Application of PBPK to Drug Development in Rare Diseases - SMi ADEMT Conferece 2016
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Transcript of Application of PBPK to Drug Development in Rare Diseases - SMi ADEMT Conferece 2016
Takeda Development Centre Europe
Application of PBPK to Drug Development in Rare Diseases A Literature Review and Novel Application
11th Annual Conference and Exhibition on ADMET Monday 13th June 2016
Pau Aceves, MPharm, MSc. Senior Clinical Pharmacologist Quantitative Clinical Pharmacology Takeda Development Center Europe
Note: Any views expressed here are entirely my own and not of my employer or any pharmaceutical industry association
Takeda Development Centre Europe
Introduction Rare Diseases – Increasing Trend in Drug Development
• End of ‘blockbuster’ model. • Incentives from FDA, EMEA • High unmet medical need • Phase II to launch timelines of
orphan drugs is 3.9 years vs. 5.42 years for non-orphan
• Higher probability of regulatory success 93% vs. 88% for non-orphan (p<0.05) [Phillips, 2012]
• Increasing strategic choice to develop drugs for rare diseases
• Opportunity to re-purpose products to orphan indication
Jarvis LM. Orphans Find a Home. C&EN, 2013 / http://www.tandfonline.com/doi/pdf/10.1517/21678707.2013.752128
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Introduction Application of PBPK
Zhao P et al. Clin Pharmacol Ther. 2011 Feb;89(2):259-67 (2011)
PBPK class of models characterize ADME processes and their underlying biological and physiological drivers.
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Systems Approaches
Systems Pharmacology
PBPK
Introduction Rare Diseases – Challenges & Modelling Opportunities
• Many rare diseases are serious and life-threatening (frail patients)
• Lack of regulatory precedence • No established endpoints • Ethical challenges • Logistical trial challenges • Small number of patients • Heterogeneity in disease • Many diseases primarily affect
pediatric patients • Co-morbidities ie. hepatic/renal
impairment • Polypharmacy, paninhibitors etc.
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Application of PBPK to Drug Development in Rare Diseases
Part 1: A Literature Review [from publically available data sources]
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Case Study 1 [Public FDA Data, 2013] Ibrutinib (Janssen) for Mantle Cell Lymphoma (MCL)
• MCL accounts for about 6% of all non-Hodgkin lymphoma cases in the US
http://www.accessdata.fda.gov/drugsatfda_docs/nda/2013/205552Orig1s000ClinPharmR.pdf
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Case Study 2 [Public FDA Data, 2014] Eliglustat (Cerdelga, Genzyme) for Gaucher disease
• Lysosomal storage disorder caused by a hereditary deficiency in the enzyme glucocerebrosidase, which affects 6,000 people in the U.S.
• PBPK used to quantify the impact of CYP2D6 polymorphisms and DDIs • Predicted 12 DDI scenarios involving CYP2D6 EM, IM, and PM • Predicted UMs would not achieve adequate conc. for therapeutic effect
• FDA approved eliglustat for treatment of EMs, IMs, or PMs of CYP2D6.
http://www.accessdata.fda.gov/drugsatfda_docs/nda/2014/205494Orig1s000ClinPharmR.pdf
Takeda Development Centre Europe
• Inhibitor of DOT1L HMT with orphan status for MLL-r.
• MLL-r is an aggressive subtype of AML and acute ALL
• PBPK model predicted PK profiles of pinometostat after i.v. Infusion in adults
• Adult model transformed to pediatric setting based on ontogeny changes
• PBPK supported clinical trial design in rare populations and provided dosing recommendations for the ongoing pediatric trial
https://ash.confex.com/ash/2014/webprogram/Paper72544.html
Case Study 3 [Waters et al., 2013] Pinometostat (Epizyme) for Mantle Cell Lymphoma (MCL)
Adults
Children
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Literature Review Summary
Drug Name How did PBPK help in rare diseases?
Ibrutinib FDA accepted the use of PBPK predictions to fill in unknown clinical gaps to help better inform the product label.
Eliglustat Inform drug development and guide clinical practice in rare diseases by simulating combined intrinsic and extrinsic factors
Pinometostat Help study designs and dose selection in rare pediatric populations. Leverage adult data. First record of prospective use of PBPK for dose selection in an on going study
Others Exendin-(9-39): Example of PBPK/PD (systems pharmacology)
Sildenafil i.v.: Streamline the Clinical Pharmacology study requirements
Ruxolitinib : First example of DDI via more than one pathway (CYP), with EMEA acceptance and label recommendation
Macicentan, Ponatinib, rhPTH[1-84], etc.
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Application of PBPK to Drug Development in Rare Diseases
Part 2: A Novel Application [Unpublished Takeda]
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PBPK in Rare Diseases Novel Application Bronchiolitis Obliterans Syndrome (BOS)
• Majority of the patients are post-lung transplant (50-60%)
• Results in fibrotic disease of the lungs with fatal outcome
• Rapid onset and is diagnosed when significant decline in pulmonary function has occurred
• Median survival of 3-4 years post diagnosis (oncology-like)
• Lack of effective therapies (none ↓ disease progression)
• High unmet medical need • Takeda re-purposed compound from oncology pipeline
• 3 Ph 1 studies ongoing, over 200 oncology patients dosed • No HV data available. No renal/hepatic impairment studies done (no CLR
data), no relevant (to BOS) DDI studies conducted.
http://www.medscape.com/viewarticle/466350_7
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PBPK in Rare Diseases Novel Application BOS Phase 1b Multiple Raising Dose Study Design
0.2 mg
0.6 mg
1.2 mg 1.2 mg
2 mg
3 mg 3 mg
4 mg
5 mg
Maximum Tolerated Dose = 6 mg [oncology]
30X
Anticipated Therapeutic Range
Cohort 1 0.2 → 1.2 mg QD Cohort 2 1.2 → 3 mg QD Cohort 3 3 → 5 mg QD
• Three sequential cohorts • Escalation within and between cohorts • Very low recruitment 0.2 pt/site/month
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Oncology Population Reference • “Low potential for DDIs in
Oncology population” • Younger, no liver/renal impairment,
less con meds, prohibited strong inhibitors/ inducers in protocols
13
PBPK in Rare Diseases Novel Application BOS DDI Challenges
Median % fm and fe in absence of inhibitor(s)
CYP1A2 Liver
CYP2B6 Liver
CYP2C8 Liver
CYP2C9 Liver
CYP2C19 Liver
CYP2D6 Liver
CYP3A4 Liver
BOS a New Paradigm • Patients are on at least one NTI as per SoC immunosupression • Likely to present with prophylactic antibiotics/antifungals i.e. fluconazole • Patients with co-morbidities i.e. renal and hepatic impairment
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PBPK in Rare Diseases Novel Application Is There Case for PBPK Modelling in BOS? • Scarce (hard to find) patient population • Narrow therapeutic index (but high unmet medical need) • Intrinsic factor challenges
– Patients are frail -> can’t do many assessments i.e. Intense PK/PD – Hepatic impairment – Renal impairment
• Extrinsic factor challenges – Taking NTIs – Taking paninhibitos (variable prescribed doses) – Taking multiple other co-medications
• Could PBPK simulations help manage risk so as to enable recruitment BOS patients without putting any individual subject at undue risk?
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• A PBPK model developed in SimCYP • The final PBPK model consisted of:
– Full ADAM model – Full distribution model – rCYP data used to estimate the in
vivo contribution of CYPs – CLR was extrapolated using
allometry • Secondary model using the observed
human CL/F to derive CLint (retrograde) • Final model qualified by comparing the
simulated PK against the observed.
15
PBPK in Rare Diseases Novel Application PBPK Estimated DDI Potential – Model Overview
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PBPK in Rare Diseases Novel Application Simulation – Ketoconazole DDI (1 pathway Inhibited)
• Simulation confirms low DDI potential with strong CYP3A4 inhibitor
0
5
10
15
20
25
30
35
40
45
144 146 148 150 152 154 156 158 160 162 164 166 168
Syst
emic
Con
cent
ratio
n (n
g/m
L)
Time - Substrate (h)
Mean Plasma Conc. vs. Time Profiles in HVs With and Without Ketoconazole
CSys Mean CSys Mean with Interaction
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
0 24 48 72 96 120 144 168
Syst
emic
Con
cent
ratio
n (n
g/m
L)
Time - Inhibitor (h)
Mean Plasma Ketoconazole Plasma concentration over Time Following 400 mg QD for 7 Days
ISys Mean 1
1.4 x AUC
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0
1
2
3
4
5
6
7
8
9
0 24 48 72 96 120 144 168
Syst
emic
Con
cent
ratio
n (m
g/L)
Time (h)
Mean Plasma Fluconazole Plasma concentration over Time Following 200 mg QD for 7 Days
ISys 1
0
5
10
15
20
25
30
35
40
45
50
144 168
Syst
emic
Con
cent
ratio
n (m
g/L)
Time (h)
Mean Plasma Conc. vs. Time Profiles in HVs With and Without Fluconazole
CSys Csys with interaction
PBPK in Rare Diseases Novel Application Simulation – Fluconazole DDI (≥2 pathways Inhibited)
• Moderate DDI (i.e. ≥ 2 & < 5-fold AUC) when ≥2 pathways are perturbed
2.3 x AUC
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0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
144 150 156 162 168
Syst
emic
Con
cent
ratio
n (m
g/L)
Time - Substrate (h)
CSys - Pop 1 CSys - Pop 2
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
144 150 156 162 168
Syst
emic
Con
cent
ratio
n (m
g/L)
Time - Substrate (h)
CSys - Pop 1 CSys - Pop 2 CSys + Interaction - Pop 2
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
144 150 156 162 168
Syst
emic
Con
cent
ratio
n (m
g/L)
Time - Substrate (h)
CSys - HVs CSys - Pop 3
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
144 150 156 162 168
Syst
emic
Con
cent
ratio
n (m
g/L)
Time - Substrate (h)
CSys - HVs CSys - Pop 3 CSys + Interaction - Pop 3
Mean Plasma Conc. vs. Time Profiles in HVs (Pop 1) and in Subjects with Mild Liver Impairment (Pop 2)
With and Without Fluconazole
PBPK in Rare Diseases Novel Application PBPK Simulations – 1 Intrinsic Factor ± Fluconazole DDI
• Moderate increase in exposure (i.e. ≥ 2 & < 5-fold AUC) in mild renal or hepatically (CPA) impaired subjects with or without Fluconazole.
2 x AUC
3.4 x AUC 2 x
AUC
3.4 x AUC
Mean Plasma Conc. vs. Time Profiles in HVs (Pop 1) and in Subjects with Mild Renal Impairment (Pop 3)
With and Without Fluconazole
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PBPK in Rare Diseases Novel Application Worst Case Simulation – Justification for MRD Starting Dose
• Starting dose Cohort 1 = 0.2 mg • Max dose Cohort 1 = 1.2 • Oncology MTD = 6 mg • Worst-case scenario simulation
with multi-factors → 4-fold • HED at NOAEL = 1 mg • Starting dose acceptable
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
144 150 156 162 168
Syst
emic
Con
cent
ratio
n (m
g/L)
Time - Substrate (h)
Mean Plasma Conc. vs. Time Profiles in HVs (Pop 1) and in Subjects with Mild Liver Impairment With
Fluconazole and Diltiazem
CSys with interaction CSys - Pop 1
3.7 x AUC
0.2 mg
0.6 mg
1.2 mg
Cohort 1
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• Study Protocol and PBPK simulated DDI table presented to FDA as part of preIND Meeting Briefing Document
• Weak DDI when a single metabolic pathway is strongly inhibited • Moderate when ≥2 pathways are perturbed; or in mild hepatics
20
PBPK in Rare Diseases Novel Application PBPK Estimated DDI Potential – Trial Simulations
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Risk Assessment
• Strategy to assess risk for each individual • Ensures safeguards, while enabling recruitment in BOS • Requires additional ‘third-party’ unblinded PK analyist
PBPK Simulation (if needed)
Blinded CP reviews Con Meds & Liver
Function
Not expected
Close to MTD
Safety Alone
Level of Monitoring
Could AUC or Cmax be >
MTD at the 3rd dose?
Safety & PK
Exceeds Exclude!
PK Analysis
Interim blinded PK analysis by
team CP
Expedited PK analysed by
unblinded CP
Learn from preliminary results
Obs. PK > MTD withdraw
Stopping Rules
Obs. PK = MTD don’t escalate
Pred. next dose PK > MTD don’t
escalate
21 CP: Clinical Pharmacologist; Obs.: Observed; Pred.: Predicted; MTD: Maximum Tolerated dose
PBPK in Rare Diseases Novel Application PBPK DDI Risk Minimization Strategy for MRD Study
BOS
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How did PBPK help in BOS?
• Quantified the previously unknown DDI risk for BOS subjects • Simulations helped communicate the risk of DDI in BOS • Helped simulate “impossible trials” / “what if” scenarios • Influenced the study exclusion criteria • Appropriateness of the starting dose for First-in-BOS MRD • Supported the DDI strategy during FDA preIND meeting • Confirmed the low impact of CYP2C19 PM in Japanese • May have helped prevent exceeding MTD exposures [as the
project and MRD study did not go ahead] • PBPK was an enabling tool to safely allow the
investigation of a drug in a complex rare disease like BOS
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Acknowledgements
• Dr. Graham Scott, Senior Director at Takeda, London. • Dr. Paul Goldsmith , Scientific Director at Takeda, London. • Dr. Lachy McLean, Vice President, Experimental Medicine
Head Immunology at Takeda, California. • Dr. Chirag Patel, Scientific Director, Clinical Pharmacology at
Takeda, Boston. • Dr. Beverly Knight, Clinical Pharmacology Lead at Pfizer,
California. • Dr. Ruth Lock, Consultant in DMPK and Clinical
Pharmacology, London. • Nuria Bech MBA, Clinical Trials Study Leader at Hoffmann-
La Roche, UK.
Takeda Development Centre Europe
Thank You