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Kotowska K, Wojciuk B, Sieńko J, Bogacz A, Stukan I, Drożdżal S, Czerny B, Tejchman K, Trybek G, Machaliński B, Kotowski M. The Role of Vitamin D Metabolism Genes and Their Genomic Background in Shaping Cyclosporine A Dosage Parameters after Kidney Transplantation. J Clin Med 2024; 13:4966. [PMID: 39201108 PMCID: PMC11355102 DOI: 10.3390/jcm13164966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Revised: 08/12/2024] [Accepted: 08/19/2024] [Indexed: 09/02/2024] Open
Abstract
Background: Kidney transplantation is followed by immunosuppressive therapy involving calcineurin inhibitors (CNIs) such as cyclosporin A. However, long-term high CNIs doses can lead to vitamin D deficiency, and genetic variations influencing vitamin D levels can indirectly impact the necessary CNIs dosage. This study investigates the impact of genetic variations of vitamin D binding protein (DBP) rs2282679 and CYP2R1 hydroxylase rs10741657 polymorphisms on the cyclosporin A dosage in kidney transplant recipients. Additional polymorphisims of genes that are predicted to influence the pharmacogenetic profile were included. Methods: Gene polymorphisms in 177 kidney transplant recipients were analyzed using data mining techniques, including the Random Forest algorithm and Classification and Regression Trees (C&RT). The relationship between the concentration/dose (C/D) ratio of cyclosporin A and genetic profiles was assessed to determine the predictive value of DBP rs2282679 and CYP2R1 rs10741657 polymorphisms. Results: Polymorphic variants of the DBP (rs2282679) demonstrated a strong predictive value for the cyclosporin A C/D ratio in post-kidney transplantation patients. By contrast, the CYP2R1 polymorphism (rs10741657) did not show predictive significance. Additionally, the immune response genes rs231775 CTLA4 and rs1800896 IL10 were identified as predictors of cyclosporin A response, though these did not result in statistically significant differences. Conclusions:DBP rs2282679 polymorphisms can significantly predict the cyclosporin A C/D ratio, potentially enhancing the accuracy of CNI dosing. This can help identify patient groups at risk of vitamin D deficiency, ultimately improving the management of kidney transplant recipients. Understanding these genetic influences allows for more personalized and effective treatment strategies, contributing to better long-term outcomes for patients.
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Affiliation(s)
- Katarzyna Kotowska
- Clinic of Maxillofacial Surgery, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland
| | - Bartosz Wojciuk
- Department of Immunological Diagnostics, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland
| | - Jerzy Sieńko
- Institute of Physical Culture Sciences, University of Szczecin, 70-453 Szczecin, Poland
| | - Anna Bogacz
- Department of Personalized Medicine and Cell Therapy, Regional Blood Center, 60-354 Poznan, Poland
| | - Iga Stukan
- Department of General Pathology, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland
| | - Sylwester Drożdżal
- Department of Nephrology, Transplantology and Internal Medicine, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland
| | - Bogusław Czerny
- Department of General Pharmacology and Pharmacoeconomics, Pomeranian Medical University in Szczecin, 71-210 Szczecin, Poland
| | - Karol Tejchman
- Department of General Surgery and Transplantation, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland
| | - Grzegorz Trybek
- Department of Interdisciplinary Dentistry, Pomeranian Medical University, 70-204 Szczecin, Poland
| | - Bogusław Machaliński
- Department of General Pathology, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland
| | - Maciej Kotowski
- Department of General Surgery and Transplantation, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland
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Marwa KJ, Kapesa A, Kamugisha E, Swedberg G. The Influence of Cytochrome P450 Polymorphisms on Pharmacokinetic Profiles and Treatment Outcomes Among Malaria Patients in Sub-Saharan Africa: A Systematic Review. Pharmgenomics Pers Med 2023; 16:449-461. [PMID: 37223718 PMCID: PMC10202199 DOI: 10.2147/pgpm.s379945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 04/24/2023] [Indexed: 05/25/2023] Open
Abstract
Background Sub-Saharan Africa (SSA) population is genetically diverse and heterogenous thus variability in drug response among individuals is predicted to be high. Cytochrome P450 (CYP450) polymorphisms is a major source of variability in drug response. This systematic review presents the influence of CYP450 single nucleotide polymorphisms (SNPs), particularly CYP3A4*1B, CYP2B6*6 and CYP3A5*3 on antimalarial drug plasma concentrations, efficacy and safety in SSA populations. Methods Searching for relevant studies was done through Google Scholar, Cochrane Central Register of controlled trials (CENTRAL), PubMed, Medline, LILACS, and EMBASE online data bases. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines were used. Two independent reviewers extracted data from the studies. Results Thirteen studies reporting the influence of CYP450 SNPs on plasma concentrations, efficacy and safety were included in the final data synthesis. CYP3A4*1B, CYP3A5*5, CYP2B6*6 and CYP2C8*2 did not affect antimalarial drug plasma concentration significantly. There was no difference in treatment outcomes between malaria patients with variant alleles and those with wild type alleles. Conclusion This review reports lack of influence of CYP3A4*1B, CYP3A5*3, CYP2C8*3 and CYP2B6*6 SNPs on PK profiles, efficacy and safety in SSA among P. falciparum malaria patients.
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Affiliation(s)
- Karol J Marwa
- Department of Pharmacology, Catholic University of Health and Allied Sciences, Mwanza, Tanzania
| | - Anthony Kapesa
- Department of Community Medicine, Catholic University of Health and Allied Sciences, Mwanza, Tanzania
| | - Erasmus Kamugisha
- Department of Biochemistry, Catholic University of Health and Allied Sciences, Mwanza, Tanzania
| | - Göte Swedberg
- Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
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Developing supervised machine learning algorithms to evaluate the therapeutic effect and laboratory-related adverse events of cyclosporine and tacrolimus in renal transplants. Int J Clin Pharm 2023:10.1007/s11096-023-01545-5. [PMID: 36848022 DOI: 10.1007/s11096-023-01545-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 01/18/2023] [Indexed: 03/01/2023]
Abstract
BACKGROUND Single nucleotide polymorphisms influence the effects of tacrolimus and cyclosporine in renal transplants. AIM We set out to use machine learning algorithms (MLAs) to identify variables that predict the therapeutic effects and adverse events following tacrolimus and cyclosporine administration in renal transplant patients. METHOD We sampled 120 adult renal transplant patients (on cyclosporine or tacrolimus). Generalized linear model (GLM), support vector machine (SVM), artificial neural network (ANN), Chi-square automatic interaction detection, classification and regression tree, and K-nearest neighbors were the chosen MLAs. The mean absolute error (MAE), relative mean square error (RMSE), and regression coefficient (β) with a 95% confidence interval (CI) were used as the model parameters. RESULTS For a stable dose of tacrolimus, the MAEs (RMSEs) of GLM, SVM, and ANN were 1.3 (1.5), 1.3 (1.8), and 1.7 (2.3) mg/day, respectively. GLM revealed that the POR*28 genotype and age significantly predicted the stable dose of tacrolimus as follows: POR*28 (β -1.8; 95% CI -3, -0.5; p = 0.006), and age (β -0.04; 95% CI -0.1, -0.006; p = 0.02). For a stable dose of cyclosporine, MAEs (RMSEs) of 93.2 (103.4), 79.1 (115.2), and 73.7 (91.7) mg/day were observed with GLM, SVM, and ANN, respectively. GLM revealed the following predictors of a stable dose of cyclosporine: CYP3A5*3 (β -80.8; 95% CI -130.3, -31.2; p = 0.001), and age (β -3.4; 95% CI -5.9, -0.9; p = 0.007). CONCLUSION We observed that various MLAs could identify significant predictors that were useful to optimize tacrolimus and cyclosporine dosing regimens; yet, the findings must be externally validated.
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Huang Q, Lin X, Wang Y, Chen X, Zheng W, Zhong X, Shang D, Huang M, Gao X, Deng H, Li J, Zeng F, Mo X. Tacrolimus pharmacokinetics in pediatric nephrotic syndrome: A combination of population pharmacokinetic modelling and machine learning approaches to improve individual prediction. Front Pharmacol 2022; 13:942129. [DOI: 10.3389/fphar.2022.942129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 09/30/2022] [Indexed: 11/17/2022] Open
Abstract
Background and Aim: Tacrolimus (TAC) is a first-line immunosuppressant for the treatment of refractory nephrotic syndrome (RNS), but the pharmacokinetics of TAC varies widely among individuals, and there is still no accurate model to predict the pharmacokinetics of TAC in RNS. Therefore, this study aimed to combine population pharmacokinetic (PPK) model and machine learning algorithms to develop a simple and accurate prediction model for TAC.Methods: 139 children with RNS from August 2013 to December 2018 were included, and blood samples of TAC trough and partial peak concentrations were collected. The blood concentration of TAC was determined by enzyme immunoassay; CYP3A5 was genotyped by polymerase chain reaction-restriction fragment length polymorphism method; MYH9, LAMB2, ACTN4 and other genotypes were determined by MALDI-TOF MS method; PPK model was established by nonlinear mixed-effects method. Based on this, six machine learning algorithms, including eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Extra-Trees, Gradient Boosting Decision Tree (GBDT), Adaptive boosting (AdaBoost) and Lasso, were used to establish the machine learning model of TAC clearance.Results: A one-compartment model of first-order absorption and elimination adequately described the pharmacokinetics of TAC. Age, co-administration of Wuzhi capsules, CYP3A5 *3/*3 genotype and CTLA4 rs4553808 genotype were significantly affecting the clearance of TAC. Among the six machine learning models, the Lasso algorithm model performed the best (R2 = 0.42).Conclusion: For the first time, a clearance prediction model of TAC in pediatric patients with RNS was established using PPK combined with machine learning, by which the individual clearance of TAC can be predicted more accurately, and the initial dose of administration can be optimized to achieve the goal of individualized treatment.
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Janssen A, Bennis FC, Mathôt RAA. Adoption of Machine Learning in Pharmacometrics: An Overview of Recent Implementations and Their Considerations. Pharmaceutics 2022; 14:pharmaceutics14091814. [PMID: 36145562 PMCID: PMC9502080 DOI: 10.3390/pharmaceutics14091814] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 08/17/2022] [Accepted: 08/22/2022] [Indexed: 11/23/2022] Open
Abstract
Pharmacometrics is a multidisciplinary field utilizing mathematical models of physiology, pharmacology, and disease to describe and quantify the interactions between medication and patient. As these models become more and more advanced, the need for advanced data analysis tools grows. Recently, there has been much interest in the adoption of machine learning (ML) algorithms. These algorithms offer strong function approximation capabilities and might reduce the time spent on model development. However, ML tools are not yet an integral part of the pharmacometrics workflow. The goal of this work is to discuss how ML algorithms have been applied in four stages of the pharmacometrics pipeline: data preparation, hypothesis generation, predictive modelling, and model validation. We will also discuss considerations before the use of ML algorithms with respect to each topic. We conclude by summarizing applications that hold potential for adoption by pharmacometricians.
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Affiliation(s)
- Alexander Janssen
- Department of Clinical Pharmacology, Hospital Pharmacy, Amsterdam University Medical Center, 1105 Amsterdam, The Netherlands
- Correspondence:
| | - Frank C. Bennis
- Quantitative Data Analytics Group, Department of Computer Science, Vrije Universiteit Amsterdam, 1081 Amsterdam, The Netherlands
| | - Ron A. A. Mathôt
- Department of Clinical Pharmacology, Hospital Pharmacy, Amsterdam University Medical Center, 1105 Amsterdam, The Netherlands
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Yeh CH, Chou YJ, Tsai TH, Hsu PWC, Li CH, Chan YH, Tsai SF, Ng SC, Chou KM, Lin YC, Juan YH, Fu TC, Lai CC, Sytwu HK, Tsai TF. Artificial-Intelligence-Assisted Discovery of Genetic Factors for Precision Medicine of Antiplatelet Therapy in Diabetic Peripheral Artery Disease. Biomedicines 2022; 10:biomedicines10010116. [PMID: 35052795 PMCID: PMC8773099 DOI: 10.3390/biomedicines10010116] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 12/30/2021] [Accepted: 01/04/2022] [Indexed: 12/15/2022] Open
Abstract
An increased risk of cardiovascular events was identified in patients with peripheral artery disease (PAD). Clopidogrel is one of the most widely used antiplatelet medications. However, there are heterogeneous outcomes when clopidogrel is used to prevent cardiovascular events in PAD patients. Here, we use an artificial intelligence (AI)-assisted methodology to identify genetic factors potentially involved in the clopidogrel-resistant mechanism, which is currently unclear. Several discoveries can be pinpointed. Firstly, a high proportion (>50%) of clopidogrel resistance was found among diabetic PAD patients in Taiwan. Interestingly, our result suggests that platelet function test-guided antiplatelet therapy appears to reduce the post-interventional occurrence of major adverse cerebrovascular and cardiac events in diabetic PAD patients. Secondly, AI-assisted genome-wide association study of a single-nucleotide polymorphism (SNP) database identified a SNP signature composed of 20 SNPs, which are mapped into 9 protein-coding genes (SLC37A2, IQSEC1, WASHC3, PSD3, BTBD7, GLIS3, PRDM11, LRBA1, and CNR1). Finally, analysis of the protein connectivity map revealed that LRBA, GLIS3, BTBD7, IQSEC1, and PSD3 appear to form a protein interaction network. Intriguingly, the genetic factors seem to pinpoint a pathway related to endocytosis and recycling of P2Y12 receptor, which is the drug target of clopidogrel. Our findings reveal that a combination of AI-assisted discovery of SNP signatures and clinical parameters has the potential to develop an ethnic-specific precision medicine for antiplatelet therapy in diabetic PAD patients.
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Affiliation(s)
- Chi-Hsiao Yeh
- Department of Thoracic and Cardiovascular Surgery, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan;
- College of Medicine, Chang Gung University, Taoyuan 333, Taiwan; (Y.-C.L.); (Y.-H.J.); (T.-C.F.)
- Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung 204, Taiwan
| | - Yi-Ju Chou
- Institute of Molecular and Genomic Medicine, National Health Research Institutes, Miaoli 350, Taiwan; (Y.-J.C.); (P.W.-C.H.); (S.-F.T.)
| | - Tsung-Hsien Tsai
- Advanced Tech BU, Acer Inc., New Taipei City 221, Taiwan; (T.-H.T.); (C.-H.L.); (Y.-H.C.)
| | - Paul Wei-Che Hsu
- Institute of Molecular and Genomic Medicine, National Health Research Institutes, Miaoli 350, Taiwan; (Y.-J.C.); (P.W.-C.H.); (S.-F.T.)
| | - Chun-Hsien Li
- Advanced Tech BU, Acer Inc., New Taipei City 221, Taiwan; (T.-H.T.); (C.-H.L.); (Y.-H.C.)
| | - Yun-Hsuan Chan
- Advanced Tech BU, Acer Inc., New Taipei City 221, Taiwan; (T.-H.T.); (C.-H.L.); (Y.-H.C.)
| | - Shih-Feng Tsai
- Institute of Molecular and Genomic Medicine, National Health Research Institutes, Miaoli 350, Taiwan; (Y.-J.C.); (P.W.-C.H.); (S.-F.T.)
| | - Soh-Ching Ng
- Department of Internal Medicine, Division of Endocrinology and Metabolism, Chang Gung Memorial Hospital, Keelung 204, Taiwan; (S.-C.N.); (K.-M.C.)
| | - Kuei-Mei Chou
- Department of Internal Medicine, Division of Endocrinology and Metabolism, Chang Gung Memorial Hospital, Keelung 204, Taiwan; (S.-C.N.); (K.-M.C.)
| | - Yu-Ching Lin
- College of Medicine, Chang Gung University, Taoyuan 333, Taiwan; (Y.-C.L.); (Y.-H.J.); (T.-C.F.)
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Keelung 204, Taiwan
| | - Yu-Hsiang Juan
- College of Medicine, Chang Gung University, Taoyuan 333, Taiwan; (Y.-C.L.); (Y.-H.J.); (T.-C.F.)
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Keelung 204, Taiwan
| | - Tieh-Cheng Fu
- College of Medicine, Chang Gung University, Taoyuan 333, Taiwan; (Y.-C.L.); (Y.-H.J.); (T.-C.F.)
- Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital, Keelung 204, Taiwan
| | - Chi-Chun Lai
- College of Medicine, Chang Gung University, Taoyuan 333, Taiwan; (Y.-C.L.); (Y.-H.J.); (T.-C.F.)
- Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung 204, Taiwan
- Department of Ophthalmology, Chang Gung Memorial Hospital, Keelung 204, Taiwan
- Correspondence: (C.-C.L.); (H.-K.S.); (T.-F.T.); Tel.: +886-2-24313131 (ext. 6101) (C.-C.L.); +886-37-206166 (ext. 31010) (H.-K.S.); +886-2-28267293 (T.-F.T.)
| | - Huey-Kang Sytwu
- National Institute of Infectious Diseases and Vaccinology, National Health Research Institutes, Miaoli 350, Taiwan
- National Defense Medical Center, Department & Graduate Institute of Microbiology and Immunology, Taipei 114, Taiwan
- Correspondence: (C.-C.L.); (H.-K.S.); (T.-F.T.); Tel.: +886-2-24313131 (ext. 6101) (C.-C.L.); +886-37-206166 (ext. 31010) (H.-K.S.); +886-2-28267293 (T.-F.T.)
| | - Ting-Fen Tsai
- Institute of Molecular and Genomic Medicine, National Health Research Institutes, Miaoli 350, Taiwan; (Y.-J.C.); (P.W.-C.H.); (S.-F.T.)
- Departments of Life Sciences and Institute of Genome Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Correspondence: (C.-C.L.); (H.-K.S.); (T.-F.T.); Tel.: +886-2-24313131 (ext. 6101) (C.-C.L.); +886-37-206166 (ext. 31010) (H.-K.S.); +886-2-28267293 (T.-F.T.)
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McComb M, Bies R, Ramanathan M. Machine learning in pharmacometrics: Opportunities and challenges. Br J Clin Pharmacol 2021; 88:1482-1499. [PMID: 33634893 DOI: 10.1111/bcp.14801] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 02/08/2021] [Accepted: 02/12/2021] [Indexed: 12/13/2022] Open
Abstract
The explosive growth in medical devices, imaging and diagnostics, computing, and communication and information technologies in drug development and healthcare has created an ever-expanding data landscape that the pharmacometrics (PMX) research community must now traverse. The tools of machine learning (ML) have emerged as a powerful computational approach in other data-rich disciplines but its effective utilization in the pharmaceutical sciences and PMX modelling is in its infancy. ML-based methods can complement PMX modelling by enabling the information in diverse sources of big data, e.g. population-based public databases and disease-specific clinical registries, to be harnessed because they are capable of efficiently identifying salient variables associated with outcomes and delineating their interdependencies. ML algorithms are computationally efficient, have strong predictive capabilities and can enable learning in the big data setting. ML algorithms can be viewed as providing a computational bridge from big data to complement PMX modelling. This review provides an overview of the strengths and weaknesses of ML approaches vis-à-vis population methods, assesses current research into ML applications in the pharmaceutical sciences and provides perspective for potential opportunities and strategies for the successful integration and utilization of ML in PMX.
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Affiliation(s)
- Mason McComb
- Department of Pharmaceutical Sciences, University at Buffalo, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Robert Bies
- Department of Pharmaceutical Sciences, University at Buffalo, University at Buffalo, State University of New York, Buffalo, NY, USA.,Institute for Computational Data Science, University at Buffalo, NY, USA
| | - Murali Ramanathan
- Department of Pharmaceutical Sciences, University at Buffalo, University at Buffalo, State University of New York, Buffalo, NY, USA.,Department of Neurology, University at Buffalo, State University of New York, Buffalo, NY, USA
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