• J Diabetes Sci Technol · Jul 2014

    Clinical Trial

    Application of PK/PD modeling and simulation to dosing regimen optimization of high-dose human regular U-500 insulin.

    • Amparo de la Peña, Xiaosu Ma, Shobha Reddy, Fernando Ovalle, Richard M Bergenstal, and Jeffrey A Jackson.
    • Eli Lilly and Company, Indianapolis, IN, USA adelapa@lilly.com.
    • J Diabetes Sci Technol. 2014 Jul 1; 8 (4): 821-9.

    AbstractPharmacokinetic/pharmacodynamic (PK/PD) studies of human regular U-500 insulin (U-500R) at high doses commonly used in clinical practice (>100 units) have not been performed. The current analysis applied PK/PD modeling/simulation to fit the data and simulate single-dose and steady-state PK/PD of U-500R high-dose regimens. Data from 3 single-dose euglycemic clamp studies in healthy obese and normal-weight patients, and normal-weight patients with type 1 diabetes were used to build the model. The model was sequential (PK inputs fed into PD component). PK was described using a 1-compartment model with first-order absorption and elimination. The model estimated separate absorption rate constants for U-500R and human regular U-100 insulin. The PD component used an effect compartment model, parameterized in terms of maximum pharmacologic effect (E(max)) and concentration to achieve 50% of E(max). The model described the data well. Steady-state PK for once-daily (QD), twice-daily (BID), or thrice-daily (TID) administration appeared to be reached 24 hours after the first dose. At steady-state, QD dosing showed the greatest fluctuations in PK/PD. BID dosing showed a gradual increase in insulin action with each dose and a fairly stable basal insulin effect. For TID dosing, activity was maintained throughout the dosing interval. PK/PD modeling/simulation of high U-500R doses supports BID or TID administration with an extended duration of activity relative to QD. TID dosing may provide slightly better full-day insulin effect. Additional PK/PD studies and randomized controlled trials of U-500R are needed to validate model predictions in patients with insulin-resistant diabetes requiring high-dose insulin.© 2014 Diabetes Technology Society.

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