IDentif.AI-Omicron: Harnessing an AI-Derived and Disease-Agnostic Platform to Pinpoint Combinatorial Therapies for Clinically Actionable Anti-SARS-CoV-2 Intervention

Blasiak, Agata; Truong, Anh T. L.; Wang, Peter; Hooi, Lissa; Chye, De Hoe; Tan, Shi-Bei; You, Kui; Remus, Alexandria; Allen, David Michael; Chai, Louis Yi Ann; Chan, Conrad En Zuo; Lye, David Chien; Tan, Gek-Yen Gladys; Seah, Shirley Gek Kheng; Chow, Edward Kai‐Hua; Ho, Dean

doi:10.1021/acsnano.2c06366

Cited by 6 publications

(3 citation statements)

References 51 publications

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“…Importantly, this approach demonstrated that while small data could successfully achieve global optimisation of efficacy and safety from a massive parameter space of combinatorial permutations, a prospectively acquired and carefully designed set of experiments was critical to pinpoint a list of actionable regimens. [ 12 37 38 44 45 46 47 48 49 50 51 52 ]…”

Section: N-of-1 Regimen Designmentioning

confidence: 99%

N-of-1 medicine

Wang,

Leong,

Lau

et al. 2024

Singapore Medical Journal

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

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Section: N-of-1 Regimen Designmentioning

confidence: 99%

N-of-1 medicine

Wang,

Leong,

Lau

et al. 2024

Singapore Medical Journal

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“…One key concept of personalized medicine is recognizing that individuals have biological variations both in biochemistry and drug responses [308]. Utilization of AI for personalized drug optimization by both drug selection and dosage has been promising [309,310]. A microfluidic POCT device for MRD and an OoC for tracking cancer migratory abilities were demonstrated for evaluation of patient-specific cancer drug efficacy by combining AI analysis [311,312].…”

Section: Personalized Drug Developmentmentioning

confidence: 99%

Microsystem Advances through Integration with Artificial Intelligence

2023

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Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier–Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.

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“…Fortunately, emerging AI-based strategies can interrogate parameter spaces of this magnitude by pairing prospective experiments with established optimization methods to yield globally optimized drug combinations. Examples include recent work to address SARS-CoV-2 drug development, blood cancers in patients, and antimicrobial resistance. − In addition, a newly developed DL model, DrugCell, simulates the response of human cancer cells when treated with therapeutics and subsequently predicts synergistic drug combinations that may improve treatment outcomes …”

mentioning

confidence: 99%