ArticlePloS one2018
Artificial neural network model for predicting the bioavailability of tacrolimus in patients with renal transplantation.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 63 citations in OpenAlex.
- Responsible Use of Artificial Intelligence to Improve Kidney Care: A Statement from the American Society of Nephrology.Journal of the American Society of Nephrology : JASN · 2026Review
- Transforming nephrology through artificial intelligence: a state-of-the-art roadmap for clinical integration.Clinical kidney journal · 2026Review
- AI-based pathomics in kidney diseases: progress and application.Renal failure · 2025Review
- Reshaping transplantation with AI, emerging technologies and xenotransplantation.Nature medicine · 2025Review
- An In-depth overview of artificial intelligence (AI) tool utilization across diverse phases of organ transplantation.Journal of translational medicine · 2025Review
- Predicting high-risk factors for postoperative inadequate analgesia and adverse reactions in cesarean delivery surgery: a prospective study.International journal of surgery (London, England) · 2025Article
- Tacrolimus trough level and oxidative stress in Tunisian kidney transplanted patients.Renal failure · 2024Article
- The Use of Machine Learning in the Diagnosis of Kidney Allograft Rejection: Current Knowledge and Applications.Diagnostics (Basel, Switzerland) · 2024Review
- Present and Future Applications of Artificial Intelligence in Kidney Transplantation.Journal of clinical medicine · 2024Review
- Article
- Artificial intelligence: a new field of knowledge for nephrologists?Clinical kidney journal · 2023Article
- Beyond Human Limits: Harnessing Artificial Intelligence to Optimize Immunosuppression in Kidney Transplantation.Journal of clinical medicine research · 2023Review
- Developing supervised machine learning algorithms to evaluate the therapeutic effect and laboratory-related adverse events of cyclosporine and tacrolimus in renal transplants.International journal of clinical pharmacy · 2023Article
- Toward generalizing the use of artificial intelligence in nephrology and kidney transplantation.Journal of nephrology · 2023Review
- Development of a particle swarm optimization-backpropagation artificial neural network model and effects of age and gender on pharmacokinetics study of omeprazole enteric-coated tablets in Chinese population.BMC pharmacology & toxicology · 2022Article
- Machine Learning for Renal Pathologies: An Updated Survey.Sensors (Basel, Switzerland) · 2022Review
- Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation.Transplant international : official journal of the European Society for Organ Transplantation · 2022Review
- Artificial intelligence and kidney transplantation.World journal of transplantation · 2021Review
- An artificial neural network-pharmacokinetic model and its interpretation using Shapley additive explanations.CPT: pharmacometrics & systems pharmacology · 2021Article
- Machine Learning Applications in Solid Organ Transplantation and Related Complications.Frontiers in immunology · 2021Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The objective of the current study was to explore the role of ABCB1 and CYP3A5 genetic polymorphisms in predicting the bioavailability of tacrolimus and the risk for post-transplant diabetes. Artificial neural network (ANN) and logistic regression (LR) models were used to predict the bioavailability of tacrolimus and risk for post-transplant diabetes, respectively. The five-fold cross-validation of ANN model showed good correlation with the experimental data of bioavailability (r2 = 0.93-0.96). Younger age, male gender, optimal body mass index were shown to exhibit lower bioavailability of tacrolimus. ABCB1 1236 C>T and 2677G>T/A showed inverse association while CYP3A5*3 showed a positive association with the bioavailability of tacrolimus. Gender bias was observed in the association with ABCB1 3435 C>T polymorphism. CYP3A5*3 was shown to interact synergistically in increasing the bioavailability in combination with ABCB1 1236 TT or 2677GG genotypes. LR model showed an independent association of ABCB1 2677 G>T/A with post transplant diabetes (OR: 4.83, 95% CI: 1.22-19.03). Multifactor dimensionality reduction analysis (MDR) revealed that synergistic interactions between CYP3A5*3 and ABCB1 2677 G>T/A as the determinants of risk for post-transplant diabetes. To conclude, the ANN and MDR models explore both individual and synergistic effects of variables in modulating the bioavailability of tacrolimus and risk for post-transplant diabetes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.