Skip to main content
SpringerLink
Log in

Exploring molecular fingerprints of different drugs having bile interaction: a stepping stone towards better drug delivery

  • Original Article
  • Published:
Molecular Diversity Aims and scope Submit manuscript

Abstract

Bile acids are amphiphilic substances produced naturally in humans. In the context of drug delivery and dosage form design, it is critical to understand whether a drug interacts with bile inside the gastrointestinal (GI) tract or not. This study focuses on the identification of structural fingerprints/features important for bile interaction. Molecular modelling methods such as Bayesian classification and recursive partitioning (RP) studies are executed to find important fingerprints/features for the bile interaction. For the Bayesian classification study, the ROC score of 0.837 and 0.950 are found for the training set and the test set compounds, respectively. The fluorine-containing aliphatic/aromatic group, the branched chain of the alkyl group containing hydroxyl moiety and the phenothiazine ring etc. are identified as good fingerprints having a positive contribution towards bile interactions, whereas, the bad fingerprints such as free carboxylate group, purine, and pyrimidine ring etc. have a negative contribution towards bile interactions. The best tree (tree ID: 1) from the RP study classifies the bile interacting or non-interacting compounds with a ROC score of 0.941 for the training and 0.875 for the test set. Additionally, SARpy and QSAR-Co analyses are also been performed to classify compounds as bile interacting/non-interacting. Moreover, forty-six recently FDA-approved drugs have been screened by the developed SARpy and QSAR-Co models to assess their bile interaction properties. Overall, this attempt may facilitate the researchers to identify bile interacting/non-interacting molecules in a faster way and help in the design of formulations and target-specific drug development.

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
Fig. 8

Similar content being viewed by others

Data availability

The data associated with this study are contained within the article or in the supplementary material.

Code availability

Not applicable.

References

  1. Mithani SD, Bakatselou V, TenHoor CN, Dressman JB (1996) Estimation of the increase in solubility of drugs as a function of bile salt concentration. Pharm Res 13:163–167. https://doi.org/10.1023/A:1016062224568

    Article  CAS  PubMed  Google Scholar 

  2. Tangerman A, van Schaik A, van der Hoek EW (1986) Analysis of conjugated and unconjugated bile acids in serum and jejunal fluid of normal subjects. Clin Chim Acta 159:123–132. https://doi.org/10.1016/0009-8981(86)90044-6

    Article  CAS  PubMed  Google Scholar 

  3. Peeters TL, Vantrappen G, Janssens J (1980) Bile acid output and the interdigestive migrating motor complex in normals and in cholecystectomy patients. Gastroenterology 79:678–681. https://doi.org/10.1016/0016-5085(80)90244-9

    Article  CAS  PubMed  Google Scholar 

  4. Stamp D, Jenkins G (2008) An overview of bile-acid synthesis, chemistry and function. In: Jenkins GJ, Hardie LJ (Ed) Bile Acids: Toxicology and Bioactivity Issue 4 of Issues in toxicology, Royal Society of Chemistry, Great Britain, pp 1–13

  5. Sugioka H, Moroi Y (1998) Micelle formation of sodium cholate and solubilization into the micelle. Biochim Biophys Acta BBA 1394:99–110. https://doi.org/10.1016/S0005-2760(98)00090-3

    Article  CAS  PubMed  Google Scholar 

  6. Friedler A (2008) Bile Acids: chemistry, biosynthesis, analysis, chemical and metabolic transformations and pharmacology. Open Chem 6:131–131. https://doi.org/10.2478/s11532-007-0072-2

    Article  Google Scholar 

  7. Darkoh C, Lichtenberger LM, Ajami N, Dial EJ, Jiang ZD, DuPont HL (2010) Bile acids improve the antimicrobial effect of rifaximin. Antimicrob Agents Chemother 54:3618–3624. https://doi.org/10.1128/AAC.00161-10

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Nanjwade BK, Patel DJ, Udhani RA, Manvi FV (2011) Functions of lipids for enhancement of oral bioavailability of poorly water-soluble drugs. Sci Pharm 79:705–727. https://doi.org/10.3797/scipharm.1105-09

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Pavlović N, Goločorbin-Kon S, Ðanić M, Stanimirov B, Al-Salami H, Stankov K, Mikov M (2018) Bile acids and their derivatives as potential modifiers of drug release and pharmacokinetic profiles. Front Pharmacol 9:1283. https://doi.org/10.3389/fphar.2018.01283

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Kim K, Yoon I, Chun I, Lee N, Kim T, Gwak HS (2011) Effects of bile salts on the lovastatin pharmacokinetics following oral administration to rats. Drug Deliv 18:79–83. https://doi.org/10.3109/10717544.2010.512024

    Article  CAS  PubMed  Google Scholar 

  11. Schlauersbach J, Kehrein J, Hanio S, Galli B, Harlacher C, Heidenreich C, Lenz B, Sotriffer C, Meinel L (2022) Predicting bile and lipid interaction for drug substances. Mol Pharm 19:2868–2876. https://doi.org/10.1021/acs.molpharmaceut.2c00227

    Article  CAS  PubMed  Google Scholar 

  12. Berrar D (2018) Bayes’ theorem and naive Bayes classifier. Encyclopedia Bioinform Comput Biol 403:412

    Google Scholar 

  13. Banerjee S, Amin SA, Jha T (2022) A fragment-based structural analysis of MMP-2 inhibitors in search of meaningful structural fragments. Comp Biol Med 144: 105360. https://doi.org/10.1016/j.compbiomed.2022.105360

  14. Chen L, Li Y, Zhao Q, Peng H, Hou T (2011) ADME evaluation in drug discovery. 10. Predictions of P-glycoprotein inhibitors using recursive partitioning and naive Bayesian classification techniques. Mol Pharm 8:889–900. https://doi.org/10.1021/mp100465q

    Article  CAS  PubMed  Google Scholar 

  15. Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50:742–754. https://doi.org/10.1021/ci100050t

    Article  CAS  PubMed  Google Scholar 

  16. Bhardwaj B, Baidya ATK, Amin SA, Adhikari N, Jha T, Gayen S (2019) Insight into structural features of phenyltetrazole derivatives as ABCG2 inhibitors for the treatment of multidrug resistance in cancer. SAR QSAR Environ Res 30:457–475. https://doi.org/10.1080/1062936X.2019.1615545

    Article  CAS  PubMed  Google Scholar 

  17. Amin SA, Adhikari N, Jha T (2021) Development of decision trees to discriminate HDAC8 inhibitors and non-inhibitors using recursive partitioning. J Biomol Struct Dyn 39:1–8. https://doi.org/10.1080/07391102.2019.1661876

    Article  CAS  PubMed  Google Scholar 

  18. Gini G, Ferrari T, Lombardo A, Cassano A, Benfenati E (2019) A new QSAR model for acute fish toxicity based on mined structural alerts. J Toxicol Risk Assess 5:2572–2580. https://doi.org/10.23937/2572-4061.1510016

    Article  CAS  Google Scholar 

  19. Chen Y, Yang H, Wu Z, Liu G, Tang Y, Li W (2018) Prediction of farnesoid X receptor disruptors with machine learning methods. Chem Res Toxicol 31:1128–1137. https://doi.org/10.1021/acs.chemrestox.8b00162

    Article  CAS  PubMed  Google Scholar 

  20. Ghosh K, Amin SA, Gayen S, Jha T (2021) Unmasking of crucial structural fragments for coronavirus protease inhibitors and its implications in COVID-19 drug discovery. J Mol Struct 1237:130366. https://doi.org/10.1016/j.molstruc.2021.130366

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Pizzo F, Lombardo A, Manganaro A, Benfenati E (2016) A new structure-activity relationship (SAR) model for predicting drug-induced liver injury, based on statistical and expert-based structural alerts. Front Pharmacol 7:442. https://doi.org/10.3389/fphar.2016.00442

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Ambure P, Halder AK, Gonzalez Diaz H, Cordeiro MN (2019) QSAR-Co: An open source software for developing robust multitasking or multitarget classification-based QSAR models. J Chem Inf Model 59:2538–2544. https://doi.org/10.1021/acs.jcim.9b00295

    Article  CAS  PubMed  Google Scholar 

  23. Halder AK, Giri AK, Cordeiro MNDS (2019) Multi-target chemometric modelling, fragment analysis and virtual screening with ERK inhibitors as potential anticancer agents. Molecules 24:3909. https://doi.org/10.3390/molecules24213909

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Ambure P, Ballesteros A, Huertas F, Camilleri P, Barigye SJ, Gozalbes R (2020) Development of generalized QSAR models for predicting cytotoxicity and genotoxicity of metal oxides nanoparticles. Int J Quant Struct Prop Rel 5:83–100. https://doi.org/10.4018/IJQSPR.20201001.oa2

    Article  CAS  Google Scholar 

  25. Ortega-Tenezaca B, Quevedo-Tumailli V, Bediaga H, Collados J, Arrasate S, Madariaga G, Munteanu CR, Cordeiro MN, González-Díaz H (2020) PTML multi-label algorithms: models, software, and applications. Curr Top Med Chem 20:2326–2337. https://doi.org/10.2174/1568026620666200916122616

    Article  CAS  PubMed  Google Scholar 

  26. Halder AK, Cordeiro MN (2021) AKT inhibitors: the road ahead to computational modeling-guided discovery. Int J Mol Sci 22:3944. https://doi.org/10.3390/ijms22083944

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, Li Q, Shoemaker BA, Thiessen PA, Yu B, Zaslavsky L, Zhang J, Bolton EE (2021) PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Res 49:D1388–D1395. https://doi.org/10.1093/nar/gkaa971

    Article  CAS  PubMed  Google Scholar 

  28. Discovery Studio 3.0, (2015). Accelrys Inc., CA, USA.

  29. Yap CW (2011) PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32:1466–1474. https://doi.org/10.1002/jcc.21707

    Article  CAS  PubMed  Google Scholar 

  30. Amin SA, Gayen S (2016) Modelling the cytotoxic activity of pyrazolo-triazole hybrids using descriptors calculated from the open source tool “PaDEL-descriptor.” J Taibah Univ Med Sci 10:896–905. https://doi.org/10.1016/j.jtusci.2016.04.009

    Article  Google Scholar 

  31. Pramanik S, Roy K (2014) Modeling bioconcentration factor (BCF) using mechanistically interpretable descriptors computed from open source tool “PaDEL-Descriptor.” Environ Sci Pollut Res 21:2955–2965. https://doi.org/10.1007/s11356-013-2247-z

    Article  CAS  Google Scholar 

  32. Khan K, Baderna D, Cappelli C, Toma C, Lombardo A, Roy K, Benfenati E (2019) Ecotoxicological QSAR modeling of organic compounds against fish: Application of fragment based descriptors in feature analysis. Aquat Toxicol 212:162–174. https://doi.org/10.1016/j.aquatox.2019.05.011

    Article  CAS  PubMed  Google Scholar 

  33. Jillella GK, Ojha PK, Roy K (2021) Application of QSAR for the identification of key molecular fragments and reliable predictions of effects of textile dyes on growth rate and biomass values of Raphidocelis subcapitata. Aquat Toxicol 238:105925. https://doi.org/10.1016/j.aquatox.2021.105925

    Article  CAS  PubMed  Google Scholar 

  34. Jillella GK, Khan K, Roy K (2020) Application of QSARs in identification of mutagenicity mechanisms of nitro and amino aromatic compounds against Salmonella typhimurium species. Toxicol In Vitro 65:4768. https://doi.org/10.1016/j.tiv.2020.104768

    Article  CAS  Google Scholar 

  35. Cereto-Massagué A, Ojeda MJ, Valls C, Mulero M, Garcia-Vallvé S, Pujadas G (2015) Molecular fingerprint similarity search in virtual screening. Methods 71:58–63. https://doi.org/10.1016/j.ymeth.2014.08.005

    Article  CAS  PubMed  Google Scholar 

  36. Fernández-de Gortari E, García-Jacas CR, Martinez-Mayorga K, Medina-Franco JL (2017) Database fingerprint (DFP): an approach to represent molecular databases. J Cheminformatics 9:1–9. https://doi.org/10.1186/s13321-017-0195-1

    Article  Google Scholar 

  37. Li Q, Wang Y, Bryant SH (2009) A novel method for mining highly imbalanced high-throughput screening data in PubChem. Bioinformatics 25:3310–3316. https://doi.org/10.1093/bioinformatics/btp589

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Xie XQ (2010) Exploiting PubChem for virtual screening. Expert Opin Drug Discov 5:1205–1220. https://doi.org/10.1517/17460441.2010.524924

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Alpay BA, Gosink M, Aguiar D (2022) Evaluating molecular fingerprint-based models of drug side effects against a statistical control. Drug Discov Today 27:103364. https://doi.org/10.1016/j.drudis.2022.103364

    Article  CAS  PubMed  Google Scholar 

  40. Xu C, Cheng F, Chen L, Du Z, Li W, Liu G, Lee PW, Tang Y (2012) In silico prediction of chemical Ames mutagenicity. J Chem Inf Model 52:2840–2847. https://doi.org/10.1021/ci300400a

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

SS and AB sincerely acknowledge the All India Council for Technical Education (AICTE), New Delhi, India for awarding the post-graduate GPAT Fellowship. SG would like to thank SERB, Govt. of India for financial assistance under the MATRICS scheme (MTR/2022/000286). We are very much grateful to the Department of Pharmaceutical Technology, Jadavpur University, Kolkata; Department of Pharmaceutical Technology, JIS University, Agarpara, Kolkata; India for providing research facilities.

Author information

Author notes

  1. Sourav Sardar and Arijit Bhattacharya have contributed equally to this work.

Authors and Affiliations

  1. Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India

    Sourav Sardar, Arijit Bhattacharya & Shovanlal Gayen

  2. Department of Pharmaceutical Technology, JIS University, 81, Nilgunj Road, Agarpara, Kolkata, West Bengal, India

    Sk. Abdul Amin

  3. Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India

    Tarun Jha

Authors
  1. Sourav Sardar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arijit Bhattacharya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sk. Abdul Amin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tarun Jha
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shovanlal Gayen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

SS: contributed to the research tool; analysis, interpretation and drafting of the manuscript. AB: contributed to the research tool; analysis, interpretation and drafting of the manuscript. SAA: analysis, interpretation and drafting the article; contributed to the final version. TJ: supervised the entire study and final approval of the version to be submitted. SG: conceived the idea, supervised the entire study and final approval of the version to be submitted. All authors discussed the results and contributed to the final manuscript.

Corresponding authors

Correspondence to Tarun Jha or Shovanlal Gayen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Conflicts of interest

The authors have no conflict of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

All co-authors have read and approved the revised manuscript.

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 (DOC 1150 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

Sardar, S., Bhattacharya, A., Amin, S.A. et al. Exploring molecular fingerprints of different drugs having bile interaction: a stepping stone towards better drug delivery. Mol Divers (2023). https://doi.org/10.1007/s11030-023-10670-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11030-023-10670-2

Keywords

Search

Navigation