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Page 1: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of
Page 2: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

Degradation Prediction Goals

QbD Approach

Degradation Knowledge Space

In Silico Prediction Tools

In Cerebro Chemistry of Drug Degradation

Prediction vs Actual Results

How well are we progressing?

Experimental Conditions

Is wet chemistry still required?

Degradation Workflow

Page 3: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

3

Degradation prediction enables understanding

labile functionalities critical in designing less

reactive, more stable analogs.

With efforts to reduce time and cost to market, the

potential for stability issues increases dramatically.

Degradation studies conducted by a chemistry

guided predictive stability approach enables

analysts to deliver stability indicating methodology

more efficiently.

Page 4: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

Stress testing of the drug substance can

help identify the likely degradation

products, which can in turn help establish the degradation pathways

and the intrinsic stability of the molecule

and validate the stability indicating

power of the analytical procedures used. The nature of the stress testing will

depend on the individual drug

substance and the type of drug product

involved.

Page 5: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

QbD - Controlling the Quality: defining design space that the product has demonstrated in development to consistently meet required specifications

Design Space(Acceptable Operating Space) Knowledge Space

(Failure to Operate)Control Space

(Normal Operating Space)

Page 6: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

Design Space

Control Space

BC

Parent

"Actual" Degradation Productsin final packaging / storage conditions

H

AB

CD

E

F

GH

Parent

I

"Potential" Degradation Products(Stress Testing Results)

Knowledge Space: Full Suite of

Conditions (All likely modes of

degradation)

B

CD

EParent

"Actual" Degradation Products(Accel. / Long-Term RT Stability)

H

Page 7: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

Exploratory Screen Development

(EDS)

Screening Designed Synthesis

(SDS)

Lead Development

(LD)

CAN Seeking (CS)

Phase 1

Page 8: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

Strong chemistry understanding applied upfront creates a solid

knowledge space in our QbD model

1- PREDICT DEGRADANTSPredict most likely degradants.

2- DESIGN PROTOCOLDevelop based on the chemistry

of the API/drug product formulation.

3- PERFORM EXPERIMENTSSample at appropriate points using

‘reasonable’ stress conditions.

4- CHALLENGE METHODOLOGYScreen degradation samples

using suitable methodology (HPLC).

5- EVALUATE PURITY/POTENCYObtain purity/potency data including mass

balancewhere appropriate.

6- SELECT KPSS/TRACK PEAKSDetermine the primary degradants.

Track KPSS across orthogonal methods.

7- IDENTIFY DEGRADANTSUtilize LC-MS, LC-NMR,.

8- DOCUMENTPrepare reports and share degradation

structures and mechanisms.

Page 9: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

Predict Degradants based on

In-cerebro-chemistry knowledge

In silico approaches and databases :

CAMEO, DELPHI, Pharma D3 and

Zeneth

Baertschi, Alsante, Santafianos Chapter 3 “The

Chemistry of Drug Degradation” Pharmaceutical Stress

Testing, Second Edition, S. Baertschi, K. Alsante, R. Reed, Editors, Informa, July 2011

Page 10: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

CAMEO: Computer Assisted Mechanistic Evaluation of Organic Reactions

By Prof. W.L. Jorgensen to predict the products of organic reactions based on the concepts of organic chemistry.

Analyzes molecule and the reactants and applies organic chemistry to predict reactions

Converted to a plug-in program available with CamSoft ChemOffice

No ability to teach new chemistry

DELPHI: Degradant Expert Leading to PHarmaceutical InsightReference: Pole et al. Molecular Pharmaceutics, Vol. 4, No. 4, 539-549, 2007.

Challenging to teach new chemistry, involves outdated code

CAMEO understands that the nitrogen is nucleophilic and looks for something to react with. In this case we have told CAMEO that hydrogen peroxide is in the reaction. CAMEO can be told what is in the reaction and can deal with new reagents and substrates.

N

NAr

O

N

N Ar

HO OH2

Compound A

Page 11: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

11

Hydrogen bond dissociation enthalpy

a model for

oxidative

susceptibility?

How hard

(energetically) is it to

pull the hydrogen

off?

BDE: A Model for Predictive Stability?

Page 12: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

Computed numbers are all over the place

However, comparison is relative. Differences wash out

(as long as we use a consistent computational model!)

Interested in relative (intramolecular) trends

Conclusion: Calculations were time consuming/

resource intensive and not sustainable model

Cl

Cl

NH

1

2

3

45

6

7

8

2'

5'

6'

9

Species Hformation1

kcal/mole

BDEkcal/mole

Hformation2

kcal/moleBDEkcal/mole

Hformation3

kcal/moleBDEkcal/mol

e

Hformation4

kcal/moleBDEkcal/mole

sertraline

29.3 22.0 19.9 10.0

-H1 47.8 70.6 41.5 71.6 40.1 72.3 32.1 74.2

-H2 63.0 85.8 56.6 86.7 50.8 83.0 42.3 84.4

-H4 55.7 78.5 50.3 80.4 47.1 79.3 39.2 81.3

-H6 87.4 110.2 81.6 111.7 73.4 105.6 65.3 107.4

-H9 53.3 76.1 48.1 78.2 43.3 75.5 35.8 77.9

-H2' 89.3 112.1 83.4 113.5 76.5 108.7 68.2 110.3

c

Page 13: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

Uses high quality knowledge base and

reasoning engine. Detailed trees showing

chemical degradation pathways have

been implemented. Including:

Level of likelihood

• Chemical formula

• Exact mass

• Degradation pathway description

• Literature references

Page 14: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

Novel "hybrid" publishing/database paradigm enabling a proactive approach to drug stability by establishing trends with functional groups to allow enhanced prediction of degradation results

Work with 2 verisons: internal – proprietary; external – published data mining

http://d3.cambridgesoft.com/

Page 15: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

15

D RH

O2 h

H+ OH-

Page 16: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

16

Degradants:

Acid/Base:

Mechanism, Conditions

Oxidation:

Mechanism, Conditions

Thermal/Humidity:

Mechanism, Conditions

Page 17: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

17

Change in MW

Functional Group Search

Page 18: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

With a benzylic functionality, oxidation will be emphasized in

our experimental protocol

1000 Degradants

Substructure searchable

Page 19: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

Predicts degradants from

API

API/Excipient Impurity

API/ low MW Excipient

Captures change in MW

Records actual results

Notebook references

LC method described

Sample prep. described

Example chromatogram

Solid state stress testing

Solution stress testing

PGI degradant alerts

Page 20: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

Flag Potential Genotoxic Impurity (PGI) structure alerts

Goal of understanding/detecting PGIs to assist analysts

Proactively identify PGIs

Developing stage sensitive strategies for controlling PGIs

Page 21: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

Search

Pharma D3

Run Predictive Software Zeneth

Design/Execute Experimental

Protocol

Identify Actual Degradants

Compare Actual Vs Predicted Degradants

Teach Zeneth/

Archive Phama D3Not predicted in Zeneth

KB 2009-2011

Study Radical Oxidation 30 mol%ACVA, 60°C

New Zeneth KB

Rule: 2012.1.0

Enrichmnet/isolation and

characterization by MS and NMRObserved but not predicted

Page 22: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

N

N NH

R'N

NH2

R NH

O

NH

R'

O

ACVA/O2 25%ACTUAL RESULT

<0.1% dissolving solvent H2O-15%MeCN,60C, 24h<0.1% 1N-HCl in 85%H2O-15%MeCN

5.0% 0.8N-KOH in 85%H2O-15%MeCN89.0% H2O2 oxidation31.0% Oxidation ACVA, 60C, 24h

XX% 0.8N KOH

XX% 0.8N KOH

XX% 0.8N KOHXX% 1.0N HCl

ACTUAL RESULTS

Page 23: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

Summary of most common degradation reactions

Link to MW Pharma D3 mining

Degradation vs. synthetic chemistry

Synthetic chemistry focuses on bond making and

stoichiometric reactions

Degradation chemistry pays attention to slow, low

yielding (0.1%) and bond breaking reactions

Page 24: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

Dehydration

Hydration

Oxidation

Oxidation

KetoneOxidation

Epimerization, Rearrangement

Hydroxyl to Ketone

Olefin

DecarboxylationMethyl Ether/

Ester Hydrolysis

Ethyl Ether Hydrolysis

Page 25: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

Four Late stage compounds evaluated and disguised as Compounds A, B, C and D for

presentation (Tofacitinib, Crizotinib, Axitinib,

Bostutinib)

Comparison of DELPHI, Zeneth 3 (knowledge base 1-3) and Zeneth 4 beta version

Predictions performed using auto Zeneth conditions

at pH 1 and 13 with an equivocal threshold

Some differences observed in pH 1 and 13 predictions

Page 26: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

DEG.

COND.

Oxidation

R (ACVA)

60C, 24h

H2O2

48 h,

25C

H2O2

FeSO4

48

h/25C

1N-HCl

RT, 48h

0.8N-

KOH

25C, 48h

High

Reactivity

>10%

>90%

X

30%

X

60%

X

Moderate

Reactivity

1-10%

1%

X

5%

X

Low

Reactivity

<1%

DEG.

COND.

Oxidation

R (ACVA)

60C, 24h

H2O2

6 days,

25C

0.1N-HCl

RT, 2.5h

0.2N-KOH

RT, 0.4h

High

Reactivity

>10%

10%

X

27%

X

Moderate

Reactivity

1-10%

7%

X

Low

Reactivity

<1%

<1%

X

X = assesment of degradation by degradation type using in-silico; in-cerebro prediction

Page 27: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

Degradant

Structure

ID Observations

API&DP Stressed

Conditions

Prediction

KB1

2009.1.0

Prediction

KB2

2009.2.0

Prediction

KB3

2011.1.0

Prediction

KB4

2011.2.0

Prediction

KB6

2012.1.3

Predicted Degs-

Observed Degs-

Total predicted Degs pH 1-

Total predicted Degs pH 13-

3*

6

39

34

2

6

31

170

3

6

158

197

5

6

188

321

A

1

API Solution

Acid/Base

Solid State Thermal

DP-

70C/75%RH/3wks

API-not observed

pH 1(yes)

pH13 (yes)STEP 1

Vey Likely

pH 1(yes)

pH13 (yes)STEP 1

Very Likely

pH 1(yes)

pH13 (yes)STEP 1

Very Likely

pH 1(yes)

pH13 (yes)STEP 1

Very Likely

pH 1(yes)

pH13 (yes)

STEP 1

Very Likely

A

2

API Solution

Not observed

Solid State Thermal

DP-

30ºC/65%RH/2y

rs

API-not observed

pH 1(no)

pH13 (no)

pH 1(no)

pH13 (no)

pH 1(no)

pH13 (no)

pH 1(no)

pH13 (no)

pH 1(yes)

pH13 (yes)

A

3

API Solution

1) ACVA Oxidation

2) H2O2 Oxidation

Solid State Thermal

DP-

30ºC/65%RH/2y

rs

DP70ºC/75%RH/3w

s

API-not observed

pH 1(no)

pH13 (no)

pH 1(no)

pH13 (no)

pH 1(no)

pH13 (no)

pH 1(no)

pH13 (no)

pH 1(yes)

pH13 (yes)

STEP 1

Likely

Amide hydrolysis

Ring cleavage

Pyrrolo Pyrimidine oxidation

Page 28: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

Degradant

Structure

ID Observations

API&DP Stressed

Conditions

Prediction

KB1

2009.1.0

Prediction

KB2

2009.2.0

Prediction

KB3

2011.1.0

Prediction

KB4

2011.2.0

Prediction

KB6

2012.1.3

A4

API Solution

Not observed

Solid State Thermal

DP-70ºC/75%RH/3wks

DP-30ºC/65%RH/2 yrs

API-not observed

pH 1(yes)

pH13 (no)STEP 3Likely

pH 1(no)

pH13 (no)pH 1(yes)

pH13 (yes)STEP 2

equivocal

pH 1(yes)

pH13 (yes)STEP 2

equivocal

pH 1(yes)

pH13 (yes)STEP 2

equivocal

A5

API Solution

ACVA Oxidation

Solid State Thermal

DP-70ºC/75%RH/3wks

DP-30ºC/65%RH/2 yrs

API-not observed

pH 1(no)

pH13 (no)

pH 1(no)

pH13 (no)

pH 1(no)

pH13 (no)

pH 1(no)

pH13 (no)pH 1(yes)

pH13 (yes)STEP 2likely

A6

API Solution

1) ACVA Oxidation

2) Fenton

3) Metals

Solid State Thermal

DP-70ºC/75%RH/3wks

DP-30ºC/65%RH/2 yrs

API-not observed

pH 1(yes)

pH13 (yes)STEP 2Likely

pH 1(yes)

pH13 (yes)STEP 2Likely

pH 1(yes)

pH13 (yes)STEP 2

equivocal

pH 1(yes)

pH13 (yes)STEP 2

equivocal

pH 1(yes)

pH13 (yes)STEP 2likely

Pyrrolo Pyrimidine oxidation

Amine dealkylation

Ring cleavage

Page 29: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

Degradant

Structure

ID Observations

API&DP Stressed

Conditions

Prediction

KB1

2009.1.0

Prediction

KB2

2009.2.0

Prediction

KB3

2011.1.0

Prediction

KB4

2011.2.0

Prediction

KB6

2012.1.3

Predicted Degs-Observed Degs-Total predicted Degs pH 1-Total predicted Degs pH 13-

1*

3

92

41

1

3

150

167

1

3

175

170

1*

3

34

60

B

3

API Solution

Not observed

Solid State Thermal

DP-70ºC/75%RH/4wks

API-not observed

pH 1(no)

pH13 (no)

pH 1(no)

pH13 (no)

pH 1(no)

pH13 (no)

pH 1(no)

pH13 (no)

pH 1(no)

pH13 (no)

B

1

API Solution

ACVA Oxidation

Solid State Thermal

API -not observed

DP(tablet) -not observed

DP(oral soln)-50ºC/4wks

pH 1(no)

pH13 (no)

pH 1(no)

pH13 (no)

pH 1(no)

pH13 (no)

pH 1(no)

pH13 (no)

pH 1(no)

pH13 (no)

Amine dealkylation

Oxidation

Page 30: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

Degradant

Structure

ID Observations

API&DP Stressed

Conditions

Prediction

KB1

2009.1.0

Prediction

KB2

2009.2.0

Prediction

KB3

2011.1.0

Prediction

KB4

2011.2.0

B2

API Solution

photo

Solid State Thermal

API-not observed

DP(tablet)-not

observed

DP(oral soln)-

observed

pH 1(yes)

pH13 (no)Very Likely

Step 1

pH 1(yes)

pH13 (yes)Very Likely

Step 1

pH 1(yes)

pH13 (yes)Very Likely

Step 1

pH 1(yes)

pH13 (yes)Very Likely

Step 1

NA Excipient API Interaction Predicted by Zeneth

Ether hydrolysis

Dimer

Page 31: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

Degradant

Structure

ID Observations

API&DP Stressed

Conditions

Prediction

KB1

2009.1.0

Prediction

KB2

2009.2.0

Prediction

KB3

2011.1.0

Prediction

KB4

2011.2.0

Prediction

KB6

2012.1.3

Predicted Degs-Observed Degs-Total predicted Degs pH 1-Total predicted Degs pH 13-

1

3

158

44

1

3

167

95

1

3

83

95

2

3

90

115

3

3

51

80

C1

API Solution

1) ACVA Oxidation

2) H2O2 Oxidation

3) NMP Oxidation

Solid State Thermal

DP-

70ºC/75%RH/6wks

API-not observed

pH 1(yes)

pH13 (yes)Very Likely

Step 1

pH 1(yes)

pH13 (yes)Very Likely

Step 1

pH 1(yes)

pH13 (yes)Very Likely

Step 1

pH 1(yes)

pH13 (yes)Very Likely

Step 1

pH 1(yes)

pH13 (yes)Very Likely

Step 1

C2

API Solution

Photo

Solid State

API photo

DP photo

pH 1(no)

pH13 (no)

pH 1(no)

pH13 (no)

pH 1(no)

pH13 (no)

pH 1(no)

pH13 (no)pH 1(yes)

pH13 (yes)Very Likely

Step 1

Sulfoxide

dimer

Page 32: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

Degradant

Structure

ID Observations

API&DP

Stressed

Conditions

Prediction

KB1

2009.1.0

Prediction

KB2

2009.2.0

Prediction

KB3

2011.1.0

Prediction

KB4

2011.2.0

Prediction

KB6

2012.1.3

C3

API Solution

Photo

Solid State

API photo

DP photo

pH 1(no)

pH13 (no)

pH 1(no)

pH13 (no)

pH 1(no)

pH13 (no)

pH 1(yes)

pH13 (yes)

Likely

Step 1

pH 1(yes)

pH13 (yes)

Likely

Step 1

NA

*

API Solution

Photo

Solid State

API photo

DP photo

pH 1(no)

pH13 (no)

pH 1(no)

pH13 (no)

pH 1(no)

pH13 (no)

pH 1(no)

pH13 (no)

pH 1(no)

pH13 (no)

Cis isolmerization

dimer

Page 33: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

Degradant

Structure

ID Observations

API&DP

Stressed

Conditions

Prediction

KB1

2009.1.0

Prediction

KB2

2009.2.0

Prediction

KB3

2011.1.0

Prediction

KB4

2011.2.0

Prediction

KB6 2012.1.3

Predicted Degs

Observed Degs

Total predicted Degs pH 1

Total predicted Degs pH 13

2

5

226

208

2*

5

192

229

5*

5

311

311

5*

5

270

294

D1

API Solution

H2O2

oxidation

Solid State

H2O2 +API

H2O2+DP

yes pH 1(yes)

pH13 (yes)

Likely

Step1

pH 1(no)

pH13 (yes)

Very Likely

Step 1

pH 1(no)

pH13 (yes)

Very Likely

Step 1

pH 1(no)

pH13 (yes)

Very Likely

Step 1

D2

API Solution

H2O2

oxidation

Solid State

H2O2 +API

Light +API

H2O2+DP

Light + API

yes pH 1(no)

pH13 (no)

-

-

pH 1(no)

pH13 (no)

-

-

pH 1(yes)

pH13 (yes)

Very Likely

Step 1

pH 1(yes)

pH13 (yes)

Very Likely

Step 1

D3

API Solution

H2O2

oxidation

Solid State

H2O2 +API

H2O2+DP

yes pH 1(no)

pH13 (no)

-

-

pH 1(no)

pH13 (no)

-

-

pH 1(no)

pH13 (yes)

Equivocal

Step 2

pH 1(no)

pH13 (yes)

Equivocal

Step 2

Oxidation

hydroxyl

Oxidation + hydroxyl

Page 34: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

Degradant

Structure

ID Observations

API&DP Stressed

Conditions

Prediction

KB1

2009.1.0

Prediction

KB2

2009.2.0

Prediction

KB3

2011.1.0

Prediction

KB4

2011.2.0

Prediction

KB6

2012.1.3

D4

Solid State

Photo

API

pH 1(yes)

pH13 (yes)

Likely

Step2

pH 1(yes)

pH13 (yes)

Likely

Step2

pH 1(yes)

pH13 (yes)

Likely

Step2

pH 1(yes)

pH13 (yes)

Likely

Step2

pH 1(yes)

pH13 (yes)

Likely

Step2

D5

Solid State

photo

API

pH 1(no)

pH13 (no)

-

-

pH 1(no)

pH13 (no)

-

-

pH 1(yes)

pH13 (no)

Equivocal

Step2

pH 1(yes)

pH13 (no)

Equivocal

Step2

pH 1(yes)

pH13 (no)

Equivocal

Step2

Ether hydrolysis

Ketone + hydrolysis

Page 35: Degradation Prediction Goals - Welcome to Lhasa Limited · Degradation Prediction Goals QbD Approach Degradation Knowledge Space In Silico Prediction Tools In Cerebro Chemistry of

Benchmarked against 4 recently filed Pfizer compounds

Zeneth’s accuracy has improved from an average

of 39% to 79% on benchmarked data

Zeneth Version Z3 Z3 Z3 Z4 Z4 Z5

Knowledge Base KB1 KB2 KB3 KB4 KB5 KB6

Drug Observed Predicted % Predicted Predicted % Predicted Predicted % Predicted Predicted % Predicted Predicted % Predicted Predicted % Predicted

A 6 3 50.0 2 33.3 3 50.0 3 50.0 5 83.3 5 83.3

B 3 1 33.3 1 33.3 1 33.3 1 33.3 1 33.3 1 33.3

C 3 1 33.3 1 33.3 1 33.3 2 66.7 2 66.7 3 100.0

D 5 2 40.0 2 40.0 5 100.0 5 100.0 5 100.0 5 100.0

Average

Predicted 39.2 35.0 54.2 62.5 70.8 79.2

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CORE INGREDIENTS EXCIPIENT IMPURITY

Varenicline -

Mannitol D-Mannose

Microcrystalline Cellulose (Avicel) Formic acid, formaldehyde, D-glucose, acetic acid

Dibasic Calcium Phosphate (A-Tab, Di-Cal)

Calcium phosphate

FILM COAT COMPONENTS

PEG 3350 Aldehydes, peroxides, organic acid, formic

acid, formaldehyde, acetic acid

Cellulose Acetate Acetic acid, D-glucose, formaldehyde, formic acid, acetic acid

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API degradant prediction process becomes too

complicated when incorporating excipients and

their impurities in one processing step as illustrated

below.

Easier to process API against individual reactant

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Examples of valenicline excipient impurity degradants in tablets

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Reaction of formaldehyde with amines (Eschweiler-Clarke)

formic acid/formaldehyde

can act as a reducing agent.

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Eschweiler-Clarke Methylation of Primary or Secondary Amine

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Reaction of formic acid with amines-amide formation

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Default Zeneth data bases below can be used to create custom

data bases for excipients and their contaminants

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Chemistry guided approach using predictive tools assists in

targeting most likely reactive functional groups and experimental

conditions to focus on (by using Zeneth, Pharma D3, etc.)

Knowledge Space

Design Space

Control Space

Fine-TunedKnowledge Space

based on chemistryguided tools

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Identifying Experimental Conditions:

Based on prediction knowledge, degradation conditions are

selected/optimized

1- PREDICT DEGRADANTSPredict most likely degradants.

2- DESIGN PROTOCOLDevelop based on the chemistry

of the API/drug product formulation.

3- PERFORM EXPERIMENTSSample at appropriate points using

‘reasonable’ stress conditions.

4- CHALLENGE METHODOLOGYScreen degradation samples

using suitable methodology (HPLC).

5- EVALUATE PURITY/POTENCYObtain purity/potency data including

mass balance where appropriate.

6- SELECT KPSS/TRACK PEAKSDetermine the primary degradants.

Track KPSS across orthogonal methods.

7- IDENTIFY DEGRADANTSUtilize LC-MS, LC-NMR,.

8- DOCUMENTPrepare reports and share degradation

structures and mechanisms.

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Knowledge Space

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Dinos Santafianos

Martin Ott and LHASA Zeneth Consortium

Steering Committee