PK-PD analysis and modelling - UCLouvain€¦ · Nov 2017 PK/PD and modelling Why modelling ? (*)...

Nov 2017 PK/PD and modelling 1

PK-PD analysis and modelling

Nov 2017 PK/PD and modelling

Why modelling ? (*)

• to move from mere description to underlying phenomena…– nature can often be better explained in terms of equations than

mere description

– this has been essential in physics (think about gravity law, radioactive decay, study of electromagnetic field and optics, … up to the equivalence of mass and energy…)

• to allow predictions over and beyond what is immediately accessible by the experience…

• to generate rules that can be applied widely…

* CAUTION: modelling in UK English but modeling in US English …

2


In vitro studies

3


Response to an antimicrobialan example with ceftobiprole and S. aureus (one strain)

Effect-over-time

0 6 12 18 24-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.50.010.2122050200

concentration(multiples of MIC)

time (h)

log1

0 C

FU

4


Response to an antimicrobialan example with ceftobiprole and S. aureus (2 strains)

Effect-over-time(2 strains)

0 6 12 18 24-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.50.010.2122050200

concentration(multiples of MIC)

time (h)

log1

0 C

FU

This where the phenomenon happens

5


Response to an antimicrobial: the modelan example with ceftobiprole and S. aureus (2 strains)

-2 -1 0 1 2 3

-1

0

1

2

3 MSSA #1

MSSA #2

log10 multiples of MIC measured at pH 7.4

∆ lo

g cf

u (2

4 h

- 0 h

)

6


Response to an antimicrobial: the modelan example with ceftobiprole and S. aureus (multiple strains)

MIC

CS

Emax

-2 -1 0 1 2 3

-1

0

1

2

3 MSSA HA-MRSA CA-MRSA

log10 multiples of MIC measured at pH 7.4

∆ lo

g cf

u (2

4 h

- 0 h

)Emin

7


Analyses

Equation for Prism

Equation:Sigmoidal dose-responseY=Bottom + (Top-Bottom)/(1+10^((LogEC50-X)))

;X is the logarithm of concentration. Y is the response;Y starts at Bottom and goes to Top with a sigmoid shape

Sigmoidal dose-response:

also called "4-parameters logistic equation", i.e.• bottom (Emin)• Top (Emax)• EC50• Hill slope

Sigmoid dose-response

-1 0 1 2 3 40.0

2.5

5.0

7.5

10.0

Hill slope = 0.5 1.0 2.0

concentration (log10)

resp

onse

Emin

Emax

EC50

8


AnalysesEquation

Equation:Sigmoidal dose-responseY=Bottom + (Top-Bottom)/(1+10^((LogEC50-X)))

;X is the logarithm of concentration. Y is the response;Y starts at Bottom and goes to Top with a sigmoid shape

9


Type of functions

how would you fit those

data

Do not forget to use the appropriate axes !

10


Type of functions

This would be a good

model

11


Run statistics

12


Run tests

13


Two examples

14


Impact of MIC on the response of intracellular bacteria to moxifloxacin

-3 -2 -1 0 1 2-3

-2

-1

0

1

2

3

SA069

SA481

NRS386

NRS192

NRS384

≤ 0.06 0.125 1.0

SA1

MIC (mg/L)

HMC 551KKH II-7924

≥ 2.0

log10 extracellular concentration (mg/L)

-3 -2 -1 0 1 2 3 4-3

-2

-1

0

1

2

3

log10 extracellular concentration (x MIC)

after normalization for MIC

Lemaire et al. Journal of Antimicrobial Chemotherapy (2011) 66:596-607

15


Colistin and inoculum effect

colistin concentration (mg/L)

low inoculum (∼ in vitro testing)

high inoculum (∼ in vivo

The extent and rate of killing of P. aeruginosa by colistin were markedly decreased at high CFUo compared to those at low CFUo.Bulita et al. Antimicrob. Agents Chemother. (2010) 54:2051-2062

16


In search of models with Prism

17


In search of models (including your own)

18


In search of models (including your own)

19


And here you are …azithromycin

-5 -4 -3 -2 -1 0 1 2-4

-3

-2

-1

0

1

2

Cmax0.5 mg/L

MIC, 0.01-0.03 mg/LCs, 3 mg/L

Log concentration (mg/L)

∆ lo

g cf

u fr

om ti

me

0 (4

8 h)

clarithromycin

-5 -4 -3 -2 -1 0 1 2-4

-3

-2

-1

0

1

2

Cmax1 mg/L

MIC, 0.008 mg/LCs, 0.06 mg/L

Log concentration (mg/L)∆

log

cfu

from

tim

e 0

(48

h)

telithromycin

-5 -4 -3 -2 -1 0 1 2-4

-3

-2

-1

0

1

2

Cmax1 mg/L

MIC, 0.008 mg/LCs, 0.04 mg/L


∆ lo

g cf

u fr

om ti

me

0 (4

8 h)

ciprofloxacin

-5 -4 -3 -2 -1 0 1 2-4

-3

-2

-1

0

1

2

Cmax4 mg/L

MIC, 0.01 mg/LCs, 0.002 mg/L


∆ lo

g cf

u fr

om ti

me

0 (4

8 h)

moxifloxacin

-5 -4 -3 -2 -1 0 1 2-4

-3

-2

-1

0

1

2

Cmax4 mg/L

MIC, 0.01 mg/LCs, 0.001 mg/L


∆ lo

g cf

u fr

om ti

me

0 (4

8 h)

finafloxacin

-5 -4 -3 -2 -1 0 1 2-4

-3

-2

-1

0

1

2

Cmax12 mg/L

MIC, 0.01 mg/LCs, 0.05 mg/L


∆ lo

g cf

u fr

om ti

me

0 (4

8 h)

20


In vivo pharmacokinetics

21


What is PK analysis and modeling ?

• Noncompartmental analysisNoncompartmental PK analysis examines total drug exposure and looks for function(s) fitting the change of concentration over time without reference to where the drug may distribute.

Analysis is simple and does not imply anything concerning the actual fate of the drug.

The results are purely descriptive and non-predictive unless the function selected is linked to physical phenomena (e.g. 1st order kinetics).


What is PK analysis and modeling ?• Compartmental analysis

Describes and predicts the concentration-time curve based on the movements of the drug between compartments (kinetic or physiological model)

Once the model is indentified, it can be used to predict the concentration at any time.

The model may be (very) difficult to develop

The simplest PK compartmental model is the one-compartmental PK model with IV bolus administration and first-order elimination.

The most complex PK models rely on the use of physiological information to ease development and validation.

23


What is PK analysis and modeling ?

• Compartmental analysis

The simplest PK compartmental model is the one-compartmental PK model with IV bolus administration and first-order kinetic elimination

This can be developed with simple software accessible to lay users such as Prism (with some sophistication sometimes)

More complex PK models rely on the use of physiological information to ease development and validation.

This requires "high capacity" software that is often impossible to use without serious introduction

24


Simple compartmental models

25


Integrating … (calculus)

26


From model to data and finding "best parameters" with a computer (curve fitting)

• choose (or enter) your equation• enter your data• enter initial parameter values

(best estimate; optional but useful)• the computer will then

– compare equation-based curve to actual data– modify parameters by successive iterations

until a "best" fit is obtained …– the limit is the number of iterations

numerical integration

27


From data to model with a computer (no calculus)

28


Example of monocopartmental analysis … (*)

equation: C = C0.e-kt

theoretical curve

Exponential-decay (1 compartment)

0.0 2.5 5.0 7.5 10.00

25

50

75

100

125

150

Co = 142 mg/L - 2 g - 70 kg - Vd = 0.2 L/kgke = 0.185 (t1/2 = 2h)plateau = 0

time (h)

conc

entr

atoi

n (m

g/L)

* this analysis and the following ones concern ceftazidime IV

29


Fitting to ideal population data (*)Ceftazidime: ideal patients

0.0 2.5 5.0 7.5 10.00

25

50

75

100

125

150

* data from a few volunteers

30


Ideal population: tests for 95 % CICeftazidime: ideal patients

0.0 2.5 5.0 7.5 10.00

25

50

75

100

125

150

31


Ideal population: residuals

ideal-valuesNonlin fit of ideal-valuesData Table-1:Residuals

0 1 2 3 4 5 6 7-7

-5

-3

-1

1

3

5

time (h)

why are they much larger

here ?

32


Real population (*)ceftazidime: real population

0.0 2.5 5.0 7.5 10.00

25

50

75

100

125

150

n = 27 patientsCo = 98 mg/L (2 g - 71.5 ± 10.9 kg)ke = 0.1865 (t1/2 = 3.716 h)plateau = - 8.2

time (h)

conc

entr

atio

n (m

g/L)

* data from several patients

33


Real population: 95 % CI ceftazidime: real population

0.0 2.5 5.0 7.5 10.00

25

50

75

100

125

150

34


Real population: residuals

0 1 2 3 4 5 6 7-35

-25

-15

-5

5

15

25

35

time (h)

resi

dual

s

35

Real population: residuals

Ideal population: Residuals

0 1 2 3 4 5 6 7-35

-25

-15

-5

5

15

25

35

time (h)

resi

dual

s

35


More complex models: accumulation / decay

equation: C = D/Vd x ka/(ka-ke) [e-ket – e-kat]

theoretical curve

Bateman function(applied to ceftazidime)

0.0 2.5 5.0 7.5 10.00

25

50

75

100

125

150

D = 2 g / 70 kg (28.57 mg/kg) - Vd = 0.2 L/kgka = 8ke = 0.3465 (t1/2 = 2h)plateau = 0

time (h)

conc

entr

atio

n (m

g/L)

36


In search of more complex models with Prism

37


Ceftazidime with Bateman function

0 1 2 3 4 5 6 70

25

50

75

100

time (h)

conc

entr

atio

n (m

g/L)

38

Accumulation / decay with Prism … (*)


real data

R2 = 0.57

38


Examples d'analyse monocompartimentale … (*)


Ceftazidime with Bateman

0 1 2 3 4 5 6 70

25

50

75

100

Prism has a

problem here !

39


When the data become really too complex…

40


The Mixed non-lin approaches

• A mixed model is a statistical model containing both fixed effects and random effects.

• These models are useful in a wide variety of disciplines in the physical, biological and social sciences.

• They are particularly useful in settings where repeated measurements are made on the same statistical units (longitudinal study), or where measurements are made on clusters of related statistical units.

• Because of their advantage in dealing with missing values, mixed effects models are often preferred over more traditional approaches such as repeated measures ANOVA.

41


The Mixed non-lin approachesDifferent softwares, but all working by numerical integration based on pre-defined models

42

Exemples avec la témocilline



Temocillin in a nutshell

• Temocillin or 6-α-methoxy-ticarcillin• Registered in 1984 for the first time (Beecham)• Maintained on the market since 1998 (Eumedica)

– BE, LU, UK and now FR

• Narrow-spectrum antibiotic (Gram-negative oriented)– Enterobacteriaceae

– B. cepacia

– Neisseria, Haemophilus, Pasteurella, Legionella

– Inactive against most strains of P. aeruginosa, Acinetobacter,Stenotrophomonas,

– no useful activity against Gram-positive and anaerobes

• Stable to most β-lactamases– Class A (including ESBL, KPC), class C (AmpC), class D (OXA-1)

– Hydrolysed by OXA-48-like (class D) and class B enzymes (metallo-enzymes)

H3C

OHN

S

NO

O-

O

H

O

OO

S

But what if you place the bulky group on the β-lactam ring ?


HHN

S

NO

O-

O

H

O

OO

S

H3C

OHN

S

NO

O-

O

H

O

OO

S

ticarcillin temocillin

water cannot

access…

Matagne et al. Biochem. J. (1993) 293, 607-411


Why me and temocillin ?

As a result …


Belgian SmPC, last revision 2012; Van Landuyt et al, AAC 1982; 22:535-40

Susceptible organisms

MIC < 1 mg/L 1 mg/L < MIC < 10 mg/L 10 mg/L < MIC < 100 mg/LMoraxella catarrhalisHaemophilus influenzaeLegionella pneumophilaNeisseria gonorrhoeaeNeisseria meningitidis

Brucella abortusCitrobacter spp.Escherichia coliKlebsiella pneumoniaePasteurella multocidaProteus mirabilisProteus spp (indole +)Providencia stuartiiSalmonella TyphimuriumShigella sonneiYersinia enterocolitica

Serratia marcescensEnterobacter spp

Intrinsically resistant organisms

anaerobesGram(+) bacteriaAcinetobacter sppPseudomonas aeruginosa

ESKAPE pathogens

Chemical stability of temocillin in concentrated solutions


De Jongh et al. Journal of Antimicrobial Chemotherapy (2008) 61:382-388 – Supplementary Material

Comparative chemical stabilities of β-lactams upon storage of concentrated solutions at 25 and/or 37°C


Conclusion Molecule Stability limit 1 reference

good temocillin > 24 h at 37°C 2 De Jongh et al. JAC 2008

aztreonam > 30 h at 37°C Chanteux et al. (abstract)

piperacillin 24 h at 37°C Viaene et al. AAC 2002

weak ceftazidime 24 h at 25°C / 8 h at 37°C Servais et al. AAC 2001

problematic cefepime color appearance within 6 h Baririan et al. JAC 2003

insufficient imipenem < 5 h Viaene et al. AAC 2002

meropenem < 5 h Viaene et al. AAC 2002

doripenem ∼ 6-10 h Berthoin et al. JAC 2010

1 > 90 % of original compound (European Pharmacopoiea) 2 stable for 3 weeks at 4°C (for home medication) (Carryn et al., J Antimicrob Chemother 2010;65:2045-2046)

JAC: J Antimicrob ChemotherAAC: Antimcrob Agents Chemother

• For β-lactams, – only the free fraction is (probably) active…


Temocillin pharmacodynamics: the lessons of β-lactams

Exemple #1 (très court):bolus et infusion continue


Application to clinical trials (ICU patients)


De Jongh et al, JAC 2008; 61:382-8

2 g q12 h

LD: 2 g; CI 4g/24h

MIC90

MIC90PK/PD Bkpt 8-16 mg/L

Monte Carlo simulation

MIC90

PK/PD target

Exemple #2 (plus long): patients de soins intensifsavec données manquantes



Temocillin project (full)

20 30

20 30

101

102

101

102

101

102

101

102

ID: 1 ID: 2 ID: 3

ID: 4 ID: 5 ID: 6

ID: 7 ID: 8 ID: 9

ID: 10 ID: 11Con

cent

ratio

n (m

g/L)

Time (h)

54


Outputs: individual curves

24 26 28 30 320

100

2002

24 26 28 30 320

50

100

1503

24 26 28 30 320

100

2004

24 26 28 30 320

100

200

5

55


Outputs: spaghetti plot (*)

24 25 26 27 28 29 30 31 320

50

100

150

200

250

time

obse

rvat

ions

Total number of subjects: 4Average number of doses per subject: 1Total/Average/Min/Max numbers of observations: 15 3.75 3 4

* not noodles !

56


Outputs: population curves

24 26 28 30 32-100

0

100

200

3002

24 26 28 30 32-100

0

100

200

3003

24 26 28 30 32-100

0

100

200

3004

24 26 28 30 32-100

0

100

200

3005

57


Outputs: population

24 25 26 27 28 29 30 31 32 33-100

-50

0

50

100

150

200

250

300

TIME

DV

Percentile 90 : Obs.Percentile 50 : Obs.Percentile 10 : Obs.Percentile 90 : 90% CIPercentile 50 : 90% CIPercentile 10 : 90% CIOutliersOutlier (area)

58


Outputs: observations vs. predictions

50 100 150 200

50

100

150

200

obse

rvat

ions

predictions

Using the population parameters

50 100 150 200

50

100

150

200

obse

rvat

ions

predictions

Using the individual parameters

59


Outputs: residuals

20 30 40-2

0

2

4

PW

RE

S

time

50 100 150 200-2

0

2

predictions

PW

RE

S

20 30 40-2

0

2

4

IWR

ES

time

50 100 150 200-2

0

2

predictions

IWR

ES

20 30 40-2

0

2

4

NP

DE

time

50 100 150 200-2

0

2

predictions

NP

DE

60

Exemple #3 (long): volontaires vs soins intensifset impact de la fraction libre


Cette partie est reprise du travail de Thèse en coursde Mr Perrin Ngougni-Pokkem

62Nov 2017 PK/PD and modelling

There is growing evidence that standard antibiotic regimens may not provide adequate drug concentrations …

J.W. Mouton et al: Int J Antimicrob Agents. 2002 Apr;19(4):323-31.Roberts et al, Br J Clin Pharmacol. 2012;73:27-36.


Critically ill patients

Pharmacokinetic alteration

A. Abdulla et al: University Medical Center Rotterdam; eposter 069; ECCMID 2017 Hosthoff et al, Swiss Med Wkly. 2016;146:w14368Roberts JA, Lipman J. Clin Pharmacokinetic 2006; 45 (8): 755-73

RRT: renal replacement therapyECMO: extra corporeal membrane oxygenation

Hyperdynamic statesIncreased cardiac out, and clearanceDecreased plasma concentrations

Altered fluid balance / Altered protein bindingIncreased volume of distributionDecreased plasma concentrations

Renal and hepatic impairmentDecreased clearanceIncreased plasma concentrations

Organ support (RRT/ECMO)Increased volume of distribution /clearanceIncreased/decreased plasma concentrations

Critically-ill patients


Consequences of PK alteration

Critically ill patients

Pharmacokinetic alteration

underdosing

Therapeutic failure/antibiotic resistance

overdosingTherapeutic

antibiotic concentration

toxic effects Therapeutic success

A. Abdulla et al: University Medical Center Rotterdam; eposter 069; ECCMID 2017 Hosthoff et al, Swiss Med Wkly. 2016;146:w14368Roberts JA, Lipman J. Clin Pharmacokinetic 2006; 45 (8): 755-73

Variability in antibiotic

concentration


The main objectives

• Current literature data are based mainly on TOTAL temocillin concentrations

• Only the free concentration is active !• Concentration in the infected tissue is important !

Part 1Pilot study

Population Pharmacokinetic Analysis and Protein Binding Characteristics of Free and total

Temocillin concentrations in Plasma of Healthy Volunteers and patients


Design of the in vitro study

Bound concentration vs free concentration of temocillin in plasma

Free Fraction at a given total concentration vs protein concentrations


Temocillin plasma protein binding In vitro study

0 25 50 75 100 125 150 175 200 22505

1015202530354045505560657075

Free fraction vs total concentration of temocillinin plasma for 4 healthy donors (D) compared with

5 patients donors (P) in vitro study

D1D2D3D4

P1P2P3P4P5

total concentration (mg/L)

free

frac

tion(

%)

For the patient donors High free fraction up to 65% Free fraction which increases with the total

concentration High variability between the patient donors.

For the healthy donors, except D4 Low free fraction between 5 to 8% Free fraction which is not influenced by the

total concentration Low variability between the healthy donors

D4: 57.03 g/LD1: 71.75 g/LD2: 84.91 g/LD3: 70.55 g/L

Plasma total protein level (mg/L)Reference range : 65-85g/L

P1: 52.89 g/LP2: 48.34 g/LP3: 61.17 g/LP4: 55.31 g/LP5: 55.53 g/L


Michaelis-Menten fitting of temocillin protein binding

Plasma protein binding of temocillin is saturable

Maximum binding is lower in patients

Bound concentration vs free concentration of temocillin in plasmaIn vitro study

0 20 40 60 80 100 1200

50

100

150

200

250

VolunPatie

Free Concentration (mg/L)

Bou

nd c

once

ntra

tion

(mg/

L)D; n=3 P; n=6


Design of the clinical study: « Phase1 »

8 healthy volunteers.

Single dose of 2g TMO in 40 min infusion; IV administration.

Blood sampling: 40min, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12h.

Study of the relationship between free fraction of temocillin vs its total concentration.

Study of the relationship between bound concentration of temocillin vs free concentration.

Principal Investigator according to Austrian drug lawMarkus Zeitlinger,MDDepartment of clinical Pharmacology,Medical University of Vienna Graph Pad 4 software


Temocillin plasma protein binding

0 50 100 150 200 2500

5

10

15

20

25

V1 = 73.58g/LV2 = 77.45g/LV3 = 66.16g/LV4 = 74.08g/LV5 = 72.45g/LV6 = 64.50g/LV7 = 65.25g/LV8 = 69.45g/L

Plasma total protein levelReference range:

65-85g/L

Mean total concentration (mg/L)

Free

frac

tion

(%)

Low free fraction (3-8%) for total concentrationsbelow 150 mg/L, and increase in free fraction upto 20% for higher total concentrations

Free fraction vs total concentration of TMO in plasma for 8 healthy volunteers (V) in vivo study compared with healthy donors (D)

in vitro study




0 20 40 60 80 100 1200

100

200

300V (in vivo study; n=8)

Free concentration (mg/L)

Bou

nd c

once

ntra

tion

(mg/

L)

Protein binding saturation observed




0 20 40 60 80 100 1200

100

200

300V (in vivo study; n=8)D (in vitro study; n=3)P (in vitro study; n=6)

Free concentration (mg/L)

Bou

nd c

once

ntra

tion

(mg/

L)

Lower Bmax for patients !

Comparison of plasma protein binding in healthy volunteers (V), healthy donors (D) and patient donors (P)

Similar protein binding saturation observed


PK modeling approaches

“Data-rich” situation Simple to implementIndividual analyzes in descriptive statistics

Subject 1

Subject 2

Subject N

Adapted from I.Delattre. 2012


Plasma total and free concentration versus timePharmacokinetic profile of free and total concentration : individual data

Free concentration decreases with the total concentration Important variability in the pharmacokinetic profiles between

the volunteers


Comparison of pharmacokinetic parameters (free vs total)

Mean and IC95% of pharmacokinetic parameters of free and total TMO

p=0.07

Free Total3

4

5

6

Tem

ocill

in h

alf l

ife (h

)

The half-life of the free temocillin has an important numerical effect; But not significant

P<0.0001

Free Total0

10

20

30

40

50

Tem

ocill

in c

lear

ance

(L/h

)

The clearance of the free temocillin is very high compared to the total

P<0.0001

Free Total0

50

100

150

200

250

300

350

Tem

ocill

in v

olum

e of

dis

trib

utio

n (L

)

The volume of distribution of the free temocillin is very high compared to the total

t1/2 = 0.693 Vd / Cl


1. Structural pharmacokinetic model

This PK profile suggests that the kinetics of the TMO is Bi-compartmental

Intravenous administration of 2 g of TMOPlasma free concentration versus time

0 1 2 3 4 5 6 7 8 9 10 11 121

10

100

1000

Time (h)

log

free

con

cent

ratio

ns (m

g/L)

Distribution andelimination phase

Elimination phase

Visual evaluation of pharmacokinetic profile


1. Goodness-of-fit plotO

bser

ved

free

conc

entr

atio

ns (m

g/L)

Individual predicted free concentrations (mg/L)

Mono compartmental model tested without covariate

Obs

erve

dfr

ee co

ncen

trat

ions

(mg/

L)

Individual predicted free concentrations (mg/L)

Bi-compartmental model tested without covariate

The correlation is better in this case and with less variability


1. Goodness-of-fit plot

Bi-compartmental model + Proportional residual error model

WRE

= (C

pred

-Cob

s) /

Cpr

ed

Individual C predicted (mg/L)

Mono compartmental model tested without covariate

Time (h)

Freq

uenc

y

WRE

Shapiro-Wilk. P=0.002WRE vs. TimeWRE vs. Cpred

Bi-compartmental model tested without covariate

WRE

= (C

pred

-Cob

s) /

Cpr

ed

Individual C predicted (mg/L) Time (h)

Freq

uenc

y

WRE

Shapiro-Wilk. P=0.214WRE vs. TimeWRE vs. Cpred

Residues should be centered on 0 95% of the population residues should

be between approximately -2 and 2 Residue distribution should be normal


2. Covariate model Relevant physiological, biological and demographic parameters that could change

the pharmacokinetic parameters

Make it possible to explain the inter and / or intra-individual variability

Parameter Mean (sd) RangeAge (yr) 32.9 (12.1) 23.0-53.0Weight (kg) 81.9 (10.9) 70.2-105.6Height (m) 1.8 (0.1) 1.7-1.9BMI (kg/m2) 24.4 (2.9) 20.7-28.9GFR (mL/min)(Cockcroft-Gault)

135.7 (16.1) 108.2-153.4

ASAT (U/L) 23.8 (4.5) 14.0-30.0ALAT(U/L) 30.1 (9.2) 18.0-45.0LDH(U/L) 158.8 (28.4) 143.0-195.0Albumin (g/L) Not analyzedTotal protein (g/L) 70.4 (4.4) 64.5-77.5

Influence volume of distribution and clearance

Variable protein binding (-->93 %)

Renal excretion (80% found in the urine in 24h)


Validation population pharmacokinetic final model

Internal Validation: Monte Carlo simulations

• Simulated profiles (n=1000) compared to observed data.

• The observed concentrations should be distributed homogeneously around the median of the simulated concentrations

• Less than 5% of observed concentrations must be outside the 5th and 95th percentiles of the simulated concentrations

External Validation


Internal Validation: Visual Predictive Checks (VPC).

0 1 2 3 4 5 6 7 8 9 10 11 120.1

1

10

100

1000

Simulated free TMO profile

0.95

0.5

0.05

Observed free concentration

Free

con

cent

ratio

n (m

g/L)

Time (h)


Temocillin pharmacodynamic targets

As every β-lactam, temocillin is – bactericidal– time-dependent

(activity is driven by the time during which the drug plasma free concentration remains above the minimum inhibitory concentrations (MIC))

40% of time > MIC is enough for bacteriostatic activity acceptable for non-immunocompromised patients

70% of time > MIC is recommended for immunocompromised patients

100% of time > MIC is suggested For critically-ill patientsthis could not only maximize efficacy but also minimize emergence of resistance

Craig WA Diagn Microbiol Infect Dis. 1995;22:89-96. PMID: 7587056.

Delattre IK et al For submission to Expert Review on Antiinfective Therapy as Special Report


Probability of Target Attainment (PTA) of plasma free temocillin concentrations

For non-immunocompromised patientsTarget: fT > BSAC breakpoint = 8 mg/L of 40% of the time, based on a mean free fraction of 6.0 ± 1.4% (mean of values observed for total concentration < 150mg/L),

PTA

Target concentrations (mg/L)Standard dosing (2g/12h)PTA = 0

newly proposed dosing (2g/8h)

PTA = 0.5


For non-immunocompromised patientsTarget: fT > BSAC breakpoint = 8 mg/L of 40% of the time, based on a mean free fraction of 13.0 ± 4.0% (mean of values observed for total concentration > 150mg/L),

PTA

Target concentrations (mg/L) newly proposed dosing (2g/8h)PTA = 1

Standard dosing (2g/12h)

PTA = 0.99



PTA

Target concentrations (mg/L)

For critically-ill patientsTarget: fT > BSAC breakpoint = 8 mg/L of 100% of the time, based on a mean free fraction of 35.0 ± 12.3% (mean of values observed for patient),

Standard dosing (2g/12h)

PTA = 0.7

newly proposed dosing (2g/8h)PTA = 1



MIC distributions of E. coli : ESBL/AmpC (n=1155) vs non-ESBL/AmpC (n=1473)

Source: Eumedica (data on file)

Which are the actual (and recent) observations ?

PK-PD analysis and modelling - UCLouvain€¦ · Nov 2017 PK/PD and modelling Why modelling ? (*)...

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