Testing for Biological Activity

1. Testing for Biological Activity Or: How I learnt to stop worrying and love the data……

2. Why is this stuff important? “siRNA studies have validated this target as critical for disease progression. The candidate compound shows clear functional and selective antagonism at low nanomolar doses and no intrinsic agonist efficacy. Furthermore, the compound shows negative AMES and hERG and does not display any wide binding liabilities. In vitro and predicited ADMET parameters were deemed to be acceptable The compound demonstrated statistically significant efficacy in the mouse wet dog shake test and the mechanical allodynia model and showed no detrimental effects in an Irwin screen. Pharmacokinetic parameters appear acceptable for the desired route of administration. We therefore recommend this derivative for clinical development.”

3. Example Screening Cascade Primary Target IC50 < 10 nM In vitro ADME Selectivity vs related targets > 10 fold In vitro ADME Cellular assay < 100 nM In vivo model < 50 mg/kg p.o. Pre-clinical evaluation

4. Recap. • Only molecules of the right shape can bind to a target protein (“Lock and key”) Drug Drug bound to target Target + = + =

5. Types of targets Enzymes Receptors Ion Channels

6. Simple in vitro assays Binding assays most commonly used procedures for primary screening of GPCRs/ion channels/carrier systems Less common for enzymes Functional assays Measures response produced by receptor/ion channel/enzyme on stimulation Antagonists assessed vs agonists

7. Binding Assays • Utilises the binding of a molecule (ligand) with high affinity (stickiness) for target (eg receptor) • Ligand traditionally tracked by labelling with a radioactive atom (usually tritium (3-H) or 125-Iodine) during synthesis. • Now also use fluorescent “tags” • Ligands must bind competitively (as opposed to irreversibly) • Measures the affinity of a test compound for the target

8. A simple binding assay compound 3H-Ligand 3H-Ligand 3H-Ligand compound compound 3H-Ligand compound 3H-Ligand 3H-Ligand

9. What does a binding assay tell us? • Affinity (Ki)- The concentration of molecule which occupies 50% of the available receptors in the absence of any other ligand • NB: for Ki a low number such as 1 nM is “high” affinity • a high number such as 1000 nM is “low” affinity by this definition • An alternative format for affinities is pKi, the -ve Log10 of Ki • In contrast, to Ki, for pKi a high number 9.0 (= 1 nM) indicates “high” affinity • A low number such as 6.0 ( = 1 mM) is lower affinity. • The affinity measure should be relatively consistent between and within binding assays.

10. Binding assays are usually run in “competition” mode The IC50 is the conc’n of a molecule which occupies 50% of available binding sites in the presence of competing radioligand. IC50 Concentration (M) • Compound A • Compound B

11. Caveats with Binding Assays • Requires a selective, high affinity, labelled ligand • may not have been identified in an early stage project • Highly simplified system • Physiological relevance? • Concentration dependence? • Does inhibition correlate with reduction in target activity? • Especially relevant for GPCRs and other signalling processes

12. Binding assays most commonly used procedures for primary screening of GPCRs/ion channels/carrier systems Less common for enzymes Functional assays For enzymes, measures inhibition of activity Measures response produced by receptor/ion channel on stimulation Antagonists assessed vs agonists Simple in vitro assays

13. Enzyme assays Determines the IC50 of inhibition of an enzymatic conversion Need to carefully minimise inter-assay variability Protein batches may have different activity levels Protein and substrate concentration alters IC50 value of inhibitor Incubation time critical if inhibitors are time dependent

14. Interaction with receptors and ion channels • Two distinct types of interaction • Agonists • Antagonists • Both can have full or partial “efficacy”

15. Agonists Agonist

16. Partial Agonists

17. “Full” or “partial” agonists 100 90% efficacy 80 60% efficacy 60 % Normal activity 40 20 0 -12 -11 -10 -9 -8 -7 -6 -5 -4 Log Concentration (M)

18. Inverse Agonists • Can also have partial inverse agonists • “damp down” active receptors Inverse Agonist

19. Antagonists Antagonist

20. Problems with native tissue assays • Low levels of receptor or target expression usual • Low signal to noise ratio = variability and difficulties of interpretation • Needs preparation • Often non-human

21. A solution -transfected cell systems • Cell DNA is transfected with DNA coding for the human target receptor • A cell line expressing relatively high amounts of human target receptor protein is selected and cultured • The high expression of receptor gives an optimal signal to noise (background) ratio and facilitates reproducibility • Can grow cells in plates for automated equipment

22. Functional Screens for profiling agonists • Functional assay identifies agonist activity or lack of it (antagonist) • Functional assays define efficacy(the relative ability of an agonist to stimulate a receptor compared with the natural agonist whose efficacy is assumed to be 100%) • Functional assays also define potency- the effective concentration of an agonist which elicits 50% of the maximum response of the native agonist (EC50)

23. Why not just use a functional assay? • Functional assays give potency and efficacy measures, why do we need binding assays as well? • The functional response to a molecule is dependent on the absolute number of receptors (the “expression level”) • The more receptors, the higher the response per quantity of drug and vice versa

24. Receptor Reserve Ligand 1x response Ligand Ligand Ligand 2x response Ligand Ligand Agonist potency and efficacy values are therefore subject to variability within and between different functional tests.

25. Assay Limitations • All these primary assays (functional and binding) have several common liabilities • Require a “standard” • Interference from test compounds • Biological relevance?

26. Removing the problem: Label-free screening. Optical detection unit Light-source Optical detection unit Light-source Polarized light Reflected light Prism Polarized light Reflected light Sensor surface with gold film Prism Sensor surface with gold film Flow channel Sample Flow channel Sample • Removes the issues around compound interference • Directly measures binding of compound and target • Most common technique is Surface Plasmon Resonance (SPR)

27. Cellular assays • For intracellular targets, once activity is confirmed, activity in cells needs to be evaluated and confirmed. • More complex system • Non-specific binding? • Off-target effects? • Cell assays are highly dependent upon the nature of the target • Often much more laborious and complex to establish and validate • Generally, the more specific the readout, the more complex the assay

28. Cellular Measurements • Cell death/survival • Colony growth • Nutrient consumption • Shape/size • Protein levels – the Western Blot • Highly useful for tracking cellular signalling, control and communication events

29. Western Blotting pS473 PKB 0 .03 .1 .3 1 3 [Drug] (mM) pS335/336 S6 S6 0 .12 .25 .5 1 2 4 8 [Drug] (nM) • IMPORTANT POINT: all assays MUST have controls! separate protein on gel

30. High Content Screening • “Gives more confidence that a compound works, not just that it’s potent”

31. Assay Limitations • All these primary assays have several common liabilities • Require a “standard” • Interference from test compounds • Biological relevance? • Cellular assays also have problems • Non–natural cells; Truly representative? • Cell penetration?

32. Testing for Drug Likeness • In vitro DMPK – • Microsomal/hepatocyte turnover • Membrane permeability • CYP450 inhibition • Pharmacokinetics – Assesses drug exposure • The effect of the drug on the body • Determination of plasma levels at different time points • Determination of brain / tissue levels etc • Pharmacodynamics – Assesses activity at target • The effect of the body on the drug • In Vivo binding / Ex Vivo binding • In Vivo functional assay eg surrogate markers, fMRI, behavioural response

33. Toxicology • Genetic Toxicity • Ames • Wide-binding screens • Off-target effects • Behavioural assessment • CNS liabilities • If these look OK, we can see if the drug actually works…..

34. In Vivo efficacy testing • Several key questions: • Why? • Which species • Which models work well? • And, importantly, which don’t……

35. Why do we still rely on in vivo models? • “……because for all we know about biochemistry, about physiology and about biology in general, living systems are still far too complex for us to model.” • Isolated enzymes cannot provide a model for what can happen in a single real cell. • And no culture of cells can recapitulate what goes on in a real organism. • The signalling, the feedback loops, the interconnectedness of these systems is (so far) too much for us to predict.

36. What can the models tell us? • Exposure • Efficacy and Toxicity • And, critically, the relationship between them • But which data can we really trust?

37. In Vivo Models: The Good • Cardiovascular • Diabetes (up to a point) • Antibacterials/Antifungals • Asthma • Osteoporosis • Hormonal Imbalances • Parkinson’s Disease (?)

38. In Vivo Models: The Bad • Obesity • COPD • Pain

39. In Vivo Models: The Ugly • Most Neurodegenerative/cognitive disorders • Oncology

40. An example: Oncology models • Satraplatin, GPC Biotech • Axitinib, Pfizer: Pfizer shuttered a late-stage trial of a pancreatic cancer drug after its data monitoring board determined that the treatment--designed to shrink tumors--had failed to extend patients' survival time. "These results were disappointing, given the trend toward prolonged survival seen in a Phase II study of axitinib in this extremely difficult-to-treat patient population," Mace Rothenberg, head of Pfizer's oncology business unit, said in a statement. The randomized, double-blind satraplatin phase III registration trial (SPARC) has failed to meet its primary endpoint of overall survival in patients with hormone-refractory prostate cancer, Pharmion Corporation and GPC Biotech AG said in a news release "We are extremely disappointed with the findings," said Bernd R. Seizinger, MD, PhD, chief executive officer of GPC Biotech.

41. An example: Oncology models • We can cure tumours in mice; why not in humans? • Tumour load unrepresentative • Heterogeneity/genetic complexity • Models driven by single/limited genetic factor • Patient tumour variability/evolution • Metabolism/Toxicology

42. Summary • Good and bad data: • a few examples from the literature……

43. A caveat: tracking data down a screening cascade.

44. A caveat: tracking data down a screening cascade.

45. A caveat: tracking data down a screening cascade.

46. By comparison….

47. Contrast with the cellular data:

48. One final note…. • All this data, however robust, can never replace or predict clinical data. • Drug discovery programmes MUST be clinically testable….. • Early clinician input and opinion should be, but often is not, integrated into the process