PKPD: PAST, PRESENT AND

FUTUREApplied to antibiotics - Prof. Dr. An Vermeulen - 23 Nov 2017

DEPARTMENT OF BIOANALYSIS

LABORATORY OF MEDICAL BIOCHEMISTRY AND CLINICAL ANALYSIS

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DOSE SELECTION MADE EASY

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IN REALITY …

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… KNOWLEDGE IS IMPRECISE

Uncertainty

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ALSO, ALL PATIENTS ARE DIFFERENT

Inherent

Biological

Variability

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− We try to establish relationships between exposure (mostly plasma

concentrations) and effects

− In a way, if we focus on PK, it is because we believe it is a biomarker for

effects, and that understanding the PK profile helps to understand the PD

(effects) profile

− Most of the times, we can not measure the concentrations at the effect site,

and we sample plasma concentrations instead (easy matrix to sample and

to measure concentrations in)

− The relationship between plasma and effect site concentrations is then

again linked mathematically

− Once a model is developed, it allows simulations of scenarios we have

not studied/for which we do not have information

− Time points w/o sampling

− Additional doses/dose regimen

− Repeated dosing (using single dose data)

− At population & individual level

− For different covariate subgroups (pediatrics, CrCL, BWT, ..)

PKPD MODELING

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PKPD MODEL APPLICATIONS

• Conceptualization and quantification

• In vitro In vivo extrapolation

• Species scaling

• Clinical trial design

• Design of new dosage forms

• Comparative efficacy/toxicity

• Patent enhancement

• Labeling advice

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PHARMACOKINETICS

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PHARMACOKINETICSPharmacokinetics is the study and mathematical characterization of the time

course of drug absorption, distribution, metabolism, and excretion (ADME)

processes that determine the time-course of drug action.

Adapted from Atkinson, Principles of Clinical Pharmacology (2001)

PRESCRIBED DOSE

DRUG PRODUCT

ABSORPTION

PLASMA

FREEPROTEINBOUND

ELIMINATION

METABOLISM RENALEXCRETION

MOST TISSUES

NONSPECIFIC

BINDING

BIOPHASE

RECEPTORBINDING

DISTRIBUTION

EFFECT

BIOPHARMACEUTICS COMPLIANCE

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NONCOMPARTMENTAL ANALYSIS

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NONCOMPARTMENTAL ANALYSIS

PRIMARY PHARMACOKINETIC PROPERTIES

van de Waterbeemd and Gifford. Nature Rev Drug Disc. 2:192-204 (2003)13

COMPARTMENTAL ANALYSIS

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COMPARTMENTAL ANALYSIS

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THE ASPECT VARIABILITY

− Modelling is essentially similar at the population level !

− But ..− At the population level, the aspect variability comes into play

− In a population analysis, the focus is on

− estimating the magnitude of this variability

− trying to identify patient characteristics that can explain (part of) this variability

− dose-adjustments in subsets? LABELLING impact !

− dose-individualisation in individuals? (avoid TDM - therapeutic drug monitoring)

− So ..− Three levels of variability have to be considered:

− IIV or BSV: inter-individual or between-subject variability

− IOV: inter- or between-occasion variability

− Intra-subject, within-subject or residual (unexplained) variability16

THE ASPECT VARIABILITY

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− Nonlinear Mixed Effects Modelling (NONMEM)

− Mixed effects− Fixed effects ()

− Random effects (, )

− Fixed effects− Estimate mean PK(PD) parameters in the population (= typical values,

central tendency, population average, ..)

− Identify factors that influence the PK (PD) parameters (demographics, lab values, PGx, smoking habits etc…)

− Random effects− Estimate unexplained variability

=> IIV, IOV and residual variability

POPULATION PK PRINCIPLES

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PHARMACODYNAMICS

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PK-PD RELATIONSHIPS: TYPE OF EFFECTS

Drug effect

Reversible

Irreversible

Direct

Indirect

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REVERSIBLE EFFECTS – DIRECT CONCENTRATION-EFFECT MODELS

)(

)ln(

50

max

50

max

p

p

p

p

p

p

p

CEC

CEEffect

CEC

CEEffect

CEffect

CEffect

CEffect

Linear

Log-linear

Power

Emax

Sigmoid Emax

Effect directly

related to plasma

concentration

EC50/IC50 values: the potency of the drug

Emax/Imax values: the efficacy of the drug

γ : sigmoidicity factor 21

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REVERSIBLE EFFECTS – INDIRECTMODELING A BIOPHASE

Segre, Il Pharmaco (1968)

Sheiner et al., CPT (1979)

eCpCeokdt

edC

eC50EC

eCmaxEE

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REVERSIBLE EFFECTS – INDIRECT

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Chemotherapeutic effects

Simple cell killing

Killing and regrowth

Cell cycle effects

Clinical therapy

Irreversible enzyme inhibition

Reactive drug metabolites

Simple drug toxicity

Carcinogenicity models

IRREVERSIBLE EFFECTS

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SIMPLE CELL KILLING

ln SF = -k * D0/CL

J. Pharm. Sci. 60:892 (1971)

Drecker functionN = R

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SIMPLE CELL KILLING

Spleen cells

Osteosarcoma cells

Cyclophosphamide - mice

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CELL KILLING – CELL GROWTH

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CELL KILLING – CELL GROWTH

ANTIBACTERIAL EFFECTS

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PKPD: PAST, PRESENT AND FUTURE …

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MIC

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MIC

kg = ks

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SUMMARY PK MEASURES

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ANTIBACTERIAL EFFECTS

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ANTIBACTERIAL EFFECTS

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Antibiotics

Thigh Lung

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MIC DISTRIBUTION

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TARGET ATTAINMENT

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TARGET ATTAINMENT

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CLINICAL AND MICROBIOLOGICAL CURE

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CLINICAL CURE

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CLINICAL BREAKPOINTPKPD BREAKPOINT

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MODELING TIME COURSE OF ANTIBIOTIC EFFECT

• Better to model time course

of bacterial growth & killing

• Differentiate between the

system (bacteria) and drug

effects

• Can address resistance development

• .. and the effects of the host’s immune

system

=> Use all available data: in vitro, animal, clinical

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SYSTEMS PHARMACOLOGY: PBPK

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… COMBINED WITH PD MODEL AT INFECTION SITE

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… ALLOWS TO DERIVE THE TISSUES WITH HIGHEST EFFECT

An VermeulenPharmacokinetics and Pharmacodynamics/PBPK/Pharmacometrics

DEPARTMENT OF BIOANALYSIS

E AnMC.Vermeulen@ugent.be

T +32 9 264 81 12

M +32 474 55 08 65

www.ugent.be

Ghent University

@ugent

Ghent University