drug metabolism and pharmacokinetics (dmpk)
TRANSCRIPT
DRUG METABOLISM AND PHARMACOKINETICS (DMPK) Lena Gustavsson, H. Lundbeck A/S, [email protected] November 2015
DMPK in Drug Discovery and Development Agenda
Introduction Optimizing pharmacokinetic properties
Absorption & bioavailability Distribution Elimination – Clearance
Understanding clearance mechanisms Drug metabolism Drug transporters
Drug drug interactions and interindividual variability Summary: Drug discovery and development
2
H. Lundbeck A/S – an introduction A pharmaceutical company with focus on brain diseases
More than 700 million people are affected by brain disease worldwide Lundbeck is dedicated to address the global burden of brain disease Psychiatric diseases e.g. bipolar disorder, depression, schizophrenia Neurologic diseases e.g. Alzheimer´s, Parkinson´s, Huntington´s
A global company with head quaters in Valby, Denmark
Total approximately 5500 employee´s Approximately 1700 employees in Denmark Full value chain from research to production
Want to know more? Go to: www.lundbeck.com/global/about-us/progress-in-mind www.youtube.com/user/progressinmind
3
Why study Drug Metabolism and PharmacoKinetics?
DRUG DISCOVERY Optimise compounds to get...
Good bioavailability – get to its target Appropriate duration (1-2 doses/day) Low potential for drug-drug interactions
…predicted to man Provide basics for understanding of
Toxicology Pharmacology
DRUG DEVELOPMENT Provide understanding of drug disposition
Preclinical animal species – tox coverage Human data
Assess the risk for drug drug interactions (DDI) Decrease risk for drug drug interactions in the clinic Impact on the design of clinical studies
Comply with guidelines from regulatory authorities
From Rowland and Tozer, 1995
2015-11-08 Lena Gustavsson
Reasons for compound attrition
Kola and Landis 2004
2015-11-08 Lena Gustavsson
Pharmacokinetics – oral administration
Dru
g co
ncen
trat
ion
in p
lasm
a
tmax
Cmax
ADME = Absorption Distribution Metabolism Excretion
2015-11-08 Lena Gustavsson
DMPK in Drug Discovery and Development Agenda
Introduction Optimizing pharmacokinetic properties
Absorption & bioavailability Distribution Elimination – Clearance
Understanding clearance mechanisms Drug metabolism Drug transporters
Drug drug interactions and interindividual variability Summary: Drug discovery and development
7
Absorption
Lipinski’s rule of 5 to predict poor permeability/absorption (Lipinski et al, Adv Drug Delivery Rev 23:3-25, 1997)
Mw > 500 Log P > 5 H-bond donors >5 H-bond acceptors > 10
Transporter substrates are exceptions from the rule.
2015-11-08 Lena Gustavsson
Is the drug absorbed? No = low bioavailability !
Permeability Caco-2
Caco-2 • Human colon epithelial cell line • Differentiates to monolayer with tight junctions
•Papp: cm/sec x 10-6
correlates to human abs
•Indication of transporter mechanisms • Automated incubations • LC-MS/MS analysis
• Alternative to Caco-2: PAMPA – artificial membrane
2015-11-08 Lena Gustavsson
Bioavailability - oral administration
Fgut
Fhep Fabs
F
F = Fgut x Fabs x Fhep
Liver Portal vein
Gut wall Gut lumen
Fhep = 1 - Ehep
2015-11-08 Lena Gustavsson
Distribution • Drug distribution is the reversible transfer of drug to and from the site of measurement (blood/plasma) • Distribution is influenced by
-perfusion – blood circulation to tissues -diffusion -physicochemical properties -binding to proteins etc
From Rowland and Tozer, 1995
2015-11-08 Lena Gustavsson
Not a real volume but a mathematical expression of the extent to which a drug distributes into tissues
-Low V – drug stays in blood/plasma -High V – drug distributes extensively into tissues
V relates the concentration at site of measurement to the total amount of drug in the body (L/kg) V=Amount drug in body/plasma concentration (L/kg bw)
Volume of distribution (V)
2015-11-08 Lena Gustavsson
Elimination: The concept of clearance (CL)
Clearance is the apparent volume of plasma completely cleared of drug per unit time
Rate of elimination = CL x C CL = Dose / AUC (iv dose) Unit: mL/min/kg
2015-11-08 Lena Gustavsson
Hepatic clearance
Bile duct
Hepatic vein
Gall Bladder
Hepatic artery
Hepatic portal vein
The liver is the major site of drug metabolism
2015-11-08 Lena Gustavsson
Drug metabolizing enzymes Route of elimination of the top 200 most
prescribed drugs in 2002
Enzymes listed in FDA guidelines • CYP: 1A2, 2B6, 2C8, 2C9,
2C19, 2D6, 3A • UGT: 1A1, 1A3, 1A4, 1A6, 1A9,
2B7, 2B15
Weinkers and Health Nat Rev Drug Discov 4:825-833, 2005
How to estimate metabolic clearance from in vitro studies?
2015-11-08 Lena Gustavsson
Metabolic stability Microsomes or
hepatocytes
Phase I +II II
metabolites
How fast is the drug eliminated by the liver ? Fast = low bioavailability ! Fast = short duration !
CLint - the intrinsic capacity of a system to clear a drug (µL/min/mg protein or cells)
CLint = = V0 / [S] Vmax Km
0,01
0,1
1
10
100
0 20 40 60 80
Time (minutes)
ln S
ub
stra
te C
on
cen
trat
ion
2015-11-08 Lena Gustavsson
Prediction of in vivo clearance from in vitro data
CLint= ln 2 /( t1/2 x protein conc)
CLint’= CLint x (mg microsomes/g liver) x (g liver/kg bw)
If well-stirred model CLhep,met,= (Qh x fu x CLint’)/ (Qh + (fu x CLint’)
CL = CL(HepMet)+CL(HepBile)+CL(Renal)+ ....
In vitro CLint
Whole liver CLint
Whole body CL
Hepatic metabolic CL
In vitro t1/2
2015-11-08 Lena Gustavsson
Interplay between V and CL Rat pharmacokinetics
1
10
100
1000
10000
0 5 10 15Time (hours)
Con
cent
ratio
n (n
mol
/L)
A
C B
Elimination half-life T1/2 = ln 2 x V / CL
CL(mL/min/kg) Vss(L/kg) T½ (h) A 20 14 10 B 70 12 3 C 80 0.6 0.5
Volume of distribution Clearance Absorption
Dosing interval? Dose?
Half-life Oral bioavailability
2015-11-08 Lena Gustavsson
DMPK in Drug Discovery and Development Agenda
Introduction Optimizing pharmacokinetic properties
Absorption & bioavailability Distribution Elimination – Clearance
Understanding clearance mechanisms Drug metabolism Drug transporters
Drug drug interactions and interindividual variability Summary: Drug discovery and development
21
Clearance mechanisms
Hepatic • Metabolism – phase I and phase II enzymes • Bile excretion – sinusoidal and canalicular transporters
Renal • Passive – glomerular filtration • Active transport
Extrahepatic metabolism • Intestinal CYP3A4 • Enzymes in blood • Other extrahepatic enzymes
Total CL = CLMetHep + CLMetBile + CLRenal + .......
2015-11-08 Lena Gustavsson
Hepatic clearance mechanisms
Hepatocyte
Canalicular membrane
Bile canaliculus
Sinusoidal membrane
Blood
Efflux transporters
Drug
Drug
Metabolite
Drug uptake transporters
Drug metabolising enzymes
2015-11-08 Lena Gustavsson
Uptake & Efflux Transporters SLCs Solute Carriers o OAT Organic Anion Transporter o OCT Organic Cation Transporter o OATP Organic Anion Transporting Polypeptides
ABC series ATP Binding Cassette transporters o MDR Multi Drug Resistance proteins o MRP Multi drug Resistance-like Proteins o White family Drosophila white eye pigment gene
Blood Hepatocyte 2015-11-08 Lena Gustavsson
From International Transporter Consortium Giacomini et al Nature Rev Drug Disc 2010 Modified marking EMA recommended transporters in blue
Drug transporters that influences drug disposition – clinical evidence
2015-11-08 Lena Gustavsson
Pravastatin
3-methylglutaryl-coenzyme A (HMG-CoA) reductase inhibitor; key enzyme in cholesterol synthesis Used for the management of hypercholesterolaemia The target is in the liver Has short t1/2 (~2h), low F (17%) but successful Has a good safety profile compared to other statins WHY?
O
O
OO
O O
O
H
Chiral
2015-11-08 Lena Gustavsson
Gut
Liver Systemic Circulation
Oral tablet
t
of t
Oral tablet
Disposition of Pravastatin
Kidney
Active secretion
Enterohepatic recirculation
MRP2
OATP1B1
Substrate for OATP1B1 MRP2
2015-11-08 Lena Gustavsson
Niemi, Clin.Pharm.Ther. 2010 Pasanen et al, Pharmacogenet. Genomics 2006 Search study N.Engl.J.Med. 2008
Simvastatin-induced myopathy increased due to increased plasma exposure - OATP1B1 polymorphism
2015-11-08 Lena Gustavsson
In vitro, animal In vivo, human
In vitro, human
In vivo, animal
Prediction of Human PK
In vitro/in vivo correlation
Allometric scaling Vss (dog, human PPB) Absorption (rat)
Scaling CL Absorption (Caco-2) Drug-drug interactions
Species differences
2015-11-08 Lena Gustavsson
DMPK in Drug Discovery and Development Agenda
Introduction Optimizing pharmacokinetic properties
Absorption & bioavailability Distribution Elimination – Clearance
Understanding clearance mechanisms Drug metabolism Drug transporters
Drug drug interactions and interindividual variability Summary: Drug discovery and development
30
Interindividual variability
Age
Sex
Genetics
Enzyme content
Liver weight
Organ blood flow
….
….
….
Nature Reviews Drug Discovery 6, 140-148 (February 2007) | doi:10.1038/nrd2173 http://www.simcyp.com
Interindividual variation in drug response
2015-11-08 Lena Gustavsson
CYP2D6 phenotypes in a Swedish population
2015-11-08 Lena Gustavsson
CYP2D6
Codeine Prodrug
Morphine Active metabolite
CYP2D6 Poor
metabolizer No formation of
morphine Lack of
analgesia
CYP2D6 Ultra-rapid metabolizer
Formation of morphine
Overdosing Adverse events
Codeine metabolism to morphine is metabolised by CYP2D6
Does the drug inhibit Cytochrome P450? Yes = Potential drug interactions!
P450
metabolite
P450
metabolite
2015-11-08 Lena Gustavsson
Drug drug interactions - CYP inhibition
Metabolism of terfenadine
OH
N
OH
OH
N
OH
COOH
Terfenadine Almost complete first pass extraction in man
Active Metabolite Responsible for efficacy in man
CYP3A4
2015-11-08 Lena Gustavsson
Ketoconazole
OO N
Cl
O
N
Cl
N
N
O
• Ketoconazole is an antifungal agent • Potent inhibitor of CYP3A4 • IC50 value <1µM • Antifungal dose is high (400mg twice
daily) • Circulating concentrations of
ketoconazole exceed IC50 for CYP3A4 inhibition
2015-11-08 Lena Gustavsson
OH
N
OH
OH
N
OH
COOH
CYP3A4
Low circulating concentrations of metabolite
High circulating concentrations of terfenadine
2015-11-08 Lena Gustavsson
Ketoconazole – terfenadine interaction
Implications of terfenadine – ketoconazole interaction
• High circulating concentrations of terfenadine • Potential to prolong QT interval of the ECG • Abnormal heart rhythm • Small numbers of patients go on to develop fatal
Torsade de Pointes (heart stops) • Led to withdrawal of terfenadine from the market • Increased questioning of Regulatory Authorities on QT
and DDIs
2015-11-08 Lena Gustavsson
•CYP1A2, 2B6, 2C8, 2C9, 2C19, 2D6, 3A4
• Human recombinant P450 enzymes
• IC50 = µM
• If IC50 < 10 µM – potential interaction • If [I]/Ki >0.1 - need to address in clinical study
• Discovery: Automated fluorescence based
• Development: LC-MS/MS analysis of metabolite
CYP
substrate
product
*
2015-11-08 Lena Gustavsson
CYP inhibition Recombinant enzymes Human liver microsomes
Induction of P450 enzymes Transcriptional regulation by nuclear hormone receptors
AhR
CYP3A CYP2C
PAH Hsp90
CAR
Arnt
RXR CYP1A
RXR
CYP2B
PXR, GR?
RXR
- NUCLEUS -
Rif
PB
Aryl hydrocarbon Receptor (AhR) • Ligands: Polyaromatic hydrocarbons, dioxins (TCDD), Omeprazol • Target genes: CYP1A1, CYP1A2, CYP1B1 Constitutive Androstane Receptor (CAR) • Ligands: Phenobarbital, CITCO • Target genes: CYP2B6
Pregnane X Receptor (PXR) • Ligands: Rifampin, Carbamazepine • Target genes: CYP3A4, CYP2C8, CYP2C9,
CYP2C19
• Cross-talk between nuclear hormone receptors (AhR, CAR, PXR, GR, Hnf4 etc)
DDI Risk Assessment
Perpetrator (inhibitor/inducer) • Enzyme/transporter IC50/Ki • Concentration plasma, liver,
intestine • Bound vs unbound • Time dependence • Also includes polymorphism
Victim (substrate) • Enzyme/transporter
phenotyping • Drug disposition e.g. clearance • Fraction of total elimination • Mechanistic understanding
Complex interactions – how to assess the risk? • Integration of data my modeling and simulation – PBPK • Iterative addition of new data • Other relevant information
- Co-medications - Biopharmaceutical Classification System etc
Physiology Based Pharmacokinetic (PBPK) Modelling and Simulation
2015-11-08 Lena Gustavsson
Jones and Rowland-Yeo 2013
PBPK modelling and simulation A DDI example – compound A
2015-11-08 Lena Gustavsson
Median % fm and fe in absence of inhibitor(s)
CYP3A4 Liver
CYP3A5 Liver
Renal
• Compound A is mainly metabolized by CYP3A4 • Assessment of DDI risks with compound A as a
”victim” • How will the plasma concentration change when co-
dosing a potent CYP3A4 inhibitor • How will the plasma concentration curve change
when co-dosing with a strong inducer of CYP3A4?
Prediction of the effect of a CYP3A4 inhibitor on the AUC of compound A
45
0.000E+00
050E+00
100E+00
150E+00
200E+00
250E+00
0 45 90 135 180 225 270 315 360 405 450
Syst
emic
Con
cent
ratio
n (n
g/m
L)
Time - Substrate (h)
CSys CSys with Interaction
200 mg itraconazole QD x 20 days 3 mg compound A on day 12 AUC ratio = 3.1
• Co-administration of itraconazole (potent CYP3A4 inhibitor) may result in a 3 fold increase in the AUC of compound A
• A clinical DDI study is required to investigate the effect in vivo
Simulation of plasma concentration curves – prediction of the effect of a CYP3A4 inducer
Compound A
• Co-administration of a strong CYP3A4 inducer, rifampicin, with compound A leads to a decrease in AUC to 20%
• High risk of loosing the pharmacological efficacy of compound A • Perform a clinical study to assess risk in vivo
Day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30AF34134 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3rif 600 600 600 600 600 600 600 600 600 600 600
Compound A
DMPK in Drug Discovery and Development Agenda
Introduction Optimizing pharmacokinetic properties
Absorption & bioavailability Distribution Elimination – Clearance
Understanding clearance mechanisms Drug metabolism Drug transporters
Drug drug interactions and interindividual variability Summary: Drug discovery and development
47
Understanding and predicting drug disposition – an iterative process of data integration
2015-11-08 Lena Gustavsson
Input data
Why study Drug Metabolism and PharmacoKinetics?
DRUG DISCOVERY Optimise compounds to get...
Good bioavailability – get to its target Appropriate duration (1-2 doses/day) Low potential for drug-drug interactions
…predicted to man Provide basics for understanding of
Toxicology Pharmacology
DRUG DEVELOPMENT Provide understanding of drug disposition
Preclinical animal species – tox coverage Human data
Assess the risk for drug drug interactions (DDI) Decrease risk for drug drug interactions in the clinic Impact on the design of clinical studies
Comply with guidelines from regulatory authorities
From Rowland and Tozer, 1995
2015-11-08 Lena Gustavsson
THANKS FOR LISTENING!
50