Rational Solvent Selection for Pharmaceutical Impurity Purge - Crystal

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Rational Solvent Selection for Pharmaceutical Impurity Purge Yuriy A. Abramov Cryst. Growth Des., Just Accepted Manuscript • DOI: 10.1021/acs.cgd.7b01748 • Publication Date (Web): 08 Jan 2018 Downloaded from http://pubs.acs.org on January 15, 2018

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Crystal Growth & Design

Rational Solvent Selection for Pharmaceutical Impurity Purge

Yuriy A. Abramov Pfizer Worldwide Research and Development, Groton, Connecticut 06340, United States [email protected]

Abstract. This study focuses on the development of computational impurity purge factor to enable rational solvent selection for purification of pharmaceutical compounds via recrystallization. Three purge factors have been proposed under SLE thermodynamic consideration based on two properties: impurity partition coefficient between solvent and crystalline pharmaceutical, and thermodynamic solubility of the crystalline pharmaceutical. A limited testing of the purge factors against experimental observations was performed, using COSMO-RS approach as implemented in the COSMOtherm software. Good and superior performance of the purge factor proportional to a difference between the impurity partition coefficient and the crystalline pharmaceutical solubility was demonstrated. As a result, a virtual solvent screening based on that factor is proposed to be used to guide selection of preferred solvents for impurity purge via recrystallization.

Keywords: impurity purge, purge factor, in silico screening, COSMO-RS, solvent selection, recrystallization

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1. Introduction Generation of process-related impurities is a key issue facing the pharmaceutical industry. Regulatory expectations for control of impurities in new drugs have been established through ICH guidelines,1-4 which outline requirements for the registration of new drugs. The knowledge of actual and potential impurities, which can arise during synthesis, purification and storage are expected to be presented upon completion of drug development. Investigations for synthetic drug substances include process-related impurities such as mutagenic impurities, byproducts, intermediates, residual solvents, and elemental impurities, are important to ensure drug safety from the point of patient exposure to impurities. For impurity control purposes, many purification steps (i.e. recrystallization, solvent liquid-liquid extraction, precipitation, distillation, column chromatography) have the ability to remove or purge process impurities. However, many of those methods appear to be expensive during scaling up at pilot plant and manufacturing levels. Since small-molecule pharmaceuticals and intermediates are traditionally isolated as crystalline solids during the synthesis, (re)crystallization is one the most common and preferred techniques of impurity purge in pharmaceutical industry.5 An impurity may also undergo a chemical reaction in the recrystallization solvent or get captured by interaction with complexing agent. Both of these approaches can be used to sterically prevent impurity incorporation into the host lattice. For example, in order to avoid the necessity of chromatography a chemoselective removal of 1,3-diol impurities from crude tertiary alcohol by performing the recrystallization in the presence of phenylboronic acid was reported.6 The applicability of complexing agent strategy was demonstrated for the greater than 90%


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purification of an active pharmaceutical ingredient (API) fenofibrate from its major impurity fenofibric acid.7 In addition, a strategy for combination of impurity complexation with nanofiltration membrane during continuous crystallization was recently reported.8 In that case a nanofiltration membrane is selected to preferentially reject the higher molecular weight impurity complex in solution, while allowing the lower molecular weight API to permeate through. The efficiency of the process was successfully demonstrated by the cooling crystallization of two API/impurity systems: the benzamide/3-nitrobenzoic acid, and the ketoprofen/ibuprofen/α,4dimethylphenylacetic acid. An impurity purge during recrystallization may also take place due to form change of the crystalline product. For example, a substantial impurity removal was achieved by recrystallization of piperidene pharmaceutical intermediate 9 and (R,R)-formoterol L-tartrate10 to their hydrate forms. An efficient and effective purification process for the reduction of impurities in dirithromycin via its acetone solvate has been also reported. 11 In a typical case of a high chemical similarity of the impurity to the API, the impurity has a strong tendency to incorporate into the host lattice, making it difficult to obtain a high purity of the API with a single recrystallization attempt. Therefore, design of recrystallization impurity purge typically involves experimental solvent screening. Such purely experimental approach is typically time consuming and expensive. An accelerated drug development greatly benefits from guidance provided by computational methods, which are currently used to support all steps related to the development of solid‐state pharmaceuticals.12 Therefore, it is important to develop a rational approach for solvent selection for an optimal impurity purge from a given API or a pharmaceutical intermediate. The only computational algorithm, suggested previously, was based on the selection of a solvent system that easily solubilizes the impurity relative to the API



over the entire temperature range.13 In addition, an impurity docking via grid searching method may provide information on specifics interaction energy of the impurity with the API crystal lattice and morphological surfaces.14 However, the latter approach ignores the role of solvent in the impurity purge and is rather applicable to estimation of impurity effect on crystal growth and morphology. The grid search approach may be also used to rank purging efficiency of different impurities based on their interaction energies with the API crystal lattice or morphological surfaces. This study focuses on the development of a theoretical impurity purge factor to enable computational solvent selection for impurity purge via recrystallization. Purge factors have been developed previously for genotoxic impurities. However, those are semiquantitative risk assessment factors, based on the assessment of key physicochemical properties of the substance in question, which are not focused on impurity purge by recrystallization.15,16

2. Approach 2.1. Thermodynamic consideration At the first step of impurity recrystallization experiment, a crystalline pharmaceutical compound (for convenience called an API throughout the manuscript) with an initial amount of impurity is fully dissolved in the recrystallization solvent (for example by heating). That is followed by crystallization of the API (for example by cooling) with new impurity content. A desired redistribution of the impurity and the API during recrystallization is schematically presented in Figure 1.

A solid/liquid equilibrium (SLE) at the end of recrystallization

experiment can be characterized by the API solution being saturated (or oversaturated within a


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metastable zone) in respect to the crystalline phase of the API. That is not the case for the impurity, which due to its low initial amount should be typically highly undersaturated relative to its own solid phase. Therefore, the SLE equilibrium of the pure crystalline API may be represented by its thermodynamic solubility in the recrystallization solvent system (referred to molar fractions):

=

∆

!"

(1)

Here is the solubility if the crystalline API, $" is the pseudochemical potential of the pure API in the supercooled liquid form as defined by Ben-Naim,17 $ %& is the pseudochemical potential of the API in solution, and ∆'() is a free energy of fusion of the API crystal.

Based on the above considerations, the thermodynamic equilibrium between the impurity in the solution and in the crystalline API may be represented by a partition coefficient between the ,-. corresponding two phases, * %&,

, rather than by the thermodynamic solubility of the

crystalline impurity (referred to molar fractions): ,-. * %&,

=

345

345

/012 !"

(2)

,-. ,-. Here $ %& and $

are chemical potentials of the impurity in solution and in the

crystalline API, respectively. There are two important tasks of the impurity purge optimization in the pharmaceutical industry. One is a decrease of amount of impurity in the final crystalline product below an acceptable level. Another is a higher recovery of the API upon the recrystallization. From



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,-. general considerations, the former optimization task may be described by a higher * %&,

property of the impurity in the selected recrystallization solvent system (equation 2). While the latter task should be generally related to the API solubility in the recrystallization solvent – the higher

, the lower recovery should be expected.

In accordance with the above thermodynamic considerations, two simplified in silico ,-. purge factors based on the * %&,

and

properties could be proposed (referred to

molar fractions): ,-. f1 = * %&,

345

f2 =

, /012 6/012

(3) 345

=

345

( /012 )( ∆ )

(4)

!"

The higher f1, the higher impurity purge can be expected. However, the f1 factor does not take into account variation of of the amount of the recrystallized API solid phase. That amount is generally defined by the API solubility in a given solvent system. The purge factor f2 is effectively related to the optimization of the both impurity purge tasks described above – a higher f2 value corresponds to a lower relative impurity content in the recrystallized API (as ,-. described by * %&,

), and/or to a higher API recovery (which is related to a lower

). ,-. Since $" , $

and ∆'() are constants for a given API/impurity system; f2 dependence 345

on the recrystallization solvent could be simplified as: f2 =

(9: ; )

!"

, where C1 is a

constant. That makes f2 solvent dependence proportional to the one based on the solubility ratio between crystalline impurity and crystalline API:

345

6/012 6/012

=


345

(9< ; )

!"

.

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An elaborated thermodynamic consideration of the impurity purge factor under assumptions of a low impurity concentration and a low solubility of the API, is presented in the Supporting Information. The proposed purge factor is defined as: ,-. ,-. => = ?* %&, ,@ − ,@ BC %& ≈ E* %&,

FG

FG

− ,@ H C %& ,

(5)

where MWAPIand MWsolv are molecular weights of the API and solvent molecules, ,-. respectively; * %&, ,@ and ,@ are a mass based partition coefficient and solubility of

the impurity and API respectively; and C %& is the solvent density. The higher f3, the higher impurity purge should be expected. ,-. A special attention should be paid to the prediction of $

property for calculation ,-. of * %&,

(equation 2). One approximation could be of neglecting the crystalline nature of

the solid API and performing predictions in amorphous (or a supercooled liquid) phase: ,-. ,-. $

= $ I-%.J . Multiple successful in silico screening applications of the supercooled

liquid approximation of the crystalline solid state were previously reported.18,19,20,21,22 The ,-. amorphous phase approximation was used in this study for prediction of * %&, ,@ property

for calculation of the purge factors f1 and f2 (equations 3 and 4, respectively). ,-. However, another approximation for $

, which takes into account a crystalline ,-. nature of the API solid phase, is proposed for * %&, ,@ prediction for calculation of the

purge factors f 3 (equation 5): ,-. ,-. $

= $ I-%.J − ∆'()

(6)



As a justification of the approximation 6 one may consider a limiting case of impurity being ,-. ,-. identical to the API molecule. In that case $ I-%.J = $" and * %&, ,@ = ,@

(see equations 1 and 2), and therefore no “impurity” purge would take place as should be expected (equation 5). In this study all three proposed impurity purge factors will be tested against two available pharmaceutically relevant experimental observations. 2.2.General consideration of limitations and advantages of the approaches Knowledge of impurity chemical structure is required to perform rational solvent selection by any of the proposed approaches. The accuracy of the purge factors prediction will strongly depend on the accuracy of ,-. prediction of underlying properties – * %&, ,@ and ,@ .

Purge factor f3 is the most scientifically sound among all three proposed factors. However, one should keep in mind that it was defined under the assumptions of a low concentration and a low solubility of the impurity and the API, respectively. In addition, no kinetics effect on impurity purge via recrystallization was considered in this study. It is well known that form change may have a profound effect on the impurity content and vice versa. 9,10,11,23 Due to the dependence of the absolute values of f3 and f2 on ∆'() , these

purge factors, in contrast to f1 one, may generally account for the solid form change effect on the impurity purge. However, ∆'() values of the solid forms should be available for the prediction

of that effect. The proposed purge factors are not directly applicable to a quantitative consideration of impurity purge due to its chemical transformation in the recrystallization solution. That would


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require consideration of specific interactions of the initial and chemically modified impurities with the API crystal lattice, for example, using the grid search approach.14 The latter limitation is also relevant to consideration of relative purging efficiency of multiple impurities in one API. Interaction energies of the impurities with the crystalline rather than amorphous API would be required for the above prediction.

3. Data Two published pharmaceutically relevant experimental observations of solvent effect on impurity purge via recrystallization were used in this study for testing of the proposed purge factors.6,9 The API/impurity systems and results of impurity purging experiments reported in those studies are summarized in Table 1. The most extensive experimental study was reported for elimination of 1,3-diol dimeric impurities generated during methyl Grignard addition to esters.6 A crude tertiary alcohol (Table 1) contained ~3% of dimers. Recrystallization from 12 solvent systems resulted in variation of the 1,3-diol impurity content in the tertiary alcohol within 1.94%.

Impurity increase was

observed during recrystallization from pentane and hexanes solutions. Removal of the impurity from the crude tertiary alcohol was achieved by performing the recrystallization in the presence of phenylboronic acid, which led to reaction with the 1,3-diol impurity to generate a cyclic boronic ester. Since the proposed purge factors are not directly applicable to the cases of chemical transformation of the impurities, phenylboronic acid was not considered as the recrystallization solvent for the test calculations. In the second study a hydrochloride salt of piperidene pharmaceutical intermediate that can crystallize as either a hydrate or an anhydrous form with very different purities was reported



(Table 1).

9

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The anhydrate or hydrate form of the piperidene intermediate was obtained by

recrystallization from acetone/water mixtures when the solvent water content was below or higher 10%, respectively. The anhydrate form is capable of accommodating up to 4.0% of the Nlinked impurity. Reported effect of recrystallization solvent composition on purity variation (within 1.3%) of the anhydrate form was used in this study for testing the proposed purge factors.

4. Calculations Since small molecule impurities are typically measured in weight faction or percentage ,-. units, all the calculations of f1, f2, f3 purge factors are based on * %&, ,@ and ,@ ,-. properties and are referred to mass based units. * %&, ,@ and ,@ predictions were

performed using conductorlike screening model for real solvents (COSMO-RS) approach, which is based on a combination of the quantum chemical dielectric continuum solvation model (COSMO) with a statistical thermodynamic treatment of surface interactions.24

It was

demonstrated in recent studies that COSMO-RS is a powerful virtual screening approach for many important pharmaceutical applications, such as for example solvents screening for solubility or crystallization;19 solvent screening for solid desolvation;18,19,20,22 coformers screening for cocrystallization18,19,20 or for an improved relative humidity stability;21 solid hydrate formation propensity;21 propensity of intra- 25 and intermolecular 26-28 hydrogen bonding. In addition, COSMO-RS approach allows a relatively accurate prediction of absolute values of partition coefficient between different media

25, 29

as well as of thermodynamic solubility in any

solvent system.20 In this study up to 8 diverse sets of conformations of all compounds under consideration were generated using LowModeMD algorithm and MMFF94x force field as implemented in


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MOE 2016 software.30 The generated conformations were further optimized in aqueous media by the DMol3 software at PBE/DNP/COSMO level of theory [30-32 ],31-33 as implemented in Material Studio 2017 software package.34 The generated screening charge densities of all ,-. conformations were used for COSMOtherm35 calculations of * %&, ,@ and ,@ properties. Since no experimental ∆'() were available, for the tertiary alcohol that property was estimated via a QSPR model embedded into COSMOtherm software (∆'() =3.2 kcal/mol). No ∆'()

prediction was possible for the anhydrous salt form of piperidene pharmaceutical

intermediate, therefore a value of ∆'() = 5.0 kcal/mol was adopted for calculation of purge

factors f2 and f3.

5. Results and discussion Results of impurity purge factors calculation for tertiary alcohol/1,3-diol impurity and piperidene pharmaceutical intermediate/N-linked impurity systems are compared with experimental observations in Table 2. A graphical side-to-side comparison of performances of three purge factors ordered according to the experimental purity observations is presented in Figure 2. For that a normalization of the purge factors values was performed for a better visualization. A relative performance of the proposed purge factors is evaluated in this section based on enrichment of solvent system selection for an optimal impurity purge. It should be noted that, according to equation A5, no linear correlation should be generally expected between any of the proposed purge factors and the experimentally observed impurity changes after recrystallization. Therefore, the simplest qualitative way of estimation of purge factors performance is to compare relative ranking of the crystallization solvents with the experimental observations. In regards to purification of tertiary alcohol, it is obvious from



Figure 2 and Table 2 that the solvent ranking provided by the purge factor f2 demonstrates a poor correlation with the experimental observations. Performance of purge factors f1 and f3 is much better; however, they also provide several ranking outliers with MEK solvent being the strongest one. The lowest values of f1 and f3 factors correctly describe pentane, hexane and n-heptane as the least favorable solvents for the 1,3-diol impurity purge from the tertiary alcohol. MEK, ACN and either toluene or 2-propanol are correctly identified as the most preferred solvents by f3 and f1 factors, respectively. At the same time, only purge factor f3 provides a correct solvents ranking for the N-linked impurity purge from the anhydrous form of the piperidene pharmaceutical intermediate (Table 2, Figure 2). Solvent ranking based on the purge factors f1 and f2 appeared to be completely opposite to the experimental observations. Perhaps a more practical estimation of overall performance of the purge factors for solvent selection for purification may be based on analysis of receiver operator characteristic (ROC) curve. An ROC curve plots a true positive predictions rate versus a false positive prediction rate for a binary classifier system. The area under the curve (AUC) measures the overall performance of the model. Predictions with AUCs values higher than 0.5 indicate that the model is better than a random selection, with the AUC value of 1.0 corresponding to a perfect model prediction. As an example, the quality of the prediction of the four best solvents (ACN, 2-propanol, toluene and MEK) for the 1,3-diol impurity purge (>1%) based on the proposed purge factors was measured by the ROC curves presented in Figure 3. A detailed side-by-side comparison of the ROC curves demonstrates a clear advantage of the purge factors f1 and f3 over f2. There is only one misranking (MTBE) in case of f1 based solvent virtual screening, resulting in an almost ideal


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enrichment of the solvents selection (AUC of 0.9, Figure 3a). Screening based on the f3 factor provides the fastest solvent enrichment rate (true positive predictions of MEK, ACN and toluene are on the top of the proposed solvent list). In spite of one strong outlier (2-propanol), f3 provides a good overall hit enrichment (AUC of 0.75, Figure 3c). The quality of the solvent selection based on the purge factor f2 appeared to be poor and close to a random (AUC of 0.60, Figure 3b).

6. Conclusions The goal of this study is to develop computational purge factor property to enable rational selection of optimal solvent selection for impurity purge by means of recrystallization. Based on general SLE thermodynamic consideration three purge factors, f1, f2, and f3 were proposed utilizing such properties as impurity partition coefficient between solvent and the crystalline API and thermodynamic solubility of crystalline API. A limited testing of all three purge factors against experimental observations of solvent enabled purification by means of recrystallization of two API/impurity systems - tertiary alcohol/1,3-diol impurity and piperidene pharmaceutical intermediate/N-linked impurity, was performed. The testing demonstrated a good and superior performance of the purge factor f3, while the poorest solvent selection enrichment was provided by the purge factor f2. As a result, a virtual solvent screening based on the f3 factor is proposed to be used to guide the experimental selection of solvents that have an increased probability of impurity purge via recrystallization. It is also outlined in this study that one should keep in mind the following limitations of the proposed purge factor. Though f3 factor may generally account for the solid form change effect on the impurity purge, ∆'() properties of the solid forms should be available for the

prediction of that effect. Also, the proposed purge factor is not directly applicable to



consideration of impurity purge due to its chemical transformation in the recrystallization solution. In addition, no kinetics effect on impurity purge via recrystallization was considered in this study.

Supporting Information The Supporting Information is available free of charge on the ACS Publications website. Derivation of the impurity purge factor f3 References 1. International Conference on Harmonisation (ICH). Guideline Q3A (R2): Impurities in New Drug Substances; October 2006. 2. International Conference on Harmonisation (ICH). Guideline Q3B (R2): Impurities in New Drug Products; 2006. 3. International Conference on Harmonisation (ICH), Guideline Q3C (R4): Impurities: Guidelines for Residual Solvents; 2009. 4. International Conference on Harmonisation (ICH) M7 Tripartite Guideline. Assessment and Control of DNA Reactive (Mutagenic) Impurities in Pharmaceuticals to Limit Potential Carcinogenic Risk, Current Step 4 version, 23 June 2014. 5. Moynihan, H. A.; Horgan, D. E., Impurity Occurrence and Removal in Crystalline Products from Process Reactions. 2017, 21, 689−704. 6. Tucker, J. L.; Couturier, M.; Leeman, K. R.; Hinderaker, M. P.; Andresen, B. M., Chemoselective removal of dimeric 1, 3-diol impurities generated from methyl grignard addition onto esters. Org. Process Res.Dev. 2003, 7, 929-932. 7. Weber, C. C.; Wood, G. P.; Kunov-Kruse, A. J.; Nmagu, D. E.; Trout, B. L.; Myerson, A. S., Quantitative Solution Measurement for the Selection of Complexing Agents to Enable Purification by Impurity Complexation. Cryst. Growth Des. 2014, 14, 3649-3657. 8. Vartak, S.; Myerson, A. S., Continuous Crystallization with Impurity Complexation and Nanofiltration Recycle. Org. Process Res.Dev. 2017, 21, 253-261. 9. Black, S. N.; Cuthbert, M. W.; Roberts, R. J.; Stensland, B., Increased chemical purity using a hydrate. Crystal growth & design 2004, 4, 539-544. 10. Tanoury, G. J.; Hett, R.; Kessler, D. W.; Wald, S. A.; Senanayake, C. H., Taking advantage of polymorphism to effect an impurity removal: development of a thermodynamic crystal form of (R, R)formoterol tartrate. Org. Process Res.Dev. 2002, 6, 855-862. 11. Wirth, D. D.; Stephenson, G. A., Purification of dirithromycin. Impurity reduction and polymorph manipulation. Org. Process Res.Dev. 1997, 1, 55-60. 12. Abramov, Y. A., Ed. Computational Pharmaceutical Solid State Chemistry; John Wiley & Sons, 2016.


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13. Nass, K. K., Rational solvent selection for cooling crystallizations. Ind. Eng. Chem. Res. 1994, 33, 1580-1584. 14. Roberts, K. J.; Hammond, R. B.; Ramachandran, V.; Docherty, R. Synthonic Engineering: From Molecular and Crystallographic Structure to the Rational Design of Pharmaceutical Solid Dosage Forms. In Computational Pharmaceutical Solid State Chemistry, Abramov, Y. A., Ed.; John Wiley & Sons, 2016; pp 175−210. 15. Teasdale, A.; Fenner, S.; Ray, A.; Ford, A.; Phillips, 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. 16. Teasdale, A.; Elder, D.; Chang, S.-J.; Wang, S.; Thompson, R.; Benz, N.; Sanchez Flores, I. H., Risk assessment of genotoxic impurities in new chemical entities: strategies to demonstrate control. Org. Process Res.Dev. 2013, 17, 221-230. 17. Ben-Naim, A. Y., Solvation thermodynamics. Springer Science & Business Media: 2013. 18. Abramov, Y. A.; Loschen, C.; Klamt, A., Rational coformer or solvent selection for pharmaceutical cocrystallization or desolvation. J. Pharm. Sci. 2012, 101, 3687-3697. 19. Loschen, C.; Klamt, A., Solubility prediction, solvate and cocrystal screening as tools for rational crystal engineering. J. Pharm.Pharmacol. 2015, 67, 803-811. 20. Loschen, C.; Klamt, A., New Developments in Prediction of Solid-State Solubility and Cocrystallization Using COSMO-RS Theory. In Computational Pharmaceutical Solid State Chemistry, Abramov, Y. A., Ed.; John Wiley & Sons, 2016; pp 211-233. 21. Abramov, Y. A., Virtual hydrate screening and coformer selection for improved relative humidity stability. CrystEngComm 2015, 17, 5216-5224. 22. Loschen, C.; Klamt, A., Computational Screening of Drug Solvates. Pharm. Res. 2016, 33, 27942804. 23. Mukuta, T.; Lee, A. Y.; Kawakami, T.; Myerson, A. S., Influence of impurities on the solutionmediated phase transformation of an active pharmaceutical ingredient. Cryst. Growth Des. 2005, 5, 1429-1436. 24. Klamt, A., The COSMO and COSMO-RS solvation models. Wiley Interdisciplinary Reviews: Computational Molecular Science 2011, 1, 699-709. 25. Shalaeva, M.; Caron, G.; Abramov, Y. A.; O’Connell, T. N.; Plummer, M. S.; Yalamanchi, G.; Farley, K. A.; Goetz, G. H.; Philippe, L.; Shapiro, M. J., Integrating intramolecular hydrogen bonding (IMHB) considerations in drug discovery using ΔlogP as a tool. J. Med. Chem. 2013, 56, 4870-4879. 26. Abramov, Y. A., Current computational approaches to support pharmaceutical solid form selection. Org. Process Res. Dev. 2012, 17, 472-485. 27. Klamt, A.; Reinisch, J.; Eckert, F.; Hellweg, A.; Diedenhofen, M., Polarization charge densities provide a predictive quantification of hydrogen bond energies. Phys. Chem. Chem. Phys. 2012, 14, 955963. 28. Klamt, A.; Reinisch, J.; Eckert, F.; Graton, J.; Le Questel, J.-Y., Interpretation of experimental hydrogen-bond enthalpies and entropies from COSMO polarisation charge densities. Phys. Chem. Chem. Phys. 2013, 15, 7147-7154. 29. Spieß, A. C.; Eberhard, W.; Peters, M.; Eckstein, M. F.; Greiner, L.; Büchs, J., Prediction of partition coefficients using COSMO-RS: solvent screening for maximum conversion in biocatalytic twophase reaction systems. Chem. Eng.Process. 2008, 47, 1034-1041. 30. MOE 2016.08, Chemical Computing Group, Inc., 1010 Sherbrooke Street West, Suite 910, Montreal, Quebec, Canada. 31. Delley, B., From molecules to solids with the DMol 3 approach. J.Chem.Phys. 2000, 113, 77567764.



32. Perdew, J. P.; Burke, K.; Ernzerhof, M., Generalized gradient approximation made simple. Phys. Rev. Lett. 1996, 77, 3865. 33. Andzelm, J.; Kölmel, C.; Klamt, A., Incorporation of solvent effects into density functional calculations of molecular energies and geometries. J.Chem.Phys. 1995, 103, 9312-9320. 34. Accelrys Software. Material Studio 2017 R2 DMol3; Accelrys Software, Inc., San Diego, CA, 2017. 35. Eckert, F.; Klamt, A., COSMOtherm, Version C3. 0, Release 17.01. COSMOlogic GmbH & Co. KG, Leverkusen, Germany 2016.

Figure Captions. Figure 1. A schematic representation of the desired redistribution of the impurity and the API during recrystallization. Figure 2. A side-to-side comparison of performances of the computational purge factors f1 , f2 and f3 ordered according to the increased purity observations. A normalization of the purge factors values was performed for a better visualization. a) tertiary alcohol/1,3-diol impurity; f1 and f2 factors are normalized against the highest values; f3 factor values are normalized against the value in ACN and are capped at value of 2.0; b) piperidene pharmaceutical intermediate/N-linked impurity; f1 and f2 factors are normalized against the highest values; f3 factor values are normalized against the lowest absolute value. Figure 3. The quality of the prediction of the four best solvents (ACN, 2-propanol, toluene and MEK) for the 1,3-diol impurity purge based on the proposed purge factors f1 (a), f2 (b) and f3 (c), as measured by the ROC curves.


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Table 1. The API/impurity systems and results of impurity purging experiments used for validation of computational purge factors.

a

,-.

Impurity change due to recrystallization in toluene was scaled down to adjust N"

of the crude tertiary alcohol in Tables 2 and 3 of the reference.6 b

(% impurity in recrystallized sample)-(% impurity prior to recrystallization).


differences


Table 2. Results of impurity purge factors calculation for tertiary alcohol/1,3-diol impurity and piperidene pharmaceutical intermediate/N-linked impurity systems.


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Rational Solvent Selection for Pharmaceutical Impurity Purge

Yuriy A. Abramov

Synopsis This study focuses on the development of computational impurity purge factor to enable rational solvent selection for purification of pharmaceutical compounds via recrystallization. Good and superior performance of the purge factor proportional to a difference between the impurity partition coefficient and the crystalline pharmaceutical solubility was demonstrated.



Figure 1 73x112mm (144 x 144 DPI)


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Figure 2a 167x110mm (144 x 144 DPI)



Figure 2b 158x106mm (144 x 144 DPI)


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Figure 3a 113x107mm (144 x 144 DPI)



Figure 3b 109x105mm (144 x 144 DPI)


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Figure 3c 111x104mm (144 x 144 DPI)


Rational Solvent Selection for Pharmaceutical Impurity Purge - Crystal

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