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La compétition technologique qui accompagne le succès des anticorps thérapeutiques se traduit par de multiples améliorations portant sur les domaines constants d’IgG. L’énorme quantité de données scientifiques et juridiques soulève la double question de leur analyse et de leur exploitation. Dans le but de dresser un état de l’art exhaustif des variants d’IgG, nous avons établi un paysage brevet des technologies d’ingénierie des IgG4, applicables au développement d’un anticorps purement antagoniste. De nombreux variants ayant été conçus pour moduler la liaison pour le FcRn, nous avons donc entrepris de mettre au point un algorithme d’apprentissage automatique permettant de prédire l’affinité du complexe Fc/FcRn qui exploite ces données. Nous avons modélisé ces variants puis calculé des descripteurs d’interface afin de constituer un set d’entraînement. Nous décrivons les performances de l’algorithme d’apprentissage, que nous avons appliqué à plus de 18000 variants aléatoires, parmi lesquels nous en avons produit et testé trois afin de comparer la prédiction in silico à l’affinité expérimentale.The technological competition that accompanies the success of therapeutic antibodies is reflected in numerous improvements in the constant IgG domains. The huge amount of scientific and legal data raises the double question of their analysis and exploitation. In order to establish a comprehensive state of the art of IgG variants, we have established a patent landscape of IgG4 engineering technologies, applicable to the development of a purely antagonistic antibody. Since many variants have been designed to modulate the binding for FcRn, we have set about developing an automatic learning algorithm to predict the affinity of the Fc / FcRn complex that exploits this data. We modeled these variants and then calculated interface descriptors to form a training set. We describe the performance of the learning algorithm, which we applied to more than 18,000 random variants, of which we produced and tested three to compare in silico prediction with experimental affinity

Quelles chaînes lourdes d’immunoglobulines pour quels anticorps d’immunostimulation ?

En cancérologie, les anticorps conduisant à une immunostimulation, ou anticorps d’immunostimulation, relèvent de différents mécanismes d’action: simple blocage de récepteurs agissant comme points de contrôle de l’immunité, élimination des lymphocytes T régulateurs infiltrant les tumeurs, action agoniste sur des récepteurs activateurs des lymphocytes, etc. Dans la mesure où ces propriétés font parfois intervenir la région Fc et la région charnière, le choix du bon isotype de chaîne lourde ou de variants de cette chaîne lourde obtenus par ingénierie peut s’avérer déterminant pour l’efficacité thérapeutique. Cette brève revue tente de tirer les premières leçons de l’expérience clinique

New structural formats of therapeutic antibodies for rheumatology

International audiencePharmaceutical companies strive continuously to develop better medications in order to remain competitive. In the arena of monoclonal antibodies and related biologics (fusion proteins containing an IgG Fc fragment), the thrust is not only toward identifying new targets, but also toward developing new molecular formats. Here, new-generation antibodies used to treat rheumatic diseases are discussed, with emphasis on relations linking structure to pharmacological effects and on the improvements expected from the new formats. Isotypic and allotypic antibody diversity has pharmacological implications and is already exploited in commercially available antibodies. Efforts to engineer the Fc fragment of the various immunoglobulin G subclasses are reviewed with reference to abatacept, ixekizumab, other mutated IgG4 antibodies currently in development, sapelizumab, anifrolumab, and tanezumab. Bispecific antibodies are a focus of increasing interest (particularly those binding to both IL-17 and TNFα) and may earn a place in the therapeutic armamentarium as a means of avoiding the use of antibody combinations. However, the construction and production of bispecific antibodies continues to raise major technological challenges. Other molecular formats involve the fusion of antibodies to cytokines or the use of nanobodies and peptibodies. These new formats are at the very early stages of development, and their clinical relevance remains unclear

Insights into the IgG heavy chain engineering patent landscape as applied to IgG4 antibody development

International audienceDespite being the least abundant immunoglobulin G in human plasma, IgG4 are used therapeutically when weak effector functions are needed. The increase in engineered IgG4-based antibodies on the market led us to study the patent landscape of IgG4 Fc engineering, i.e., patents claiming modifications in the heavy chain. Thirty-seven relevant patent families were identified, comprising hundreds of IgG4 Fc variants focusing on removal of residual effector functions (since IgG4s bind to FcγRI and weakly to other FcγRs), half-life enhancement and IgG4 stability. Given the number of expired or soon to expire major patents in those 3 areas, companies developing blocking antibodies now have, or will in the near future, access to free tools to design silenced, half-life extended and stable IgG4 antibodies

Harnessing Fc/FcRn Affinity Data from Patents with Different Machine Learning Methods

International audienceMonoclonal antibodies are biopharmaceuticals with a very long half-life due to the binding of their Fc portion to the neonatal receptor (FcRn), a pharmacokinetic property that can be further improved through engineering of the Fc portion, as demonstrated by the approval of several new drugs. Many Fc variants with increased binding to FcRn have been found using different methods, such as structure-guided design, random mutagenesis, or a combination of both, and are described in the literature as well as in patents. Our hypothesis is that this material could be subjected to a machine learning approach in order to generate new variants with similar properties. We therefore compiled 1323 Fc variants affecting the affinity for FcRn, which were disclosed in twenty patents. These data were used to train several algorithms, with two different models, in order to predict the affinity for FcRn of new randomly generated Fc variants. To determine which algorithm was the most robust, we first assessed the correlation between measured and predicted affinity in a 10-fold cross-validation test. We then generated variants by in silico random mutagenesis and compared the prediction made by the different algorithms. As a final validation, we produced variants, not described in any patents, and compared the predicted affinity with the experimental binding affinities measured by surface plasmon resonance (SPR). The best mean absolute error (MAE) between predicted and experimental values was obtained with a support vector regressor (SVR) using six features and trained on 1251 examples. With this setting, the error on the log(KD) was less than 0.17. The obtained results show that such an approach could be used to find new variants with better half-life properties that are different from those already extensively used in therapeutic antibody development

All or nothing: Survival, reproduction and oxidative balance in Spotted Wing Drosophila (Drosophila suzukii) in response to cold

International audienceWinter severity and overwintering capacity are key ecological factors in successful invasions, especially in ectotherms. The integration of physiological approaches into the study of invasion processes is emerg- ing and promising. Physiological information describes the mechanisms underlying observed survival and reproductive capacities, and it can be used to predict an organism’s response to environmental per- turbations such as cold temperatures. We investigated the effects of various cold treatments on life his- tory and physiological traits of an invasive pest species, Drosophila suzukii, such as survival, fertility and oxidative balance. This species, a native of temperate Asian areas, is known to survive where cold tem- peratures are particularly harsh and has been recently introduced into Europe and North America. We found that cold treatments had a strong impact on adult survival but no effect on female’s fertility. Although only minor changes were observed after cold treatment on studied physiological traits, a strong sex-based difference was observed in both survival and physiological markers (antioxidant defences and oxidative markers). Females exhibited higher survival, reduced oxidative defences, less damage to nucleic acids, and more damage to lipids. These results suggest that D. suzukii relies on a pathway other than oxidative balance to resist cold injury. Altogether, our results provide information concerning the mech- anisms of successful invasion by D. suzukii. These findings may assist in the development of population models that predict the current and future geographic ranges of this species

Applying artificial intelligence to accelerate and de-risk antibody discovery

As in all sectors of science and industry, artificial intelligence (AI) is meant to have a high impact in the discovery of antibodies in the coming years. Antibody discovery was traditionally conducted through a succession of experimental steps: animal immunization, screening of relevant clones, in vitro testing, affinity maturation, in vivo testing in animal models, then different steps of humanization and maturation generating the candidate that will be tested in clinical trials. This scheme suffers from different flaws, rendering the whole process very risky, with an attrition rate over 95%. The rise of in silico methods, among which AI, has been gradually proven to reliably guide different experimental steps with more robust processes. They are now capable of covering the whole discovery process. Amongst the players in this new field, the company MAbSilico proposes an in silico pipeline allowing to design antibody sequences in a few days, already humanized and optimized for affinity and developability, considerably de-risking and accelerating the discovery process