Integration of genome-scale metabolic networks into whole-body PBPK models shows phenotype-specific cases of drug-induced metabolic perturbation

Abstract

Drug-induced perturbations of the endogenous metabolic network are a potential root cause of cellular toxicity. A mechanistic understanding of such unwanted side effects during drug therapy is therefore vital for patient safety. The comprehensive assessment of such drug-induced injuries requires the simultaneous consideration of both drug exposure at the whole-body and resulting biochemical responses at the cellular level. We here present a computational multi-scale workflow that combines whole-body physiologically based pharmacokinetic (PBPK) models and organ-specific genome-scale metabolic network (GSMN) models through shared reactions of the xenobiotic metabolism. The applicability of the proposed workflow is illustrated for isoniazid, a first-line antibacterial agent against Mycobacterium tuberculosis, which is known to cause idiosyncratic drug-induced liver injuries (DILI). We combined GSMN models of a human liver with N-acetyl transferase 2 (NAT2)-phenotype-specific PBPK models of isoniazid. The combined PBPK-GSMN models quantitatively describe isoniazid pharmacokinetics, as well as intracellular responses, and changes in the exometabolome in a human liver following isoniazid administration. Notably, intracellular and extracellular responses identified with the PBPK-GSMN models are in line with experimental and clinical findings. Moreover, the drug-induced metabolic perturbations are distributed and attenuated in the metabolic network in a phenotype-dependent manner. Our simulation results show that a simultaneous consideration of both drug pharmacokinetics at the whole-body and metabolism at the cellular level is mandatory to explain drug-induced injuries at the patient level. The proposed workflow extends our mechanistic understanding of the biochemistry underlying adverse events and may be used to prevent drug-induced injuries in the future.

A Physiology-Based Model of Human Bile Acid Metabolism for Predicting Bile Acid Tissue Levels After Drug Administration in Healthy Subjects and BRIC Type 2 Patients

“…For such a differentiated BA pool the necessary metabolisation steps which are catalysed by the intestinal microbiome need to be integrated. The tools for the vertical integration of metabolic network models into PBPK models already exist and can directly be added to the current PBBA model (Krauss et al, 2012; Cordes et al, 2018).…”

Insights from pharmacokinetic models of host-microbiome drug metabolism

“…Pharmacokinetic models can be combined with host genome-scale models to simulate drug and nutrient effects on host physiology, which can lead to dietary recommendations for enhanced drug effect, 24 predicted drug-drug interactions 25 or adverse drug reactions. [26][27][28] Since intestinal motility, nutrient intake, 29,30 and drugs affect bacterial growth, 31 physiology and microbiome composition, 32,33 incorporation of genome-scale metabolic models of the microbiota into the pharmacokinetic modeling framework might further enhance the ability of these integrative models to predict in vivo metabolism of specific drugs. Improved in silico estimation of drug absorption, 34,35 and putative host [36][37][38][39] and bacterial [40][41][42] xenobiotic biotransformation in combination with systematic experimental studies of microbial drug metabolism, 6 will facilitate accurate prediction of in vivo drug metabolism with integrative pharmacokinetic models in the near future.…”

Quantitative systems pharmacology of interferon alpha administration: A multi-scale approach

“…In the past, genome scale network models have been integrated in PBPK models [51, 52]. Also PBPK models have been coupled with simplistic or abstract dynamic cellular network models [53–55].…”