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1 Introduction to Human Challenge Models (HCMs) for Respiratory Viruses and the Application of Quantitative Pharmacology to Enhance Drug Development Craig Rayner PharmD MBA 22 nd August 2016

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Page 1: Introduction to Human Challenge Models (HCMs) for ... · immunological status can be tightly controlled to isolate drug effect • PK, PD (Virologic, clinical, biomarker) and safety

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Introduction to Human Challenge Models (HCMs) for Respiratory Viruses and the Application of Quantitative Pharmacology to Enhance Drug Development

Craig Rayner PharmD MBA

22nd August 2016

Page 2: Introduction to Human Challenge Models (HCMs) for ... · immunological status can be tightly controlled to isolate drug effect • PK, PD (Virologic, clinical, biomarker) and safety

Disclosures and Acknowledgements

The information is derived from publically available sources and key

material for further reading is provided. Any representation/interpretation

does not necessarily reflect perspectives of others.

Data presented represents the efforts of many colleagues, over many

years across many organizations

- Roche / Genentech

- d3 Medicine LLC

- ICPD, University at Buffalo, Monash University

- Alios Biopharma, hVIVO, University of Tennessee

- 360 Biolabs

Primary investigators and clinical trials staff

Clinical trials participants

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Page 3: Introduction to Human Challenge Models (HCMs) for ... · immunological status can be tightly controlled to isolate drug effect • PK, PD (Virologic, clinical, biomarker) and safety

Related Work

• Publications

1. Wollacott AM et al. Safety and Upper Respiratory Pharmacokinetics of the Hemagglutinin Stalk-

Binding Antibody VIS410 Support Treatment and Prophylaxis Based on Population Modeling of

Seasonal Influenza A Outbreaks. EBioMedicine. 2016 Feb 26;5:147-55

2. Kamal M, Rayner C et al. A Drug-Disease Model Describing the Effect of Oseltamivir Neuraminidase

Inhibition on Influenza Virus Progression. Antimicrob Agents Chemother (2016).

3. Rayner C et al. Pharmacokinetic-pharmacodynamic determinants of oseltamivir efficacy using data

from phase 2 inoculation studies. Antimicrob Agents Chemother (2013).

4. Dobrovolny H, Rayner C et al. Assessing mathematical models of influenza infections using features

of the immune response. PLoS One (2013).

5. Reddy MB, · Yang KH, · Rao G, · Rayner CR, · Nie J, · Pamulapati C, · Marathe BM, · Forrest A, ·

Govorkova EA. Oseltamivir Population Pharmacokinetics in the Ferret: Model Application for

Pharmacokinetic/Pharmacodynamic Study Design. PLoS ONE 10/2015; 10(10)

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Page 4: Introduction to Human Challenge Models (HCMs) for ... · immunological status can be tightly controlled to isolate drug effect • PK, PD (Virologic, clinical, biomarker) and safety

Related Work• Abstracts

1. Lutz J, Patel K et al. A mechanistic model describing

the effect of respiratory syncytial virus (RSV) kinetics

on clinical symptom score by presatovir (GS-5806).

ACOP7 (2016 accepted).

2. Fidler M, Patel K et al. A Symptom Driven Multiscale

Model of Influenza. ISIRV (2016 accepted).

3. Patel K et al. Population PK/PD Modeling of Human

Respiratory Syncytial Virus Infection and the Antiviral

Effect of AL-8176. IDWeek (2015).

4. Patel K et al. Population

Pharmacokinetic/Pharmacodynamic (PK/PD) Modeling

of MHAA4549A, an Anti-influenza Monoclonal

Antibody, In Subjects Challenged With Influenza A

Virus. ICAAC (2015).

5. Patel K et al. Population modelling of influenza viral

kinetics, immune response, symptom dynamics and

the effect of oseltamivir. PAGANZ (2015).

6. Rayner C, Kirkpatrick et al. A Novel Interdisciplinary

Pharmacometric Approach: A Systems Approach to

Support Pharmacology to the Payer. ACOP (2015).

7. Patel K at al. Modelling the kinetics of human

respiratory syncytial virus (RSV) and clinical disease

symptoms. ACOP (2014).

4

8. Wu D, Chaiyakunapruk N, Pratoomsoot C, Lee K, Chong HY,

Nelson NE, Smith PF, Kirkpatrick CM, Kamal M, Nieforth K,

Dall G, · Toovey S, ·Kong DC, · Kamauu A, Rayner CR. Cost-

Utility Analysis of Optimal Dosing of Oseltamivir Under Pandemic

Influenza Using a Novel Approach: Linking Health Economics

and Transmission Dynamic Models Value in Health 11/2014;

17(7):A807.

9. Wu D, Chaiyakunapruk N, Pratoomsoot C, Lee K, Chong HY,

Nelson NE, Smith PF, Kirkpatrick CM, Kamal M, Nieforth K,

Dall G, · Toovey S, ·Kong DC, · Kamauu A, Rayner CR.

Oseltamivir use in an Influenza Outbreak: Linking Pharmacology

to Pharmacoeconomics. IDWeek 2014 Meeting of the Infectious

Diseases Society of America; 10/2014

10. Smith PF, Kirkpatrick CM, Rayner CR Wu D, Chaiyakunapruk N,

Pratoomsoot C, Lee K, Chong HY, Nelson NE, Nieforth K, Dall

G, · Toovey S, ·Kong DC, Kamauu A, Kamal M. Estimating

Health Outcomes of Antiviral Use in Influenza Outbreaks by

Linking PK/PD and Epidemiology via a Transmission Dynamic

Model: A Novel Approach. IDWeek 2014 Meeting of the

Infectious Diseases Society of America; 10/2014

11. Rayner CR, Bulik CC, Kamal MA, Reynolds DK, Toovey S,

Hammel JP, Smith PF, Bhavnani SM, Ambrose PG, Forrest A.

Pharmacokinetic-Pharmacodynamic (PK-PD) Determinants of

Oseltamivir Efficacy Using Data from Two Phase 2 Inoculation

Studies. XIV International Symposium on Respiratory Viral

Infections (ISRVI), Istanbul, Turkey, 23–26 March 2012

12. Yang KH, Reddy M, Pamulapati C, Rayner CR,

Forrest A. Development of a Pharmacokinetic

Model for Oseltamivir in Ferrets Using Iterative

Two-Stage Analysis. American College of Clinical

Pharmacy Virtual Poster Symposium, May 22–

24, 2012

13. E. Hershberger et al. Pharmacokinetics of the

Hemagglutinin (HA) Stalk-Binding Antibody,

VIS410, in a Human Challenge Model of Infection

with a p2009 H1N1 Virus. ISIRV 2016

14. GG Rao et al. Population Modelling of Influenza A

Kinetics, Innate Immune Response & Symptom

Dynamics. 54th Interscience Conference on

Antimicrobial Agents and Chemotherapy (ICAAC).

Washington, DC Sept 2014.

15. M Fidler et al. Modeling the Spread of Pandemic

Influenza in the United States: Impact of Antiviral

Interventions, Pharmacology, and Resistance.

American Conference on Pharmacometrics;

October 2014, Las Vegas, NV.

16. E Lakota et al. Development and optimization of

an adaptive study design for respiratory virus

human challenge models. ACCP, 2014.

Page 5: Introduction to Human Challenge Models (HCMs) for ... · immunological status can be tightly controlled to isolate drug effect • PK, PD (Virologic, clinical, biomarker) and safety

Background

• Human challenge models (HCM) are employed as part of many

development programs for ARI such as influenza, rhinovirus and respiratory

syncytial virus (RSV)

• Infections are deliberately induced under carefully controlled and monitored

conditions, involving virus inocula of known virulence and provenance

• Key variables, such as baseline infection load, timing of Rx, and

immunological status can be tightly controlled to isolate drug effect

• PK, PD (Virologic, clinical, biomarker) and safety can be diligently evaluated

• Quantitative pharmacology can optimize HCM study design and analysis to

enhance drug development for respiratory viruses.

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Page 6: Introduction to Human Challenge Models (HCMs) for ... · immunological status can be tightly controlled to isolate drug effect • PK, PD (Virologic, clinical, biomarker) and safety

HCMs as Applied to Respiratory Viruses

• Highly controlled nature of experimental

pharmacology HCMs enables “rich and clean”

interrogation of drug effect

• Intensive safety surveillance

• Specialist units creates opportunity for novel

designs (eg. adaptive design to capture ER

surface, expansive dose ranges, frequent

invasive sampling) to mitigate expense and

accelerate

• Not dependent on seasonal disease, means

study durations can be short

• Rich data enables quantitative pharmacological

approaches (MBM) to support development

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Pro’s Con’s

• Regulatory challenges in obtaining an

acceptable inocula

• Ethical considerations (infecting volunteers,

transmission risk)

• Transferability to the field is debated as

important variability ignored including virus

(strains, virulence, replication, IC50, tropism,

baseline VL), time to Rx, host immune status

etc), abrogated/different clinical course of

disease

• Artefact of study including inoculation

techniques, impost of frequent invasive

sampling (dilution, loss of virus)

• Specialist clinical trial units, strain availability

and need to pre-screen to obtain sero-negative

subjects can create availability bottlenecks and

high costs

Page 7: Introduction to Human Challenge Models (HCMs) for ... · immunological status can be tightly controlled to isolate drug effect • PK, PD (Virologic, clinical, biomarker) and safety

Considerations for Quantitative Pharmacology Application to HCM of Respiratory Viruses

- Case 1: HCM for Oseltamivir for influenza

- Case 2: HCM for ALS-008176 for RSV

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Page 8: Introduction to Human Challenge Models (HCMs) for ... · immunological status can be tightly controlled to isolate drug effect • PK, PD (Virologic, clinical, biomarker) and safety

CASE 1 - HCM FOR OSELTAMIVIR FOR INFLUENZA

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Methods – Pooled Available Ph2 Studies

• Healthy volunteers in Studies PV15616 and NP15717 were experimentally infected with

influenza (TCID50 of virus 106)and treatment was initiated with oseltamivir or placebo after 24h

• Symptoms (feeling feverish, headache, muscle ache, sore throat, cough, overall discomfort,

nasal symptoms) individually ranked b.i.d. for symptom severity (0, 1, 2, or 3) for 9 days

• Nasal lavage b.i.d. for viral culture Days 1–3 and o.d. on Days 4–8

Study

Number of

subjects

(infected) Virus

IC50

(nM) Dosing regimens PK Sampling

PV15616 80 (69)Influenza

A/Texas0.18

20, 100 or 200mg b.i.d.

or

200mg q.d.

or placebo for 5 days

Sparse PK samples

pre-dose and on Days

3, 4 and 7

NP15717 60 (46)Influenza

B/Yamagata16.76

75 or 150mg b.i.d.

or placebo b.i.d. for

5 days

Intensive PK on Days

1 and 5

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Methods - PK

• A robust population PK model based on nine clinical studies was developed to provide individual

OC exposures

• Individual predicted steady-state exposures for OC were determined: AUC0–24h, Cmin, Cmax

CLoc = clearance of OC; CLop = clearance of OP; F = fractional bioavailability

of OP; Fm = fractional bioavailability of oseltamivir that is metabolised;

Ka = the first order rate of absorption ; Q = inter-compartmental clearance

term; Voc = volume of OC; Vop = central volume of OP; Vp = peripheral volume

of OP

(Fm)

Oral

Depot

Ka

(F)

Central (OP)

Vop

Q/Vop

Q/Vp

Peripheral

(OP) Vp

CLop/Vop

Central (OC)

Voc

CLoc/Voc

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Independent Variables

Exposure, PK-PD

or potency measureDemographic / other

AUC0–24h Age

AUC0–24h:IC50 ratio Race

Cmax Sex

Cmax:IC50 ratio Height

Cmin Weight

Cmin:IC50 ratio Body mass index

Total daily dose Creatinine clearance

Treatment regimen Antipyretic anilides

IC50

Note: co-linearity reduced focus to AUC related metrics

consistent with PKPD index from preclinical models

Methods:

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12JAMA. 1999;282(13):1240-1246. doi:10.1001/jama.282.13.1240

The viral titer area under the curve value was lower in the combined oseltamivir group (n=56) compared with placebo (n=13); P=.02.

“Typical” time-course of viral load (PV15616)

Time-course is a blend of “fact”

and experimental “fiction”

- Inoculation

- Serial nasal washings

- Measurement

Detection of PK/PD effect influenced by VT time-course and form of dependent variable (eg. Peak VT<rate of decline< AUC<< TSVR =Tshed )

Page 13: Introduction to Human Challenge Models (HCMs) for ... · immunological status can be tightly controlled to isolate drug effect • PK, PD (Virologic, clinical, biomarker) and safety

13JAMA. 1999;282(13):1240-1246. doi:10.1001/jama.282.13.1240

“Typical” symptom and cytokine time-course (PV15616)

The total symptom score area under the curve value was lower in the combined oseltamivir groups (n=56) compared with placebo

(n=13); P=.05; Cytokine levels for days 1, 3, 5, and 9 were determined using commercially available enzyme-linked immunosorbent

assay kits. Asterisk indicates P≤.01; dagger, P≤.001; and double dagger, P<.05.

Patel et al. ICAAC 2015

MHAA4549A

Oseltamivir

HCM viruses can provide biophase PK, VK, cytokines and symptoms to inform MBMs

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Understand the inputs!

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Sampling procedures

- Procedure (disruptive, dilutive)

- Replicate (same/different site)

- Pooled/Combined(wash + swab)

- Standardisation (within study,

across studies)

VT / biomarker measurement

- Sampling error

- Method (culture/ PCR)

- Performance (BLQ)

It is essential to have a detailed understanding of the sampling and measurement methodologies

CROs and PROs

- Validated instruments

- Relevance to HCM

- Fever

- Composite vs

individual symptom

scores

- Frequency / learning

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Methods:

Time-to-event Continuous

• Time to alleviation of composite symptom

score

• Time at which any of the seven individual

symptoms scores were >1 to the time at

which all applicable individual symptom

scores were ≤1

• Time to cessation of viral shedding

• Time from the first positive viral culture to the

time of the first negative viral culture

• Composite symptom score AUC

– Symptom scores added together and AUC of

composite score calculated from the time at

which any of the seven individual symptoms

scores were >1 to the time at which all

applicable individual symptom scores were ≤1

• Viral titre AUC

– The AUC of the viral titre values spanning the

time from the first positive viral culture to the

time of the first negative viral culture

• Peak viral titre– The maximum viral titre value achieved for

each subject during study Days 1–9

A diverse set of univariate and multivariable PK-PD analyses were performed

against the following dependent variables:

Page 16: Introduction to Human Challenge Models (HCMs) for ... · immunological status can be tightly controlled to isolate drug effect • PK, PD (Virologic, clinical, biomarker) and safety

Results

• Higher OC Exposures are

Associated with a Faster

Cessation of Viral Shedding - Peak VT and AUCVT ND

• Higher OC Exposures are

Associated with a Lower

Severity of Illness

• A Faster Time to Alleviation of

Composite Symptoms is Seen

with Higher OC Exposures

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Findings consistent with observations of exposure-response nested within Phase III trials1,2

1Evaluation of Pharmacokinetic-Pharmacodynamic (PK-PD) Relationships for Influenza

Symptom and Quality of Life (QOL) Endpoints Among Oseltamivir- Treated Patients

(ICAAC 2014); 2Pharmacokinetic-Pharmacodynamic (PK-PD) Evaluation of the Impact

of Oseltamivir on Influenza Viral Endpoints (ICAAC 2014)

Page 17: Introduction to Human Challenge Models (HCMs) for ... · immunological status can be tightly controlled to isolate drug effect • PK, PD (Virologic, clinical, biomarker) and safety

CASE 2 - ALS008176 INOCULATION STUDY FOR RSV

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- PCR guided Rx start- Adaptive design (safety, PK, PD) to establish ER/DR

-008176 is an oral nucleoside prodrug converted intracellularly to its active

triphosphate ALS-008112.

Methods

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Results ALS-8176 Dose-Response in a Human RSV Challenge Model

500 mg LD/500 mg q12h

500 mg LD/ 150 mg q12h

375 mg q12hLonger VT time-course than influenza HCM

ALS-8176 had significant improvements on VT AUC, Time to undetectable PCR, Peak VT and AUC symptom score

N Engl J Med 373;21 (2015)

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K Patel, et al. ID Week 2015

Even Abbreviated Designs Enable MBM Approaches

Rich PK/PD data provided in RSV HCM enables quantitative pharmacology approaches to address complex nucleoside pharmacology

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Considerations for Quantitative Pharmacology for Respiratory Virus HCMs

• Critical to understand sampling and assay methods as they can impact

integrity of observations and assumptions

• Consider the time-course of PD markers (eg. VK) to guide the most

appropriate form of a PD variable

• Don’t forget about other endpoints, as HCM virus can cause symptoms

• Consider potential for MBM to connect PK, VK, immune-system

components, and symptoms to enable better extrapolation to clinic

(especially for complex examples such as nucleoside analogues)

• Numerous opportunities to optimise HCMs through quantitative

pharmacology methods (adaptive design, optimal design based on ER)

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