Degradation Prediction Goals. Degradation Knowledge Space. Prediction vs Actual Results. Experimental Conditions. Degradation Workflow.

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Dwain Murphy
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2 Degradation Goals QbD Approach Degradation Knowledge Space In Silico Tools In Cerebro Chemistry of Drug Degradation vs Actual Results How well are we progressing? Experimental Conditions Is wet chemistry still required? Degradation Workflow

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. 3

and validate the stability indicating power of the analytical procedures used.

4 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.

Control Space (Acceptable Operating Space)

5 QbD - Controlling the Quality: defining design space that the product has demonstrated in development to consistently meet required specifications Design Space Control Space (Acceptable Operating Space) (Normal Operating Space) Knowledge Space (Failure to Operate)

B C Parent "Actual" Degradation Products in final packaging / storage conditions H A B C D E F Parent G H "Potential" Degradation Products (Stress Testing Results) I Control

6 B C Parent "Actual" Degradation Products in final packaging / storage conditions H A B C D E F Parent G H "Potential" Degradation Products (Stress Testing Results) I Control Space Knowledge Space: Full Suite of Conditions (All likely modes of degradation) Design Space C B D E Parent H "Actual" Degradation Products (Accel. / Long-Term RT Stability)

7 Exploratory Screen Development (EDS) Screening Designed Synthesis (SDS) Lead Development (LD) CAN Seeking (CS) Phase 1

Strong chemistry understanding applied upfront creates a solid knowledge space in our QbD model 1- PREDICT DEGRADANTS Predict most likely degradants.

6- SELECT KPSS/TRACK PEAKS Determine the primary degradants. Track KPSS across orthogonal methods.

8 Strong chemistry understanding applied upfront creates a solid knowledge space in our QbD model 1- PREDICT DEGRADANTS Predict most likely degradants. 5- EVALUATE PURITY/POTENCY Obtain purity/potency data including mass balancewhere appropriate. 2- DESIGN PROTOCOL Develop based on the chemistry of the API/drug product formulation. 6- SELECT KPSS/TRACK PEAKS Determine the primary degradants. Track KPSS across orthogonal methods. 3- PERFORM EXPERIMENTS Sample at appropriate points using reasonable stress conditions. 7- IDENTIFY DEGRADANTS Utilize LC-MS, LC-NMR,. 4- CHALLENGE METHODOLOGY Screen degradation samples using suitable methodology (HPLC). 8- DOCUMENT Prepare reports and share degradation structures and mechanisms.

Santafianos Chapter 3 The Chemistry of Drug Degradation Pharmaceutical Stress

9 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

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.

10 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 Compound A N Ar N HO OH 2 N N O Ar 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. DELPHI: Degradant Expert Leading to PHarmaceutical Insight Reference: Pole et al. Molecular Pharmaceutics, Vol. 4, No. 4, , Challenging to teach new chemistry, involves outdated code

11 BDE: A Model for Predictive Stability? Hydrogen bond dissociation enthalpy a model for oxidative susceptibility? How hard (energetically) is it to pull the hydrogen off? 11

8 HN 1 c 9 7 2 Computed numbers are all over the place However, comparison is relative.

) Interested in relative (intramolecular) trends Conclusion: Calculations were time consuming/ resource intensive and not

formation 3 kcal/mole BDE kcal/mol e H formation 4 kcal/mole BDE kcal/mole sertralin e 29.3 22.0 19.9 10.0 -H 1 47.8 70.6 41.5 71.

12 8 HN 1 c 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 6 5 6' 5' Cl 4 3 2' Cl Species H formation 1 kcal/mol e BDE kcal/mole H formation 2 kcal/mole BDE kcal/mole H formation 3 kcal/mole BDE kcal/mol e H formation 4 kcal/mole BDE kcal/mole sertralin e H H H H H H 2'

13 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

14 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

15 H + OH- O 2 h D RH 15

16 Degradants: Acid/Base: Mechanism, Conditions Oxidation: Mechanism, Conditions Thermal/Humidity: Mechanism, Conditions 16

17 Change in MW Functional Group Search 17

18 1000 Degradants Substructure searchable With a benzylic functionality, oxidation will be emphasized in our experimental protocol

19 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

assist analysts Proactively identify PGIs

20 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

Search Pharma D3 New Zeneth KB Rule: 2012

21 Search Pharma D3 New Zeneth KB Rule: Teach Zeneth/ Archive Phama D3 Run Predictive Software Zeneth Not predicted in Zeneth KB Compare Actual Vs Predicted Degradants Design/Execute Experimental Protocol Study Radical Oxidation 30 mol%acva, 60 C Observed but not predicted Identify Actual Degradants Enrichmnet/isolation and characterization by MS and NMR

22 XX% 0.8N KOH O NH 2 XX% 0.8N KOH R N H N R' R' XX% 0.8N KOH XX% 1.0N HCl N N N H N H O ACVA/O 2 25% ACTUAL RESULT ACTUAL RESULTS <0.1% dissolving solvent H 2 O-15%MeCN,60C, 24h <0.1% 1N-HCl in 85%H 2 O-15%MeCN 5.0% 0.8N-KOH in 85%H 2 O-15%MeCN 89.0% H 2 O 2 oxidation 31.0% Oxidation ACVA, 60C, 24h

23 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

24 Epimerization, Rearrangement Ethyl Ether Hydrolysis Decarboxylation Hydroxyl to Ketone Olefin Dehydration Methyl Ether/ Ester Hydrolysis Ketone Oxidation Oxidation Hydration Oxidation

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

25 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 s performed using auto Zeneth conditions at ph 1 and 13 with an equivocal threshold Some differences observed in ph 1 and 13 predictions

26 DEG. COND. High Reactivity >10% Moderate Reactivity 1-10% Low Reactivity <1% Oxidation R (ACVA) 60 C, 24h >90% X H 2 O 2 48 h, 25C 30% X H 2 O 2 FeSO4 48 h/25c 60% X 1N-HCl RT, 48h 1% X 0.8N- KOH 25C, 48h 5% X DEG. COND. High Reactivity >10% Moderate Reactivity 1-10% Low Reactivity <1% Oxidation R (ACVA) 60 C, 24h <1% X H 2 O 2 6 days, 25C 7% X 0.1N-HCl RT, 2.5h 10% X 0.2N-KOH RT, 0.4h 27% X X = assesment of degradation by degradation type using in-silico; in-cerebro prediction

27 Degradant Structure ID Observations API&DP Stressed Conditions KB KB KB KB KB Predicted Degs- Observed Degs- Total predicted Degs ph 1- Total predicted Degs ph 13-3* A 1 API Solution Acid/Base Solid State Thermal DP- 70C/75%RH/3wks API-not observed STEP 1 Vey Likely STEP 1 Very Likely STEP 1 Very Likely STEP 1 Very Likely STEP 1 Very Likely Amide hydrolysis API Solution Not observed Ring cleavage A 2 Solid State Thermal DP- 30ºC/65%RH/2y rs API-not observed API Solution 1) ACVA Oxidation 2) H 2 O 2 Oxidation Pyrrolo Pyrimidine oxidation A 3 Solid State Thermal DP- 30ºC/65%RH/2y rs DP70ºC/75%RH/3w s API-not observed STEP 1 Likely

Degradant Structure ID Observations API&DP Stressed Conditions KB1

28 Degradant Structure ID Observations API&DP Stressed Conditions KB KB KB KB KB API Solution Not observed A4 Solid State Thermal DP-70ºC/75%RH/3wks DP-30ºC/65%RH/2 yrs API-not observed STEP 3 Likely STEP 2 equivocal STEP 2 equivocal STEP 2 equivocal Amine dealkylation API Solution ACVA Oxidation A5 Solid State Thermal DP-70ºC/75%RH/3wks DP-30ºC/65%RH/2 yrs API-not observed STEP 2 likely Pyrrolo Pyrimidine oxidation Ring cleavage 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 STEP 2 Likely STEP 2 Likely STEP 2 equivocal STEP 2 equivocal STEP 2 likely

29 Degradant Structure ID Observations API&DP Stressed Conditions KB KB KB KB KB Predicted Degs- Observed Degs- Total predicted Degs ph 1- Total predicted Degs ph 13-1* * API Solution Not observed B 3 Solid State Thermal DP-70ºC/75%RH/4wks API-not observed Amine dealkylation API Solution ACVA Oxidation Oxidation B 1 Solid State Thermal API -not observed DP(tablet) -not observed DP(oral soln)-50ºc/4wks

30 Degradant Structure ID Observations API&DP Stressed Conditions KB KB KB KB API Solution photo Ether hydrolysis B2 Solid State Thermal API-not observed DP(tablet)-not observed DP(oral soln)- observed Very Likely Step 1 Very Likely Step 1 Very Likely Step 1 Very Likely Step 1 NA Excipient API Interaction Predicted by Zeneth Dimer

31 Degradant Structure ID Observations API&DP Stressed Conditions KB KB KB KB KB Predicted Degs- Observed Degs- Total predicted Degs ph 1- Total predicted Degs ph Sulfoxide C1 API Solution 1) ACVA Oxidation 2) H 2 O 2 Oxidation 3) NMP Oxidation Solid State Thermal DP- 70ºC/75%RH/6wks API-not observed Very Likely Step 1 Very Likely Step 1 Very Likely Step 1 Very Likely Step 1 Very Likely Step 1 C2 API Solution Photo Solid State API photo DP photo Very Likely Step 1 dimer

32 Degradant Structure ID Observations API&DP Stressed Conditions KB KB KB KB KB API Solution Photo Cis isolmerization C3 Solid State API photo DP photo Likely Step 1 Likely Step 1 NA * API Solution Photo Solid State API photo DP photo dimer

33 Degradant Structure ID Observations API&DP Stressed Conditions KB KB KB KB KB Predicted Degs Observed Degs Total predicted Degs ph 1 Total predicted Degs ph * * * Oxidation D1 API Solution H 2 O 2 oxidation Solid State H 2 O 2 +API H 2 O 2 +DP yes Likely Step1 Very Likely Step 1 Very Likely Step 1 Very Likely Step 1 hydroxyl D2 API Solution H 2 O 2 oxidation Solid State H 2 O 2 +API Light +API H 2 O 2 +DP Light + API yes Very Likely Step 1 Very Likely Step 1 Oxidation + hydroxyl D3 API Solution H 2 O 2 oxidation Solid State H 2 O 2 +API H 2 O 2 +DP yes Equivocal Step 2 Equivocal Step 2

34 Degradant Structure ID Observations API&DP Stressed Conditions KB KB KB KB KB D4 Solid State Photo API Likely Step2 Likely Step2 Likely Step2 Likely Step2 Likely Step2 Ether hydrolysis D5 Solid State photo API Equivocal Step2 Equivocal Step2 Equivocal Step2 Ketone + hydrolysis

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.

35 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 B C D Average Predicted

CORE INGREDIENTS EXCIPIENT IMPURITY Varenicline - Mannitol Microcrystalline Cellulose (Avicel) Dibasic Calcium Phosphate (A-Tab, Di- Cal) FILM COAT COMPONENTS PEG 3350 Cellulose Acetate D-Mannose

36 CORE INGREDIENTS EXCIPIENT IMPURITY Varenicline - Mannitol Microcrystalline Cellulose (Avicel) Dibasic Calcium Phosphate (A-Tab, Di- Cal) FILM COAT COMPONENTS PEG 3350 Cellulose Acetate D-Mannose Formic acid, formaldehyde, D-glucose, acetic acid Calcium phosphate Aldehydes, peroxides, organic acid, formic acid, formaldehyde, acetic acid Acetic acid, D-glucose, formaldehyde, formic acid, acetic acid

37 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

38 Examples of valenicline excipient impurity degradants in tablets

39 Reaction of formaldehyde with amines (Eschweiler-Clarke) formic acid/formaldehyde can act as a reducing agent.

40 Eschweiler-Clarke Methylation of Primary or Secondary Amine

41 Reaction of formic acid with amines-amide formation

43 Default Zeneth data bases below can be used to create custom data bases for excipients and their contaminants

44 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.) Control Space Design Space Knowledge Space Fine-Tuned Knowledge Space based on chemistry guided tools

Identifying Experimental Conditions: Based on prediction knowledge, degradation conditions are selected/optimized 1- PREDICT DEGRADANTS Predict most likely degradants.

6- SELECT KPSS/TRACK PEAKS Determine the primary degradants. Track KPSS across orthogonal methods. 3- PERFORM EXPERIMENTS Sample at appropriate points using reasonable stress conditions.

45 Identifying Experimental Conditions: Based on prediction knowledge, degradation conditions are selected/optimized 1- PREDICT DEGRADANTS Predict most likely degradants. 5- EVALUATE PURITY/POTENCY Obtain purity/potency data including mass balance where appropriate. 2- DESIGN PROTOCOL Develop based on the chemistry of the API/drug product formulation. 6- SELECT KPSS/TRACK PEAKS Determine the primary degradants. Track KPSS across orthogonal methods. 3- PERFORM EXPERIMENTS Sample at appropriate points using reasonable stress conditions. 7- IDENTIFY DEGRADANTS Utilize LC-MS, LC-NMR,. 4- CHALLENGE METHODOLOGY Screen degradation samples using suitable methodology (HPLC). 8- DOCUMENT Prepare reports and share degradation structures and mechanisms.

46 Knowledge Space

48 Dinos Santafianos Martin Ott and LHASA Zeneth Consortium Steering Committee

Organic Chemistry of Drug Degradation

Organic Chemistry of Drug Degradation Li New Jersey Email: minli88@yahoo.com Publishing Contents Chapter 1 Introduction 1 1.1 Drug Impurities, and the Importance of Understanding Drug Chemistry 1 Characteristics