Effective drug combination for
            <i>Caenorhabditis elegans</i>
            nematodes discovered by output-driven feedback system control technique

Li, Yiyang; Njus, Zach; Kong, Taejoon; Su, Wenqiong; Ho, Chih‐Ming; Pandey, Santosh

doi:10.1126/sciadv.aao1254

Cited by 38 publications

(31 citation statements)

References 58 publications

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“…One important aspect in this setup was the dimensions of the long and thin connecting channels between the main channel and the micro chambers, which ensured a controlled diffusion and inhibited turbulences and cross-flow [ 68 ]. Ding et al used a similar motility assay to analyze drug efficacy in Caenorhabditis elegans [ 69 ].…”

Section: Continuous Flow Diffusion and Concentration Gradients-bamentioning

confidence: 99%

Microfluidic Devices for Drug Assays

2018

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In this review, we give an overview of the current state of microfluidic-based high-throughput drug assays. In this highly interdisciplinary research field, various approaches have been applied to high-throughput drug screening, including microtiter plate, droplets microfluidics as well as continuous flow, diffusion and concentration gradients-based microfluidic drug assays. Therefore, we reviewed over 100 recent publications in the field and sorted them according to their microfluidic approach. As a result, we are showcasing, comparing and discussing broadly applied approaches as well as singular promising ones that might contribute to shaping the future of this field.

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Section: Continuous Flow Diffusion and Concentration Gradients-bamentioning

confidence: 99%

Microfluidic Devices for Drug Assays

2018

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“…If the subject's response to inputs is dynamic, CURATE.AI can constantly re‐calibrate the treatment inputs as the subject's response evolves during the course of treatment, resulting in robust and sustainable interventional optimization. Additionally, the quadratic surfaces comprising the core of CURATE.AI have been implemented in drug combination discovery platforms to aid in narrowing expansive drug libraries, find the most efficacious combinations from a large parameter space (e.g., dose ranges), and even elucidate and understand the relationships of underlying mechanisms within in vivo and in vitro systems of interest representative of various oncological and infectious disease indications, such as multiple myeloma and tuberculosis …”

Section: Introductionmentioning

confidence: 99%

Harnessing CURATE.AI as a Digital Therapeutics Platform by Identifying N‐of‐1 Learning Trajectory Profiles

Kee

Weiyan

Blasiak

et al. 2019

Advanced Therapeutics

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Conventional therapeutic interventions, which range from drug treatment to learning and training regimens, are often given at a fixed dose/intensity. This often leads to sub-optimal responses, or even none at all. Similarly, fixed intensity training can lead to plateaus in learning trajectories and training outcomes. This barrier will impact the field of digital therapeutics, where drug-based therapies may be complemented or replaced by learning and training platforms. A potential solution is to optimize training by identifying N-of-1 (single subject) training profiles that can then enhance learning trajectories through individualized training regimens. In this study, CURATE.AI, a mechanism-independent and indication-agnostic artificial intelligence (AI) platform, is used to identify N-of-1 learning trajectory profiles for healthy volunteers trained on the Multi-Attribute Task Battery (MATB), a flight deck simulator developed by the National Aeronautics and Space Administration and United States Air Force. By leveraging modulated MATB training intensity in a prospective study, CURATE.AI successfully develops N-of-1 learning trajectory profiles that may actionably mediate training optimization on the single-subject level, by dynamically identifying training inputs that drive the best possible scoring outcome. Therefore, CURATE.AI-guided training may serve as a powerful optimization platform for digital therapy, student learning, cognitive decline prevention, and other indications. and non-pharmacologic, often involve fixed or static magnitudes of treatment. [18][19][20][21][22][23][24][25][26] These treatments may be represented by dosage in the pharmacologic setting, or training intensities in learning-related domains. While fixed-dose drug treatment and fixed-intensity training are often used as a standard of care,

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“…The AI‐PRS approach is a new paradigm for identifying highly synergistic drug regimens for the treatment of disease based upon the observation that drug‐dose inputs are correlated with phenotypic outputs (e.g., amelioration of a disease state) in numerous biological systems by a parabolic response surface; such a surface is described by a quadratic algebraic equation . The AI‐PRS approach, which is output driven and hence agnostic to drug mechanism, has been applied to identify synergistic drug combinations to address a large variety of medical problems including treatment of cancer (hepatocellular, breast, ovarian, colon, renal, and bladder carcinoma; multiple myeloma; acute lymphoblastic leukemia) and infectious diseases (herpes simplex virus‐1 infection; parasitic nematode infection) and suppression of rejection in liver transplantation …”

Section: Introductionmentioning

confidence: 99%

AI‐Enabled Parabolic Response Surface Approach Identifies Ultra Short‐Course Near‐Universal TB Drug Regimens

Horwitz

Clemens

Lee

2019

Advanced Therapeutics

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Tuberculosis (TB) is a major health problem that causes more deaths worldwide than any other single infectious disease. Current multidrug therapy for tuberculosis is exceedingly lengthy, leading to poor drug adherence, and consequently the emergence of drug resistance. Hence, much more rapid treatments are needed. Experimentally identifying the most synergistic drug combinations among available drugs is complicated by the astronomical number of possible drug-dose combinations. This problem is dealt with by the use of an artificial-intelligence-enabled parabolic response surface platform in conjunction with an in vitro Mycobacterium tuberculosis-infected macrophage cell culture assay amenable to high-throughput screening. This strategy allows rapid identification of the most effective drug-dose combinations by testing only a small fraction of the total drug-dose efficacy response surface. The same platform is then used to optimize the in vivo doses of each drug in the most potent regimens. Thus, regimens are identified that are dramatically more effective than the Standard Regimen in treating TB in a mouse model-a model broadly predictive of drug efficacy in humans. The most effective regimens reported herein shorten the duration of treatment required to achieve relapse-free cure by 80% and are suitable for treating both drug-sensitive and most drug-resistant cases of tuberculosis.

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Effective drug combination for Caenorhabditis elegans nematodes discovered by output-driven feedback system control technique

Abstract: An engineering approach yields a highly potent combination of four antinematode drugs at lower dosages than individual drugs.

Cited by 38 publications

References 58 publications

Microfluidic Devices for Drug Assays

Microfluidic Devices for Drug Assays

Harnessing CURATE.AI as a Digital Therapeutics Platform by Identifying N‐of‐1 Learning Trajectory Profiles

AI‐Enabled Parabolic Response Surface Approach Identifies Ultra Short‐Course Near‐Universal TB Drug Regimens

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