{'fraction_non_alphanumeric': 0.020418084589207585, 'fraction_numerical': 0.003403014098201264, 'mean_word_length': 6.297872340425532, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Pharmacokinetic modeling of simvastatin, nelfinavir and their interaction in humans

Background: Simvastatin, a commonly used HMG-CoA reductase inhibitor, is extensively metabolized by CYP3A4. Therefore, co-administration of simvastatin and CYP3A4 inhibitor can affect simvastatin pharmacokinetics. Nelfinavir, a protease inhibitor, and its major metabolite (M8) are known to be potent CYP3A4 inhibitors. When simvastatin and nelfinavir are co-administered, simvastatin pharmacokinetics is significantly altered and may result in an increased risk of rhabdomyolysis. Objective: To develop a mathematical model describing a drug-drug interaction between simvastatin and nelfinavir in humans. Methods: Eligible pharmacokinetic studies were selected from Pubmed database and concentration time course data were digitally extracted and used for model development. Compartmental pharmacokinetic models for simvastatin and nelfinavir were developed separately. A drug-drug interaction model of simvastatin and nelfinavir was subsequently developed using the prior information. Finally, the final drug-drug interaction modeled was validated against observed simvastatin concentrations. Results: Three compartmental pharmacokinetic models were successfully developed. Simvastatin pharmacokinetics was best described by a one compartment model for simvastatin linked to its active form, simvastatin hydroxy acid. Nelfinavir pharmacokinetics could be adequately described by a one compartment parent-metabolite model. Our final drug-drug interaction model predicted an increase in simvastatin exposure which is in line with clinical observations linking the simvastatin-nelfinavir combination to an increased risk of rhabdomyolysis. Conclusion: Simvastatin-nelfinavir pharmacokinetic interaction can be explained by our final model. This model framework will be useful in further advanced developing other mechanism based drug-drug interaction model used to predict the risk of rhabdomyolysis occurrence in patients prescribed simvastatin and nelfinavir concurrently.