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.
The genotype of a patient determines the extent of drug-induced metabolic perturbations on the endogenous cellular network of the liver. A team around Lars Kuepfer at Germany’s RWTH Aachen University developed a computational workflow that links drug pharmacokinetics at the whole-body level with a cellular network of the liver. The authors used the competitive cofactor and energy demands in endogenous and drug metabolism to establish a multi-scale model for the antibiotic isoniazid. Their model quantitatively describes how isoniazid pharmacokinetics alter the intracellular liver biochemistry and the utilization of extracellular metabolites in different patient genotypes. The study outlines how a mechanistic understanding of genotype-dependent drug-induced metabolic perturbations may help to explain diverging incidence rates of toxic events in different patient subgroups. This could reduce the occurrence of toxic side effects during drug treatments in the future.