Regional differences of tachykinin effects on smooth muscle and pacemaker potentials of the stomach, duodenum, ileum and colon of an emetic model, the house musk shrews

Liu, Julia Y.H.; Deng, Ye; Hui, Jessica; Du, Peng; Ng, Heidi S.H.; Lu, Zhonghua; Yang, Luting; Liu, Luping; Khalid, Ammara; Ngan, Man-Piu; Cui, Dexuan; Ji, Bin; Chan, Sze Wa; Rudd, John A.

doi:10.1016/j.npep.2022.102300

Cited by 2 publications

(4 citation statements)

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“…Datasets accumulated in the database were produced using previously described methodologies 1 – 6 . In brief, 24 features, including dominant frequency, average frequency, dominant power, amplitude, period, velocity; percentage of contribution in power spectrum divided into percentages of brady-rhythm, normal-rhythm, and tachy-rhythm; signal stability and complexity features including multiscale sample entropy and detrended fluctuation analysis divided into various time window scale; and wave propagation features including change in dominant propagation patterns were automatically extracted using customized and automated analytical programs 4 .…”

Section: Methodsmentioning

confidence: 99%

“…This report also emphasize the advantages of using standardized drug screening methodology to create EF drug databases, allowing highly-consistent and massive amount of comparisons to be performed between numerous of drugs simultaneously, which could quickly advance the unexplored knowledge on drug-induced effects on EF of GI pacemaker activity, or even discover novel correlations which we had never considered before. Using our established standardized drug testing methodology with the MEA technology 1 – 6 and automated data analytical pipeline 4 , any novel EF drug profile and AE prediction result may be created in 2 to 3 days. This is expected to bring game-changing impact towards decision making in drug discovery.…”

Section: Conclusion and Future Studiesmentioning

confidence: 99%

“…Gastrointestinal (GI) motility is highly coordinated by rhythmic electrical signals, called pacemaking activity or slow-waves, controlled by network of interstitial cells of Cajal (ICC). These propagating electrical signals can be easily recorded using a microelectrode array (MEA) platform, and drug-induced effects on GI pacemaking activity can be easily tested using a standardized methodology 1 – 6 . It is proposed that these electrical signals are the languages representing healthy bowel movement and reflecting non-conscious health and disease conditions of our body, such as, vomiting, diarrhea and constipation.…”

Section: Introductionmentioning

confidence: 99%

“…The current study aims to decode and translate these signals with the help of artificial intelligence (AI) technology, to identify correlations between GI pacemaker activity and health and disease. We collected and integrated a large amount of drug testing data on GI pacemaker activity in our previous publication and unpublished studies, where we used a standardized and robust MEA methodology to test different drugs at different effective doses in different GI segments including, stomach, duodenum, ileum and colon 1 – 6 . We built a new type of electrophysiological drug database based on changes in 24 signal features extracted from recorded GI pacemaker activity before and after acute drug treatment, which are referred to as “electrical” features (EFs).…”

Section: Introductionmentioning

confidence: 99%

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Predicting drug adverse effects using a new Gastro-Intestinal Pacemaker Activity Drug Database (GIPADD)

Liu

Rudd

2023

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Electrical data could be a new source of big-data for training artificial intelligence (AI) for drug discovery. A Gastro-Intestinal Pacemaker Activity Drug Database (GIPADD) was built using a standardized methodology to test drug effects on electrical gastrointestinal (GI) pacemaker activity. The current report used data obtained from 89 drugs with 4867 datasets to evaluate the potential use of the GIPADD for predicting drug adverse effects (AEs) using a machine-learning (ML) approach and to explore correlations between AEs and GI pacemaker activity. Twenty-four “electrical” features (EFs) were extracted using an automated analytical pipeline from the electrical signals recorded before and after acute drug treatment at three concentrations (or more) on four-types of GI tissues (stomach, duodenum, ileum and colon). Extracted features were normalized and merged with an online side-effect resource (SIDER) database. Sixty-six common AEs were selected. Different algorithms of classification ML models, including Naïve Bayes, discriminant analysis, classification tree, k-nearest neighbors, support vector machine and an ensemble model were tested. Separated tissue models were also tested. Averaging experimental repeats and dose adjustment were performed to refine the prediction results. Random datasets were created for model validation. After model validation, nine AEs classification ML model were constructed with accuracy ranging from 67 to 80%. EF can be further grouped into ‘excitatory’ and ‘inhibitory’ types of AEs. This is the first time drugs are being clustered based on EF. Drugs acting on similar receptors share similar EF profile, indicating potential use of the database to predict drug targets too. GIPADD is a growing database, where prediction accuracy is expected to improve. The current approach provides novel insights on how EF may be used as new source of big-data in health and disease.

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Section: Methodsmentioning

confidence: 99%