BACKing up clinical data with modellingregist2.virology-education.com/2016/17HIVHEPPK/27...Parameters Predicted (n = 400) Observed (n = 29) Avg. EFV dose from breast milk (µg/kg/day)

BACKing up clinical data

with modelling

Marco Siccardi

What is modelling?

INPUT OUTPUT

EXPERIMENTALVARIABILITY

CLINICAL

UNDERSTAND UNDERPINNING MECHANISMS

SIMULATE CLINICAL SCENARIOS

PRE-CLINICAL EXPOSURE

ADME

EFFICACY/TOXICITY

Gastrointestinal tract,

peritoneum, skin,

muscle, lungs, peritoneum

Absorption

Anatomical barriers

(blood brain barrier,

blood genital barrier)

Passive and active diffusion in tissues

Hepatic, intestinal,

pulmonary metabolism

Drug administration

Oral, intravenous,

subcutaneous, intramuscular,

inhalation

Distribution

Metabolism

Protein

binding

Systemic

circulation

Target site

Biliary, renal,

pulmonary excretion

Excretion

BOTTOM-UP

AbsorptionDistribution

MOLECULAR/CELLULAR PROCESSES

MetabolismElimination

GENETICS

DEMOGRAPHICS

GENDER

COMORBIDITIES

Gastrointestinal tract,

peritoneum, skin,

muscle, lungs, peritoneum

Absorption

Anatomical barriers

(blood brain barrier,

blood genital barrier)

Passive and active diffusion in tissues

Hepatic, intestinal,

pulmonary metabolism

Drug administration

Oral, intravenous,

subcutaneous, intramuscular,

inhalation

Distribution

Metabolism

Protein

binding

Systemic

circulation

Target site

Biliary, renal,

pulmonary excretion

Excretion

TOP-DOWN

GENETICS

DEMOGRAPHICS

GENDER

COMORBIDITIES




BOTTOM-UP

Lungs

Portal Vein

RV LV

Small Intestine Tissue

Elimination

Pancreas

Spleen

Kidneys

Arte

ries

Liver

Stomach

Bones

Brain

Gonads

Skin

Thymus

Heart

IM Depot

Implant

Muscle

Adipose

Ve

ins

Phase I enzymes

Apparent permeability

CLEARANCEVOLUME OF

DISTRIBUTION

BIOAVAILABILITY

Concentrations

Effe

ct

DRUG-DRUG INTERACTIONS

OPTIMISATION OF NOVEL FORMULATIONS

TISSUE PENETRATION

PK IN SPECIAL POPULATIONS

Physiologically Based Pharmacokinetic Modelling

Population Pharmacokinetic Modelling

CHARACTERISATION

of KEY PK VARIABLES

Clearance

Vss

Rate of absorption

COMPARTIMENTAL

MODEL

IDENTIFICATION and

MATHEMATICAL DESCRIPTION

OF PREDICTORS

SIMULATION OF

CLINICAL SCENARIOS

• Top-down analysis of 485 publications (“david back” & Liverpool)

• 34599 keywords from Scopus

• 100 most frequent words

Modelling applications



Adult model structure Female model structure Nursing mother model structure

Child model structure

Model adequately described EFV

PK> 90% of all individual observed data points within thepredictive interval

11

Parameters Predicted (n = 400) Observed (n = 29)

Avg. EFV dose from breast milk

(µg/kg/day)

412 (82.3-2170) 428 (164-1610)

Max. EFV dose from breast milk

(µg/kg/day)

571 (131-2430) 809 (215-2760)

Infant [EFV]: 10 days-1 month (µg/mL) 0.22 (0.061-0.77) 0.19 (0.071-0.705)

Infant [EFV]: 1-3 months (µg/mL) 0.19 (0.037-0.81) 0.18 (0.036-0.504)



Prediction of PK parameters in mothers, milk

and children


Dickinson et al

Savic et al 2012


0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

0 5 10 15 20 25 30

Pla

sm

a c

on

ce

ntr

ati

on

s (

µg

/ml)

Time (days)

A broad variaty of tecnhological platforms, routes of administration to support innovative drug delivery strategies.

LIPOSOMES MICELLES SOLID LIPID

NANOPARTICLES

CO-NANOPRECIPITATES SPIONs SOLID DRUG

NANOPARTICLES

Routes of administration Drug delivery strategiesLONG-ACTING ORAL BIOAVIALABILITY

Nanomedicine for drug delivery

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0 500 1000 1500 2000

Pla

sma

co

nce

ntr

atio

ns

(mg

/mL)

Time (hr)

EXPERIMENTAL DATA IN SILICO SIMULATIONS VALIDATION against ORAL PK DATA

Use of Physiologically Based Modelling to identify LA candidates

CHARACTERISATION of KEY PK VARIABLES

Clearance

VssK = 0.0046 hr-1

K = 0.00046 hr-1

K = 0.023 hr-1

SIMULATION OF VARIOUS LONG-ACTING

STRATEGIES

Regulatory relevance

PBPK has great potential value to support benefit–risk evaluations

PBPK provides a mechanistic basis for extrapolation beyond the clinical

trial population, reducing uncertainty, and enabling better labeling around

drug–drug interactions and in special populations

“PBPK-thinking” in drug development is encouraged, as it

leads to a mechanistic understanding of the processes mediating drug

disposition

PopPK identifies the patient factors that cause changes in the dose-

concentration relationship and therefore can support dosage

modification and optimisation.

Recognition of the importance of developing optimum dosing strategies has

led to a surge in the use of the population PK approach in new drug

development and the regulatory process.

from “guidance for industry” by FDA

• Raltegravir (RAL) Pharmacokinetics (PK) and Safety in HIV-1 Exposed Neonates at High Risk of Infection (IMPAACT P1110) – O_2

• In silico pharmacokinetic/pharmacodynamic simulation of long acting tenofovirinjectable formulation for pre exposure prophylaxis strategies. O_14

• PBPK/PD Modeling and Simulations to Guide Dose Recommendation of Amlodipine after Co-administration with Viekirax or Viekira Pak – O_16

• Physiologically-based simulation of daclatasvir pharmacokinetics with antiretroviral inducers and inhibitors of cytochrome P450 and drug transporter – O_21

• Population Pharmacokinetics of Raltegravir and Raltegravir Glucuronide in Healthy Adults Receiving UGT1A1 Modulators Ritonavir, Ketoconazole or Rifampicin – P_64

• A semimechanistic Enzyme-Turnover Model for Simulating Darunavir/CobicistatPharmacokinetics over 72h Following Drug Cessation in Healthy Volunteers – P_65

• Population Pharmacokinetics and Pharmacodynamics Model Linking TDF/FTC with the dNTP Pool – P66

• Population pharmacokinetics of rilpivirine in HIV-1 infected patients treated with the single tablet regimen rilpivirine/tenofovir/emtricitabine – P_67

• Population Pharmacokinetic Analysis of Velpatasvir, a Pangenotypic HCV NS5A Inhibitor in Healthy and Hepatitis C Virus-Infected Subjects – P_27

17th International Workshop on Clinical Pharmacology of HIV & Hepatitis Therapy

TOP-DOWN




GENETICS

DEMOGRAPHICS

GENDER

COMORBIDITIES

BOTTOM-UP

Special populations Dose optimisationDDIs

Formulations

Penetration into tissues

Clinical trialsAnimal models

In vitro

data

Basic

research

Clinical

research

Pilot studies

Acknowledgments

David BackAndrew OwenSaye KhooRajith Kumar ReddyNeill LiptrottPaul CurleyDarren MossOwain RobertsLaura DIckinsonLee TathamJames HobsonAdeniyi OlagunjuMegan NearyChristina ChanJustin ChiongLaura ElseHenry PertinezAlessandro SchipaniSteve RannardTom McDonaldMarco GiardielloSharon MurphyFiona HattonSam AutyAndy Dwyer Jose Molto

Catia MarzoliniManuel Battegay

Kim ScarsiAnthony PodanyCourtney Fletcher

Marta Boffito

Charles FlexnerCaren Meyers

Giovanni Di Perri

Stefano Bonora

Andrea Calcagno

Antonio D’Avolio

All models are wrong, but some are useful…. I think we need more

data.

Documents

BACKing up clinical data with modellingregist2.virology-education.com/2016/17HIVHEPPK/27...Parameters Predicted (n = 400) Observed (n = 29) Avg. EFV dose from breast milk (µg/kg/day)