Population pharmacokinetic modeling of missed mycophenolate mofetil doses: Impact on exposure and exploration of mitigation strategies.

Background: Missed doses of mycophenolate mofetil (MMF) are frequent in transplant recipients and may lead to subtherapeutic exposure to mycophenolic acid (MPA), potentially compromising graft function. However, current guidance on how to manage such deviations remains empirical and is not supported by pharmacokinetic evidence.

Methods: We used two validated population pharmacokinetic models of MPA to simulate steady-state exposure in virtual cohorts of renal transplant recipients treated with MMF.

Large-Scale Real-World Monitoring of Mycophenolic Acid Exposure in Liver Transplantation: Impact of Bayesian Dose Adjustment.

Background: Mycophenolate mofetil is widely used in liver transplantation but poses dosing challenges owing to the narrow therapeutic window and high pharmacokinetic variability of its active moiety, mycophenolic acid (MPA). Overexposure increases the risk of adverse effects, whereas underexposure increases the risk of rejection and graft loss. The ImmunoSuppressant Bayesian Dose Adjustment (ISBA) platform estimates the MPA area under the curve (AUC)0-12h using 3 post-dose samples (20 minutes, 1 hour, 3 hours) and provides dose recommendations to target an AUC0-12h of 30-60 mg·h/L.

Estimation of Overall Cyclosporine Exposure Using Machine Learning.

Background: Cyclosporine (CsA), an immunosuppressant widely used in solid-organ transplantation, requires precise therapeutic drug monitoring to balance its efficacy and toxicity. The interdose area under the concentration-time curve (AUC0-12 h) is considered to be a superior metric of drug exposure compared with single concentration measurements but is, nevertheless, resource-intensive. Machine learning (ML) offers a novel approach for AUC prediction by leveraging patient-specific data without relying on traditional pharmacokinetic assumptions.

Oncological and Functional Outcomes of Hemi-Ablation Versus Focal Ablation for Localized Prostate Cancer Using Irreversible Electroporation.

: Irreversible electroporation (IRE) is a novel ablative treatment modality for localized prostate cancer and aims at achieving oncological control while minimizing the related side effects. We present the functional and oncological outcomes of focal IRE ablation versus hemi-ablation from a single-center patient series. : Men with histologically confirmed low-intermediate risk prostate cancer received focal IRE ablation or hemi-ablation.

Pharmacokinetic/Pharmacodynamic Modelling and Monte Carlo Simulations to Predict Cytomegalovirus Viral Load in Pediatric Transplant Recipients Treated with (val)Ganciclovir.

Background And Objectives: Cytomegalovirus (CMV) infection poses significant challenges in pediatric transplant recipients. Ganciclovir and its prodrug valganciclovir are primary treatments because of their potent antiviral effects. Balancing efficacy and toxicity is particularly critical in children.

Use of Artificial Intelligence in Current Fight Against Antimicrobial Resistance.

Antimicrobial resistance (AMR) poses a significant global health threat, with projections indicating it could surpass cancer in mortality rates by 2050 if left unaddressed. Optimizing antimicrobial dosing is critical to mitigate resistance and improve clinical outcomes. Traditional approaches, including population pharmacokinetics (PK) models and Bayesian estimation, are limited by mechanistic hypothesis requirements and complexity.

Optimizing CMV therapy: Population pharmacokinetics and Monte Carlo simulations for letermovir and maribavir dosage.

Purpose: This study seeks to reassess and enhance the dosing strategies of letermovir and maribavir for treating cytomegalovirus (CMV) infection, aiming to propose adjustments that could improve therapeutic effectiveness.

Methods: Pre-existing population pharmacokinetic models were used alongside Monte Carlo simulations to evaluate the dosing strategies of letermovir and maribavir in CMV treatment. The simulations assessed the probability of target attainment for current and alternative dosing regimens, including scenarios with missed doses.

Comparative evaluation of Allplex HPV28 and Anyplex II HPV28 assays for high-risk HPV genotyping in cervical samples.

Background/objectives: Human papillomavirus (HPV) genotyping is essential for cervical cancer screening and prevention. The AllplexTM HPV28 real-time PCR kit, using different chemistry and results analysis compared with its predecessor, the AnyplexTM II HPV28 kit, has recently been launched. This study aims to compare the AllplexTM HPV28 and AnyplexTM II HPV28 assays in detecting and genotyping the 13 high-risk (HR)-HPV types.

Estimation of Ganciclovir Exposure in Adults Transplant Patients by Machine Learning.

Introduction: Valganciclovir, a prodrug of ganciclovir (GCV), is used to prevent cytomegalovirus infection after transplantation, with doses adjusted based on creatinine clearance (CrCL) to target GCV AUC0-24 h of 40-60 mg*h/L. This sometimes leads to overexposure or underexposure. This study aimed to train, test and validate machine learning (ML) algorithms for accurate GCV AUC0-24 h estimation in solid organ transplantation.

Ciprofloxacin Dosage Optimization in Cystic Fibrosis Through Therapeutic Drug Monitoring.

Background: Ciprofloxacin (CIP) is effective against many Gram-negative pathogens and penetrates well into respiratory secretions and pulmonary tissues, thus making it useful for treating respiratory infections in patients with cystic fibrosis (CF).

Methods: A 13-year-old patient with severe CF and an acute respiratory exacerbation from multidrug-resistant Pseudomonas aeruginosa was treated with 700 mg of CIP every 8 hours. Bronchial secretions confirmed that P.

Killing several birds with one stone: A multi-indication population pharmacokinetic model and Bayesian estimator for enteric-coated mycophenolate sodium.

Aims: Mycophenolic acid (MPA), the active component of enteric-coated mycophenolate sodium (EC-MPS), exhibits highly variable pharmacokinetics. Only a few population pharmacokinetic (popPK) models and Bayesian estimators (MAP-BE) exist for estimating MPA AUC and all in renal transplantation. This study aimed to develop a popPK model and MAP-BE for MPA AUC estimation using a limited sampling strategy (LSS) in solid organ transplant (SOT), haematopoietic stem cell (HSC) recipients and patients with autoimmune diseases (AID) on EC-MPS.

Leveraging machine learning in limited sampling strategies for efficient estimation of the area under the curve in pharmacokinetic analysis: a review.

Objective: Limited sampling strategies are widely employed in clinical practice to minimize the number of blood samples required for the accurate area under the curve calculations, as obtaining these samples can be costly and challenging. Traditionally, the maximum a posteriori Bayesian estimation has been the standard method for the area under the curve estimation based on limited samples. However, machine learning is emerging as a promising alternative for this purpose.

The effects of maternal voice on pain during placement of peripherally inserted central catheter in neonates.

Background: Peripherally inserted central catheter (PICC) are a necessary procedure for preterm newborns care. Despite the use of analgesic treatments, its insertion can be painful. Our objective was to study the effect of maternal voice on pain during PICC insertion.

To be or not to be, when synthetic data meet clinical pharmacology: A focused study on pharmacogenetics.

The use of synthetic data in pharmacology research has gained significant attention due to its potential to address privacy concerns and promote open science. In this study, we implemented and compared three synthetic data generation methods, CT-GAN, TVAE, and a simplified implementation of Avatar, for a previously published pharmacogenetic dataset of 253 patients with one measurement per patient (non-longitudinal). The aim of this study was to evaluate the performance of these methods in terms of data utility and privacy trade off.

Everolimus Personalized Therapy: Second Consensus Report by the International Association of Therapeutic Drug Monitoring and Clinical Toxicology.

The Immunosuppressive Drugs Scientific Committee of the International Association of Therapeutic Drug Monitoring and Clinical Toxicology established the second consensus report to guide therapeutic drug monitoring (TDM) of everolimus (EVR) and its optimal use in clinical practice 7 years after the first version was published in 2016. This version provides information focused on new developments that have arisen in the last 7 years. For the general aspects of the pharmacology and TDM of EVR that have retained their relevance, readers can refer to the 2016 document.

Daptomycin Dosage Optimization in Renal Impairment Using Model-Informed Precision Dosing.

Background: Daptomycin's efficacy and toxicity are closely related to its exposure, which can vary widely among individuals. The patient, a 59-year-old male with an estimated glomerular filtration rate (eGFR) of 12 mL/min/1.73 m² and a weight of 64 kg, was treated with 850 mg of daptomycin every other day for infective endocarditis caused by methicillin-resistant Staphylococcus aureus (MRSA).

Application of machine-learning models to predict the ganciclovir and valganciclovir exposure in children using a limited sampling strategy.

Intravenous ganciclovir and oral valganciclovir display significant variability in ganciclovir pharmacokinetics, particularly in children. Therapeutic drug monitoring currently relies on the area under the concentration-time (AUC). Machine-learning (ML) algorithms represent an interesting alternative to Maximum-a-Posteriori Bayesian-estimators for AUC estimation.

A Machine Learning Algorithm to Predict the Starting Dose of Daptomycin.

Background And Objective: The dosage of daptomycin is usually based on body weight. However, it has been shown that this approach yields too high an exposure in obese patients. Pharmacokinetic and pharmacodynamic indexes (PK/PD) have been proposed for daptomycin's antibacterial effect (AUC/CMI >666) and toxicity (C0 > 24.

Synergizing pharmacometrics and pharmacovigilance for medication error management: the case of secukinumab.

Purpose: To demonstrate the effective integration of pharmacometrics and pharmacovigilance in managing medication errors, highlighted by a case involving secukinumab in a patient with hidradenitis suppurativa.

Methods: We present the case of a 41-year-old male with progressive hidradenitis suppurativa, unresponsive to multiple antibiotic regimens and infliximab treatment. Due to a medication error, the patient received 300 mg of secukinumab daily for 4 days instead of weekly, totaling 1200 mg.

A machine learning approach to predict daptomycin exposure from two concentrations based on Monte Carlo simulations.

Daptomycin is a concentration-dependent lipopeptide antibiotic for which exposure/effect relationships have been shown. Machine learning (ML) algorithms, developed to predict the individual exposure to drugs, have shown very good performances in comparison to maximum a posteriori Bayesian estimation (MAP-BE). The aim of this work was to predict the area under the blood concentration curve (AUC) of daptomycin from two samples and a few covariates using XGBoost ML algorithm trained on Monte Carlo simulations.