Modelling and simulation to help define MABEL and Starting dose in FIH studies

Transcription

1 Modelling and simulation to help define MABEL and Starting dose in FIH studies B Laurijssens, BEL Pharm Consulting. Steven W Martin, Pharmacometrics Group, Dept Clinical Pharmacology, Pfizer, Sandwich Labs, Kent, UK.

2 How should we select a starting dose? Base the starting dose on toxicology in animals Base the starting dose on Pharmacology in animals Base the starting dose on toxicology in animals and expected pharmacology in humans Slide 2

3 What is the right thing to do? 1. More accurately predict exposure in humans Scaling to man should use state of the art approaches PK scaling to humans 2. Prediction of Pharmacology in Humans (MABEL) Inter-species differences in binding, relative time (e.g. normalized to lifespan), signalling or pathway differences should be taken into account 3. Exposure-Toxicity relationship in animals Need to take account of NOAEL To estimate a safety margin All predictions are relative to observed toxicology Pharmacology usually evident prior to toxicology 4. Apply a Safety Margin/Factor to predicted dose To ensure the safety and wellbeing of subjects in the trial Slide 3

4 4. What drives the safety factor? Allow a safety adjustment based on level of risk 1. Safety Margin depends upon patient population In oncology patients willing to accept smaller safety margin than in healthy volunteers 2. Higher risk molecules would be those that: Are novel Are very potent Are agonists Have low species cross-reactivity Have steep dose-response curves Have a high degree of uncertainty in the calculation of the MABEL. 3. Additional consideration should be given to molecules with pleiotropic effect Biological cascade or cytokine release leading to amplification of an effect that might not be sufficiently controlled by a physiologic feedback mechanism (eg in the immune system or blood coagulation system) Slide 4

5 How do we determine safe starting dose? Pharmacokinetics 1. Prediction in humans Allometric Scaling Mechanistic Models PBPK Models Pharmacology 1. Prediction in humans Receptor occupancy (Kd) PK model + RO Mechanistic PKPD models 1. Calculate MABEL 2. Perform risk assessment of drug 3. Potentially apply safety factor 4. Quote FIH dose relative to NOAEL Slide 5

6 Modelling is vital when the concentration or doseresponse relationships are not straight forward 1. Time delays between pharmacokinetic and Pharmacodynamics Active metabolites Equilibrium delays (e.g. Oxycodone) indirect response models 2. Complex Pharmacodynamics Response Biological systems that develop tolerance or rebound (Benzodiazepine & exaggerated anxiety) 3. Complex Pharmacological System Transduction delays (e.g. AMG531, EPO, GNRH agonists) Drug response is the results of the interaction on many physiological pathways; e.g. inflammation, bone remodelling, Endocrine pathways. 4. Complexities in translation of PD effect across species Slide 6

7 When would we want to use Modelling to estimate MABEL? When the drug response takes time to develop and is not directly related to plasma concentrations. May be due to an active metabolite or equilibrium delay between plasma concentration and biophase (eg Oxycodone) causing Hysteresis. Dose Anticlockwise Hysteresis Cp Disposition Equilibrium Delay Ce Biophase Distribution Direct PD Response Pupil Diam (mm from Baseline) Elapsed time after dosing Lalovic Clin Pharm Ther Pharmacokinetics Pharmacodynamics Oxycodone ng/ml Slide 7

8 When would we want to use Modelling to estimate MABEL? When there is a lag time in drug response even after the drug reaches the biophase (e.g. AMG531, EPO, INFa) Dose PK AMG531 Cp Disposition Biosignal Transduction delays PD Response Time (days) PD Pk PD Slide 8

9 Outline: Starting Dose Calculation for FIH Predicting PK in humans Predicting human pharmacology (MABEL) Pulling it all together Working example Conclusions Slide 9

10 Methods for predicting human PK 1. Allometric scaling Simplest approach Very reliable for some small molecules and biotherapeutics (not TMD) Relates PK parameter to body weight 2. (Semi-)mechanistic-based PK models Takes into account species and disease state differences Better for extrapolation beyond dose-range Simcyp Simulator for small molecules 3. Physiologically-based PK models May be most accurate method to predict across species [not being covered in this course] Slide 10

11 1. Allometric Scaling for PK Prediction Allometric scaling relates a variable to size Variable: Vd, CL, Size: Body weight, maximum life span, brain weight, body surface area X = a * W b a: coefficient, b :allometric scaling exponent Log [Vss (ml)] Rhesus Monkey Mice Human Predicted Vss = 188*Wt(0.95) Usually performed on log-log scale Exponent tends to be similar across molecules CL: b= 0.75 [α BSA] Vd: b = 1 [α BW] t½: b = 0.25 [1-0.75] Log[Bwt (Kg)] Slide 11

12 Methods for predicting human PK 1. Allometric scaling Simplest approach Very reliable for some small molecules and biotherapeutics (not TMD) Relates PK parameter to body weight 2. (Semi-)mechanistic-based PK models More reliable for target-mediated disposition (membrane bound targets) Takes into account species and disease state differences Better for extrapolation beyond dose-range Simcyp Simulator for small molecules 3. Physiologically-based PK models May be most accurate method to predict across species [not being covered in this course] Slide 12

13 2. Mechanistic PK models: why are they needed? Serum Concentration (ng/ml) Membrane bound, internalizing antigen which binds MAb and undergoes target mediated disposition (TMD) Monkeys Time (Days) mg/kg 0.01 mg/kg 0.1 mg/kg 1.0 mg/kg 3.0 mg/kg Note: Non-linearity occurs where Cp >>> Kd (3x10-12 M) Concentration (ug/ml) Parameters Scaled to Humans Inter-compartmental Cl and VoL Production and elimination of antigen Human parameters Linear component cl and vo Predicted concentrations Observed concentrations Humans Predicted Human Observed Time (h) Slide 13

14 Outline: Starting Dose Calculation for FIH Predicting PK in humans Predicting human pharmacology (MABEL) Pulling it all together Working example Conclusions Slide 14

15 Methods used for predicting human pharmacology Predicted receptor occupancy (Kd) PK model coupled with receptor occupancy PK model coupled with ex vivo assay PK model as empirical PK/PD model in absence of biomarker or other in vivo PD data Mechanistic PKPD models Slide 15

16 Why is receptor occupancy not straight forward, especially for Biotherapeutics? 1. [MAb] is similar to target levels More complex equation needed 2. Limited distribution leads to C plasma >> C biophase Target C plasma needs to be much higher 3. On and off-rates at receptor quite slow for Mab Equilibrium calculations (e.g. Kd based) may over predict receptor occupancy. 4. Target (receptors or ligands) have inherent turnover rates 5. Binding of target by antibody changes kinetics of target (i.e. internalization) resulting in RO that is not predictable by pharmacological equilibrium approaches Slide 16

17 1. Estimating Receptor Occupancy: MAb Standard Receptor Occupancy Calculation for [MAb]>>target (Also termed the Kd of Duff calculation) What do we need to calculate RO Only K D Slide 17

18 2. Diffusion barrier to biophase Panitumumab (Anti-EGF MAb) Plasma concentration 100- fold >> Tissue Concentration Slide 18

19 3. Impact of changing Koff rate on RO calculations Ab Dose kel + PKPD Model (TMD) Ag Rin kdeg k on k off Ab Ag kint Duff Formula Dose Ab + Ag k on k off Ab Ag Slide 19

20 4. Impact of Target Turnover on RO Estimation At high receptor turnover rates, higher molar excess of MAb is required (Not accounted for in simple Kd model) Ab Dose kel + Dose Ab + PKPD Model (TMD) Ag Rin kdeg Duff Formula Ag k on k off k on k off Ab Ag kint Ab Ag Dose For 10% Receptor Occupancy PKPD prediction Duff formula Receptor turnover half- life (hours) Slide 20

21 4. Impact of target/receptor turnover: variability across species and target Slide 21

22 Methods used for predicting human pharmacology Predicted receptor occupancy (Kd) PK model coupled with receptor occupancy PK model coupled with ex vivo assay PK model as empirical PK/PD model in absence of biomarker or other in vivo PD data Mechanistic PKPD models Slide 22

23 4. Impact of target turnover: Impact on FIH dose selection Subcutaneous Dosing Slide 23

24 PKPD model-based approach to MABEL Establish a mechanism-based model in a relevant animal species to demonstrate the relationship between dose and RO. Determine RO and pharmacological effect relationship. Extrapolate model to humans using all relevant data (literature, in vitro human etc) Perform simulations considering both uncertainty in model parameters and in scale-up This approach should help select a more rational starting dose for FIH within the minimum anticipated biological effect level (MABEL) principles using all relevant literature and project level data Slide 24

25 100 What is an appropriate level of receptor occupancy for first dose? 90% RO may be appropriate for certain ANTAGONISTS Receptor Occupancy (%) ~10% RO may be more appropriate for most ANTAGONIST Will also depend on: target (novel? ) therapeutic area (oncology?) preclinical toxicology profile pharmacology (immunomodulatory?) Effector Mechanisms ADCC or ADC are NOT directly related to RO Dose (mg/kg) <10% RO is more appropriate for an AGONIST Slide 25

26 FIH Starting Dose Working Example Molecule: IgG1 Fusion Protein (Peptide) NPLATE (AMG531) Action: Agonist Mechanism: Promotes the viability and growth of megakaryocyte colonyforming cells, increasing platelet production Target: Binds the TPO receptor (peptide is distinctly different from TPO) Competitor info: Similar mechanism Genentech rhu-tpo, Amgen PEG-MGDF Background information: Molecular weight = 59,000 Da Platelets 2.3 x L TPO Receptors/ Platelet 56 (Baseline) TPO Receptors nm Kd = 14 nm Slide 26

27 AMG-531 Preclinical Data Available In vitro studies Platelet binding relative to TPO Rabbit, mice, rat, monkey, human MK-CFU proliferation assays Monkey and human In vivo studies PK FcRn knock-out and wild type (single dose) 1 mth tox in rat and monkey Slide 27

28 In vitro results: Platelet binding across species Competition assay with TPO number of receptors/platelet human : ~60 monkey : ~30 rat : ~6 rabbit : ~2 MK-CFU proliferation assays Similar results in both monkeys and humans for AMG531 and MGDF What can you conclude from this information? species specificity? AMG-531 competes at least as effectively as TPO, for the same binding sites on Mpl receptor. Monkeys appear the most similar to humans for binding and in proliferation assay, Also observed with first generation compounds Slide 28

29 In vivo results: AMG-531 PK in FcRn KO & WT Mice What can you conclude from this information? PK characteristics in wild type mouse? Slower Clearance and smaller volume of distribution Role of FcRn receptor? Fc receptor binding activates a salvage pathway that prolongs serum levels Slide 29

30 AMG531: Prediction of human PK Predicted human Vss = 122 ml/kg Predicted human CL = 7.8 ml/hr/kg 3 Human Predicted Log [Vss (ml)] Rhesus Monkey Mice Human Predicted Vss = 380*Wt(0.89) Log [ CL(mL/hr)] Mice Rhesus Monkey Cl = 9.53*Wt(0.97) Log[Bwt (Kg)] Log[Bwt (Kg)] Is allometric scaling appropriate for this type of molecule? Are the parameter estimates and exponents reasonable? Slide 30

31 AMG-531 In vivo pharmacology studies Mice Single dose 100 to 1000 µg/kg Platelet numbers increased 4.7 to 8.4 fold MABEL dose <10 µg/kg Monkeys Single dose Doses 2000 µg/kg Platelet numbers increased 2-fold Does this tell you anything about species sensitivity? Mice appear to be a more sensitive species than monkeys Slide 31

32 Toxicology Information Monkeys Dosing 3x week (4 wks) Doses 500, 1000 and 5000 µg/kg NOAEL = 5000 µg/kg Platelet count increased 4-6- fold at NOAEL Rats Dosing 3x/week (4 weeks) Doses 10, 30 and 100 µg/kg LOAEL = 10 µg/kg myelofibrosis of bone marrow Platelet count markedly (>6-fold) increased at all doses Does this tell you anything about species sensitivity? Rodents appear to be the more sensitive species Slide 32

33 PKPD Approach: Platelet kinetics PKPD Model developed for PEG-rHu-MGDF Effects of PEG-rHu-MGDF PKPD model of platelet kinetics was used to predict the effects of AMG-531 in humans allometric scaling of PK in vitro potency scaling Predicted no effect dose in humans for AMG-531 was 10 µg/kg Slide 33

34 Pharmacology Approach Estimate Receptor Occupancy DRUG AMG Total Number TPO Receptors On Platelets Kd = 14 nm Drug-receptor Complex Dose 0.01 mg/kg (i.e. 10ug/kg) Mwt ~ 59,000 Plasma Volume 2.5 L AMG-531 = nm (IV Cmax ) Platelets 2.3 x L TPO Receptors/ Platelet = 56 (Baseline) TPO Receptors = nm AMG-531-TPO Receptor Complex nm (At Equilibrium) 25% Receptor Occupancy See Excel Spreadsheet Calculator to estimate receptor occupancy Slide 34

35 Predicting Pharmacology in Humans for AMG-531 Rats LOAEL = 10 µg/kg Cmax = ~200 ng/ml AUC = ~80 ng*hr/ml Human Equivalent (HED) Dose 1.61 µg/kg Cmax# = ~32 ng/ml AUC# = ~2.1 ng*hr/ml Cynomolgus Monkeys NOAEL = 5000 µg/kg Cmax = ~100 µg/ml AUC = ~ 1000 µg*hr/ml Human Equivalent (HED) Dose 1600 µg/kg Cmax# = ~ 32 µg/ml AUC# = ~ 230 µg*hr/ml Apply Safety Margin 10 fold MRSD = 0.16 µg/kg Apply Safety Margin 10 fold MRSD = 160 µg/kg # Based upon Allometric Scaling of CL and Volume (slide 40) Based upon FDA Guidance on HED; divide animal dose by 6.2 rat, 3.1 Slide monkey. 35

36 AMG-531 What is a safe starting dose? Which species do you think is most predictive of human rat, monkey? How should we predict human PK? Allometric or TMD PKPD modelling? Compare the starting doses from various methods? 1% RO = Dose of 0.3 ug/kg PKPD Approach = Dose 10 ug/kg HED Approach Rat = Dose 0.16 ug/kg HED Approach Cyno = Dose = 160 ug/kg What should be the FIH starting dose? What is the predicted pharmacological response at that dose? Slide 36

37 Decisions made prior to FIH for AMG-531 Which species is most predictive of human monkey (based upon MGDF scaling) How should we predict human PK? Allometric (provided good prediction) What is a safe starting dose for FIH? 10 µg/kg based upon mechanistic PKPD model What is the predicted pharmacological response at that dose? 10 µg/kg no effect dose Slide 37

38 AMG-531 FIH results: First cohort Unanticipated Robust Pharmacology Platelet Count (10 9 /L)1000 Placebo First Dose 10µg/kg Normal Study Day Slide 38

39 AMG531: Findings after 1 st Cohort New In vitro binding studies showed human cell lines to have a much higher affinity (slower off rate) than monkeys If they had more robust Koff data then FIH starting dose would have been 0.3 ug/kg Subsequent in vitro studies indicate order of species sensitivity Mouse>Human>Rat>Monkey>Rabbit Slide 39

40 Pharmacology Approach Estimate Receptor Occupancy DRUG AMG Total Number TPO Receptors On Platelets Kd = 0.6 nm Drug-receptor Complex Dose 0.01 mg/kg (i.e. 10ug/kg) Mwt ~ 59,000 Plasma Volume 2.5 L AMG-531 = 1.87 nm (IV Cmax ) Platelets 2.3 x L TPO Receptors/ Platelet = 56 (Baseline) TPO Receptors = nm AMG-531-TPO Receptor Complex nm (At Equilibrium) 89% Receptor Occupancy Additional in vitro study indicates AMG-531 binds more tightly (slower off rate) to human cells than monkey to give a lower Kd (0.6 vs 14 nm) Slide 40

41 AMG531: FIH dose de-escalation Bing Wang et al. Clinical Pharmacology & Therapeutics (6) Slide 41

42 Conclusions Know your Human! Pharmacology Be clear and transparent about the underlying assumptions quality of the data risks strength of assumptions made Manage uncertainty Sensitivity analysis, worst case scenarios, and robust designs safety margins, proper monitoring can address these Merely calculating RO is not sufficient you need to predict a pharmacological response in humans to estimate MABEL. Case by Case Try the new web based software from Implore Foal Labs Slide 42

43 Slide 43

44 Steady-State calculation of 1:1 binding of a drug to a target Based on A+B C where Kd = [A][B]/[C] Assuming i) no turnover (elimination) of target, ii) no loss of drug to distribution & clearance and iii) instantaneous equilibrium This is useful for estimation of a dose of a biotech product in man for the first minutes to hours of exposure, before significant distribution and clearance of drug take place. To take distribution, clearance and competing entities into account requires far more extensive differential equation systems together with supporting biological literature. Philip Lowe, June Inputs Dose 0,0003 mg/kg Kd 0,6 nm (Initial estimate nm) Molecular wt g/mole (IgG) Cell count Bas 2,00E+11 cells per L blood (Hospital laboratory ranges) Target per cell 59 receptors per cell Body weight 70 kg Volume plasm 2,5 L 2.5 L for standard 70 kg human Volume blood 5 L 5 L for standard 70 kg human Avogadro 6,02E+23 per mole Calculations: with expression of receptor Dose_nmoles 0,36 nmoles TDc 0,14 nm 2,8 mg/l or µg/ml Total Drug concentration TTc 0,039 nm Total Target concentration DTc 0,007 nm Drug-Target complex concentration Occupancy 18,4 percent Quick calculation: assume receptor Occupancy 19,2 percent