ABSTRACT
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.
Subject(s)
Artificial Intelligence , Drug Development , Precision Medicine , Artificial Intelligence/trends , Humans , Drug Development/methods , Drug Development/trends , Precision Medicine/methods , Precision Medicine/trends , Drug Discovery/methods , Drug Discovery/trends , Machine Learning , Computer SimulationABSTRACT
The offer of anti-cancer drugs has recently been disrupted by the introduction of checkpoint inhibitors on the market. Currently, one anti-CTLA-4, two anti-PD-1 and two anti-PD-L1 are authorized in the European Union, in seven different types of cancer. The clinical development of these therapies is still in full swing: in July 2017, more than 1 500 clinical trials were evaluating anti-PD-1, anti-PD-L1 and anti-CTLA-4 drugs in about twenty different locations and this number continues to increase. In the short term in France, other immunotherapies, the CAR-T cells, will complete this therapeutic arsenal. These immunotherapies appear as a real revolution in the treatment of some cancers. Nevertheless, many issues are associated with these therapies, particularly regarding the identification of good responders, the proper use of these drugs including the management of therapeutic strategies and safety profile, as well as the organization of care. In addition, the expenses associated with ipilimumab, nivolumab and pembrolizumab are substantial and almost tripled in one year, going from 120 million euros in 2015 to more than 340 million euros in 2016. This raises the question of the ability of the current healthcare system to maintain equitable access to innovation and best treatments for all patients. For all these reasons, the French National Cancer Institute decided to dedicate its thematic annual report on these innovative immunotherapies, targeting in particular checkpoint inhibitors and CAR-T cells, in order to produce an inventory of current data and an analysis regarding the different issues associated with these therapies.
