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(1)

APS - Progress within the IMI OrBiTo project – predictive tools for oral biopharmaceutics

GSK Stevenage

Development of the compound database

Tuesday 13th May 2014

Kristin Lacy, Alison Margolskee,

(2)

WP4 Objectives–within OrBiTo

Refine existing in silico mechanistic tools to improve

absorption modeling

Define the gaps in the models

Propose tools to calculate model performance

Change the inputs to the models

Amin Rostami and Xavier Pepin

2014-05-13

g

p

Change the algorithms to better reflect

biology and drug formulation behaviour

Monitor performance

(3)

Requirements for the OrBiTo compound database

Be novel (capture EFPIA data)

Be secure, accessible to all partners

Data capture offline with standard

tools

Be searchable

Feasibility to selectively blind some

Amin Rostami and Xavier Pepin

2014-05-13

Feasibility to selectively blind some

fields

Allow interaction whilst maintaining

full or partial anonymity

Be flexible to allow new fields to be

captured

(4)

OrBiTo Database – How does it work?

Amin Rostami and Xavier Pepin

2014-05-13

1.

Data gathering using Excel

Plug-in developed by Simcyp

XML File

2.

An XML file is generated containing

the data collected in the Excel plug-in

3.

XML file is imported into the

OrBiTo database which is

stored within the ‘Cloud’

4.

Users query the database using

the ‘OrBiTo Query Language’

5.

Data is downloaded from the database

and the Excel plug-in is re-populated with

the downloaded data.

(5)

Backups of the database are taken once a week so if any

failures were to happen then we would be able to rebuild the

database.

OrBiTo Database – Backups

All backups are stored within the ‘Cloud’ and are kept for 90

(6)

OrBiTo Database – How/when was it built ?

Amin Rostami and Xavier Pepin

2014-05-13

August

2012

: Pre-project discussions started between Sanofi

& Simcyp to decide what type of data, metadata was to be

captured and in what units and format. A decision about

how the data would be collated was also made during this

time (Excel plug-in).

Data type Metadata Value Unit Format Comments For what purpose Minimal dataset

Main challenge with the drug Indicate the main challenge from picklist and also free text Text Drop down menu 0 Important pKa Indicate basic"B" of acidic "A" nature + method of obtention (calculated or measured, indicate batch number used) no unit

n / 3 matrix [n values rounded to +/- 0.1 / "A" for acid or "B" base/

Method of obtention]

Indicate all the pKas of the drug Calculate ionization, distribution, permeability vs pH Compulsory

f T l l h diff i ffi i Molecular weight of active moiety None g.mol-1 1 value +/- 1 g/mol For blinding purposes the use of ranges could be made.

Proposal to round it to +/- 1 g/mol

To calculate the diffusion coefficient (dissolution rate) +/- 1 g/mol Compulsory API Log P Shake flask, pH-metric, pH-UV, HPLC, CE no unit (log ratio) [1 value +/- 0.1 / Method of

obtention = DDM5] log P = Log D of the unionized species

Anticipation of many partition properties with/through biological membranes. Anticipation of solubility in micellar systems, use in the ACAT model for absorption scaling factors

Compulsory

API Log D = LogP @pHn Shake flask, pH-metric, pH-UV, HPLC, CE no unit (log ratio)

n / 3 matrix (n pH values / n Log D values +/- 0.1/Method of obtention = DDM5]

Choose as many log D as different types of ionized species based on the pKas. Choose pH rationnaly, i.e. pH1 = pKa1-2 ; pH2 = (pka1+pKa2)/2...etc or best reconstruct full lipophilicity profile

QSAR modeling, anticipation of many partition properties with/through biological membranes. Anticipation of solubility in micellar systems

Compulsory

Apparent API permeability through membrane (Bi-directional)

Indicate membrane nature (strain of cells or artificial) + drug concentration in donor+ direction or transport + pH in donor + pH in acceptor + recovery + inhibitor nature + inhibitor concentration + BSA concentrations in donor + value

nm/s

n/14 matrix [n values Papp (+ - 0.1 nm.s-1) / membrane nature = DDM1 / drug concentration in donor (µM)/ "A2B" or "B2A"/ pH in donor + - 0.1/ pH in acceptor + -0.1/ recovery (+ - 1%)/ inhibitor name / inhibitor concentration (µM)/ BSA concentration in donor (%)/BSA concentration in acceptor (%)/ Agitation rate (rpm)/ type of agitation = free text/ Size of Unstirred Water Layer(µm)/other info = free text/ Cross reference to method PxMn]

Need to define a list of reference drugs to be tested at a certain concentration in the apical compartment to serve as reference for the Papp measurements coming from different labs, provide the Papp of these "reference drugs" in a separate table with open name for the drug but similar format indicated in format column

Use to run/ validate in silico tools

(7)

OrBiTo Database – How/when was it built ?

Amin Rostami and Xavier Pepin

2014-05-13

November 2012: 1

st

Excel plug-in was developed. Based on the

structure of the Excel plug-in and the type of data collated, the

database architecture was designed.

(8)

OrBiTo Database – How/when was it built ?

Amin Rostami and Xavier Pepin

2014-05-13

December 2012: Agreement on numbering and blinding strategy

January 2013: First usable Excel plug-in 1.1 distributed to EFPIA

partners for T4.2, T4.3 & T4.4

(9)

OrBiTo Database – How/when was it built ?

Amin Rostami and Xavier Pepin

2014-05-13

Oct 2012 - June 2013: Development of the OrBiTo web database by

SimCYP/Certara

May 2013: Sanofi to pay the web cloud services to cover project duration

6

th

June -Sept 2013: Data Upload open to EFPIA

Sept 2013-March 2014 Data gap analysis & update (T4.6)

Data gap identification

feed-back to EFPIA

Gap filling if possible

SimCYP/ Manchester Uni + EFPIA

(10)

What is currently in the OrBiTo database

API database characteristics: 86 compounds

Considerable proportion of solubility limited compounds comparable to

other databases.

(11)

What is currently in the OrBiTo database - Formulations

43.1

7.8

4.4 3.6

2.0

1.6 0.6

IR crystalline

IR solution aqueous

P l

d

l

Database mainly consisting of immediate-release solutions and tablets

with a small proportion of controlled-release

37.0

Prolonged release

IR solution non aqueous

IR amorphous

Undefined

Delayed release

IR emulsion

(12)

OrBiTo database – PK studies

Studies = 489

Arms = 1576

1214 in fasted

274 in fed state

88 no mention of prandial state

(13)

OrBiTo database – Administration routes

IV

215

Oral

1278

Other (specify)

( p

y)

29

SC

4

Unspecified

50

Total

1576

(14)

Other administration routes

Human or canine administration to the lower segments of the

intestine

Other route\APIs

A2853

A3837

A5766

A6598

A6646

A6939

Total

Ascending colon

3

3

1

2

1

1

11

dog (beagle)

1

1

human

3

2

2

1

8

rat (specify strain)

1

1

2

OrBiTo database – Administration routes

Descending colon or rectal

4

4

human

4

4

Distal Small Bowel

1

1

1

1

4

dog (beagle)

1

1

human

1

1

1

3

Jejunum

1

1

2

dog (beagle)

1

1

human

1

1

Stomach

1

1

human

1

1

Total

4

4

3

3

7

1

22

(15)

Data gap analysis – Task 4.6

1- Objective of the gap analysis : Improve data quality

through gap analysis and active participation of EFPIA

2- Select dataset for task 4.9 : evaluation of performance of

existing PBPK tools using blinded human dataset : Bottom-up

anticipation of human PK and systematic evaluation of model

anticipation of human PK and systematic evaluation of model

failures

Run by 3 independent partners

comparison of selections

(16)

API data files

(17)

Data gap analysis – Example of data quality

Apparent permeability

5 APIs with no permeability data

(some are drugs of pro-drugs)

Large disparity in # of datapoints

Majority with 2 or 1 datapoint

# of permeability

data

# of

APIs

0

5

1

16

2

30

3

4

4

8

In Feb 2014 : Only 35 API with

reference compound Papp

Issue for scaling to human Peff

5

2

6

5

7

1

8

5

9

1

10

4

12

2

14

3

(18)

Filling the gaps: Strategy

Commenting on missing parameters through

orbitodatabase.eu

Email updates twice per week on the status of parameter

information for ‘key parameters’

Parameters of interest:

LogP/D

Solubility

Solubility

Permeability

Clearance

Particle size

fup

BP

Human oral PK data

Human i.v. PK data

(19)

Filling the gaps: Strategy

Commenting on missing parameters through

orbitodatabase.eu

Unique feature of info communication through database whilst

(20)

Data gap analysis – T4.6

(21)
(22)

Data gap analysis – T4.6

Other criteria for clustering

Formulations types

(23)

Task 4.6 : gap analysis : Final selection

Final selection included 43 APIs

In order to expand M&S API set some deficiencies were allowed,

including: Lack of blood-to-plasma ratios, lack of fumic and clearance

informed via allometric scaling.

(24)

Modelling and simulation task: Plan and progress

The final 43 APIs were allocated

based on available man months

with a considerable overlap for

10 APIs in order to test operator

differences between sites.

Deadline is currently set for end

of September 2014.

EFPIA

EFPIA

EFPIA

EFPIA

API

API

database

database

Gap

Gap

analysis

analysis

API

API

selection

selection

M&S Task

M&S Task

Statistical

Statistical

evaluation

evaluation

ACAT

ACAT

ACAT

ACAT

ADAM

ADAM

ADAM

ADAM

GI

GI--SSim

im

GI

GI--SSim

im

March – September 2014

March 2014

Dec 2013 – Feb 2014

Academic

Academic

contributors

contributors

and EFPIA

and EFPIA

Academic

Academic

contributors

contributors

and EFPIA

and EFPIA

Based on

Based on

Selection

Selection

Criteria

Criteria

Based on

Based on

Selection

Selection

Criteria

Criteria

June 2013 – Feb 2014

November 2014

(25)

Uses of the OrBito compound database

T4.9 (Feb-Nov 2014)

Gap analysis

Bottom up anticipation of human PK from in

vitro and animal data

Systemic evaluation of model performance

based on quality indicators (T4.8)

Evaluation of gaps depending on

biopharmaceutical space/type of formulation

T4.7 (March– Dec 2014)

Regular updates

Completion of missing data / Incomplete data (EFPIA)

Continuous improvement (new fields,

new data, new simulations)

monitor

goodness of fit to increase performance

Completion of missing data / Incomplete data (EFPIA)

Creation of new fields in the database in support of

other WPs work (SimCYP)

WP1-3 : Identification of drugs

of interest for performing tests

in vitro or in vivo

T4.20 : testing of various improvement

options for in silico models

(26)

OrBiTo database creation and improvement –

Acknowledgements

Kristin Lacy

Steve Andrews

Philip Hayward

Ian Gledhill

Shriram Pathak

Amin Rostami and Xavier Pepin

2014-05-13

Shriram Pathak

Adam Darwich

Alison Margolskee

All the EFPIA partners

(27)

2013

2014

2015

2016

2017

Planning

2014 : Biggest year for WP4

Completion of T4.6, T4.9, T4.7, T4.5

(28)

Status – WP4

Review of available PBPK models

and gaps in physiology is

accepted and available online

Data gap analysis is completed

(T4.6)

Important updates

T4.5 not started

T4.5 proposition to integrate

WP3 data in vivo instead since

RIVM database unavailable

Issues

Mitigations

Amin Rostami and Xavier Pepin

2014-04-23

Overall status

Tasks

Milestones/

Deliveries

People

Comm.

Budget

T4.9 is started

T4.7 Increase of budget in

SimCYP to perform this task (for

maintaining database and adding

new fields)

Change from last report

Completed

References

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