Artificial neural network and bioavailability of the immunosuppression drug. Issue 4 (August 2020)

• Record Type: Journal Article
• Authors: Naushad, Shaik Mohammad; Kutala, Vijay Kumar
• Is Part Of: Current opinion in organ transplantation. Volume 25:Issue 4(2020:Aug.)
• Publication Date: 2020-08
• Subjects: artificial neural network model -- classification and regression trees model -- immunosuppressive drugs -- renal and liver transplantation; Transplantation of organs, tissues, etc -- Periodicals; Immunosuppression -- Periodicals; Transplantation immunology -- Periodicals; 617.954
• DOI: 10.1097/MOT.0000000000000770
• Languages: English
• ISSNs: 1087-2418

Abstract

Abstract : Purpose of review: The success of organ transplant is determined by number of demographic, clinical, immunological and genetic variables. Artificial intelligence tools, such as artificial neural networks (ANNs) or classification and regression trees (CART) can handle multiple independent variables and predict the dependent variables by deducing the complex nonlinear relationships between variables. Recent findings: In the last two decades, several researchers employed these tools to identify donor-recipient matching pairs, to optimize immunosuppressant doses, to predict allograft survival and to minimize adverse drug reactions. These models showed better performance characteristics than the empirical dosing strategies in terms of sensitivity, specificity, overall accuracy, or area under the curve of receiver-operating characteristic curves. The performance of the models was dependent directly on the input variables. Recent studies identified protein biomarkers and pharmacogenetic determinants of immunosuppressants as additional variables that increase the precision in prediction. Accessibility of medical records, proper follow-up of transplant cases, deep understanding of pharmacokinetic and pharmacodynamic pathways of immunosuppressant drugs coupled with genomic and proteomic markers are essential in developing an effective artificial intelligence platform for transplantation. Summary: Artificial intelligence has a greater clinical utility both in pretransplantation and posttransplantation periods to get favourable clinical outcomes, thus ensuring successful graft survival.