Taking Translation into the Clinic: Can We Improve the Probability of Success?

Transcription

1 Taking Translation into the Clinic: Can We Improve the Probability of Success? Janssen R&D/Cardiovascular & Metabolism Therapeutic Area Nicholas Di Prospero, MD, PhD Translational Medicine Group Disclaimer: the contents of the presentation are the views and opinions of the presenter and do not reflect those of Janssen Research and Development, LLC

2 Translational Medicine: a Pharmaceutical Perspective Bench to Bedside: Goal: to develop novel therapies for human disease New therapies must increase value to: Patients: improved efficacy (QOL), fewer side effects, easier administration, no drug-drug interactions Providers/payers: improved outcomes (less morbidity and mortality), improved compliance Target product profile (TPP) Translational Medicine: the interface between drug discovery and early clinical development Assess risk/benefit as early as possible 2

3 Traditional Drug Development Process Hypothesis testing Model efficacy, PK, toxicology Discovery Phase 1 Safety and tolerability PK Proof of concept Dose ranging Phase 2 Phase 3 Proof of efficacy clinically meaningful Proof of commercial concept NDA 3

4 The Drug Development Problem (Millions) Attrition: 9 NMEs = 1 drug to market Cost: >68% in clinical development (53% after phase 1) Time: ~13-15 years Paul, SM et al, Nat Rev Drug Discov. 9, (2010) 4

5 The Pharmaceutical Industry Response Discovery Early Development: Phase 1/2 Increase Spending in R&D Late Development: Phase 3 Approved Drug 5

6 The Outcome of the Response Number of approved NMEs relatively constant despite increasing amounts of R&D spending Increasing NME input has not increased the number of drug approvals Congressional Budget Office, Oct

7 Why did the response fail? Issues: Metric misalignment Discovery Unrealized technical expectations (genomics, models) Increased commercial competition Increased regulatory requirements Early Development: Phase 1/2 Late Development: Phase 3 Approved Drug The leaders of major corporations including pharmaceuticals have incorrectly assumed that R&D was scalable, could be industrialized, and could be driven by detailed metrics and automation Jean-Pierre Garnier 7

8 The Drug Development Problem Worsening Approved NME cost increasing at an unsustainable rate Net income in industry predicted to be flat R&D budget is a % of total income need to do more with less Munos, B, Nat Rev Drug Discov. 8, (2009) 8

9 The Drug Development Opportunity Use biomarkers to provide critical information earlier Identify winners/losers early ( Go/No-Go criteria relative to TPP) Limited investment to Phase 2 (~5%): Fail fast, fail cheap Increase resources/funds to evaluate other NME (maximize return on investment) 9

10 Biomarkers Tools to increase early clinical data and facilitate decision making

11 Definition of Biomarkers Biomarker* a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention Proximal biomarker a measurement that reflects interaction of drug with its target (ie, target engagement) Distal biomarker an endpoint that occurs in the pathophysiological cascade; change in the endpoint is expected to reflect a meaningful alteration in the disease process (ie, disease modifying) Surrogate endpoint* expected to predict clinical benefit (or harm, or lack of benefit) based on epidemiologic, therapeutic, pathophysiologic, or other scientific evidence (a biomarker intended to substitute for a clinical endpoint) Clinical endpoint* a characteristic or variable that reflects how a patient feels, functions, or survives Mechanistic/drug specific: protein levels, enzyme activity, etc Tumor marker (CEA) Viral load (HIV) Fasting plasma glucose Pulmonary function tests Hemoglobin A1c LDL cholesterol Blood pressure Morbidity (incidence of renal failure, incidence of MI, etc) Mortality *NIH Biomarkers Working Group, Clin Pharm Ther. 69(3), (2001)

12 Definition of Biomarkers Biomarker* a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention Proximal biomarker a measurement that reflects interaction of drug with its target (ie, target engagement, mode of action) Distal biomarker an endpoint that occurs in the pathophysiological cascade; change in the endpoint is expected to reflect a meaningful alteration in the disease process (ie, disease modifying) Surrogate endpoint* expected to predict clinical benefit (or harm, or lack of benefit) based on epidemiologic, therapeutic, pathophysiologic, or other scientific evidence (a biomarker intended to substitute for a clinical endpoint) Clinical endpoint* a characteristic or variable that reflects how a patient feels, functions, or survives Translational Mechanism/drug specific: protein levels, enzyme activity, etc Tumor marker Viral load Fasting plasma glucose Pulmonary function tests Hemoglobin A1c LDL cholesterol Blood pressure Morbidity (incidence of renal failure, incidence of MI, etc) Mortality *NIH Biomarkers Working Group, Clin Pharm Ther. 69(3), (2001)

13 Flow of Translational Biomarkers Biomarker Caveats: Valid (reproducible) Correlated Quantitative Multiple/consistent STOP Preclinical data Progress compound Proximal Distal Frank, R. Nat Rev Drug Discov. 2, (2003) 13

14 Biomarker Application In Early Development Standard safety and fit-for-purpose biomarkers Predict longer-term efficacy Evaluate against TPP Hypothesis testing Model efficacy, PK, toxicology Develop biomarker strategy to evaluate mechanism and safety Phase 1 Safety and tolerability PK Target Engagement Proof of pharmacology / concept Dose ranging Phase 2 Phase 3 Proof of efficacy - clinically meaningful Proof of commercial concept NDA Discovery Modify dose-escalation (real-time) Dose-selection for evaluating pharmacology (mechanism fully engaged) 14

15 Case Studies Biomarkers in early clinical development of treatments for type 2 diabetes mellitus

16 Type 2 Diabetes Mellitus (T2DM) Affects >300 million people worldwide (WHO)* 2-4x increased risk of CVD Deaths projected to double by 2030 Significant cause of renal failure, blindness, and lower limb amputations Despite current therapies, many patients not well-controlled Loss of efficacy over time (progressive β-cell failure) Side effect limitations (eg, hypoglycemia, GI effects, weight gain, loss BMD) Compliance issues New therapeutics (TPP): Need to complement existing therapies Improved efficacy and durability Fewer side effects * UKPDS, Lancet 352: (1998) 16

17 Pathogenesis of Hyperglycemia in Patients with T2DM Therapeutic Targets Incretin axis defects Excess glucagon Obesity / excess fat Pancreas Sulfonylureas GLP-1 agonists DPP-4 inhibitors Meglitinides Insulin Decreased -cell function Insulin Hepatic Glucose Production Liver Metformin GLP-1 agonists DPP-4 inhibitors Glucose Production Insulin Resistance Muscle PPARγ agonists Metformin Glucose Uptake Kidney Glucose re-absorption see review by: DeFronzo R, Diabetes 58: (2009) 17

18 Renal Glucose Filtration and Re-absorption Under normal conditions in healthy individuals, >97% filtered glucose is reabsorbed glomerulus proximal convoluted tubule distal convoluted tubule Renal glucose is reabsorbed through sodium-dependent transporters (SGLT): SGLT1 and SGLT2 (other SGLT?) SGLT2 is a high-capacity, low-affinity glucose transporter (S1 and S2 segments) SGLT1 is a low-capacity, high-affinity glucose transporter (S3 segment) glucose is filtered in the glomerulus SGLT2 (major) Glucose reabsorbed to systemic circulation SGLT1 (minor) loop of Henle urine collecting duct No glucose in urine Wright EM et al, Physiol Rev. 19: (2011)

19 Novel T2DM Target: SGLT2 Blockade Individuals with inherited SGLT2 deficiency have benign renal glucosuria Urinary glucose excretion (UGE) up to ~100 g/24 hrs Not associated with clinical sequela May have utility with other drugs given different MOA glucose is filtered in the glomerulus glomerulus proximal convoluted tubule SGLT2 SGLT1 X Less glucose reabsorbed distal convoluted tubule collecting duct loop of Henle urine Glucosuria

20 Canagliflozin: Animal Model Pharmacodynamics Canagliflozin (4b-3) selective inhibitor of SGLT2 Induces (dose-dependent) UGE in rats Reduced blood glucose 48% in hyperglycemic rat model Did not affect glucose levels in normoglycemic rats (see reference) After 4 weeks of treatment, reduced body weight and improved glucose handling was observed in both mouse and rat models of diabetes Nomura, S. et al., J Med Chem. 53, (2010)

21 Biomarkers in T2DM Using Canagliflozin Biomarker a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention Proximal biomarker a measurement that reflects interaction of drug with its target (ie, target engagement) UGE Distal biomarker an endpoint that occurs in the pathophysiological cascade; change in the endpoint is expected to reflect a meaningful alteration in the disease process (ie, disease modifying) Surrogate endpoint expected to predict clinical benefit (or harm, or lack of benefit) based on epidemiologic, therapeutic, pathophysiologic, or other scientific evidence (a biomarker intended to substitute for a clinical endpoint) Clinical endpoint a characteristic or variable that reflects how a patient feels, functions, or survives

22 Single Ascending Dose Study: Normal Subjects NHV: males (6+2/cohort) Canagliflozin dose dependently increased UGE to a maximum of ~70 g/day Glucose loss = calories/day Safety: No hypoglycemic events No meaningful change in plasma or urine electrolytes No change in 24-hr creatinine clearance Sha S. Diabetes, Obesity and Metabolism, 13: (2011)

23 Renal Glucose Threshold (RT G ) Urinary Glucose Excretion (g/d) Healthy Subjects Below RT G Minimal Glucosuria Occurs RT G Above RT G Glucosuria Occurs Renal threshold (RT G ) is the blood glucose level above which UGE occurs RT G calculated using UGE, PG, and GFR Plasma Glucose (mg/dl) Healthy individuals: RT G = ~180 mg/dl Nair S, J Clin Endocrinol Metab. 95:34-42 (2010) Rave K, Nephrol Dial Transplant. 21: (2006)

24 Single Ascending Dose Study: Normal Subjects Canagliflozin dose dependently decreased mean RT G maximally to ~60 mg/dl RT G may be more useful in subjects with T2DM with more variable PG and GFR Data supports target engagement and supports continued development Sha S. Diabetes, Obesity and Metabolism, 13: (2011)

25 Biomarkers in T2DM Using Canagliflozin Biomarker a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention Proximal biomarker a measurement that reflects interaction of drug with its target (ie, target engagement) UGE RT G Distal biomarker an endpoint that occurs in the pathophysiological cascade; change in the endpoint is expected to reflect a meaningful alteration in the disease process (ie, disease modifying) Surrogate endpoint expected to predict clinical benefit (or harm, or lack of benefit) based on epidemiologic, therapeutic, pathophysiologic, or other scientific evidence (a biomarker intended to substitute for a clinical endpoint) Clinical endpoint a characteristic or variable that reflects how a patient feels, functions, or survives

26 Multiple Ascending Dose Study: T2DM Subjects Double-blind, placebo-controlled, single and multiple (14 day) ascending dose study (16 cana + 4 pbo/cohort) Canagliflozin increased mean 24-hr UGE dose-dependently Sha, S. et al., American Diabetes Association Poster Session June 2010

27 Multiple Ascending Dose Study: T2DM Subjects RT G elevated in subjects with T2DM* Dose-dependent relationship more evident than with UGE Demonstrated that RT G lowered over 24-hrs at doses 200 mg/day Sha, S. et al., American Diabetes Association Poster Session June 2010 *Rahmoune H, Diabetes. 54: (2005)

28 Biomarkers in T2DM Using Canagliflozin Biomarker a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention Proximal biomarker a measurement that reflects interaction of drug with its target (ie, target engagement) UGE RT G Distal biomarker an endpoint that occurs in the pathophysiological cascade; change in the endpoint is expected to reflect a meaningful alteration in the disease process (ie, disease modifying) Surrogate endpoint expected to predict clinical benefit (or harm, or lack of benefit) based on epidemiologic, therapeutic, pathophysiologic, or other scientific evidence (a biomarker intended to substitute for a clinical endpoint) Clinical endpoint a characteristic or variable that reflects how a patient feels, functions, or survives Hemoglobin A1c (DCCT, 1993; UKPDS, 1998*) Morbidity (nephropathy, neuropathy, and retinopathy)

29 Biomarkers in T2DM Using Canagliflozin Biomarker a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention Proximal biomarker a measurement that reflects interaction of drug with its target (ie, target engagement) Distal biomarker an endpoint that occurs in the pathophysiological cascade; change in the endpoint is expected to reflect a meaningful alteration in the disease process (ie, disease modifying) Surrogate endpoint expected to predict clinical benefit (or harm, or lack of benefit) based on epidemiologic, therapeutic, pathophysiologic, or other scientific evidence (a biomarker intended to substitute for a clinical endpoint) Clinical endpoint a characteristic or variable that reflects how a patient feels, functions, or survives UGE RT G 24-hr weighted mean glucose Body weight Hemoglobin A1c (DCCT, 1993; UKPDS, 1998*) Morbidity (nephropathy, neuropathy, and retinopathy)

30 plasma glucose 24-hour Mean Plasma Glucose Approximate Plasma Glucose Profile PP Excursion (Area 2 = ½ PP Excursion 4) FPG ~ 4 hr Area 1 = FPG 24 From the DCCT study, Rohlfing found a relationship between WMG and HbA1c By substituting equations and using a best statistical fit model: ΔHbA1c = 0.03 x ΔWMG (mg/dl) time (hr) 24-hr WMG = (Glucose AUC)/24 = (FPG x 24) + (3 x ½ PP Excursion 4) Therefore, at steady state, ΔWMG of ~18 mg/dl would predict = ΔHbA1c of ~0.5% (>16 weeks Tx) Δ WMG = Δ FPG + (Δ PP Excursion/4) Rohlfing, C. et al., Diabetes care 25, (2002)

31 Multiple Ascending Dose Study (T2DM Subjects) Sha, S. et al., American Diabetes Association Poster Session June 2010

32 Multiple Ascending Dose Study:HbA1c Prediction Dose 24-h WMG (minus PBO) Predicted HbA1c 30 mg QD 100 mg QD 200 mg QD 400 mg QD 300 mg BID 5 mg/dl 0.1% 29 mg/dl 0.8 % 39 mg/dl 1.1 % 46 mg/dl 1.3 % 37 mg/dl 1.1 % Sha, S. et al., American Diabetes Association Poster Session June 2010

33 Multiple Ascending Dose Study: Weight Change Sha, S. et al., American Diabetes Association Poster Session June 2010

34 Biomarker Application In Early Development Electrolytes, creatinine, renal biomarkers Disease modifying activity: ΔWMG Meet Go and TPP criteria Hypothesis testing Model efficacy, PK, toxicology Develop biomarker strategy to evaluate mechanism and safety Phase 1 Safety and tolerability PK Target Engagement Proof of pharmacology / concept Dose ranging Phase 2 Phase 3 Proof of efficacy - clinically meaningful Proof of commercial concept NDA Discovery UGE and RT G revealed dose-dependent changes in NHV and T2DM 34

35 Phase 2b Data: T2DM Subjects on stable metformin dose ( 1500 mg/day) Rosenstock, J. et al., European Assoc. Study Diabetes Poster Session Sept 2010

36 Phase 2b Data: T2DM Subjects Rosenstock, J. et al., European Assoc. Study Diabetes Poster Session Sept 2010

37 Canagliflozin Biomarker Summary UGE revealed maximal activity at doses 200 mg QD in NHV RT G elevated in subjects with T2DM and contributes to hyperglycemia Canagliflozin significantly lowered RT G RT G defined dose-dependent and maximal 24-hr effects Maximal reduction of WMG at doses 200 mg QD in subjects with T2DM at steady-state (14 days) Predict HbA1c reduction Acceleration of program/resources to Phase 2b 37

38 Pathogenesis of Hyperglycemia in Patients with T2DM Therapeutic Targets Incretin axis defects Excess glucagon Obesity / excess fat Pancreas Sulfonylureas GLP-1 agonists DPP-4 inhibitors Meglitinides Insulin Decreased -cell function Insulin Hepatic Glucose Production Liver Metformin GLP-1 agonists DPP-4 inhibitors Glucose Production Insulin Resistance Muscle PPARγ agonists Metformin Glucose Uptake PPARγ agonists: weight gain, bone loss, edema, cancer risk Need for new insulin sensitizers 38

39 Novel Insulin Sensitizer: Drug JNJ PBO 3 mg/kg 10 mg/kg 30 mg/kg PBO 3 mg/kg 10 mg/kg 30 mg/kg High-Fat Diet Induced Obesity Mouse Model: JNJ - 10 mg/kg Animals develop obesity and insulin resistance Treatment dose-dependently reduced weight gain and fasting insulin levels Treatment improved glucose disposition after an OGTT 39

40 Novel Insulin Sensitizer: Drug JNJ Proximal Biomarkers*: 1. Receptor Antagonism 2. Endogenous Ligand Levels 3. Phenotypic Change Drug JNJ Distal Biomarkers: hr WMG 2. HOMA-IR Phenotypic Change Ligand *Could not validate proximal biomarkers in animal models due to species difference (ie, no dose selection guide relative to PD effects) 40

41 Single Ascending Dose Study: Normal Subjects Linear PK in NHV from 25mg to 2500 mg 40 Phenotypic Change Following Single Ascending Doses of Drug JNJ Maximal phenotypic change at 1250 mg Maximal antagonism and ligand changes also at 1250 mg Similar effects in 10 day MAD study Change from Predose Baseline (%) Placebo 25 mg 100 mg 250 mg 500 mg 1250 mg 2500 mg Time (hours post-dose)

42 LS Mean ± SE (mg/dl) Phase 2a Study hr WMG: Change from Baseline 20 Placebo PIO JNJ 250 JNJ day study in subjects with T2DM 4 arms: PBO, positive control, two doses JNJ based on preclinical PK and maximal clinical PD effect PIO and JNJ 250 effects: statistically significant Skewed data at JNJ 1000: non-parametric testing showed significant effect Treatment N LS Mean SD PBOsubtracted 95% CI P-value 28 day Δ Placebo Pioglitazone 30 mg QD (-47, -8) JNJ 250 mg BID (-40, -1) JNJ 1000 mg BID (-33, 6)

43 Phase 2a Study Post-hoc: 24-hr WMG mean change from baseline at Day 28 (parametric analysis of pooled data) Placebo Pioglitazone 30 mg QD JNJ- 250 and 1000 LS Mean Δ WAG (mg/dl) PBO subtracted LS Mean NA % CI (mg/dl) NA 47; 8 33; 0 p-value NA Predicted HbA1c lowering = ~0.5% (assuming no further timedependent changes) Could be acceptable to progress if demonstrated insulin sensitization 43

44 Phase 2a Study HOMA-IR: Change from Baseline LS Mean ± SE (mg/dl) Placebo PIO JNJ 250 JNJ 1000 HOMA-IR - Index of hepatic insulin resistance (based on FPG and FPI) - Moderate agreement with gold standard (clamp-measured) PIO is statistically significant No indication that JNJ is acting as an insulin sensitizer Treatment N LS Mean SD PBOsubtracted 95% CI P-value 28 day Δ Placebo Pioglitazone 30 mg QD (-3.6, 0) JNJ 250 mg BID (-2.0, 1.5) JNJ 1000 mg BID (-2.5, 1.1)

45 JNJ Biomarker Summary Demonstrated maximal proximal biomarker activity at dose 1000 mg, but no preclinical data available to confirm magnitude of biomarker activity required for glycemic effects Statistically significant reduction in WMG with JNJ Predicted HbA1c and lack of insulin sensitization did not meet TPP No-Go Early termination with limited at-risk spending allow for reallocation of money/resources. 45

46 Proven Utility Commercial success through the application of biomarkers: the sitagliptin program

47 Incretin The Incretin Axis Endogenous Glucoregulatory System Glucagon-Like Peptide-1 (GLP-1) Glucose-Dependent Insulinotropic Peptide (GIP) After food ingestion Meal Meal bolus Neural innervation GI tract L-cells Time (min) At -cell: GLP-1/GIP Release of INSULIN At -cell: GLP-1 Release of GLUCAGON INS / GLN INS Delayed gastric emptying Glucose production Glucose uptake Decreased post-meal (and fasting) glucose 47

48 Active GLP-1 DPP-4 and Biomarker Strategy Sitagliptin X Dipeptidyl peptidase 4 (DPP-4) GLP 9-39 inactive metabolite Proximal: 1) Target engagement: DPP-4 activity 2) Mechanism: GLP-1/GIP levels Distal: 1) OGTT: Insulin/Glucagon levels Glucose levels Meal Insulin Glucagon Glucose Time (min) t 1/2 2-3 minutes

49 Sitagliptin Biomarker and Clinical Development Preclinical Data: > 80% DPP-4 inhibition Provided maximum increase in active GLP-1 and GIP Provided maximum reduction in glucose AUC Suggested > 80% as appropriate PD target SAD/MAD Study established likely dose range 24 hr DPP-4 inhibition > 80% achieved at a dose of 100 mg once-daily Bergman, AJ. Clin Ther. 28, (2006)

50 Sitagliptin Biomarker and Clinical Development Phase 1: three-period, single dose cross-over study in patients with T2DM 200 mg 25 mg PBO active GLP-1 active GIP Herman, GA. J. Clin Endocrinol Metab. 91, (2006)

51 Plasma Glucose (mg/dl) Sitagliptin Biomarker and Clinical Development Phase 1: three-period, single dose cross-over study in patients with T2DM insulin active GLP-1 and GIP levels glucagon plasma glucose AUC Herman, GA. J. Clin Endocrinol Metab. 91, (2006)

52 Sitagliptin Summary Biomarker Caveats: Valid (reproducible) Correlated Quantitative Multiple/consistent DPP-4 Activity Preclinical data GLP-1 and GIP levels; Insulin and Glucagon Proximal Glucose AUC levels STOP Distal Net Result*: Phase 1b to 2b Saved 1.5 years 1 st to Market *Krishna, R. AAPS Journal. 10, (2008) 52

53 Summary Crisis in drug development: attrition and increasing costs Biomarkers in industry: tools to obtain information earlier Proximal biomarkers: Target engagement: enzyme activity, phenotypic changes, antagonism, etc. Mechanistic: protein levels (incretins), UGE, insulin, glucagon, HOMA-IR, etc. Distal biomarkers: disease modifying Glucose changes (OGTT, 24-hr WMG), body weight, food intake, etc. Safety: general and fit-for-purpose Facilitate decision making: Valid, correlated, multiple, quantitative, preclinical data GO/NO-GO criteria increase POS, reduce cost/time Biomarker challenges: Proximal biomarkers may be difficult/impractical Many diseases lack reliable distal biomarkers/surrogate endpoints Translational Medicine: industry perspective 53

54 Thank You