[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.

Use of non-invasive cardiac investigations to predict clinical endpoints after coronary bypass graft surgery in coronary artery disease patients: results from the prognosis and evaluation of risk in the coronary operated patient (PERISCOP) study.

AIMS: Little is known about which patients who have undergone coronary bypass surgery are at risk of future clinical cardiovascular events and may benefit from further medical treatment. We sought to determine if routine non-invasive cardiac investigations performed early after surgery were able to stratify the risk of cardiovascular events in this population. METHODS: Two thousand and sixty-five consecutive patients were enrolled in a prospective multicenter study (PERISCOP). Exercise testing, echocardiography, and 24-h ambulatory ECG monitoring were performed at day 20+/-10 after coronary bypass surgery. Follow-up was performed 1 year after coronary bypass surgery. Causes of all hospitalisation and death occurring within 1 year were documented and classified by an End-point Committee. The principal endpoint was the combination of all-cause deaths and cardiovascular events requiring hospitalisation (myocardial infarction, unstable or severe angina, stroke, congestive heart failure). RESULTS: The 1-year frequency of first events was 155 (8%). In multivariate analysis, exercise duration <420s (RR=1.68; 95% CI: 1.13-2.49), exercise induced ST segment depression >1mm (RR=1.90; 95% CI: 1.18-3.05), and left ventricular (LV) dysfunction (wall motion index <1.15) (RR=1.97; 95% CI: 1.10-3.51) were independent predictors of cardiovascular events and deaths. Ambulatory ECG monitoring had no predictive value. CONCLUSIONS: Exercise testing and echocardiography performed early after coronary bypass surgery are able to identify high-risk patients who may benefit from intensive secondary prevention.