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Classical structural identifiability methodology applied to low-dimensional dynamic systems in receptor theory
Mathematical modelling has become a key tool in pharmacological analysis, towards understanding dynamics of cell signalling and quantifying...
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Identifiability of enzyme kinetic parameters in substrate competition: a case study of CD39/NTPDase1
CD39 (NTPDase1—nucleoside triphosphate diphosphohydrolase 1) is a membrane-tethered ectonucleotidase that hydrolyzes extracellular ATP to ADP and ADP...
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Saddle-Reset for Robust Parameter Estimation and Identifiability Analysis of Nonlinear Mixed Effects Models
Parameter estimation of a nonlinear model based on maximizing the likelihood using gradient-based numerical optimization methods can often fail due...
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Estimating dynamic individual coactivation patterns based on densely sampled resting-state fMRI data and utilizing it for better subject identification
As a complex dynamic system, the brain exhibits spatially organized recurring patterns of activity over time. Coactivation patterns (CAPs), which...
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Structural identifiability and sensitivity
Ordinary differential equation models often contain a large number of parameters that must be determined from measurements by estimation procedure....
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Mechanistic inference of the metabolic rates underlying \(^{13}\)C breath test curves
AbstractCarbon stable isotope breath tests offer new opportunities to better understand gastrointestinal function in health and disease. However, it...
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Analysis of cellular kinetic models suggest that physiologically based model parameters may be inherently, practically unidentifiable
Physiologically-based pharmacokinetic and cellular kinetic models are used extensively to predict concentration profiles of drugs or adoptively...
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Assessing parameter identifiability in compartmental dynamic models using a computational approach: application to infectious disease transmission models
BackgroundMathematical modeling is now frequently used in outbreak investigations to understand underlying mechanisms of infectious disease dynamics,...
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MPBPK-TMDD models for mAbs: alternative models, comparison, and identifiability issues
The aim of the present study was to evaluate model identifiability when minimal physiologically-based pharmacokinetic (mPBPK) models are integrated...
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Inferring pulmonary exposure based on clinical PK data: accuracy and precision of model-based deconvolution methods
Determining and understanding the target-site exposure in clinical studies remains challenging. This is especially true for oral drug inhalation for...
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Development and validation of a nomogram for predicting sever cancer-related fatigue in patients with cervical cancer
ObjectiveCancer-related fatigue (CRF) has been considered the biggest influencing factor for cancer patients after surgery. This study aimed to...
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Causal machine learning for predicting treatment outcomes
Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby...
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A physiologically-based pharmacokinetic model to describe antisense oligonucleotide distribution after intrathecal administration
Antisense oligonucleotides (ASOs) are promising therapeutic agents for a variety of neurodegenerative and neuromuscular disorders, e.g., Alzheimer’s,...
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Optimal Experimental Design for Systems and Synthetic Biology Using AMIGO2
Dynamic modeling in systems and synthetic biology is still quite a challenge—the complex nature of the interactions results in nonlinear models,...
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Note on importance of correct stoichiometric assumptions for modeling of monoclonal antibodies
Pharmacokinetic modeling of monoclonal antibodies (mAbs) with non-linear binding is based on equations of the target-mediated drug disposition (Mager...
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Structural identifiability for mathematical pharmacology: models of myelosuppression
Structural identifiability is an often overlooked, but essential, prerequisite to the experiment design stage. The application of structural...
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Reinforcement Learning
Over the last two decades, the model-based approach to analysing functional magnetic resonance imaging (fMRI) data has been adopted across the...
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Edge time series components of functional connectivity and cognitive function in Alzheimer’s disease
Understanding the interrelationships of brain function as measured by resting-state magnetic resonance imaging and neuropsychological/behavioral...
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An integrated modelling approach for targeted degradation: insights on optimization, data requirements and PKPD predictions from semi- or fully-mechanistic models and exact steady state solutions
The value of an integrated mathematical modelling approach for protein degraders which combines the benefits of traditional turnover models and fully...
