for paraphrase generation[END_REF] [START_REF] Nisioi | Exploring neural text simplification models[END_REF], which consider the simplification as a monolingual translation task; (3) rule-based approaches [START_REF] Carroll | Simplifying text for language-impaired readers[END_REF] [START_REF] Belder | Lexical simplification[END_REF], which design and exploit specifically defined simplification rules. Whatever the approach, the common point is the need for resources, such as dedicated simplification corpora, syntactic transformation rules, and lexical resources in which difficult words are associated with simpler synonyms, like {atrocity; cruelty}. Yet, synonyms are not the only lexical information necessary for the simplification. Hence, the few existing works on the typology of simplification [START_REF] Brunato | Defining an annotation scheme with a view to automatic text simplification[END_REF] [START_REF] Koptient | Simplification-induced transformations: typology and some characteristics[END_REF] show that lexical substitution can also be performed with extended forms of abbreviations, hyperonyms, hyponyms, paraphrases and definitions. Such lexical resources must be reliable and propose simpler equivalents for technical terms.

The purpose of our work is to build such reliable lexical resource for French, with the focus on medical language and terminology. Therefore, we need (1) to identify lexical equivalents (synonyms, hyperonyms, definitions, etc.) for technical medical terms, and (2) to assign readability scores to technical terms and to their equivalents.

In our work, term or technical term correspond to terms that need to be simplified during the simplification process. They can correspond to syntactically simple (one word, like comedo or hematuria) or complex (more than one word, like systemic lupus erythematosus) sequences. Paraphrases or equivalences are the simplified layman versions with the same, or very close, meaning. Both elements are associated within the same pair {technical term; paraphrase}.

In what follows, we first present the methods designed for the identification of lexical equivalents for technical terms (Section II). We then describe the approaches for rating the lexicon (technical terms and their equivalents) according to the readability and evaluate the results (Section III). Finally, we conclude with some perspectives for future work (Section IV).

II. IDENTIFICATION OF LEXICAL EQUIVALENTS FOR

TECHNICAL MEDICAL TERMS In this section, we introduce the corpora used and explain the methods proposed for the identification of lexical equivalents (synonyms, hyperonyms, definitions, etc.) for technical terms.

A. Corpora

We use the CLEAR corpus [START_REF] Grabar | Clear -simple corpus for medical French[END_REF], which contains comparable documents differenciated by their technicality and difficulty. In this corpus, technical documents are associated with their simpler or simplified versions. The corpus contains 16,313 pairs of texts (over 57M word occurrences in technical texts and over 35M word occurrences in simplified texts), which are provided from three sources:

• Drug leaflets from the French ministry of health [START_REF]Base de données publique des médicaments (Public Database of Drugs)[END_REF].

The technical part contains drug leaflets created for medical doctors, while the simple part contains patient package inserts that can be found in drug boxes. These two kinds of documents are created by pharmaceutical companies almost independently from one another; • Abstracts of systematic reviews from the Cochrane collaboration [START_REF] Cochrane Reviews | [END_REF]. The technical part contains technical abstracts, while the simple part contains the manually simplified versions of these technical abstracts; • Encyclopedia articles from collaborative online encyclopedias. The technical part contains medicinerelated articles from French Wikipedia [START_REF]Wikipédia l'encyclopédie libre (Wikipedia the free encyclopedia[END_REF], while the simple part contains the corresponding articles from the French children encyclopedia called Vikidia [START_REF] Vikidia | [END_REF].

We also use a forum corpus collected from masante.net. This forum provides the possibility for users to ask healthrelated questions, which are answered by medical doctors. We exploit 6,139 answers available totaling 315,362 word occurrences.

B. Methods for Identification of Lexical Equivalents

We propose several methods for the identification of lexical equivalents (synonyms, hyperonyms, definitions, etc.) for technical terms. We also evaluate the extracted equivalents with the precision measure (percentage of correct equivalents among the extractions proposed by a given method). Each pair {technical terms; paraphrase} was validated manually by one person with training in NLP (Natural Language Processing) but no training in medicine. Table I summarises the extraction results provided by each method and their precision. In the [START_REF] Zeng | A text corpora-based estimation of the familiarity of health terminology[END_REF] 11,641 -English medical abbreviations [START_REF] Schwartz | A simple algorithm for identifying abbreviation definitions in biomedical text[END_REF] 785 95 French medical terms [START_REF] Deléger | Paraphrase acquisition from comparable medical corpora of specialized and lay texts[END_REF] 147 67 French medical terms [START_REF] Cartoni | Découverte de patrons paraphrastiques en corpus comparable: une approche basée sur les n-grammes (Discovery of paraphrastic patterns in comparable corpora: an n-gram based approach)[END_REF] 109 66

second part of 1) Extraction of Equivalents from Parallel Aligned Sentences: Manually aligned parallel sentences from the CLEAR corpus are first manually annotated for transformations observed during the simplification of technical sentences. The annotation is done within the YAWAT annotator [START_REF] Germann | Yawat: Yet another word alignment tool[END_REF]. The annotations focus on several types of transformations, among which the most frequent are: (1) synonymy ({excipients; composants} ({excipients; components}), {céphalées; maux de têtes} ({cephalalgia; headaches})), [START_REF] Siddharthan | Syntactic simplification and text cohesion[END_REF] hyperonymy ({clyndamicine; ce médicament} ({clyndamicin; this drug})), (3) hyponymy ({benzodiazépines; bromazépam} ({benzodiazepines; bromazépam})), ( 4) part-of-speech shift ({peuvent se manifester; apparition} ({can appear; occur-rence})), [START_REF] Caseli | Building a Brazilian Portuguese parallel corpus of original and simplified texts[END_REF] formal shift ({des médicaments; un médicament} ({drugs; drug})). Once the transformations are annotated, we extract the equivalents which correspond to synonyms and hyperonyms, and which are the easiest to exploit during the simplification. This resource includes 626 technical terms with their equivalents. Due to the method, fully relying on manual annotation, this set of equivalents shows 100% precision.

2) Definitions of Technical Terms: Definition context of terms, like est un (is a) or défini comme (defined as) are exploited to extract definitions of medical terms. An example is given in [START_REF] Vettori | Supporting deaf children's reading skills: the many challenges of text simplification[END_REF]. Technical terms are first detected and, if they occur within definition contexts, the entire sentence is extracted. 2,037 candidate definitions are extracted. After the validation, we keep 1,028 definitions (68% precision). 3) Reformulations of Technical Terms: Reformulations usually indicate that there are technical terms and that they are explained by the speaker [START_REF] Antoine | Exploitation de reformulations pour l'acquisition d'un vocabulaire expert/non expert (Exploitation of reformulations for the acquisition of expert/non-expert vocabulary)[END_REF]. We exploit several linguistic markers: (1) brackets like in [START_REF] Caseli | Building a Brazilian Portuguese parallel corpus of original and simplified texts[END_REF], in which the technical word hématurie (hematuria) is reformulated in trop de globules rouges dans vos urines (too much of red blood cells in urine); [START_REF] Siddharthan | Syntactic simplification and text cohesion[END_REF] explicit reformulation markers like c'est-à-dire (that is (to say)), autrement dit (in other words), l'équivalent (the equivalent) or encore appelé (also called). In example [START_REF] Brunato | Defining an annotation scheme with a view to automatic text simplification[END_REF], the technical term périménopause (perimenopause) is reformulated in période qui entoure la ménopause (period which surrounds the menopause).

(5)

Vous avez effectivement une hématurie (trop de globules rouges dans vos urines).

(Indeed, you have hematuria (too many red blood cells in urine).) [START_REF] Brunato | Defining an annotation scheme with a view to automatic text simplification[END_REF] La prise de poids est normale dans la périménopause, c'est à dire la période qui entoure la ménopause.

(Weight gain is expected during perimenopause, that is the period which surrounds the menopause.)

This method provides 7,959 correct pairs {technical term; paraphrase} which overall precision is 60%. With this kind of method, it is also necessary to verify the direction of the relation: where is the technical and where is its layman paraphrase. During the extraction, we consider that the longer sequence is the paraphrase, contrary to the technical term, which is usually a single-word expression or a noun phrase. This feature is also checked in Section III.

4) Word Morphology:

In the biomedical language, word morphology may be indicative of technical terms and of their possible paraphrases, like in myalgia, composed of myo-(muscle) and algia (pain), and meaning muscle ache. We exploit information on word morphology in two ways:

• The terms are first analyzed morphologically with Dérif [START_REF] Namer | Morphologie, Lexique et TAL : l'analyseur DériF (Dérif Analyzer)[END_REF] in order to transform them into morphological bases and affixes: myocardique (myocardial) is analyzed into myo (muscle) and carde (heart). Then, we look into the corpus and search for syntactic groups that contain these words (muscle and heart in this example). In this way, we can find the sequence heart muscle meaning muscle du coeur in French. This method provides 1,128 paraphrases for technical terms with 86% precision;

• We start with a set of Latin and Greek affixes (430 prefixes and 103 suffixes) and their semantics, like dipsy meaning thirst, a meaning absence/without, logy meaning study of, or angio meaning blood vessel. We then combine every prefix with every suffix [START_REF] Kloehn | Improving consumer understanding of medical text: Development and validation of a new subsimplify algorithm to automatically generate term explanations in english and spanish[END_REF] to coin possible medical terms. In this way, we obtain 15,405 possible medical terms, which are then validated manually: this results in 1,939 terms (13% precision). Supposing that medical terms are compositional, we also combine the meaning of their morphological components for the creation of paraphrases: angiologie (angiology) is paraphrased in étude des vaisseaux sanguins (study of blood vessels), while adipsie (adipsy) is paraphrased in absence de soif (absence of thirst).

5) Expansion of Abbreviations: Abbreviations are commonly used in the medical language, like LCR (CSF) meaning liquide cérébro-spinal (cerebrospinal fluid) in example [START_REF] Glavas | Simplifying lexical simplification: Do we need simplified corpora?[END_REF]. Unless already known, abbreviations are difficult to understand by patients: it is then necessary to provide expanded forms of abbreviations. We extract the expanded forms of abbreviations with an adapted version of published algorithm [START_REF] Schwartz | A simple algorithm for identifying abbreviation definitions in biomedical text[END_REF], which processes two kinds of structures: expanded form (abbreviation) like in [START_REF] Glavas | Simplifying lexical simplification: Do we need simplified corpora?[END_REF], and abbreviation (expanded form). We extract 8,148 abbreviations with precision 94%. [START_REF] Glavas | Simplifying lexical simplification: Do we need simplified corpora?[END_REF] On l'appelle aussi liquide cérébro-spinal (LCR). (It is also called cerebrospinal fluid (CSF).)

6) Exploitation of an Online Medical Dictionary: We also exploit already available lexicons from online sources [27]. For each medical term, we keep the first sentence of the definition, which is expected to describe precisely the term. We obtain 1,165 additional medical terms and their paraphrases.

III. COMPUTING THE READABILITY OF TECHNICAL

TERMS AND OF THEIR EQUIVALENTS In this section, we compute the readability scores for technical terms and their layman equivalences. The purpose is (1) to assign the readability scores to each term and paraphrase, (2) to verify if paraphrases are indeed easier than technical terms, (3) if necessary, to switch the place of terms with their equivalents, which can be relevant with some automatic methods like reformulation extraction, and more specifically (4) to provide indication on simplicity of terms and their equivalents, which can be later used by simplification systems. For instance, some technical terms have more than one equivalent, which differ by their readability. In this situation, it is necessary to choose the equivalent which suits the best the simplification task.

Over one hundred readability formulas have been proposed by researchers [START_REF] Franc | Les apports du traitement automatique du langage à la lisibilité du franc ¸ais langue étrangère (Contributions of Natural Language Processing to the readability of French as a foreign language)[END_REF], from which we choose just few for our work. These are linear regression formulas. They are mainly dedicated for rating the readability at the level of texts. These readability indexes are not considered to be very reliable and are often criticized [START_REF] Zeng | A text corpora-based estimation of the familiarity of health terminology[END_REF]. Nevertheless, we consider that they can provide useful information on readability of terms and paraphrases. In the rest of this section, we first present the selected readability formulas and how we adapt them for the processing of terms and paraphrases (Section III-A). We then propose our computational readability models adapted to paraphrases, and based on a set of features and machine learning algorithms (Section III-B). The models are evaluated with Precision (correctness), Recall (exhaustiveness) and Fmeasure (harmonic mean of Precision and Recall). Finally, we present the results obtained with the readability models and indexes (Section III-C).

A. Linear Regression Readability Formulas

Dale index [START_REF] Dale | A formula for predicting readability[END_REF] is one of the first readability formulas proposed: Dale = 0.15x1 + 0.04x2, where x1 represents the percentage of words missing from the basic vocabulary, and x2 represents the average number of words per sentence. The higher Dale index, the less the text is readable. We adapt this formula to terms in French as follows: x1 is the percentage of words missing form the Catach list [START_REF] Catach | Les listes orthographiques de base du franais (LOB) : les mots les plus frquents et leurs formes flchies les plus frquentes (Basic lists of spelling in French: the more frequent words and their more frequent inflected forms)[END_REF], which is the French set with 400 basic words; and x2 is the number of words in a given paraphrase or term. When applied to paraphrases, this formula provides readability scores between 0.08 (être malade(being sick)) and 15.4 (éruption faciale, douleur articulaire, anomalies musculaires, fièvre (facial rash, articular pain, muscle abnormality, fever)). The scores of terms are lower.

Kandel index [START_REF] Kandel | Application de l'indice de flesch la langue francaise [applying flesch index to french language[END_REF] is the French adaptation of the very popular Flesch formula [START_REF] Flesch | A new readability yardstick[END_REF]: Kandel = 207-(1.015 * ASL)-(73.6 * ASW ), where ASL is the average number of words in each sentence, and ASW the average number of syllables. The index values are expected to fall between 0 and 100: 0 to 30 for texts difficult to understand, and starting from 70 for texts easily understandable by adults. In our experiments, we consider that ASL is the number of words in the paraphrase, and ASW the average number of syllables per word. When applied to paraphrases, the index scores are uneven and fall outside the expected scale, going from -188.58 (hypertension intracrânienne bénigne (benign intracranial hypertension)) up to 204.96 (condylomes acuminés (acuminated condyloma)).

Mesnager index [START_REF] Mesnager | Lisibilité des textes pour enfants : un nouvel outil ? (Readability of texts for children: a new tool?[END_REF] is a variant of the Dale index: M esnager = (1/2 * AC) + (1/3 * P ), where AC is the percentage of words missing from the basic vocabulary [START_REF] Catach | Les listes orthographiques de base du franais (LOB) : les mots les plus frquents et leurs formes flchies les plus frquentes (Basic lists of spelling in French: the more frequent words and their more frequent inflected forms)[END_REF], and P the average number of words in sentences. The index values are supposed to be between 6 (easy text) and 25 (difficult text). In our case, we consider that P is the number of words in paraphrases and terms. When applied to our data, the formula provides scores between 0.66 (être malade (being sick) or point noir (blackhead)) and 69.3 (éruption faciale (facial rash), douleur articulaire (articular pain), anomalies musculaires (muscle abnormality), and fièvre (fever)).

Sitbon index [START_REF] Sitbon | Lisibilité et recherche d'information : vers une meilleure accessibilité (Readability and Information Seeking: towards a better accessibility[END_REF] is one of the rare formulas designed for sentences (and not for texts): Sitbon = 1.12 * ADV -0.69 * CON + 6.48 * cohesion + 15.58, where ADV and CON are, respectively, the number of adverbs and conjuctions, and cohesion is the number of phonemes divided by the number of letters. There is no reference scale of values for the Sitbon index. When applied to our data, the index provides scores between 18.05 (groupe de glandes et de cellules du corps fabriquant et libérant des hormones dans le sang, qui contrôlent de nombreuses fonctions comme la croissance, la reproduction, le sommeil, la faim et le métabolisme (group of glands and cells in the body that make and deliver hormones in blood, that control many functions such as growth, reproduction, sleep, hunger and metabolism)) and 25.37 (protéine normalement fabriquée par le placenta lors de la grossesse habituellement non présente dans le sang d'une femme en bonne santé qui n'est pas enceinte ou d'un homme en bonne santé (protein that is normally made by placenta during pregnancy, and usually missing in blood of healthy non-pregnant women or healthy men)). We can see that the scale of values is very narrow and offers reduced discrimination of readability.

Smith index [START_REF] Smith | Devereaux readability index[END_REF] is also adapted to sentences: L = -6.49+ 1.56W L + 0.19SL, where WL is the average number of letters in words, and SL is the number of words in the sentence. When applied to our data, the formula shows scores between -1.44 (étude de l'os (study of bone)) and 17.29 (concrétions gastrointestinales (gastrointestinal concretions)). Contrary to other indexes, difficult paraphrases are not the longer ones but rather those composed of polylexical units, like gastro-intestinal (gastrointestinal).

B. Computational Readability Models

For designing the computational readability models, we choose the descriptors mainly issued from the existing typology [START_REF] Gala | A model to predict lexical complexity and to grade words (Un modèle pour prédire la complexité lexicale et graduer les mots)[END_REF]. The purpose is to design a set of descriptors easy to compute and to use:

• number of letters, usually indicating the length, and complexity, of terms and of their equivalents; • number of phonemes. To obtain the number of phonemes, we use the database Lexique3 [START_REF] New | Une base de données lexicales du francais contemporain sur internet : Lexique (A lexical database for contemporary french: LEXIQUE)[END_REF]. It provides over 140,000 French lemmas and associated information, such as their phonetic transcription, number of syllables, and part-of-speech tag. For words missing in Lexique3, we use the Epitran module [START_REF] Mortensen | Epitran: Precision G2P for many languages[END_REF] adapted to French; • number of syllables. Lexique3 is also used to obtain the number of syllables. For words missing in Lex-ique3, we use Epitran and then their syllabation [START_REF] Pallier | Syllabation des représentations phonétiques de brulex et de lexique (Syllabation of phonetics representation of Brulex and Lexique)[END_REF]; • cohesion between phonemes and spelling corresponds to the ratio between the number of phonemes and number of letters. It provides values between 0 and 2: 0 if no difference, 1 if one or two differences, and 2 if more than two differences. Words with higher values of cohesion are supposed to be less readable; • frequency is also obtained from Lexique3. For words missing in Lexique3, we fix the frequency to 0 because these are supposed to be rare words; • presence in the Catach list [START_REF] Catach | Les listes orthographiques de base du franais (LOB) : les mots les plus frquents et leurs formes flchies les plus frquentes (Basic lists of spelling in French: the more frequent words and their more frequent inflected forms)[END_REF], which is the basic set of French words; • syllable components, which corresponds to three complexity levels according to the structure of syllables (coined with consonants C, vowels V and semiconsonants Y ) and their frequency. For instance, syllables like CYV, V, CVC, CV are very frequent in French, while syllables like CCVC, VCC,VC, YV, CVY are much less frequent in French.

We have to predict two classes for terms and equivalents: simple and difficult. Training of the biclass models is done on independent reference data: manually rated medical lexicon annotated according to the difficulty of words [START_REF] Grabar | A large rated lexicon with french medical words[END_REF]. This lexicon contains 29,641 medical words. Three classifiers (MultiLayer Perceptron M LP , Decision Tree DT and Random Forest RF ), implemented within the Python library ScikitLearn [START_REF] Pedregosa | Scikit-learn: Machine learning in Python[END_REF], are used. The models are next applied to terms and their paraphrases from the lexicon. The more the prediction is close to 0 the more difficult is the sequence, and the more it is close to 1 the simpler is the sequence. When the sequence contains more than one word, which is the majority of cases, models are first applied to each non-grammatical word, and then we compute the average probability of the whole sequence to be classified as simple or complex. The probabilities of three algorithms are taken into account individually. For instance, in abaissement de la température (decrease in temperature), all the algorithms predict that abaissement (decrease) is simple (with probability value 1) and that température (temperature) is simple (with probability 1). This gives the average score 1 for each algorithm, and the term is considered as simple by all of them. As for ablation de l'abdomen (ablation of abdomen), M LP and RF predict that the two words of the paraphrase are simple (probability 1), while DT predicts that ablation is simple and abdomen is difficult. This gives the average score 1 for M LP and RF , and 0.5 for DT . Overall, this term is also considered as simple but with lesser probability.

C. Results

The result of this step is that technical terms and paraphrases are rated for their readability with the five classical readability indexes (Dale, Kandel, Mesnager, Sitbon and Smith) and by the proposed computational readability models. In Table III, we present some examples of technical terms and of their equivalents, and indicate their readability scores. In the first line, we indicate the interpretation of the readability values according to indexes and models. For instance, with Dale, high scores are expected to be associated with difficult terms, while low scores are expected to be associated with simple terms. The Sitbon index is rather sensitive to long terms and paraphrases. In the examples provided, technical terms precede the paraphrases. For instance, comédon (comedo) is recognized to be difficult to undestand by all measures: Dale, Mesnager, Sitbon and Smith indexes are high, Kandel is low, and the three computational models MLP, DR, RF show the value 0. As expected, its paraphrase point noir (blackhead) is recognized to be easy to understand: Dale, Mesnager, Sitbon and Smith indexes are low, Kandel is high, while the three comutational models MLP, DR, RF show the value 1. The picture may be different with other pairs {term; paraphrase}. For instance, in the pair {vomissement (vomiting); être malade (being sick)}, both elements are considered as understandable by computational models and Sitbon, while other indexes consider that the paraphrase être malade (being sick) is simpler than the term vomissement (vomiting). The pairs, in which terms are paraphrased with long sequences, may be more difficult to be rated by the indexes and models. This is the case of système endocrinien (endocrine system) and its paraphrase groupe de glandes et de cellules du corps fabriquant et libérant des hormones dans le sang, qui contrôlent de nombreuses fonctions comme la croissance, la reproduction, le sommeil, la faim et le métabolisme (group of glands and cells in the body that make and deliver hormones in blood, that control many functions such as growth, reproduction, sleep, hunger and metabolism). Hence, the length of the paraphrase may introduce additional readability factor, which should also be considered in chosing the paraphrases for simplification. Overall, indexes and models provide useful information for the selection of lexical substitutes for technical terms.

The scores also permit us to compare the readability within pairs and indicate that order of terms and their paraphrases is correct. In few cases, the length of paraphrases decreases their readability, but overall their readability remains acceptable.

IV. CONCLUSION Automatic text simplification is an NLP field whose purpose is to make texts more easily understandable by common readers. While an important progress has been done in this field, the main barrier is still related to the availability of suitable data, such as corpora and lexica. We propose a set of experiments designed for the creation of a lexicon with French technical medical terms and their layman paraphrases. Several approaches and methods are developed and applied for the automatic extraction of paraphrases. The results from each method are evaluated with precision metric and usually show that the extractions are reliable with over 68% precision. Overall, the lexicon contains 11,272 pairs {technical term; paraphrase} for 6,937 different technical terms. Terms and paraphrases from this lexicon are then rated for their readability with several adapted readability indexes and with specifically designed computational models. Globally, we observe that paraphrases are indeed easier to understand than technical terms. This rated lexicon will be exploited by simplification systems and we expect that readability scores will help to choose the best lexical substitutions. The lexicon will be made available for the research community.

( 4 )

 4 L'angiographie est une technique d'imagerie médicale portant sur les vaisseaux sanguins qui ne sont pas visibles sur des radiographies standards. (Angiography is a medical imaging technique for blood vessels which are not visible with standard imaging.)

TABLE I

 I

	. SUMMARY OF DIFFERENT METHODS PROPOSED:
	NUMBER OF CORRECT EXTRACTIONS AND THEIR PRECISION,
	AND THEIR COMPARISON WITH THE EXISTING WORK
	Methods	# extractions	Precision
	Parallel sentences	626	100
	Definitions	1,028	68
	Reformulation	7,959	60
	Morphological analysis	1,128	86
	Morphological affixes and roots	1,939	13
	Abbreviations	8,148	94
	Online resources	1,165	100
	English medical terms

  TableI, we indicate some existing work on acquisition of equivalents for medical terms in English[START_REF] Zeng | A text corpora-based estimation of the familiarity of health terminology[END_REF][START_REF] Schwartz | A simple algorithm for identifying abbreviation definitions in biomedical text[END_REF] and in French[START_REF] Deléger | Paraphrase acquisition from comparable medical corpora of specialized and lay texts[END_REF][START_REF] Cartoni | Découverte de patrons paraphrastiques en corpus comparable: une approche basée sur les n-grammes (Discovery of paraphrastic patterns in comparable corpora: an n-gram based approach)[END_REF]. The most known resource is the Consumer Health Vocabulary (CHV) in English[START_REF] Zeng | A text corpora-based estimation of the familiarity of health terminology[END_REF], while

there is no comparable resources in other languages, such as French. The methods exploited for the creation of CHV are both manual and automatic. Overall, CHV contains 141,213 unique layman terms, among which 11,641 terms are lexically different from their technical terms. This lexicon is the closest work to what we present in this section. Our lexicon currently contains 11,272 pairs {technical terms; paraphrase} for 6,937 different terms. Because of their specific linguistic function, abbreviations are not included in the lexicon. Besides, several other extractions need yet to be validated manually.

  Table II indicates Precision, Recall and F-measure obtained during the training with a 10-fold cross-validation set. We can see that all classifiers show good results, M LP being the best in this task with overall results over 90%.

TABLE II

 II

	. RESULTS OF THE READABILITY MODEL ON
	TRAINING REFERENCE DATA WITH 10-FOLD
		CROSS-VALIDATION
		Precision	Recall	F-measure
	MLP	90.3	90.4	90.0
	DT	88.7	89.0	88.6
	RF	89.2	89.5	89.2

TABLE III .

 III EXAMPLES OF RATING OF TECHNICAL TERMS AND OF THEIR EQUIVALENTS groupe de glandes et de cellules du corps fabriquant et libérant des hormones dans le sang, qui contrôlent de nombreuses fonctions comme la croissance, la reproduction, le sommeil, la faim et le métabolisme (group of glands and cells in the body that make and deliver hormones in blood, that control many functions such as growth, reproduction, sleep, hunger and metabolism) par le placenta lors de la grossesse habituellement non présente dans le sang d'une femme en bonne santé qui n'est pas enceinte ou d'un homme en bonne santé (protein that is normally made by placenta during pregnancy, and usually missing in blood of healthy non-pregnant women or healthy men)

	Terms and their equivalents	Dale	Kandel	Mesnager	Sitbon	Smith	MLP	DT	RF
	difficult	high	low	high	high	high	0	0	0
	simple	low	high	low	low	low	1	1	1
	comédon (comedo)	15.04	-235.615	66.33	22.06	4.62	0	0	0
	point noir (blackhead)	0.08	102.77	0.66	21.34	0.91	1	1	1
	vomissements (comiting)	15.04	-88.415	66.33	20.98	12.42	1	1	1
	être malade (being sick)	0.08	65.98	0.66	20.76	1.69	1	1	1
	lupus érythémateux disséminé (systemic lupus erythematosus)	15.12	-65.91	66.99	21.06	7.6	0.33	0.33	0.66
	éruption faciale, douleur articulaire, anomalies musculaires, fièvre (facial eruption, articular	15.4	16.91	69.3	21.11	5.082	0.67	0.67	0.67
	pain, muscular abnormalies, fever)
	condylomes acuminés (condylomata acuminata)	15.08	204.97	66.66	15.58	-6.11	0	0	0
	verrues génitales (genital warts)	15.08	20.97	66.66	20.035	6.37	1	1	1
	système endocrinien (endocrine system)	15.08	131.37	66.66	21.7	7.93	0.5	0.5	0.5
		9.97	73.7	49.26	18.05	8.00	0.67	0.67	0.5
	alpha-foetoprotéine (afp) (alpha-foetoproteine (AFP))	15.08	-15.83	66.66	19.9	12.61	0	0	0
	protéine normalement fabriquée 8.42	86.45	42.28	25.37	7.17	1	1	1
	ostéologie (osteology)	15.04	-126.23	66.33	21.412	9.3	0	0	0
	étude de l'os (study of bones)	7.66	94.57	34.32	19.11	-1.44	1	1	0.5
	bézoards (bezoars)	15.04	-126.23	66.33	21.25	6.18	0	0	0
	concrétions gastro-intestinales (gastrointestinal concretions)	15.08	204.94	66.66	21.41	17.29	0.5	0.5	0.5

ACKNOWLEDGMENT

This work was funded by the French National Agency for Research (ANR) as part of the CLEAR project (Communication, Literacy, Education, Accessibility, Readability), ANR-17-CE19-0016-01.


text:
Rémi Cardon
email: remi.cardon@univ-lille.fr

Natalia Grabar
email: natalia.grabar@univ-lille.fr

French Biomedical Text Simplification: When Small and Precise Helps

We present experiments on biomedical text simplification in French. We use two kinds of corpora -parallel sentences extracted from existing health comparable corpora in French and WikiLarge corpus translated from English to French -and a lexicon that associates medical terms with paraphrases. Then, we train neural models on these parallel corpora using different ratios of general and specialized sentences. We evaluate the results with BLEU, SARI and Kandel scores. The results point out that little specialized data helps significantly the simplification.

Introduction

The goal of text simplification is to make complex texts accessible to a given target audience (children, foreigners, diseased people...) or to make complex texts to be more easily processed by NLP applications. There is little existing work on biomedical text simplification in English [START_REF] Peng | iSimp: A sentence simplification system for biomedical text[END_REF][START_REF] Shardlow | Neural text simplification of clinical letters with a domain specific phrase table[END_REF], and on text simplification for the general language in French [START_REF] Abdul Rauf | Simplification automatique de texte dans un contexte de faibles ressources[END_REF][START_REF] Gala | Alector: A Parallel Corpus of Simplified French Texts with Alignments of Misreadings by Poor and Dyslexic Readers[END_REF][START_REF] Sauvan | Text simplification to help individuals with low vision read more fluently[END_REF][START_REF] Brouwers | Syntactic sentence simplification for french[END_REF].

Research on text simplification is usually performed on open domain English. Currently used methods rely on deep learning approaches and require large parallel monolingual corpora in which one complex sentence is paired with one or more simplified versions [START_REF] Nisioi | Exploring neural text simplification models[END_REF][START_REF] Cooper | CombiNMT: An exploration into neural text simplification models[END_REF]. Two English datasets are commonly used: (1) Newsela [START_REF] Xu | Problems in current text simplification research: New data can help[END_REF], a corpus with news articles that are manually re-written according to four levels of simplification, and (2) WikiLarge [START_REF] Zhang | Sentence simplification with deep reinforcement learning[END_REF], issued from a compilation of three previously released simplification corpora all extracted from Wikipedia [START_REF] Zhu | A monolingual tree-based translation model for sentence simplification[END_REF][START_REF] Woodsend | Learning to simplify sentences with quasi-synchronous grammar and integer programming[END_REF][START_REF] Kauchak | Improving text simplification language modeling using unsimplified text data[END_REF]. The availability of corpora and resources plays indeed a very important role and condition the feasibility of the NLP research.

We build and test models for biomedical text simplification in French. We first describe the data used (Section 2) and various configurations of the experiments (Section 3). We then indicate the evaluation principles (Section 4), and present and discuss the results (Section 5). We also provide the WikiLarge FR corpus and a set of native parallel sentences in French, from general and biomedical languages. FR 297,494 12,753,567 296,402 12,695,192 992 42,676 We use two sets of parallel corpora dedicated to the simplification. One set is obtained from the freely available simplification corpus CLEAR in French [START_REF] Grabar | CLEAR -simple corpus for medical French[END_REF]. This is a comparable corpus, which contains three types of texts (medical literature reviews, drug information, and medical articles from Wikipedia and Vikidia), and from which parallel sentences were extracted. This parallel corpus contains 4,596 sentence pairs, which is not sufficient when using the current state-of-the-art methods, that rely on deep learning. Another corpus is obtained thanks to the automatic translation of WikiLarge in French. The translation has been done using OpenNMT-py [START_REF] Klein | OpenNMT: Opensource toolkit for neural machine translation[END_REF] with the default parameters, and the En-Fr model provided. This WikiLarge FR parallel corpus contains almost 300,000 sentence pairs. Table 1 indicates the volume of data in both corpora. We segmented the CLEAR corpus into train, validation and test sets: 100 examples for testing, three times as many for validation, and the rest for training. WikiLarge FR is already segmented in these three sets, yet we decided to reduce the WikiLarge FR test set from 359 pairs to 100 to make it comparable with the CLEAR test set. As these two corpora contain data from Wikipedia, we checked for duplicates to avoid having identical pairs in two different sets, and we found none.

Linguistic Data

We also use a lexicon that proposes layman paraphrases for technical medical terms, like {hypotension; baisse de la tension artérielle} ({hypotension; decrease in arterial pressure} ). This lexicon has been built using medical terminologies [START_REF] Da Lindberg | The Unified Medical Language System[END_REF] and corpora (CLEAR corpus and various discussion fora) in French. The lexicon currently contains 7,580 paraphrases for 4,516 medical terms.

Experimental Protocol

The purpose of the experimental protocol is to focus on two aspects:

1. Impact of the general and medical language corpora, for which we use different ratios of WikiLarge FR and CLEAR sets. Since we aim at the simplification of biomedical texts and since the CLEAR corpus is not large, we always use all of the CLEAR training and validation sets. Then, we gradually add WikiLarge FR examples to the training set: we start with a model trained on the same number of examples from both corpora (ratio 1:1), and increase examples from WikiLarge FR respecting the ratios 1:5, 1:10, 1:25, 1:50, and up to 1:75. The 1:75 ratio value is an approximation, it corresponds to the case where the entire WikiLarge FR training set is used in the experiment. Each additional set with WikiLarge FR examples is selected randomly and added to the previous set. These experiments permit to observe the robustness of the models when tested on biomedical and general language, and to observe whether these additional examples improve the results;

2. Impact of lexicon. We perform the same experiments to which we feed the lexicon. The lexicon is exploited in two ways: (1) during the simplification phase with the OpenNMT-py --phrase_table flag that it usually used for dealing with unknown words. That flag was used in the same way in a work for simplification of clinical letters in English [START_REF] Shardlow | Neural text simplification of clinical letters with a domain specific phrase table[END_REF] and (2) during the training phase for which we add the entire lexicon to the training set.

We refer to the first set of experiments as NPT (no phrase table), the second set of experiments as PTS (phrase table used in the simplification phase) and the third set of experiments as PTT (phrase table added to the training set). Across each series of experiments, the validation and test sets are the same, while the training sets differ in the ratios and whether or how the lexicon is used. We use OpenNMT-py for the sentence simplification with the following configuration: two bidirectional LSTM layers of 500 units for the encoder and the decoder, ADAM optimizer, learning rate 0.001, dropout probability 0.3, attention dropout probability 0.2. Each individual training took about five hours on a Geforce RTX 2070 GPU. During the simplification phase, we use the --replace_unk flag which tells the program to copy unknown words from the input to the output, except for the PTS set of experiments where it is replaced by the --phrase_table flag that uses the lexicon.

We report three baselines: (1) one model trained on CLEAR only, (2) another model trained on Wiki-Large FR only, and (3) the identity baseline which corresponds to the case where the output is the copy of the input.

Evaluation

We evaluate the models using several metrics: (1) BLEU [START_REF] Papineni | Bleu: a method for automatic evaluation of machine translation[END_REF], initially designed for the evaluation in machine translation, is also used in text simplification which can be seen as a monolingual translation task. It compares the system output with the reference data. This metric gives a rough indication of the performance of a system, especially regarding grammaticality and meaning preservation, but it is not a strong indicator for simplification [START_REF] Sulem | BLEU is not suitable for the evaluation of text simplification[END_REF]; (2) SARI [START_REF] Xu | Optimizing statistical machine translation for text simplification[END_REF] is currently considered as the most common metric for text simplification. SARI is computed by comparing the system output against the reference, and against the input as well. It should be noted that SARI is more reliable when several references are available [START_REF] Alva-Manchego | Data-driven sentence simplification: Survey and benchmark[END_REF][START_REF] Zhang | Sentence simplification with deep reinforcement learning[END_REF], which is not the case in our experiments; (3) Kandel [START_REF] Kandel | Application de l'indice de flesch à la langue française[END_REF]) is a readability metric. It does not compare the output with the reference or the input, and only provides formal indicators such as sentence length and number of syllables per word. It is an adaptation of the Flesch [START_REF] Flesch | A new readability yardstick[END_REF] readability measure -that was designed for English -to the French language. The absolute indexes are not informative by themselves: the measure is described to be more relevant for comparisons. Higher scores mean that the text should be easier to read.

We computed the first two metrics with the EASSE evaluation suite for automatic text simplification (Alva-Manchego et al 2 indicates the SARI, BLEU and Kandel scores when testing the models on WikiLarge FR and CLEAR test sets. The first three rows show the baseline results. According to the Kandel scores, medical sentences from CLEAR are indeed less readable than sentences from Wikipedia. We can also see that training on CLEAR performs very poorly on the general language (<1 BLEU and 20.52 SARI). The performances are also poor on medical language but BLEU is way higher than on the general language (21.59 vs 0.15). This is due to the fact that the model trained on WikiLarge FR only performs quite well on WikiLarge FR (39.08 BLEU score) but poorly on CLEAR (9.72 BLEU score), which indicates that the model trained on general language cannot handle domain specific data. When data from two corpora are used for the training, BLEU and SARI improve to up to 49.7 BLEU (PTT 1:75) and 39.05 SARI (PTS 1:50) on WikiLarge FR. This indicates that the volume of data is crucial. The input lexicon has little effect when used during the simplification phase. Yet, with several models, the lexicon proves to be efficient when used during the training phase. When testing on CLEAR, BLEU and SARI scores are higher: up to 50.23 BLEU (NPT 1:75) and 40.94 SARI (PTT 1:5). Here, all models perform better examples including the lexicon (PTS and PTT)this is a substantial improvement. Finally, as the Kandel index advantages models that output short sentences with no consideration to their contents, we will simply observe that no model worsens the Kandel readability score of the original texts -no model produces an output with a Kandel index that is lower than the identity baseline.

Table 3 shows some simplification examples provided for WikiLarge FR, and Table 4 shows some simplification examples for CLEAR.

In Table 3, the sentence Le 14 octobre 1960, le candidat à la présidence John F. Kennedy a proposé le concept de ce qui est devenu le Peace Corps sur les marches de l'Union du Michigan. (On October 14, 1960, presidential candidate John F. Kennedy proposed the concept of what became the Peace Corps on the Union Steps of Michigan.) is processed. the baseline models provide either no changes (WikiLarge FR) or changes that are not meaningful (CLEAR). Indeed, the CLEAR baseline model applied to the WikiLarge FR test example proposes a grammatical sentence, but which has no semantic relation to the input (cancer is medicine). This draws attention to the fact that simplification is very sensitive to the training data and needs caution. We can see that the CLEAR baseline model only outputs words related to the medical domain, regardless of the input. Hence, real improvement in quality is obtained with other models. Indeed, except the CLEAR baseline, other examples for WikiLarge correspond to the state-of-the-art transformations.

As for CLEAR (Table 4), we illustrate the modifications on a sentence that comes from drug information released by the French Ministry of Health: une hypotension artérielle peut être observée en cas d'administration intraveineuse trop rapide, inférieure à 60 minutes (voir rubrique 4.2). (arterial hypotension may be observed if intravenous administration is too rapid, less than 60 minutes (see section 4.2).). The source document is aimed at physicians, whereas the reference is aimed at patients and can be found in drug boxes. The only changes in the reference are the truncation of "minutes" and the deletion of the mention of another section. NPT examples are close to the reference except for the truncation, while PTS and PTT are more creative: PTS 1:75 transforms hypotension in lower than normal blood pressure, while PTT 1:75 transforms hypotension in decrease in blood pressure. Lexically, these are correct transformations and an improvement over the reference. Yet, by mechanically replacing hypotension, PTS creates an ungrammatical sentence in French.

Conclusion

We addressed the biomedical text simplification in French, which is, to our knowledge, the first attempt to perform this task with a machine translation technique. In order to cope with the lack of French data for simplification, we translated to French a freely available English corpus. We made the translation available, as well as the parallel data from CLEAR1 . Using those data we achieved improvements over the baselines for French biomedical text simplification. Indeed, baselines produce incorrect and imperfect simplifications, but the results are significantly improved with large datasets.

We propose several experiments that indicate that (1) an automatically translated resource can help in a low resource setting, (2) a small amount of good quality specialized data can significantly improve overall performances, (3) multiword units processing can be dealt with by adding a lexicon to the training set. That is to say that mixing data of different linguistic nature helps simplification. Overall, we can obtain interesting simplification results which often prove to be more creative than the reference and yet correct. Besides, the impact of large native data for simplification should also be studied.

Table 1 :

 1 Size of the two parallel corpora

	100	4,302

Table 2 :

 2 Evaluation metrics for the various experiments obtained on WikiLarge FR and CLEAR

	., 2019)
	5 Quantitative and Qualitative Results
		WikiLarge FR	CLEAR
	Models	BLEU SARI Kandel BLEU SARI Kandel
	Identity baseline 60.02 25.05 81.15	55.00 23.73 76.67
	WikiLarge FR	39.08 37.61 89.71	9.72 30.97 95.58
	CLEAR	0.15 20.52 94.32	21.59 22.07 84.15
	NPT 1:1	5.83 25.60 98.20	26.23 38.10 84.26
	NPT 1:5	14.82 30.38 96.23	29.86 39.20 80.43
	NPT 1:10	33.74 35.01 92.97	41.05 38.32 80.02
	NPT 1:25	25.88 34.44 92.26	37.24 40.34 78.12
	NPT 1:50	44.48 38.93 90.52	49.16 35.36 79.09
	NPT 1:75	49.67 38.02 89.71	50.23 33.91 79.11
	PTS 1:5	15.06 30.28 103.29 30.00 39.10 82.06
	PTS 1:10	33.70 35.12 102.17 40.29 38.32 79.42
	PTS 1:25	26.16 34.44 99.84	37.17 40.09 79.04
	PTS 1:50	44.49 39.05 100.63 48.16 35.33 89.08
	PTS 1:75	49.70 38.26 97.76	47.61 34.27 78.43
	PTT 1:5	23.98 33.68 95.56	39.07 40.94 87.36
	PTT 1:10	30.94 34.05 94.61	38.17 36.38 86.72
	PTT 1:25	37.29 34.74 91.40	42.92 39.14 88.22
	PTT 1:50	32.68 36.73 98.81	49.72 37.52 90.60
	PTT 1:75	34.20 36.47 89.05	40.16 38.58 92.35
	Table

  Models Test on WikiLarge FR Source Le 14 octobre 1960, le candidat à la présidence John F. Kennedy a proposé le concept de ce qui est devenu le Peace Corps sur les marches de l'Union du Michigan. (On October 14, 1960, presidential candidate John F. Kennedy proposed the concept of what became the Peace Corps on the Union Steps of Michigan.) Reference John F. Kennedy, un candidat à la présidence, a proposé l'idée de ce qui devint le Peace Corps sur les marches de l'Union du Michigan le 14 octobre 1960. (John F. Kennedy, a presidential candidate, proposed the idea of what became the Peace Corps on the Union steps of Michigan En 1960, le candidat du président John F. Kennedy a suggéré le Peace Corps sur les marches de l'Union du Michigan. (In 1960, President John F. Kennedy's candidate suggested the Peace Corps on the Union steps of Michigan.) NPT 1:50 En 1960, le candidat à la présidence John F. Kennedy a proposé l'idée du Peace Corps sur les marches de l'Union du Michigan. (In 1960, presidential candidate John F. Kennedy proposed the idea of the Peace Corps on the Union Steps of Michigan.) PTT 1:75 John F. Kennedy a proposé le concept de ce qui est devenu le Peace Corps sur les marches de l'Union du Michigan. (John F. Kennedy proposed the concept of what became the Peace Corps on the Union steps of Michigan.)

		on October 14, 1960.)
	Wikilarge	No changes made
	CLEAR	le cancer est de la médecine (cancer is medicine)
	NPT 1:25

Table 3 :

 3 Examples for simplification of sentences from WikiLarge FR CLEAR le traitement de la naissance de la naissance de l' repos [...] de l' repos de la médecine [...] de la médecine de la peau [...] de la peau de la (treatment of the birth of the birth of the rest [...] of the rest of the medicine [...] of the medicine of the skin [...] of the skin of the)

	Models	Test on CLEAR
	Source	une hypotension artérielle peut être observée en cas d'administration intraveineuse trop
		rapide, inférieure à 60 minutes (voir rubrique 4.2) (arterial hypotension may be observed if
		intravenous administration is too rapid, less than 60 minutes (see section 4.2).)
	Reference	une hypotension artérielle peut être observée en cas d'administration intraveineuse trop
		rapide , inférieure à 60 min (arterial hypotension may be observed if intravenous administration is
		too rapid, less than 60 min.)
	WikiLarge	Une artérielle artérielle peut être observée en cas de crise intraveineuse trop rapide et
		inférieure à 60 minutes ( (An arterial arterial may be observed if intravenous crisis is too rapid and
		less than 60 minutes)
	NPT 1:50	une hypotension artérielle peut être observée en cas d'administration intraveineuse trop
	& 1:75	rapide, inférieure à 60 minutes (arterial hypotension may be observed if intravenous administration
		is too rapid, less than 60 minutes)
	PTT 1:75	une tension inférieure à la normale artérielle peut être observée en cas d' administration
		intraveineuse trop rapide , inférieure à 60 minutes (lower than normal blood pressure may be
		observed if intravenous administration is too rapid, less than 60 minutes.)
	PTT 1:50	une hypotension artérielle peut être observée en cas d'administration intraveineuse trop
		rapide, inférieure à 60 minutes ( voir rubrique « 3 ) (arterial hypotension may be observed if
		intravenous administration is too rapid, less than 60 minutes (see section " 3))
	PTT 1:75	une diminution de la tension artérielle peut être observée en cas d' administration in-
		traveineuse trop rapide , inférieure à 60 minutes (a decrease in blood pressure may be observed
		if intravenous administration is too rapid, less than 60 minutes)

Table 4 :

 4 Examples for simplification of sentences from CLEAR than the baselines: with as few as ∼4,000 examples of domain-specific data (for NPT) and ∼10,000

http://natalia.grabar.free.fr/resources.php

Acknowledgements

This work was funded by the French National Agency for Research (ANR) as part of the CLEAR project (Communication, Literacy, Education, Accessibility, Readability), ANR-17-CE19-0016-01. The authors would like to thank the reviewers for their helpful comments and questions that permitted to improve the overall quality of the paper.


Lama Ahmad

Stéphane Plancqueel

Noureddine Lazar

Hafsa Korri-Youssoufi

Inès Li

La Sierra-Gallay

Herman Van Tilbeurgh

Laurent Salmon
email: laurent.salmon@universite-paris-saclay.fr

De La Sierra-Gallay

Novel N-substituted 5-phosphate-D-arabinonamide derivatives as strong inhibitors of phosphoglucose isomerases: synthesis, structure-activity relationship and crystallographic studies

Keywords: enzyme inhibitor, phosphate, monosaccharide, phosphoglucose isomerase, autocrine motility factor Highlights

come

Introduction

Phosphoglucose isomerase (PGI, E.C. 5.3.1.9) is a cytosolic enzyme that catalyses the reversible interconversion of D-glucose 6-phosphate (G6P) and D-fructose 6-phosphate (F6P) in glycolysis. In addition to isomerase activity, PGI displays anomerase activity between anomers of D-glucopyranose 6-phosphate (/-G6P) [1] and between those of D-fructofuranose 6-phosphate (/-F6P) [2] (Scheme 1A). The F6P to G6P isomerization reaction proceeds by a proton transfer mechanism between C-1 and C-2 of the substrates, concomitant with a proton transfer between O-1 and O-2, and involves a 1,2-cis-enediolate high-energy intermediate (HEI). Designed as HEI analogues, 5-phospho-D-arabinonohydroxamate (5PAH, 1) [3] and 5-phospho-D-arabinonate (5PAA, 2) [4] are the strongest PGI inhibitors reported to date (Scheme 1B). In complement with kinetic and physico-chemical studies, a number of crystal structures of PGI, including PGI alone (human PGI: PDB code 1IAT [5], rabbit PGI: PDB code 1HM5 [6]) and in complex with substrate D-fructose 6-phosphate (rabbit PGI : PDB code 1HOX [7]) or inhibitors 1 (rabbit PGI: PDB code 1KOJ [8]), 2 (rabbit PGI: PDB code 1G98 [9], human PGI: PDB code 1NUH [START_REF] Davies | The structure of human phosphoglucose isomerase complexed with a transition-state analogue[END_REF]), and D-sorbitol-6-phosphate (rabbit PGI, PDB code 1XTB [START_REF] Lee | The crystal structure of rabbit phosphoglucose isomerase complexed with D-sorbitol-6-phosphate, an analog of the open chain form of D-glucose-6-phosphate[END_REF]), led to a detailed mechanism of the overall catalyzed interconversion. [3] and 5-phospho-D-arabinonate (5PAA, 2) [4], two strong PGI inhibitors reported as HEI analogues.

PGI is also known as a moonlighting protein [START_REF] Jeffery | Moonlighting proteins[END_REF] with a number of extracellular activities including roles as neuroleukin [13,[START_REF] Chaput | The neurotrophic factor neuroleukin is 90% homologous with phosphohexose isomerase[END_REF], differentiation and maturation mediator [START_REF] Xu | The differentiation and maturation mediator for human myeloid leukemia cells shares homology with neuroleukin or phosphoglucose isomerase[END_REF], antigen in rheumatoid arthritis [START_REF] Matsumoto | Arthritis provoked by linked T and B cell recognition of a glycolytic enzyme[END_REF], surface antigen in sperm agglutination [START_REF] Yakirevich | Cloning of a glucose phosphate isomerase/neuroleukin-like sperm antigen involved in sperm agglutination[END_REF], inhibitor toward myofibrilbound serine proteinase [START_REF] Cao | Purification of a Novel Serine Proteinase Inhibitor from the Skeletal Muscle of White Croaker (Argyrosomus argentatus)[END_REF], and autocrine motility factor (AMF) [START_REF] Liotta | Tumor cell autocrine motility factor[END_REF][START_REF] Watanabe | Tumor cell autocrine motility factor is the neuroleukin/phosphohexose isomerase polypeptide[END_REF]. AMF is a cytokine secreted by a large variety of tumor cells that stimulates motility of cancer cells in vitro and metastases development in vivo [START_REF] Watanabe | Purification of human tumor cell autocrine motility factor and molecular cloning of its receptor[END_REF]. AMF is a validated biomarker of cancer progression [START_REF] Bodansky | Serum phosphohexose isomerase in cancer. II. as an index of tumor growth in metastatic carcinoma of the breast[END_REF][START_REF] Schwartz | Comparative values of phosphatases and other serum enzymes in following patients with prostatic carcinoma[END_REF][START_REF] Guirguis | Detection of Autocrine Motility Factor in Urine as a Marker of Bladder Cancer[END_REF][START_REF] Baumann | The diagnostic validity of the serum tumor marker phosphohexose isomerase (PHI) in patients with gastrointestinal, kidney, and breast cancer[END_REF][START_REF] Filella | Serum phosphohexose isomerase activities in patients with colorectal cancer[END_REF], high levels of the cytokine in urine or serum of cancer patients being clearly related to poor prognosis. While discovered independently, in terms of sequence and 3D structure [5,[START_REF] Tanaka | Inhibition Mechanism of Cytokine Activity of Human Autocrine Motility Factor Examined by Crystal Structure Analyses and Site-directed Mutagenesis Studies[END_REF], human AMF is identical to human PGI but with different functionalities and locations. Through activation of the AMF membrane receptor (AMFR) of tumor cells, AMF has been shown to decrease cell adhesion and to induce their motility [START_REF] Watanabe | Purification of human tumor cell autocrine motility factor and molecular cloning of its receptor[END_REF][START_REF] Tsutsumi | Autocrine motility factor signaling enhances pancreatic cancer metastasis[END_REF]. For the purpose of designing new, specific, sensitive and non-invasive diagnostic tools for the early detection of an ongoing cancer process, we are looking for suitable bioreceptor molecules that could be grafted onto an electrode for efficient electrochemical detection of AMF in urine and/or serum of cancer patients. Because the AMF recognition site involves, at least in part, the active site of human PGI, enzyme substrates and competitive inhibitors might be used as bioreceptors. Indeed, we designed a highly efficient carbohydrate-based electrochemical biosensor with sensitivity in the pM range using the PGI substrate F6P [START_REF] Devillers | Carbohydrate-based electrochemical biosensor for detection of a cancer biomarker in human plasma[END_REF].

In order to find new strong AMF-PGI inhibitors that could be potentially used as anticancer agents and/or as bioreceptors for carbohydrate-based electrochemical biosensors, we report in this study the synthesis and kinetic evaluation of several new human PGI inhibitors derived from the synthon 5-phospho-D-arabinono-1,4-lactone 3 and primary amines 4a-k to afford the corresponding N-substituted 5-phosphate-D-arabinonamide derivatives 5a-k (Scheme 2). Although not designed as HEI analogue inhibitors of the enzyme catalyzed isomerization reaction, several of these compounds appear as new strong PGI inhibitors, including N-(5-phosphate-D-arabinoyl)-2-(amino)ethanamine (5PAED, 5j, Scheme 2) for which we report a K i value of 0.7 µM, and the crystal structure at 2.38 Å resolution of its complex with AMF-PGI (we previously reported the use of 5PAED 5j in a biosensor for AMF detection in a separate detailed electrochemical study [START_REF] Ahmad | Electrochemical detection of the human cancer biomarker 'autocrine motility factor-phosphoglucose isomerase' based on a biosensor formed with a monosaccharidic inhibitor[END_REF]).

Analysis of the active site interactions, in addition to kinetic evaluations of several of the new inhibitors synthesized in this study, clearly shows that AMF-PGI is able to accept a rather large modification of the C-1 "head" part of the ligand bound at the PGI active site.

All of these compounds 5a-k (Scheme 2) were also evaluated as potential inhibitors of type I zinc(II) phosphomannose isomerase (PMI), the enzyme that reversibly catalyzes the isomerization of D-mannose 6-phosphate (M6P, epimer of G6P on C-2) to F6P [31]. Indeed, 5PAH (1, Scheme 1) is known to strongly inhibit both PGI [3] and PMI [START_REF] Roux | Inhibition of type I and type II phosphomannose isomerases by the reaction intermediate analogue 5-phospho-D-arabinonohydroxamic acid supports a catalytic role for the metal cofactor[END_REF] catalyzed isomerization reactions.

Therefore, the discovery of PGI-specific inhibitors that do not inhibit PMI is of obvious therapeutic interest. Indeed, none of the synthesized compounds 5a-k seem to be PMI inhibitors. Contrarily to PGI, the PMI active site does accommodate only substrate or true HEI analogues.

Results and discussion

2.1. Chemistry N-Substituted 5-phosphate-D-arabinonamide derivatives 5a-k were all obtained by reaction of the respective commercially available primary amines 4a-k on 5-dihydrogeno-phospho-Darabinono-1,4-lactone 3 [3,[START_REF] Hardré | Competitive inhibitors of yeast phosphoglucose isomerase: synthesis and evaluation of new types of phosphorylated sugars from the synthon D-arabinonolactone-5phosphate[END_REF] through nucleophilic substitution and subsequent ring opening in anhydrous MeOH (except 5f which was synthesized in anhydrous DMF for solubility reasons) upon stirring 1.5 h at room temperature (Scheme 2). Following concentration of the reaction mixture, ionexchange chromatography followed by lyophilization afforded pure N-substituted 5-phosphate-Darabinonamide derivatives 5a-k in yields ranging from 70 to 100%. All compounds were fully characterized by 1 H, 13 C, and 31 P NMR, and high-resolution mass spectrometry analyses (see Experimental section). Scheme 2. Synthesis of N-substituted 5-phosphate-D-arabinonamide derivatives 5a-k through nucleophilic addition of the respective primary amines 4a-k on the synthon 5-phospho-D-arabinono-1,4-lactone 3.

Inhibition kinetics

N-Substituted 5-phosphate-D-arabinonamide derivatives 5a-k were first evaluated as potential inhibitors of PMIs from Escherichia coli (EcPMI, Sigma-Aldrich) and Candida albicans (CaPMI) [31] on the M6P to F6P isomerization reaction [START_REF] Gracy | Studies on phosphomannose isomerase I. Isolation, homogeneity measurements, and determination of some physical properties[END_REF]. IC 50 values ranging from 1600 to 175 µM were obtained (see Supp. Info. SI1), which shows that none of these compounds behave as good PMI inhibitors in comparison to K M values for M6P of 330 µM [START_REF] Roux | The reaction mechanism of type I phosphomannose isomerases: New information from inhibition and polarizable molecular mechanics studies[END_REF] and 200 µM [START_REF] Bernard | Selenomethionine Labelling of Phosphomannose Isomerase Changes its Kinetic Properties[END_REF], respectively.

As illustrated in Table 1, compounds 5a-k appear to be inhibitors of both rabbit muscle and human PGI [START_REF] Ahmad | Electrochemical detection of the human cancer biomarker 'autocrine motility factor-phosphoglucose isomerase' based on a biosensor formed with a monosaccharidic inhibitor[END_REF] catalyzed isomerization reactions [START_REF] Gracy | Phosphoglucose isomerase of human erythrocytes and cardiac tissue[END_REF], with IC 50 values ranging from 30 (5a vs. RmPGI) to 0.91 µM (5j vs. hPGI), in comparison to K M values for F6P = 60 µM and 50 µM, respectively. As could be expected from their high sequence similarity (93%), the kinetic parameters are similar for both PGIs. Surprisingly compounds 5c, 5d, 5f, and 5g are rather good PGI inhibitors with respective IC 50 values of 3.1, 4.9, 2.9, and 1.5 µM (vs. hPGI), despite the fact that their N-substituted Nmethyl-methylimidazolyl) (5c), ethylimidazolyl (5d), 2-methylpyridyl (5f), and 2-methylfuranyl (5g) amide groups on C-1 are rather voluminous. A hypothesis could be that their aromatic substituents interact favorably with the positively charged Arg272 residue at the active site through favorable cation- interactions. Indeed, such interactions in structural biology are found to be common, particularly with the side chain of Arg [START_REF] Gallivan | Cation-π interactions in structural biology[END_REF][START_REF] Mahadevi | Cation-π Interaction: Its Role and Relevance in Chemistry[END_REF]. Other compounds with aromatic substituents display slightly higher IC 50 values for RmPGI of 11 µM (methylimidazolyl, 5b), 15 µM (propylimidazolyl, 5e), and 14 µM (methylthiofuranyl, 5h), however still in the same range. These small IC 50 differences between aromatic compounds are difficult to rationalized. When compared to 5c (methyl-methylimidazolyl) and 5f (2-methylpyridyl) which are similar in size, 5e (propylimidazolyl) bears a longer substituent on C-1 which could impairs its cation- energy interaction versus Arg. Also, inhibitor 5b (methylimidazolyl) is not as good as 5c (methylmethylimidazolyl) probably because of a lower cycle electron density. Compounds bearing nonaromatic substituents display higher IC 50 values with 30 µM (methylethylsulfide, 5a), and 13 µM (methylethylsulfone, 5i), except for 5j and 5k which are particular cases. Indeed, these two compounds bearing ethylamino (5j, 5PAED) and -hydroxypropylamino (5k, 5PADP) Nsubstituted amide groups were originally designed to interact favorably with negatively charged Glu357 active site residue, their primary amino group being most likely protonated at physiological pH. Compounds 5j and 5k appear as new strong PGI inhibitors, with respective IC 50 values of 1.00 and 2.86 µM vs. RmPGI, and 0.91 and 0.95 µM vs. hPGI. It is noteworthy that these values are quite close to those we measured for 5PAH (1) and 5PAA (2), respectively 0.80 and 0.46 µM in our experimental conditions (50 mM HEPES, pH 7.1, 25 °C). The remarkable point is that 5PAED (5j) and 5PADP (5k) are not HEI analogue inhibitors, contrarily to 5PAH (1) and 5PAA (2). Obviously, unexpected but favorable interactions must take place at the active site with 5PAED (5j) and 5PADP (5k), as illustrated below by the crystal structure of hPGI-5PAED complex. Favorable electrostatic interactions probably take place between the protonated Arg residue at the active site and the non-protonated amino group of inhibitors 5j (ethylamino) and 5k (-hydroxypropylamino). The inhibition constants (K i ) were measured for the best three hPGI inhibitors 5g, 5j, and 5k vs. RmPGI and hPGI. Surprisingly, Lineweaver-Burk representation of the initial velocity V o as a function of F6P concentration at various inhibitor concentrations reveals that these inhibitors are indeed mixed inhibitors (see Supp. Info. SI2), with respective K i values = 1.5, 0.7, and 0.5 µM, and K i ' values = 5.0, 2.3, and 2.6 µM for hPGI (Table 1). In our experimental conditions, these compounds bind not only to the free enzyme E (K i ), but also to the enzyme-substrate complex ES (K i '). Substrate binding to PGI probably allows the inhibitor to bind to an allosteric site. However, we suspect that the inhibitor only binds to the active site of the enzyme, as shown by the crystallographic study presented below for the complex hPGI-5PAED (5j). Such sub-micromolar K i values measured for 5j and 5k are only slightly higher than the K i values we measured for the competitive inhibitors 5PAH (1) and 5PAA (2), respectively 0.19 and 0.15 µM. Although not designed as HEI analogue inhibitors, 5j (5PAED) and 5k (5PADP) appear to be new strong PGI inhibitors with K M /K i ratios (hPGI) equals about 70 and 100, respectively. Interestingly, the crystallographic study described below will show an expected binding mode of 5PAED (5j) at the hPGI active site.

Crystallographic studies

Co-crystallization of hPGI in complex with 5PAED (5j) gave rod shaped crystals that belonged to the space group symmetry P4 3 (a = b = 96.110 Å, c = 271.450 Å) (Supp. Info. SI3). X-ray diffraction data were collected at PROXIMA 2 beamline (SOLEIL) and the structure was solved at 2.38 Å (PDB code 6XUH) by molecular replacement using the coordinates of the hPGI crystal structure (PDB 1IAT) [5]. The asymmetric unit contains four copies of PGI. Two form a biological dimer and the other two form dimers through crystallographic symmetry axes (the protein is only active in dimeric form [START_REF] Achari | Glucose-6-phosphate isomerase[END_REF]). PGI contains 556 amino acids and is composed of a large and a small domain. The large N-terminal domain is formed by four parallel and two anti-parallel β strands surrounded by α helices. The small domain contained between the residues 115 to 288 is made by a five parallel β-stranded sheet surrounded by α helices. A third domain formed by residues 511-556 extends towards the other monomer. The active site is located between the C-terminal domain and the small domain. Each of the two active sites of the homodimer is composed of amino acids of one monomer and the His388 residue of the other monomer (Figure 1A). The binding mode of the 5PAED compound (5j) to hPGI was not completely as expected.

While a 5PAED (5j) molecule was unambiguously bound in the active site of one subunit of the homodimer (Figure 1B), the residual electron density at the other subunit did not correspond to 5PAED. We interpreted this density as a bound 5PAA (2) moiety (Figure 1A). Similarly, one molecule of 5PAED (5j) and one molecule of 5PAA (2) are found in each of the other two monomers in the asymmetric unit. The co-crystallization process extended over a period of 17 days which likely allowed 5PAED (5j) to decompose to 5PAA (2) and ethylenediamine, either by hydrolysis or by reversible formation of the lactone precursor 3, which further hydrolyzed to 5PAA (2). Indeed, we checked by NMR analysis (see Supp. Info SI4) that 5PAED (5j) was stable enough in aqueous medium for kinetic evaluation of its inhibitory properties, but most likely not sufficiently stable to avoid partial decomposition during the co-crystallization process. Upon soaking of hPGI crystals in mother liquid containing 5PAED, we exclusively identified 5PAA bound at the enzyme active sites. 5PAED (5j) most likely entirely decomposed to 5PAA (2) during the one day equilibration time of a 5 mM 5PAED drop against the crystallization buffer or during the subsequent 8 h soaking time of the hPGI crystals in the equilibrated inhibitor drop. The structure of hPGI-5PAA (2) was solved at 1.95 Å (PDB code 6XUI). Except for the locations of water molecules, the 3D structure is identical to the deposited hPGI-5PAA 2.5 Å structure (PDB code 1NUH) [START_REF] Davies | The structure of human phosphoglucose isomerase complexed with a transition-state analogue[END_REF], and also very similar to the 1.9 Å rabbit muscle PGI-5PAA (RmPGI-5PAA) structure (PDB code 1G98) [9], and will therefore not be further described. Figure 2 shows an overview of the interactions of 5PAED (5j) with active site residues of hPGI. The residues correspond to those identified in other PGI structures such as RmPGI-5PAA (PDB code 1G98), RmPGI-5PAH (PDB code 1KOJ) and hPGI-5PAA (PDB code 1NUH) [8][9][START_REF] Davies | The structure of human phosphoglucose isomerase complexed with a transition-state analogue[END_REF]. The same residues are found at the active site with, in addition, the implication of Gly271. Eleven residues form the active site of the enzyme and provide a network of hydrogen bonds with 5PAED (5j), including six residues (Ser159, Ser209, Lys210, Lys518, Thr211 and Thr214) which anchor the phosphoryl group, showing its importance for binding of the inhibitor. Ser159 is involved in a H-bond with an oxygen atom (2.9 Å) of the phosphoryl group via its hydroxyl group. This same oxygen is interacting with the C nitrogen of Lys210 at a distance of 2.9 Å. Another oxygen atom of the phosphoryl group establishes three Hbonds at distances of 2.9, 2.0 and 3.1 Å respectively with the hydroxyl groups of Ser209 and the two threonines Thr214 and Thr211. The latter also interacts via its NH group with another oxygen atom of the phosphoryl group at a distance of 3.1 Å. Lys518 is in interaction with O5 oxygen atom of the phosphoryl group. Indeed, and according to the structure of RmPGI with F6P (PDB 1HOX) [7], this lysine, which does not interact directly with the substrate in its cyclic form, approaches after opening of the cycle to interact with the substrate under its linear form. This structure supports the results already described in the literature highlighting the importance of phosphoryl group in the anchoring of the inhibitor.

The hPGI-5PAED structure confirms that Glu357 is correctly positioned to act as the catalytic base of the isomerization process. Glu357 is in interaction with nitrogen N1 (2.6 and 2.9 Å) of 5PAED which corresponds to the C1 carbon of the substrate F6P and is at a distance of 3.3 Å of the atom corresponding to the C2 of G6P: it is at the level of these two carbon atoms that the catalytic base pulls the hydrogen from the substrate to bring to the 1,2-cis-enediolate HEI. Gly271, for its part, establishes a hydrogen bond with N2 nitrogen of 5PAED [d(NH-N2) = 2.9 Å]. Arg272 is also well positioned to interact with this same nitrogen. The latter is probably unprotonated (NH 2 form, pK a ~ 9) so that it can interact with the nitrogen of arginine, which is very probably protonated at the active site (pK a ~ 12). This structural study shows that, contrary to what we initially postulated, the terminal amine functional group of 5PAED does not interact with Glu357 via an ionic bond, but establishes three H-bonds with Arg272 and Gly271 (from one monomer). The polyhydroxylated skeleton participates via four H-bonds when mooring the inhibitor at the active site. His388 (from the second monomer) participates in two of these H-bonds: it interacts via its carbonyl and the imidazole nitrogen with the two oxygen atoms O2 (2.6 Å) and O4 (3.0 Å), respectively, of 5PAED (5j). The latter corresponds to the O5 cyclic oxygen atom of F6P, which confirms the role attributed to His388 derived from the structure of RmPGI-F6P (PDB 1HOX) [7]: His388 catalyzes the ring opening step of the cyclic substrate by protonating the F6P furanose oxygen or pyranose G6P atom.

Gly158 completes the hydrogen bonding network by interacting via its nitrogen with the O2 and O3 oxygen atom of the inhibitor. Detailed structural alignments of the hPGI-5PAED (5j) structure with those of RmPGI-5PAH (1) (PDB code 1KOJ, [8]) depicted in Figure 3A and RmPGI-5PAA (2) (PDB code 1G98, [9]) depicted in Figure 3B show small but significant differences. Indeed, Lys518 that interacts with O4 and O5 oxygen atoms of inhibitors 5PAH (Figure 3A) or 5PAA (Figure 3B), reorients to interact with O4 and the Thr214 side chain oxygen atom. Another important observation can be made: the good superposition of the terminal -NH 2 functional group of the 5PAED of the hPGI-5PAED structure with the hydroxyl group of the -NHOH functional group (distance 1.5 Å) of the hPGI-5PAH complex structure (Figure 3A). This hydroxamic acid functional group provides two H-bonds with the two nitrogens of Arg272. It is replaced by the NH 2 group of 5PAED (5j) which, in addition to interacting with Arg272 via two H-bonds, involves an additional third H-bond to Gly271. In the case of the RmPGI-5PAA structure (Figure 3B), a water molecule is well-positioned between the carboxylate functional group of 5PAA and Arg272, also offering two H-bonds to this residue. Here again one can observe a good superposition of the terminal -NH 2 functional group of 5PAED with this water molecule (distance 1.0 Å). Overall, the crystallographic study of the hPGI-5PAED complex shows that the active site of PGIs, and more particularly of hPGI is relatively tolerant for modification of the head "C-1" part of its inhibitors, unlike PMIs. This observation is supported by kinetic assays which gives K i values in the same order of magnitude for 5PAH (1), 5PAA (2), and 5PAED (5j), respectively 0.19, 0.15, and 0.7 µM.

Conclusions

In conclusion, we report the synthesis of new potentially anti-metastatic compounds, that are good to excellent inhibitors of hPGI-AMF and which do not mimic the 1,2-cis-enediolate functional group of the HEI involved in the F6P to G6P isomerization reaction, both in terms of coplanarity and steric hindrance. One of these new PGI inhibitors, namely N-(5-phosphate-D-arabinoyl)-2-(amino)ethanamine (5PAED, 5j) displays a low K i value = 0.7 µM. The X-ray structure of this inhibitor within the hPGI-AMF active site was resolved at 2.38 Å resolution and showed its unexpected mode of interaction at the active site of the enzyme and explained its strong inhibitory power. Overall, the crystallographic study of the hPGI-5PAED complex shows that the active site of PGIs, and more particularly of hPGI, as supported by kinetic assays, is relatively tolerant for modification of the head "C-1" part of its inhibitors, unlike other aldose-ketose isomerases like PMIs. The fact that hPGI inhibitors might be functionalized at the head C-1 part offers the possibility of future design of original bioreceptors to be grafted on biosensors for the efficient detection of the cancer biomarker AMF-PGI. We recently reported such a biosensor using 5PAED (5j) as bioreceptor [START_REF] Ahmad | Electrochemical detection of the human cancer biomarker 'autocrine motility factor-phosphoglucose isomerase' based on a biosensor formed with a monosaccharidic inhibitor[END_REF]. Here, we demonstrate how such a bioreceptor could interact at the active site of hPGI and open the door to the development of future original carbohydrate-based biosensors for the early detection of biomarkers of therapeutic interest.

Experimental section

Chemistry

Reagents and methods

All chemical reagents were of analytical grade, obtained from Sigma Aldrich, and used without further purification. Solvents were obtained from SDS or VWR. MeOH was dried by refluxing with Mg/I 2 , then distilled and used immediately. Flash chromatography was performed using silica gel (35-70 μm, E. Merck) under N 2 pressure. Unless otherwise stated, all organic extracts were dried over Na 2 SO 4 and filtered. Concentration of solutions was performed under diminished pressure at temperature < 30 °C using rotary evaporator. All air-and moisture-sensitive reactions were performed under an atmosphere of argon. Analytical TLC was performed using Silica Gel 60 F 254 pre-coated aluminum plates (E. Merck). Spots were visualized by treatment with 5% ethanolic H 2 SO 4 followed by heating and/or by absorbance of UV light at 254 nm. NMR spectra (see Supp.

Info. SI6) were recorded at 297 K in D 2 O with Bruker DRX 300 ( 1 H at 300.13 MHz and 13 C at 75.47 MHz) or DPX 250 ( 1 H at 250.13 MHz, 13 C at 62.90 MHz, and 31 P at 101.26 MHz) spectrometer using NMR Notebook 2.0 WinNMR software. Chemical shift are reported in ppm (δ) and coupling constants in Hz (J ij ). 1 H NMR spectra were referenced to internal residual HOD (δ 4.78). 13 C NMR spectra were referenced to dioxane (δ 67.4). In most cases, COSY, HSQC, and/or DEPT135 NMR spectra were recorded for assigning resonances. Infrared spectra were recorded with a FTIR Bruker IFS-66 spectrometer. High-resolution mass spectrometry (HRMS) analyses were performed by electrospray with negative (ESI -) ionization mode.

General procedure for the synthesis of N-substituted 5-phosphate-D-arabinonamide derivatives 5a-k

In a round-bottom flask placed under argon, containing D-arabinono-1,4-lactone 5-phosphate 3 (0.50 mmol) in 5 mL anhydrous methanol (DMF for 5f synthesis), primary amine 4a-k (2.0 mmol, 4 eq) was added. The reaction mixture was stirred at rt for 1.5 h. Following concentration of the reaction mixture, the residue was eluted with water on a Dowex 50WX4 ion-exchange column (100-200 mesh, Na + form) and lyophilized to afford the target compound 5a-k. 13 2). Table 2. Crystallographic data collection and refinement statistics of hPGI-5PAED (5j) complex. [42] using the structure of hPGI at 1.6 Å resolution (PDB code 1IAT). The model was build semiautomatically after density modification using Parrot, REFMAC5 and BUCCANEER [START_REF] Cowtan | The Buccaneer software for automated model building. 1. Tracing protein chains[END_REF]. At this step (for the hPGI-5PAED complex), 2112 residues were uniquely allocated to 4 chains (90.3% of completeness by residues built) with a FOM value of 0.86 and R/Rfree = 30/35. The model was further improved by iterative cycles of manual rebuilding using COOT [START_REF] Emsley | Coot: model-building tools for molecular graphics[END_REF] and refinement using REFMAC5 program [START_REF] Murshudov | REFMAC5 for the refinement of macromolecular crystal structures[END_REF]. This yielded a R/R free factor of 26/32%. The final model contains 4 chains of residues 1 to 556, 2 molecules of 5PAED, 2 molecules of 5PAA and 271 water molecules.

Crystallographic data collection

The structure of the hPGI soaked with 5PAED was solved using the same protocol and we observed a molecule of 5PAA instead 5PAED. We then obtained the structure of a hPGI-5PAA complex at

Accession numbers

Coordinates and structure factors have been deposited in the Protein Data Bank for hPGI-5PAED (5j) and hPGI-5PAA (2) complexes with accession number 6XUH and 6XUI, respectively.

Scheme 1 .

 1 Scheme 1. (A) Reversible reaction catalyzed by phosphoglucose isomerase (PGI) between /-Dglucopyranose 6-phosphate (/-G6P) and /-D-fructofuranose 6-phosphate (/-F6P) through the 1,2-cis-enediolate high-energy intermediate (HEI). (B) Representation of 5-phosphate-Darabinohydroxamic acid (5PAH, 1) [3] and 5-phospho-D-arabinonate (5PAA, 2) [4], two strong PGI

Figure 1 .

 1 Figure 1. (A) Three-dimensional X-ray crystal structure of the complex between homodimeric human phosphoglucose isomerase (cartoon presentation with one subunit colored in pink and the other in yellow) and inhibitors 5-phospho-D-arabinonate (5PAA, 2) and N-(5-phosphate-Darabinoyl)-2-(amino)ethanamine (5PAED, 5j) respectively located in each of the two enzyme active

Figure 2 .

 2 Figure 2. Representation of the active site of human phosphoglucose isomerase (hPGI) in complex with N-(5-phosphate-D-arabinoyl)-2-(amino)ethanamine (5PAED, 5j). Only amino acids directly in interaction with the inhibitor are depicted. H-Bonds are drawn as grey dashed lines (H-bond distances were omitted for clarity, but are available in Supp. Info. SI5). Residues of monomer A are shown in light brown, and that of monomer B (His388) are shown in pale yellow.

Figure 3 .

 3 Figure 3. Active site view of the structural alignments of human phosphoglucose isomerase (hPGI) in complex with N-(5-phosphate-D-arabinoyl)-2-(amino)ethanamine (5PAED, 5j) depicted in light brown and: (A) rabbit muscle PGI in complex with 5-phosphate-D-arabinohydroxamic acid (5PAH, 1, light blue, PDB 1KOJ [8]); (B) rabbit muscle PGI in complex with 5-phospho-D-arabinonate (5PAA, 2, light green, PDB 1G98 [9]). Some amino acids were omitted for clarity. H-Bonds are drawn as dashed lines and distances are given in angström.

4 (

 4 C NMR (D 2 O, 75 MHz) δ C 175.5 (C-1), 70.9 (C-3), 70.7 (C-2), 69.8 (C-4), 65.1 (C-5), 37.9 (C-6), 32.C7), 14.2 (C-8); 31 P NMR (D 2 O, 121.5 MHz) δ P 4.7 ppm; HRMS (ESI-) m/z: Calcd for C 8 H 17 NO 8 PS [M-2Na+H] -318.0413, Found 318.0420.4.1.2.2. N-[(5-Phosphate-D-arabinoyl)-1-(imidazol-2-yl)methanamine (5PAIm, 5b)White solid, 100% yield; FTIR  3500-3200 (OH, NH), 3000-2820 (CH, CH 2 , CH arom ), 1652 (C=O), 1544 (NH), 1436 (C=C), 1089 (P-O, C-O, C-N), 975 (P-O) cm -1 ; 1 H NMR (D 2 O, 300 MHz) δ H 6.96 (m, 2H, H 8 +H 9 ), 4.45 (m, 3H, H 2 +H 6 +H 6' ), 3.99-3.78 (m, 4H, H 3 +H 4 +H 5 +H 5' ) ppm;13 C NMR (D 2 O, 75 MHz) δ C 176.0 (C-1), 144.8 (C-7), 122.0 (C-8, C-9), 71.0 (C-3), 70.9 (C-2), 69.9 (d, JC 4,P = 4.6 Hz), 65.0 (C-5), 36.3 (C6) ppm; 31 P NMR (D 2 O, 121.5 MHz) δ P 4.9 ppm; HRMS (ESI-) m/z: Calcd for C 9 H 15 N 3 O 8 P [M-2Na+H] -324.0597, Found 324.0600. 4.1.2.3. N-(5-Phosphate-D-arabinoyl)-1-(1-methylimidazol-2-yl)methanamine (5PAMIm, 5c) Brown solid, 100% yield; FTIR  (cm -1 ) 3500-3200 (OH, NH), 3000-2800 (CH, CH 2 , CH arom ), 1648 (C=O), 1534 (NH), 1451-1417 (C=C), 1091 (P-O, C-O, C-N), 977 (P-O); 1 H NMR (D 2 O, 300 MHz) δ H 6.97 (s, 1H, H 8 ), 6.83 (s, 1H, H 9 ), 4.43-4.40 (m, 3H, H 2 +H 6 +H 6' ), 3.88-3.72 (m, 4H, H 3 +H 4 +H 5 +H 5' ), 3.50 (s, 2H, H 10 ); 13 C NMR (D 2 O, 75 MHz) δ C 176.6 (C-1), 144.5 (C-7), 126.0 (C-8), 122.8 (C-9), 71.5 (C-3), 71.2 (C-2), 70.3 (d, C-4, JC 4,P = 5.2 Hz), 65.2 (C-5), 35.0 (C-6), 32.5 0.1 M Tris, pH 8.5, 15% glycerol and 5 mM 5PAED before flash cooled in liquid nitrogen. Diffraction data were collected at the Proxima 2 beam line, synchrotron SOLEIL (Gif-sur-Yvette, France), at 100 K on a EIGER X 9M area detector. The crystal belongs to space group P4 3 with cell dimensions a = b = 96.110 Å and c = 271.450 Å and contains four PGI molecules per asymmetric unit (see Supp. Info. SI3), with an estimated solvent content of 42.4%. Crystals from hPGI alone were also soaked 8 h in a buffer containing 5 mM of 5PAED (in 50 mM sodium chloride, 20 mM Tris, pH 7.5). They were cryoprotected in 32.5% PEG4000, 0.1 M magnesium chloride, 0.1 M Tris, pH 8.5, 13.9% glycerol and 5 mM 5PAED and flash cooled in liquid nitrogen. Diffraction data were collected at the Proxima 1 beam line, synchrotron SOLEIL (Gif-sur-Yvette, France), at 100 K on a Dectris Pilatus 6M detector. The hPGI crystal soaked with 5PAED belongs to space group P2 1 2 1 2 1 with cell dimensions a =80.55 Å, b = 106.95 Å, c = 270.40 Å and contains four PGI molecules per asymmetric unit, with an estimated solvent content of 47.8%. Data were indexed and processed with XDS [41] (Table

0 a

 0 parameters (Å, °) a = 96.11, b = 96.11, c = 271.45, α = β = γ = 90.= 80.55, b = 106.95, c = 270.4, α = β = γ = 90.

Table 1 .

 1 Kinetic parameters describing the inhibition of rabbit muscle and human phosphoglucose

isomerases with compounds 1, 2 and 5a-k a

  Competitive inhibition; c In ref.[3]: K i = 0.195 ± 0.006 µM (50 mM TRIS HCl buffer, pH 8.0, 30 °C); d In ref.[4]: K i = 0.46 µM (100 mM MOPS buffer, pH 7.0, 30 °C); e Value not determined; f Mixed inhibition.

		IC 50 (µM)	K i (µM)	K i ' (µM)
	Inhibitor	RmPGI	hPGI	RmPGI	hPGI	hPGI
	1 b 2 b e 0.888 ± 0.006 0.796 ± 0.006	0.21 ± 0.06 c e	0.19 ± 0.06 e	-e
	5j f	1.00 ± 0.02	0.91 ± 0.09	1.0 ± 0.1	0.7 ± 0.1	2.3 ± 0.1
	5k f	2.86 ± 0.02	0.95 ± 0.05	e	0.52 ± 0.07	2.6 ± 0.4
	a Conditions: 50 mM HEPES buffer, pH 7.1, 25 °C (RmPGI : rabbit muscle phosphoglucose
	isomerase, hPGI: human phosphoglucose isomerase); b

  (65.3) † Values in parentheses refer to the highest resolution shell : 2.52 to 2.38 or 2.07 to 1.95 for the co-crystal or the soaked complex, respectively Structure of hPGI-5PAED complex was solved by molecular replacement (program PHASER)

	Resolution limits † (Å) Number of observations † Number of unique reflections R-meas † (%) Completeness † (%) I/σ † (I) CC (1/2) Number of non-hydrogen atoms (protein/ligands/water) R/R free (%) R.M.S.D. Bonds (Å)/angles (°) Average temperature factors (protein/ligands/waters) 99.8Refinement statistics 47.32 -2.38 (2.52 -2.38) 48.26-1.95 (2.07-1.95) 339425(53656) 627727(98542) 97562(15379) 170097(26904) 11.9(97.4) 10.9(111.7) 99.2(97.0) 99.3(98.1) 8.12(1.57) 10.40(1.33) 99.5(82.1) 17748/66/271 17748/144/1469 26.61/31.69 17.07/21.93 0.01/1.44 0.02/1.85 75.26/64.6/42.5 31.29/44.13/38.56 4.5. Structure determination and refinement statistics

  1.95 Å resolution. The final hPGI-5PAA model contains 4 chains of residues 1 to 556, 4 molecules of 5PAA, 4 molecules of polyethylene glycol, 2 molecules of glycerol and 1469 water molecules with a R/Rfree of 17/22%. Molecular structures, figures and scheme were generated with Pymol 2.0.5, Gimp 2.6.6, Chemdraw Pro 12.0 (Perkin-Elmer Informatics Inc., Cambridge, MA, USA) and Powerpoint softwares. Crystallographic data collection and refinement statistics of hPGI-5PAED complex are given in Table2.

Acknowledgements

LA conducted this work in fulfilment of the requirements for a Ph.D. from the University of Paris-Saclay. One of the team (ECBB) is a member of the Laboratory of Excellence in Research on Medication and Innovative Therapeutics (LERMIT) supported by a grant from the Agence Nationale de la Recherche (ANR-10-LABX-33). This work received support from the French Infrastructure for Structural Biology (FRISBI ANR-10-INSB-0501). We thank the staff of the Proxima1 and Proxima2 beamlines at the SOLEIL synchrotron for assistance during the diffraction data collections. Jean-Pierre Baltaze (ICMMO) is gratefully acknowledged for his valuable help in NMR spectrocopy.

Declaration of Competing interest

The authors have declared no conflict of interest.

Supplementary Information

Supplementary data to this article can be found online. Supp. Info. SI6: 13 C and 1 H NMR spectra of compounds 5a-k.

Supp