Drugs prescribed for various medical conditions can cause harmful
liver side effects. Lab experiments with liver cells can help reveal the
underlying molecular mechanisms by which these drugs cause liver
damage, which could inform better prevention and treatment efforts.
However, lab experiments alone cannot reliably predict actual effects in
To improve translation of lab data to patients, Christoph Thiel of
RWTH Aachen University, Germany, and colleagues recently developed a new
strategy that uses computational modeling to simulate how liver cells
in the body respond to different doses of different drugs. The approach
integrates experimental observations with knowledge of how drugs are
distributed and metabolized after they enter the body.
‘A computational modeling approach has been used by researchers to analyze and compare the toxic effects of fifteen different drugs on the liver.’
The researchers had previously demonstrated their approach in a
proof-of-concept study. In the new study, the approach was applied to
simulate and compare the potentially toxic liver effects of fifteen
different drugs at clinically relevant doses.
The scientists developed whole-body models to simulate the fate of
each drug after ingestion and validated the models using experimental
data from scientific literature. These models were then coupled with lab
data to predict each drug's effects on the liver at patient level. The
researchers found that the drugs fell into different groups that caused
similar responses, including which genes would be transcribed in
response to toxic doses.
While further validation is required, the method has the potential
to lead to faster diagnosis of toxic liver side effects in patients. It
could help reveal which gene transcripts could serve as early signs of
toxicity and which drug combinations might be particularly dangerous,
for both new and existing drugs.
"Consistently applied to the design of clinical development
programs, the approach presented has the potential to early identify
medical and economic risks of new drugs," says study co-author Lars