Artificial intelligence aids in development of nanomedicines for cancer management

Seminars in Cancer Biology, Journal Year: 2023, Volume and Issue: 89, P. 61 - 75

Published: Jan. 20, 2023

Language: Английский

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Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases

Pharmacological Research, Journal Year: 2023, Volume and Issue: 189, P. 106706 - 106706

Published: Feb. 20, 2023

Liver cancers are the fourth leading cause of cancer-related mortality worldwide. In past decade, breakthroughs in field artificial intelligence (AI) have inspired development algorithms cancer setting. A growing body recent studies evaluated machine learning (ML) and deep (DL) for pre-screening, diagnosis management liver patients through diagnostic image analysis, biomarker discovery predicting personalized clinical outcomes. Despite promise these early AI tools, there is a significant need to explain 'black box' work towards deployment enable ultimate translatability. Certain emerging fields such as RNA nanomedicine targeted therapy may also benefit from application AI, specifically nano-formulation research given that they still largely reliant on lengthy trial-and-error experiments. this paper, we put forward current landscape along with challenges management. Finally, discussed future perspectives how multidisciplinary approach using could accelerate transition medicine bench side clinic.

Language: Английский

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Artificial Intelligence in Clinical Oncology: From Data to Digital Pathology and Treatment

American Society of Clinical Oncology Educational Book, Journal Year: 2023, Volume and Issue: 43

Published: May 1, 2023

Recently, a wide spectrum of artificial intelligence (AI)–based applications in the broader categories digital pathology, biomarker development, and treatment have been explored. In domain these included novel analytical strategies for realizing new information derived from standard histology to guide selection development predict response. therapeutics, AI-driven drug target discovery, design repurposing, combination regimen optimization, modulated dosing, beyond. Given continued advances that are emerging, it is important develop workflows seamlessly combine various segments AI innovation comprehensively augment diagnostic interventional arsenal clinical oncology community. To overcome challenges remain with regard ideation, validation, deployment oncology, recommendations toward bringing this workflow fruition also provided clinical, engineering, implementation, health care economics considerations. Ultimately, work proposes frameworks can potentially integrate domains sustainable adoption practice-changing by community drive improved patient outcomes.

Language: Английский

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Discovery of Broad‐Spectrum Repurposed Drug Combinations Against Carbapenem‐Resistant Enterobacteriaceae (CRE) Through Artificial Intelligence (AI)‐Driven Platform

Advanced Therapeutics, Journal Year: 2024, Volume and Issue: 7(3)

Published: Jan. 12, 2024

Abstract Antimicrobial resistance challenges the sustainability of healthcare systems and results in substantial economic losses worldwide. This issue is further aggravated by paucity new drugs treatment options. In this study, an artificial intelligence (AI)‐derived platform termed IDentif.AI utilized to accelerate development effective therapeutic options for carbapenem‐resistant Enterobacteriaceae (CRE). Twelve Food Drug Administration‐approved are selected vitro inhibitory efficacy 155 combinations consisting various determined at different concentrations against both Klebsiella pneumoniae Escherichia coli . Correlating these experimental data via AI‐derived relationship, rapidly determines a ranked list drug search space over half million possible combinations. Meropenem found strongly synergize with low doses anticancer bleomycin, showing broad‐spectrum, bactericidal activity nine isolates across three CRE species rich minimal media no synergistic cytotoxicity on mammalian cells. Synergy also detected between bleomycin other key carbapenems clinical use (imipenem, ertapenem). Bleomycin/carbapenem appears be promising combination therapy treating infections. shows profound capability identifying pan‐active family bacteria through surveying strains from parallel.

Language: Английский

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Overcoming Pilotitis in Digital Medicine at the Intersection of Data, Clinical Evidence, and Adoption

Advanced Intelligent Systems, Journal Year: 2022, Volume and Issue: 4(9)

Published: May 26, 2022

Worldwide, digital medicine technologies are being developed at a rapid rate. While these offer the potential to transform and revolutionize health care, many risk of stalling remaining in pilot stage, known as “pilotitis,” thus never reaching true potential. Therefore, overcoming “pilotitis” increase uptake is global concern. To date, several authors have proposed solutions overcome various barriers owing technologies, such regulatory frameworks patients’ data ownership; however, areas require further consideration. This perspective piece identifies three adoption implementation proposes approaches for how them. Addressing may provide pathway success improve patient outcomes efficiency healthcare delivery.

Language: Английский

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The IDentif.AI-x pandemic readiness platform: Rapid prioritization of optimized COVID-19 combination therapy regimens

npj Digital Medicine, Journal Year: 2022, Volume and Issue: 5(1)

Published: June 30, 2022

IDentif.AI-x, a clinically actionable artificial intelligence platform, was used to rapidly pinpoint and prioritize optimal combination therapies against COVID-19 by pairing prospective, experimental validation of multi-drug efficacy on SARS-CoV-2 live virus Vero E6 assay with quadratic optimization workflow. A starting pool 12 candidate drugs developed in collaboration community infectious disease clinicians first narrowed down six-drug then interrogated 50 regimens at three dosing levels per drug, representing 729 possible combinations. IDentif.AI-x revealed EIDD-1931 be strong upon which multiple drug combinations can derived, pinpointed number interactions, were further reconfirmed variants B.1.351 (Beta) B.1.617.2 (Delta). prioritized promising for clinical translation immediately adjusted re-executed new an path towards optimizing therapy following pandemic emergence.

Language: Английский

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Physicians’ Perspectives on AI in Clinical Decision Support Systems: Interview Study of the CURATE.AI Personalized Dose Optimization Platform

JMIR Human Factors, Journal Year: 2023, Volume and Issue: 10, P. e48476 - e48476

Published: Sept. 10, 2023

Background Physicians play a key role in integrating new clinical technology into care practices through user feedback and growth propositions to developers of the technology. As physicians are stakeholders involved iteration process, understanding their roles as users can provide nuanced insights workings these technologies that being explored. Therefore, physicians’ perceptions be critical toward validation, implementation, downstream adoption. Given increasing prevalence decision support systems (CDSSs), there remains need gain an in-depth expectations implementation. This paper explores CURATE.AI, novel artificial intelligence (AI)–based stage personalized dosing CDSSs, practice. Objective study aims understand perspectives CURATE.AI for work gather on considerations implementation AI-based CDSS tools. Methods A total 12 participants completed semistructured interviews examining knowledge, experience, attitudes, risks, future course combination therapy platform, CURATE.AI. Interviews were audio recorded, transcribed verbatim, coded manually. The data thematically analyzed. Results Overall, 3 broad themes 9 subthemes identified thematic analysis. covered perceived significant across various stages development, including trial, mass Conclusions laid out ways interpreted CDSS, research pointed during different exploration layered with relevant researchers.

Language: Английский

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Dose optimization of an adjuvanted peptide-based personalized neoantigen melanoma vaccine

PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(3), P. e1011247 - e1011247

Published: March 1, 2024

The advancements in next-generation sequencing have made it possible to effectively detect somatic mutations, which has led the development of personalized neoantigen cancer vaccines that are tailored unique variants found a patient’s cancer. These can provide significant clinical benefit by leveraging immune response eliminate malignant cells. However, determining optimal vaccine dose for each patient is challenge due heterogeneity tumors. To address this challenge, we formulate mathematical optimization problem based on previous model encompasses cascade produced patient. We propose an approach identify doses, considering fixed vaccination schedule, while simultaneously minimizing overall number tumor and activated T validate our approach, perform silico experiments six real-world trial patients with advanced melanoma. compare results applying those suboptimal (the used its deviations). Our simulations reveal regimen higher initial doses lower final may lead reduction size certain patients. offers promising improving outcomes.

Language: Английский

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N-of-1 medicine

Singapore Medical Journal, Journal Year: 2024, Volume and Issue: 65(3), P. 167 - 175

Published: March 1, 2024

Abstract The fields of precision and personalised medicine have led to promising advances in tailoring treatment individual patients. Examples include genome/molecular alteration-guided drug selection, single-patient gene therapy design synergy-based combination development, these approaches can yield substantially diverse recommendations. Therefore, it is important define each domain delineate their commonalities differences an effort develop novel clinical trial designs, streamline workflow rethink regulatory considerations, create value healthcare economics assessments, other factors. These segments are essential recognise the diversity within domains accelerate respective workflows towards practice-changing healthcare. To emphasise points, this article elaborates on concept digital health medicine-enabled N-of-1 medicine, which individualises regimen dosing using a patient’s own data. We will conclude with recommendations for consideration when developing based emerging digital-based platforms.

Language: Английский

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Nanotechnology and Artificial Intelligence in Cancer Treatment

Next research., Journal Year: 2025, Volume and Issue: unknown, P. 100179 - 100179

Published: Jan. 1, 2025

Language: Английский

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