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Computational and Experimental Screening Approaches to Aripiprazole Salt Crystallization

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Abstract

Introduction

The screening of multicomponent crystal system (MCC) is a key method for improving physicochemical properties of active pharmaceutical ingredients (APIs). The challenges associated with experimental salt screening include a large number of potential counterions and solvent systems and tendency to undergo disproportionation to produce free form during crystallization. These challenges may be mitigated by a combination of experimental and computational approaches to salt screening. The goal of this study is to evaluate performance of the counterion screening methods and propose and validate novel approaches to virtual solvent screening for MCC crystallization.

Methods

The actual performance of the ΔpKa > 3 rule for counterion selection was validated using multiple screenings reports. Novel computational models for virtual solvent screening to avoid MCC incongruent crystallization were proposed. Using the ΔpKa rule, 10 acid counterions were selected for experimental aripiprazole (APZ) salt screening using 10 organic solvents. The experimental results were used to validate the proposed novel virtual solvent screen models.

Results

Experimental APZ salt screening resulted in a total of eight MCCs which included glucuronate, mesylate, oxalate, tartrate, salicylate and mandelate. The new model to virtually screen solvents provided a general agreement with APZ experimental findings in terms of selecting the optimal solvent for MCC crystallization.

Conclusion

The rational selection of counterions and organic solvents for MCC crystallization was presented using combined novel computational model as well as experimental studies. The current virtual solvent screen model was successfully implemented and validated which can be easily applied to newly discovered APIs.

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Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to Brahma Reddy from MSN Pharmaceuticals (Hyderabad, India) for

providing us with the API sample to carry out our research work. The authors greatly thankful to J-Star Research Inc (A Porton Company) for providing us with necessary support to conduct laboratory work as well as computational work.

Funding

Not applicable.

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Authors and Affiliations

  1. J-Star Research Inc., 6 Cedarbrook Drive, Cranbury, NJ, 08512, USA

    Harsh S. Shah, Caroline Michelle, Tian Xie, Kaushalendra Chaturvedi, Shanming Kuang & Yuriy A. Abramov

  2. Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA

    Yuriy A. Abramov

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  1. Harsh S. Shah
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  2. Caroline Michelle
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  3. Tian Xie
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  4. Kaushalendra Chaturvedi
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  5. Shanming Kuang
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Contributions

Yuriy Abramov (YA) and Shamming Kuang (SK) were involved with idea conceptualization. YA proposed the virtual screening approaches and performed all the calculations. SK together with Harsh S. Shah (HS) designed the experimental study. Caroline Michelle (CM) performed methodology and investigations. HS was involved in data curation and formal analysis. YA and HS writing- original draft preparation, Tian Xie performed NMR data analysis and interpretation, reviewing and editing. Kaushalendra Chaturvedi assisted with methodology, lab support for experiments execution. Final review, edits and supervision was performed by SK and YA.

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Correspondence to Harsh S. Shah or Yuriy A. Abramov.

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Shah, H.S., Michelle, C., Xie, T. et al. Computational and Experimental Screening Approaches to Aripiprazole Salt Crystallization. Pharm Res 40, 2779–2789 (2023). https://doi.org/10.1007/s11095-023-03522-z

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