Our research interest is in the development and application of computational technologies for environmental chemicals, nanoparticles and drugs in animals and humans to address research questions related to nanomedicine, animal-derived food safety assessment, and environmental chemical risk assessment. The computational technologies used in our lab include physiologically based pharmacokinetic (PBPK), pharmacokinetics/pharmacodynamics (PK/PD), toxicogenomics, machine learning, and artificial intelligence (AI) approaches. The long-term goal of our research is to develop AI-assisted computational approaches to support decision-making in human health, animal health, and environmental health (i.e., one health approach). Currently, we have three specific projects.

Our projects:

1. To develop computational methods and PBPK models for drugs in food-producing animals, such as cattle, swine, sheep, goats, chickens, and turkeys. The objective of this project is to use these PBPK and AI models/methods to predict withdrawal intervals after extralable use of drugs in food animals to help protect safety of animal-derived food products, including meat, milk, and eggs.


2. To integrate PBPK modeling with AI approaches, such as artificial neural networking (ANN) to determine the key physicochemical properties in the delivery of nanoparticles to the tumor site and other target organs. The objective of this project is to develop AI-assisted computational models to help design safe nanomedicine.

3. To develop PBPK models for environmental chemicals, such as per- and polyfluoroalkyl substances (PFAS) in animals and humans of different life stages, including fetal, neonatal, gestational, and lactational periods. The objective of this project is to integrate these models with in vitro and in vivo animal toxicity data as well as human epidemiological data to inform exposure assessment, dose-response analysis, and risk assessment, and ultimately helping public health decision-making.

Our Expertise:

  • Non-compartmental pharmacokinetic analysis
  • Compartmental pharmacokinetic modeling
  • Population pharmacokinetic analysis using nonlinear mixed effects (NLME) modeling
  • Physiologically based pharmacokinetic (PBPK) modeling
  • Population physiologically based pharmacokinetic modeling using Monte Carlo simulations
  • Bayesian population physiologically based pharmacokinetic modeling using Markov chain Monte Carlo simulations
  • Pharmacokinetic-pharmacodynamic (PK/PD) modeling
  • Physiologically based pharmacokinetic-pharmacodynamic (PBPK/PD) modeling
  • Probablistic risk modeling and assessment
  • Machine learning methods, such as multiple linear regression, k-nearest neighbors, random forest and support vector machine
  • Artificial neural network and deep learning