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Improvement in Dose Selection: FDA Perspective. IDSA/ISAP/FDA Workshop April 16, 2004 Jenny J Zheng, Ph.D. Pharmacometrician DPEIII/OCPB/CDER/FDA. Objectives. To discuss the studies to support dose selection To discuss a model based quantitative approach to guide dose selection
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Improvement in Dose Selection: FDA Perspective IDSA/ISAP/FDA Workshop April 16, 2004 Jenny J Zheng, Ph.D. Pharmacometrician DPEIII/OCPB/CDER/FDA
Objectives • To discuss the studies to support dose selection • To discuss a model based quantitative approach to guide dose selection • To discuss approaches to further advance the use of PK/PD for dose selection
Dose Selection • Efficacy • Safety • Resistance
Studies to Support Dose Selection 1. Microbiology / in vitro data • Susceptibility data for pathogens relevant to the indication • Protein binding • Post antibiotic effect (PAE) 2. Pre-clinical data • PK/PD studies in various animal/in vitro models • Efficacy studies in various therapeutic infection models • PK studies in the animals used in therapeutic infection models
Studies to Support the Dose Selection 3. Phase 1 pharmacokinetic studies • PK studies in healthy subjects • Phase 2 studies • Efficacy • Safety
Time to Communicate with FDA • Early communication is encouraged. • Prior to phase 2/3 studies
How to Select Dose • Rationale for dose selection in antimicrobial drug applications is variable. • Very often the dose is selected based on mean PK profile in relation to MIC for relevant pathogens. • The PK variability of drug is not considered. • The relationship between a concentration- time profile and a MIC is not always clear. • A quantitative approach is recommended.
Quantitative Approach • What is quantitative approach? • Use modeling and simulation tools to quantitatively predict the outcome. • Advantage of quantitative approach • Quantitative (predictive) • Decision is more logical and transparent • Objective
Quantitative Approach • Can pathogen be killed at dose X? • A PK/PD index can be obtained from pre-clinical studies and be used to predict the bacterial killing/inhibition effect . • If the PK/PD index for the unbound drug in the subject is above the PK\PD index obtained from pre-clinic: YES. • Otherwise: NO Dose X PK Protein binding Pathogen
The Goal • To predict the percentage of patients who could reach a PK/PD target at a range of doses. • Dose/doses can be determined for clinical trials: • Phase 3 (fixed dose): majority of subjects would reach the PK/PD target. • Phase 2 dose ranging studies: the doses should be selected so that it is differentiable with regard the percent of subjects who could reach the PK/PD target among the doses.
The Method • PK data PK model (parameters and its variability) PK profiles at doses even not studied.
The Method • Distribution of PK/PD index at dose X 77%
The Method X4 X3 X2 MIC90=2 mg/mL X1 MIC90=1 mg/mL MIC90=0.5 mg/mL
Antimicrobial Model • Successful treatment of infection involves the interactions of host, drug and bacteria Pharmacokinetics Tissue penetration ? Host Drug ? • Susceptibility of pathogens • PK/PD relationship • PAE • Killing rate • Other metrics ? Immune system ? Bacteria s • The factors not being considered may represent potential limitations.
PK/PD Studies in Human • The predictability of PK/PD relationship in animals for treatment of infections in human is not clear. • The PK/PD relationship for most of drugs was established only in animals. • To improve understanding the PK/PD relationship in human: • Phase 2/3 studies
Summary • A model based quantitative approach is informative for dose selection. • With the potential limitations, the dose should be selected based on the totality of available data. • Dose selection: Microbiology Pre-clinic Phase 1 Phase 2 Phase 3
Future • Well designed dose ranging phase 2 studies in appropriate infections. • PK/PD relationship in human • Safety • Improvement of the model used in quantitative approach. • Efficacy + Safety + Resistance • Evaluation of indices other than AUC/MIC, Cmax/MIC, and T>MIC. • Development of optimal duration of therapy.