SpringerLink
Log in

Neural Network Models for Predicting Solubility and Metabolism Class of Drugs in the Biopharmaceutics Drug Disposition Classification System (BDDCS)

  • Original Research Article
  • Published:
European Journal of Drug Metabolism and Pharmacokinetics Aims and scope Submit manuscript

Abstract

Background and Objective

The biopharmaceutics drug disposition classification system (BDDCS) categorizes drugs into four classes on the basis of their solubility and metabolism. This framework allows for the study of the pharmacokinetics of transporters and enzymatic metabolization on biopharmaceuticals, as well as drug–drug interactions in the body. The objective of the present study was to develop computational models by neural network models and structural parameters and physicochemical properties to estimate the class of a drug in the BDDCS system.

Methods

In this study, deep learning methods were utilized to explore the potential of artificial and convolutional neural networks (ANNs and CNNs) in predicting the BDDCS class of 721 substances. The structural parameters and physicochemical properties [Abraham solvation parameters, octanol-water partition (log P) and over the pH range 1–7.5 (log D), number of rotatable bonds, hydrogen bond acceptor numbers, as well as hydrogen bond donor count] are calculated with various software. These compounds were then split into a training set consisting of 602 molecules and a test set of 119 compounds to validate the models.

Results

The results of this study showed that neural network models using applied parameters of the drug, i.e., log D and Abraham solvation parameters, are able to predict the class of solubility and metabolism in the BDDCS system with good accuracy.

Conclusions

Neural network models are well equipped to deal with the relations between the structural parameters and physicochemical properties of drugs and BDDCS classes. In addition, log D is a more suitable parameter compared with log P in predicting BDDCS.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. Wu CY, Benet LZ. Predicting drug disposition via application of BCS: Transport/absorption/ elimination interplay and development of a biopharmaceutics drug disposition classification system. Pharm Res. 2005;22(1):11–23. https://doi.org/10.1007/s11095-004-9004-4.

    Article  CAS  PubMed  Google Scholar 

  2. Bocci G, Benet LZ, Oprea TI. Can BDDCS illuminate targets in drug design? Drug Discov Today. 2019;24(12):2299–306. https://doi.org/10.1016/j.drudis.2019.09.021.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Charalabidis A, Sfouni M, Bergström C, Macheras P. The biopharmaceutics classification system (BCS) and the biopharmaceutics drug disposition classification system (BDDCS): beyond guidelines. Int J Pharm. 2019;566:264–81. https://doi.org/10.1016/j.ijpharm.2019.05.041.

    Article  CAS  PubMed  Google Scholar 

  4. Abdolmaleki A, Ghasemi JB, Ghasemi F. Computer aided drug design for multi-target drug design: SAR /QSAR, molecular docking and pharmacophore methods. Curr Drug Targets. 2017;18(5):556–75. https://doi.org/10.2174/1389450117666160101120822.

    Article  CAS  PubMed  Google Scholar 

  5. Patel V, Shah M. Artificial intelligence and machine learning in drug discovery and development. Intell Med. 2022;2(3):134–40. https://doi.org/10.1016/j.imed.2021.10.001.

    Article  Google Scholar 

  6. Vatansever S, Schlessinger A, Wacker D, Kaniskan HÜ, ** J, Zhou MM, Zhang B. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: state-of-the-arts and future directions. Med Res Rev. 2021;41(3):1427–73. https://doi.org/10.1002/med.21764.

    Article  PubMed  Google Scholar 

  7. Hanafi A, Amani A. Effect of processing/formulation parameters on particle size of nanoemulsions containing ibuprofen: an artificial neural networks study. Pharm Sci. 2021;27(2):230–7. https://doi.org/10.34172/PS.2020.74.

    Article  CAS  Google Scholar 

  8. Goodfellow I, Bengio Y, Courville A. Deep learning. MIT Press; 2016.

    Google Scholar 

  9. Yamashita R, Nishio M, Do RKG, Togashi K. Convolutional neural networks: an overview and application in radiology. Insights Imaging. 2018;9(4):611–29. https://doi.org/10.1007/s13244-018-0639-9.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Golfar Y, Shayanfar A. Prediction of biopharmaceutical drug disposition classification system (BDDCS) by structural parameters. J Pharm Pharm Sci. 2019;22(1):247–69. https://doi.org/10.18433/jpps30271.

    Article  CAS  PubMed  Google Scholar 

  11. Broccatelli F, Cruciani G, Benet LZ, Oprea TI. Bddcs class prediction for new molecular entities. Mol Pharm. 2012;9(3):570–80. https://doi.org/10.1021/mp2004302.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Lobo S. Is there enough focus on lipophilicity in drug discovery? Expert Opin Drug Discov. 2020;15(3):261–3. https://doi.org/10.1080/17460441.2020.1691995.

    Article  PubMed  Google Scholar 

  13. Ghotbi G, Hamzeh-Mivehroud M, Taghvimi A, Davaran S, Dastmalchi S. Investigation of experimental and in silico physicochemical properties of thiazole-pyridinium anti-acetylcholinesterase derivatives with potential anti-Alzheimer’s activity. Pharm Sci. 2021;27(3):366–77. https://doi.org/10.34172/PS.2020.81.

    Article  CAS  Google Scholar 

  14. Papich MG, Martinez MN. Applying Biopharmaceutical Classification System (BCS) criteria to predict oral absorption of drugs in dogs: challenges and pitfalls. AAPS J. 2015;17(4):948–64. https://doi.org/10.1208/s12248-015-9743-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Jouyban A, Acree WE, Michael H Jr. Abraham and his developed parameters: various applications in medicine, chemistry and biology. Pharm Sci. 2022;28(2):170–3. https://doi.org/10.34172/PS.2022.1.

    Article  Google Scholar 

  16. Aliagas I, Gobbi A, Lee ML, Sellers BD. Comparison of log P and log D correction models trained with public and proprietary data sets. J Comp-Aided Mol Des. 2022;36(3):253–62. https://doi.org/10.1007/s10822-022-00450-9.

    Article  CAS  Google Scholar 

  17. Benet LZ, Broccatelli F, Oprea TI. BDDCS applied to over 900 drugs. AAPS J. 2011;13(4):519–47. https://doi.org/10.1208/s12248-011-9290-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Hosey CM, Chan R, Benet LZ. BDDCS predictions, self-correcting aspects of BDDCS assignments, BDDCS assignment corrections, and classification for more than 175 additional drugs. AAPS J. 2016;18(1):251–60. https://doi.org/10.1208/s12248-015-9845-2.

    Article  CAS  PubMed  Google Scholar 

  19. Bocci G, Oprea TI, Benet LZ. State of the art and uses for the biopharmaceutics drug disposition classification system (BDDCS): new additions, revisions, and citation references. AAPS J. 2022. https://doi.org/10.1208/s12248-022-00687-0.

    Article  CAS  PubMed  Google Scholar 

  20. Ulrich N, Endo S, Brown TN, Watanabe N, Bronner G, Abraham MH, Goss KU, UFZ-LSER database v 3.2.1 [Internet], Leipzig, Germany, Helmholtz Centre for Environmental Research-UFZ. 2017. http://www.ufz.de/lserd. Accessed on Aug 2022.

  21. Reaxys, https://www.reaxys.com/. Accessed on Aug 2022.

  22. ACD/labs, https://ilab.acdlabs.com. Accessed on August 2022.

  23. Siramshetty V, Williams J, Nguyễn ÐT, Neyra J, Southall N, Mathé E, Xu X, Shah P. Validating ADME QSAR models using marketed drugs. SLAS Discov. 2021;26(10):1326–36. https://doi.org/10.1177/24725552211017520.

    Article  PubMed  Google Scholar 

  24. Mohammadi SM, Shayanfar A, Emami S, Jouyban A. Effects of amount of excess solid, the type of stirring and sedimentation time on solubility of sodium phenytoin and lamotrigine. ADMET DMPK. 2018;6(4):269–78. https://doi.org/10.5599/admet.621.

    Article  Google Scholar 

  25. Tinworth CP, Young RJ. Facts, patterns, and principles in drug discovery: appraising the rule of 5 with measured physicochemical data. J Med Chem. 2020;63(18):10091–108. https://doi.org/10.1021/acs.jmedchem.9b01596.

    Article  CAS  PubMed  Google Scholar 

  26. Kah M, Brown CD. Log D: lipophilicity for ionisable compounds. Chemosphere. 2008;72(10):1401–8. https://doi.org/10.1016/j.chemosphere.2008.04.074.

    Article  CAS  PubMed  Google Scholar 

  27. Benet LZ. The role of BCS (biopharmaceutics classification system) and BDDCS (biopharmaceutics drug disposition classification system) in drug development. J Pharm Sci. 2013;102(1):34–42. https://doi.org/10.1002/jps.23359.

    Article  CAS  PubMed  Google Scholar 

  28. Bhal SK, Kassam K, Peirson IG, Pearl GM. The rule of five revisited: applying log D in place of log P in drug-likeness filters. Mol Pharm. 2007;4(4):556–60. https://doi.org/10.1021/mp0700209.

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

    Aryan Ashrafi

  2. Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran

    Kiarash Teimouri

  3. Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran

    Farnaz Aghazadeh

  4. Pharmaceutical Analysis Research Center, Tabriz University of Medical Sciences, Tabriz, Iran

    Ali Shayanfar

  5. Faculty of Pharmacy, Tabriz University of Medical Sciences, Golgasht St., Tabriz, 5166614766, East Azerbaijan, Iran

    Ali Shayanfar

Authors
  1. Aryan Ashrafi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kiarash Teimouri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Farnaz Aghazadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ali Shayanfar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ali Shayanfar.

Ethics declarations

Funding

A.S. would like to acknowledge financial support from the Vice Chancellor for Research of Tabriz University of Medical Sciences, Tabriz, Iran (no.: 66944).

Conflict of Interest

Aryan Ashrafi, Kiarash Teymouri, Farnaz Aghazadeh, and Ali Shayanfar have no conflicts of interest.

Ethical Approval

Not applicable.

Consent to Participate

Not applicable.

Availability of Data

All data are available as a supplementary file (Table S1) on the journal’s website along with the published article.

Code Availability

It is available online in the following link: https://github.com/AryanAshrafi/Neural-Network-models-for-predicting-BDDCS-system.

Author Contributions

Aryan Ashrafi was responsible for data analysis and interpretation and drafting the article; Kiarash Teymouri for data analysis and interpretation and drafting the article; Farnaz Aghazadeh for data collection; and Ali Shayanfar for design of the work, supervision of the project, and critical revision of the article. All authors read and approved the final manuscript.

Consent to Publish

Not applicable.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (XLSX 172 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

Ashrafi, A., Teimouri, K., Aghazadeh, F. et al. Neural Network Models for Predicting Solubility and Metabolism Class of Drugs in the Biopharmaceutics Drug Disposition Classification System (BDDCS). Eur J Drug Metab Pharmacokinet 49, 1–6 (2024). https://doi.org/10.1007/s13318-023-00861-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13318-023-00861-5

Search

Navigation