Skip to content
2000
  1. Home
  2. A-Z Publications
  3. CNS & Neurological Disorders - Drug Targets (Formerly Current Drug Targets - CNS & Neurological Disorders)
  4. Fast Track Articles
  5. Fast Track Article
image of BBBper: A Machine Learning-based Online Tool for Blood-Brain Barrier (BBB) Permeability Prediction

Abstract

Aims

Neuronal disorders have affected more than 15% of the world's population, signifying the importance of continued design and development of drugs that can cross the Blood-Brain Barrier (BBB).

Background

BBB limits the permeability of external compounds by 98% to maintain and regulate brain homeostasis. Hence, BBB permeability prediction is vital to predict the activity of a drug-like substance.

Objective

Here, we report about developing BBBper (Blood-Brain Barrier permeability prediction) using machine learning tool.

Method

A supervised machine learning-based online tool, based on physicochemical parameters to predict the BBB permeability of given chemical compounds was developed. The user-end webpage was developed in HTML and linked with back-end server by a python script to run user queries and results.

Result

BBBper uses a random forest algorithm at the back end, showing 97% accuracy on the external dataset, compared to 70-92% accuracy of currently available web-based BBB permeability prediction tools.

Conclusion

The BBBper web tool is freely available at http://bbbper.mdu.ac.in.

© 2024 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cnsnddt/10.2174/0118715273328174241007060331
2024-10-16
2024-11-26

  • From This Site

    /content/journals/cnsnddt/10.2174/0118715273328174241007060331
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Obermeier B. Daneman R. Ransohoff R.M. Development, maintenance and disruption of the blood-brain barrier. Nat. Med. 2013 19 12 1584 1596 10.1038/nm.3407 24309662
    [Google Scholar]
  2. Alahmari A. Blood-Brain Barrier Overview: Structural and Functional Correlation. Neural Plast. 2021 2021 1 10 10.1155/2021/6564585 34912450
    [Google Scholar]
  3. Benz F. Liebner S. Structure and Function of the Blood-Brain Barrier (BBB). Handb Exp Pharmacol. 2020 3 31 10.1007/164_2020_404
    [Google Scholar]
  4. Summerfield S.G. Read K. Begley D.J. Obradovic T. Hidalgo I.J. Coggon S. Lewis A.V. Porter R.A. Jeffrey P. Central nervous system drug disposition: the relationship between in situ brain permeability and brain free fraction. J. Pharmacol. Exp. Ther. 2007 322 1 205 213 10.1124/jpet.107.121525 17405866
    [Google Scholar]
  5. Pardridge W.M. Drug transport across the blood-brain barrier. J. Cereb. Blood Flow Metab. 2012 32 11 1959 1972 10.1038/jcbfm.2012.126 22929442
    [Google Scholar]
  6. Lipinski C.A. Drug-like properties and the causes of poor solubility and poor permeability. J. Pharmacol. Toxicol. Methods 2000 44 1 235 249 10.1016/S1056‑8719(00)00107‑6 11274893
    [Google Scholar]
  7. Lipinski C.A. Lombardo F. Dominy B.W. Feeney P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings 1PII of original article: S0169-409X(96)00423-1. The article was originally published in Advanced Drug Delivery Reviews 23 (1997) 3–25. 1. Adv. Drug Deliv. Rev. 2001 46 1-3 3 26 10.1016/S0169‑409X(00)00129‑0 11259830
    [Google Scholar]
  8. Pajouhesh H. Lenz G.R. Medicinal chemical properties of successful central nervous system drugs. NeuroRx 2005 2 4 541 553 10.1602/neurorx.2.4.541 16489364
    [Google Scholar]
  9. Rautio J. Laine K. Gynther M. Savolainen J. Prodrug approaches for CNS delivery. AAPS J. 2008 10 1 92 102 10.1208/s12248‑008‑9009‑8 18446509
    [Google Scholar]
  10. Luco J.M. Prediction of the brain-blood distribution of a large set of drugs from structurally derived descriptors using partial least-squares (PLS) modeling. J. Chem. Inf. Comput. Sci. 1999 39 2 396 404 10.1021/ci980411n 10192950
    [Google Scholar]
  11. Crivori P. Cruciani G. Carrupt P.A. Testa B. Predicting blood-brain barrier permeation from three-dimensional molecular structure. J. Med. Chem. 2000 43 11 2204 2216 10.1021/jm990968+ 10841799
    [Google Scholar]
  12. Doniger S. Hofmann T. Yeh J. Predicting CNS permeability of drug molecules: comparison of neural network and support vector machine algorithms. J. Comput. Biol. 2002 9 6 849 864 10.1089/10665270260518317 12614551
    [Google Scholar]
  13. Kortagere S. Chekmarev D. Welsh W.J. Ekins S. New predictive models for blood-brain barrier permeability of drug-like molecules. Pharm. Res. 2008 25 8 1836 1845 10.1007/s11095‑008‑9584‑5 18415049
    [Google Scholar]
  14. Shen J. Cheng F. Xu Y. Li W. Tang Y. Estimation of ADME properties with substructure pattern recognition. J. Chem. Inf. Model. 2010 50 6 1034 1041 10.1021/ci100104j 20578727
    [Google Scholar]
  15. Cheng F. Li W. Zhou Y. Shen J. Wu Z. Liu G. Lee P.W. Tang Y. admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties. J. Chem. Inf. Model. 2012 52 11 3099 3105 10.1021/ci300367a 23092397
    [Google Scholar]
  16. Guerra A. Páez J.A. Campillo N.E. Artificial neural networks in ADMET modeling: Prediction of blood-brain barrier permeation. QSAR Comb. Sci. 2008 27 5 586 594 10.1002/qsar.200710019
    [Google Scholar]
  17. Zhang L. Zhu H. Oprea T.I. Golbraikh A. Tropsha A. QSAR modeling of the blood-brain barrier permeability for diverse organic compounds. Pharm. Res. 2008 25 8 1902 1914 10.1007/s11095‑008‑9609‑0 18553217
    [Google Scholar]
  18. Muehlbacher M. Spitzer G.M. Liedl K.R. Kornhuber J. Qualitative prediction of blood–brain barrier permeability on a large and refined dataset. J. Comput. Aided Mol. Des. 2011 25 12 1095 1106 10.1007/s10822‑011‑9478‑1 22109848
    [Google Scholar]
  19. Camacho D.M. Collins K.M. Powers R.K. Costello J.C. Collins J.J. Next-Generation Machine Learning for Biological Networks. Cell 2018 173 7 1581 1592 10.1016/j.cell.2018.05.015 29887378
    [Google Scholar]
  20. Russell SJ Norvig P Artificial Intelligence - A Modern Approach Pearson 2016
    [Google Scholar]
  21. Huang E.T.C. Yang J.S. Liao K.Y.K. Tseng W.C.W. Lee C.K. Gill M. Compas C. See S. Tsai F.J. Predicting blood–brain barrier permeability of molecules with a large language model and machine learning. Sci. Rep. 2024 14 1 15844 10.1038/s41598‑024‑66897‑y 38982309
    [Google Scholar]
  22. Kumar V. Banerjee A. Roy K. Breaking the Barriers: Machine-Learning-Based c-RASAR Approach for Accurate Blood–Brain Barrier Permeability Prediction. J. Chem. Inf. Model. 2024 64 10 4298 4309 10.1021/acs.jcim.4c00433 38700741
    [Google Scholar]
  23. Dichiara M. Cosentino G. Giordano G. Pasquinucci L. Marrazzo A. Costanzo G. Amata E. Designing drugs optimized for both blood–brain barrier permeation and intra-cerebral partition. Expert Opin. Drug Discov. 2023 19 3 1 13 10.1080/17460441.2023.2294118 38145409
    [Google Scholar]
  24. Shaker B. Lee J. Lee Y. Yu M.S. Lee H.M. Lee E. Kang H.C. Oh K.S. Kim H.W. Na D. A machine learning-based quantitative model (LogBB_Pred) to predict the blood–brain barrier permeability (logBB value) of drug compounds. Bioinformatics 2023 39 10 btad577 10.1093/bioinformatics/btad577 37713469
    [Google Scholar]
  25. Kumar R. Sharma A. Alexiou A. Bilgrami A.L. Kamal M.A. Ashraf G.M. DeePred-BBB: A Blood Brain Barrier Permeability Prediction Model With Improved Accuracy. Front. Neurosci. 2022 16 858126 10.3389/fnins.2022.858126 35592264
    [Google Scholar]
  26. Adenot M. Lahana R. Blood-brain barrier permeation models: discriminating between potential CNS and non-CNS drugs including P-glycoprotein substrates. J. Chem. Inf. Comput. Sci. 2004 44 1 239 248 10.1021/ci034205d 14741033
    [Google Scholar]
  27. Gao Z. Chen Y. Cai X. Xu R. Predict drug permeability to blood–brain-barrier from clinical phenotypes: drug side effects and drug indications. Bioinformatics 2017 33 6 901 908 10.1093/bioinformatics/btw713 27993785
    [Google Scholar]
  28. Wang Z. Yang H. Wu Z. Wang T. Li W. Tang Y. Liu G. In Silico Prediction of Blood–Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods. ChemMedChem 2018 13 20 2189 2201 10.1002/cmdc.201800533 30110511
    [Google Scholar]
  29. Martins I.F. Teixeira A.L. Pinheiro L. Falcao A.O. A Bayesian approach to in silico blood-brain barrier penetration modeling. J. Chem. Inf. Model. 2012 52 6 1686 1697 10.1021/ci300124c 22612593
    [Google Scholar]
  30. Fan R.E. Chen P.H. Lin C.J. Working set selection using second order information for training support vector machines. J. Mach. Learn. Res. 2005 6 1889 1918
    [Google Scholar]
  31. Anastasiadis AD Magoulas GD Vrahatis MN New globally convergent training scheme based on the resilient propagation algorithm. Neurocomputing 2005 64 253 270 10.1016/j.neucom.2004.11.016
    [Google Scholar]
  32. Breiman L. Random Forests. Mach. Learn. 2001 45 1 5 32 10.1023/A:1010933404324
    [Google Scholar]
  33. Fawcett T. An introduction to ROC analysis. Pattern Recognit. Lett. 2006 27 8 861 874 10.1016/j.patrec.2005.10.010
    [Google Scholar]
  34. Gevrey M. Dimopoulos I. Lek S. Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecol. Modell. 2003 160 3 249 264 10.1016/S0304‑3800(02)00257‑0
    [Google Scholar]
  35. Nembrini S. König I.R. Wright M.N. The revival of the Gini importance? Bioinformatics 2018 34 21 3711 3718 10.1093/bioinformatics/bty373 29757357
    [Google Scholar]
  36. Martinez-Taboada F. Redondo J.I. The SIESTA (SEAAV Integrated evaluation sedation tool for anaesthesia) project: Initial development of a multifactorial sedation assessment tool for dogs. PLoS ONE 2020 15 4 e0230799
    [Google Scholar]
  37. Menze B.H. Kelm B.M. Masuch R. Himmelreich U. Bachert P. Petrich W. Hamprecht F.A. A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinformatics 2009 10 1 213 10.1186/1471‑2105‑10‑213 19591666
    [Google Scholar]
  38. Xiong G. Wu Z. Yi J. Fu L. Yang Z. Hsieh C. Yin M. Zeng X. Wu C. Lu A. Chen X. Hou T. Cao D. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res. 2021 49 W1 W5 W14 10.1093/nar/gkab255 33893803
    [Google Scholar]
  39. Kumar N. Patiyal S. Choudhury S. Tomer R. Dhall A. Raghava G.P.S. DMPPred: a tool for identification of antigenic regions responsible for inducing type 1 diabetes mellitus. Brief. Bioinform. 2023 24 1 bbac525 10.1093/bib/bbac525 36524996
    [Google Scholar]
  40. Shaker B. Yu M.S. Song J.S. Ahn S. Ryu J.Y. Oh K.S. Na D. LightBBB: computational prediction model of blood–brain-barrier penetration based on LightGBM. Bioinformatics 2021 37 8 1135 1139 10.1093/bioinformatics/btaa918 33112379
    [Google Scholar]
  41. Daina A Michielin O Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017 7 42717 10.1038/srep42717
    [Google Scholar]
  42. Kelder J. Grootenhuis P.D.J. Bayada D.M. Delbressine L.P.C. Ploemen J.P. Polar molecular surface as a dominating determinant for oral absorption and brain penetration of drugs. Pharm. Res. 1999 16 10 1514 1519 10.1023/A:1015040217741 10554091
    [Google Scholar]
  43. Shityakov S. Neuhaus W. Dandekar T. Förster C. Analysing molecular polar surface descriptors to predict blood-brain barrier permeation. Int. J. Comput. Biol. Drug Des. 2013 6 1/2 146 156 10.1504/IJCBDD.2013.052195 23428480
    [Google Scholar]
  44. Palm K. Stenberg P. Luthman K. Artursson P. I Polar molecular surface properties predict the intestinal absorption of drugs in humans. Pharm. Res. 1997 14 5 568 571 10.1023/A:1012188625088 9165525
    [Google Scholar]
  45. van de Waterbeemd H. Camenisch G. Folkers G. Chretien J.R. Raevsky O.A. Estimation of blood-brain barrier crossing of drugs using molecular size and shape, and H-bonding descriptors. J. Drug Target. 1998 6 2 151 165 10.3109/10611869808997889 9886238
    [Google Scholar]
  46. Radouane A. Péhourcq F. Tramu G. Creppy E.E. Bannwarth B. Influence of lipophilicity on the diffusion of cephalosporins into the cerebrospinal fluid. Fundam. Clin. Pharmacol. 1996 10 3 309 313 10.1111/j.1472‑8206.1996.tb00311.x 8836706
    [Google Scholar]
  47. Castillo-Garit J.A. Casanola-Martin G.M. Le-Thi-Thu H. Pham-The H. Barigye S.J. A Simple Method to Predict Blood-Brain Barrier Permeability of Drug- Like Compounds Using Classification Trees. Med. Chem. 2017 13 7 664 669 10.2174/1573406413666170209124302 28185535
    [Google Scholar]
  48. Singh M. Divakaran R. Konda L.S.K. Kristam R. A classification model for blood brain barrier penetration. J. Mol. Graph. Model. 2020 96 107516 10.1016/j.jmgm.2019.107516 31940508
    [Google Scholar]
/content/journals/cnsnddt/10.2174/0118715273328174241007060331
Loading
BBBper: A Machine Learning-based Online Tool for Blood-Brain Barrier (BBB) Permeability Prediction
Bentham Science Publishers ; https://doi.org/10.2174/0118715273328174241007060331
/content/journals/cnsnddt/10.2174/0118715273328174241007060331
/content/journals/cnsnddt/10.2174/0118715273328174241007060331
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher's website along with the published article.

file format download Download

  • Article Type:     Research Article
Keywords: random forest ; Python ; BBB permeability ; Web-server tool ; supervised machine learning ; Blood-brain permeation

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test