Simulation and prediction of in vivo drug metabolism in human populations from in vitro data

  title={Simulation and prediction of in vivo drug metabolism in human populations from in vitro data},
  author={Amin Rostami-Hodjegan and Geoffrey T Tucker},
  journal={Nature Reviews Drug Discovery},
The perceived failure of new drug development has been blamed on deficiencies in in vivo studies of drug efficacy and safety. Prior simulation of the potential exposure of different individuals to a given dose might help to improve the design of such studies. This should also help researchers to focus on the characteristics of individuals who present with extreme reactions to therapy. An effort to build virtual populations using extensive demographic, physiological, genomic and in vitro… 

Translation of In Vitro Metabolic Data to Predict In Vivo Drug–Drug Interactions: IVIVE and Modeling and Simulations

This chapter provides an overview of these translational aspects and describes the prerequisite data, models and tools which can be applied for the prediction of metabolic drug–drug interactions (M-DDI).

An integrated modelling approach for in vitro to in vivo extrapolations

The in vivo toxicity of drugs will be forecasted by coupling the predicted target tissue concentration profiles to dose-response relationships observed in vitro by developing prediction models for ADME processes and integrating them into generic physiologically based pharmacokinetic models.

Impact of Physiologically Based Pharmacokinetic Modeling and Simulation in Drug Development

The variety of questions that can be addressed by prospective or retrospective application of modeling and simulation and the impact this can have on clinical drug development are highlighted.

Physiologically based approaches towards the prediction of pharmacokinetics: in vitro–in vivo extrapolation

The concepts and application of the major physiologically based prediction tools to extrapolate clearance, tissue distribution, and rate and extent of absorption from minimal in vitro or animal in vivo input data are outlined.

In Vivo-In Vitro-In Silico Pharmacokinetic Modelling in Drug Development

The current status of in vitro-in vivo extrapolation of hepatic clearance or physiologically based pharmacokinetic modelling is surveyed and some examples are given, highlighting advantages and disadvantages in applying them at various phases of drug development.

Utilizing in vitro transporter data in IVIVE-PBPK: an overview

A snapshot of challenges or shortcomings of in vitro and in vivo techniques for understanding the contribution of drug transporters to a drug’s pharmacokinetics is provided.

The role of quantitative ADME proteomics to support construction of physiologically based pharmacokinetic models for use in small molecule drug development

The role of PBPK modeling in drug discovery and development is described, the assumptions involved in integrating protein abundance data are outlined, and the advances made and expected in determining abundance of relevant proteins through mass spectrometric techniques are described.

PBPK: Integrating In Vitro and In Silico Data in Physiologically Based Models

To assist with interpretation of drug pharmacokinetics (PK) in animals and humans, AU: Your article has been copyedited to be consistent with the style and usage of the rest of the volume. The

Physiologically-based pharmacokinetics in drug development and regulatory science.

Specific advances and contemporary challenges with respect to predicting the processes of drug clearance, distribution, and absorption are reviewed, together with the ability to anticipate the quantitative extent of PK-based drug-drug interactions and the impact of age, genetics, disease, and formulation.



Application of in silico approaches to predicting drug--drug interactions.

  • S. EkinsS. Wrighton
  • Biology, Chemistry
    Journal of pharmacological and toxicological methods
  • 2001

Database analyses for the prediction of in vivo drug-drug interactions from in vitro data.

The use of the total hepatic input concentration of inhibitor together with in vitro K(i) values was the most successful method for the categorization of putative CYP inhibitors and for identifying negative drug-drug interactions.

Prediction of in vivo drug clearance from in vitro data. I: Impact of inter-individual variability

The Simcyp® Population-Based ADME Simulator was used to predict median drug clearances and their associated variance from in vitro data and regardless of whether microsomal binding was considered, the predicted fold variability fell within 2-fold of the observed variability.

An evaluation of the in vitro metabolism data for predicting the clearance and drug-drug interaction potential of CYP2C9 substrates.

In vitro metabolic screening can be questioned as a compound selection tool without a proven in vitro-in vivo correlation, due to uncertainty in calculating in vivo kinetics from in vitro enzyme kinetic data.


This study investigated the effect of parallel elimination pathways as a possible reason for false positives and over-predictions in drug-drug interactions and concluded that incorporating parallel pathways provides a valuable step forward in making quantitative predictions of drug- drug interactions from in vitro data.

Prediction of Hepatic Metabolic Clearance Based on Interspecies Allometric Scaling Techniques and In Vitro-In Vivo Correlations

In contrast to purely empirical approaches, the physiological approach to predicting clearance gives an opportunity to integrate some of these complexities and, therefore, should provide more confidence in the prediction of clearance in humans.

Prediction of metabolic drug clearance in humans: In vitro–in vivo extrapolation vs allometric scaling

IVIVE is more reliable than AS in predicting human clearance values for drugs mainly metabolized by CYP450 enzymes, suggesting that the place of AS methods in pre-clinical drug development warrants further scrutiny.

Metabolism: Scaling-up from In Vitro to Organ and Whole Body

In vitro studies involving purified isoenzymes, subcellular fragments, slices, and isolated cells can, in many instances, provide invaluable mechanistic insight inasmuch as the system allows for easy control of experimental variables.

Prediction of in vivo drug clearance from in vitro data. II: Potential inter-ethnic differences

Only partial success in predicting ethnic differences in clearance indicates the need for larger and more reliable databases on relevant variables and in silico predictions might be used with more confidence to decrease theneed for repeating pharmacokinetic studies in different ethnic groups.