A NEW IN-VITRO MODEL TO PREDICT THE IN VIVO BEHAVIOR OF DRUGS BASED ON PARALLEL ARTIFICIAL MEMBRANE AND PLASMA

PROTEIN BINDING.

Roberto Bozic October 27-29, 2008

• oral absorption,

• brain uptake

• pharmacokinetic and metabolic properties

Lipophilicity :is expressed as partition or distribution coefficient (Log P or Log D) between octanol/aqueous phases. It is an important physicochemical parameter influencing :

Plasma Protein Binding:is a reversible association of a drug with the proteins of the plasma compartment of blood. Albumin is the major component of plasma proteins.PPB has influence on :

• determination of margin in safety assessment/toxicology studies

• the efficacy of a drug

• drug metabolism and pharmacokinetics

• drug-drug interactions (low influence)

• blood-brain barrier penetration

INTRODUCTION

INTRODUCTION

• Lipophilicity :• Shake flask (96 well plate)

• RP-HPLC techniques

• IAM (immobilized artificial membrane chromatography)

• Liposome chromatography

• pH-metric technique

• Plasma Protein Binding:• Equilibrium dialysis (gold std

method)

• Ultrafiltration

• Ultracentrifugation

• Chromatographic techniques (immobilized-albumin support coupled with HPLC)

Experimental methods:

PAMPA Basics

Lipid membrane

Acceptor

Filter Support

Donor

Acceptor

Donor

Membrane

DIFFUSION PROCESSASSEMBLY

PAMPA (Parallel Artificial Membrane Permeability Assay)

• to develop an in vitro model to investigate the influence of a lipidic membrane on the protein binding of drugs, and to obtain a rank ordering of them.

• To develop, compatibly with the needs of the modern drug discovery process, a highly automated process allowing the rapid turnaround of in vitro data using appropriate analytical, MS-based, methods to assess widely diverse compounds.

PURPOSE

ASSAY PRINCIPLE

- Assay based on the 96-well plate format.

- Human Serum Albumin (at physiological concentration, 600uM or 43 g/l in HBSS buffer pH=7.4); it is not immobilized on any surface.

- Hydrophobic filter membrane impregnated with 15% soy lecithin in n-dodecane.

Lipid membrane

Filter Support

Drug + HSA Drug-HSA

Drug

SAMPLE PREPARATION

Solution 10 μM of drug in HSA

Dilution with HSA solution

(4.3% of HSA in HBSS buffer with 25 mM Hepes, pH=7.4)

Vortex 30’’Centrifugation 3700 rpm, 15’

Incubation for 3 h, at 37 °C, under shaking .

LC-MS/MS analysis

It was purified by protein precipitation with MeCN

An aliquot of this solution was collected at different time (range time profile: t= 0÷20 h)

Solution 10 mM in DMSO of drug

The solution was placed in a filter plate coated with a lipidic membrane (15% soy lecithin in n-dodecane)

How much does lipidic membrane affect PPB?

0.010.020.030.040.050.0

60.070.080.090.0

100.0

0 200 400 600 800 1000 1200 1400

Time (min)

% d

rug

in m

embr

ane

(HS

A==

>mem

br.)

Compound PPB (%) (*) % membrane retention (**)

warfarin 99±1 0

propranolol 87±6 82

propranolo

warfarin

(*) J. G. Hardman, L. E. Limbird, A. G. Gilman. Book “Goodman & Gilman's The Pharmacological Basis of Therapeutics”,9th ed. (1996).

(**) data obtained experimentally by PAMPA assay

Distribution kinetics of warfarine and propranolol.

LC-MS/MS EQUIPMENTS

LC-system HP1100 binary pump

Autosampler CTC PAL

Mass spectrometer

Triple Quadrupole - API 2000 (PE-Sciex )

Source TIS

Mobile phase A 95% Ammonium formate 10 mM pH 4.0 + 5% acetonitrile

Mobile phase B 5% Ammonium formate 10 mM pH 4.0 + 95% acetonitrile

Software Analyst 1.4.1 and Automaton 1.3 (PE-Sciex)

ANALYTICAL PROCEDURE

MASS PARAMETERS OPTIMIZATION

SAMPLES ANALYSIS BY LC-MS/MS

• FIA (Flow injection Analysis)

• Software: Automaton version 1.3 (PE Sciex)

• run time: 1 min (the optimization of the mass parameters requires three consecutive runs, 3.0 min, for each compound)

• Inj. Vol.: 20 uL

• Isocratic Conditions (FIA): mobile phase 20%A / 80% B

• flow rate: 200µl/min

• Analytical Guard Column SB-C8 4.6 x 12.5 mm, 5-Micron (Zorbax - Agilent Technologies)

•Software: Analyst version 1.4.1 (PE Sciex)

•Inj. Vol.: 20 µL

•Gradient Conditions:

Step Tempo Flusso (L/min)

%A %B

0 0.0 1500 100 0

1 0.0 1500 0 100

2 0.15 1500 0 100

3 0.20 600 0 100

4 1.00 600 0 100

5 1.35 600 100 0

6 1.45 1500 100 0

ANALYTICAL PROCEDURE

MASS PARAMETERS OPTIMIZATED

Compound MW MRM Transition

Polarity DP (Declustering

Potential)

CE (Collision Energy)

WARFARIN 308.0 309163 + 45 22

CHLORPROMAZINE 318.1 318.958.1 + 65 70

TAMOXIFEN 371.1 372.2128.9 + 80 45

VINBLASTINE 810.2 811.5224.4 + 80 70

ASTEMIZOLE 458.2 459.3135 + 65 45

LOPERAMIDE 476.2 477.4266.2 + 65 30

IMIPRAMINE 280.2 28186 + 45 30

PROPRANOLOL 259.0 260.156.1 + 45 45

VERAPAMIL 454.0 455.3165.1 + 80 45

NICARDIPINE 479.2 480.191.1 + 10 70

CLOZAPINE 326.1 327269.9 + 45 30

NIFEDIPINA 346.1 347.3315.1 + 25 17

Method validation: precision

Intra-Assay

Time (min) 1200 180 84 21 10 1.5

1 Mean 80.1 62.0 44.1 25.0 28.5 16.1

S.D. 3.9 0.7 6.2 3.1 2.5 0.3

%CV 4.8 1.2 14.1 12.2 8.9 1.8

2 Mean 84.1 62.2 45.0 27 22.3 14.4

S.D. 1.7 5.3 4.0 1.3 0.7 1.7

%CV 2.1 8.2 8.8 4.9 3.0 11.7

Nicardipine

Time (min)

1200 180 84 21 10 1.5

Mean 82.1 63.3 44.6 25.6 25.4 15.3

S.D. 3.5 3.9 4.7 3.0 3.9 1.4

%CV 4.2 3.7 10.5 11.7 15.3 9.0

Inter-Assay

The model has been applied to 11 commercial drugs:

• with high Plasma Protein Binding

PPB data taken from literature source (*)

• with high membrane retention data obtained experimentally by PAMPA assay (Automated Parallel Artificial Membrane Permeability Assay)

• Solubility @ pH=7 > 30 uMsolubility data obtained experimentally

COMPOUND SELECTION

(*) J. G. Hardman, L. E. Limbird, A. G. Gilman. Book “Goodman & Gilman's The Pharmacological Basis of Therapeutics”, 9th ed. (1996).

COMPOUND SELECTIONCompounds PPB %(*) Membrane

retention %(**)

Solubility @ pH7(**) (uM)

WARFARIN 99 ±1 0 228CHLORPROMAZINE 95-98 96 185

TAMOXIFEN >98 93 67

VINBLASTINE 99 96 225

ASTEMIZOLE 97 99 187

LOPERAMIDE 97 95 225

IMIPRAMINE 90.1 ±1.4 94 225

PROPRANOLOL 87 ±6 82 225

VERAPAMIL 90 ±2 85 187

NICARDIPINE 98-99.5 96 41

CLOZAPINE >95 92 225

NIFEDIPINA 96 ±1 83 161

(*) J. G. Hardman, L. E. Limbird, A. G. Gilman. Book “Goodman & Gilman's The Pharmacological Basis of Therapeutics”,9th ed. (1996).(**) data obtained experimentally

NON-SPECIFIC BINDING EVALUATION

Compounds Non-specific Binding %

WARFARIN 6.2

CHLORPROMAZINE 0.2

TAMOXIFEN 0.5

VINBLASTINE 6.3

ASTEMIZOLE 2.4

LOPERAMIDE 1.0

IMIPRAMINE 1.0

PROPRANOLOL 1.9

VERAPAMIL 0.2

NICARDIPINE 2.7

CLOZAPINE 0.4

NIFEDIPINA 7.3

- The non-specific binding was investigated using the same procedure above described but in absence of lipidic membrane.

- Results show a negligible influence of non-specific binding. 

RESULTS AND DISCUSSION

0.010.020.030.040.050.060.070.080.090.0

100.0

0 500 1000 1500

Time (min)

% d

rug

in

mem

bra

ne

(HS

A=

=>

mem

br.

)

Vinblastine

Tamoxifen

Chlorpromazine

Loperamide

Astemizole

Propranolol

imipramine

Verapamil

Nicardipine

Clozapine

Nifedipine

Distribution kinetics of 11 commercial drugs.

- 11 commercial drugs: with high PPB and high membrane retention.

- These kinetic profiles show a different behavior of these 11 compounds and allowed their rank ordering.

RESULTS AND DISCUSSION

Distribution kinetics, comparison between acids, neutral and bases compounds.

- Acids showed a stronger protein binding than neutral or basic compounds and a lower trend to distribute on lipidic membrane.

- Neutral compound showed a higher trend to protein binding than bases.

- Bases resulted more absorbed on lipidic membrane

0.010.020.030.040.050.060.070.080.090.0

100.0

0 500 1000 1500

Time (min)

% d

rug

in m

embr

ane

(HS

A==

>mem

br.)

Vinblastine

Warfarine

Propranolol

imipramine

Verapamil

Nicardipine

Clozapine

Nifedipine

Compound pKa (a) species(c)

warfarin 4.82 a

nifedipine 2.69 (b) n

nicardipine 7.17 n

vinblastine 2.0; 11.4; 14,6 (b) n

propranolol 9.53 c

imipramine 9.51 c

verapamil 9.07 c

clozapine 7.9; 4.4 c

(a) Taken from ref 12. (b) predicted by ACDLABS. (c) Dominant species at physiological pH: n=neutral or anpholite, a=anion and c=cation.

Conclusions

• To try out basic compounds in plasma (instead of HSA), to evaluate the contribution of 1-acid glycoprotein on bases protein binding.

• To estimate the correlation between these data and pharmacokinetics properties of compounds.

• Further development of the method in terms of high throughput, particularly automation of sample preparation.

Next steps:

• A LC-MS/MS-based medium throughput method has been development.

• A good precision, compatible with drug discovery needs, was showed.

• The model was able to rank order compounds with similar properties

in term of PPB and Lipophilicity.

REFERENCES

(1) Kerns EH. J. Pharm. Sci. 2001, 90, 1838-1858(2) Borchardt RT, Kerns EH, Lipinski CA, Thakker DR, Wang B. Book “PharmaceuticalProfiling in Drug

Discovery for Lead Selection”, AAPS PRESS, Arington, VA, 2004, pp 127-182.(3) Caron G, Ermondi G, Lorenti M. J. Med. Chem. 2004, 47, 3949-3961.(4) Testa B, Crivori P, Reinst M, Carrupt PA. Book “The influence of Lipophilicity on the Pharmacokinetics

Behaviour of Drugs: Concepts and Examples”. Kluvert Academic Publisher: Norwell, MA, 2000, pp 179-211.(5) Borchardt RT, Kerns EH, Lipinski CA, Thakker DR, Wang B. Book “Pharmaceutical Profiling in Drug

Discovery for Lead Selection”, AAPS PRESS, Arington, VA, 2004, pp 327-336.(6) Testa , Kramer SD, Wunderli-Allespach H, Folkers G (Eds.). Book “Pharmacokinetic Profiling in Drug

Research”, VHCA, Zurich, 2006, pp 119-141.(7) Testa , Kramer SD, Wunderli-Allespach H, Folkers G (Eds.). Book “Pharmacokinetic Profiling in Drug

Research”, VHCA, Zurich, 2006, pp 165-202.(8) Banker MJ, Clark TH, Williams JA, J. Pharm. Sci. 2003, 92, 967-974. (9) Schuhmecher J, Buhner K, Witt-laido J Pharm Sci, 2000, 89, 1008-1021.(10) Loidl-Stahlhofen A, Hartmann T, Schottner M, Rohring C, Brodowsky H, Schmitt J, Keldenich J. Pharmaceutical Research, 2001, 18, 12, 1782-1788.(11) Elisabet Lazaro, Philip J. Lowe, Xavier Briand, Bernard Faller. J. Med. Chem. 2008, 51, 2009-2017.(12) Avdeef A., Book “Absorption and Drug Development”, Wiley-Interscience, a John Wiley & Sons, Inc.,

Publication, 2003. (13) Joel Griffith Hardman, Lee E. Limbird, Alfred G. Gilman. Book “Goodman & Gilman's The Pharmacological

Basis of Therapeutics”, 9th ed. (1996).

Back-up slidesBack-up slides

• Setup of analytical method• Setup of sample preparation conditions • Method validation (reference compounds,

reproducibility)• Non-specific binding evaluation• Method application on commercial drugs having

high PPB and high membrane retention• Method application on acidic, basic and neutral

compounds

SUMMARY

ASSAY PRINCIPLE

 

-----------------------------------------------------------Membrane

Drug + HSA Drug-HSA

Drug

Method validation: precision

Time (min) 1200 180 84 21 10 1.5

1 Mean 86.9 63.6 50.1 18.0 19.4 18.7

S.D. 0.5 3.1 6.4 1.0 3.7 2.0

%CV 0.5 4.9 12.8 5.7 18.9 10.6

2 Mean 86.4 64.2 52.2 24.4 18.7 18.2

S.D. 3.9 2.0 3.0 4.0 1.7 3.0

%CV 4.5 3.2 5.7 16.4 9.0 16.7

Time (min)

1200 180 84 21 10 1.5

Mean 86.6 63.9 51.2 21.8 18.0 18.4

S.D. 2.8 2.4 4.6 4.5 1.3 2.1

%CV 3.2 3.7 9.0 20.7 7.2 11.4

Intra-Assay Inter-Assay

Clozapine

PAMPA* (Parallel Artificial Membrane Permeability Assay)

* Kansy M et al. J. Med Chem, 1998, 41, 1007-1009

Artificial Membrane on Filter

Acceptor chamber

Donor chamber

PAMPALow Membrane Retention High Membrane Retention

Calcoli PAMPAIl calcolo della permeabilità si basa su una versione modificata della seguente equazione11:

 

M = è la quantità totale di farmaco nel sistema meno la quantità di campione persa nella membrana.

 CA (t) = è la concentrazione di farmaco nella cella accetrice al tempo t

CA (0) = è la concentrazione di farmaco nella cella accetrice al tempo 0

 VA = è il volume della cella accetrice

 VD = è il volume della cella donatrice

 Pe = è la permeabilità effettiva

 A = è l’area del filtro

 t = è il tempo di permeazione

 La ritenzione percentuale in membrana, %R, è calcolata mediante la seguente equazione:

 

 

M = è la quantità di farmaco in D (Donor), A (Acceptor) al tempo (0) e alla fine dell’esperimento (t).

tVV

AP

VVM

AVVM

AAD

e

ADADeCtC

)11

(

))0(()()(

100)(

%)0(

)()()0( xM

MMMR

D

tAtDD

P-ION In House

Analysis UV LC-MS/MS

Conc. 50 uM 10 uM

Membrane 2% Phosphatidylcholine in Dodecane

15% Soy Lecithin in Dodecane

Incubation Time 15h 4h

Temperature Room 37°C

Replicates 2-3 1

Reference External Internal

P-ION vs In House Model

Unionized drug molecule Ionized drug molecule

Least soluble

pKa / pH

LogP

Most soluble

Most lipophilic,most permeable

Least lipophilic, least permeable

SpH

Simple model for Passive Diffusion

Advantages & Disadvantages of PAMPA

• Advantages– Direct measurement of

passive diffusion (Clean number)

– Easy to set up– Higher Throughput– Highly Reproducible– Non cell based assay (less

labor intensive)– Another Partition Coefficient

(membrane retention)– Data more directly

amenable to in silico modeling

• Disadvantages– No passive paracellular

info– No active uptake