oral absorption, brain uptake pharmacokinetic and metabolic properties

1. A NEW IN-VITRO MODEL TO PREDICT THE IN VIVO BEHAVIOR OF DRUGS BASED ON PARALLEL ARTIFICIAL MEMBRANE AND PLASMA PROTEIN BINDING.Roberto BozicOctober 27-29, 2008

2. INTRODUCTION 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. Lipophilicity : is expressed as partition or distribution coefficient (Log P or Log D) between octanol/aqueous phases. It is an important physicochemical parameterinfluencing : • oral absorption, • brain uptake • pharmacokinetic and metabolic properties 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

3. 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) INTRODUCTION Experimental methods:

4. Acceptor Membrane Donor PAMPA Basics ASSEMBLY DIFFUSION PROCESS Acceptor Lipid membrane Filter Support Donor PAMPA (Parallel Artificial Membrane Permeability Assay)

5. PURPOSE • 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.

6. Drug + HSA Drug-HSA Lipid membrane Drug Filter Support 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.

7. SAMPLE PREPARATION Solution 10 mM in DMSO of drug Dilution with HSA solution (4.3% of HSA in HBSS buffer with 25 mM Hepes, pH=7.4) Solution 10 μM of drug in HSA Incubation for 3 h, at 37 °C, under shaking. The solution was placed in a filter plate coated with a lipidic membrane (15% soy lecithin in n-dodecane) An aliquot of this solution was collected at different time (range time profile: t= 0÷20 h) It was purified by protein precipitation with MeCN Vortex 30’’ Centrifugation 3700 rpm, 15’ LC-MS/MS analysis

8. How much does lipidic membrane affect PPB? Distribution kinetics of warfarine and propranolol. propranolo warfarin (*) J. G. Hardman, L. E. Limbird, A. G. Gilman. Book “Goodman & Gilman'sThe Pharmacological Basis of Therapeutics”,9th ed. (1996). (**) data obtained experimentally by PAMPA assay

9. LC-MS/MS EQUIPMENTS

10. ANALYTICAL PROCEDURE 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:

11. MASS PARAMETERS OPTIMIZATED

12. Method validation: precision Nicardipine Intra-Assay Inter-Assay

13. COMPOUND SELECTION 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 uM solubility data obtained experimentally (*) J. G. Hardman, L. E. Limbird, A. G. Gilman. Book “Goodman & Gilman'sThe Pharmacological Basis of Therapeutics”, 9th ed. (1996).

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

15. NON-SPECIFIC BINDING EVALUATION • 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.

16. RESULTS AND DISCUSSION 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.

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

18. Conclusions • 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. Next steps: • 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.

19. 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'sThe Pharmacological Basis of Therapeutics”, 9th ed. (1996).

20. Back-up slides

21. SUMMARY • 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

22. Drug + HSA Drug-HSA ASSAY PRINCIPLE -----------------------------------------------------------Membrane Drug

23. Method validation: precision Clozapine Intra-Assay Inter-Assay

24. Acceptor chamber Artificial Membrane on Filter Donor chamber PAMPA* (Parallel Artificial Membrane Permeability Assay) * Kansy M et al. J. Med Chem, 1998, 41, 1007-1009

25. PAMPA Low Membrane Retention High Membrane Retention

26. Calcoli PAMPA Il 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).

27. P-ION vs In House Model

28. Simple model for Passive Diffusion SpH Least soluble Most soluble Unionized drug molecule Ionized drug molecule pKa / pH LogP Least lipophilic, least permeable Most lipophilic, most permeable

29. 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 Advantages & Disadvantages of PAMPA