Pharmacology of Anti- Infective Agents in 2005: The Basics and Beyond Prof. Hartmut Derendorf University of Florida
Resistance Development
Approved Antibacterial Agents 1983-2004
Pharmacodynamics Pharmacokinetics conc. vs effect conc. vs time 0.4 Conc. Effect 0.0 Time 0 25 Conc. (log) 10 -3 PK/PD effect vs time 1 Effect 0 Time 0
Time above MIC Time above MIC C max /MIC C max /MIC 16 16 16 16 C max C max Concentration (µg/mL) Concentration (µg/mL) Concentration (µg/mL) Concentration (µg/mL) 12 12 12 12 8 8 8 8 MIC MIC MIC MIC 4 4 4 4 0 0 0 0 24 24 12 12 24 24 0 0 6 6 18 18 12 12 0 0 6 6 18 18 Time (hours) Time (hours) Time (hours) Time (hours) t > MIC t > MIC AUC 24 /MIC AUC 24 /MIC 16 16 PD Concentration (µg/mL) Concentration (µg/mL) PK 12 12 8 8 MIC MIC MIC Serum 4 4 0 0 24 24 12 12 0 0 6 6 18 18 Time (hours) Time (hours)
Pharmacokinetics • Tissue Distribution • Protein Binding Problems:
Protein Binding of Cephalosporines Cephapirin 62 Cefonicid 98 Moxalactam 53-67 Ceftriaxone 90-95 Cefprozil 40 Cefoperazone 89-93 Cefotaxime 36 Cefazolin 89 Cefpodoxime 25 Cefotetan 85 Ceforanide 80-82 Cefamandole 74 Cefoxitin 73 Cephalothin 71 Cefmetazole 70 Cefixime 65
vascular space extravascular space binding to extracellular plasma biological protein material binding blood cell tissue cell binding, binding, diffusion into diffusion into blood cells, tissue cells, binding to binding to intracellular intracellular biological biological material material
Tissue Concentrations Tissue can be looked at as an aqueous dispersed system of biological material. It is the concentration in the water of the tissue that is responsible for pharmacological activity. Total tissue concentrations need to be interpreted with great care since they reflect hybrid values of total amount of drug (free + bound) in a given tissue ‘Tissue-partition-coefficients’ are not appropriate since they imply homogenous tissue distribution
The free (unbound) concentration of the drug at the receptor site should be used in PK/PD correlations to make prediction for pharmacological activity
Blister Fluid • Blister fluid is a ‘homogenous tissue fluid’ • Protein binding in blister fluid needs to be considered
Ampicillin Cloxacillin � Serum � Free blister fluid
Microdialysis Perfusate Dialysate Interstitium Capillary Cell
Clinical study Cefpodoxime and Cefixime • To compare the soft tissue distribution of these two antibiotics after 400mg oral dose in healthy male volunteers by microdialysis • Two way cross-over, single oral dose study
Microdialysis
Clinical Microdialysis Cefixime Cefpodoxime 400 mg po 400 mg po plasma muscle free plasma 6 6 plasma muscle free plasma Concentration (mg/L) Concentratoin (mg/L) 5 5 4 4 3 3 2 2 1 1 0 0 0 2 4 6 8 10 0 2 4 6 8 10 Time (h) Time (h) Liu & Derendorf, JAC 50, 19 (2002)
Pharmacokinetics Cefpodoxime Cefixime AUC P [mg*h/L] 22.4 (8.7) 25.7 (8.4) AUC T [mg*h/L] 15.4 (5.2) 7.4 (2.1) C max, P [mg/L] 3.9 (1.2) 3.4 (1.1) C max,T [mg/L] 2.1 (1.0) 0.9 (0.3)
Conclusion Microdialysis has opened the door to get better information about the drug concentrations at the site of action. This, in combination with appropriate PK/PD- models, will allow for better dosing decisions than traditional approaches based on blood concentrations and MIC.
Pharmacodynamics Problems: • MIC is imprecise • MIC is monodimensional • MIC is used as a threshold • When MIC does not explain the data, patches are used (post-antibiotic effect, sub-MIC effect)
MIC The Current Paradigm MIC is poison for the mind. H. Mattie (1994), after a long after-dinner discussion
Concentration-dependent vs. Time-dependent Craig 1991
connector tubing waste flask Kill Curves Auto-dilution system reservoir pump
Kill Curves of Ceftriaxone S. pneumoniae ATCC6303 H. influenzae ATCC10211 MIC: 20 ng/mL MIC: 5 ng/mL
Kill Curves of Ceftriaxone S. pneumoniae ATCC6303 H. influenzae ATCC10211 MIC: 20 ng/mL MIC: 5 ng/mL
PK-PD Model ⎛ ⎞ ⋅ k C dN ⎜ ⎟ = − ⋅ max f k N ⎜ ⎟ + dt EC C ⎝ ⎠ 50 f Maximum Growth Rate Constant k Maximum Killing Rate Constant k-k max Initially, bacteria are in log growth phase
Single Dose Piperacillin vs. E. coli 10 14 control 10 13 10 12 10 11 10 10 2g 10 9 CFU/mL 4g 10 8 10 7 8g 10 6 10 5 10 4 10 3 10 2 10 1 10 0 0 2 4 6 8 10 Time (h)
PK-PD Model In animals Bacterial survival fraction of P. aeruginosa in a neutropenic mouse model at different doses (mg/kg) of piperacillin (Zhi et al., 1988)
Dosing Interval Piperacillin (2g and 4g) vs. E. coli q24h q8h q4h 50µg/mL q24h 50µg/mL q8h 50µg/mL q4h 10 11 10 11 10 11 10 10 10 10 10 10 10 9 10 9 10 9 10 8 10 8 10 8 CFU/mL CFU/mL CFU/mL 10 7 10 7 10 7 10 6 10 6 10 6 10 5 10 5 10 5 10 4 10 4 10 4 10 3 10 3 10 3 10 2 10 2 10 2 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 Time (h) Time (h) Time (h) 100µg/mL q24h 100µg/mL q8h 100µg/mL q4h 10 11 10 11 10 11 10 10 10 10 10 10 10 9 10 9 10 9 10 8 10 8 10 8 CFU/mL CFU/mL CFU/mL 10 7 10 7 10 7 10 6 10 6 10 6 10 5 10 5 10 5 10 4 10 4 10 4 10 3 10 3 10 3 10 2 10 2 10 2 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 Time (h) Time (h) Time (h)
Example 1 • Same PK • Different EC 50 • Same MIC (Sensitivity) • Same t>MIC • Different k max • Same AUC/MIC (Maximum Kill Rate) • Same C max /MIC • Same k (Growth Rate)
PK-PD modeling based on Kill Curves Condition 1 Condition 2 250 250 10 8 10 8 10 7 10 7 200 200 Antibiotic Conc (ng/mL) Antibiotic Conc (ng/mL) 6 6 10 10 150 150 CFU/mL CFU/mL 5 5 10 10 10 4 10 4 100 100 3 3 10 10 50 50 2 2 10 10 0 0 10 1 10 1 0 5 10 15 20 25 0 5 10 15 20 25 Time (hour) Time (hour) Control (CFU/mL) Treated (CFU/mL) Antibiotic concentration
Example 2 • Same PK • Different EC 50 • Same MIC (Sensitivity) • Same t>MIC • Different k • Same AUC/MIC (Growth Rate) • Same C max /MIC • Same k max (Maximum Kill Rate)
PK-PD modeling based on Kill Curves Condition 1 Condition 2 250 250 10 8 10 8 10 7 10 7 200 200 Antibiotic Conc (ng/mL) Antibiotic Conc (ng/mL) 6 6 10 10 150 150 CFU/mL CFU/mL 5 5 10 10 10 4 10 4 100 100 3 3 10 10 50 50 2 2 10 10 0 0 10 1 10 1 0 5 10 15 20 25 0 5 10 15 20 25 Time (hour) Time (hour) Control (CFU/mL) Treated (CFU/mL) Antibiotic concentration
MIC (mg/L) MIC (mg/L) Cefpodoxime Cefixime Haemophilus 0.06-0.12 0.06 influenzae Moraxella 0.12-0.25 0.12 catarrhalis Streptococcus pneumoniae 0.03 0.25 (penicillin- sensitive) Streptococcus pneumoniae 0.12 1.0 (penicillin- intermediate)
Cefpodoxime Cefixime a b Cefpodoxime: H. influenzae Cefixime: H. influenzae 10 8 10 8 control control 10 7 10 7 0.03 0.03 10 6 10 6 H. influenzae CFU/mL CFU/mL 10 5 10 5 0.06 0.13 10 4 10 4 1.0 0.95 10 3 10 3 10 2 10 2 0 1 2 3 4 5 6 0 1 2 3 4 5 6 Time (h) Time (h) c d Cefpodoxime: M. catarrhalis Cefixime: M. catarrhalis 10 8 10 8 control control 10 7 10 7 0.038 0.075 10 6 10 6 M. catarrhalis CFU/mL CFU/mL 10 5 10 5 0.15 0.15 10 4 0.75 10 4 0.75 10 3 10 3 10 2 10 2 0 1 2 3 4 5 6 0 1 2 3 4 5 6 Time (h) Time (h) e f Cefpodoxime: S. pneumo-sensitive Cefixime: S. pneumo-sensitive 10 9 10 9 control control 10 8 10 8 CFU/mL CFU/mL 10 7 10 7 0.02 0.18 10 6 10 6 S. pneumococci 10 5 10 5 0.24 0.03 10 4 10 4 0.72 0.12 10 3 10 3 10 2 10 2 0 1 2 3 4 5 6 0 1 2 3 4 5 6 Time (h) Time (h)
200 mg Cefpodoxime bid vs. 400 mg Cefixime qd a b Cefpodoxime: 200 mg bid on S. pneumo-sensitive Cefixime: 400 mg qd on S. pneumo-sensitive 10 9 10 9 10 8 10 8 10 7 10 7 S. pneumococci-penS 10 6 10 6 CFU/mL CFU/mL 10 5 10 5 10 4 10 4 10 3 10 3 10 2 10 2 10 1 10 1 10 0 10 0 0 6 12 18 24 0 6 12 18 24 Time (h) Time (h) c d Cefpodoxime: 200 mg bid on S. pneumo-interme Cefixime: 400 mg qd on S. pneumo-interme 10 9 10 9 10 8 10 8 10 7 10 7 10 6 10 6 S. pneumococci-penI CFU/mL CFU/mL 10 5 10 5 10 4 10 4 10 3 10 3 10 2 10 2 10 1 10 1 10 0 10 0 0 6 12 18 24 0 6 12 18 24 Time (h) Time (h)
Modified E max Model: ⎛ ⎞ ⎛ ⎞ ⎛ ⎞ C ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⋅ − + ⋅ r k 1 k C ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ + 1 2 Dose ( ) ⎝ IC C ⎠ ⎝ ⎠ dN − ⋅ = − 50 r ⋅ ⋅ − ⎜ ⎟ z t k N 1 e + dt ⎜ EC C ⎟ ka 50 ⎜ ⎟ ⎝ ⎠ ( ) ( ) ( ) Cp ke − ⋅ − − α ⋅ − = ⋅ k t t − t t C C e e e lag lag r 0 α kill (-) Cr k 0 k ecr Comparing to E max model: Bacteria ⎛ ⎞ Cr ⎜ ⎟ = − + K k 1 k ⎜ ⎟ + max 1 2 ⎝ ⎠ IC Cr 50
Two sub-population model Drug (C) OBS: same growth rate for sensitive (S) and resistant (R) Killing f s (C) Bacteria (S) Growth ( k 0 ) Bacteria (R) f r (C) Bacteria pool
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