Tigecycline-Tetracycline Combination Modeling against Pseudomonas aeruginosa: Application of the General Pharmacodynamic Interaction Term in Various Interaction Models
Amelia N. Deitchman (1), Astrid Broeker (1), Sebastian G. Wicha (2,3), Johannes Kast (1), Ravi Shankar Prasad Singh (1), Hartmut Derendorf (1)
(1) University of Florida, Gainesville, FL, USA (2) Uppsala University, Uppsala, Sweden, (3) University of Hamburg, Hamburg, Germany
Objectives: The enhanced activity of combination tigecycline (TIG) and tetracycline (TET) against Pseudomonas aeruginosa has been previously described in in vitro static time kill curve experiments [1]. We aimed to develop a PK/PD model based on this data to comprehensively describe the antibacterial interaction and effect for this drug combination. This analysis also aimed to explore the use of an interaction term (previously presented as the general pharmacodynamic interaction (GDPI) model [2]) applied to empiric and mechanism-based models.
Methods: Static time-kill curve modeling was performed with NONMEM (Ver.7.3). Using previously developed models for the TIG and TET alone [3,4] as base models, a collective model was developed to describe a combined dataset of mono and combination therapy. Change in objective function value, visual predictive checks, and goodness-of-fit plots were utilized to evaluate fit of standard and modified Bliss Independence, Loewe additivity, and competitive inhibition models. GDPI terms were incorporated into models on EC50 or Emax terms to improve interaction description.
Results: Generally, many GDPI models had improved performance compared to their respective parent models. A competitive inhibition model with a GDPI term on Emax (INT 0.415) best described the TIG-TET interaction and was selected as the model to move forward for future analyses (EC50 2.7 mg/L TIG and 8.86 mg/L TET, KmaxTIG 1.49 h-1, KmaxTET 1.35 h-1, Hill factor 1.88). The final model was a two subpopulation (susceptible and persistent resting) bacterial model with load dependent transfer from the susceptible to persistent state (kSR 1.25 h-1), and drug degradation for both TIG (kdegTIG 0.0909 h-1) and TET (kdegTET 0.0539 h-1). Relative standard errors of estimates were below 30%.
Conclusions: TIG and TET effects alone and in combination in vitro against P. aeruginosa were described using a semi-mechanistic GDPI competitive inhibition model, which will be combined with clinical pharmacokinetic information to evaluate combination dosing regimens. This exploration has also demonstrated the flexibility of the GDPI term as well as its ability to generally improve model fit of empiric or mechanism-based interaction models.
Acknowledgement: This work supported in part by the NIH/NCATS Clinical and Translational Science Award to the University of Florida UL1 TR000064.
References:
[1] Deitchman A, Singh R, Zoehner A, Derendorf H. In Vitro Pharmacodynamics of Tigecycline and Tetracycline Combinations against Pseudomonas aeruginosa. ECCMID Poster Presentation 2015. Copenhagen, Denmark.
[2] Wicha SG, Chen C, Clewe O, Simonsson USH. A general pharmacodynamic interaction model based on the Bliss Independence criterion. PAGE Oral Presentation 2016. Lisbon, Portugal.
[3] Deitchman AN, Kast J, Voelkner A, Derendorf H. Describing Tigecycline Activity against Pseudomonas aeruginosa: Modeling of In Vitro Time-Kill Curves. ICAAC/ICC Poster Presentation 2015. San Diego, CA.
[4] Deitchman AN, Kast J, Derendorf H. Tetracycline against Pseudomonas aeruginosa: Pharmacokinetic/Pharmacodynamic Modeling of In Vitro Time-Kill Curves. PAGE Poster Presentation 2016. Lisbon, Portugal.