Chenyan Zhao (1), Anders N. Kristoffersson (1), David D. Khan (1), Pernilla Lagerbäck (2), Ulrika Lustig (3), Sha Cao (3), Otto Cars (2), Dan I. Andersson (3), Diarmaid Hughes (3), Elisabet I. Nielsen (1), Lena E.Friberg (1)
(1) Dept of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden (2) Dept of Medical Sciences, Uppsala University, Uppsala, Sweden (3) Dept of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
Introduction/Objectives: Resistance to ciprofloxacin (CIP) is common for E. coli worldwide and colistin (CST) may overcome and/or prevent emergence of resistance when co-administered. This study aimed to quantify any CIP and CST interaction by an in silico PKPD model to explore if the combination results in an improved anti-bacterial effect compared to either drug alone.
Materials/methods: In vitro static time-kill experiments were performed with four E. coli strains: MG1655 WT (MICCIP=0.023 mg/L, MICCST=0.5 mg/L), two isogenic mutant strains (MICCIP=0.38 and 1.0 mg/L, MICCST=0.5 mg/L) and one clinical strain (MICCIP=0.047 mg/L, MICCST=0.75 mg/L). Bacteria were exposed to each of the antibiotics in the concentration range 0.0625–16xMIC for up to 32 hours. CST concentrations were measured and an in silico model was built to describe the change in bacteria-free CST concentrations in the tubes[1]. To characterize the bacterial counts under CST monodrug exposure, a previously developed model for P. aeruginosa exposed to CST[2] was adopted and refined. The CIP effect on all 4 strains have been characterized earlier[3]. Optimal design (OD) based on the combination of monodrug models facilitated the selection of concentrations and sampling times for the drug combination experiments. Interaction models, quantifying the combined antibiotic effect on the bacteria, were investigated. The drug effect under various clinically achievable doses of CIP and CST were compared using predictions from the final PKPD model.
Results: Overall 300 time-kill curves were available for analysis (99 curves for CST as monodrug, 129 for CIP monodrug and 72 for CST and CIP in combination). During PKPD modelling, CST starting (0 hour) concentrations were set to the measured with inter-experimental variability (fixed to 10% CV). Time-varying CST concentrations were satisfactorily characterized by a model with two compartments representing bound and unbound CST. The CST monodrug model described the data well, with the four strains sharing the same model structure and the three isogenic strains sharing parameter values. CST resistance was modelled by adaptive resistance[2] with the resistance onset rate being drug concentration-independent. Through OD, the chosen concentration range for combination experiments was CIP of 0.125-2xMIC and CST of 0.125-0.375xMIC. The general pharmacodynamic drug interaction (GPDI) model[4] collapsed to a concentration-independent interaction function, which could fit the interaction data better than a power interaction model[5] (dOFV=604, df=2). The interaction parameters were assumed to impact the drug potency (EC50). The parameter estimates indicated that when co-administered, CST increased CIP EC50 by 21.5% and 47.6% for clinical and three isogenic strains, respectively. The impact of CIP on CST was negligible except for the clinical strain where CST EC50decreased by 36.6%. Model predictions indicated that CST+CIP has a positive combination effect for the clinical strain with a higher and longer lasting bacterial killing than for either drug alone. For the isogenic strains, the superiority of the combination over monodrug exposure was dependent on the relative exposure of the two drugs, but not worse than CST alone when the CIP monodrug effect appeared to be higher than the combination.
Conclusions: A PKPD model was successfully developed to characterize observed in vitro E. coli bacterial counts over time when exposed to CIP and CST alone and in combination. A positive combination effect of CIP and CST were seen in most cases, but the interaction was both strain- and concentration-dependent. The clinical benefit of the combination needs to be further explored.
References:
[1] Karvanen M, Malmberg C, Lagerbäck P, Friberg LE, Cars O. Colistin Is Extensively Lost during Standard In Vitro Experimental Conditions. Antimicrob Agents Chemother. 2017;61(11):1-9.
[2] Mohamed AF, Cars O, Friberg LE. A pharmacokinetic/pharmacodynamic model developed for the effect of colistin on Pseudomonas aeruginosa in vitro with evaluation of population pharmacokinetic variability on simulated bacterial killing. J Antimicrob Chemother. 2014;69(5):1350-1361.
[3] Nielsen EI, Khan DD, Cao S, et al. Can a pharmacokinetic/pharmacodynamic (PKPD) model be predictive across bacterial densities and strains? External evaluation of a PKPD model describing longitudinal in vitro data. J Antimicrob Chemother. 2017;72(11):3108-3116.
[4] Wicha SG, Chen C, Clewe O, Simonsson USH. A general pharmacodynamic interaction model identifies perpetrators and victims in drug interactions. Nat Commun. 2017;8(1):1-11.
[5] Mohamed AF, Kristoffersson AN, Karvanen M, Nielsen EI, Cars O, Friberg LE. Dynamic interaction of colistin and meropenem on a WT and a resistant strain of Pseudomonas aeruginosa as quantified in a PK/PD model. J Antimicrob Chemother. 2016;71(5):1279-1290.
Reference: PAGE 28 (2019) Abstr 9188 [www.page-meeting.org/?abstract=9188]
Poster: Drug/Disease Modelling - Infection