Development of a physiologically based pharmacokinetic model of organic solvent in rats

Microfluidic-Based Multi-Organ Platforms for Drug Discovery

Development of predictive multi-organ models before implementing costly clinical trials is central for screening the toxicity, efficacy, and side effects of new therapeutic agents. Despite significant efforts that have been recently made to develop biomimetic in vitro tissue models, the clinical application of such platforms is still far from reality. Recent advances in physiologically-based pharmacokinetic and pharmacodynamic (PBPK-PD) modeling, micro- and nanotechnology, and in silico modeling have enabled single- and multi-organ platforms for investigation of new chemical agents and tissue-tissue interactions. This review provides an overview of the principles of designing microfluidic-based organ-on-chip models for drug testing and highlights current state-of-the-art in developing predictive multi-organ models for studying the cross-talk of interconnected organs. We further discuss the challenges associated with establishing a predictive body-on-chip (BOC) model such as the scaling, cell types, the common medium, and principles of the study design for characterizing the interaction of drugs with multiple targets.

Analysis of algorithms predicting blood:air and tissue:blood partition coefficients from solvent partition coefficients for prevalent components of JP-8 jet fuel

Algorithms predicting tissue and blood partition coefficients (PCs) from solvent properties were compared to assess their usefulness in a petroleum mixture physiologically based pharmacokinetic/pharmacodynamic model. Measured blood:air and tissue:blood PCs for rat and human tissues were sought from literature resources for 14 prevalent jet fuel (JP-8) components. Average experimental PCs were compared with predicted PCs calculated using algorithms from 9 published sources. Algorithms chosen used solvent PCs (octanol:water, saline or water:air, oil:air coefficients) due to the relative accessibility of these parameters. Tissue:blood PCs were calculated from ratios of predicted tissue:air and experimental blood:air values (PCEB). Of the 231 calculated values, 27% performed within +/- 20% of the experimental PC values. Physiologically based equations (based on water and lipid components of a tissue type) did not perform as well as empirical equations (derived from linear regression of experimental PC data) and hybrid equations (physiological parameters and empirical factors combined) for the jet fuel components. The major limitation encountered in this analysis was the lack of experimental data for the selected JP-8 constituents. PCEB values were compared with tissue:blood PCs calculated from ratios of predicted tissue:air and predicted blood:air values (PCPB). Overall, 68% of PCEB values had smaller absolute % errors than PCPB values. If calculated PC values must be used in models, a comparison of experimental and predicted PCs for chemically similar compounds would estimate the expected error level in calculated values.

QSAR modeling of blood:air and tissue:air partition coefficients using theoretical descriptors

Human blood:air, human and rat tissue (fat, brain, liver, muscle, and kidney):air partition coefficients of a diverse set of organic compounds were correlated and predicted using structural descriptors by employing CODESSA-PRO and ISIDA programs. Four and five descriptor regression models developed using CODESSA-PRO were validated on three different test sets. Overall, these models have reasonable values of correlation coefficients (R(2)) and leave-one-out correlation coefficients (R(cv)(2)): R(2) = 0.881-0.983; R(cv)(2) = 0.826-0.962. Calculations with ISIDA resulted in models based on atom/bond sequences involving two to three atoms with statistical parameters that were similar to those of models obtained with CODESSA-PRO (R(2) = 0.911-0.974; R(cv)(2) = 0.831-0.936). A mixed pool of molecular and fragment descriptors did not lead to significant improvement of the models.

Whole body pharmacokinetic models

The aim of the current review is to summarise the present status of physiologically based pharmacokinetic (PBPK) modelling and its applications in drug research, and thus serve as a reference point to people interested in the methodology. The review is structured into three major sections. The first discusses the existing methodologies and techniques of PBPK model development. The second describes some of the most interesting PBPK model implementations published. The final section is devoted to a discussion of the current limitations and the possible future developments of the PBPK modelling approach. The current review is focused on papers dealing with the pharmacokinetics and/or toxicokinetics of medicinal compounds; references discussing PBPK models of environmental compounds are mentioned only if they represent considerable methodological developments or reveal interesting interpretations and/or applications.The major conclusion of the review is that, despite its significant potential, PBPK modelling has not seen the development and implementation it deserves, especially in the drug discovery, research and development processes. The main reason for this is that the successful development and implementation of a PBPK model is seen to require the investment of significant experience, effort, time and resources. Yet, a substantial body of PBPK-related research has been accumulated that can facilitate the PBPK modelling and implementation process. What is probably lagging behind is the expertise component, where the demand for appropriately qualified staff far outreaches availability.