Skip to main content

Advertisement

SpringerLink
Log in

Predicting Glass-Forming Ability of Pharmaceutical Compounds by Using Machine Learning Technologies

  • Research Article
  • Applications of Machine Learning and A.I. in Pharmaceutical Development and Technology
  • Published:
AAPS PharmSciTech Aims and scope Submit manuscript

Abstract

Low aqueous solubility is a common and serious challenge for most drug substances not only in development but also in the market, and it may cause low absorption and bioavailability as a result. Amorphization is an intermolecular modification strategy to address the issue by breaking the crystal lattice and enhancing the energy state. However, due to the physicochemical properties of the amorphous state, drugs are thermodynamically unstable and tend to recrystallize over time. Glass-forming ability (GFA) is an experimental method to evaluate the forming and stability of glass formed by crystallization tendency. Machine learning (ML) is an emerging technique widely applied in pharmaceutical sciences. In this study, we successfully developed multiple ML models (i.e., random forest (RF), XGBoost, and support vector machine (SVM)) to predict GFA from 171 drug molecules. Two different molecular representation methods (i.e., 2D descriptor and Extended-connectivity fingerprints (ECFP)) were implemented to process the drug molecules. Among all ML algorithms, 2D-RF performed best with the highest accuracy, AUC, and F1 of 0.857, 0.850, and 0.828, respectively, in the testing set. In addition, we conducted a feature importance analysis, and the results mostly agreed with the literature, which demonstrated the interpretability of the model. Most importantly, our study showed great potential for developing amorphous drugs by in silico screening of stable glass formers.

Graphical Abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data Availability

The authors confirm that the data supporting the findings of this study are available within the article.

Abbreviations

GFA:

Glass-forming ability

AI:

Artificial intelligence

ML:

Machine learning

RF:

Random forest

SVM:

Support vector machine

SHAP:

SHapley Additive exPlanations

ECFP:

Extended-connectivity fingerprints

IG:

Information gain

ACC:

Accuracy

F1:

F1 score

ROC:

Receiving operating characteristics

AUC:

Area under the curve

GF:

Glass former

References

  1. Savjani KT, Gajjar AK, Savjani JK. Drug solubility: importance and enhancement techniques. 1957;2012. https://doi.org/10.5402/2012/195727.

  2. Jermain S v., Brough C, Williams RO. Amorphous solid dispersions and nanocrystal technologies for poorly water-soluble drug delivery – an update. Int J Pharm. 2018;535(1–2):379–392. https://doi.org/10.1016/J.IJPHARM.2017.10.051.

  3. Adachi M, Hinatsu Y, Kusamori K, et al. Improved dissolution and absorption of ketoconazole in the presence of organic acids as pH-modifiers. Eur J Pharm Sci. 2015;76:225–30. https://doi.org/10.1016/J.EJPS.2015.05.015.

    Article  CAS  PubMed  Google Scholar 

  4. Balakrishnan A, Rege BD, Amidon GL, Polli JE. Surfactant-mediated dissolution: contributions of solubility enhancement and relatively low micelle diffusivity. J Pharm Sci. 2004;93(8):2064–75. https://doi.org/10.1002/JPS.20118.

    Article  CAS  PubMed  Google Scholar 

  5. Jain NK, Gupta U. Application of dendrimer–drug complexation in the enhancement of drug solubility and bioavailability. 2008;4(8):1035–1052. 101517/17425255481035.

  6. Kolašinac N, Kachrimanis K, Homšek I, Grujić B, Urić Z, Ibrić S. Solubility enhancement of desloratadine by solid dispersion in poloxamers. Int J Pharm. 2012;436(1–2):161–70. https://doi.org/10.1016/J.IJPHARM.2012.06.060.

    Article  PubMed  Google Scholar 

  7. Almeida Sousa L, Reutzel-Edens SM, Stephenson GA, Taylor LS. Supersaturation potential of salt, co-crystal, and amorphous forms of a model weak base. Published online. 2016. https://doi.org/10.1021/acs.cgd.5b01341.

    Article  Google Scholar 

  8. Bogner RH, Murdande SB, Pikal MJ, Shanker RM. Solubility advantage of amorphous pharmaceuticals: II. application of quantitative thermodynamic relationships for prediction of solubility enhancement in structurally diverse insoluble pharmaceuticals. Pharm Res. 2010;27(12):2704-2714. https://doi.org/10.1007/S11095-010-0269-5/TABLES/4.

  9. Rasenack N, Müller BW. Dissolution rate enhancement by in situ micronization of poorly water-soluble drugs. Pharmaceutical Research. 2002;19(12):1894–900. https://doi.org/10.1023/A:1021410028371.

    Article  CAS  PubMed  Google Scholar 

  10. Patel VR, Agrawal YK. Nanosuspension: an approach to enhance solubility of drugs. J Adv Pharm Technol Res. 2011;2(2):81. https://doi.org/10.4103/2231-4040.82950.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Rams-Baron M, Jachowicz R, Boldyreva E, Zhou D, Jamroz W, Paluch M. Why amorphous drugs? Amorphous Drugs. Published online 2018:1–7. https://doi.org/10.1007/978-3-319-72002-9_1.

  12. Zografi G, Sciences ANJ of P, undefined. Interrelationships between structure and the properties of amorphous solids of pharmaceutical interest. Elsevier. 2017 Accessed November 13, 2022. https://www.sciencedirect.com/science/article/pii/S0022354916414115.

  13. Baird JA, van Eerdenbrugh B, Taylor LS. A Classification system to assess the crystallization tendency of organic molecules from undercooled melts. J Pharm Sci. 2010;99(9):3787–806. https://doi.org/10.1002/JPS.22197.

    Article  CAS  PubMed  Google Scholar 

  14. Wyttenbach N, Kirchmeyer W, Alsenz J, Kuentz M. Theoretical considerations of the Prigogine-Defay ratio with regard to the glass-forming ability of drugs from undercooled melts. Mol Pharm. 2016;13(1):241–50. https://doi.org/10.1021/ACS.MOLPHARMACEUT.5B00688/SUPPL_FILE/MP5B00688_SI_001.PDF.

    Article  CAS  PubMed  Google Scholar 

  15. Jiang J, Ma X, Ouyang D, Williams Iii RO. Emerging artificial intelligence (AI) technologies used in the development of solid dosage forms. Pharmaceutics. 2022;14(11):2257. https://doi.org/10.3390/PHARMACEUTICS14112257.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Wang W, Ye Z, Gao H, Ouyang D. Computational pharmaceutics - a new paradigm of drug delivery. J Control Release. 2021;338:119–36. https://doi.org/10.1016/J.JCONREL.2021.08.030.

    Article  CAS  PubMed  Google Scholar 

  17. Jiang J, Peng HH, Yang Z, et al 2022 The applicationsof machine learning (ML) in designing dry powder for inhalation by using thin-film-freezing technology. Int J Pharm. 2022 626:122179. https://doi.org/10.1016/J.IJPHARM.2022.122179.

  18. Ma X, Kittikunakorn N, Sorman B, et al. Application of deep learning convolutional neural networks for internal tablet defect detection: high accuracy, throughput, and adaptability. J Pharm Sci. 2020;109(4):1547–57. https://doi.org/10.1016/J.XPHS.2020.01.014.

    Article  CAS  PubMed  Google Scholar 

  19. Westphal E, Seitz HA. machine learning method for defect detection and visualization in selective laser sintering based on convolutional neural networks. Addit Manuf. 2021;41:101965. https://doi.org/10.1016/J.ADDMA.2021.101965.

    Article  Google Scholar 

  20. Ficzere M, Alexandra Mészáros L, Kállai-Szabó N, et al. Real-time coating thickness measurement and defect recognition of film coated tablets with machine vision and deep learning. Int J Pharm. 2022;623:121957. https://doi.org/10.1016/J.IJPHARM.2022.121957.

    Article  CAS  PubMed  Google Scholar 

  21. Jiang Junhuang, Lu Anqi, Ma Xiangyu, Ouyang Defang, Williams O. Robert III. The applications of machine learning to predict the forming of chemically stable amorphous solid dispersions prepared by hot-melt extrusion. International Journal of Pharmaceutics: X. 2023;5:100164. https://doi.org/10.1016/j.ijpx.2023.100164.

    Article  CAS  PubMed  Google Scholar 

  22. Dong J, Gao H, PharmSD Ouyang D. A novel AI-based computational platform for solid dispersion formulation design. Int J Pharm. 2021;604:120705. https://doi.org/10.1016/J.IJPHARM.2021.120705.

    Article  CAS  PubMed  Google Scholar 

  23. Han R, Xiong H, Ye Z, et al. Predicting physical stability of solid dispersions by machine learning techniques. J Control Release. 2019;311–312:16–25. https://doi.org/10.1016/J.JCONREL.2019.08.030.

    Article  PubMed  Google Scholar 

  24. Han R, Yang Y, Li X, Ouyang D. Predicting oral disintegrating tablet formulations by neural network techniques. Asian J Pharm Sci. 2018;13(4):336–42. https://doi.org/10.1016/J.AJPS.2018.01.003.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Breiman L. Random forests. Mach Learn. 2001;45(1):5–32. https://doi.org/10.1023/A:1010933404324.

    Article  Google Scholar 

  26. Adankon MM, Cheriet M. Support Vector Machine. Encyclopedia of Biometrics. Published online 2009:1303–1308. https://doi.org/10.1007/978-0-387-73003-5_299.

  27. Chen T, CGacm sigkdd international conference on knowledge, 2016 undefined. Xgboost: A scalable tree boosting system. dl.acm.org. 2016;13–17-August-2016:785–794. https://doi.org/10.1145/2939672.2939785.

  28. Ke G, Meng Q, Finley T, et al Lightgbm: a highly efficient gradient boosting decision tree. papers.nips.cc. Accessed January 27, 2022. http://papers.nips.cc/paper/6907-a-highly-efficient-gradient-boosting-decision-tree.

  29. Toropov AA, Toropova AP, Martyanov SE, et al. Comparison of SMILES and molecular graphs as the representation of the molecular structure for QSAR analysis for mutagenic potential of polyaromatic amines. Chemom Intell Lab Syst. 2011;109(1):94–100. https://doi.org/10.1016/J.CHEMOLAB.2011.07.008.

    Article  CAS  Google Scholar 

  30. Alhalaweh A, Alzghoul A, Kaialy W, Mahlin D, Bergström CAS. Computational predictions of glass-forming ability and crystallization tendency of drug molecules. Mol Pharm. 2014;11(9):3123–32. https://doi.org/10.1021/MP500303A/ASSET/IMAGES/LARGE/MP-2014-00303A_0008.JPEG.

    Article  CAS  PubMed  Google Scholar 

  31. Edueng K, Bergström CAS, Gråsjö J, Mahlin D. Long-term physical (in)stability of spray-dried amorphous drugs: relationship with glass-forming ability and physicochemical properties. Pharmaceutics. 2019;11(9):425. https://doi.org/10.3390/PHARMACEUTICS11090425.

  32. Atawa B, Couvrat N, Coquerel G, Dargent E, Saiter A. Impact of chirality on the glass forming ability and the crystallization from the amorphous state of 5-ethyl-5-methylhydantoin, a chiral poor glass former. Int J Pharm. 2018;540(1–2):11–21. https://doi.org/10.1016/J.IJPHARM.2018.01.050.

    Article  CAS  PubMed  Google Scholar 

  33. Wyttenbach N, Kuentz M. Glass-forming ability of compounds in marketed amorphous drug products. Eur J Pharm Biopharm. 2017;112:204–8. https://doi.org/10.1016/J.EJPB.2016.11.031.

    Article  CAS  PubMed  Google Scholar 

  34. Kapourani A, Vardaka E, Katopodis K, Kachrimanis K, Barmpalexis P. Crystallization tendency of APIs possessing different thermal and glass related properties in amorphous solid dispersions. Int J Pharm. 2020;579:119149. https://doi.org/10.1016/J.IJPHARM.2020.119149.

  35. Blaabjerg LI, Lindenberg E, Löbmann K, Grohganz H, Rades T. Is there a correlation between the glass forming ability of a drug and its supersaturation propensity? Int J Pharm. 2018;538(1–2):243–9. https://doi.org/10.1016/J.IJPHARM.2018.01.013.

    Article  CAS  PubMed  Google Scholar 

  36. Lapuk SE, Mukhametzyanov TA, Schick C, Gerasimov A v. Crystallization kinetics and glass-forming ability of rapidly crystallizing drugs studied by Fast Scanning Calorimetry. Int J Pharm. 2021;599:120427. https://doi.org/10.1016/J.IJPHARM.2021.120427.

  37. Safna Hussan KP, Thayyil MS, Deshpande SK, Jinitha T, v., Manoj K, Ngai KL. Molecular dynamics, physical and thermal stability of neat amorphous amlodipine besylate and in binary mixture. Eur J Pharm Sci. 2018;119:268–78. https://doi.org/10.1016/J.EJPS.2018.04.030.

    Article  CAS  PubMed  Google Scholar 

  38. Baghel S, Cathcart H, Redington W, O’Reilly NJ. An investigation into the crystallization tendency/kinetics of amorphous active pharmaceutical ingredients: a case study with dipyridamole and cinnarizine. Eur J Pharm Biopharm. 2016;104:59–71. https://doi.org/10.1016/J.EJPB.2016.04.017.

    Article  CAS  PubMed  Google Scholar 

  39. Sahakijpijarn S, Moon C, Koleng JJ, Christensen DJ, Williams RO. Development of remdesivir as a dry powder for inhalation by thin film freezing. Pharmaceutics. 2020; 12(11):1002. https://doi.org/10.3390/PHARMACEUTICS12111002.

  40. Blaabjerg LI, Bulduk B, Lindenberg E, Löbmann K, Rades T, Grohganz H. Influence of glass forming ability on the physical stability of supersaturated amorphous solid dispersions. J Pharm Sci. 2019;108(8):2561–9. https://doi.org/10.1016/J.XPHS.2019.02.028.

    Article  CAS  PubMed  Google Scholar 

  41. Baird JA, Santiago-Quinonez D, Rinaldi C, Taylor LS. Role of viscosity in influencing the glass-forming ability of organic molecules from the undercooled melt state. Pharm Res. 2012;29(1):271–84. https://doi.org/10.1007/S11095-011-0540-4/FIGURES/5.

    Article  CAS  PubMed  Google Scholar 

  42. Yang L, Shami A. On hyperparameter optimization of machine learning algorithms: theory and practice. Neurocomputing. 2020;415:295–316. https://doi.org/10.1016/J.NEUCOM.2020.07.061.

    Article  Google Scholar 

  43. Ye Z, Ouyang D. Prediction of small-molecule compound solubility in organic solvents by machine learning algorithms. Ye and Ouyang Journal of Cheminformatics. 2021;13:98. https://doi.org/10.1186/s13321-021-00575-3.

    Article  CAS  PubMed  Google Scholar 

  44. Kopitar L, Cilar L, Kocbek P, Stiglic G. Local vs. global interpretability of machine learning models in type 2 diabetes mellitus screening. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2019;11979 LNAI:108–119. https://doi.org/10.1007/978-3-030-37446-4_9/FIGURES/3.

  45. Jadhav Swati, He Hongmei, Jenkins Karl. Information gain directed genetic algorithm wrapper feature selection for credit rating. Appl Soft Comput. 2018;69:541–53. https://doi.org/10.1016/j.asoc.2018.04.033.

    Article  Google Scholar 

  46. Nurzyńska K, Booth J, Roberts CJ, McCabe J, Dryden I, Fischer PM. Long-term amorphous drug stability predictions using easily calculated, predicted, and measured parameters. Mol Pharm. 2015;12(9):3389–98. https://doi.org/10.1021/ACS.MOLPHARMACEUT.5B00409/ASSET/IMAGES/LARGE/MP-2015-00409X_0008.JPEG.

    Article  PubMed  Google Scholar 

  47. Hall LH, Kier LB. The molecular connectivity chi indexes and kappa shape indexes in structure-property modeling. Published online January 5, 2007:367–422. https://doi.org/10.1002/9780470125793.CH9.

  48. Wildman SA, Crippen GM. Prediction of physicochemical parameters by atomic contributions. J Chem Inf Comput Sci. 1999;39(5):868–73. https://doi.org/10.1021/CI990307L/ASSET/IMAGES/LARGE/CI990307LF00002.JPEG.

    Article  CAS  Google Scholar 

  49. Gasteiger J, Marsili M. Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges. Tetrahedron. 1980;36(22):3219–28. https://doi.org/10.1016/0040-4020(80)80168-2.

    Article  CAS  Google Scholar 

  50. Mahlin D, Bergström CAS. Early drug development predictions of glass-forming ability and physical stability of drugs. Eur J Pharm Sci. 2013;49(2):323–32. https://doi.org/10.1016/J.EJPS.2013.03.016.

    Article  CAS  PubMed  Google Scholar 

  51. Kawakami K. Crystallization tendency of pharmaceutical glasses: relevance to compound properties, impact of formulation process, and implications for design of amorphous solid dispersions. Pharmaceutics 2019, 2019;11(5):202. https://doi.org/10.3390/PHARMACEUTICS11050202.

Download references

Author information

Authors and Affiliations

  1. Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, Texas, 78712, USA

    Junhuang Jiang & Robert O. Williams III

  2. State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, 999078, China

    Defang Ouyang

Authors
  1. Junhuang Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Defang Ouyang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Robert O. Williams III
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Junhuang Jiang: conceptualization, methodology, data curation, software, visualization, writing—original draft. Defang Ouyang: conceptualization, validation, writing—review and editing. Robert O. Williams III: conceptualization, writing—review and editing, supervision.

Corresponding author

Correspondence to Robert O. Williams III.

Ethics declarations

Conflicts of interest

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 570 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, J., Ouyang, D. & Williams, R.O. Predicting Glass-Forming Ability of Pharmaceutical Compounds by Using Machine Learning Technologies. AAPS PharmSciTech 24, 103 (2023). https://doi.org/10.1208/s12249-023-02535-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1208/s12249-023-02535-6

Keywords

Search

Navigation