mirabilis - welcome to lhasa limited · • assume ttc is 100 ppm • assume charge (initial conc)...

MirabilisUsing Mirabilis to Support Purge Arguments Under

ICH M7

[email protected]

Dr Michael Burns

Scientist

mailto:[email protected]

Outline

• Background

• Introduction to paper-based approach

• Industrial case studies

• Introduction to the Mirabilis project

• Regulatory advocacy• Break for Questions

• Introduction to the Mirabilis software

Background

3

• The threat posed by (potential) mutagenic impurities, (P)MIs, in drug substances arises from the use of reagents such as alkylating agents within the synthesis.

• What makes them useful reagents in synthesis, is also what makes them (P)MIs.

• Virtually all syntheses will involve the use of mutagenic or potentially mutagenic reagents or possess potential risk arising from a (P)MI formed in the process.

• Any synthetic drug therefore may have a latent (P)MI-related risk.

Evaluating Risk Posed by Mutagenic Impurities

4

• Fundamentally there is a need to assess the risk posed by mutagenic impurities.

• Are there any mutagenic impurities present in the final product at levels of concern?

• Historically the emphasis has been to analytically test for every MI.

• Significant analytical challenges.• Also takes little account of knowledge of intrinsic reactivity /

physico-chemical parameters.

• Question Posed – Could a systematic way be found that takes into account factors that reduce the risk of potential carryover?

Example of the analytical challenge

5

• Synthetic Intermediate – multiple stages from final API

• Exists in two forms

• Thermally sensitive

• Non-chromophoric

• Involatile

• Derivatisation also very tricky

O

O

OH

O

OHOH

Cl

Required use of NMR yet purge calculation showed purge >100,000

Purge Factor Calculation – Basic Principles

6

• The following key factors were defined in order to assess the potential carry-over of a MI:

• reactivity, solubility, volatility, and any additional physical process designed to eliminate impurities e.g. chromatography.

• Score assigned on the basis of the physicochemical properties of the MI relative to the process conditions.

• These are then simply multiplied together to determine a ‘purge factor’ (for each stage)

• The overall purge factor is a multiple of the factors for individual stages.

• Predicted purge is then compared to required purge (this being based on the safety limit and initial level introduced into the process).

Original Purge Prediction Scoring System

7

• The original scoring system was based on basic principles – referred to as “paper” assessment because not automated (manual calculation via spreadsheet).

• Reactivity shown to have largest effect.

• Other factors (especially solubility) would also influence purging.

• Scoring system originally designed to be conservative:

• On validation this was experimentally observed.

• Decided this should be retained rather than seeking absolute parity.

Paper Assessment – Case Study – AZD9056

8

Paper Assessment – Case Study – AZD9056

9

Calculated: 10,000Measured: 112,000(Solubility, Reactivity)

Calculated: 3Measured: 10(Solubility)

Calculated: 10,000Measured: 38,500(Solubility, Volatility)

• Comparison of the overall predictions with the experimental results shows that the

calculated purge factor has good correlation with each impurity.

• Under-predicts the purge capacity of the process.

• Clearly demonstrates risk of carry-over to be low

• Predictions indicated where formation of an impurity needed to be regulated through process control, rather than relying on the ability of the process to eliminate it.

Purge predictions clearly proved useful, but where

do they fit into a control strategy?

ICH M7: Control Strategy Options for Mutagenic Impurities

10

Section 8 - CONTROL

• Greater capability in terms of mechanism to prove absence.

• Options other than to simply test for presence in final API.

• Ability to more widely use chemical / process based arguments to assess purging.

• Expressed in terms of a series of control options.

ICH M7 – Mutagenic ImpuritiesSection 8 – CONTROL

11

• What does the guideline state?

• Where the control strategy relies on process controls in lieu of analytical testing there must be understanding of how the process chemistry and process parameters impact levels of mutagenic impurities.

• The risk assessment can be based on physicochemical properties and process factors that influence the fate and purge of an impurity

• This includes chemical reactivity, solubility, volatility, ionisability and any physical process steps designed to remove impurities.

• These factors are built into both the paper-based approach and also Mirabilis

ICH M7 – Mutagenic ImpuritiesSection 8 – CONTROL - Defines a series of control options

12

Option 2

• Test for the impurity in the specification for a raw material, starting material or intermediate at permitted level.

Option 1

• Test for the impurity in the drug substance.

Option 4

• Predicted to be removed by processing based on process understanding – no testing required.

Option 3

• Test at intermediate stage with a higher limit + understanding of process capacity.

Impacted by purge predictions

What is the right order?

Which data are required?

Control Option 4How do I apply this in practice?

13

• The principle of relating the physico-chemical properties of the mutagenic impurity to the chemical process is defined in the concept of purge factor calculations.

• OPRD paper referenced directly in ICH M7

14

Further Examples of Purge

Predictions in the Context of ICH

M7

Is this simply about avoiding analytical testing?

15

Nitro pyridine (impurity) in a starting material

• 3 Step reaction –• Alcohol converted to alkyl halide. • Coupled to a thiol.• Oxidation step.

• Only final step isolated.• Impurity is unreactive/highly

soluble*/non-volatile.• Predicted in steps 1 and 2 no purge.

• Spiking experiment 3000ppm.• Only reduced to 2000ppm.

Purge calculations showed that control at starting material is required

*comparable to the API

GSK183390A

16

• Methyl hydrazine is used in stage 2 to cyclise the diketone to produce the pyrazole ester.

• Thionyl chloride is used to activate the pyrazole via the formation of an acid chloride which is

then reacted with the benzylamine to form the API.

Theoretical Purge Factors for GTIs in GSK183390A Synthetic Process

17

GTI Stage Reactivity

H=100,

M=10,

L=1

Solubility

F=10,

M=3, L=1

Volatility

H=10,

M=3, L=1

Cumulative total

per stage

Rationale for Purge

Factor

CH3NHNH2 2 100 10 3 3000 Bp of CH3NHNH2 88-

90°C, solvent removed

by distillation /partial

drying.

3 1 3 3 9 Product isolated and

dried.

4 10 10 3 300 Likely reaction with

SOCl2 and also with

the acyl chloride

formed (step j)

Product isolated and

dried.

ALL 8.1 x 106

SOCl2 4 (j) 100 10 10 10000 Highly reactive,

volatile Bp 79°C4 (k) 100 10 10 10000 Reacts instantaneously

in aqueous

environment.

1.0 x 108

Option 4 approach supported by purge calculation for two reactive mutagens

MK-8876

18Org. Process Res. Dev. 2015, 19, 1531-1535

(P)M

I

Pre

dic

ted

Me

asu

red

(to

LO

D)

EDC 1010 >50,000*

MeI 106 > 105

ICH2Cl 106 > 2 x 105

ArB(OH)2 3 x 104 > 106

BBA 1000 > 250,000

Carbazole 100 > 375

*LOD after 3 stages

Purge ToolHow do predicted values compare to actual measured?

19

• Several examples by consortium members indicate the system shows a systematic bias.

• It under-predicts – typically by a factor of around 10.

• This is important! In order to maintain robustness it should not over-predict.

Development of an in silico tool

20

The Mirabilis Consortium

21

• Merck & Co., Inc

• Novartis

• Pfizer

• Polpharma

• Roche

• Sanofi

• Sunovion

• Takeda

• UCB

• Vertex

• Achaogen

• Astra Zeneca

• Abbvie

• Bayer

• Celgene

• Excella

• Esteve

• Galapagos

• GSK

• Janssen

• Lilly

Mirabilis software

22

• Mirabilis established to take basic principles of paper-based predictions and augment them.

• Key concepts:• Use of an in silico template allows for greater consistency in

terms of how predictions are structured and reported.

• Transparent predictions, based on information from a knowledge base.

• Knowledge management & pre-competitive knowledge sharing.

Origins of the Knowledge Base

23

• The initial basis of the knowledge base was a reactivity grid developed by AstraZeneca;

• This demonstrated both the predicted reactivity and confidence based on the reactant / reaction.

• Also outlined those where a (P)MI may be formed as product.

Origins of Knowledge Base Matrix

24

• A consortium collaboration exercise resulted in an “expert elicitation” for each reactivity purge factor

• Each member was given the reaction grid and asked to give their expert opinion on whether they agreed or disagreed with the proposed reactivity purge factors.

• Lhasa collated the results and modified the grid accordingly• Where members agreed on a reactivity purge factor then a

consensus call was made.

• For those without consensus, a conservative call was made.

Expansion of the Knowledge Matrix

25

• Where current understanding of reactivity is inadequate, additional work is being done to address this.

Reaction Type Reagent Solvent Reactive?

1 Reduction H2 Pd/C Dioxane No

2 NaBH4 MeOH, THF, DCM No

3 LiAlH4 THF No

4 DIBAL-H THF, DCM No

5 Oxidation H2O2 DCE, DCM, CH3CN Yes

6 Peracetic Acid DCM Yes*

7 Oxone CH3CN, H2O, H2O:CH3CN Yes**

8 TEMPO DCM Yes***

9 Acids Aq HCl CH3CN, THF No

10 Conc. H2SO4 H2O No

11 Aq H2SO4 H2O, Dioxane, CH3CN No

12 HBr/HOAc DCM No

13 Bases Aqueous NaHCO3 CH3CN No

14 10% NaOH CH3CN, Dioxane, H2O No

15 50% NaOH H2O Yes

16 DBU CH3CN, DCE No

17 Amide Bond Formation CDI (with benzoic acid) DCM No

18 EDAc/HOPO (with benzoic acid) DMF No

19 Benzoyl chloride THF No

20 Nucleophiles MeOH THF No

21 Benzyl amine THF No

22 Other Reagents SOCl2 DCE No

23 NCS DCE No

24 NCS/TEA DCE No

25 NBS DCE Yes****

26 Boc2O/TEA THF No

27 TMSCl/TEA THF No

28 Cross-Coupling RuPhos-Pd complex (25 mol%), K2CO3, THF/H2O ?

29 Pd2dba3 (12.5 mol%), PtBu3HBF4 (25 mol%), TEA, THF ?

Observations –This knowledge is used to support predictions

*Reaction was complete within 5 minutes at -78°C

**Reaction was complete within 5 minutes at 2.5°C

***Reaction was complete within 5 minutes at 2.8°C

****Reaction was complete within 5 minutes at 3.2°C

Reaction Grid -Experimental Work

• Data from reactions can be used to determine purge factors i.e. want to normalise to match scoring system

26

For majority experimental purge >> 100• By normalising to

scale we retain conservatism.

Regulatory Advocacy

27

• Purge prediction principles and scoring rubric well established and used in paper assessments.

• ICH M7 provides framework for control strategy options.

• Mirabilis provides a partially automated and living knowledge base to assist scientists and regulators in making and reporting purge predictions.

• The Mirabilis consortium developed a framework to implement this technology consistently into (P)MI workflows throughout development and commercialisation.

• Goal: consistent application and presentation of purge prediction science to help drive broad regulatory acceptance.

Mirabilis regulatory workflow publication

28

Goal: establish framework to leverage purge predictions to inform selection of control strategy during development, which in turn informs both data collection and regulatory reporting recommendations

Mirabilis (P)MI Purge Prediction Decision Tree

29

Select ICH M7 Option 1,2 or 3 commercial strategy,

as appropriate

Impurity requires management as (P)MI

Determine Purge Ratio (PR) in current API route for (P)MI

Predicted purge factor for (P)MIPurge Ratio = -----------------------------------------------------------------------------

Required purge factor to achieve TTC or PDE for (P)MI

Select ICH M7 Option 4 commercial strategy

Yes No

Select initial ICH M7 control strategy for (P)MI during development based on Purge Ratio. Implement recommended experimental data collection and regulatory reporting strategies based upon Purge

Ratio (next slide)

Does final data package support

commercial ICH M7 Option 4 strategy ?

Key premise: purge excess dictates data collection needs and regulatory reporting practices

Example of calculation of Purge Ratio

30

Purge Ratio prediction of (P)MI “X” (a process reagent)• Assume TTC is 100 ppm• Assume charge (initial conc) is 1 eq or 106 ppm• 104 purge factor (106 / 100 ppm) needed to achieve TTC• Therefore to achieve a 103 Purge Ratio (i.e. three order magnitude

more purge predicted than required to achieve TTC), Mirabilis must predict a 107 cumulative purge factor

Predicted purge factor for (P)MIPurge Ratio = -----------------------------------------------------------------------------

Required purge factor to achieve TTC or PDE for (P)MI

So how does one consistently apply the (P)MI Purge Ratio to lab workflows and regulatory reporting ?

When Purge Ratio > 1000…

31

Data Collection Recommendations

Collection of additional experimental data not necessary to support scientific rationale for non-commercial or commercial API routes.

Regulatory Reporting Recommendations

Report “unlikely to persist” or cumulative predicted purge factor and Purge Ratio for non-commercial API routes in regulatory submissions.

Replace with summary of key elements of predicted purge factorcalculations and Purge Ratio for commercial API routes in regulatory submissions.

Option 4 recommended

Example presentation in regulatory dossier when Purge Ratio > 1000 in commercial route

32

<insert chemical

structure of (P)MI “X”>

Point of introduction Stage 2 of 5

(P)MI TTC 50 ppm

Assumed initial concentration

and rationale for selection

106 ppm at start of Stage 2 because

“X” charge is 1 equivalent

Required Purge Factor to achieve

TTC

2 x 104 = 106 ppm initial conc / 50

ppm TTC

Predicted Purge Factor

2 x 108 (source Mirabilis software vx.x)

Key factors: 1000x purge in Stage 2

driven by reactivity and solubility,

purge in Stages 3-5 driven by solubility

Purge Ratio 1 x 104 = 2 x 108 / 2 x 104

Control Strategy Option 4

No supporting experimental data collection recommended when Purge Ratio is large

When Purge Ratios > 100x and <1000x

33


Collection of additional non-trace experimental data (solubility, reactivity, and volatility) recommended to support scientific rationale for both non-commercial andcommercial API routes.

Collection of additional trace PMI analysis not necessary to support scientific rationale for non-commercial or commercial API routes


Report the cumulative predicted purge factor and Purge Ratio for non-commercial API routes in regulatory submissions.

Replace with summary of key elements of predicted purge factor calculations, Purge Ratio, and supporting non-trace data on purge properties for commercial API routes in regulatory submissions.

Option 4 recommended

Example presentation in regulatory dossier when Purge Ratio > 100x and <1000x in commercial route

34

<insert chemical

structure of (P)MI “X”>


(P)MI TTC 50 ppm

Assumed initial concentration and

rationale for selection

106 ppm at start of Stage 3

1 eq. added to reaction

Required Purge Factor to achieve

TTC2 x 104 [106 ppm initial/50 ppm TTC]



Key factors: 1000x purge in Stage 3

driven by reactivity and solubility, purge

in Stages 4-5 driven by solubility

Purge Ratio 300

Supporting experimental data

(P)MI solubility in solvent x (Stage 3

crystallisation solvent) is 50 mg/mL;

(P)MI “X” measured in isolated product Y

(Stage 3) as ND<200 ppm; additional

6000x purging below this level predicted

in Stage 4 + Stage 5


For moderate Purge Ratios, some supporting data recommended but not necessarily trace level measurements

When Purge Ratios <100x

35


For non-commercial API routes, experimentally measure (P)MI purging, including trace (P)MI analyses as appropriate, to support scientific rationale.

Note: Additional data are expected to support an Option 4 control strategy when (P)MI Purge Ratio <<100x. For commercial API routes, detailed experimental fate & purge studies are expected to support a commercial Option 4 control strategy.


Report summary of key elements of predicted purge factor calculations, Purge Ratio, and supporting non-trace or trace data for non-commercial API routes in regulatory submissions.

Replace with complete summary of predicted purge factor calculations, Purge Ratio, supporting trace and non-trace fate and purge data for commercial API routes in regulatory submissions

Option 4 possible with strong data package, otherwise Options 1, 2, or 3 are recommended

Control of late stage (P)MIs identified within the Atovaquone Second Generation Process

36

• Predicted / required ratio for (P)MIs 1 and 2 <100:

Conservative and based solely on reactivity.

Informs ICH M7 Option 1 control for (P)MIs unless a strong data package available.

• Confirmed measured purge is >> predicted purge such that measured / required

ratio > 1000:

Data included combination of observed levels in isolated materials (LC UV), fate and effects, spiking and supporting trace analysis (NMR).

• Acquired data and good process understanding justifies an ICH M7 Option 4

rationale.

Example presentation in regulatory dossier when Purge Ratio <100x in commercial route

37

<insert chemical

structure of

(P)MI “X”>


(P)MI TTC 50 ppm

Assumed initial concentration and

rationale for selection

106 ppm at start of Stage 4

1 eq. added to reaction

Required Purge Factor to achieve TTC 2 x 104 [106 ppm initial/50 ppm TTC]



Key factors: 1000x purge in Stage 4 driven

by reactivity and solubility, purge in Stage 5

driven by solubility

Purge Ratio 50

Supporting experimental data

(P)MI solubility in solvent x (Stage 4

crystallisation solvent) is 50 mg/mL; (P)MI

“X” measured in Y commercial batches of

API at ND<5 ppm


For lower Purge Ratios, trace supporting data package required to justify an Option 4 strategy, otherwise select Option 1, 2, or 3 as appropriate

Summary of recommendations for option 4 submissions

38

• Purge ratio >1000 requires only very basic detail regarding the purge calculation.

• Purge ratio <1000 but >100 requires more increased detail regarding how the purge value was derived.

• Purge ratio <100 requires an in depth analysis to describe the purge assignment, mechanisms, literature and possible experimental data (e.g. Spiking)

This approach applies an additional layer of conservatism on top of an already conservatively biased system

Conclusions

81

• The purge tool concept provides an efficient and effective way of assessing the risk posed by a (P)MI.

• A consortium member analysis determined in-house savings of 8,000 h per annum utilising this approach.

• CRITICALLY - The development of this as an in silicotool provides the basis for a systematic approach based on knowledge, one aligned directly with principles defined in ICH M7.

Lhasa Limited

Granary Wharf House, 2 Canal Wharf

Leeds, LS11 5PS

Registered Charity (290866)

Company Registration Number 01765239

+44(0)113 394 6020

[email protected]

www.lhasalimited.org

Obrigado! Questions?

References

83

• Teasdale, A.; Fenner, S.; Ray, A.; Ford, A.; Philips, A. A tool for the semiquantitative assessment of potentially genotoxic impurity (PGI) carryover into API using physicochemical parameters and process conditions. Org. Process Res. Dev. 2010, 14, 943-945. DOI: 10.1021/op100071n

• Teasdale, A.; Elder, D.P. Risk assessment of genotoxic impurities in new chemical entities: Strategies to demonstrate control. Org. Process Res. Dev. 2013, 17, 221-230. DOI: 10.1021/op300268u

• Elder, D.P.; Okafo, G.; McGuire, M. Assessment of predictivity of semiquantitative risk assessment tool: Pazopanib hydrochloride genotoxic impurities. Org. Process Res. Dev. 2013, 17, 1036-1041. DOI: 10.1021/op400139z

• McLaughlin, M.; Dermenjian, R.K.; Jin, Y.; Klapars, A.; Reddy, M.V.; Williams, M.J. Evaluation and control of mutagenic impurities in a development compound: purge factor estimates vs measured amounts. Org. Process Res. Dev. 2015, 19, 1531-1535. DOI: 10.1021/acs.oprd.5b00263

• Lapanja, N.; Zupančič, B.; Časar, R.T.; Orkič, D.; Uštar, M.; Satler, A.; Jurca, S.; Doljak, B. A generic industry approach to demonstrate efficient purification of potential mutagenic impurities in the synthesis of drug substances. Org. Process Res. Dev. 2015, 19, 1524-1530. DOI: 10.1021/acs.oprd.5b00061

• Barber, C; Antonucci, V; Baumann, J-C.; Brown, R.; Covey-Crump, E.; Elder, D.; Elliott, E.; Fennell, J.W.; Gallou, F.; Ide, N.D.; Jordine, G.; Kallemeyn, J.M.; Lauwers, D.; Looker, A.R.; Lovelle, L.E.; McLaughlin, Molzahn, R.; Ott, M.; Schils, D.; Schulte Oestrich, R.; Stevenson, N.; Talavera, R.; Teasdale, A.; Urquhart, M.W.; Varie, D.L.; Welch, D. A consortium-driven framework to guide the implementation of ICH M7 Option 4 control strategies. Regul. Toxicol. Pharmacol., 2017, 90, 22-28. DOI: 10.1016/j.yrtph.2017.08.008

• Betori, R.C.; Kallemeyn, J.M; Welch, D.S. A kinetics-based approach for the assignment of reactivity purge factors. Org. Process Res. Dev. 2015, 19, 1517-1521. DOI: 10.1021/acs.oprd.5b00257

BACK UP SLIDES

84

Future work

• Improvements to be introduced in the future include:

• The use of machine learning in purge assessment

• Progression towards automation of solubility purge factors

85

Solubility Predictions – Augmentation

86

Approaches:• Based on experimental data

• Measured values or Reaction System • Applicable where (P)MI is a reagent.

• Important to factor in - initial solubility and required solubility at end of reaction.

In reality at point of isolation of desired product, level of (P)MI may be <1% of initial level. Thus if intrinsically soluble at start of reaction small changes in solvent system unlikely to affect solubility.

THF

THF

3-aminopropan-1-ol

AZD9056 Aldehyde

AZD9056 Imine

AZD9506 Free Base

Solubility Predictions - Augmentation

87

• Approaches continuedExtrapolation

• It is possible to extrapolate solubility from one solvent to another

• It is even possible to do ‘ab initio’ calculations.

Surrogate data • May be possible to find data for

similar structures in the literature

at least for chemical transformations.

Theoretical impurity (level <<0.1%)

Lite

ratu

re d

ata

–ra

nge

of

solv

ents

>1

0g/

l

Volatility Arguments of Purge Factors

Background

89

• Can the proposed purge factors be supported by data?

• For the purposes of this analysis, will assume the volatility purge is the result of a distillation where the mass is reduced by 90% (fairly typical of a solvent swap)

• For simplicity a single process solvent will be assumed, but the conclusions would hold for a multi-solvent system.

Volatility

90

Before After

Mass = MMass Fraction of PMI

= xPMI

Mass = M’Mass Fraction of PMI

= x’PMI

Total Mass of PMI= M.xPMI

Total Mass of PMI= M’.x’PMI

For the case where the PMI is more volatile than the process solvent (lower boiling), the VLE for the system would look like this assuming ideal behaviour:

PMISolvent

Temp During a distillation the pot composition will move towards the left of the diagram and therefore: x’PMI < xPMI

The assumption of a 90% reduction in mass means M compared to M’ already gives a 9 fold purge of PMI (even if the PMI fraction were unchanged). Combined with the reduction in mass fraction from the distillation, this supports the proposed purge factors of 10 and 3 for instances when the PMI is more volatile than the solvent.

Vap.

Liq.

Volatility

91

The analysis on the previous slide assumed the PMI and solvent show ideal behaviour. It is theoretically possible that they could form a minimum boiling azeotrope (A)The VLE diagram this would look like this:

PMISolvent

Temp

A

If the initial PMI mass fraction is greater than the azeotropic composition (ie to the right of point A), the mass fraction of the PMI will increase during distillation ie: x’PMI > xPMI

If the initial PMI mass fraction is less than the azeotropic composition (i.e. to the left of point A), the final mass fraction will always be less than the initial (i.e. the relationship shown on the previous slide will hold true).

The situation where an azeotrope causes an enrichment of PMI during a distillation is considered very unlikely. The concentration of PMIs is generally low and therefore any azeotropes formed will have a higher PMI concentration and so not cause enrichment.

In either case a 9 fold purge from the reduction of mass during this distillation would still be observed.

Volatility - Conclusion

92

• For the case of a distillation the proposed purge factors of 3 and 10 are justified for the case where the PMI is more volatile than the solvent.

• Assuming a purge factor of 1 for cases where the PMI is less volatile than the reaction solvent is taking a “worst-case” approach as, providing the PMI has some volatility, no credit is given to the fact that some of it will be removed during the operation.

Physical Processing

93

• Schule et al demonstrated the effectiveness of a wash sequence to remove Acetamide

• Purges >> factor applied by Mirabilis (factor =10)

94

Further Development

Purge Factor = 1(10, 100)

• Why the purge factor has been assigned• Summary of data from literature, experiments etc

• Impurity reaction with individual components

• Impurity reaction in real scenario• Mechanisms?• Products?

• Scope and effects (eg temp, solvent, structural)

Literature references

Literatureexamples and supplementary

info

Experimental examples and supplementary

info

Reaction mining database summary and supplementary

info

Links to other models?

Treatment of Excess Reagents

95

• Equivalents EDC used = 1.5, isolated yield = 97%

• Based on chemistry first principles, to achieve this yield 1 equivalent of EDC must be consumed

- in-process LC data also indicated complete conversion of starting acid

• Therefore, conservative maximum EDC remaining at the end of the reaction is 0.5 equivalent (i.e. 500,000 ppm) (i.e. 1.5-0.97 = 0.53)

• Take 0.5 equivalents at the end of the reaction as the realistic starting point for purge prediction

mirabilis - welcome to lhasa limited · • assume ttc is 100 ppm • assume charge (initial conc)...

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