In Vitro- In Vivo Extrapolation by Physiologically Based Kinetic Modeling: Experience With Three Case Studies and Lessons Learned

Abstract

Physiologically based kinetic (PBK) modeling has been increasingly used since the beginning of the 21st century to support dose selection to be used in preclinical and clinical safety studies in the pharmaceutical sector. For chemical safety assessment, the use of PBK has also found interest, however, to a smaller extent, although an internationally agreed document was published already in 2010 (IPCS/WHO), but at that time, PBK modeling was based mostly on in vivo data as the example in the IPCS/WHO document indicates. Recently, the OECD has published a guidance document which set standards on how to characterize, validate, and report PBK models for regulatory purposes. In the past few years, we gained experience on using in vitro data for performing quantitative in vitro-in vivo extrapolation (QIVIVE), in which biokinetic data play a crucial role to obtain a realistic estimation of human exposure. In addition, pharmaco-/toxicodynamic aspects have been introduced into the approach. Here, three examples with different drugs/chemicals are described, in which different approaches have been applied. The lessons we learned from the exercise are as follows: 1) in vitro conditions should be considered and compared to the in vivo situation, particularly for protein binding; 2) in vitro inhibition of metabolizing enzymes by the formed metabolites should be taken into consideration; and 3) it is important to extrapolate from the in vitro measured intracellular concentration and not from the nominal concentration to the tissue/organ concentration to come up with an appropriate QIVIVE for the relevant adverse effects.

Keywords: new approach methodologies (NAMs); reverse dosimetry; risk assessment; toxicodynamics; toxicokinetics.

FIGURE 1

Optimized dose in reverse dosimetry with optimization for concentrations in the target organ of toxicity (liver). Condition/scenario a: hepatic tissue: blood partition coefficient of 3.01 calculated by the algorithm of Schmitt, (2008a; Schmitt, 2008b). Condition/scenario b: hepatic tissue: blood partition coefficient of 11.1 derived from the in vitro data (concentration in human hepatic cells/concentration in the supernatant) calculated from the in vitro concentration–time data (Truisi et al., 2015). Orange color: concentrations in arterial blood; blue color: concentration in the liver. Orange line and dashed-dotted blue line: condition (a); orange dashed line and blue dotted line: condition (b). Modeling using the hepatic tissue: blood partition coefficient calculated by using the in vitro measured concentration in the cell lysate and the concentration in the medium fits the in vitro data well; however, the resulting dose related to beginning liver toxicity is within the therapeutic range. Modeling using the hepatic tissue: blood partition coefficient calculated by the algorithm (Schmitt, 2008a; Schmitt, 2008b) did not well describe the concentration in the medium when optimized for the liver concentration; however, the resulting dose related to beginning liver toxicity is in accordance with the clinical observations.

FIGURE 2

Simulated (lines) and observed (points) plasma concentration–time profiles of amiodarone following i.v. administration of 400 mg. Conditions: fu of 0.06 and hepatic clearance calculated from in vitro data (Trivier et al., 1993) (blue line) or alternatively obtained from in vitro data (Pomponio et al., 2015a) (red line), compared with the average plasma concentration–time data of seven patients with cardiac arrhythmias (Andreasen et al., 1981).

FIGURE 3

Simulated plasma CPF concentration–time profile in non-pregnant (black line) vs. pregnant (red line) population (n = 100) after a single oral dose of 2 mg/kg.