Artificial neural network model for predicting the bioavailability of tacrolimus in patients with renal transplantation

Thishya, Kalluri; Vattam, Kiran Kumar; Naushad, Shaik Mohammad; Raju, Shree Bhushan; Kutala, Vijay Kumar

doi:10.1371/journal.pone.0191921

Cited by 65Pharmacogenetics and Drug MetabolismRenal Transplantation Outcomes and TreatmentsDrug Transport and Resistance Mechanisms

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Artificial neural network model for predicting the bioavailability of tacrolimus in patients with renal transplantation

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Thishya, Kalluri, Vattam, Kiran Kumar, Naushad, Shaik Mohammad, Raju, Shree Bhushan, Kutala, Vijay Kumar·2018·PLoS ONE

DOI 10.1371/journal.pone.0191921PMC PMC5886400

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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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All three studies comparing logistic regression to machine learning algorithms for predicting stable tacrolimus dose in kidney transplant patients showed a superior predictive ability of machine learning tools over linear regression models, albeit with a relatively small difference.
“All studies showed a superior predictive ability of machine learning tools over the linear regression models, albeit with a relatively small difference.”
From Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation (2022) · cites this paper as [32]

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V Haufroid, 2004, The effect of CYP3A5 and MDR1 (ABCB1) polymorphisms on cyclosporine and tacrolimus dose requirements and trough blood levels in stable renal transplant patients, Pharmacogenetics, 14, 147, 10.1097/00008571-200403000-00002 · DOI

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Authors: Thishya, K., Vattam, K. K., Naushad, S. M., Raju, S. B., & Kutala, V. K.
Affiliations: Nizam's Institute of Medical Sciences (IN)
Corresponding author: Kutala, Vijay Kumar
ORCID: Naushad, Shaik Mohammad
Kutala, Vijay Kumar
Venue: PLoS ONE
Publisher: Public Library of Science (PLoS)
Published: 2018-04-05
Volume: 13
Issue: 4
Pages: e0191921
ISSN: 1932-6203 (ISSN-L)
DOI: 10.1371/journal.pone.0191921
PMC: PMC5886400
OpenAlex: W2795471351
Cited by: 65
References: 47
Citations by year: 2026: 3 · 2025: 8 · 2024: 9 · 2023: 6 · 2022: 11 · 2021: 14 · 2020: 12 · 2019: 1
FWCI: 7.43
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: PLoS ONE (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: Department of Biotechnology, Ministry of Science and Technology (BT/PR7964/MED/12/606/2013)
Topics: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
Health Sciences › Medicine › Transplantation › Renal Transplantation Outcomes and Treatments
Health Sciences › Medicine › Oncology › Drug Transport and Resistance Mechanisms
Keywords: tacrolimus, bioavailability, kidney transplantation, ABCB1 polymorphisms, CYP3A5*3, post-transplant diabetes mellitus, pharmacogenetics, drug-drug interactions, artificial neural networks, multifactor dimensionality reduction, logistic regression
MeSH terms: Adolescent, Adult, Biological Availability, Female, Genotype, Humans, Male, Middle Aged, Kidney Transplantation, Tacrolimus, Neural Networks, Computer, ATP Binding Cassette Transporter, Subfamily B, Computational Biology, Polymorphism, Single Nucleotide, Cytochrome P-450 CYP3A, Young Adult

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Artificial neural network model for predicting the bioavailability of tacrolimus in patients with renal transplantation

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