DOSE SELECTION FOR ANTI- INFECTIVE DRUGS: INDUSTRY PERSPECTIVE Dennis M. Grasela, PharmD, PhD Executive Director, Infectious Diseases Department of Clinical Discovery Bristol-Myers Squibb Pharmaceutical Research Institute FDA/IDSA/ISAP Workshop 15-16 April 2004
DOSE SELECTION FOR ANTI-INFECTIVE DRUGS: INDUSTRY PERSPECTIVE Dennis M. Grasela, PharmD, PhD Executive Director, Infectious Diseases Department of Clinical.
Jan 17, 2016
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DOSE SELECTION FOR ANTI-INFECTIVE DRUGS: INDUSTRY PERSPECTIVE
Dennis M. Grasela, PharmD, PhD
Executive Director, Infectious Diseases
Department of Clinical Discovery
Bristol-Myers Squibb Pharmaceutical Research Institute
FDA/IDSA/ISAP Workshop15-16 April 2004
Bristol-Myers Squibb Company
OUTLINE
Exposure-Response (PK/PD) approach to dose selection
Factors driving the use of PK/PD based drug development
Potential cost benefits
Bristol-Myers Squibb Company
OUTLINE
Exposure-Response (PK/PD) approach to dose selection
Factors driving the use of PK/PD based Factors driving the use of PK/PD based drug developmentdrug development
Potential cost benefitsPotential cost benefits
Bristol-Myers Squibb Company
ELEMENTS OF PK/PD-BASED APPROACH TO DOSE SELECTION
Use in vitro MIC values to determine the height of the microbiological hurdle for key pathogen(s)
Use PK/PD data from in vitro hollow-fiber and in vivo animal models of infection to define PD-linked parameter and target values for key pathogen(s)
Use PK/PD modeling in ‘proof-of-principle’ studies to examine Exposure-Response relationship
Combine PK/PD-based knowledge and Monte Carlo simulations to define dose and schedule for Phase III studies
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MIC DISTRIBUTION FOR S. pneumoniae
3 5 107
957
104 2 0 1 00
200
400
600
800
1000
0.03 0.06 0.12 0.25 0.5 1 2 4 8
M I C (mg/L)
Fre
qu
en
cy
• Data from the SENTRY Antimicrobial Surveillance Program
• 1179 isolates (1998-2002), obtained from patients aged <7 years
• MIC90 - 0.25 mg/L
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0
1
2
3
4
5
6
7
8
9
10
0 4 8 12 16 20 24 28 32
USE OF IN VITRO MODELS TO DEFINE PD-TARGET for S. pneumoniae
Time (hours)Time (hours)
Lo
g C
FU
/mL
Lo
g C
FU
/mL
10141724
343845LEV
Lister D. AAC. 2002; 46: 69-74
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USE OF IN VIVO ANIMAL MODELS TO DEFINE PD-TARGET for S. pneumoniae
Mattoes HM et al. AAC. 2001; 45: 2092-2097
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USE OF CLINICAL DATA TO CONFIRM PD-TARGET for S. pneumoniae
Ambrose PG, et al. AAC. 2001; 45: 2793-2797
Probability of Achieving Target AUC:MIC Ratio for S. pneumoniae
Probability
0.05
0.045
0.04
0.035
0.03
0.025
0.02
0.015
0.01
0.005
00 50 100 150 200 250 300 350 400
free AUC:MIC
94%
Ambrose PG, Grasela D. ICAAC 1999Bristol-Myers Squibb Company
Ambrose PM, Grasela DM. Diagn Microbiol & Infect Dis. 2000; 38: 151-157.
Bristol-Myers Squibb Company
OUTLINE
Exposure-Response (PK/PD) approach Exposure-Response (PK/PD) approach to dose selectionto dose selection
Factors driving the use of PK/PD based drug development
Potential cost benefitsPotential cost benefits
Bristol-Myers Squibb Company
DRIVERS FOR THE USE OF PK/PD Internal Factors
Knowledge-based decision making Dose selection/confirmation
Examination of the effect of administering a dose not studied during development
Selection of target indication(s)
Enhanced understanding of the drug
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DRIVERS FOR THE USE OF PK/PD Internal Factors
Use of population-based PK/PD analyses Can help explain differences in response among
individuals receiving the same dose (e.g., covariate analyses)
Can help identify at risk sub-populations and define risk-benefit ratios and/or risk management strategies
Can help guide the use of pharmacogenomics
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DRIVERS FOR THE USE OF PK/PD External Factors
FDA Expectations/Opportunities Guidances (http://www.fda.gov/cder/guidance)
Population PK guidance (1999)
Exposure-Response guidance (5April 2003)
FDA Modernization Act of 1997 (FDAMA)Global Expectations
Data-driven responses to regulatory questions and/or ‘What if scenarios’
Benefit-Risk assessment
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DRIVERS FOR THE USE OF PK/PD External Factors
Section 111 of FDAMA provides for:
“use of PK bridging studies in Pediatric studies of new drugs”
FDA Modernization Act of 1997 (FDAMA)
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DRIVERS FOR THE USE OF PK/PD External Factors
Section 115 of FDAMA provides for:
“new drug approval based upon evidence from a single adequate and
well controlled trial, supported by confirmatory scientific evidence from
other studies (e.g., Phase II PK/PD studies) in the NDA”
FDA Modernization Act of 1997 (FDAMA)
Bristol-Myers Squibb Company
OUTLINE
Exposure-Response (PK-PD) approach Exposure-Response (PK-PD) approach to dose selectionto dose selection
Factors driving the use of PK-PD based Factors driving the use of PK-PD based drug developmentdrug development
Potential cost benefits
Bristol-Myers Squibb Company
COST BENEFITS
Population PK can obviate the need for selected clinical trials (e.g., age-gender, renal impairment, etc..)
Position sponsor to utilize provisions in the ICH E5 guidelines for the use of PK bridging studies for submission in Japan, etc…
Position sponsor to utilize provisions in Sections 111 and 115 of FDAMA
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COST BENEFITS
Selection of indications, based on PK/PD evaluation of antimicrobial spectrum
Smaller sample sizes associated with exposure-response vs. dose-response approaches
Selection of optimal dose may lower sample size requirements for non-inferiority trials
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Sample Size Saving with Selection of Optimal Dose in Non-inferiority Trials
Anticipated Responsefor Comparator
Projected Responsefor BMS Compound
Sample Size(n/arm)
85% 80% 985
85% 85% 219
85% 90% 83
85% 95% 38
Assume 90% power, with a delta of 10%, and all subjects enrolled are fully evaluable
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COST BENEFITS
Higher quality submissions which could: facilitate regulatory review enhance relationship with regulatory authorities minimize post-submission questions
Facilitate transition to novel dosage forms based on PK studies only, if PK/PD relationship is known
Provide basis for data-driven market differentiation
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SUMMARY
Selecting the optimal dose(s) for the treatment of infection(s) is important in order to: Maximize efficacy Minimize toxicity Minimize resistance development
Using an exposure-response approach to dose selection and drug development can: Facilitate knowledge-based decision making Optimize trial designs Streamline development and related costs
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