Role of PK/PD in Evidence based Medicine. Tausif... · Role of PK/PD in Evidence based Medicine...

Role of PK/PD in Evidence based Medicine

Tausif Ahmed, PhD

Asst. Director,

Modeling & Simulation, GLP-BA and Met-ID

Piramal Enterprises Ltd, Mumbai

Third Annual Conference, Indian Association for Statistics in Clinical Trials

Agenda

� Introduction

� Historical perspectives

� PK-PD Models

� Applications of PK/PD

� Challenges in PK/PD modeling

� Summary

Is it safe? Does it work?

Does it work in double blind trials?

KNOWLEDGE

LEVEL

Drug Development Cycle

Drug Discovery & Development - Attrition Rate

Reasons for Phase 2 Failures 2008-2010

Nature Reviews: Drug Discovery, May 2011

Reasons for Phase 3 Failures 2008-2010

Nature Reviews: Drug Discovery, Feb 2011

Agenda

� Introduction


� PK-PD Models



� Summary

– Pharmacometrics – still an emerging science

– Science that quantifies drug, disease, and trial information to aid efficient drug

development and/or regulatory decisions

– Pharmacometrics - a collection of model-based approaches used to

• extract from data & organize our understanding of a system’s behavior in a

concise manner

• do so in a language (i.e. mathematics) that allows simulation of the system

output

– Pharmacometric Models - three broad classes:

• Exposure-Response Models- specifically describe the relationships

among dose, drug concentration in blood (or another matrix), and clinical

response (effectiveness and undesirable effects)

• Disease Models- aim to describe disease progression

• Clinical Trial Models- describe patient demographics, adherence, dropout

rates, trial structure, and so on

Pharmacometrics defined

A Brief History of Pharmacometrics

THE LATE 60’S:

A PREMATURE BIRTH ?

1968: The Birth of PK/PD

• Presence of a delay between norepinephrine concentration-time

profiles and the kinetics of pharmacological response, i.e. blood

pressure-time data

• Gino Segre introduced the concept of a hypothetical effect

compartment to account for this delay

• This allowed an empirical description of time-dissociated kinetics

Segre G. Kinetics of interaction between drugs and biological systems. FarmacoSci. 1968

Oct;23(10):907-18

• NONLIN software only introduced in a year later by Carl Metzler

• It was written in FORTRAN-66 programming language for mainframe

computers

• Long gap of PK/PD publications until 1979

• CM Metzler. A Users Manual for NONLIN. Technical Report 7297 69 7292 005. Upjohn

Co., Kalamazoo, Michigan (1969)

1968: Was it a Premature Birth?

THE LATE 70’S:

A REBIRTH


1979: Rebirth

• Lewis Sheiner and coworkers made Segre’s model more popular. They were

the first to formalize this concept into a model to describe hysteresis caused

by distribution to the biophase

• It was reborn as the ―Link Model

• SheinerLB, Stanski DR, Vozeh S, Miller RD, Ham J. Simultaneous modeling of

pharmacokinetics and pharmacodynamics: application to d-tubocurarine Clin Pharmacol

Ther. 1979 Mar;25(3):358-71


THE LATE 80’S:

TODDLING

80’s: Growing Application

• Growing use of PK/PD modeling, with applications to diverse

therapeutic areas (mainly cardiovascular)

• Source: Pubmed search (Key-words: ―pharmacodynamic AND modeling)

80’s: Growing Application

• Growing use of PK/PD modeling, with applications to diverse

therapeutic areas (mainly cardiovascular)


THE LATE 90’S:

A STEEP LEARNING CURVE

90’s: The Advent of Mechanism-Based PK/PD

• In so called – Indirect Physiological Response (IPR) models, the drug

concentration is no longer related to the PD variable itself. Instead, it

is assumed to modulate upstream and/or down stream regulation

mechanisms


• Significant increase in the number of publications. Predominantly

theoretical until 1993. Growing number of increasingly complex

applications afterwards

• Source: Pubmed search (Key-words: ―pharmacodynamic and modeling)


TODAY:

A MATURE DISCIPLINE

• Application of integrated drug-disease-trial models to optimize clinical

development programs with respect to therapeutic potential, R&D

productivity and commercial value

Today: A Mature Discipline

Predict, LearnConfirm, Save

Predict, LearnConfirm, Save

Preclinical Phase

Clinical Phase

Drug Model: PK/PD

Post-NDA Phase

eIND IND EOP2a EOP2 NDA 6 mo safetypreIND VGDS

��Quantitative Analysis &/or Simulation

Safety Model: learn ‘at risk’ population, detect early or avoid risk

PK/PD Bridging

• Pediatrics

• Elderly

• Dosage forms

��

��

Disease Model: detect change, qualify new biomarkers, simulate trial design��

Label Update

BenefitRisk

��

Efficacy/Safety Benefit/Risk

Approval• Drug• Label

��

Individual Dosing

Cross-trial analysis: dose-response (efficacy/safety)

��Dose

RangingConfirming

S S��PK/PD

Dose-escalationPOP

S��Human PK/PD Prediction

Simulate (S) ��

• Dosing

• Human proof of principle

• Phase 3 trial design

• Value

Target Product Profile


1999 2003

• ...............as well as regulatory decisions about labeling and approval


Confidential

Operation of M&S

Modeling & Simulation

DMPK

Pharmacology

Biomarkers

Clinical ResearchData Management & Biostatistics IT

Imaging

Software's: WinNonlin,

NONMEM, R, S-plus,

ADAPT, SAS

• Preclinical & in-vitro studies

• Single and multiple dose pharmacokinetics

• Absolute bioavailability & dose proportionality

• Metabolism and drug interactions

• Food effects studies; Bioequivalence studies

• Special population studies – age, gender, race

• Pharmacokinetics in the target population

• Disease states such as renal and liver impairment

• Pharmacokinetic (PK) Modeling

• In vitro-in vivo correlation (IVIVC)

• Pharmacodynamic (PD) Modeling

• Population Pharmacokinetic (PopPK) Modeling

• Pharmacokinetic/ Pharmacodynamic (PK/PD) Modeling

• Physiology Based Pharmacokinetic (PBPK) Modeling

• Clinical Trial Simulation

Domain

Agenda

� Introduction


� PK-PD Models



� Summary

PK/PD Modeling

Pharmacokinetics(PK)

Pharmacodynamics(PD)

Effe

ct

Concentration

Con

cent

ratio

n

Time

Effe

ct

Time

Pharmacokinetics/Pharmacodynamic Modeling(PK/PD)

What the body does to the drug

What the drug does to the body

MODELSimplified description of some aspect of realityHELPS IN PREDICTION

- Linear model

- Log-linear model

- Emax - model

- Sigmoid Emax – model

- Inhibitory models

- Effect Compartment

- Indirect Models

- Tolerance Models

Pharmacodynamic Models

� Drug must “interact” with a “receptor substance” to elicit an

activity

� Drug(D)+Receptor(R)↔ [DR] →Effect

� Rearrangement leads to Michaelis-Menten Equation

- D - Free drug concentration, Emax - Maximal effect,

- KD - Binding constant

PD Models: Basic Principles

EffectE D

DDK=

•+

max

Maximum Effect Model

Inhibitory Effect Model

Direct Effect Model

Biophase Distribution Model

Indirect Link Model

Analgesic effect of 400 mg oral ibuprofen quantified by

subjective pain intensity rating

Suri et al., Int J Clin Pharmacol Ther 1997, 35, 1-8 Effect

Ck10

D

k1

e

ka

Ce

D DoseC Plasma concentrationCe Effect compartment

concentrationk10, ka, k1e, ke0First-order rate constants

Emax-model

ke0

b

� Distributional delay between plasma and effect site concentration

� Dissociated time courses of concentration and effect

− Concentration maximum before effect maximum− Effect intensity increasing despite decreasing plasma

concentrations− Effects persist beyond the time plasma concentrations

are detectable

Counterclockwise hysteresis loop

Indirect Link Models

� The general indirect response model assumes that the change in the response parameter, which is related to the effect, is governed by an input or production process (zero-order rate constant kin) and an output or degradation process (first-order rate constant kout)

� Temporal dissociation between the concentration time course and the effect-time course (hysteresis) due to synthesis of a protein, reduction in a synthesis rate (reduction in hormonal levels)

� Thus, the rate of change in response (R) is described by

Indirect Response Models

Response (R)ki

n

kout

Production Degradation

Rkkdt

dRoutin

×-=

Dayneka et al., J Pharmacokinet Biopharm 1993, 21, 457-78

Indirect Response Models

Pharmacokinetics Pharmacodynamics

k21Ck10

Modulation of Input or Production Process

D

ka

Response

k0in

kout

+-

RkCIC

CIk

dt

dRoutin

⋅−

+⋅−⋅=

50

max0 1

RkCSC

CSk

dt

dRoutin

⋅−

+⋅+⋅=

50

max0 1

-

+

100 maxmax≤<> IS

+

-

Agenda

� Introduction


� PK-PD Models



� Summary

Example 1: Dose-response Relationship

� Artemisinin derivatives commonly used to treat drug resistant falciparum malaria

� Doses of artesunate used in mono therapy and combination treatment- derived

empirically

� PD end point- PCT (Parasite clearance time)

� 47 adult patients with acute uncomplicated falciparum malaria and parasitemia were

randomized to receive a single oral dose of artesunate: 0 - 250 mg

� Inhibitory sigmoid Emax model fitted to dose vs shortening of PCT

� Emax was estimated as 28.6 h, and the 50% effective dose was 1.6 mg/kg bw

� No reduction in PCTs with the use of single oral doses of artesunate higher than 2 mg/kg,

and this reflects the average lower limit of the maximally effective dose


0 4 8 12 16 Time (hr)

Pla

sm

a C

oncentr

ation

AUC

Time above MIC

MIC

Peak

Trough

AUC / MIC

Efficacy of Aminoglycosides

Safety of Aminoglycosides

Efficacy of New Quinolones

Efficacy of b-lactam, macrolides, glycopeptides

Example 2: PK-PD Modelsin Preclinical Drug Development- Antimicrobials

Example 2: PK/PD Modelsin Preclinical Drug Development- Antimicrobials

� Telithromycin (Tel)- belongs to the class of antimicrobial, ketolides

� Effective against penicillin and macrolide resistant gram +ive Streptococcus pneumoniae

� Thigh infection model: CD-1 mice rendered neutropenic by ip injection of cyclophosphamide

� Colonies of S. pneumoniae (106- 107 CFU/mL) appx. 0.1 mL inocculum injected in thigh of

mice, 2h prior to initiation of antimicrobial therapy

� 2h post-infection, Tel 50 or 100 mpk dose administered

� PK- blood collected at regular intervals till 24 hours post-dose

� PD- 2h- post infection, Tel doses ranging from 25-200 mg/kg administered at different

dosing regimens

� After 24h of treatment, mice were sacrificed and thighs removed and CFU counted vs control

� PK-PD analysis done using Inhibitory Emax model


� AUC/MIC- strong determinant of the response against S. pneumoniae

� Maximal efficacy and bacterial inhibition against S. pneumoniae strains were predicted

by AUC/MIC and Cmax/MIC ratios of appx 1000 and 200, respectively


Confidential

Example 3: Preclinical Development-Diabetes

� xx (antidiabetic drug)

� PK-PD Analysis of data from the study in rat, hamsters and ob/ob mice

� Key results:

� PD Analysis: IC50 for xx ranged from 100-300 ng/mL across 3

different preclinical disease models (concn. vs biomarker levels

� Similar concn. expected in clinic for efficacy

� Good correlation between biologic response and biomarker (r = 0.85)

� Biomarker focus right from preclinical stage

� Compare and combine with data from all the preclinical studies : Build

knowledge-base for extrapolation to clinical trials

Confidential

Preclinical Data

� xx in Hamsters

y = 0.0218x + 103.38

R2 = 0.40

80

90

100

110

120

130

140

0 200 400 600 800 1000

Day 21 TG Level

Day

21

Bo

dy

Wt

Response vs Biomarker model

Disease Progression model

PK-PD (Response) model

PK-PD (Biomarker) model

Confidential

Prediction of FIH Dose

CL*MLP y = 1.3113x + 1.6455R2 = 0.9746

-1.00

0.00

1.00

2.00

3.00

4.00

5.00

-2.0 -1.0 0.0 1.0 2.0 3.0

log BW (kg)

log

CL

(m

L/m

in)

� Use of allometric scaling in predicting FIH dose� Other alternative approaches for FIH dose prediction: NOAEL from preclin. Species� FIH dose prediction based on efficacious doses in preclin. disease models� In vitro-In vivo correlation: Prediction human clearance from human hepatocyte intrinsic

clearance data� Simulated human PK profiles and correlation with efficacy/toxicity- Integrated approach

NOAEL Cmax= 11, 000 ng/mLToxic dose Cmax: 42, 000 ng/mL

Efficacious conc.

Confidential

Disease Progression Model- Diabetes

• Change in fasting plasma glucose (FPG) concentrations modeled as a function of Cp via an indirect-effect model on the assumption that drug xx reduces glucose by increasing theremoval rate of glucose from the plasma compartment

� Models developed based on phase I/IIa data help make valid predictions for larger phase II/III trials

FPG

HbA1c

)1(50

max

CEC

CEKout +

⋅+⋅inK

inK ' outK '

cHbAKFPGKdt

cdHbAoutin 1''

1 ⋅−⋅=

HbAlc

FPG

Dru

g C

onc.

Time (Week)

FPGCEC

CEKK

dt

dFPGoutin ⋅

+⋅+−= )1(

50

max

Cmt 1 Cmt 2

1st order Oral Absorption


Confidential

� Simulation of multiple dose profile of drug x based on single dose PK

� Correlate exposure to efficacy in deciding the proposed doses for RMD study

� Decide dosing regimen (QD vs BID) based on efficacy and safety

Design of Phase I RMD Study- QD Dosing

SS reached by day 4

Target Cmax= 80900 ng/mL

Agenda

� Introduction


� PK-PD Models



� Summary

Current Problem in PK/PD

� Today, we do not have an adequate understanding of the clinicalefficacy/ MOA for most disease states

� Do not have an adequate understanding of the MOA for clinical toxicity

� This is the reason for the lack of suitable biomarkers and surrogatemarkers

� Validation of PD biomarkers

� Correlation of PK/PD model with safety or efficacy outcomes- Need todevelop disease progression models

� Validated Assay (reproducible, high precision….)

� Understanding of pharmacologic behavior of the drug andpathophysiology of the disease

Future research needs to address above areas

(Colburn, Washington, 1999)

Practical Aspects

CORPORATE PRESSURES

• mergers, consolidation,

small start-up

• Changing corporate

philosophy and structure is

changing the development

process

• Increased Productivity

THUSDevelop Innovative Drugsfaster with reduced risk, more effective cheaper

New Discovery Approaches:

� e.g combinatorial chemistry

� computational approaches

� robotic systems

More drug candidates in shorter period of timechallenges and opportunities in the PK/PD area

Pharmacometrics Status in India

� Search of website http://www.ctri.nic.in (clinical trials registry- India, Indian Council

of Medical Research) with key words “phase II/III trials 2010” reveals: None of the

trials have pharmacometrics (PM) component

� PM is at infancy stage in India

� Efforts are made to impart trainings in the fields of PK/PD data analysis and clinical

protocol writing

� Preconference workshop entitled “Pharmacokinetics: protocol development, conduct

and analysis” organized by South Asian chapter of ACCP at PLSL in Aug, 2011

� Trained (hands-on/didactic lectures) about 50 medical and pharmacy grad and post

graduates

� PAGIN formed in 2008: Formed to provide PM training in India

� ICMR grant to Sponsor “Research Methodology Workshop on PK/PD” held at ACTREC

(Advanced Centre for Training Research and Education in Cancer), Navi Mumbai from

May 1-7, 2012 to train post graduate students in the field of PM (co hosted by

Piramal)

Pharmacometrics Status in India

Summary

� Increasing awareness and understanding of PK-PD in drug discovery and dev

� Emphasis on biomarkers relating to target modulation for novel targets

� Strong collaboration between discovery DMPK, biology, IT, statistician and clinical

biomarker groups

� Translation of preclinical biomarkers to the clinical setting

� Start early and transfer PK-PD knowledge from discovery to development; refine model

as more data becomes available

� PK-PD provides a scientific basis for optimal FIH dose

� Optimal use of PK-PD modeling and simulation- fewer failed compounds, fewer study

failures and smaller number of studies needed for registration: Save time and money

Pharmacokinetics/Pharmacodynamics an uphill climb – but nice view!

Thanks

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