Purpose
Managing the pharmacokinetic variability of immunosuppressive drugs after pediatric hematopoietic stem cell transplantation (HSCT) is a clinical challenge. Thus, the aim of our study was to ...design and validate a decision support tool predicting the best first cyclosporine oral dose to give when switching from intravenous route.
Methods
We used 10-years pediatric HSCT patients’ dataset from 2008 to 2018. A tree-augmented naïve Bayesian network model (method belonging to artificial intelligence) was built with data from the first eight-years, and validated with data from the last two.
Results
The Bayesian network model obtained showed good prediction performances, both after a 10-fold cross-validation and external validation, with respectively an AUC-ROC of 0.89 and 0.86, a percentage of misclassified patients of 28.7% and 35.2%, a true positive rate of 0.71 and 0.65, and a false positive rate of 0.12 and 0.14 respectively.
Conclusion
The final model allows the prediction of the most likely cyclosporine oral dose to reach the therapeutic target specified by the clinician. The clinical impact of using this model needs to be prospectively warranted. Respecting the decision support tool terms of use is necessary as well as remaining critical about the prediction by confronting it with the clinical context.
...the use of corticosteroids or anti-IL6 antibody in addition to standard of care is proposed for critically ill patients with COVID-19. ...corticosteroids are well-known risk factors for IFI and ...identified as a negative outcome predictor of invasive aspergillosis 4, whereas IFI were reported in patients treated with anti-IL6 antibody 5. ...it is expected that incidence of IFI may increase with the more extensive use of corticosteroids or other immunomodulating therapies, and this, mostly in patients with risk factors for CAPA such as older age, initial antibiotic usage of beta-lactamase inhibitor combination, and chronic obstructive pulmonary disease (COPD) 1. Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
The authors report on a case of a 59-year-old man hospitalized in the intensive care unit (ICU) because of severe SARS-COV-2 infection (COVID-19). Background: The patient had several comorbidities, ...including liver cirrhosis. He developed ventilation-associated bacterial pneumonia for which he was administered cefepime at an initial dose of 2 g/8 h. Therapeutic drug monitoring was performed, showing overexposure with an initial trough concentration of > 60 mg/L. Methods: Analysis of pharmacokinetic (PK) data and model-based dose adjustment was performed using BestDose software. Results: The patient had unexpected PK parameter values. Serum creatinine was only moderately increased, whereas measured creatinine clearance based on urine collection showed impaired renal function. Bacterial minimum inhibitory concentration (MIC) was also considered in the dosing decisions. After dose reduction to 0.5 g/8 h, the cefepime trough concentration progressively declined and reached the target values by the end of the therapy. A post-hoc analysis provided a different interpretation of drug overexposure. Conclusion: This case report illustrates how physiological, microbiological, and drug concentration data can be used for model-based dosage individualization of cefepime in ICU patients.
We present a unified quantitative approach to predict the
in vivo
alteration in drug exposure caused by either cytochrome P450 (CYP) gene polymorphisms or CYP-mediated drug–drug interactions (DDI). ...An application to drugs metabolized by CYP2C19 is presented. The metrics used is the ratio of altered drug area under the curve (AUC) to the AUC in extensive metabolizers with no mutation or no interaction. Data from 42 pharmacokinetic studies performed in CYP2C19 genetic subgroups and 18 DDI studies were used to estimate model parameters and predicted AUC ratios by using Bayesian approach. Pharmacogenetic information was used to estimate a parameter of the model which was then used to predict DDI. The method adequately predicted the AUC ratios published in the literature, with mean errors of −0.15 and −0.62 and mean absolute errors of 0.62 and 1.05 for genotype and DDI data, respectively. The approach provides quantitative prediction of the effect of five genotype variants and 10 inhibitors on the exposure to 25 CYP2C19 substrates, including a number of unobserved cases. A quantitative approach for predicting the effect of gene polymorphisms and drug interactions on drug exposure has been successfully applied for CYP2C19 substrates. This study shows that pharmacogenetic information can be used to predict DDI. This may have important implications for the development of personalized medicine and drug development.
Cytochrome P450 2D6 (CYP2D6) gene polymorphisms influence the exposure to tramadol (T) and its pharmacologically active metabolite, O-demethyl tramadol (O-dT). Tramadol has been considered as a ...candidate probe drug for CYP2D6 phenotyping. The objective of the CYTRAM study was to investigate the value of plasma O-dT/T ratio for CYP2D6 phenotyping. European adult patients who received IV tramadol after surgery were included. CYP2D6 genotyping was performed and subjects were classified as extensive (EM), intermediate (IM), poor (PM), or ultra-rapid (UM) CYP2D6 metabolizers. Plasma concentrations of tramadol and O-dT were determined at 24 h and 48 h. The relationship between O-dT/T ratio and CYP2D6 phenotype was examined in both a learning and a validation group. Genotype data were obtained in 301 patients, including 23 PM (8%), 117 IM (39%), 154 EM (51%), and 7 UM (2%). Tramadol trough concentrations at 24 h were available in 297 patients. Mean value of O-dT/T ratio was significantly lower in PM than in non-PM individuals (0.061 ± 0.031 versus 0.178 ± 0.09, p < 0.01). However, large overlap was observed in the distributions of O-dT/T ratio between groups. Statistical models based on O-dT/T ratio failed to identify CYP2D6 phenotype with acceptable sensitivity and specificity. Those results suggest that tramadol is not an adequate probe drug for CYP2D6 phenotyping.
Optimal immunosuppressive therapy in acquired severe aplastic anemia (SAA) remains to be refined, especially cyclosporine (CsA) use. Current recommendations state that CsA trough blood concentrations ...(TBC) should be maintained between 200 and 400 ng/mL despite the lack of supporting data. This study aimed at quantifying relationships between CsA exposure and neutrophil response and determining an effective range for CsA TBC. Twenty-three SAA patients treated with CsA were retrospectively analyzed. Nonlinear mixed effect modeling approaches were used to develop a pharmacokinetic-pharmacodynamic model. The pharmacokinetic model described the relationships between CsA doses and TBC. The pharmacodynamic model allowed to estimate boundaries for optimal CsA effects, neutrophils being used as biomarker of response. A time-to-event model linked effective concentration to time-to-therapeutic success. CsA TBC were adequately described by a two-compartment model with first-order absorption, a lag time, and a linear elimination. The efficient range of CsA TBC was estimated between 87 and 120 ng/mL. Model-based simulations and external validation in three additional patients confirmed these results. This original modeling approach was successful in describing the relationship between CsA TBC and neutrophil response in SAA patients. Although further evaluation of the model is necessary, this work suggests that an optimal CsA TBC target of 100 ng/mL would be associated with a better neutrophil response in children with SAA.
Thanks to advancements in medical care, a majority of patients with sickle cell disease (SCD) worldwide live beyond 18 years of age, and therefore, patients initially followed in paediatric ...departments are then transferred to adult departments. This paediatric-adult care transition is a period with an increased risk of discontinuity of care and subsequent morbidity and mortality. During this period, the patient will have to manage new interlocutors and places of care, and personal issues related to the period of adolescence. To take into consideration all these aspects, an interesting approach is to use the whole system approach to the patient, as presented in the biopsychosocial approach. The aim of this trial is to evaluate the impact of the proposed biopsychosocial paediatric-adult transition programme.
The DREPADO study is a multicentre randomised control trial comparing a control group (Arm A) to an interventional group with a paediatric-adult transition programme based on a biopsychosocial approach (Arm B). To be included, patients should have the SS, SC, or Sβ form of sickle cell disease and be aged between 16 and 17 years. The randomisation in a 1:1 ratio assigns to Arm A or B. The primary outcome is the number of hospital admissions and emergencies for complications in the index hospital, in the 2 years after the first consultation in the adult department of care. Secondary outcomes consider the quality of life, but also include coping skills such as sense of self-efficacy and disease knowledge. To provide patient and parent knowledge and coping skills, the transition programme is composed of three axes: educational, psychological, and social, conducted individually and in groups.
By providing self-care knowledge and coping skills related to SCD and therapeutics, helping empower patientsin relation to pain management and emotions, and facilitating the relationship to oneself, others, and care in Arm B of the DREPADO study, we believe that the morbidity and mortality of patients with SCD may be reduced after the proposed paediatric-adult transition programme.
ClinicalTrials.gov, ID: NCT03786549; registered on 17 December 2018; https://clinicaltrials.gov/.
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