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Research Article

QSAR model to predict Kp,uu,brain with a small dataset, incorporating predicted values of related parameter

ORCID Icon, ORCID Icon, , &
Pages 885-897 | Received 02 Sep 2022, Accepted 14 Nov 2022, Published online: 24 Nov 2022

References

  • United Nations New York, world population prospects, 2019. Available at https://population.un.org/wpp/Publications/Files/WPP2019_Highlights.pdf (accessed January 29, 2021).
  • M.S. Alavijeh, M. Chishty, M.Z. Qaiser, and A.M. Palmer, Drug metabolism and pharmacokinetics, the blood-brain barrier, and central nervous system drug discovery, NeuroRX 2 (2005), pp. 554–571. doi:10.1602/neurorx.2.4.554.
  • S.H. Preskorn, CNS drug development: Lessons learned part 3: Psychiatric and central nervous system drugs developed over the last decade-implications for the field, J. Psychiatr. Pract. 23 (2017), pp. 352–360. doi:10.1097/PRA.0000000000000258.
  • S. Tietz and B. Engelhardt, Brain barriers: Crosstalk between complex tight junctions and adherens junctions, J. Cell Biol. 209 (2015), pp. 492–506. doi:10.1083/jcb.201412147.
  • X. Wang, X. Duan, G. Yang, X. Zhang, L. Deng, H. Zheng, C. Deng, J. Wen, N. Wang, C. Peng, X. Zhao, and Y. Wel, Honokiol crosses BBB and BCSFB, and inhibits brain tumor growth in rat 9L intracerebral gliosarcoma model and human U251 xenograft glioma model, PLoS One 6 (2011), pp. e18490. doi:10.1371/journal.pone.0018490.
  • W.M. Pardridge, Blood-brain barrier biology and methodology, J. Neurovirol. 5 (1999), pp. 556–559. doi:10.3109/13550289909021285.
  • S. Ayloo and C. Gu, Transcytosis at the blood-brain barrier, Curr. Opin. Neurobiol. 57 (2019), pp. 32–38. doi:10.1016/j.conb.2018.12.014.
  • X. Liu and C. Chen, Strategies to optimize brain penetration in drug discovery, Curr. Opin. Drug Discov. Devel. 8 (2005), pp. 505–512.
  • J.C. Kalvass and T.S. Maurer, Influence of nonspecific brain and plasma binding on CNS exposure: Implications for rational drug discovery, Biopharm. Drug Dispos. 23 (2002), pp. 327–328. doi:10.1002/bdd.325.
  • J.C. Kalvass, T.S. Maurer, and G.M. Pollack, Use of plasma and brain unbound fractions to assess the extent of brain distribution of 34 drugs: Comparison of unbound concentration ratios to in vivo p-glycoprotein efflux ratios, Drug Metab. Dispos. 35 (2007), pp. 660–666. doi:10.1124/dmd.106.012294.
  • S.G. Summerfield, A.J. Stevens, L. Cutler, M. Del Carmen Osuna, B. Hammond, S.P. Tang, A. Hersey, D.J. Spalding, and P. Jeffrey, Improving the in vitro prediction of in vivo central nervous system penetration: Integrating permeability, P-glycoprotein efflux, and free fractions in blood and brain, J. Pharmacol. Exp. Ther. 316 (2006), pp. 1282–1290. doi:10.1124/jpet.105.092916.
  • J.C. Kalvass, E.R. Olson, M.P. Cassidy, D.E. Selley, and G.M. Pollack, Pharmacokinetics and pharmacodynamics of seven opioids in P-glycoprotein-competent mice: Assessment of unbound brain EC50,u and correlation of in vitro, preclinical, and clinical data, J. Pharmacol. Exp. Ther. 323 (2007), pp. 346–355. doi:10.1124/jpet.107.119560.
  • L. Di, H. Rong, and B. Feng, Demystifying brain penetration in central nervous system drug discovery, Miniperspective, J. Med. Chem. 56 (2013), pp. 2–12. doi:10.1021/jm301297f.
  • X. Liu, C. Chen, and B.J. Smith, Progress in brain penetration evaluation in drug discovery and development, J. Pharmacol. Exp. Ther. 325 (2008), pp. 349–356. doi:10.1124/jpet.107.130294.
  • M. Hammarlund-Udenaes, M. Friden, S. Syvanen, and A. Gupta, On the rate and extent of drug delivery to the brain, Pharm. Res. 25 (2008), pp. 1737–1750. doi:10.1007/s11095-007-9502-2.
  • A. Reichel, Addressing central nervous system (CNS) penetration in drug discovery: Basics and implications of the evolving new concept, Chem. Biodivers. 6 (2009), pp. 2030–2049. doi:10.1002/cbdv.200900103.
  • C.S. Chaurasia, M. ller, E.D. Bashaw, E. Benfeldt, J. Bolinder, R. Bullock, P.M. Bungay, E.C.M. DeLange, H. Derendorf, W.F. Elmquist, M. Hammarlund-Udenaes, C. Joukhadar, D.L. Kellogg Jr, C.E. Lunte, C.H. Nordstrom, H. Rollema, R.J. Sawchuk, B.W.Y. Cheung, V.P. Shah, L. Stahle, U. Ungerstedt, D.F. Welty, and H. Yeo, AAPS-FDA workshop white paper: Microdialysis principles, application, and regulatory perspectives, J. Clin. Pharmacol. 47 (2007), pp. 589–603. doi:10.1177/0091270006299091.
  • C.C. Orozco, K. Atkinson, S. Ryu, G. Chang, C. Keefer, J. Lin, K. Riccardi, R.K. Mongillo, D. Tess, K.J. Filipski, A.S. Kalgutkar, J. Litchfield, D. Scott, and L. Di, Structural attributes influencing unbound tissue distribution, J. Med. Chem. 185 (2020), pp. 111813. doi:10.1016/j.ejmech.2019.111813.
  • P. Andre, M. Debray, J.M. Scherrmann, and S. Cisternino, Clonidine transport at the mouse blood-brain barrier by a new H+ antiporter that interacts with addictive drugs, J. Cereb. Blood Flow Metab. 29 (2009), pp. 1293–1304. doi:10.1038/jcbfm.2009.54.
  • E. Dolgikh, I.A. Watson, P.V. Desai, G.A. Sawada, S. Morton, T.M. Jones, and T.J. Ramb, QSAR model of unbound brain-to-plasma partition coefficient, Kp,uu,brain: Incorporating P-glycoprotein efflux as a variable, J. Chem. Inf. Model. 56 (2016), pp. 2225–2233. doi:10.1021/acs.jcim.6b00229.
  • M. Friden, S. Winiwarter, G. Jerndal, O. Bengtsson, H. Wan, U. Bredberg, M. Hammarlund-Udenaes, and M. Antonsson, Structure-brain exposure relationships in rat and human using a novel data set of unbound drug concentrations in brain interstitial and cerebrospinal fluids, J. Med. Chem. 52 (2009), pp. 6233–6243. doi:10.1021/jm901036q.
  • H. Chen, S. Winiwarter, M. Friden, M. Antonsson, and O. Engkvist, In silico prediction of unbound brain-to-plasma concentration ratio using machine learning algorithms, J. Mol. Graph. Model. 29 (2011), pp. 985–995. doi:10.1016/j.jmgm.2011.04.004.
  • S. Varadharajan, S. Winiwarter, L. Carlsson, O. Engkvist, A. Anantha, T. Kogej, M. Fridén, J. Stålring, and H. Chen, Exploring in silico prediction of the unbound brain-to-plasma drug concentration ratio: Model validation, renewal, and interpretation, J. Pharm. Sci. 104 (2015), pp. 1197–1206. doi:10.1002/jps.24301.
  • I. Loryan, V. Sinha, C. Mackie, A. van Peer, W.H. Drinkenburg, A. Vermeulen, D. Heald, M. Hammarlund-Udenaes, and C.M. Wassvik, Molecular properties determining unbound intracellular and extracellular brain exposure of CNS drug candidates, Mol. Pharm. 12 (2015), pp. 520–532. doi:10.1021/mp5005965.
  • H. Liu, K. Dong, W. Zhang, S.G. Summerfield, and G.C. Terstappen, Prediction of brain:blood unbound concentration ratios in CNS drug discovery employing in silico and in vitro model systems, Drug Discov. Today 23 (2018), pp. 1357–1372. doi:10.1016/j.drudis.2018.03.002.
  • Y.Y. Zhang, H. Liu, S.G. Summerfield, C.N. Luscombe, and J. Sahi, Integrating in silico and in vitro approaches to predict drug accessibility to the central nervous system, Mol. Pharm. 13 (2016), pp. 1540–1550. doi:10.1021/acs.molpharmaceut.6b00031.
  • S.S. Julia, Drug discovery world, (2013). Available at https://www.ddw-online.com/the-great-neuro-pipeline-brain-drain-why-big-pharma-hasnt-given-up-on-cns-disorders-909-201310.
  • J. Ma, R.P. Sheridan, A. Liaw, G.E. Dahl, and V. Svetnik, Deep neural nets as a method for quantitative structure-activity relationships, J. Chem. Inf. Model. 55 (2015), pp. 263–274. doi:10.1021/ci500747n.
  • B. Ramsundar, B. Liu, Z. Wu, A. Verras, M. Tudor, R.P. Sheridan, and V. Pande, Is multitask deep learning practical for pharma?, J. Chem. Inf. Model. 57 (2017), pp. 2068–2076. doi:10.1021/acs.jcim.7b00146.
  • J. Wenzel, H. Matter, and F. Schmidt, Predictive multitask deep neural network models for ADME-Tox properties: Learning from large data sets, J. Chem. Inf. Model. 59 (2019), pp. 1253–1268. doi:10.1021/acs.jcim.8b00785.
  • Y. Xu, J. Ma, A. Liaw, R.P. Sheridan, and V. Svetnik, demystifying multitask deep neural networks for quantitative structure-activity relationships, J. Chem. Inf. Model. 57 (2017), pp. 2490–2504.
  • S. Sosnin, D. Karlov, I.V. Tetko, and M.V. Fedorov, Comparative study of multitask toxicity modeling on a broad chemical space, J. Chem. Inf. Model. 59 (2019), pp. 1052–1072. doi:10.1021/acs.jcim.8b00685.
  • ADMET predictor, ver 9.0 Simulations Plus, Lancaster, USA. (2018). Available at https://www.simulations-plus.com/software/admetpredictor.
  • Maestro, Schrödinger K.K., New York, USA. (2019). available at https://www.schrodinger.com/maestro.
  • C.W. Yap, PaDEL-descriptor, ver 2.21; software available at http://www.yapcwsoft.com/dd/padeldescriptor.
  • Dragon ver 7.0, Talete srl. Milano, Italy, (2021). Available at http://www.talete.mi.it/products/dragon_description.htm.
  • K. Stepnik and W. Kukula-Koch, In silico studies on triterpenoid saponins permeation through the blood-brain barrier combined with postmortem research on the brain tissues of mice affected by astragaloside iv administration, Int. J. Mol. Sci. 21 (2020), pp. 2534. doi:10.3390/ijms21072534.
  • S. Sato, K. Tohyama, and Y. Kosugi, Investigation of MDR1-overexpressing cell lines to derive a quantitative prediction approach for brain disposition using in vitro efflux activities, Eur. J. Pharm. Sci. 142 (2020), pp. 105119. doi:10.1016/j.ejps.2019.105119.
  • R. Jansson, U. Bredberg, and M. Ashton, Prediction of drug tissue to plasma concentration ratios using a measured volume of distribution in combination with lipophilicity, J. Pharm. Sci. 97 (2008), pp. 2324–2339.
  • X. Han, X. Zheng, Y. Wang, X. Sun, Y. Xiao, Y. Tang, and W. Qin, Random forest can accurately predict the development of end-stage renal disease in immunoglobulin a nephropathy patients, Ann. Transl. Med. 7 (2019), pp. 234. doi:10.21037/atm.2018.12.11.
  • T.M. Deist, F.J.W.M. Dankers, G. Valdes, R. Wijsman, I.C. Hsu, C. Oberije, T. Lustberg, J. van Soest, F. Hoebers, A. Jochems, I. El Naqa, L. Wee, O. Morin, D.R. Raleigh, W. Bots, J.H. Kaanders, J. Belderbos, M. Kwint, T. Solberg, R. Monshouwer, J. Bussink, A. Dekker, and P. Lambin, Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers, Med. Phys. 45 (2018), pp. 3449–3459. doi:10.1002/mp.12967.
  • K. Tan, W. Ma, F. Wu, and Q. Du, Random forest-based estimation of heavy metal concentration in agricultural soils with hyperspectral sensor data, Environ. Monit. Assess. 191 (2019), pp. 446. doi:10.1007/s10661-019-7510-4.
  • M. Al-Mukhtar, Random forest, support vector machine, and neural networks to modelling suspended sediment in Tigris River-Baghdad, Environ. Monit. Assess. 191 (2019), pp. 673. doi:10.1007/s10661-019-7821-5.
  • H. Singh, S. Singh, D. Singla, S.M. Agarwal, and G.P. Raghava, QSAR based model for discriminating EGFR inhibitors and non-inhibitors using Random forest, Biol. Direct 10 (2015), pp. 10. doi:10.1186/s13062-015-0046-9.
  • Random forest package information, Available at https://cran.rproject.org/web/packages/randomForest/index.html.
  • Caret package information, Available at https://cran.r-project.org/web/checks/check_results_caret.html.
  • P.P. Graczyk, Gini coefficient: A new way to express selectivity of kinase inhibitors against a family of kinases, J. Med. Chem. 50 (2007), pp. 5773–5779. doi:10.1021/jm070562u.
  • P. Domingos, The role of Occam’s razor in knowledge discovery, Data Min. Knowl. Discov. 3 (1999), pp. 409–425. doi:10.1023/A:1009868929893.
  • QikProp, Schrödinger K.K., New York, USA. (2019). available at http://gohom.win/ManualHom/Schrodinger/Schrodinger_2012_docs/general/qikprop_props.pdf.
  • D.F. Veber, S.R. Johnson, H.Y. Cheng, B.R. Smith, K.W. Ward, and K.D. Kopple, Molecular properties that influence the oral bioavailability of drug candidates, J. Med. Chem. 45 (2002), pp. 2615–2623. doi:10.1021/jm020017n.
  • C.W. Fong, Permeability of the blood-brain barrier: Molecular mechanism of transport of drugs and physiologically important compounds, J. Membr. Biol. 248 (2015), pp. 9778–9779. doi:10.1007/s00232-015-9778-9.
  • H. Kodaira, H. Kusuhara, E. Fuse, J. Ushiki, and Y. Sugiyama, Quantitative investigation of the brain-to-cerebrospinal fluid unbound drug concentration ratio under steady-state conditions in rats using a pharmacokinetic model and scaling factors for active efflux transporters, Drug Metab. Dispos. 42 (2014), pp. 983–989. doi:10.1124/dmd.113.056606.
  • R. Jansson, U. Bredberg, and M. Ashton, Prediction of drug tissue to plasma concentration ratios using a measured volume of distribution in combination with lipophilicity, J. Pharm. Sci. 97 (2008), pp. 2324–2329.
  • Y.E. Yun and A.N. Edginton, Correlation-based prediction of tissue-to-plasma partition coefficients using readily available input parameters, Xenobiotica 43 (2013), pp. 839–852. doi:10.3109/00498254.2013.770182.
  • Y. Xu, J. Ma, A. Liaw, R.P. Sheridan, and V. Svetnik, Demystifying multitask deep neural networks for quantitative structure-activity relationships, J. Chem. Inf. Model. 57 (2017), pp. 2490–2504.

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