Dataset Information

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

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

SUBMITTER: Blasiak A

PROVIDER: S-EPMC9244889 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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

Blasiak Agata A Truong Anh T L ATL Remus Alexandria A Hooi Lissa L Seah Shirley Gek Kheng SGK Wang Peter P Chye De Hoe H Lim Angeline Pei Chiew APC Ng Kim Tien KT Teo Swee Teng ST Tan Yee-Joo YJ Allen David Michael DM Chai Louis Yi Ann LYA Chng Wee Joo WJ Lin Raymond T P RTP Lye David C B DCB Wong John Eu-Li JE Tan Gek-Yen Gladys GG Chan Conrad En Zuo CEZ Chow Edward Kai-Hua EK Ho Dean D

NPJ digital medicine 20220630 1

IDentif.AI-x, a clinically actionable artificial intelligence platform, was used to rapidly pinpoint and prioritize optimal combination therapies against COVID-19 by pairing a prospective, experimental validation of multi-drug efficacy on a SARS-CoV-2 live virus and Vero E6 assay with a quadratic optimization workflow. A starting pool of 12 candidate drugs developed in collaboration with a community of infectious disease clinicians was first narrowed down to a six-drug pool and then interrogated ...[more]

PMID: 35773329

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Project description:Background: Current standard of care (SOC) regimens against nontuberculous mycobacteria (NTM) usually result in unsatisfactory therapeutic responses, primarily due to multi-drug resistance and antibiotic susceptibility-guided therapies. In the midst of rising incidences in NTM infections, strategies to develop NTM-specific treatments have been explored and validated. Methods: To provide an alternative approach to address NTM-specific treatment, IDentif.AI was harnessed to rapidly optimize and design effective combination therapy regimens against Mycobacterium abscessus (M. abscessus), the highly resistant and rapid growth species of NTM. IDentif.AI interrogated the drug interaction space from a pool of 6 antibiotics, and pinpointed multiple clinically actionable drug combinations. IDentif.AI-pinpointed actionable combinations were experimentally validated and their interactions were assessed using Bliss independence model and diagonal measurement of n-way drug interactions. Results: Notably, IDentfi.AI-designed 3- and 4-drug combinations demonstrated greater %Inhibition efficacy than the SOC regimens. The platform also pinpointed two unique drug interactions (Levofloxacin (LVX)/Rifabutin (RFB) and LVX/Meropenem (MEM)) that may serve as the backbone of potential 3- and 4-drug combinations like LVX/MEM/RFB, which exhibited 58.33±4.99 %Inhibition efficacy against M. abscessus. Further analysis of LVX/RFB via Bliss independence model pointed to dose-dependent synergistic interactions in clinically actionable concentrations. Conclusions: IDentif.AI-designed combinations may provide alternative regimen options to current SOC combinations that are often administered with Amikacin, which has been known to induce ototoxicity in patients. Furthermore, IDentif.AI pinpointed 2-drug interactions may also serve as the backbone for the development of other effective 3- and 4-drug combination therapies. The findings in this study suggest that this platform may contribute to NTM-specific drug development.

Project description:Oral feeding competency is a major determinant of length of stay in the neonatal intensive care unit. An infant must be able to consistently demonstrate the ability to take all required enteral nutrition by mouth before discharge home. Most infants born prematurely (<37 weeks) will require days, if not weeks, to master this oral feeding competency skill. Inappropriately timed feeding attempts can lead to acute and long-term morbidities, prolonged hospitalizations, and increased health-care costs. Previously, a panel of five genes involved in essential developmental pathways including sensory integration (nephronophthisis 4, Plexin A1), hunger signaling [neuropeptide Y2 receptor (NPY2R), adenosine-monophosphate-activated protein kinase (AMPK)], and facial development (wingless-type MMTV integration site family, member 3) required for oral feeding success were identified in neonatal saliva. This study aimed to translate these five transcriptomic biomarkers into a rapid proteomic platform to provide objective, real-time assessment of oral feeding skills, to better inform care, and to improve neonatal outcomes. Total protein was extracted from saliva of 10 feeding-successful and 10 feeding-unsuccessful infants matched for age, sex, and post-conceptional age. Development of immunoassays was attempted for five oral feeding biomarkers and two reference biomarkers (GAPDH and YWHAZ) to normalize for starting protein concentrations. Normalized protein concentrations were correlated to both feeding status at time of sample collection and previously described gene expression profiles. Only the reference proteins and those involved in hunger signaling were detected in neonatal saliva at measurable levels. Expression patterns for NPY2R and AMPK correlated with the gene expression patterns previously seen between successful and unsuccessful feeders and predicted feeding outcome. Salivary proteins associated with hunger signaling are readily quantifiable in neonatal saliva and may be utilized to assess oral feeding readiness in the newborn. This study lays the foundation for the development of an informative, rapid, proteomic platform to assess neonatal oral feeding maturation.

Dataset Information

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

Publications

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

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