drug discovery: Where are we now? How did we RSC February 2013

Transcription

1 The role of pharmacokinetics in drug discovery: Where are we now? How did we get th here??where are we going? Peter Webborn RSC February 2013

2 Why conduct PK studies in animals? The primary purpose of pre-clinical pharmacokinetic studies is to validate the tools that will be used to predict human kinetics 2

3 The role of pharmacokinetics in drug discovery Overview 1. The past - The 4 key steps that got us here 2. PK studies / data / tactics in a modern Drug Discovery programme 3. The future When free plasma concentrations don t tell us everything 3

4 How did we get here? Four key Developments 1. A Bioanalytical breakthrough 2. A Pharmacokinetic breakthrough 3. An Experimental breakthrough 4. A Conceptual change 4

5 Bioanalytical breakthrough LC-MS : The Thermospray interface Thermospray Source design, Blackley et al (1978) 5 Historically Gas chromatography (+derivatisation) Thin layer chromatography uv HPLC LC-MS (triple quadrupole) Key for analysis in drug discovery - Sensitive, Selective, Generic, Fast

6 Pharmacokinetic breakthrough The Introduction of clearance concepts The well stirred liver model CL Q. fub. CL Q fu int b.cl int 6

7 Pharmacokinetic breakthrough Clearance Six cornerstones of understanding The Clearance is the volume of blood cleared of drug per unit time 1. Clearance (not half-life) is the best measure of the efficiency of an elimination process 2. Clearance is the scaling factor between the iv dose you give and the AUC you get! 3. Clearance relates the rate of elimination (ng/min) to the substrate concentration (ng/ml) V = CL x S 4. Determined from CL = Dose/AUC iv (units of flow) 5. Is influenced by plasma protein binding and by blood flow 6. Intrinsic clearance or CL int - relates free drug concentration to the rate of elimination. 7

8 Experimental breakthrough Prediction of clearance from in vitro data Also: Rane A, Wilkinson GR and Shand DG, Prediction of hepatic extraction ratio from in vitro measurement of intrinsic clearance. J Pharrnacol Exp Ther 200: , Also No need to monitor appearance of metabolites, can be derived from loss of parent compound - and - From V/S not Vmax / Km 8

9 Conceptual breakthrough PK properties are predictable and amenable to optimisation Amounts and routes of elimination. Types of metabolism. Rates and affinities The compound The Chemical series SAR Dose and half-life Concentration and clearance Name and structure t Physicochemical properties Predictions don t always have to be right If you know what to expect you are more likely to spot the unexpected 9

10 Conceptual breakthrough - Example SAR - Ionisation and Volume of Distribution (Vss) logd Acid Base Neutral Vss tends to be acid < neutral < base Little influence of log D 7.4 Why do we see this?

11 Understand drug behaviour anticipating risks/issues Examples: Acidic drugs Acids Low permeability High Albumin affinity Poor Absorption risk Uptake Transporter substrate? Difficult to measure very high ppb Low Vss Renal CL Biliary CL Risk of short t½ Interspecies differences Interspecies differences Enterohepatic recirculation Intrinsic clearance must be low Interpretation of PK data Interspecies differences Interspecies differences Acyl Glucuronide reactivity risk Interspecies differences Glucuronidation Gut metabolism Interspecies differences Standard microsome assays no use

12 Where are we now? Why conduct PK studies in animals? - How to get most value from studies? - How to use optimally to progress projects? What have we learnt about experimental systems? - Accuracy, reproducibility - Optimal protocols - Cassette dosing Specific issues - Plasma protein binding - Formulation choice - Hepatic uptake transporters t 12

13 Why conduct PK studies in animals? The primary purpose of pre-clinical pharmacokinetic studies is to validate the tools that will be used to predict human kinetics 13

14 Why conduct PK studies in animals? Why do you want to know the... Clearance Half-life Bioavailability Volume of distribution*...of a compound in animals? 14

15 Assessing risk What you really want to know how well you were able to predict in vivo kinetics - Clearance - microsome /hepatocyte CL int - in silico - Volume of distribution Physicochemical properties Vssu across species - Bioavailability - Permeability /solubility 1 st pass metabolism I understand why this compound behaves the way it does Or I have no idea why this compound behaves the way it does If I can predict the kinetics in rat and dog, I have a reasonable case to ask you to believe I can predict human kinetics Or If I can t predict the kinetics in rat and dog, why should anyone think I can predict human kinetics 15

16 Predicting hepatic metabolic clearance In vitro scaling factors, fu P, R b, fu inc Lab specific correction Raw Scaled Predicted CLint CLint In vivo CLint Well stirred model (WSM) Predicted in vivo clearance Log(Q Qh*CL b )/(Qh-CL b) b A regression approach adjusts for systematic underpredictions observed when scaling in vitro CLint directly using the well stirred model, unbound fractions in blood and the in vitro matrix, and physiological scaling factors. Riley, McGinnity and Austin (2005) Log(CLint*SF*fu b/fu inc) ) This is commonly seen, and is not understood Correction factor is associated with the assay, not the compound

17 Setting criteria for an acceptable IVIVE. 1-sided 90% upper prediction limit 2-sided 80% prediction interval Log 10 Observed CL Lint in vivo (ml l/min/kg) Project 3 Project 1 Rat reference set Project 2 Having an optimised, standardised method puts the focus on the compound, not the scaling method Allows a common understanding of scaling and non-scaling compounds, and uncertainty in predictions. Log 10 Predicted CLint in vivo (ml/min/kg)

18 Project example - IVIV correlations RAT DOG Compounds in general scaled well in rats if LogD was kept below 3 Clarity of message / rules all can understand Allows focus on compound not scaling method

19 Effective use of PK data - focus on prediction validation Re-enforce positive project behaviours Predict - measure - learn Ensure correct use of in vitro data Build trust in in silico /in vitro systems Supporting prediction continuum: In silico - in vitro - PK - PKPD Understanding relative risk/uncertainty in extrapolations Efficient projects work in chemical series that are predictable Candidate drugs that are understood, are less risky 19

20 A Problem... Projects like having compounds tested Less beneficial behaviours Overriding belief that more data = better informed Rigid screening cascades More success in testing cascade validates molecules Projects like to demonstrate t progress It s our best compound to date let s get a full data package Consequences Many measurements that fit with predictions Many results we learn nothing from Much data gathered on incrementally better compounds Too much data to manage unfocussed optimisation strategies Management of study requests I ll run any test you are prepared to make a decision on 20

21 A PK screening strategy t Enabling the next decision i HI LO Number of studies Fold under- prediction of rat CL 21

22 The Power of Databases What can we learn from having PK data on 1000 s of compounds? SAR Cassette dosing studies Yielding a better understanding of Variability (inter-animal /inter-study) Protocol enhancement diagnosisi - N=3 v n=2 - First time point after iv bolus - Cannulated v non-cannulated animals 22

23 Renal Clearance Model Key descriptors: Lipophilicity Ability of compound to carry a positive charge

24 Use of Reference compounds to track assay performance Mean Clearance over 5 months CL ml/min/kg Non-cannulated Animals Cannulated Animals 0 06/05/ /06/ /08/ /10/ /11/ /01/2014 Date of study Reference compounds - Key advantage of cassette studies 24

25 How many animals to use? n=2 vs n=3 Impact on Vss estimates Vss within Count %ofvalues 0-20% % % 24 4 >40%

26 Impact on CL - 2min or 5 min first sample in AUC Change i % C ~Clearance (ml/min/kg) N=2750

27 Charnwood rat iv PK data 2min or 5 min first sample - Impact on CL % Change in AUC 73% of TV within 10% 83% of JVC within 10% 90% of TV within 20% 93% of JVC within 20% Clearance (ml/min/kg) N=2750

28 Specific Issues Plasma protein binding Formulation choice Transporters 28 Author 00 Month Year Set area descriptor Sub level 1

29 Only one of these statements is mechanistically correct... Because of the high plasma protein binding, free plasma concentrations will be very low Because of the high plasma protein binding, total plasma concentrations will be very high

30 Protein Binding - Don t let it trip you up! In vitro systems A closed system Free levels l driven by binding In vivo systems An open system Free levels l driven by elimination rate 30 Smith et al (2010) Nature Drug Discovery Dec;9(12):929-39

31 Choice of formulation The formulation should be appropriate for the conclusion that will be drawn from the study e.g. If you want to draw conclusions about likely human bioavailability /absorption, a clinically relevant formulation should be used. If you are assessing exposure prior to an efficacy study, ensure the formulation is tolerated for the duration of study + does not affect the PD endpoint 31

32 Hepatic Uptake transporters Uptake and efflux transporters have made understanding drug clearance more complicated.....some simple concepts are no longer valid Additivity of clearance - only applies to parallel l processes Transporter modelling requires barriers, serial processes and concentration gradients across membranes 32

33 Uptake is the rate-limiting step in the overall hepatic elimination of pravastatin at steady-state in rats Yamazaki, M., Akiyama, S., Nishigaki, R., Sugiyama, Y Pharmaceutical Research 13 (10), 1559 What is this really saying? Consider this example: in a 3 step reaction: For the formation of D (excretion into bile) - The rate determining step is always the slowest step in the process. For loss of A, the rate determining step is always k1 (plasma clearance) In this scenario: The rate of conversion of A to B depends on k1, k2 and k4. For poorly permeable compounds uptake is the rate determining step in the plasma clearance of active uptake substrates (because the back-rate is insignificant) 33

34 The future More Chemical diversity - Oligonucleotides - Extremely polar molecules - Intravenous antibiotics - Drug Antibody conjugates - Nanotechnology delivery systems Instrumentation t ti - New interfaces More sensitive - Higher throughput - No chromatography More reliance on predictions -Cost - Trust Moving beyond plasma - Mass Spectrometry Imaging (MSI) 34

35 When (free) plasma concentrations can t tell the whole story Free drug hypothesis It is the unbound drug that is in equilibrium with the target At equilibrium the free drug concentration in plasma and tissues are the same Problem areas Poorly perfused tissues Hypoxic regions Substrates for drug transporters Active / toxic metabolites Low target off-rates Local administration (eg lung, skin) Mass spectrometry Imaging Key points Drug / metabolite / biomarkers / metabonomics in tissues Not free drug Not all compounds/ not all studies a targeted approach Resolution is not at the cellular level ~100µm (10µm) Several rapidly evolving technologies (eg MALDI, DESI, LESA, SIMS) 35

36 Mass Spectrometry Imaging A unique insight into PK and PKPD MALDI MSI If you can get the same answer through tissue homogenisation Don t do MSI 36 Challenges Sensitivity and spatial resolution

37 Drug localisation as a driver of toxicity Polymyxin nephrotoxicity Aiding compound design PMB1 PMB AZ1 The technique can meet speed/volume requirements of discovery Programmes Need to translate results to man 37

38 Summary PK is a well established component of drug discovery There is now a big opportunity to exploit PK databases PK resource management remains a challenge The future will be PKPD and translation MSI is a rapidly developing technique with real potential ti to solve both efficacy and toxicity related problems 38