A5047936756- Statistical Methods in Clinical Trials
- Optimal Experimental Design Methods
- Statistical Methods and Bayesian Inference
- Advanced Multi-Objective Optimization Algorithms
- Statistical Methods and Inference
- Health Systems, Economic Evaluations, Quality of Life
- Probabilistic and Robust Engineering Design
- Antibiotics Pharmacokinetics and Efficacy
- Anesthesia and Pain Management
- Spaceflight effects on biology
- Advanced Causal Inference Techniques
- Advanced Statistical Methods and Models
- Pain Management and Opioid Use
- Neutropenia and Cancer Infections
- Sepsis Diagnosis and Treatment
- Gene Regulatory Network Analysis
- Renal Transplantation Outcomes and Treatments
- Pain Mechanisms and Treatments
- Biosimilars and Bioanalytical Methods
- Hemodynamic Monitoring and Therapy
- Anesthesia and Sedative Agents
- Computational Drug Discovery Methods
- Pharmacogenetics and Drug Metabolism
- Nutrition and Health in Aging
- Chronic Lymphocytic Leukemia Research
Uppsala University
2013-2024
Stockholm South General Hospital
2020
Karolinska Institutet
2019-2020
KTH Royal Institute of Technology
2019
Pharmacometrics (PM) and machine learning (ML) are both valuable for drug development to characterize pharmacokinetics (PK) pharmacodynamics (PD). Pharmacokinetic/pharmacodynamic (PKPD) analysis using PM provides mechanistic insight into biological processes but is time- labor-intensive. In contrast, ML models much quicker trained, offer less insights. The opportunity of predictions PK as input a PKPD model could strongly accelerate efforts. Here exemplified by rifampicin, widely used...
Population pharmacokinetic (PK)-pharmacodynamic (PKPD) models are increasingly used in drug development and academic research; hence, designing efficient studies is an important task. Following the first theoretical work on optimal design for nonlinear mixed-effects models, this research theme has grown rapidly. There now several different software tools that implement evaluation of Fisher information matrix population PKPD. We compared evaluated following five tools: PFIM, PkStaMp, PopDes,...
Abstract The inclusion of covariates in pharmacometric models is important due to their ability explain variability drug exposure and response. Clear communication the impact needed support informed decision making clinical practice development. However, effectively conveying these effects key stakeholders makers can be challenging. Forest plots have been proposed meet needs. forest for illustration covariate pharmacometrics are complex combinations model predictions, uncertainty estimates,...
ABSTRACT Pharmacometric modeling establishes causal quantitative relationships between administered dose, tissue exposures, desired and undesired effects patient’s risk factors. These models are employed to de-risk drug development guide precision medicine decisions. However, pharmacometric tools have not been designed handle today’s heterogeneous big data complex models. We set out design a platform that facilitates domain-specific its integration with modern analytics foster innovation...
Introduction: In pharmacometric modeling, modellers choose the covariate-parameter scope to consider. For example, all covariates on structural parameters (a full model), only one of parameters, or different sets parameters. Scope reductions aim simplify models, reduce runtime, and enhance stability, usually based assumption that some are unaffected by certain covariates. However, in practice such assumptions may not hold lead biased estimates [1]. The current work considers omission bias...
Abstract Efanesoctocog alfa is a first‐in‐class high‐sustained factor VIII (HSF) replacement therapy for treatment of hemophilia A. This article presents population pharmacokinetics (PopPK) efanesoctocog and repeated time‐to‐event (RTTE) analysis bleeding episodes in adults/adolescents (≥12 years age) children (<12 years). The final PopPK dataset contained pooled data from 277 patients (4405 post‐dose [FVIII] activity records) two Phase 1/2a studies (NCT03205163; EudraCT 2018‐001535‐51),...
Because the sepsis-induced pharmacokinetic (PK) modifications need to be considered in aminoglycoside dosing, present study aimed develop a population PK model for amikacin (AMK) severe sepsis and subsequently propose an optimal sampling strategy suitable Bayesian estimation of drug parameters. Concentration-time profiles AMK were obtained from 88 critically ill septic patients during first 24 hours antibiotic treatment. The was developed using nonlinear mixed effects modeling approach....
The full random-effects model (FREM) is a method for determining covariate effects in mixed-effects models. Covariates are modeled as random variables, described by mean and variance. captures the estimated covariances between individual parameters covariates. This approach robust against issues that may cause reduced performance methods based on estimating fixed (e.g., correlated covariates where cannot be simultaneously identified fixed-effects methods). FREM parameterization...
Marzeptacog alfa (MarzAA) is under development for subcutaneous treatment of episodic bleeds in patients with hemophilia A/B and was studied a phase III trial evaluating MarzAA compared standard‐of‐care (SoC) on‐demand use. The work presented here aimed to evaluate SoC bleeding events on standardized four‐point efficacy scale (poor, fair, good, excellent). Two continuous‐time Markov modeling approaches were explored; four‐state model analyzing all four categories improvement two‐state...
Abstract The full random‐effects model (FREM) is an innovative and relatively novel covariate modeling technique. It differs from other approaches in that it treats covariates as observations captures their impact on parameters using covariances. These unique characteristics mean FREM insensitive to correlations between implicitly handles missing data. In practice, this implies are less likely be excluded the scope light of observed has been shown a useful method for small datasets, but its...
Aims This study characterized the population pharmacokinetics of edoxaban in patients with symptomatic deep‐vein thrombosis and/or pulmonary embolism Hokusai‐VTE phase 3 study. The impact protocol‐specified 50% dose reductions applied to body weight ≤ 60 kg, creatinine clearance (CL cr ) 30 50 ml min –1 or concomitant P‐glycoprotein inhibitor on exposure was assessed using simulations. Methods sparse data from Hokusai‐VTE, 9531 concentrations collected 3707 patients, were pooled 13 1...
During drug development, a key step is the identification of relevant covariates predicting between-subject variations in response. The full random effects model (FREM) one full-covariate approaches used to identify nonlinear mixed models. Here we explore ability FREM handle missing (both completely at (MCAR) and (MAR)) covariate data compare it fixed-effects (FFEM) approach, applied either with complete case analysis or mean imputation. A global health dataset (20 421 children) was develop...
Understanding the uncertainty in parameter estimates or derived secondary variables is important all data analysis activities. In pharmacometrics, this often done based on standard errors from variance-covariance matrix of estimates. Confidence intervals way are by definition symmetrical, which may lead to implausible outcomes, and will require translation generate uncertainties variables. An often-used alternative numerical percentile estimation by, for example, nonparametric bootstraps...
Abstract Purpose Tepotinib is a highly selective MET inhibitor approved for treatment of non-small cell lung cancer (NSCLC) harboring ex14 skipping alterations. Analyses presented herein evaluated the relationship between tepotinib exposure, and efficacy safety outcomes. Methods Exposure–efficacy analyses included data from an ongoing phase 2 study (VISION) investigating 500 mg/day in NSCLC Efficacy endpoints objective response, duration progression-free survival. Exposure–safety VISION,...
A time and sampling intensive pretransplant test dose design was to be reduced, but at the same optimized so that there no loss in precision of predicting individual pharmacokinetic (PK) estimates posttransplant dosing. The following variables were simultaneously: times, ciclosporin dose, second infusion duration, administration order, using a published population PK model as prior information. original reduced from 22 samples 6 samples/patient both doses (intravenous oral) administered...