Novel models for the prediction of drug–gene interactions

ABSTRACT

Introduction

Adverse drug reactions (ADRs) are among the leading causes of death, and frequently associated with drug–gene interactions (DGIs). In addition to pharmacogenomic programs for implementation of genetic preemptive testing into clinical practice, mathematical modeling can help to understand, quantify and predict the effects of DGIs in vivo. Moreover, modeling can contribute to optimize prospective clinical drug trial activities and to reduce DGI-related ADRs.

Areas covered

Approaches and challenges of mechanistical DGI implementation and model parameterization are discussed for population pharmacokinetic and physiologically based pharmacokinetic models. The broad spectrum of published DGI models and their applications is presented, focusing on the investigation of DGI effects on pharmacology and model-based dose adaptations.

Expert opinion

Mathematical modeling provides an opportunity to investigate complex DGI scenarios and can facilitate the development process of safe and efficient personalized dosing regimens. However, reliable DGI model input data from in vivo and in vitro measurements are crucial. For this, collaboration among pharmacometricians, laboratory scientists and clinicians is important to provide homogeneous datasets and unambiguous model parameters. For a broad adaptation of validated DGI models in clinical practice, interdisciplinary cooperation should be promoted and qualification toolchains must be established.

KEYWORDS:

• Dose adaptation
• drug–drug–gene interaction
• drug–gene interaction
• mathematical modeling
• pharmacodynamics
• pharmacogenetics
• pharmacokinetics
• physiologically based pharmacokinetics
• population pharmacokinetics

Article highlights

• Pharmacokinetic modeling can be helpful to explore and predict drug–gene interaction (DGI) scenarios. A review of the scientific literature revealed the growing importance of DGI modeling.

• Modeling approaches to mathematically describe DGIs are presented and DGI related model input parameters are discussed.

• Common applications of DGI models from the realm of population pharmacokinetic and physiologically based pharmacokinetic modeling are highlighted.

• From the authors’ point of view, DGI model development is impeded by incomplete and inhomogeneous in vivo and in vitro measurements.

• Interdisciplinary cooperation among pharmacometricians, laboratory scientists and clinicians are essential to develop high-quality DGI models.

• Model-guided dose optimization is currently not broadly adapted in clinical practice due to lack of user-friendly model implementations, DGI-related knowledge gaps by non-pharmacometricians as well as difficulties attributing responsibilities.

This box summarizes key points contained in the article.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer Disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose

Supplementary material

Supplemental data for this article can be accessed here.

Additional information

Funding

This paper was funded by the Robert Bosch Stiftung (Stuttgart, Germany) (Matthias Schwab), the European Commission Horizon 2020 UPGx grant 668353 (Matthias Schwab), a grant from the German Federal Ministry of Education and Research (BMBF 031L0188D) (Matthias Schwab), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy—EXC 2180—390900677 (Matthias Schwab), the German Federal Ministry of Education and Research (BMBF, Horizon 2020 INSPIRATION grant 643271), under the frame of ERACoSysMed (Thorsten Lehr) and the BMBF (OSMOSES grant 031L0161C) (Thorsten Lehr).