Pharmacology of Anti- Infective Agents in 2005: The Basics and - - PowerPoint PPT Presentation

Pharmacology of Anti- Infective Agents in 2005: The Basics and Beyond

• Prof. Hartmut Derendorf

University of Florida

Resistance Development

Approved Antibacterial Agents

1983-2004

Pharmacokinetics

• conc. vs time

Conc. Time

25 0.0 0.4

PK/PD

effect vs time

Time Effect

1

Pharmacodynamics

• conc. vs effect

10-3

• Conc. (log)

Effect

6 18 24 12 Concentration (µg/mL) 8 12 16 4

MIC Cmax

Time (hours)

Cmax/MIC

6 18 24 12 Concentration (µg/mL) 8 12 16 4

MIC Cmax

Time (hours)

Cmax/MIC

6 18 24 12 Concentration (µg/mL) 8 12 16 4

MIC t > MIC

Time (hours)

Time above MIC

6 18 24 12 Concentration (µg/mL) 8 12 16 4

MIC t > MIC

Time (hours)

Time above MIC

6 18 24 12 Concentration (µg/mL) 8 12 16 4

MIC

Time (hours)

AUC24/MIC

6 18 24 12 Concentration (µg/mL) 8 12 16 4

MIC

Time (hours)

AUC24/MIC

PK PD

Serum MIC

Pharmacokinetics

Problems:

• Protein Binding
• Tissue Distribution

Protein Binding of Cephalosporines

Cephapirin 62 Moxalactam 53-67 Cefprozil 40 Cefotaxime 36 Cefpodoxime 25 Cefonicid 98 Ceftriaxone 90-95 Cefoperazone 89-93 Cefazolin 89 Cefotetan 85 Ceforanide 80-82 Cefamandole 74 Cefoxitin 73 Cephalothin 71 Cefmetazole 70 Cefixime 65

vascular space extravascular space

plasma protein binding blood cell binding, diffusion into blood cells, binding to intracellular biological material tissue cell binding, diffusion into tissue cells, binding to intracellular biological material binding to extracellular biological 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

Interstitium Capillary Cell Perfusate Dialysate

Clinical study Cefpodoxime and Cefixime

• To compare the soft tissue distribution
• f these two antibiotics after 400mg
• ral dose in healthy male volunteers by

microdialysis

• Two way cross-over, single oral dose

study

Microdialysis

Clinical Microdialysis Cefixime

400 mg po

Cefpodoxime

400 mg po

1 2 3 4 5 6 2 4 6 8 10 Time (h) Concentratoin (mg/L)

plasma muscle free plasma

1 2 3 4 5 6 2 4 6 8 10 Time (h) Concentration (mg/L)

plasma muscle free plasma Liu & Derendorf, JAC 50, 19 (2002)

Pharmacokinetics

Cefpodoxime Cefixime AUCP [mg*h/L] 22.4 (8.7) 25.7 (8.4) AUCT [mg*h/L] 15.4 (5.2) 7.4 (2.1) Cmax, P [mg/L] 3.9 (1.2) 3.4 (1.1) Cmax,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

Kill Curves

flask reservoir tubing connector pump waste Auto-dilution system

Kill Curves of Ceftriaxone

• H. influenzae ATCC10211

MIC: 5 ng/mL

• S. pneumoniae ATCC6303

MIC: 20 ng/mL

Kill Curves of Ceftriaxone

• S. pneumoniae ATCC6303

MIC: 20 ng/mL

• H. influenzae ATCC10211

MIC: 5 ng/mL

PK-PD Model

N C EC C k k dt dN

f f

⋅ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ + ⋅ − =

50 max Maximum Growth Rate Constant k Maximum Killing Rate Constant k-kmax Initially, bacteria are in log growth phase

Single Dose

Piperacillin vs. E. coli

2 4 6 8 10

Time (h)

100 101 102 103 104 105 106 107 108 109 1010 1011 1012 1013 1014

CFU/mL

control 2g 8g 4g

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

5 10 15 20 25 10 2 10 3 10 4 10 5 10 7 10 8 10 9 10 10 10 11 CFU/mL 10 6 Time (h) 50µg/mL q24h 5 10 15 20 25 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 10 11 10 10 CFU/mL 50µg/mL q8h Time (h) 5 10 15 20 25 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 10 10 10 11 CFU/mL 50µg/mL q4h Time (h) 5 25 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 10 11 10 20 15 CFU/mL 10 10 100µg/mL q24h Time (h) 5 10 15 20 25 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 10 10 10 11 CFU/mL 100µg/mL q8h Time (h) 5 10 15 20 25 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 10 10 10 11 CFU/mL 100µg/mL q4h Time (h)

Example 1

• Same PK
• Same MIC
• Same t>MIC
• Same AUC/MIC
• Same Cmax/MIC
• Same k

(Growth Rate)

• Different EC50

(Sensitivity)

• Different kmax

(Maximum Kill Rate)

Condition 1

Condition 2

5 10 15 20 25

Time (hour)

10

1

10

2

10

3

10

4

10

5

10

6

10

7

10

8

CFU/mL

50 100 150 200 250

Antibiotic Conc (ng/mL)

5 10 15 20 25

Time (hour)

10

1

10

2

10

3

10

4

10

5

10

6

10

7

10

8

CFU/mL

50 100 150 200 250

Antibiotic Conc (ng/mL)

PK-PD modeling based on Kill Curves

Control (CFU/mL) Treated (CFU/mL) Antibiotic concentration

Example 2

• Same PK
• Same MIC
• Same t>MIC
• Same AUC/MIC
• Same Cmax/MIC
• Same kmax

(Maximum Kill Rate)

• Different EC50

(Sensitivity)

• Different k

(Growth Rate)

Condition 1

Condition 2

5 10 15 20 25

Time (hour)

10

1

10

2

10

3

10

4

10

5

10

6

10

7

10

8

CFU/mL

50 100 150 200 250

Antibiotic Conc (ng/mL)

5 10 15 20 25

Time (hour)

10

1

10

2

10

3

10

4

10

5

10

6

10

7

10

8

CFU/mL

50 100 150 200 250

Antibiotic Conc (ng/mL)

PK-PD modeling based on Kill Curves

Control (CFU/mL) Treated (CFU/mL) Antibiotic concentration

1.0 0.12

Streptococcus pneumoniae (penicillin- intermediate)

0.25 0.03

Streptococcus pneumoniae (penicillin- sensitive)

0.12 0.12-0.25

Moraxella catarrhalis

0.06 0.06-0.12

Haemophilus influenzae

MIC (mg/L) Cefixime MIC (mg/L) Cefpodoxime

Cefpodoxime Cefixime

a

Time (h)

CFU/mL

Cefpodoxime: H. influenzae

b

CFU/mL

Time (h)

Cefixime: H. influenzae

c

CFU/mL Time (h)

Cefpodoxime: M. catarrhalis

d

CFU/mL

Time (h)

Cefixime: M. catarrhalis

e

Time (h)

CFU/mL Cefpodoxime: S. pneumo-sensitive

f

Time (h)

CFU/mL

Cefixime: S. pneumo-sensitive

• H. influenzae
• M. catarrhalis
• S. pneumococci

200 mg Cefpodoxime bid vs. 400 mg Cefixime qd

a

CFU/mL Time (h)

Cefpodoxime: 200 mg bid on S. pneumo-sensitive

b

Cefixime: 400 mg qd on S. pneumo-sensitive Time (h) CFU/mL

c

CFU/mL

Time (h)

Cefpodoxime: 200 mg bid on S. pneumo-interme

d

CFU/mL

Cefixime: 400 mg qd on S. pneumo-interme

Time (h)

• S. pneumococci-penS
• S. pneumococci-penI

Modified Emax Model:

( )

t z r r

e N C EC C k C IC C k k dt dN

⋅ −

− ⋅ ⋅ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎜ ⎜ ⎜ ⎝ ⎛ + ⋅ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ + ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ + − ⋅ − = 1 1

50 2 50 1

( ) ( )

( )

lag lag e

t t t t k r

e e C C

− ⋅ − − ⋅ −

− ⋅ =

α

Dose ka Cp ke α kill (-) Cr k0 kecr Bacteria

2 50 1 max

1 k Cr IC Cr k K + ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ + − =

Comparing to Emax model:

Two sub-population model

Drug (C)

fs(C)

Growth ( k0)

fr(C)

Bacteria (R) Bacteria (S)

Bacteria pool

Killing OBS: same growth rate for sensitive (S) and resistant (R)

Two sub-population Emax model

( ) ( )

t z s s t z r r

e Ns C EC C K k dt dNs e Nr C EC C K k dt dNr

⋅ − ⋅ −

− ⋅ ⋅ ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ + ⋅ − = − ⋅ ⋅ ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ + ⋅ − = 1 1

50 max 50 max

Ns Nr Nt + =

Model Comparison – E. coli

control 4 8 12 16 20 24 28 32 36 40 44 48

Time (hours)

100 101 102 103 104 105 106 107 108 109 1010 1011 1012

CFU/mL

0.03 0.06 0.13 0.25 0.5

• E. coli (MIC=0.013 mg/L)

t (h) CFU/mL

12 24 36 48 10-5 10-4 10-3 10-2 10-1 100 101 102 103 104 105 106 107 control 0.03 0.25 0.5 0.13 0.06

• E. coli

Two sub-population model (simultaneous fit) Modified Emax model (simultaneous fit)

Faropenem Daloxate

After oral administration, faropenem daloxate is rapidly absorbed and immediately converted in plasma to its active moiety faropenem Advantages of using the pro-drug instead of faropenem sodium:

• higher oral bioavailability (70-80%)
• less gastrointestinal side effects

0.0001 0.01 1 100 10000 20 40 60 80 t (h) C F U C h a n g e No N 0.0001 0.01 1 100 10000 20 40 60 80 t (h) C F U C h a n g e No N

1 2 3 4 5 20 40 60 80 t (h) C (m g/L) 2 4 6 8 10 12 14 20 40 60 80 t (h) C (m g/L)

450 mg q24 150 mg q8

Faropenem daloxate 300 mg q12h Fed

2 4 6 8 10 12 14 20 40 60 80 t (h) C (mg/L)

Fasted

2 4 6 8 10 12 14 20 40 60 80 t (h) C (mg/L)

Faropenem daloxate 300 mg q12h

• S. pneumo. #49619

2 4 6 8 10 12 14 20 40 60 80 t (h) C (mg/L) 0.0001 0.001 0.01 0.1 1 10 100 1000 10000 100000 20 40 60 80 t (h) CFU Change No N

Fed

2 4 6 8 10 12 14 20 40 60 80 t (h) C (mg/L)

0.0001 0.001 0.01 0.1 1 10 100 1000 10000 100000 20 40 60 80 t (h) CFU Change No N

Fasted

Faropenem daloxate 300 mg q12h

2 4 6 8 10 12 14 10 20 30 40 50 60 70 80 t(h) C (m g /L ) Fed Fasted 0.001 0.01 0.1 1 10 100 10 20 30 40 50 60 70 80 t (h) C (m g /L ) Fed Fasted

Semilogarithmic scale Normal scale EC50 0.026 mg/L

Piperacillin in patients

Piperacillin serum and muscle levels in healthy patients and intensive care patients after single iv dose of 4g

Brunner et al, 2000

Piperacillin in patients

Piperacillin kill curves (MIC: = 2 mg/l and =4 mg/l)

Sauermann et al, 2003

Summary

• A simple comparison of serum concentration and MIC is

usually not sufficient to evaluate the PK/PD- relationships af anti-infective agents.

• Protein binding and tissue distribution are important

pharmacokinetic parameters that need to be

• considered. Microdialysis can provide information on

local exposure.

• PK-PD analysis based on MIC alone can be misleading.
• Microbiological kill curves provide more detailed

information about the PK/PD-relationships than simple MIC values.

Proposal

Wild Card Patent Extension

A company that receives approval for a new antibiotic, or a new indication for an existing antibiotic, that treats a targeted pathogen would be permitted to extend the market exclusivity period for another of the company’s FDA-approved drugs.

ISAP

International Society of Anti- Infective Pharmacology

• Workshops at ECCMID and ICAAC
• Symposia at ECCMID and ICAAC
• Spring 2004: Joint Symposium with FDA and IDSA
• Website with slides, presentations and tons of

information

www.isap.org

Acknowledgements

Edgar Schuck Qi Liu Ping Liu Teresa Dalla Costa Amparo de la Peña Ariya Khunvichai Arno Nolting Wanchai Treeyaprasert Stephan Schmidt Elizabeth Potocka Markus Müller Kenneth Rand Alistair Webb Maria Grant Andreas Kovar Olaf Burkhardt