首页|University of Bath Reports Findings in Machine Learning (Assessing the anticholi nergic cognitive burden classification of putative anticholinergic drugs using d rug properties)
University of Bath Reports Findings in Machine Learning (Assessing the anticholi nergic cognitive burden classification of putative anticholinergic drugs using d rug properties)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news originating from Bath, United Kingdom, by NewsRx correspondents, research stated, "This study evaluated the use of mach ine learning to leverage drug absorption, distribution, metabolism and excretion (ADME) data together with physicochemical and pharmacological data to develop a novel anticholinergic burden scale and compare its performance to previously pu blished scales. Experimental and in silico ADME, physicochemical and pharmacolog ical data were collected for antimuscarinic activity, blood-brain barrier penetr ation, bioavailability, chemical structure and P-glycoprotein (P-gp) substrate p rofile." Our news journalists obtained a quote from the research from the University of B ath, "These five drug properties were used to train an unsupervised model to ass ign anticholinergic burden scores to drugs. The model performance was evaluated through 10-fold cross-validation and compared with the clinical Anticholinergic Cognitive Burden (ACB) scale and nonclinical Anticholinergic Toxicity Scores (AT S) scale, which is based primarily on muscarinic binding affinity. In silico sof tware (ADMET Predictor) used for screening drugs for their blood-brain barrier ( BBB) penetration correctly identified some drugs that do not cross the BBB. The mean area under the curve for the unsupervised and ACB scale based on the five s elected variables was 0.76 and 0.64, respectively. The unsupervised model agreed with the ACB scale on the classification of more than half of the drugs (49 of 88) agreed on the classification of less than half the drugs in the ATS scale (1 2 of 25). Our findings suggest that the commonly used ACB scale may misclassify certain drugs due to their inability to cross the BBB. By contrast, the ATS scal e would misclassify drugs solely depending on muscarinic binding affinity withou t considering other drug properties."
BathUnited KingdomEuropeBlood Brai n BarrierBlood-Brain BarrierBrain ResearchCentral Nervous SystemCyborgsDrugs and TherapiesEmerging TechnologiesHealth and MedicineMachine Learni ng
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