Conc-effect - Handouts PDF

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Drug effects and concentration/effect – relationships Jörgen Bengtsson Division of Pharmacokinetics and Drug Therapy Department of Pharmacy Uppsala University 1

Outline • • • • • • •

Drug effects – what do we actually measure? Graded versus quantal response The shifting baseline Concentration – effect relationships Duration of effect Therapeutic window Conclusion Chapter 3, 9 and 10 in Tozer & Rowland Essentials of PK and PD, 2nd Ed

3

Why care about PK and PD? Half-life

Membrane

Clearance Volume of distribution

Conc in plasma

Membrane transport

Absorption

Dose

Conc in target organ

Effect

Pharmacodynamics: - Emax, EC50, slope - effect delay - tolerance development

First-pass metabolism

4

PK/PD relationships Side-effect

Drug Therapy

Plasma Conc.

Biomarker

Effect

6

What do we measure? •

(or response) – Characteristic or variable that measures how a patient feels, functions or survives • Example: survival after cancer treatment

• – Any biochemical feature or facet that can be used to measure the progress of disease or the effects of treatment. • Example: PSA in prostate cancer

Finding good biomarkers is very important in drug development! 7

What do we measure? • – Biomarker intended to substitute for a clinical endpoint. On the causal pathway to the clinical effect. • Blood pressure reduction as a measure of reduced risk for stroke or renal failure • S-cholesterol vs. survival/mortality

8

What do we measure – • Pain: – No pain

Worst possible pain

• Subjective measurements may be more objective ones! •  Quality of life (QOL) assessments

than

9

Testosterone Concentration (ng/ml)

Testosterone concentration (ng/ml)

• Continuous variables (= graded response) – Temperature, blood pressure, hormone levels, forced expiratory volume (FEV), etc – Most common for biomarkers and surrogates, less common for clinical endpoints 10 9 8 7 6 5 4 3 2 1 0 0

10

20

30

40

Time (days)

50

60

10

• – Number of seizures, incontinent episodes, etc. – Can only take on integer values • – first metastasis, death, headache relief, etc. 100

Graft Survival (%)

90

80 0

2

4

6

Time (months)

8

10

11

Ordinal variables • Ordered categorical – semiquantitative – None/mild/moderate/severe (symptoms or effects) – Complete Response, Partial Response, Stable disease, Progressive Disease (e.g. 5 Cancer) Sedation score

4 3

• • • • • •

2 1 0 0

1 =Fully awake 2 =Drowsy but answers when spoken to 3 =Answers slowly when spoken to 4 =Reacts when spoken to but does not answer 5 =Reacts only to pain 6= Does not react to pain 5

10

15

20

Tim e after ad m ission with stroke (h ou rs)

25 12

The shifting baseline (1) A drug effect is very often a change from some kind of baseline (baseline pain, mmHg, oC etc.): True drug response + Placebo response + Baseline = Measured effect But the baseline is not always stable: Long term changes: Short term change: 14

The shifting baseline (2)

(T&R 3-4)

15

The shifting baseline (3) Dose-dependency in the effect intensity of prednisone on muscle strength in patients with Duchenne dystrophy:

0.75 mg/kg daily 0.3 mg/kg daily Disease progression Placebo

Griggs et al. Arch Neurol 44:383-388 (1991) & Fig 3-3 T&R

16

The shifting baseline (4) Diurnal (circadian) rythm:

Common response of antihypertensive drugs is a decrease in diastolic BP of 5 – 10 mmHg

17

The shifting baseline (5) Diurnal (circadian) rythm:

18

The shifting baseline (6) “Placebo effect” may also be due to shifting baseline: 20

Feels worse, takes ”drug”

Symptom

15

Feels better

10 5 0

0

Time 12

24

Thus, baseline needs to be taken into account in the evaluation of a drug effect! 19

Concentration – Effect relationships This lecture will deal with direct effects. Effect

Concentration Effect

Time

Concentration

That is, there is a direct relationship between drug concentration and effect, independent on whether the concentration is increasing or decreasing.

21

) 120 100

Effect

80

E

60

Effect at half Emax

40

20 0 0

2

4

6

8

10

Concentration (linear scale)

T&R: ”C50”

E = E0 = Emax= C = EC50=  =

Effect Effect at zero concentration (baseline/placebo effect) Maximum effect Concentration Concentration at half-maximal effect slope factor

23

Example: metoprolol and blood pressure

SBP = systolic blood pressure

Luzier et al. 1999

20 volunteers:

Men

Women

Emax (% reduction)

18.0

17.8

EC50 (ng/mL)

28

25



6.2

2.5

24

The sigmoidal Emax model with a logarithmic concentration scale

Reduction in premature ventricular contraction frequency (%)

Log – linear region between 20 – 80 % of maximal effect

Tocainide plasma concentrations (ug/mL)

25

The influence of the slope factor () 𝐸𝑚𝑎𝑥 × 𝐶 𝛾 𝐸= 𝐸𝐶50𝛾 + 𝐶 𝛾 Linear scale

Semilogarithmic scale Slope factor

T&R p 34

Slope factor

27

The importance of the slope factor () (The discussion in T&R pp 36-37 is not quite clear)

slope factor, e.g. = 1: the effect is less sensitive to changes in concentrations

slope factor, e.g. = 5, the effect is more sensitive to small changes in concentrations 28

C-E relationship of alfentanil (with 66% N2O/O2) to prevent response to procedures during surgery.

[Ausems et al. 1988]

29

Simplifications of the Emax model

Emax  C E EC50  C

Drug effect

The Emax model, (E0 = 0) and  = 1: 100 90 80 70 60 50 40 30 20 10 0

Emax

Emax/2

0

EC50

20

40

Plasma drug concentration

60 31

Simplifications of the Emax model (2): the linear model 120

100

Effect

80

60 40

At effects < 30 % of Emax, the relationship can be approximated to a straight line

20

0 0

2

4

6

8

10

Conc 32

Simplifications of the sigmoidal Emax model (2): the linear model Emax  C E   C EC50

Emax  C  E EC50 

Emax   C   EC50

S = slope of the line (if  ≈ 1)

Prothrombin time AUC

If C...