Integration of a microfluidic multicellular coculture array with machine learning analysis to predict adverse cutaneous drug reactions
Lor Huai Chong, Terry Ching, Hui Jia Farm, Gianluca Grenci, Keng‐Hwee Chiam, Yi‐Chin Toh
Abstract
model achieves an average of 87.5% accuracy (correct prediction rate), 75% specificity (prediction rate of true negative drugs), and 100% sensitivity (prediction rate of true positive drugs). We then employ the MCA and the SVM training algorithm to prospectively identify the skin-sensitizing likelihood and mechanism-of-action for obeticholic acid (OCA), a farnesoid X receptor (FXR) agonist which has undergone clinical trials for non-alcoholic steatohepatitis (NASH) with well-documented cutaneous side effects.
Topics & Concepts
Multicellular organismMicrofluidicsDrug reactionDrugNanotechnologyComputer scienceChemistryComputational biologyBiologyPharmacologyMaterials scienceCellBiochemistryBiosimilars and Bioanalytical MethodsAdvancements in Transdermal Drug DeliveryMonoclonal and Polyclonal Antibodies Research