1	Automation In Pharmaceutical Profiling Edward Kerns, Li Di, Susan Petusky, Susan Li, Donna Huryn
2	We Overlook Properties At Our Peril
3	Pharmaceuticals - Holistic Model
4	Changing Strategy in Discovery Aids Success
5	Barrier Model Assists Assay Selection
6	Barrier / Assay Model – In Vivo “ADME”
7	Barriers Also Restrict Drug Leads In Vitro
8	Lack of Information Can be Deadly
9	“ADME” Is Not Just In Vivo Anymore
10	Apply Property Data Throughout Discovery
11	Focus on Properties With Impact
12	Pharmaceutical Profiling: Mission Determine properties of compounds Physicochemical, metabolic, toxicologic, pharmacokinetic In silico, in vitro, in vivo Deliver timely data in useful format Apply data to benefit discovery Enhance lead selection and optimization Improve experiment planning & interpretation Multidisciplinary collaboration
13	Automation of Profiling Methods: Examples Integrity & Purity Solubility Permeability Stability CYP450 Inhibition
14	Integrity & Purity Right compound? HTS compound may have degraded or be misidentified Structure-Activity Relationships (SAR) Purity high? Impurities cause false activities and properties
15	Integrity & Purity Schematic: LC/UV/MS
16	Permeability Pass lipid membranes? Absorption – Intestinal Cells – Target tissue Cells – In vitro bioassay
17	Permeability Schematic: PAMPA
18	Permeability Profile: PAMPA Detail pION PSR4^®
19	PAMPA-BBB Assay PAMPA Modification for BBB Same instrumentation Brain lipid High correlation
20	Solubility Sufficient for GI absorption? Improve with structure modification Sufficient for bioassays? Not be soluble at test concentration or during preparation Correct activity (SAR) Soluble during reagent preparation?
21	Solubility Schematic
22	Solubility Profile: Detail
23	Stability Remain intact in discovery experiments? Activity or inactivity due to degradation Use conditions of activity experiments Does clearance reduce exposure? Modify structure to increase t_1/2
24	Stability: Relevant Discovery Conditions Metabolic 37^o C, 0.25 hr (Liver) Neutral pH 7.4, 37^o C, 24 hr (Assay Buffer) Plasma 37^o C, 4 hr (Blood) Acid pH 1.5, 37^o C, 4 hr (Stomach) Basic pH 8, 37^o C, 24 hr (Intestine) Heat pH 7.4, 45^o C, 24 hr (Storage) Light pH 7.4, HI light, 24 hr (Light)
25	Stability Schematic
26	Microsomal Incubation: Plate Detail
27	Stability Quantitation: FIA MS/MS
28	Stability Profile: FIA MS/MS
29	Stability Follow-up Mechanism of degradation Structure elucidation Rapid LC/MS/MS Synthesize stable structures
30	CYP450 Inhibition Inhibit CYP450 Isozyme(s)? Potential Drug-Drug interactions Safety concern Product removal Determine early to correct or drop series
31	CYP450 Inhibition: Experiment
32	CYP450 Inhibition Schematic
33	CYP450 Inhibition: Detail
34	Tactics for Properties in Discovery Discovery activity assay barriers Parallel activity and property evaluation Iterative structure redesign
35	Tactic: Understand Discovery Experiment Barriers
36	Tactic: Parallel Activity/Property Optimization
37	Tactic: Iterative Structure Redesign
38	Future Directions Enhanced assays (Q3) Quickness Quantity Quality Higher level automation BMS, Pfizer, Eli Lilly Enhanced property optimization Guidance for medicinal chemists Visualization & Consultation IV/IV correlation software
39	Conclusions Many discovery problems are property-related Property information enhances drug discovery Planning, Interpreting, Modifying, Prioritizing, Diagnosing Discovery scientists are learning to apply data Continued improvement in assays is important Automation enhances property assays Quickness, Quantity, Quality
40	Log P 2004 Symposium
41	Acknowledgements Wyeth Li Di Susan Petusky Susan Li Teresa Kleintop Mark Tischler Larry Mallis Nelson Huang Donna Huryn Oliver McConnell Guy Carter