[IL-17A and IL-17F: from discovery to target of biologics - an illustrative example of translational research]. / IL-17A et IL-17F : de la découverte au ciblage thérapeutique - Un exemple de médecine translationnelle.

Interleukin (IL)-17A and then IL-17F have been discovered through their roles in chronic inflammatory diseases. These cytokines share 50% of sequence homology and bind to the same receptor made of the IL-17RA et IL-17RC chains. While they have rather similar pro-inflammatory effects, slight differences exist depending on the cell type considered or whether there is TNF or not. Indeed, there is a synergistic effect of TNF and IL-17A or IL-17F on many cell types. In addition, the interactions between immune and stromal cells also modulate their effects which vary according to stromal cell subtype. The identification of IL-17A and IL-17F roles in inflammatory diseases, as psoriasis, has led to the development of inhibitors of those cytokines. Anti-IL-17A, then anti-IL-17A/F and now anti-IL-17RA have been approved for different diseases and are particularly efficient in psoriasis. Their use is expending to other diseases like psoriatic arthritis and spondyloarthritis. Last, the recent understanding of the importance of stromal cells during chronic inflammation explains the relative inefficacy of such inhibitors in some other diseases.

Title: IL-17A et IL-17F : de la découverte au ciblage thérapeutique - Un exemple de médecine translationnelle. Abstract: L'interleukine (IL)-17A puis l'IL-17F ont été découvertes tour à tour pour leur rôle joué dans les maladies inflammatoires chroniques. Elles ont une homologie de séquence d'environ 50 % et partagent le même récepteur formé des chaînes IL-17RA et IL-17RC. Si elles ont des effets pro-inflammatoires assez similaires, il existe néanmoins quelques différences selon le type cellulaire considéré et selon la présence ou non de TNF, autre cytokine avec laquelle elles ont une synergie d'action. La troisième variable venant moduler leurs effets réside dans les interactions entre cellules immunes et cellules stromales, qui, là encore, varient selon le type de cellules stromales. La mise en évidence de leur rôle dans le psoriasis a notamment conduit au développement d'inhibiteurs de l'IL-17A, puis à la fois de l'IL-17A et de l'IL-17F et enfin d'un de leurs récepteurs. Ces inhibiteurs sont utilisés avec succès dans cette pathologie, et leur indication a été étendue progressivement au rhumatisme psoriasique et à certaines formes de spondylarthrite. Enfin, la récente compréhension de l'importance des cellules stromales dans la réaction inflammatoire chronique permet d'expliquer l'efficacité variable de ces biothérapies dans certaines pathologies.

[Protein induced proximity and targeted degradations by new degraders: concepts, developments, challenges for clinical applications]. / Induction de proximité et dégradation de cibles thérapeutiques par les nouveaux dégradeurs : quels concepts, quels développements, quel futur ?

The review is focused on recent drug discovery advances based on targeted protein degradation strategies. This new area of research has exploded leading to the development of potential drugs useful in a large variety of human diseases. They first target disease relevant proteins difficult to counteract with other classical strategies and extend now to aggregates, organelles, nucleic acids or lipidic droplets. These degraders engaged either the ubiquitin-proteasome system for PROTACs and molecular glues (first generation), or the lysosomal system via endosome-lysosome degradation (LYTACs) and autophagy-lysosome degradation (ATTEC, AUTAC, AUTOTAC) (following generations of degraders). PROTACs have expanded from the orthodox heterobifunctional ones to new derivatives such as homo-PROTACs, pro-PROTACs, CLIPTACs, HaloPROTACs, PHOTOTACs, Bac-PROTACs, AbTACs, ARN-PROTACs. The small molecular-weight molecular glues induce the formation of new ternary complexes which implicate the targeted protein and an ubiquitin ligase E3 allowing the protein ubiquinitation followed by its proteasomal degradation. Lysosomal degraders (LYTAC, ATTEC, AUTAC, AUTOTAC) specifically recognize extracellular and membrane proteins or dysfunctional organelles and transport them into lysosomes where they are degraded. They overcome the limitations observed with proteasomal degradations induced by PROTAC and molecular glues and demonstrate their potential to treat human diseases, especially neurodegenerative ones. Pharmaceutical companies are engaged at the world level to develop these new potential drugs targeting cancers, immuno-inflammatory and neurodegenerative diseases as well as a variety of other ones. Efficiency and risks for these novel therapeutic strategies are discussed.

Title: Induction de proximité et dégradation de cibles thérapeutiques par les nouveaux dégradeurs : quels concepts, quels développements, quel futur ? Abstract: La recherche dans le domaine de la dégradation ciblée des protéines s'est considérablement développée conduisant à l'élaboration de nouveaux outils chimiques à visée thérapeutique, les dégradeurs, potentiellement utiles dans diverses pathologies. Une grande variété d'objets à dégrader appartenant à divers compartiments intra- ou extracellulaires (protéines, complexes ou agrégats, organelles, acides nucléiques, gouttelettes lipidiques) a été ciblée à l'aide de ligands déjà existants, d'autres restent à découvrir. Les molécules de première génération, PROTAC et colles moléculaires, utilisent le système ubiquitine-protéasome pour détruire spécifiquement des protéines pathogéniques, certaines considérées jusqu'à présent comme inaccessibles en tant que cibles thérapeutiques. Au cours des cinq dernières années, ont été développés de nouveaux types de PROTAC hétéro-bifonctionnels comme les homo-PROTAC, pro-PROTAC, CLIPTAC, HaloPROTAC, PHOTOTAC, Bac-PROTAC, mais aussi des PROTAC macromoléculaires comme les AbTAC et ARN-PROTAC. Du fait de la grande diversité des substrats dégradés par les lysosomes, de nouveaux dégradeurs impliquant deux voies distinctes ont été ensuite produits : les chimères LYTAC pour la voie endosome-lysosome et les chimères ATTEC, AUTAC et AUTOTAC pour la voie autophagie-lysosome, augmentant ainsi considérablement le champ d'action des dégradeurs. Ces nouvelles molécules reconnaissent spécifiquement des protéines et/ou des organelles et permettent leur transport dans les lysosomes où ils sont dégradés. Les succès obtenus, que ce soit par dégradation protéasomale ou lysosomale pour plusieurs dizaines de dégradeurs (preuves de concepts et études cliniques en cours), expliquent l'intérêt quasi mondial des industries pharmaceutiques pour ces nouvelles molécules. Les challenges posés par leur développement et leur utilisation en clinique sont discutés.

The role and future prospects of artificial intelligence algorithms in peptide drug development.

Peptide medications have been more well-known in recent years due to their many benefits, including low side effects, high biological activity, specificity, effectiveness, and so on. Over 100 peptide medications have been introduced to the market to treat a variety of illnesses. Most of these peptide medications are developed on the basis of endogenous peptides or natural peptides, which frequently required expensive, time-consuming, and extensive tests to confirm. As artificial intelligence advances quickly, it is now possible to build machine learning or deep learning models that screen a large number of candidate sequences for therapeutic peptides. Therapeutic peptides, such as those with antibacterial or anticancer properties, have been developed by the application of artificial intelligence algorithms.The process of finding and developing peptide drugs is outlined in this review, along with a few related cases that were helped by AI and conventional methods. These resources will open up new avenues for peptide drug development and discovery, helping to meet the pressing needs of clinical patients for disease treatment. Although peptide drugs are a new class of biopharmaceuticals that distinguish them from chemical and small molecule drugs, their clinical purpose and value cannot be ignored. However, the traditional peptide drug research and development has a long development cycle and high investment, and the creation of peptide medications will be substantially hastened by the AI-assisted (AI+) mode, offering a new boost for combating diseases.

Weak links: Advancing target-based drug discovery by identifying the most vulnerable targets.

Mycobacterium tuberculosis remains the most common infectious killer worldwide despite decades of antitubercular drug development. Effectively controlling the tuberculosis (TB) pandemic will require innovation in drug discovery. In this review, we provide a brief overview of the two main approaches to discovering new TB drugs-phenotypic screens and target-based drug discovery-and outline some of the limitations of each method. We then explore recent advances in genetic tools that aim to overcome some of these limitations. In particular, we highlight a novel metric to prioritize essential targets, termed vulnerability. Stratifying targets based on their vulnerability presents new opportunities for future target-based drug discovery campaigns.

Antibiotic Development: Lessons from the Past and Future Opportunities.

The challenge of antimicrobial resistance is broadly appreciated by the clinical and scientific communities. To assess progress in the development of medical countermeasures to combat bacterial infections, we deployed information gleaned from clinical trials conducted from 2000 to 2021. Whereas private sector interest in cancer grew dramatically over this period, activity to combat bacterial infections remained stagnant. The comparative ambivalence to antimicrobial resistance is reflected in the number of investigative drugs under clinical investigation, their stage of development and most troublingly, a declining number of organizations that are actively involved in the development of new products to treat bacterial infections. This drop reflects the exits of many companies that had previously developed antibacterial agents.

[The revolution of AI in drug development]. / L'intelligence artificielle, une révolution dans le développement des médicaments.

Artificial intelligence and machine learning enable the construction of predictive models, which are currently used to assist in decision-making throughout the process of drug discovery and development. These computational models can be used to represent the heterogeneity of a disease, identify therapeutic targets, design and optimize drug candidates, and evaluate the efficacy of these drugs on virtual patients or digital twins. By combining detailed patient characteristics with the prediction of potential drug-candidate properties, artificial intelligence promotes the emergence of a "computational" precision medicine, allowing for more personalized treatments, better tailored to patient specificities with the aid of such predictive models. Based on such new capabilities, a mixed reality approach to the development of new drugs is being adopted by the pharmaceutical industry, which integrates the outputs of predictive virtual models with real-world empirical studies.

Title: L'intelligence artificielle, une révolution dans le développement des médicaments. Abstract: L'intelligence artificielle (IA) et l'apprentissage automatique produisent des modèles prédictifs qui aident à la prise de décisions dans le processus de découverte de nouveaux médicaments. Cette modélisation par ordinateur permet de représenter l'hétérogénéité d'une maladie, d'identifier des cibles thérapeutiques, de concevoir et optimiser des candidats-médicaments et d'évaluer ces médicaments sur des patients virtuels, ou des jumeaux numériques. En facilitant à la fois une connaissance détaillée des caractéristiques des patients et en prédisant les propriétés de multiples médicaments possibles, l'IA permet l'émergence d'une médecine de précision « computationnelle ¼ offrant des traitements parfaitement adaptés aux spécificités des patients.

Refining the impact of genetic evidence on clinical success.

The cost of drug discovery and development is driven primarily by failure1, with only about 10% of clinical programmes eventually receiving approval2-4. We previously estimated that human genetic evidence doubles the success rate from clinical development to approval5. In this study we leverage the growth in genetic evidence over the past decade to better understand the characteristics that distinguish clinical success and failure. We estimate the probability of success for drug mechanisms with genetic support is 2.6 times greater than those without. This relative success varies among therapy areas and development phases, and improves with increasing confidence in the causal gene, but is largely unaffected by genetic effect size, minor allele frequency or year of discovery. These results indicate we are far from reaching peak genetic insights to aid the discovery of targets for more effective drugs.

Discovery of a structural class of antibiotics with explainable deep learning.

The discovery of novel structural classes of antibiotics is urgently needed to address the ongoing antibiotic resistance crisis1-9. Deep learning approaches have aided in exploring chemical spaces1,10-15; these typically use black box models and do not provide chemical insights. Here we reasoned that the chemical substructures associated with antibiotic activity learned by neural network models can be identified and used to predict structural classes of antibiotics. We tested this hypothesis by developing an explainable, substructure-based approach for the efficient, deep learning-guided exploration of chemical spaces. We determined the antibiotic activities and human cell cytotoxicity profiles of 39,312 compounds and applied ensembles of graph neural networks to predict antibiotic activity and cytotoxicity for 12,076,365 compounds. Using explainable graph algorithms, we identified substructure-based rationales for compounds with high predicted antibiotic activity and low predicted cytotoxicity. We empirically tested 283 compounds and found that compounds exhibiting antibiotic activity against Staphylococcus aureus were enriched in putative structural classes arising from rationales. Of these structural classes of compounds, one is selective against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci, evades substantial resistance, and reduces bacterial titres in mouse models of MRSA skin and systemic thigh infection. Our approach enables the deep learning-guided discovery of structural classes of antibiotics and demonstrates that machine learning models in drug discovery can be explainable, providing insights into the chemical substructures that underlie selective antibiotic activity.

Small-Molecule Lead-Finding Trends across the Roche and Genentech Research Organizations.

The origin of small-molecule leads that were pursued across the independent research organizations Roche and Genentech from 2009 to 2020 is described. The identified chemical series are derived from a variety of lead-finding methods, which include public information, high-throughput screening (both full file and focused), fragment-based design, DNA-encoded library technology, use of legacy internal data, in-licensing, and de novo design (often structure-based). The translation of the lead series into in vivo tool compounds and development candidates is discussed as are the associated biological target classes and corresponding therapeutic areas. These analyses identify important trends regarding the various lead-finding approaches, which will likely impact their future application in the Roche and Genentech research groups. They also highlight commonalities and differences across the two independent research organizations. Several caveats associated with the employed data collection and analysis methodologies are included to enhance the interpretation of the presented information.

Newly Emerging Strategies in Antiviral Drug Discovery: Dedicated to Prof. Dr. Erik De Clercq on Occasion of His 80th Anniversary.

Viral infections pose a persistent threat to human health. The relentless epidemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global health problem, with millions of infections and fatalities so far. Traditional approaches such as random screening and optimization of lead compounds by organic synthesis have become extremely resource- and time-consuming. Various modern innovative methods or integrated paradigms are now being applied to drug discovery for significant resistance in order to simplify the drug process. This review provides an overview of newly emerging antiviral strategies, including proteolysis targeting chimera (PROTAC), ribonuclease targeting chimera (RIBOTAC), targeted covalent inhibitors, topology-matching design and antiviral drug delivery system. This article is dedicated to Prof. Dr. Erik De Clercq, an internationally renowned expert in the antiviral drug research field, on the occasion of his 80th anniversary.