Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

A Sulfur Tripod Glycoconjugate that Releases a High-Affinity Copper Chelator in Hepatocytes

International audienceReleased in the cell: Three N-acetylgalactosamine units, which recognize the asialoglycoprotein receptor, were tethered through disulfide bonds to the three coordinating thiol functions of a sulfur tripod ligand that has a high affinity for CuI (see scheme). The resulting glycoconjugate can be considered as a prodrug, because after uptake by hepatic cells the intracellular reducing glutathione (GSH) releases the high-affinity intracellular CuI chelator

Hepatocyte Targeting and Intracellular Copper Chelation by a Thiol-Containing Glycocyclopeptide

International audienceMetal overload plays an important role in several diseases or intoxications, like in Wilson’s disease, a major genetic disorder of copper metabolism in humans. To efficiently and selectively decrease copper concentration in the liver that is highly damaged, chelators should be targeted at the hepatocytes. In the present work, we synthesized a molecule able to both lower intracellular copper, namely Cu(I), and target hepatocytes, combining within the same structure a chelating unit and a carbohydrate recognition element. A cyclodecapeptide scaffold displaying a controlled conformation with two independent faces was chosen to introduce both units. One face displays a cluster of carbohydrates to ensure an efficient recognition of the asialoglycoprotein receptors, expressed on the surface of hepatocytes. The second face is devoted to metal ion complexation thanks to the thiolate functions of two cysteine side-chains. To obtain a chelator that is active only once inside the cells, the two thiol functions were oxidized in a disulfide bridge to afford the glycopeptide P3. Two simple cyclodecapeptides modeling the reduced and complexing form of P3 in cells proved a high affinity for Cu(I) and a high selectivity with respect to Zn(II). As expected, P3 becomes an efficient Cu(I) chelator in the presence of glutathione that mimics the intracellular reducing environment. Finally, cellular uptake and ability to lower intracellular copper were demonstrated in hepatic cell lines, in particular in WIF-B9, making P3 a good candidate to fight copper overload in the liver

A liver-targeting Cu(l) chelator relocates Cu in hepatocytes and promotes Cu excretion in a murine model of Wilson's disease

International audienceCopper chelation is the most commonly used therapeutic strategy nowadays to treat Wilson's disease, a genetic disorder primarily inducing a pathological accumulation of Cu in the liver. The mechanism of action of Chel2, a liver-targeting Cu(i) chelator known to promote intracellular Cu chelation, was studied in hepatic cells that reconstitute polarized epithelia with functional bile canaliculi, reminiscent of the excretion pathway in the liver. The interplay between Chel2 and Cu localization in these cells was demonstrated through confocal microscopy using a fluorescent derivative and nano X-ray fluorescence. The Cu(i) bound chelator was found in vesicles potentially excreted in the canaliculi. Moreover, injection of Chel2 either intravenously or subcutaneously to a murine model of Wilson's disease increased excretion of Cu in the faeces, confirmingin vivobiliary excretion. Therefore, Chel2 turns out to be a possible means to collect and excrete hepatic Cu in the faeces, hence restoring the physiological pathway

The FANCM:p.Arg658* truncating variant is associated with risk of triple-negative breast cancer

Breast cancer is a common disease partially caused by genetic risk factors. Germline pathogenic variants in DNA repair genes BRCA1, BRCA2, PAM, ATM, and CHEK2 are associated with breast cancer risk. FANCM, which encodes for a DNA translocase, has been proposed as a breast cancer predisposition gene, with greater effects for the ER-negative and triple-negative breast cancer (TNBC) subtypes. We tested the three recurrent protein-truncating variants FANCM:p.Arg658*, p.Gln1701*, and pArg1931* for association with breast cancer risk in 67,112 cases, 53,766 controls, and 26,662 carriers of pathogenic variants of BRCA1 or BRCA2. These three variants were also studied functionally by measuring survival and chromosome fragility in FANCM(-/-) patient-derived immortalized fibroblasts treated with diepoxybutane or olaparib. We observed that FANCM:p.Arg658* was associated with increased risk of ER-negative disease and TNBC (OR = 2.44, P = 0.034 and OR = 3.79; P = 0.009, respectively). In a country-restricted analysis, we confirmed the associations detected for FANCM:p.Arg658* and found that also FANCM:p.Arg1931* was associated with ER-negative breast cancer risk (OR = 1.96; P = 0.006). The functional results indicated that all three variants were deleterious affecting cell survival and chromosome stability with FANCM:p.Arg658* causing more severe phenotypes. In conclusion, we confirmed that the two rare FANCM deleterious variants p.Arg658* and p.Arg1931* are risk factors for ER-negative and TNBC subtypes. Overall our data suggest that the effect of truncating variants on breast cancer risk may depend on their position in the gene. Cell sensitivity to olaparib exposure, identifies a possible therapeutic option to treat FANCM-associated tumors