Intelligent Pharmacy's applications of cyclosporine

Abstract

Cyclosporine (CsA) is a classical immunosuppressant that requires therapeutic drug monitoring to determine efficacy and adverse effects due to the individualized nature of its drug metabolism. Emerging computer technology has helped to give birth to new approaches to drug therapy. Pharmacokinetic models are used to identify covariates affecting efficacy, predict changes in cyclosporine metabolism and assist in the individualization of CsA treatment. Machine learning algorithms can be used to predict CsA concentrations and adverse events, big data mining can identify rare adverse reactions to CsA in the real world, and the application of internet-based home pharmacy services will yield long-term patient follow-up data. In the future, we will use more computer technology in combination with drug therapy to provide patients with a full range of pharmacy services.

Keywords

Intelligent pharmacy / Cyclosporine / Pharmacokinetic model / Machine learning / Big data

References

[1] Colombo D, Ammirati E. Cyclosporine in transplantation - a history of converging timelines. J Biol Regul Homeost Agents. 2011 Oct-Dec;25(4):493–504.

[2] Méndez A, Monforte V, Berastegui C, et al. High intra-individual variability of cyclosporine pharmacokinetics in lung transplant recipients without cystic fibrosis. Clin Transplant. 2014 Jun;28(6):743–748.

[3] Umpiérrez M, Guevara N, Ibarra M, et al. Development of a population pharmacokinetic model for cyclosporine from therapeutic drug monitoring data. BioMed Res Int. 2021 Apr 8;2021:3108749.

[4] Gao X, Bian ZL, Qiao XH, et al. Population pharmacokinetics of cyclosporine in Chinese pediatric patients with acquired aplastic anemia. Front Pharmacol. 2022 Jul 26;13:933739.

[5] Mao J, Qiu X, Qin W, et al. Factors affecting time-varying clearance of cyclosporine in adult renal transplant recipients: a population pharmacokinetic perspective. Pharm Res (N Y). 2021 Nov;38(11):1873–1887.

[6] Zapke SE, Willmann S, Grebe SO, et al. Comparing predictions of a PBPK model for cyclosporine with drug levels from therapeutic drug monitoring. Front Pharmacol. 2021 May 14;12:630904.

[7] Wu XW, Liu XY, Long EW, et al. Research progress of machine learning in clinical drug therapy. Chinese General Practice. 2022; 25 (2): 254- 258.

[8] Sridharan K, Shah S. 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 Feb;27.

[9] Mao J, Chen Y, Xu L, et al. Applying machine learning to the pharmacokinetic modeling of cyclosporine in adult renal transplant recipients: a multi-method comparison. Front Pharmacol. 2022 Oct 24;13:1016399.

[10] Song L, Huang CR, Pan SZ, et al. A model based on machine learning for the prediction of cyclosporin A trough concentration in Chinese allo-HSCT patients. Expet Rev Clin Pharmacol. 2023 Jan;16(1):83–91.

[11] Tong H, Phan NVT, Nguyen TT, et al. Review on databases and bioinformatic approaches on pharmacogenomics of adverse drug reactions. Pharmgenomics Pers Med. 2021 Jan 13;14:61–75.

[12] Schmedt N, Andersohn F, Garbe E. Signals of progressive multifocal leukoencephalopathy for immunosuppressants: a disproportionality analysis of spontaneous reports within the US Adverse Event Reporting System (AERS). Pharmacoepidemiol Drug Saf. 2012 Nov;21(11):1216–1220.

[13] Hatahira H, Abe J, Hane Y, et al. Drug-induced gingival hyperplasia: a retrospective study using spontaneous reporting system databases. J Pharm Health Care Sci. 2017 Jul 19;3:19.

[14] Dong Lingli, Huang Anbin, Zhang Shengtao, et al. Smart system of disease management for patient follow-up and medication refill during the COVID-19 epidemic period: a multicentre retrospective cohort study. Lancet. 2020 Nov;396(S1):S1.

RIGHTS & PERMISSIONS

2023 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).