Modeling new immunoregulatory therapeutics as antimicrobial alternatives for treating Clostridium difficile infection

Leber, Andrew; Hontecillas, Raquel; Abedi, Vida; Tubau-Juni, Nuria; Zoccoli-Rodriguez, Victoria; Stewart, Caroline; Bassaganya‐Riera, Josep

doi:10.1016/j.artmed.2017.05.003

Cited by 30 publications

(27 citation statements)

References 48 publications

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“…Constant Training Intensity over Multiple Sessions Experiment: Twentyeight individuals (12 males, 27 right-hand dominant, mean age of 23.0, age range of [19][20][21][22][23][24][25][26][27][28][29][30] were recruited for a 5-day training study. On each day, participants completed several cognitive tasks for up to 90 min total, including a 10 min session of the MATB-II.…”

Section: Methodsmentioning

confidence: 99%

“…Alternating Testing and Training Blocks Experiment: Six individuals (1 male, all right-hand dominant, mean age of 23.2, age range of [21][22][23][24][25][26][27][28][29] were randomized into two groups, and then each completed a single session of the AF-MATB. The data from two additional participants could not be used due to technical difficulties with the computer.…”

Section: Methodsmentioning

confidence: 99%

“…[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. This often leads to sub-optimal responses, or even none at all.…”

mentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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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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“…In previous in vitro studies, we harnessed neural networks to reveal that drugs and their doses (inputs) can be correlated to quantifiable measures of treatment efficacy and safety (outputs) through a parabolic response surface (PRS), which is governed by a second order polynomial . Importantly, unlike most conventional approaches which often involve fixed or static magnitudes of treatment, AI‐PRS is a disease indication‐agnostic approach that has been subsequently used to optimize both drug and dose selection as well as dynamic dosing from the preclinical through clinical stages of development without the need for complex disease mechanism data . More specifically, the PRS approach can be described using the following equation

V false(C false) = x_{0} + \sum x_{i} C_{i} + \sum y_{i} C_{i}^{2} + \sum z_{i j} C_{i} C_{j}

V ( C ) is the efficacy, or viral load in this study, C i is the dose of drug i , and x 0 , x i , y i , and z ij are patient‐specific constants that are determined during the clinical calibration process .…”

Section: Introductionmentioning

confidence: 99%

Harnessing Artificial Intelligence to Optimize Long‐Term Maintenance Dosing for Antiretroviral‐Naive Adults with HIV‐1 Infection

Shen

Liu

et al. 2019

Advanced Therapeutics

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Antiretroviral therapy (ART) serves as a mainstay in treating human immunodeficiency virus (HIV)infection. An HIV patient is traditionally administered the same ART regimen for life, even if his/her viral load has been reduced by several orders of magnitude from the initial viral load. Dose reduction in ART has been clinically explored in a trial-and-error manner to reduce side effects and improve ART sustainability. Using artificial intelligence (AI), we have discovered that drugs and doses inputs can be related to viral load reduction through a Parabolic Response Surface (PRS). The AI-PRS platform can rationally guide a clinically-actionable approach to identify optimized population-wide and personalized dosing. In this prospective pilot clinical trial, a combination regimen of tenofovir (TDF), efavirenz (EFV) and lamivudine (3TC) is administered to ten patients. Using AI-PRS, a 33% reduction in the long-term TDF maintenance dose (200 mg) is identified compared to standard regimens (300 mg). This regimen keeps the HIV viral load below 40 copies/mL with no relapse during a 144-week observation period. This study demonstrates that AI-PRS can potentially serve as a scalable approach to optimize and sustain the long-term management of HIV as well as a broad spectrum of other indications.

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“…Moreover, based on specific pathological markers, ML predicted the risk of sepsis with a high degree of confidence, even in the presence of incomplete and imbalanced data . ML algorithms were utilized to develop an in silico clinical trial pipeline for evaluating the efficacy of C. difficile treatments, and the model predicted that three treatments that are currently under development (antitoxin antibodies, LANCL2 activation and faecal microbiome transplantation) would all be superior to antibiotics in terms of the clinical outcomes . Applying ML tools to large microbiome data may be the cornerstone for new methods of data analysis which will assist clinicians in decision making with regard to whether antimicrobial therapy is needed in the treatment of patients with suspected infectious disease, and which will also contribute to unravelling the highly complex association of microbe communities within body niches to determine which groups or types of bacteria should be surveilled, in order to identify patients with cancer who are most at risk of developing invasive microbial infections (Fig.…”

mentioning

confidence: 99%

Machine learning for tackling microbiota data and infection complications in immunocompromised patients with cancer

Espinoza

2018

J Intern Med

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Modeling new immunoregulatory therapeutics as antimicrobial alternatives for treating Clostridium difficile infection

Cited by 30 publications

References 48 publications

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

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

Harnessing Artificial Intelligence to Optimize Long‐Term Maintenance Dosing for Antiretroviral‐Naive Adults with HIV‐1 Infection

Machine learning for tackling microbiota data and infection complications in immunocompromised patients with cancer

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