Miyanou Rosales-Hurtado

Filomena Sannio

Lindita Lari

Federica Verdirosa

Georges Feller

Elodie Carretero

Yen Vo-Hoang

Patricia Licznar-Fajardo

Jean-Denis Docquier

Laurent Gavara
email: laurent.gavara@umontpellier.fr

Zidovudine-β-Lactam Pronucleoside Strategy for Selective Delivery into Gram-Negative Bacteria Triggered by β-Lactamases

Keywords: lactamase, bacterial resistance, Gram-negative pathogen, zidovudine

come

In early 1900's, bacterial infections represented one of the most significant causes of death worldwide. This plague has been circumvented by the development of several classes of antibiotics, including the β-lactams, which show optimal efficacy and selectivity. [START_REF] Fernandes | β-Lactams: Chemical Structure, Mode of Action and Mechanisms of Resistance[END_REF] Almost immediately after the introduction of penicillin, penicillin-resistant strains of Staphylococcus aureus emerged in the clinical setting. More generally, due to the natural selective pressure when exposed to exogenous agents, bacteria developed an arsenal of mechanisms to escape the therapeutic effect of antibiotics. [START_REF] Peterson | Antibiotic Resistance Mechanisms in Bacteria: Relationships Between Resistance Determinants of Antibiotic Producers, Environmental Bacteria, and Clinical Pathogens[END_REF] Although the fast discovery and development of new antibacterial drugs allowed to adequately address the problem of resistance (a period known as the golden age of antibiotic discovery), the situation drastically changed in the late 90's with the emergence of multi-drug resistance bacteria and the progressive disinterest of pharmaceutical companies in antibiotic R&D, leading to a substantial erosion of available therapeutic options. [START_REF] Silver | Challenges of Antibacterial Discovery[END_REF] Nowadays, besides multidrug-resistant bacteria, the prevalence of isolates showing extensively-drug or pan-drug resistance phenotypes is increasing in the clinical setting. 4,[START_REF] Cassini | Attributable Deaths and Disability-Adjusted Life-Years Caused by Infections with Antibiotic-Resistant Bacteria in the EU and the European Economic Area in 2015: A Population-Level Modelling Analysis[END_REF] Currently, global deaths attributable to antimicrobial resistance is estimated to exceed 1.2 million, raising worldwide awareness. [START_REF] Miethke | Towards the Sustainable Discovery and Development of New Antibiotics[END_REF] Among other responses (antibiotic stewardship, better and faster diagnosis), the development of new antibiotics remains urgent, especially against the so-called "critical priority" pathogens, as established by WHO, which include carbapenem-resistant Gram-negative bacteria (Enterobacterales, Acinetobacter, and Pseudomonas). [START_REF]Tackling drug-resistant infections globally : final report and recommendations / the Review on Antimicrobial Resistance chaired by Jim O'Neill[END_REF] β-lactam antibiotics still represent the mainstay of antibacterial therapy, accounting for more than 50% of antibiotics prescribed worldwide. [START_REF] Bassetti | Current and Future Perspectives in the Treatment of Multidrug-Resistant Gram-Negative Infections[END_REF] Numerous families were developed such as penicillins, cephalosporins, monobactams, and carbapenems, and are all characterized by a four membered β-lactam ring, crucial for their antibacterial activity. A major mechanism of β-lactam resistance in Gram-negative pathogens is the production of enzymes able to hydrolyze the amide bond of the β-lactam ring, called β-lactamases. [START_REF] Bush | Epidemiology of β-Lactamase-Producing Pathogens[END_REF] They are classified into 4 molecular classes (A, B, C and D) but can be divided into two main families based on their mechanism of action. The Serine-β-Lactamases (SBLs, classes A, C and D) are related to serine hydrolase enzymes (SBLs) [START_REF] Lohans | New Delhi Metallo-β-Lactamase 1 Catalyzes Avibactam and Aztreonam Hydrolysis[END_REF] and the Metallo-β-Lactamases (MBLs, class B) are characterized by the presence of one or two zinc atoms into the active site. [START_REF] Bahr | Metallo-β-lactamases in the Age of Multidrug Resistance: From Structure and Mechanism to Evolution, Dissemination, and Inhibitor Design[END_REF] Their hydrolytic action results from nucleophilic attack of serine residue or water molecule, according to β-lactamase type, on the β-lactam carbonyl. β-lactamases show an extraordinary diversity (with > 7,800 unique enzymes) and evolutionary potential, leading to the emergence of clinical variants (by either mutation a or the acquisition of novel from the environmental resistome) with peculiar substrate profile, such as extended-spectrum β-lactamases (ESBLs) or carbapenem-hydrolyzing enzymes (e.g., KPC or OXA-type carbapenemases). [START_REF] Naas | Beta-Lactamase Database (BLDB) -Structure and Function[END_REF] The latter represent a major threat as they confer resistance to the last-resort and life-saving carbapenems, a major therapeutic resource for the treatment of infections caused by MDR isolates. Furthermore, resistant bacterial strains commonly produce several β-lactamases, sometimes both serine-and metallo-carbapenemases. In this context, the development of new effective βlactam-based therapies is still necessary but extremely challenging. [START_REF] Docquier | An Update β-Lactamase Inhibitor Discovery and Development[END_REF] One strategy is represented by the development of combinations with β-lactamase inhibitors, such as clavulanate or, more recently, avibactam or vaborbactam. Nevertheless, resistance to such new combinations is already observed in the clinical setting, highlighting the need for further innovation in the field. [START_REF] Bush | Overcoming β-Lactam Resistance in Gram-Negative Pathogens[END_REF] The development of new drugs based on original strategies is fundamental to tackle this growing major threat. [START_REF] Laws | Antibiotic Resistance Breakers: Current Approaches and Future Directions[END_REF] In this context, the design of compounds able to reverse bacterial resistance to β-lactam agents, by exploiting the specific β-lactam fission mechanism leads to a new type of hybrid antibacterial drugs. 16- 18 A first generation of dual agents was developed based on a fissile covalent combination of two antibiotics (Scheme 1). Several studies have explored the association of cephalosporin with a fluoroquinolone agent (such as Ro 23-9424, 1). [START_REF] Pfaller | RO 23-9424, a New Cephalosporin 3'-Quinolone: In-Vitro Antimicrobial Activity and Tentative Disc Diffusion Interpretive Criteria[END_REF] With such compounds, the penicillin-binding proteins (PBPs) are initially irreversibly inactivated through the formation of a covalent adduct with the βlactam moiety, which, after subsequent spontaneous rearrangement release fluoroquinolone moiety, inhibiting bacterial DNA gyrases (Scheme 1). The chemical codrug activation has been widely studied and different leaving groups have been introduced as well as self-immolative linkers to improve stability and other biological properties. [START_REF] Wang | Design and Synthesis of 3-Hydroxy-Pyridin-4(1H)-Ones-Ciprofloxacin Conjugates as Dual Antibacterial and Antibiofilm Agents against Pseudomonas Aeruginosa[END_REF][START_REF] Wang | A Fusion Protein of RGD4C and β-Lactamase Has a Favorable Targeting Effect in Its Use in Antibody Directed Enzyme Prodrug Therapy[END_REF][START_REF] Christenson | Pharmacokinetics of Ro 23-9424, a Dual-Action Cephalosporin, in Animals[END_REF] Due to the ever-expanding heterogeneity and simultaneous and increased production of β-lactamases in MDR clinical isolates, inactivation of PBPs mediated by such codrugs is seriously compromised. Prodrug 2, based on a similar approach by exploiting the β-lactamase-mediated release of a fluoroquinolone from a β-lactam scaffold, was also developed. [START_REF] Evans | Exploitation of Antibiotic Resistance as a Novel Drug Target: Development of a β-Lactamase-Activated Antibacterial Prodrug[END_REF] In this case, inactivation of PBPs was not desirable, and only β-lactamases producers were targeted. Recently, specific prodrugs to fight MBLs were designed with a cephalosporin-MBL inhibitor conjugate 3. [START_REF] Van Haren | Cephalosporin Prodrug Inhibitors Overcome Metallo-β-Lactamase Driven Antibiotic Resistance[END_REF][START_REF] Jackson | A Cephalosporin Prochelator Inhibits New Delhi Metallo-β-Lactamase 1 without Removing Zinc[END_REF][START_REF] Tehrani | Novel Cephalosporin Conjugates Display Potent and Selective Inhibition of Imipenemase-Type Metallo-β-Lactamases[END_REF] The hydrolysis of the β-lactam ring is mediated by the MBL enzymes that will then be inhibited by the released entity. This adjuvant strategy needs a combination with a β-lactam agent (e.g., meropenem) to provide bacterial inhibition. Scheme 1. Some dual β-lactam agents described in the literature enzymatically activated (Enz: PBPs or βlactamases) All these approaches are mainly based on cephalosporin core as the promoiety. The use of clinically valuable β-lactam antibiotic scaffolds, such as especially third-generation cephalosporin (e.g., ceftazidime, ceftriaxone, cefotaxime), which still represents the most widely used β-lactam subclass, is expected to maintain and even increase global bacterial resistance and compromise the antibacterial activity of potentially life-saving new β-lactam agents (e.g., cefiderocol). [START_REF] Aoki | A New Siderophore Cephalosporin Exhibiting Potent Activities against Pseudomonas Aeruginosa and Other Gram-Negative Pathogens Including Multi-Drug Resistant Bacteria: Structure Activity Relationship[END_REF] We here propose an alternative approach to disrupt this domino effect leading to worsening resistance by protecting crucial β-lactam therapeutic subclasses through the investigation of a novel design of β-lactamase-activated prodrugs.

◼ RESULTS AND DISCUSSION

Prodrugs design. Antibiotic therapies are never specific to the bacterial pathogens and gut microbiota disturbance, [START_REF] Ramirez | Antibiotics as Major Disruptors of Gut Microbiota[END_REF] or superinfection phenomenon [START_REF] Patangia | Impact of Antibiotics on the Human Microbiome and Consequences for Host Health[END_REF] are very commonly observed side effects. To provide selective treatments, it is necessary to specifically target (opportunistic) pathogens, especially multidrug-resistant strains, which represent a common cause of difficult-to-treat hospital-acquired infections. Our approach relies on the consideration that, in Gram-negative bacteria, the production of one-or more β-lactamases, more importantly carbapenem-hydrolyzing enzymes, represents a common resistance mechanism found in resistant strains, where the level of β-lactamase production often correlates with the level of resistance. [START_REF] Bush | Epidemiology of β-Lactamase-Producing Pathogens[END_REF] Therefore, the use of the β-lactam motif as a prodrug carrier will provide a new way to hijack one of the most common bacterial resistance mechanisms and offer the opportunity to remodulate the spectrum of activity of antibacterial compounds, limited to βlactamase producers, with potentially beneficial effects such as (a) reducing the selective pressure contributing to the dissemination of resistant strains and (b) limiting the undesirable effect of broadspectrum antibiotics on the gut microbiota and the damaging consequences of antibiotic-associated dysbiosis. We decided to focus on the structurally simplest β-lactam core, thus leading to a novel monobactam-based prodrug approach. Interestingly, this β-lactam subclass is underexploited and aztreonam is the only monobactam agent, thus sharing this structural minimalism (although with some structural specificities), to be approved for the treatment of bacterial infections in humans. [START_REF] Ramsey | A Review of the Pharmacokinetics and Pharmacodynamics of Aztreonam[END_REF] To prevent any anti-PBP activity and avoid β-lactamase stability (as the efficient cleavage of the β-lactam is actually desired here), all usual β-lactam designs, such as aminothiazole ring, oxime moiety, or βlactam substitutions, will be avoided. [START_REF] Decuyper | In Silico Design and Enantioselective Synthesis of Functionalized Monocyclic 3-Amino-1-Carboxymethyl-β-Lactams as Inhibitors of Penicillin-Binding Proteins of Resistant Bacteria[END_REF][START_REF] Decuyper | Antibacterial and β-Lactamase Inhibitory Activity of Monocyclic β-Lactams[END_REF][START_REF] Poeylaut-Palena | A Minimalistic Approach to Identify Substrate Binding Features in B1 Metallo-β-Lactamases[END_REF] At position 3 (R part, Scheme 2) of the azetidinone ring, we focused on first generation side chains of β-lactam agents to combine optimal β-lactamase recognition, while protecting the most effective substitutions present in marketed drugs. Thus, a list of side chains was drawn up from known derivatives (Scheme 2): penicillin V (a), clavulanic acid (c), penicillin G (g), cephalothin (h), reported SBL inhibitors (d and e), [START_REF] Buynak | Cephalosporin-Derived Inhibitors of β-Lactamase. Part 4: The C3 Substituent[END_REF] inspired from serine-protease inhibitors (f) [START_REF] Shah | Orally Active Beta-Lactam Inhibitors of Human Leukocyte Elastase-1. Activity of 3,3-Diethyl-2-Azetidinones[END_REF] or chemical intermediate (b). The next step consisted in the selection of the antibacterial agent to be released. Drug repurposing, i.e., the use of existing drugs for a different therapeutic application is a common strategy presenting potential advantages, such as decreased costs and minimized risks during drug development. [START_REF] Zhan | Drug Repurposing: An Effective Strategy to Accelerate Contemporary Drug Discovery[END_REF] In the complicated field of antibiotics, numerous active substances have been considered and one of the most promising class is represented by biomolecules belonging to the nucleoside analog class. [START_REF] Liu | Drug Repurposing for Next-Generation Combination Therapies against Multidrug-Resistant Bacteria[END_REF] Nucleoside Analogs (NAs) are indeed a major therapeutic class, mainly known as antiviral and anticancer agents. They display pleiotropic effects in all fundamental genetic information processes in both eukaryotic and prokaryotic cells. For now, the most relevant NA studied for its antibacterial activity is zidovudine (AZT, 4, Scheme 2). Initially developed as an anticancer agent, it is currently intensively used in AIDS treatment. [START_REF] Thomson | Nucleoside Analogues as Antibacterial Agents[END_REF] Several studies show it is a potent antibacterial agent against relevant opportunistic pathogens, such as E. coli, one of the first causes of hospitalacquired infections, including on β-lactamase-producing isolates. [START_REF] Elwell | Antibacterial Activity and Mechanism of Action of 3'-Azido-3'-Deoxythymidine (BW A509U)[END_REF][START_REF] Hu | Zidovudine Enhances Activity of Carbapenems against NDM-1-Producing Enterobacteriaceae[END_REF] To obtain the desired pronucleoside 5, AZT has to be covalently attached to the monobactam promoiety. This linker will be achieved by an ether function between the 5' hydroxyl group of AZT and the β-lactam moiety. By masking the phosphorylation site at position 5' of AZT, all potential intrinsic biological activities of the corresponding pronucleoside 5a-h are prevented (Scheme 2). Lactamase-mediated hydrolysis of the β-lactam ring will lead to the unstable intermediate 6, which will spontaneously rearrange to release AZT through a rearrangement and elimination process similar to that described previously. Two sequential approaches were implemented according to the nature of R moiety. First, in order to explore, optimize and develop the synthetic pathway, the side chain of penicillin V was introduced by acylation reaction of L-serine compound 11 to get the starting material 7a (Scheme 4). Second, the same synthesis was performed with a Boc residue as the protecting group (from 7b) allowing access to a broad range of acyl substituents on this position at the end of the synthetic pathway. Carboxylic acid functions of 7a-b were activated by EDCI/HOBt coupling condition, then benzylhydroxylamine was introduced to give corresponding hydroxamate 8a-b. The hydroxyl functions were replaced by a mesyl leaving group, and then the cyclization step was performed in presence of K2CO3 to provide β-lactam compounds 9a-b. The deprotection of hydroxamate function needed the N-O bond cleavage. The use of Raney-Ni amalgam [START_REF] Kiyota | Synthesis of (-)-Tabtoxinine-β-Lactam, the Phytotoxin of Tobacco Wildfire Disease[END_REF] or a two-step process based on hydrogenation on Pd/C and then hydroxylamine deprotection by TiCl3 [START_REF] Mattingly | Titanium Trichloride Reduction of Substituted N-Hydroxy-2-Azetidinones and Other Hydroxamic Acids[END_REF] were shown to be ineffective conditions in our case. Finally, deprotected β-lactam rings 12a-b were obtained by a solution of SmI2 in THF in good yields. [START_REF] Keck | Reductive Cleavage of N-O Bonds in Hydroxylamine and Hydroxamic Acid Derivatives Using SmI2/THF[END_REF] However, this step was highly sensitive to dissolved oxygen level and quality of commercial solution of samarium. Non-acyl substituents were also introduced at position 3 (Substituent R). The commercially available azetidinone 12c was protected by a TBDMS group to provide the corresponding β-lactam 13. The enolate 14 was obtained after the action of LDA and anion-trapping by the addition of chlorotrimethylsilane reagent. This intermediate was not isolated and directly engaged in the next step in presence of LDA to trap an electrophile reagent. [START_REF] Ogilvie | Oxidation of 3-Alkylidene-β-Lactams. A Preparation of 3-Alkenyl-3-Hydroxy-β-Lactams[END_REF] First, the 2-pyridinecarboxaldehyde was used to condense on the enolate form to give a separable mixture of isomers 15d (Z) and 15e (E). In the same way, allyl bromide electrophile was also selected to decorate the β-lactam ring by a substitution reaction to provide the allyl derivative 15f in good yield. Finally, the silyl ether protecting group was removed by a solution of TBAF in THF to give corresponding β-lactam compounds 12d-f. [START_REF] Aoyama | Synthesis and Pharmacological Evaluation of 3-Benzylazetidine-2-One-Based Human Chymase Inhibitors[END_REF] The correct identification between the two Z and E isomers was based on the 1 H NMR chemical shift of the vinylic proton and confirmed by NOE experiments (see SI for detailed spectrum of 12d). [START_REF] Urbach | 3-Alkenyl-2-Azetidinones as Fatty Acid Amide Hydrolase Inhibitors[END_REF] The next stage of the synthesis was done on the different synthesized or commercially available β-lactam precursors 12a-f. To prepare intermediates 16a-f, the nitrogen atom has to be condensed on glyoxalate moiety. Allyl group on the ester function as protecting group has been chosen and the corresponding glyoxalate reagent was prepared in two steps according to the literature method. [START_REF] Buetti-Weekly | Development of a Safe and Scalable Process for the Preparation of Allyl Glyoxalate[END_REF] Hydroxyl intermediates 16a-f can be purified or directly engaged in the next step. The acylation reaction was performed by acetic anhydride to give corresponding protected compounds 10a-f in good yields. The last reaction consisted in Lewis acid activation of acetyl function to generate N-acyl-iminium intermediate able to react with AZT. [START_REF] Wu | Scaffold Diversity from N-Acyliminium Ions[END_REF] Several experimental conditions were investigated: Lewis acid (AlCl3, BF3-Et2O, ZnCl2, LiCl, and Mg(OCl4)2), solvents (THF, DCM, and DMF) or temperatures (-20°C to reflux). Initially, acetylated compound 10a was used as a reference. Two procedures quickly emerged as being the more efficient based on BF3-Et2O in CH2Cl2 at 0°C or AlCl3 in THF at RT. In the first case, the use of BF3-Et2O leads to the N-Acyl iminium intermediate that was trapped by the hydroxyl group of the AZT to provide corresponding conjugate 17a in the form of an inseparable diastereomeric mixture. Nevertheless, in presence of AlCl3, only one diastereomer was obtained with a good yield and next synthesis steps will be done on it. This specificity was valid only for 10a and with other acetylated compounds 10b-f, only BF3-Et2O Lewis acid proved to be efficient, leading each time to a mixture of diastereomers when possible. The last stage of the synthetic pathway consisted in a sequence of successive deprotection reactions with a diversity step on the amino function at position 3 of the βlactam heterocycle. The Boc group of 17b was removed by a solution of TFA in CH2Cl2 at RT to give the deprotected compound 18 with a low yield over two steps. To introduce the N-acyl side chain, the carboxylic acid function of the corresponding substituent was activated by EDCI/HOBt mixture and then condensed on amino compound 18 to provide conjugates 17g-h. It was initially considered the use of a palladium catalyst to remove the allyl protecting group. Finally, the more economical and convenient based-catalyzed ester hydrolysis was successfully applied to give final pronucleosides 5a-h without any significant side product formation. [START_REF] Decuyper | In Silico Design and Enantioselective Synthesis of Functionalized Monocyclic 3-Amino-1-Carboxymethyl-β-Lactams as Inhibitors of Penicillin-Binding Proteins of Resistant Bacteria[END_REF] To prevent any solubility issue, all final compounds 5a-h were purified in reverse phase in presence of ammonium acetate to provide the corresponding sodium salt. During this purification step, some diastereoisomeric mixtures have proven to be separable. In this context, compounds 5f and 5g led to pure diastereoisomers 5f', 5f", 5g', and 5g" for further biological evaluations. Inspired by described methods coupled with the N-acyl iminium chemistry strategy, a straightforward and original synthetic pathway was developed. That allowed the obtention of 10 final pronucleosides 5a-h in 4-9 steps with an overall yield in 5-37% range. All compounds were fully characterized by NMR and mass spectroscopy with a UV purity ≥95% (see supporting information for all spectra). All the pronucleosides compounds obtained were subjected to biological evaluation.

Enzyme assays and reactivity towards various β-lactamases. Initially, kinetic experiments to monitor compound hydrolysis by UV spectroscopy were carried out. β-lactams are well-known to absorb light in the UV range (220-300), allowing the development of direct spectrophotometric assays. [START_REF] Waley | A Spectrophotometric Assay of β-Lactamase Action on Penicillins[END_REF] Sadly, AZT also displayed an even stronger absorbance at these wavelengths, thus preventing any discernible measurement (see SI figures S1-S4). Furthermore, such method would not unambiguously identify the nature of the released product as AZT. To overcome this issue, an indirect approach was implemented by preincubating the compounds with various β-lactamases, the release of AZT being measured as a result of bacterial growth inhibition in a disk diffusion assay of E. coli XL-1 susceptible laboratory strain (MIC value of 0.015 mg.mL -1 for AZT), with suitable negative control (compound incubated without βlactamase). As a positive control of growth inhibition of E. coli XL-1 strain, AZT was spotted on blank disks (quantities, 0.005-10 µg), showing a dose-dependent effect. The final control, performed in parallel in all subsequent experiments, consisted in 0.625µg AZT, resulting in a growth inhibition zone diameter of 40 ± 2 mm (See supporting information Table S1). A representative panel of 12 βlactamases was assayed with all possible molecular classes including various substrate specificities and different modes of action (Table 1). As a model, the pronucleoside 5a was chosen and preincubated for 30 min at 2 µM concentration with each β-lactamase (tested at concentrations 0.5 and 0.01 mg.mL - 1 ). Results are reported in table 1 (all pictures of diffusion disks are provided in SI, figure S5). The highest antibacterial activity, i.e., reflecting the optimal release of AZT from the prodrug, was obtained with class A β-lactamases (19-38 mm). Interestingly, significant compound conversion was also observed at low enzyme concentrations (0.01 mg.mL -1 ), particularly with the KPC-2 carbapenemase (Table 1). Almost similar inhibition levels were obtained at 0.5 mg.mL -1 for class B metallo-β-lactamases although AZT conversion was drastically reduced at lower concentrations, except, to some extent, in the presence of the VIM-2 enzyme. This result is consistent with the substrate profile of metallo-β-lactamases for which monobactams are poor substrates. [START_REF] Bebrone | Metallo-β-Lactamases (Classification, Activity, Genetic Organization, Structure, Zinc Coordination) and Their Superfamily[END_REF] Preferred substrates of class C enzymes (AmpC-type) are generally cephalosporins over monobactams or carbapenems (cephalosporinase activity). Nonetheless, weak 5a conversion was detected with Enterobacter cloacae AmpC and the plasmid-encoded CMY-4 β-lactamase. Among class D enzymes, the narrow-spectrum FUS-1 (OXA-85) yielded a limited release of AZT, but the activity of OXA-46 and, more interestingly, the carbapenem-hydrolyzing OXA-48 β-lactamase, allowed for a conversion rate similar to that observed with other enzymes, although no conversion could be observed in the presence of lower enzyme concentrations. Since class A β-lactamases, and notably the epidemiologically relevant KPC-2 carbapenemase, appeared as the most active enzymes on compound 5a, further biochemical experiments were carried out to further characterize the interaction and KPC-2-mediated hydrolysis of this compound.

Characterization of beta-lactamase-mediated hydrolysis by ITC.

Based on previous panel results, the KPC-2 enzyme was chosen as a model for the determination of the kinetic parameters for the hydrolysis of pronucleosides 5a-h using Isothermal Titration Calorimetry (ITC). [START_REF] D'amico | Kinetics and Energetics of Ligand Binding Determined by Microcalorimetry: Insights into Active Site Mobility in a Psychrophilic α-Amylase[END_REF] Based on the enthalpy value of β-lactam amide bond hydrolysis, kcat and KM values were determined. [START_REF] Wang | Characterization of β-Lactamase Activity Using Isothermal Titration Calorimetry[END_REF] The method was validated by determining the kinetic parameters of hydrolysis of meropenem , yielding values of kcat and KM comparable to that reported in the literature (Table 2). [START_REF] Wang | Phenotypic and Enzymatic Comparative Analysis of the KPC Variants, KPC-2 and Its Recently Discovered Variant KPC-15[END_REF] All considered prodrugs are structurally equivalent except for the nature of the side chain at the 3-position of the β-lactam ring. These experiments allowed to obtain relevant SAR data. First, the effect of this substitution was investigated on the affinity and hydrolytic activity of the KPC-2 enzyme. No enzymatic activity was highlighting the requirement of a more complex side chain for enzyme recognition and hydrolysis. Substitutions by allyl groups, such as in the diastereoisomers 5f' or 5f", did not improve compounds reactivity towards KPC-2. The introduction of a pyridine moiety seemed more favorable. Indeed, with the E-configuration of the carbon-carbon double bond, compound 5e displayed low but measurable turnover rates, although with a relatively high KM value, leading to an overall poor catalytic efficiency (kcat/KM). However, the Z-configurated pyridine 5d showed a 10-fold higher kcat and 10-fold lower KM values, compared to E-configuration, allowing a significant improvement of the catalytic efficiency.

Based on literature data, only E-configuration was explored to design β-lactamase inhibitors (due to the necessary intramolecular cyclization step for the inhibition mechanism), [START_REF] Buynak | Cephalosporin-Derived Inhibitors of β-Lactamase. Part 4: The C3 Substituent[END_REF] but Z-configurated substrates seemed better suited to accommodate themselves into the active site of KPC-type enzymes.

The main type of explored substitution at 3-position was acyl-side chain like most marketed β-lactam agents. The Boc-protected compound 5b displayed unexpected pretty good recognition even, to the best of our knowledge, it was never described in the literature as an appropriate side chain. The two isolated epimers 5g' and 5g" inspired by the benzylic penicillin G side chain were evaluated separately to understand the significance of the stereochemistry of the hemiaminal carbon atom. As expected, only one epimer, the compound 5g", was strongly active whereas the other one 5g' displayed almost no activity. It is a consistent result because, in the β-lactam agent series, it is well known that only one stereochemistry is allowed, the R configuration. The pure epimer 5a with the side chain of the penicillin V showed a similar activity profile than that of 5g", probably with the same carbon stereochemistry.

Unfortunately, the other epimer of 5a was never obtained in pure form to confirm this hypothesis. Finally, the best result was achieved by the analog of the cephalothin 5h characterized by a thiophene residue. The KM value was in the same range as the other best compounds (around 1-4 mM) but the kcat was significantly better (350 s -1 ). The resulting catalytic efficiency for 5h hydrolysis was interestingly similar to that of the substrate meropenem (≈250 mM -1 s -1 ), indicating that a significant rate of conversion of our compounds should be expected with at least some clinically relevant β-lactamases. However, this series of pronucleosides 5a-g is characterized by high values of KM when compared to that of β-lactam substrates, which is probably due to the presence of the monobactam core, generally known to display lower affinity against KPC-type β-lactamase. Hopefully, this is compensated by significantly high turnover rates, exceeding that of meropenem by up to 60-fold. Considering that metallo-β-lactamases showed a surprising ability to convert our monobactam-based pronucleoside, the hydrolysis of compound 5a was also further investigated in the presence of NDM-1 (Table 2). As previously, kinetic parameters of meropenem hydrolysis were measured and compared to that reported in the literature, showing a satisfactory agreement. [START_REF] Ali | The Role of Conserved Residues in the Catalytic Activity of NDM-1: An Approach Involving Site Directed Mutagenesis and Molecular Dynamics[END_REF] Although high KM values were obtained, an expected data based on our current knowledge on MBL spectrum of activity, a significantly high turnover rate (15 s -1 ) could be measured, in agreement with what was observed in earlier disk diffusion-based experiments. This is relevant because it might translate into a moderate, though potentially sufficient, conversion rate to observe AZT release, which exerts its antibacterial activity at very low concentrations.

Chemical and enzymatic stability of pronucleosides. Before any microbiological evaluations, the chemical stability of conjugates in aqueous solution has to be carefully determined. Indeed, zidovudine (AZT) displays a very strong antibacterial activity with MIC values well under 0.5 μg.ml -1 on susceptible stains. [START_REF] Elwell | Antibacterial Activity and Mechanism of Action of 3'-Azido-3'-Deoxythymidine (BW A509U)[END_REF] Thus, even a minor chemical instability with subsequent AZT release may represent a significant biasing the interpretation of in vitro antibacterial susceptibility testing. To monitor the chemical stability, a two-step process was implemented. In the first step, a representative panel of prodrugs (5a, 5d-e, and 5h) were evaluated by tandem mass spectroscopy coupled to HPLC. A 0.9 % NaCl physiological solution, previously used in enzyme assays (see Table 1), was used as the aqueous media and the compounds were further incubated at 37 °C. Calibration curves were obtained for each compound and AZT, and then, stability was monitored over a period of 16h. Almost all considered compounds proved to be completely stable, with no detectable AZT formation, except for compound 5e. In the case of E-pyridine 5e, a quick degradation was observed with the detection of AZT from the first sample associated with decreasing intensity of starting compound (all chromatograms and result tables are provided in SI, Table S4). The underlying degradation mechanism is unknown, but it seems specific for the E configuration (intensity of Z isomer 5d decreases slightly but with no detection of AZT release). To fully demonstrate the specific enzymatic activation of the prodrugs, the same experiments were conducted in presence of CTX-M-15 class A β-lactamase. The same preincubation conditions were used as those described in Table 1, and a full conversion of 5a into AZT was observed after 30 min. To confirm these stability data, the two best compounds 5a and 5h and the presumably unstable 5e were selected for a further NMR-based study. Compounds were solubilized in deuterium water at 37 °C and several NMR spectra were generated for 14 h. NMR characteristic peaks of AZT were carefully monitored, and no significant degradation was observed with 5a and 5h (all spectra are provided in SI, Figures S15-S16). In the case of 5e, AZT peaks emerged after 20 min only, confirming its propensity to self-degradation. Based on NMR integration of proton 5' of AZT, 50% of starting compound 5e was hydrolyzed with subsequent release of corresponding AZT after a period of 6.7 h (see SI Figures S17-S18).

In vitro antibacterial activity of pronucleosides. Only best and most stable compounds 5a and 5h were selected for further microbiological evaluations. AZT was used as the reference compound in such assays. MIC values were first determined using a set of isogenic E. coli BL21 (DE3)-derived laboratory strains producing a single β-lactamase (Table 3, entries 1-5). Based on ITC results and epidemiology relevance, strains producing the KPC-3, NDM-1, IMP-1, and Enterobacter cloacae AmpC β-lactamases were used. It was also verified that all corresponding E. coli strains were highly susceptible to AZT (MIC ranging from 0.03-0.5 μg.mL -1 ). No significant antibacterial activity (16-32 µg.mL -1 ) could be measured for compounds 5a and 5h with the parent strain in the absence of the vector. In agreement with our previous results (including ITC), the strain producing the KPC-3 carbapenemase was highly susceptible to both prodrugs 5a and 5h, with MIC values very similar to that of AZT, indicating a significant rate of conversion of the prodrug. This further confirms the ability of KPC-type β-lactamases to accommodate and efficiently hydrolyze our β-lactam-based carrier moiety also in the context of the bacterial periplasm. Metallo-β-lactamase-producing laboratory strains (IMP-1 and NDM-1) were less susceptible to the pronucleosides, although a moderate level of compound conversion could be observed, more particularly 5a with NDM-1 and 5h with IMP-1. Although difficult to interpret, these data may also reflect the different functional properties of these two enzyme subtypes. Similarly, the E. cloacae AmpC-producing strain showed a strong dependence on the nature of the compound, being able to efficiently convert prodrug 5h only whereas acyl-side-chain equivalent β-lactam agents, amoxicillin and cephalothin, are both known to be good substrates. [START_REF] Jacoby | AmpC β-Lactamases[END_REF] To rule out any spontaneous activation of the prodrug in the culture medium (matrix effect), MIC values of 5a were also determined on all E. coli BL21 (DE3) strains after 6h of incubation at 37°C in the culture medium. No significant modification of MIC value was observed compared to provided values in Table 3 without the incubation step, confirming that lower MICs were relying specifically on the β-lactamase-mediated release of AZT. These results, as previously mentioned, showed that the prodrug compounds were also able to enter into the bacterial periplasm and that these β-lactamases could exert their hydrolytic activity on such substrates in a cellular context. To further confirm this aspect, 2 types of strains were selected with no or very weak β-lactamase production (Table 3, entries 6 and 7). The prodrug 5a showed no growth inhibition on both strains demonstrating the necessity of β-lactamase enzymes to trigger it. The narrower antibacterial spectrum of conjugates allows for a specific targeting of β-lactamaseproducing opportunistic pathogens and not antibiotic-susceptible bacteria found in the healthy gut microbiota. Finally, the activity of such prodrugs was also determined with a small panel of carbapenemase-producing MDR clinical isolates (Table 2, entries 8-11). Although these isolates were susceptible to AZT, the MIC values of the prodrugs were significantly higher. However, we found it extremely encouraging to observe MIC values as low as 4-8 µg.mL -1 on a NDM-1-producing clinical isolate, further supporting the rationale and validity of our approach. In the case of KPC-producing bacteria, only the K. pneumoniae SI-353 strain displayed a moderate inhibition with MIC values of 32 and 16 µg.mL -1 with 5a and 5h, respectively, reflecting the importance of other factors in the susceptibility to our prodrug, such as modifications of the outer membrane permeability. Indeed, these clinical isolates are genetically heterogeneous and thus characterized by a different combination of mechanism of resistance and potentially unknown other factors compromising the activity of our prodrugs. To rule out any potential permeability-related issue, the MICs of the prodrugs were determined in the presence of a sub-inhibitory concentration of colistin, a polymyxin antibiotic known for its membrane permeabilizing activity, but no modification was observed. Thus, outer membrane diffusion does not seem to be the main factor involved. It is likely to result from a combination of factors, which deserves further detailed investigation. Nevertheless, in almost every case, the thiophene substitution of 5h seems slightly favorable compared to the penicillin V side chain of 5a. Solubility and preliminary toxicity evaluations. To consider further potential clinical developments, the solubility and toxicity profiles of prodrugs 5a-h were investigated. Final compounds were initially isolated in neutral form (e.g., with carboxylic acid function) displaying a moderate aqueous solubility capacity. Sodium salts were later obtained, which showed a significantly high solubility (≥ 100 µM).

The potential cytotoxicity of compounds AZT, 5a, and 5h was evaluated on human HeLa cells. No membrane-damaging activity or cytotoxic effects could be detected at concentrations up to 128 µg.mL - 1 , indicating that our prodrugs are comparable to AZT. Those preliminary results are favorable for further optimizations and developments.

◼ CONCLUSION

The development of new approaches leading to antibacterial agents is a crucial step towards addressing and minimizing the potentially dramatic of antimicrobial resistance, already accounting for 1.3 M deaths worldwide. This study addresses this expectation to use bacterial resistance to trigger βlactam conjugates: the higher will be the level of β-lactamase expression, the more vulnerable will be the bacteria. First, based on the literature, a synthetic pathway was developed to obtain prodrugs in good yields. AZT was chosen as the antibacterial agent while several substitutions on the β-lactam ring were considered. Finally, 10 final compounds 5a-h were obtained and characterized. It was possible in some cases to separate the diastereomers allowing to measure the impact of the configuration on the biological activity. Nevertheless, it is still necessary to optimize this separation step or to develop a stereoselective synthesis. The unambiguously determination of absolute configuration of this asymmetric center also remains to be established.

Second, the reactivity of the various synthesized prodrugs towards a representative panel of βlactamases was investigated. To this aim, indirect detection of released AZT after pre-incubation with different β-lactamases provided interesting preliminary information. KPC-2 was identified as the βlactamase able to catalyze compound conversion more efficiently, although some metallo-βlactamases also surprisingly, considering our use of a monobactam core, showed this ability. A calorimetric titration study was used to determine the enzymatic parameters of KPC-2 for the hydrolysis of our prodrugs and allowed to establish some interesting SAR considerations. Different pharmacomodulations were evaluated showing the necessity of specific substitution motifs in particularly based on acyl side chains of regular β-lactam agents (penicillin V and G, and cephalothin).

Into class A β-lactamase-β-lactam binary complex, these substituents are generally oriented toward a Ω-loop of the enzyme involved in the stabilization of the acyl-enzyme intermediate. [START_REF] Shimamura | Acyl-Intermediate Structures of the Extended-Spectrum Class A β-Lactamase, Toho-1, in Complex with Cefotaxime, Cephalothin, and Benzylpenicillin*[END_REF] This crucial loop is located next to the catalytic pocket, and it is described to well accommodate small aromatic side chains, especially in the case of KPC variants. [START_REF] Alsenani | Structural Characterization of the D179N and D179Y Variants of KPC-2 β-Lactamase: Ω-Loop Destabilization as a Mechanism of Resistance to Ceftazidime-Avibactam[END_REF] In this context, compound 5c, with no substituents, is not able to favorably interact with this Ω-loop and probably explains the lack of activity. Several hydrogen bond networks define the interaction between the Ω-loop and a β-lactam substrate, although these may vary in different β-lactamases. However, a conserved Asn residue is involved in a crucial hydrogen bond with the carbonyl of the β-lactam acyl amide function. [START_REF] Jacob | Engineering a Novel β-Iactamase by a Single Point Mutation[END_REF] The necessity of the carbonyl motif is well illustrated with the better affinities of acylated compounds 5a, 5g and 5h compared to that exhibited by carbon-carbon derivatives 5d-f. Finally, thiophene moiety of 5h seems to be slightly favorable than other acyl-derivatives which is consistent with literature data. [START_REF] Caselli | Structural, and Antimicrobial Analyses of β-Lactam Side Chain Recognition by β-Lactamases[END_REF] Moreover, specific chirality of stereogenic center proved to be crucial for the activity. Even if the affinities were globally lower compared to that of meropenem, two compounds, 5a and 5h, emerged with interesting values, especially because of their high turnover rates. Even in the case of the metalloenzyme NDM-1, the monobactam prodrug remained an appropriate substrate contrary to aztreonam. That was demonstrated with a good MIC value on clinical isolate expressing NDM-1 enzyme. The chemical stability was also evaluated, showing that most compounds were not prone to selfconversion or degradation in aqueous medium. Microbiological assays, first performed using isogenic laboratory strains producing different β-lactamases, resulted in low MIC values, confirming the specific and β-lactamase-mediated release of AZT in a cellular context. Nevertheless, the results were more contrasted on clinical isolates, although undoubtfully interesting. Preliminary solubility and toxicity studies showed that the compounds 5a and 5h also presented a favorable profile for future clinical development.

This pioneering study demonstrated that it was possible to target pathogenic bacteria by using the expression of β-lactamases to selectively release antibacterial agents, this obtaining compounds specifically targeting antibiotic-resistant bacteria. This has led to the possibility of developing targeted antibiotic therapies leading to a potentially drastic reduction of off-target effects, particularly on intestinal microbiota. A larger study is underway to incorporate other active substances than AZT as well as to modify the nature of the transporter to increase affinity towards β-lactamases.

◼ METHODS General methods. Nuclear Magnetic Resonance (NMR) spectra were recorded with a Bruker Avance 400 or 500 or 600 spectrometer (for 1 H and [START_REF] Docquier | An Update β-Lactamase Inhibitor Discovery and Development[END_REF] C NMR) and Bruker Avance 400 spectrometer (376.5 for 19 F). Chemical shifts (δ) and coupling constants (J) are given in ppm and Hz, respectively, using residual solvent signals as reference for the 1 H and [START_REF] Docquier | An Update β-Lactamase Inhibitor Discovery and Development[END_REF] C. The following abbreviations are used: s = singlet, d = doublet, t = triplet, q = quartet, br s = broad signal, dd = double doublet, dt = double of triplet, m = multiplet. High-resolution mass spectra (HRMS) were registered on a JEOL JMS-SX-102A mass spectrometer. Reactions were monitored with Merck Kieselgel 60F254 precoated aluminum silica gel plates (0.25 mm thickness) which were visualized under 254 nm light or by charring with a ninhydrin solution in butanol or with a vanillin solution in ethanol followed by heating or iodine stains. Purification experiments were carried out on Silica Gel premium Rf grade (40-63 µm) or were performed on a Biotage Isolera One using a 20-50 µm packed silica cartridge or using reverse phase preparative HPLC on Akta pure C18 column (Luna 21.2 x 100 mm, 5 µm, 100 Å). Eluent A: Water (10 mM AcONH4 or 0.1% TFA or Neutral); B: Acetonitrile (Neutral or 0.1% TFA, respectively) with flow rate: 20 mL/min. LC-MS analyses were obtained on the Waters Alliance 2690 HPLC, coupled to a Waters Micromass Quattro micro API (electrospray ionization mode, ESI+). Conditions: RP C18 Chomolith High-Resolution column (25 x 4.6 mm) using a linear gradient (0-100%) of water and acetonitrile (each containing 0.1% of formic acid) over 3 min and at a flow rate of 3 mL/min, mono λ detection (214 nm). Positive ion electrospray mass spectra were acquired at a solvent flow rate of 0.15 μL.min -1 . Nitrogen was used as both the nebulizing and drying gas. The data were obtained in a scan mode in 0.7 s intervals. UPLC analyses were obtained on the Waters Acquity H-Class UPLC, coupled to a Waters Zspray (electrospray ionization mode, ESI+). Conditions: Acquity UPLC® peptide HSS T3 column (2.1 x 50 mm, 1.8 µm, 100 Å) using a linear gradient (0-100%) of water and acetonitrile (each containing 0.1% of formic acid) over 5 min and at a flow rate of 0.8 mL.min -1 flow rate, photodiode array detection (TUV -214 nm). Positive ion electrospray mass spectra were acquired at a solvent flow rate of 0.8 μL.min -1 . Nitrogen was used as both the nebulizing and drying gas. The data were obtained in a scan mode in 0.2 s intervals. The purity of all synthetic compounds was determined by UPLC analysis and was greater than 95%.

Enzyme assays and release of AZT from prodrugs. The β-lactam derivative 5a (2.5 µL) at 5 mg.mL -1 was pre-incubated with 8µL of different enzymes at a concentration of 0.5 mg/mL (Deposit A), 0.01 mg.mL -1 (Deposit B), and no enzyme (Deposit C) in 9.5 µL of 0.9% of NaCl solution. After 30 minutes at room temperature, 2 µl were plated on Müller-Hinton (MH) agar containing E. coli XL1 strain and incubated at 37 °C for 24 hours. The same method is used for MICs determination described below.

Determination of kinetic parameters of hydrolysis by ITC. Enzyme activity was recorded by ITC on a

MicroCal ITC200 (GE-Malvern) equipped with a 200 µl Hastelloy sample cell and an automated 40 µl glass syringe rotating at 1000 rpm. Compounds were solubilized in 10 mM Hepes-NaOH, 0.15 M NaCl, pH 7.5 and both enzymes were diluted to the desired concentrations with the same buffer. For compounds 5a-h, the buffer also contained 1% DMSO and KPC-2 was brought to the same final DMSO concentration. The activity was recorded and calculated as described, using a molar enthalpy value for β-lactam amide bond hydrolysis of 12 kcal/mol at 25°C. The reaction rate constant kcat (s -1 ) is defined here as the number of amide bonds hydrolyzed per second and per active site. For Km determination, activities were recorded at decreasing substrate concentrations, and the data points were fitted to the Michaelis-Menten equation.

MICs determination.

The minimum inhibitory concentrations (MICs) of considered inhibitors were determined in triplicate using cation-supplemented Mueller-Hinton broth and a bacterial inoculum of 5 × 10 4 CFU/well, as recommended by the CLSI and the European Committee on Antimicrobial Susceptibility Testing (EUCAST document "Terminology relating to methods for the determination of susceptibility of bacteria to antimicrobial agents", 2000). The MIC values of compounds were determined on E. coli BL21(DE3)-derivative laboratory strains hyperproducing a single β-lactamase and carbapenemase-producing clinical isolates part of our collection.

Stability assays by ultra-performance liquid chromatography separation and tandem mass spectrometry. Identification and stability experiments were carried out by reverse phase liquid chromatography on a Waters Acquity Iclass UPLC system coupled to a high-resolution mass spectrometer (Xevo TQD Waters), equipped with an electrospray ionization source and controlled by MassLynx v.4.2 software for data acquisition. All the analyses were carried out using a Acquity UPLC BEH C18 2.1 x 50 mm column at 40°C. A flow rate of 0.8 mL.min -1 and a gradient of (0-100%) B over 3.5 min were used: eluent A, water/0.1% HCO2H; eluent B, acetonitrile/0.1% HCO2H. The mass spectrometer operated in positive or negative ionization mode and parameters were set as follows: source block temperature, 150 °C, nebulizer nitrogen flow rate, 50 L/hour, capillary voltage, 2.0 or 2.5 kV; desolvation temperature, 550 or 600 C; desolvation nitrogen gas flow, 900 or 1000 L/hour. The different investigated parameters were cone voltage (CV), collision energy (CE), ionization mode, and MRM channels. Specific settings for each compound are provided in Supporting Information (Table S4). All samples were prepared by dilution in a commercial physiological solution (0.9% NaCl). In case of enzymatic assay, a filtration on Amicon Ultra-0.5 Centrifugal Filter Unit, MWCO 3 kDa is performed before injection. A calibration curve was determined for each compound as follows: samples used for the calibration were injected (1µl) in triplicate from the lowest concentration (0.0001mg.ml -1 ) to highest one (0.01 mg.ml -1 ) and the calibration curves were plotted with the Targetlynx tool from Masslynx. Then, samples used for stability experiments were injected (1µl) at 0.005mg/mL at 37°C and monitored every hour over 24 hours. The stability curves were plotted with the Targetlynx tool from Masslynx. The MRM channel monitoring of AZT apparition and molecule degradation were plotted at the same time to check the release.

Stability assays by NMR. Pronucleosides were dissolved in 600 µL of D2O at a concentration of 10 mM. The 1 H NMR spectra were measured on a Bruker 600 MHz spectrometer at various time points at 310 K.

Toxicity assays. The potential cytotoxic activity of compounds was evaluated using the commercially available integrity assay (CytoTox 96 nonradioactive cytotoxicity assay, Promega). The compounds were tested for their ability to induce the lysis of HeLa cells by measuring the release of lactate dehydrogenase (LDH) after incubating the HeLa cell cultures (20,000 cells/well) for 24 h (37 °C, 5% CO2) in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum, 4.5 mg.mL -1 glucose, and 2 mM L-glutamine in the absence and presence of varying concentrations of the compounds (up to 250 μM). Further controls included samples containing the medium only or in which cell lysis was induced by the addition of 9% Triton X-100 (maximum LDH release control). The percentage of cytotoxicity was computed as 100 × (sample LDH release)/ (maximum LDH release). The variation in the percentage of cytotoxicity allowed us to compute an IC50 value (compound concentration inducing 50% cytotoxicity).

◼ ASSOCIATE CONTENT

The Supporting Information is available free of charge at https://pubs.acs.org/doi/xxxx Detailed synthetic procedures with all NMR spectrum associated. Supporting figures and schemes cited in the text. Miscellaneous data from biological and stability evaluations.

◼ AUTHOR INFORMATION

Corresponding author

Laurent Gavara -Institut des Biomolécules Max Mousseron, Univ Montpellier, CNRS, ENSCM, 34293 Montpellier, France; orcid.org/0000-0003-0146-1848; Email: laurent.gavara@umontpellier.fr

Scheme 4 .

 4 Scheme 4. Synthetic pathway of pronucleosides 5a-g. a Not isolated, b yield over two steps, c yield over three steps, d Not isolated except for 17c with 20% yield. Reagents and conditions: (a) Phenoxyacetyl chloride (1.3 eq.), Na2CO3, CH3CN-H20, RT; (b), Benzylhydroxylamine (1.3 eq.), EDCI.HCl (1.1 eq.), HOBt (1.1 eq.), DIPEA (1.5-3 eq.), THF, 0°C; (c), MsCl (3 eq.), pyridine, 0°C, Ar; (d) K2CO3 (1.5-3 eq.), acetone, 60-80°C; (e) SmI2 [0.1M] (5 eq.), THF-H2O, 0°C, Ar; (f) TBDMSCl (1.15 eq.), DIEA, DCM anhy., RT, Ar; (g) TMSCl (1.2 eq.), LDA [1 M], THF anhy., -78°C, Ar; (g) Electrophile (1.2 eq.), LDA [1 M], THF, -78°C, Ar; (i) TBAF [1 M], THF anhy., RT, Ar; (j) Allyl glyoxalate (3 eq.), DMF anhy., 90°C; (k) Ac2O (10 eq.), pyridine, RT; (l) BF3-Et2O (anhy.), DCM anhy.,0 °C, Ar; (m) AlCl3 (2 eq.), THF anhy., RT, Ar; (n) TFA 50%, DCM, RT; (o) carboxylic acid compound (1.3 eq.), EDCI.HCl (1.1 eq.), HOBt (1.1 eq.), DIPEA (3.5 eq.), THF, 0°C; (p) NaOH [0.1 M], THF, H2O, RT.

Table 1 .

 1 β-lactamase-mediated release of AZT measured by disk-diffusion after incubation in the presence of βlactamases representative of the four structural classes.E. coli growth inhibition zone diameter (mm) after preincubation of 5a with a β-lactamase a

			Class A			Class B			Class C			Class D
	[Enzyme] mg.mL -1	KPC-2 SHV-12 TEM-1 IMP-1 NDM-4 VIM-2 AmpC CMY-4 FOX-7 FUS-1 OXA-46 OXA-48
	0.5	38	36	35	28	36	32	24	25	-	19	31	33
	0.01	38	28	19	-b	-	13	-	-	-	-	-	-

a performed in duplicate with 30 min of preincubation at RT between 5a and the corresponding β-lactamase and transfer onto E. coli XL1-inoculated agar plate (see Materials and Methods for details). b No growth inhibition detected.

Table 2 .

 2 Kinetic parameters of KPC-2 and NDM-1 for the hydrolysis of pronucleosides, determined by ITC.

			KPC-2
	Cpd	kcat (s -1 ) a	KM (mM) a	kcat/KM (mM -1 s -1 )
	Meropenem b	5.8 ± 0.1 (4.1)	0.023 ± 0.002 (0.016)	252.2 (256)
	5a e	105 ± 3	4.1 ± 0.2	25.6
	5b	96.1 ± 2.2	5.3 ± 0.2	18.1
	5c	<0.003 c	-	-
	5d	3.2 ± 0.1	0.45 ± 0.04	7.1
	5e	0.21 ± 0.01	3.1 ± 0.3	0.07
	5f' e	0.02 d	-	-
	5f" e	0.09 d	-	-
	5g' e	≥0.06 d	≥3.8 d	-
	5g" e	127.6 ± 2.1	3.3 ± 0.1	38.7
	5h	351.4 ± 8.8	1.5 ± 0.08	234.3
			NDM-1
	Cpd	kcat (s -1 ) a	KM (mM) a	kcat/KM (mM -1 s -1 )
	Meropenem b	109 ± 1 (144.9)	0.06 ± 0.003 (0.06)	1816.7 (2415)
	5a e	15.5 ± 0.4	3.6 ± 0.1	4.3

a Values are mean ± SD; b KPC-2 data are from Wang et al.,

[START_REF] Wang | Phenotypic and Enzymatic Comparative Analysis of the KPC Variants, KPC-2 and Its Recently Discovered Variant KPC-15[END_REF]

NDM-1 data are from Ali et al.;

[START_REF] Ali | The Role of Conserved Residues in the Catalytic Activity of NDM-1: An Approach Involving Site Directed Mutagenesis and Molecular Dynamics[END_REF]

. c not detected, below detection limit; d approximate values due to the very low activity; e pure diastereomer compound.

Table 3 .

 3 MIC determination by Broth micro-dilution method a

	Entry	Bacterial strain	MIC b (µg.mL -1 ) AZT f 5a	5h
	1 c	E. coli BL21 (DE3)	0.06	32		16
	2 d	E. coli BL21 (DE3) pET9a-KPC-3	0.5	1	0.25
	3 d	E. coli BL21 (DE3) pET9a-IMP-1	0.03	8		2
	4 d	E. coli BL21 (DE3) pET9a-NDM-1	0.06	2		4
	5 d	E. coli BL21 (DE3) pET9a-AmpC	0.125	32		2
	6 c	E. coli ATCC25922	4	>256	ND
	7 c	K. pneumoniae ATCC13833	0.25	128	ND
	8 e	E. coli SI44 KPC-3	2	64		64
	9 e	K. pneumoniae SI-353 KPC-3	4	32		16
	10 e	K. pneumoniae SI-109 KPC-3	2	>128	>128
	11 e	E. coli B28 NDM-1	0.25	8		4

a Triplicate experiment; b Minimal Inhibitory Concentration; c reference isolate; d isogenic β-lactamase producer, e clinical isolate; f positive control experiment with AZT alone; ND, Not Determined

Author contributions

M.R-H. synthesized, purified, and characterized compounds, conducted stability assays, and wrote the supporting information, F.S. and F.V. were in charge of enzymatic, microbiological, and toxicity assays, L.L. synthesized, purified, and characterized compounds, G.F. performed and interpreted ITC measurements, E.C. monitored LCMSMS stability assays, Y.V-H. and P. L-F. performed and interpreted microbiological assays, J-D. D. designed and interpreted biological evaluations and contributed to project design, L.G. designed, funded, and supervised the project, and wrote the draft. All authors contributed to edit this manuscript.

Notes

The authors declare no competing financial interest.

◼ ACKNOWLEDGMENTS

This work, the ABR-Breaker project, was supported by Agence Nationale de la Recherche (ANR-19-CE18-0003-01). We also want to thank analytical and SynBio3 platforms and in particular Mr. Luc Brunel, Dr. Pascal Verdié and Mr. Pierre Sanchez for mass spectrometry analyses. Background image of the graphical abstract is designed by macrovector_official / Freepik.

◼ ABBREVIATIONS

AIDS, Acquired ImmunoDeficiency Syndrome; AmpC, Ampicillin C; AZT, Zidovudine; β-L, Beta-Lactamase; DEAD, DiEthyl AzoDicarboxylate; EDCI, 1-Ethyl-3-(3-Dimethylaminopropyl)CarbodIimide; ESBL, Extended-Spectrum Beta-Lactamase; HOBt, 1-Hydroxybenzotriazole; IMP, Imipenemase; ITC, Isothermal Titration Calorimetry; KPC, Klebsiella pneumoniae Carbapenemases; LDA, Lithium DiisopropylAmide; MBL, Metallo-Beta-Lactamase; NA, Nucleoside analog; NDM, New Delhi Metallobeta-lactamase; OXA, Oxacillinase; PBPs, penicillin-binding proteins; SARs, Structure-Activity relationships; SBL, Serine-Beta-Lactamase; TBDMS, Tert-ButylDiMethylSilyl; TFA, TriFluoroacetic Acid.

For Table of Contents Use Only

Zidovudine-β-lactam pronucleoside strategy for selective delivery into Gram-negative bacteria triggered by β-lactamases

Miyanou Rosales-Hurtado, a Filomena Sannio, b Lindita Lari, a Federica Verdirosa, b Georges Feller, c Elodie Carretero, a Yen Vo-Hoang, d Patricia Licznar-Fajardo, d Jean-Denis Docquier, b,e Laurent Gavara a, *


Thomas Mesquida
email: thomas.mesquida@cea.fr

Manon Dampfhoffer
email: manon.dampfhoffer@cea.fr

Thomas Dalgaty
email: thomas.dalgaty@cea.fr

Pascal Vivet
email: pascal.vivet@cea.fr

Amos Sironi
email: asironi@prophesee.ai

Christoph Posch
email: cposch@prophesee.ai

G2N2: Lightweight event stream classification with GRU graph neural networks

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

(referred to as events) upon the detection of relative light intensity changes in a purely eventdriven and asynchronous fashion.

The state of the art solution for treating such data has typically been to convert a stream of event-data back into 2D frames (called dense or event-frames) which serve as the input to convolutional neural networks [START_REF] Gehrig | End-to-end learning of representations for asynchronous event-based data[END_REF][START_REF] Liu | Adaptive time-slice block-matching optical flow algorithm for dynamic vision sensors[END_REF][START_REF] Maqueda | Event-based vision meets deep learning on steering prediction for selfdriving cars[END_REF]. While this works well in practice, there are some potential pitfalls. For instance, not only are the precise temporal relationships between event-generating bodies distorted, but the opportunity for sparse and event-driven real-time computation is largely lost.

An emerging alternative solution are Event-Graph Neural Networks (EGNNs) [START_REF] Bi | Graph-based object classification for neuromorphic vision sensing[END_REF][START_REF] Dalgaty | Hugnet: Hemi-spherical update graph neural network applied to low-latency event-based optical flow[END_REF][START_REF] Mitrokhin | Learning visual motion segmentation using event surfaces[END_REF][START_REF] Schaefer | Aegnn: Asynchronous eventbased graph neural networks[END_REF]. Rather than compacting events into frames, EGNNs build a graph in which each event is a vertex and edges are formed between vertices based on a search within a Euclidean ellipsoidal volume around each vertex. Edges can also be used to preserve the original spatiotemporal structure of the input event-data, which can thereafter be leveraged in graph convolution [START_REF] Fey | Fast graph representation learning with pytorch geometric[END_REF]. Early results have shown that EGNNs can outperform CNNs based on event-frames over a range of tasks, despite a greatly reduced model footprint [START_REF] Bi | Graph-based object classification for neuromorphic vision sensing[END_REF][START_REF] Mitrokhin | Learning visual motion segmentation using event surfaces[END_REF][START_REF] Schaefer | Aegnn: Asynchronous eventbased graph neural networks[END_REF]. However, due largely to the computationally expensive graph building procedure, efficient and real-time operation remains problematic. Furthermore, while Event-Graphs (EGs) naturally capture short range temporal dependencies via edges formed to events generated in the past, these dependencies will be limited to times on the order of the temporal search radiuspotentially only some tens or hundreds of milliseconds.

In this article, to solve these two problems, we combine the recently proposed hemispherical update graph neural network method (i.e., HUGNet) [START_REF] Dalgaty | Hugnet: Hemi-spherical update graph neural network applied to low-latency event-based optical flow[END_REF] with gated recurrent units (GRUs) [START_REF] Cho | Learning phrase representations using rnn encoder-decoder for statistical machine translation[END_REF] in GRU Graph Neural Network (G2N2). The GRU layers analyse temporal patterns in the features extracted by the Graph layers. Relative to other computationally costly EG methods, HUGNet prevents future nodes to influence past results, enabling lightweight feature extractor. This limitation in information sharing increased Optical Flow accuracy [START_REF] Dalgaty | Hugnet: Hemi-spherical update graph neural network applied to low-latency event-based optical flow[END_REF] and we extend this result to classification. We apply our model to event stream classification using the DVS-Lip dataset [START_REF] Tan | Multigrained spatio-temporal features perceived network for event-based lip-reading[END_REF] and compare ourselves to other fully-spherical EGNNs as well as CNNs based on event-frames. The specific contributions of this work are as follows:

• We combine EG neural network feature extractors with gated recurrent units in G2N2, enabling lightweight modelling of temporal dependencies in event-data.

• We show that EG neural networks are better feature extractors than convolutional neural networks for event-data.

• We observe that hemi-spherical EG updates outperform fully-spherical updates.

• We propose a mask-based data augmentation method called Maskout that significantly boosts performance for both EG and convolutional models.

Related Work

Event-camera data have often been treated by using convolutional neural network architectures -developed initially for application to traditional static dense frames [START_REF] Gehrig | End-to-end learning of representations for asynchronous event-based data[END_REF][START_REF] Gehrig | E-raft: Dense optical flow from event cameras[END_REF][START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF][START_REF] Maqueda | Event-based vision meets deep learning on steering prediction for selfdriving cars[END_REF][START_REF] Perot | Learning to detect objects with a 1 megapixel event camera[END_REF][START_REF] Zhu | Ev-flownet: Self-supervised optical flow estimation for event-based cameras[END_REF]. In order to convert event-data into frames, there are a number of techniques. Most often this is achieved by counting the number of generated events at each pixel within a temporal window of some tens or hundreds of milliseconds [START_REF] Gehrig | End-to-end learning of representations for asynchronous event-based data[END_REF][START_REF] Liu | Adaptive time-slice block-matching optical flow algorithm for dynamic vision sensors[END_REF]. In fact, empirical results have found that CNNs using event-frames could outperform CNNs using standard frames [START_REF] Maqueda | Event-based vision meets deep learning on steering prediction for selfdriving cars[END_REF] on the same task. To avoid completely discarding temporal correlations, some works have proposed the use of time surfaces [START_REF] Lagorce | Hots: a hierarchy of event-based time-surfaces for pattern recognition[END_REF][START_REF] Sironi | Hats: Histograms of averaged time surfaces for robust event-based object classification[END_REF] where pixel intensities correspond to, for example, the time since the last event was generated at each pixel. Other works also combine time surfaces with event counting [START_REF] Zhu | Ev-flownet: Self-supervised optical flow estimation for event-based cameras[END_REF] or use recurrent neural networks to convert event streams to frames [START_REF] Cannici | A differentiable recurrent surface for asynchronous event-based data[END_REF]. Event networks [START_REF] Dutson | Event neural networks[END_REF] processed asynchronously could keep temporal details but at the expense of important memory overhead. Spiking neural networks have been applied to event-data [START_REF] Amir | A low power, fully event-based gesture recognition system[END_REF][START_REF] Zenke | Superspike: Supervised learning in multilayer spiking neural networks[END_REF]. Although they are naturally compatible with asynchronous and eventbased data, spiking models generally fail to attain the performance of CNNs on the same task [START_REF] Baldwin | Time-ordered recent event (tore) volumes for event cameras[END_REF][START_REF] Emre O Neftci | Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks[END_REF] due to spiking activation and requiring backpropagation through time.

In recent years, graph neural networks [START_REF] Fey | Fast graph representation learning with pytorch geometric[END_REF][START_REF] Hamilton | Inductive representation learning on large graphs[END_REF][START_REF] Thomas | Semi-supervised classification with graph convolutional networks[END_REF][START_REF] Veličković | Graph attention networks[END_REF] have been utilized to process event-camera data by constructing graphs to which graph convolutions are applied. Events are typically represented by two spatial coordinates (i.e., pixel address) and a timestamp. The timestamp is scaled through multiplication with a constant and a KD-tree data structure is then constructed [START_REF] Zhou | Real-time kd-tree construction on graphics hardware[END_REF]. By performing a K-nearest neighbour search on the resulting tree, each event is then connected to those in its vicinity by an edge [START_REF] Bi | Graph-based object classification for neuromorphic vision sensing[END_REF][START_REF] Mitrokhin | Learning visual motion segmentation using event surfaces[END_REF][START_REF] Schaefer | Aegnn: Asynchronous eventbased graph neural networks[END_REF] -thereby creating an EG. The use of EGs enables the retention of the fine spatiotemporal structure of the eventdata in the graph edges -for example, as normalised vectors describing local spatiotemporal differences [START_REF] Fey | Fast graph representation learning with pytorch geometric[END_REF]. Despite promising early results, the existing EG building paradigms incur a latency which is too large to be compatible with real-time operation. Current methods construct graphs using a fully-spherical search where edges are formed from past to future events and also from future to past events. This approach has two significant drawbacks. Firstly, event-level predictions cannot be made instantaneously. Newly arrived events may be updated by an event arriving within a time equal to the temporal search radius multiplied by the number of graph layers, which may prove critical in time-sensitive applications. Secondly, the node embeddings of previously arrived events within the same temporal window are subject to change, and graph convolutions may need to be applied several times, which can be particularly problematic for high-resolution event cameras that generate millions of events per second [START_REF] Finateu | A 1280×720 back-illuminated stacked temporal contrast event-based vision sensor with 4.86µm pixels, 1.066geps readout, programmable event-rate controller and compressive data-formatting pipeline[END_REF]. To address these issues, new hemi-spherical methods have been proposed [START_REF] Dalgaty | Hugnet: Hemi-spherical update graph neural network applied to low-latency event-based optical flow[END_REF] which may allow for the processing of event streams in real-time. Events are limited to form edges from past events to events that will be generated in the future only. This eliminates the need to reapply graph convolutions after each new event arrival and permits instant event-level predictions and faster processing of event streams.

Event-based lip reading was recently proposed as a challenging neuromorphic benchmark task [START_REF] Tan | Multigrained spatio-temporal features perceived network for event-based lip-reading[END_REF] whereby an event-camera records the activity of speakers mouths as they utter words -the objective being to classify the word spoken. Existing solutions range from the application of several branches of 2D and 3D convolutions applied to event-data in respective voxel grids of differing temporal granularity [START_REF] Tan | Multigrained spatio-temporal features perceived network for event-based lip-reading[END_REF] to using reservoir computing as feature extractors [START_REF] Yoo | Rn-net: Reservoir nodes-enabled neuromorphic vision sensing network[END_REF].

Method

Building Event Graphs

The first step when using EGNNs is to build, or update, an existing EG from an event stream. Event streams S can be described as a series of events e i , each of which arrives at a time t i with pixel coordinates (x i , y i ) and a polarity p i such that S = (e i ) = (x i , y i ,t i , p i ). A widely used method in the state of the art is to define a limited number of nearest neighbours N by performing a Euclidean search around each event within a temporal radius r t and a pixel radius r xy . Edges are defined between these N nearest neighbours in order to create an Event-Graph G = (V, E) composed of Vertices V and Edges E. The resulting graph G is processed using graph convolutions in EGNNs in order to perform a desired task. It should be noted that, during GPU-based training, EG creation is generally not performed as described above, in an on the fly fashion, as performing the above steps event-by-event is highly time consuming. While methods have been proposed to permit EGs to be updated sparsely in an asynchronous fashion [START_REF] Schaefer | Aegnn: Asynchronous eventbased graph neural networks[END_REF], the state of a particular vertex can only be guaranteed after a time equal to the number of graph convolution layers multiplied by the temporal search radius has elapsed (see Supplementary materials). Furthermore, the vertex features (also referred to as node embeddings) may be modified several times as new events arrive with repeated graph convolutions. This not only imposes a lower-bound on the prediction latency, but also implies a large amount of computation. EG creation may be greatly simplified through the hemi-spherical graph updating algorithm of HUGNet [START_REF] Dalgaty | Hugnet: Hemi-spherical update graph neural network applied to low-latency event-based optical flow[END_REF] (see Algorithm 1). Here, vertices can only receive data via directed edges from the events generated in the past.

Algorithm 1 Sparse hemi-spherical update Input: New event ev = (x, y,t, p), Event Graph G = (V, E), radii
However, hemi-spherical EGs have only been demonstrated to be effective for eventbased optical flow prediction which is largely based on temporally and geometrically local events. It is not yet clear whether the method will also perform favourably relative to fullyspherical EGs for other problems too -such as classifying graphs. For this study, we use

Network topologies

EGNN feature extractor

The proposed EGNN feature extractor is inspired from [START_REF] Dalgaty | Hugnet: Hemi-spherical update graph neural network applied to low-latency event-based optical flow[END_REF] and is depicted in Fig. 2(a). It uses two types of EGNN layers: B-Spline [START_REF] Fey | Splinecnn: Fast geometric deep learning with continuous b-spline kernels[END_REF] and GCNconv [START_REF] Thomas | Semi-supervised classification with graph convolutional networks[END_REF]. B-Spline convolution incorporates complex vertex feature sharing mechanism, which leverages spatiotemporal differences between vertices in the edges to modulate message passing. On the other hand, GCNconv convolution is more straightforward whereby features from neighbouring vertices are averaged before being multiplied by a shared weight kernel. Therefore, the exact relative positions of neighbouring vertices do not influence the convolution. Our EGNN feature extractor is composed of an input B-Spline convolutional layer followed by four GCNconv layers. We found this was a good compromise between the powerful, but computationally expensive, B-spline convolution and the more lightweight GCN convolution. We use 7 input features -event coordinates, approximate normal to local event plan and polarity -as in [START_REF] Dalgaty | Hugnet: Hemi-spherical update graph neural network applied to low-latency event-based optical flow[END_REF].

In each layer, the graph convolutions result in vertices of sixty-four features. The vertex features from each layer are then concatenated together resulting in a 320-size embedding that is fed to the task head.

CNN feature extractor

We design two CNN-based feature extractors in order to compare with the EGNN-based one. For this comparison to be fair, we aim at designing a CNN-based feature extractor that has the same number of parameters as the EGNN feature extractor. Inspired by [START_REF] Tan | Multigrained spatio-temporal features perceived network for event-based lip-reading[END_REF], both convolutional feature extractors perform a 3D convolution on a volume of events in the first layer which is then followed by series of 2D convolutional layers. Note that this resembles our proposed EGNN feature extractor, whereby a more complex layer (3D conv and B-Spline) is applied to the input data, before light layers (2D conv and GCN) build upon these features. The difference between the two CNN implementations is the way in which feature maps from different layers are handled in order to create the embedding vector fed to the task head as shown in Fig. 2. The first implementation (CNN-Concat) takes inspiration from our EGNN feature extractor, whereby global mean pooling is applied to each of the feature maps in each layer resulting in 256 values which are concatenated together. The second takes a more conventional approach based on depthwise and pointwise convolutions (CNN-DWPW). More details are provided in the supplementary material.

Task heads

The task head is a neural network that takes as input the 320-size vector of the feature extractor and outputs a task dependent prediction. In this case the output is a probability distribution over the 100 possible spoken words. In order to study the ability of EGNNs to capture temporal dependencies we design three task heads. The first one is a Feed Forward Task Head (FFTH), similar to that used in [START_REF] Bi | Graph-based object classification for neuromorphic vision sensing[END_REF]. The embedding of each vertex is processed by a series of fully connected layers and the output probability distribution is computed for all vertices in the graph. These outputs are then averaged over the full input sequence and a prediction is made based on the most probable class. The two other task heads are based on gated recurrent units (GRUs). Here a single embedding is calculated for all events within a given time intervals by performing global average pooling on all vertex features. The size of this time interval is determined by the rate of the GRU and in this paper is typically chosen to be either 30Hz or 75Hz. We study the impact of a Small GRU Task Head (SGTH) and a Medium GRU Task Head (MGTH). The dimensions and number of layers of each head are detailed in Fig. 2.

Supervised classification learning

An EG was built for each of the spoken words in the dataset -each containing all of the events generated as each word is spoken. Each model is trained over 100 epochs using batches of sixteen single word EGs, the AdamW optimisation algorithm [START_REF] Loshchilov | Fixing weight decay regularization in adam[END_REF] and a cross entropy loss. We use a plateau loss scheduling whereby after ten epochs, if the average loss over the epoch has not diminished by 5%, the learning rate is halved. The reported accuracy corresponds to that achieved on the test set after the last epoch of training. We additionally leverage a variety of data augmentation techniques. The baseline data augmentations applied to the EGs are random temporal distortion, edge dropout [START_REF] Rong | Dropedge: Towards deep graph convolutional networks on node classification[END_REF] and horizontal flip as in [START_REF] Dalgaty | Hugnet: Hemi-spherical update graph neural network applied to low-latency event-based optical flow[END_REF]. Temporal distortion randomly stretches the EG such that the word is spoken at rate between 50% slower to 50% faster than the original recording. Edge dropout of probability 25% will randomly remove edges in the graph at training time and horizontal flip has a 50% to mirror the input EG along its vertical axis. Dropout is also used before and in the GRU layers with 20% probability. On top of this, we also propose a new data augmentation technique we refer to as Maskout, which we originally devised as a means of mitigating potential overfitting of the GRU layers.

Simply, we divide the input EG into regular temporal intervals of duration T , in sync with GRU frames. We then randomly mask out intervals (i.e., delete the events they contain from the graph) -although preserving edges between vertices that traverse the resulting empty regions. For each EG we randomly choose N mask regions to maskout where an integer number of sequential regions of duration T is randomly chosen by sampling a number between one and L mask . This permits the masked regions to be of a variable time duration. We also apply all of these data augmentations to the event-frames with the exception of edge dropout.

Experiments

DVS Lip

The DVS-Lip dataset is a machine learning dataset designed for automatic lip-reading using event cameras. The dataset includes 19,871 valid word samples spoken by 40 volunteers (20 male, 20 female). The vocabulary contains a total of 100 words, divided into two parts: visually similar word pairs and common words. In order to evaluate the generalization of trained methods, 30 volunteers are used for training and 10 for evaluation. The dataset includes both the event stream and intensity images (25FPS) output from the event camera, and mouth-centered crops of size 128x128 pixels are extracted using face detection. An example can be seen in Fig. 1.

Results

We first look at the impact of the task head on the HUGNet EGNN feature extractor -in particular to understand whether the GRU can bring about an improvement in performance due to its ability to better capture temporal dependencies. We then demonstrate the significant performance boost due to our new data augmentation technique Maskout and study the impact of the hemi-spherical update feature extractor relative to the more computationally intensive fully-spherical one. Finally, using the optimal configuration of our model, we perform a benchmarking against the two CNN based approaches as well as larger CNNs from the state of the art on eventbased lip reading.

Task head

Table 1 presents the results on DVS Lip achieved by the HUGNet EGNN feature extractor combined with the three different task heads.

The utility of the GRU in modelling temporal dependencies can be clearly seen in comparing the first two lines of the table. Whereas the FFTH head has access only to the features calculated by the EGNN using the information propagated forward in time between vertices connected by edges, the small GRU task head is able to integrate this information over the full spoken word and obtain a higher test accuracy. The medium sized GRU brings about another increase in performance at the expense of a slight increase in the number of operations per second and the total number of parameters. We observed that further increases in GRU size (i.e., large GRU) only very marginally improved the accuracy beyond that of EGNN + MGTH -which we will refer to as G2N2 in the remainder of the article. [START_REF] Dalgaty | Hugnet: Hemi-spherical update graph neural network applied to low-latency event-based optical flow[END_REF] vs Spherical Update. GRU is processed at 75 Hz. Latency represents the time between creation and output feature computation for an event.

Event-graph data augmentation

In Table 2 we report the effect of applying our data augmentation scheme Maskout on G2N2 and the impact of its two parameters N mask and L mask . We observed that by applying Maskout, over different combinations of parameters, the test accuracy of G2N2 was dramatically boosted by between 4.8% and 7.6%. The optimal configuration was the application of eight randomly positioned masks with a duration of between 33ms up to 200 ms (six slices of 33ms). The spoken word EGs will often be composed to several isolated EG islands between which message passing is not possible as the maximum length of masks is greater than the search radius, possibly explaining the gains.

Comparison of hemi-and fully-spherical EGs

In order for our EGNN feature extractor to be lightweight, we opted to use the hemi-spherical update graph neural network (HUGNet) [START_REF] Dalgaty | Hugnet: Hemi-spherical update graph neural network applied to low-latency event-based optical flow[END_REF] as opposed to other, more computationally costly, fully-spherical EG approaches [START_REF] Bi | Graph-based object classification for neuromorphic vision sensing[END_REF][START_REF] Mitrokhin | Learning visual motion segmentation using event surfaces[END_REF][START_REF] Schaefer | Aegnn: Asynchronous eventbased graph neural networks[END_REF]. While HUGNet, despite its reduction in EG complexity, outperformed fully-spherical approaches on optical flow estimation tasks it is important to understand whether this advantage might also generalise to other computer vision tasks and whether it is a detriment to the performance of G2N2. We therefore present in Table 3 a comparison between hemi-spherical (HUG) and fully-spherical (Fullyspherical). In this experiment, we also used a GRU rate of 75Hz since we found this to be advantageous compared to 30Hz, for a relatively small increase in the number of operations per second (see supplementary material for a sweep of the GRU rate). Intriguingly, as was reported to be the case for optical flow estimation [START_REF] Dalgaty | Hugnet: Hemi-spherical update graph neural network applied to low-latency event-based optical flow[END_REF], HUGNet outperforms its fully-spherical counterpart by 1.1% indicating a generality of the hemi-spherical EG approach. In order to emphasise advantages of the HUGNet based in terms of delay we also note the latency after an event has been generated before it can be used by the task head. When HUGNet can process in real time, standard approach adds 250 ms since there are five layers.

CNN Benchmarking

We finally compare our optimal G2N2 model to the two convolutional benchmarks of a similar model size as well as other approaches from the state of the art. The results are summarised in Table 4. Neither CNN-Concat nor CNN-DWPW are capable of attaining a performance close to that of G2N2 -with respective gaps of 6.2% and 3.4%. This shows that, for this task, the EGNN is a much more effective feature extractor than CNNs of the same size based on event-frames. Furthermore, in comparing the number of operations per second, we see that G2N2 is able to achieve this performance with up to 84% fewer calculations. This can largely be attributed to the sparse and event-driven manner in which EGNNs compute, relative to CNNs which do not exploit the inherent sparsity of event-data. We include in the same table other solutions to event-based spoken word classification from the state of the art. Remarkably G2N2 outperforms most of these methods despite the drastic reduction in the model size. G2N2 was only bettered by MSTP, by 2.7%, despite the fact it requires over two orders of magnitude more parameters. It should also be noted that, although outperformed by G2N2, CNN-Concat and CNN-DWPW also obtain reasonably competitive performances relative to other larger CNNs. We observed in our experiments that this is in large part due to our data augmentation technique Maskout.

Discussion and Perspectives

We have addressed the limitations of EGNNs in capturing long-term dependencies for spoken word classification. Our proposed solution, G2N2, combines EGNNs with GRU task heads, effectively grasping these temporal dependencies and improving performance. Additionally, our data augmentation technique, Maskout, has shown a remarkable performance boost of up to 7.4% for G2N2. Further exploration of augmentation methods and adaptation from other vision domains is warranted for EGs.

Benchmarking EGNNs against CNNs of similar size demonstrated the superior feature extraction capability of EGNNs. This advantage may stem from the preservation of precise spatiotemporal information captured by event cameras, as well as the distinct characteristics of kernel sizes in CNNs and search radii in EGNNs. Furthermore, EGNNs significantly reduced the number of calculation operations per second compared to CNNs, benefiting from the sparsity of event-based data and avoiding redundant measurements.

G2N2's lightweight nature and the use of a hemi-spherical update EGNN contribute to its computational efficiency compared to fully-spherical EG feature extractors. We observed that G2N2 with a hemi-spherical feature extractor consistently outperformed its fullyspherical counterpart, highlighting the need for further investigation into this phenomenon.

However, the utilization of GRU layers in G2N2 requires a return to the frame-based paradigm for the task head, introducing latency overhead and limiting fully asynchronous inference. Future work will focus on exploring alternatives to GRU task heads to achieve a lightweight and ultra-low latency implementation without relying on frame buffering.

Figure 1 :

 1 Figure 1: EGNN and CNN feature extractor are compared in term of accuracy and number of operations per second to process DVS Lip dataset. The comparison is done using the same training hyperparameters, task heads and number of parameters in feature extractors.the same task. To avoid completely discarding temporal correlations, some works have proposed the use of time surfaces[START_REF] Lagorce | Hots: a hierarchy of event-based time-surfaces for pattern recognition[END_REF][START_REF] Sironi | Hats: Histograms of averaged time surfaces for robust event-based object classification[END_REF] where pixel intensities correspond to, for example, the time since the last event was generated at each pixel. Other works also combine time surfaces with event counting[START_REF] Zhu | Ev-flownet: Self-supervised optical flow estimation for event-based cameras[END_REF] or use recurrent neural networks to convert event streams to frames[START_REF] Cannici | A differentiable recurrent surface for asynchronous event-based data[END_REF]. Event networks[START_REF] Dutson | Event neural networks[END_REF] processed asynchronously could keep temporal details but at the expense of important memory overhead. Spiking neural networks have been applied to event-data[START_REF] Amir | A low power, fully event-based gesture recognition system[END_REF][START_REF] Zenke | Superspike: Supervised learning in multilayer spiking neural networks[END_REF]. Although they are naturally compatible with asynchronous and eventbased data, spiking models generally fail to attain the performance of CNNs on the same task[START_REF] Baldwin | Time-ordered recent event (tore) volumes for event cameras[END_REF][START_REF] Emre O Neftci | Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks[END_REF] due to spiking activation and requiring backpropagation through time.In recent years, graph neural networks[START_REF] Fey | Fast graph representation learning with pytorch geometric[END_REF][START_REF] Hamilton | Inductive representation learning on large graphs[END_REF][START_REF] Thomas | Semi-supervised classification with graph convolutional networks[END_REF][START_REF] Veličković | Graph attention networks[END_REF] have been utilized to process event-camera data by constructing graphs to which graph convolutions are applied. Events are typically represented by two spatial coordinates (i.e., pixel address) and a timestamp. The timestamp is scaled through multiplication with a constant and a KD-tree data structure is then constructed[START_REF] Zhou | Real-time kd-tree construction on graphics hardware[END_REF]. By performing a K-nearest neighbour search on the resulting tree, each event is then connected to those in its vicinity by an edge[START_REF] Bi | Graph-based object classification for neuromorphic vision sensing[END_REF][START_REF] Mitrokhin | Learning visual motion segmentation using event surfaces[END_REF][START_REF] Schaefer | Aegnn: Asynchronous eventbased graph neural networks[END_REF] -thereby creating an EG. The use of EGs enables the retention of the fine spatiotemporal structure of the eventdata in the graph edges -for example, as normalised vectors describing local spatiotemporal differences[START_REF] Fey | Fast graph representation learning with pytorch geometric[END_REF]. Despite promising early results, the existing EG building paradigms incur a latency which is too large to be compatible with real-time operation. Current methods construct graphs using a fully-spherical search where edges are formed from past to future events and also from future to past events. This approach has two significant drawbacks. Firstly, event-level predictions cannot be made instantaneously. Newly arrived events may be updated by an event arriving within a time equal to the temporal search radius multiplied by the number of graph layers, which may prove critical in time-sensitive applications. Secondly, the node embeddings of previously arrived events within the same temporal window are subject to change, and graph convolutions may need to be applied several times, which can be particularly problematic for high-resolution event cameras that generate millions of events per second[START_REF] Finateu | A 1280×720 back-illuminated stacked temporal contrast event-based vision sensor with 4.86µm pixels, 1.066geps readout, programmable event-rate controller and compressive data-formatting pipeline[END_REF]. To address these issues, new hemi-spherical methods have been proposed[START_REF] Dalgaty | Hugnet: Hemi-spherical update graph neural network applied to low-latency event-based optical flow[END_REF] which may allow for the processing of event streams in real-time. Events are limited to form edges from past events to events that will be generated in the future only. This eliminates the need to reapply graph convolutions after each new event arrival and permits instant event-level predictions and faster processing of event streams.Event-based lip reading was recently proposed as a challenging neuromorphic benchmark task[START_REF] Tan | Multigrained spatio-temporal features perceived network for event-based lip-reading[END_REF] whereby an event-camera records the activity of speakers mouths as they utter words -the objective being to classify the word spoken. Existing solutions range from

Figure 2 :

 2 Figure 2: Topologies used in this paper. Top: feature extractors. Bottom: Task heads. ReLU activations not shown for the sake of clarity. Conv-based features extractor also make use of BatchNorm during training. Conv layers use 3x3(x3) kernel size, stride of 1 for the first 2 layers and 2 afterwards. (a) EGNN feature extractor. (b) CNN-Concat feature extractor. (c) CNN-DWPW feature extractor. (d) Feed Forward Task Head (FFTH). (e) Small GRU Task Head (SGTH). (f) Medium GRU Task Head (MGTH). a time radius r t of 100 ms, pixel r xy of 10 pixels and form edges only from the 30 closest neighbors. These values were chosen in order to maintain the same aspect ratio of DVS Lip samples (1.2 s in average, 128 pixels) and ensure that most of the vertices are connected to thirty neighbouring vertices.

Table 1 :

 1 Comparison of Task Heads (TH). FF = Feed Forward, SG = Small GRU, MG = Medium GRU. G2N2 represents EGNN + MGTH.

	Task Head	Acc test	Parameters	GOPs/s
	EGNN + FFTH	53.6 %	132 k	1.35
	EGNN + SGTH	55.9 %	156 k	1.10
	EGNN + MGTH (G2N2)	58.7 %	413 k	1.67

Table 2 :

 2 Impact of Maskout data augmentation with a hemi-spherical update graph and GRU task head (G2N2) on the accuracy. The GRU frequency is 30 Hz. N mask and L mask are the number of masks and maximum length for the EG respectively and T =33ms.

	N mask	0	4	4	4	4	6	8	10
	L mask	-	4	6	8	10	6	6	6
	Acc (%)	58.7	64.1	65.0	65.4	65.2	65.9	66.1	63.5
	Neighbor Search Search radii		Search Volume	Latency		Acc
	Fully-spherical	(10 pix, 50 ms)	V		250 ms		68.3 %
	HUG	(10 pix, 100 ms)	V		0 ms		69.4%

Table 3 :

 3 Comparison of EG building parameters. Hemi-spherical Update Graph (HUG)

Table 4 :

 4 Comparison of DVS Lip accuracy and network complexity in number of parameters.

		Mode	Rate (Hz)	Acc	Parameters GOPs/s
	CNN-Concat (This work)	Event Frame	75	63.2 %	418 k	6.57
	CNN-DWPW (This work) Event Frame	75	66.0 %	405 k	8.81
	G2N2 (This Work)	Event Graph	75 (GRU)	69.4 %	413 k	1.68
	RN-Net [31]	Event Frame	33	67.5 %	7.5 M
	[20]	Video Frame	25	65.5 %	11.2 M
	ACTION-Net[30]	Event Frame	25	68.8 %	28.1 M
	MSTP[28]	Event Frame	(25,175)	72.1 %	60.3 M

Acknowledgements This work is partly funded thanks to the French national program "Programme d'Investissements d'Avenir, IRT Nanoelec" ANR-10-AIRT-05. This publication was made possible by the use of the FactoryIA supercomputer, financially supported by the Ile-De-France Regional Council.