BACKGROUND: Defining a drug's therapeutic index (TI) is important for patient safety and regulating the development of generic drugs. For many drugs, the TI is unknown. A systematic approach was developed to characterize the TI of a drug using therapeutic drug monitoring and electronic health record (EHR) data with pharmacokinetic (PK) modeling. This approach was first tested on phenytoin, which has a known TI, and then applied to lamotrigine, which lacks a defined TI. METHODS: Retrospective EHR data from patients in a tertiary hospital were used to develop phenytoin and lamotrigine population PK models and to identify adverse events (anemia, thrombocytopenia, and leukopenia) and efficacy outcomes (seizure-free). Phenytoin and lamotrigine concentrations were simulated for each day with an adverse event or seizure. Relationships between simulated concentrations and adverse events and efficacy outcomes were used to calculate the TI for phenytoin and lamotrigine. RESULTS: For phenytoin, 93 patients with 270 total and 174 free concentrations were identified. A de novo 1-compartment PK model with Michaelis-Menten kinetics described the data well. Simulated average total and free concentrations of 10-15 and 1.0-1.5 mcg/mL were associated with both adverse events and efficacy in 50% of patients, resulting in a TI of 0.7-1.5. For lamotrigine, 45 patients with 53 concentrations were identified. A published 1-compartment model was adapted to characterize the PK data. No relationships between simulated lamotrigine concentrations and safety or efficacy endpoints were seen; therefore, the TI could not be calculated. CONCLUSIONS: This approach correctly determined the TI of phenytoin but was unable to determine the TI of lamotrigine due to a limited sample size. The use of therapeutic drug monitoring and EHR data to aid in narrow TI drug classification is promising, but it requires an adequate sample size and accurate characterization of concentration-response relationships.
BACKGROUND: Defining a drug's therapeutic index (TI) is important for patient safety and regulating the development of generic drugs. For many drugs, the TI is unknown. A systematic approach was developed to characterize the TI of a drug using therapeutic drug monitoring and electronic health record (EHR) data with pharmacokinetic (PK) modeling. This approach was first tested on phenytoin, which has a known TI, and then applied to lamotrigine, which lacks a defined TI. METHODS: Retrospective EHR data from patients in a tertiary hospital were used to develop phenytoin and lamotrigine population PK models and to identify adverse events (anemia, thrombocytopenia, and leukopenia) and efficacy outcomes (seizure-free). Phenytoin and lamotrigine concentrations were simulated for each day with an adverse event or seizure. Relationships between simulated concentrations and adverse events and efficacy outcomes were used to calculate the TI for phenytoin and lamotrigine. RESULTS: For phenytoin, 93 patients with 270 total and 174 free concentrations were identified. A de novo 1-compartment PK model with Michaelis-Menten kinetics described the data well. Simulated average total and free concentrations of 10-15 and 1.0-1.5 mcg/mL were associated with both adverse events and efficacy in 50% of patients, resulting in a TI of 0.7-1.5. For lamotrigine, 45 patients with 53 concentrations were identified. A published 1-compartment model was adapted to characterize the PK data. No relationships between simulated lamotrigine concentrations and safety or efficacy endpoints were seen; therefore, the TI could not be calculated. CONCLUSIONS: This approach correctly determined the TI of phenytoin but was unable to determine the TI of lamotrigine due to a limited sample size. The use of therapeutic drug monitoring and EHR data to aid in narrow TI drug classification is promising, but it requires an adequate sample size and accurate characterization of concentration-response relationships.
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Authors: Sarah Jane Commander; Daniel Gonzalez; Karan R Kumar; Tracy Spears; Michael Cohen-Wolkowiez; Kanecia O Zimmerman; Stephen J Balevic; Christoph P Hornik Journal: Cardiol Young Date: 2021-08-05 Impact factor: 1.023