Pharmaceutical Research

, 36:35 | Cite as

Machine Learning Models for the Prediction of Chemotherapy-Induced Peripheral Neuropathy

  • Peter Bloomingdale
  • Donald E. MagerEmail author
Research Paper



Chemotherapy-induced peripheral neuropathy (CIPN) is a common adverse side effect of cancer chemotherapy that can be life debilitating and cause extreme pain. The multifactorial and poorly understood mechanisms of toxicity have impeded the identification of novel treatment strategies. Computational models of drug neurotoxicity could be implemented in early drug discovery to screen for high-risk compounds and select safer drug candidates for further development.


Quantitative-structure toxicity relationship (QSTR) models were developed to predict the incidence of PN. A manually curated library of 95 approved drugs were used to develop the model. Molecular descriptors sensitive to the incidence of PN were identified to provide insights into structural modifications to reduce neurotoxicity. The incidence of PN was predicted for 60 antineoplastic drug candidates currently under clinical investigation.


The number of aromatic nitrogens was identified as the most important molecular descriptor. The chemical transformation of aromatic nitrogens to carbons reduced the predicted PN incidence of bortezomib from 32.3% to 21.1%. Antineoplastic drug candidates were categorized into three groups (high, medium, low) based on their predicted PN incidence.


QSTR models were developed to link physicochemical descriptors of compounds with PN incidence, which can be utilized during drug candidate selection to reduce neurotoxicity.


ADMET predictor chemotherapy-induced peripheral neuropathy machine learning toxicity QSAR 



Absorption, distribution, metabolism, excretion, and toxicity


Artificial neural network


Common terminology criteria for adverse events


Chemotherapy-induced peripheral neuropathy


European Organization for Research and Treatment of Cancer


European Medicines Agency


Food and Drug Administration


National Cancer Institute


Pharmacokinetic and pharmacodynamic


Peripheral neuropathy


Quality of Life Questionnaire-CIPN20


Quantitative structure-activity relationship


Quantitative-structure toxicity relationship


Support vector machine


Acknowledgments and Disclosures

We would like to thank Simulations Plus, Inc. for providing us with an academic license for ADMET Predictor™, ADMET Modeler™, and MedChem Designer™. Additionally, we would like to acknowledge Dr. Michael Lawless, Senior Principle Scientist at Simulations Plus, for his insightful suggestions throughout this project.

Supplementary material

11095_2018_2562_MOESM1_ESM.docx (343 kb)
Supplementary Figure 1 Molecular descriptor frequency and sensitivity of individual models (a) ANN non-transformed, (b) ANN log-transformed, (c) SVM 2% cutoff, and (d) SVM 10% cutoff of the individual models. (DOCX 342 kb)
11095_2018_2562_MOESM2_ESM.xlsx (15 kb)
Supplementary Table I (XLSX 14 kb)
11095_2018_2562_MOESM3_ESM.xlsx (22 kb)
Supplementary Table II (XLSX 22 kb)


  1. 1.
    Siegel RL, Miller KD, Jemal A. Cancer statistics, 2017. CA Cancer J Clin. 2017;67(1):7–30.PubMedCrossRefGoogle Scholar
  2. 2.
    Bluethmann SM, Mariotto AB, Rowland JH. Anticipating the "silver tsunami": prevalence trajectories and comorbidity burden among older Cancer survivors in the United States. Cancer Epidemiol Biomark Prev. 2016;25(7):1029–36.CrossRefGoogle Scholar
  3. 3.
    Quasthoff S, Hartung HP. Chemotherapy-induced peripheral neuropathy. J Neurol. 2002;249(1):9–17.PubMedCrossRefGoogle Scholar
  4. 4.
    Miaskowski C, Mastick J, Paul SM, Topp K, Smoot B, Abrams G, et al. Chemotherapy-induced neuropathy in Cancer survivors. J Pain Symptom Manag. 2017;54(2):204–18 e202.CrossRefGoogle Scholar
  5. 5.
    Wolf S, Barton D, Kottschade L, Grothey A, Loprinzi C. Chemotherapy-induced peripheral neuropathy: prevention and treatment strategies. Eur J Cancer. 2008;44(11):1507–15.PubMedCrossRefGoogle Scholar
  6. 6.
    Jaggi AS, Singh N. Mechanisms in cancer-chemotherapeutic drugs-induced peripheral neuropathy. Toxicology. 2012;291(1–3):1–9.PubMedCrossRefGoogle Scholar
  7. 7.
    Carozzi V, Canta A, Chiorazzi A. Chemotherapy-induced peripheral neuropathy: what do we know about mechanisms? Neurosci Lett. 2015;596:90–107.PubMedCrossRefGoogle Scholar
  8. 8.
    Staff NP, Grisold A, Grisold W, Windebank AJ. Chemotherapy-induced peripheral neuropathy: a current review. Ann Neurol. 2017.Google Scholar
  9. 9.
    Duch W, Swaminathan K, Meller J. Artificial intelligence approaches for rational drug design and discovery. Curr Pharm Des. 2007;13(14):1497–508.PubMedCrossRefGoogle Scholar
  10. 10.
    Burbidge R, Trotter M, Buxton B, Holden S. Drug design by machine learning: support vector machines for pharmaceutical data analysis. Comput Chem. 2001;26(1):5–14.PubMedCrossRefGoogle Scholar
  11. 11.
    Zhang L, Tan J, Han D, Zhu H. From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discov Today. 2017;22(11):1680–5.PubMedCrossRefGoogle Scholar
  12. 12.
    Council NR. Toxicity testing in the 21st century: a vision and a strategy: National Academies Press; 2007.Google Scholar
  13. 13.
    Raies AB, Bajic VB. In silico toxicology: computational methods for the prediction of chemical toxicity. Wiley Interdiscip Rev Comput Mol Sci. 2016;6(2):147–72.PubMedPubMedCentralCrossRefGoogle Scholar
  14. 14.
    Bloomingdale P, Housand C, Apgar JF, Millard BL, Mager DE, Burke JM, et al. Quantitative systems toxicology. Curr Opin Toxicol. 2017;4:79–87.PubMedPubMedCentralCrossRefGoogle Scholar
  15. 15.
    van de Waterbeemd H. Gifford E. ADMET in silico modelling: towards prediction paradise? Nat Rev Drug Discov. 2003;2(3):192–204.PubMedGoogle Scholar
  16. 16.
    Mager DE. Quantitative structure-pharmacokinetic/pharmacodynamic relationships. Adv Drug Deliv Rev. 2006;58(12–13):1326–56.PubMedCrossRefGoogle Scholar
  17. 17.
    Moroy G, Martiny VY, Vayer P, Villoutreix BO, Miteva MA. Toward in silico structure-based ADMET prediction in drug discovery. Drug Discov Today. 2012;17(1–2):44–55.PubMedCrossRefGoogle Scholar
  18. 18.
    Waring MJ, Arrowsmith J, Leach AR, Leeson PD, Mandrell S, Owen RM, et al. An analysis of the attrition of drug candidates from four major pharmaceutical companies. Nat Rev Drug Discov. 2015;14(7):475–86.PubMedCrossRefGoogle Scholar
  19. 19.
    Piccini JP, Whellan DJ, Berridge BR, Finkle JK, Pettit SD, Stockbridge N, et al. Current challenges in the evaluation of cardiac safety during drug development: translational medicine meets the critical path initiative. Am Heart J. 2009;158(3):317–26.PubMedCrossRefGoogle Scholar
  20. 20.
    Mager PP. Structure-neurotoxicity relationships applied to organophosphorus pesticides. Toxicol Lett. 1982;11(1–2):67–71.PubMedCrossRefGoogle Scholar
  21. 21.
    Cronin MT. Quantitative structure-activity relationship (QSAR) analysis of the acute sublethal neurotoxicity of solvents. Toxicol in Vitro. 1996;10(2):103–10.PubMedCrossRefGoogle Scholar
  22. 22.
    Makhaeva GF, Radchenko EV, Baskin II, Palyulin VA, Richardson RJ, Zefirov NS. Combined QSAR studies of inhibitor properties of O-phosphorylated oximes toward serine esterases involved in neurotoxicity, drug metabolism and Alzheimer's disease. SAR QSAR Environ Res. 2012;23(7–8):627–47.PubMedCrossRefGoogle Scholar
  23. 23.
    Estrada E, Molina E, Uriarte E. Quantitative structure-toxicity relationships using TOPS-MODE. 2. Neurotoxicity of a non-congeneric series of solvents. SAR QSAR Environ Res. 2001;12(5):445–59.PubMedCrossRefGoogle Scholar
  24. 24.
    Hur J, Guo AY, Loh WY, Feldman EL, Bai JP. Integrated systems pharmacology analysis of clinical drug-induced peripheral neuropathy. CPT Pharmacometrics Syst Pharmacol. 2014;3:e114.PubMedPubMedCentralCrossRefGoogle Scholar
  25. 25.
    Chong PH, Boskovich A, Stevkovic N, Bartt RE. Statin-associated peripheral neuropathy: review of the literature. Pharmacotherapy. 2004;24(9):1194–203.PubMedCrossRefGoogle Scholar
  26. 26.
    Etminan M, Brophy JM, Samii A. Oral fluoroquinolone use and risk of peripheral neuropathy: a pharmacoepidemiologic study. Neurology. 2014;83(14):1261–3.PubMedCrossRefGoogle Scholar
  27. 27.
    J-I A. Reduced HOMO− LUMO gap as an index of kinetic stability for polycyclic aromatic hydrocarbons. J Phys Chem A. 1999;103(37):7487–95.CrossRefGoogle Scholar
  28. 28.
    Duraiswamy AJ, Lee MA, Madan B, Ang SH, Tan ES, Cheong WW, et al. Discovery and optimization of a porcupine inhibitor. J Med Chem. 2015;58(15):5889–99.PubMedCrossRefGoogle Scholar
  29. 29.
    Gramatica P, Sangion A. A historical excursus on the statistical validation parameters for QSAR models: a clarification concerning metrics and terminology. J Chem Inf Model. 2016;56(6):1127–31.PubMedCrossRefGoogle Scholar
  30. 30.
    Reed AE, Weinstock RB, Weinhold F. Natural population analysis. J Chem Phys. 1985;83(2):735–46.CrossRefGoogle Scholar
  31. 31.
    Hajduk PJ, Sauer DR. Statistical analysis of the effects of common chemical substituents on ligand potency. J Med Chem. 2008;51(3):553–64.PubMedCrossRefGoogle Scholar
  32. 32.
    Pennington LD, Moustakas DT. The necessary nitrogen atom: a versatile high-impact design element for multiparameter optimization. J Med Chem. 2017;60(9):3552–79.PubMedCrossRefGoogle Scholar
  33. 33.
    Fukui K, Yonezawa T, Shingu H. A molecular orbital theory of reactivity in aromatic hydrocarbons. J Chem Phys. 1952;20(4):722–5.CrossRefGoogle Scholar
  34. 34.
    Pearson RG. Absolute electronegativity and hardness correlated with molecular orbital theory. Proc Natl Acad Sci. 1986;83(22):8440–1.PubMedCrossRefGoogle Scholar
  35. 35.
    Traquandi G, Ciomei M, Ballinari D, Casale E, Colombo N, Croci V, et al. Identification of potent pyrazolo[4,3-h]quinazoline-3-carboxamides as multi-cyclin-dependent kinase inhibitors. J Med Chem. 2010;53(5):2171–87.PubMedCrossRefGoogle Scholar
  36. 36.
    Scully SS, Tang AJ, Lundh M, Mosher CM, Perkins KM, Wagner BK. Small-molecule inhibitors of cytokine-mediated STAT1 signal transduction in beta-cells with improved aqueous solubility. J Med Chem. 2013;56(10):4125–9.PubMedPubMedCentralCrossRefGoogle Scholar
  37. 37.
    Hong SP, Liu KG, Ma G, Sabio M, Uberti MA, Bacolod MD, et al. Tricyclic thiazolopyrazole derivatives as metabotropic glutamate receptor 4 positive allosteric modulators. J Med Chem. 2011;54(14):5070–81.PubMedCrossRefGoogle Scholar
  38. 38.
    Pennington LD, Croghan MD, Sham KK, Pickrell AJ, Harrington PE, Frohn MJ, et al. Quinolinone-based agonists of S1P(1): use of a N-scan SAR strategy to optimize in vitro and in vivo activity. Bioorg Med Chem Lett. 2012;22(1):527–31.PubMedCrossRefGoogle Scholar
  39. 39.
    Yuan Y, Zaidi SA, Elbegdorj O, Aschenbach LC, Li G, Stevens DL, et al. Design, synthesis, and biological evaluation of 14-heteroaromatic-substituted naltrexone derivatives: pharmacological profile switch from mu opioid receptor selectivity to mu/kappa opioid receptor dual selectivity. J Med Chem. 2013;56(22):9156–69.PubMedPubMedCentralCrossRefGoogle Scholar
  40. 40.
    Borrmann T, Abdelrahman A, Volpini R, Lambertucci C, Alksnis E, Gorzalka S, et al. Structure-activity relationships of adenine and deazaadenine derivatives as ligands for adenine receptors, a new purinergic receptor family. J Med Chem. 2009;52(19):5974–89.PubMedCrossRefGoogle Scholar
  41. 41.
    Zheng X, Bauer P, Baumeister T, Buckmelter AJ, Caligiuri M, Clodfelter KH, et al. Structure-based identification of ureas as novel nicotinamide phosphoribosyltransferase (Nampt) inhibitors. J Med Chem. 2013;56(12):4921–37.PubMedCrossRefGoogle Scholar
  42. 42.
    Bregman H, Nguyen HN, Feric E, Ligutti J, Liu D, McDermott JS, et al. The discovery of aminopyrazines as novel, potent Na(v)1.7 antagonists: hit-to-lead identification and SAR. Bioorg Med Chem Lett. 2012;22(5):2033–42.PubMedCrossRefGoogle Scholar
  43. 43.
    Horne DB, Tamayo NA, Bartberger MD, Bo Y, Clarine J, Davis CD, et al. Optimization of potency and pharmacokinetic properties of tetrahydroisoquinoline transient receptor potential melastatin 8 (TRPM8) antagonists. J Med Chem. 2014;57(7):2989–3004.PubMedCrossRefGoogle Scholar
  44. 44.
    Pryde DC, Dalvie D, Hu Q, Jones P, Obach RS, Tran TD. Aldehyde oxidase: an enzyme of emerging importance in drug discovery. J Med Chem. 2010;53(24):8441–60.PubMedCrossRefGoogle Scholar
  45. 45.
    Kundu TK, Velayutham M, Zweier JL. Aldehyde oxidase functions as a superoxide generating NADH oxidase: an important redox regulated pathway of cellular oxygen radical formation. Biochemistry. 2012;51(13):2930–9.PubMedPubMedCentralCrossRefGoogle Scholar
  46. 46.
    Li H, Kundu TK, Zweier JL. Characterization of the magnitude and mechanism of aldehyde oxidase-mediated nitric oxide production from nitrite. J Biol Chem. 2009;284(49):33850–8.PubMedPubMedCentralCrossRefGoogle Scholar
  47. 47.
    Areti A, Yerra VG, Naidu V, Kumar A. Oxidative stress and nerve damage: role in chemotherapy induced peripheral neuropathy. Redox Biol. 2014;2:289–95.PubMedPubMedCentralCrossRefGoogle Scholar
  48. 48.
    Diamond S, Boer J, Maduskuie TP Jr, Falahatpisheh N, Li Y, Yeleswaram S. Species-specific metabolism of SGX523 by aldehyde oxidase and the toxicological implications. Drug Metab Dispos. 2010;38(8):1277–85.PubMedCrossRefGoogle Scholar
  49. 49.
    Uetrecht J. N-oxidation of drugs associated with idiosyncratic drug reactions. Drug Metab Rev. 2002;34(3):651–65.PubMedCrossRefGoogle Scholar
  50. 50.
    Knowles SR, Uetrecht J, Shear NH. Idiosyncratic drug reactions: the reactive metabolite syndromes. Lancet. 2000;356(9241):1587–91.PubMedCrossRefGoogle Scholar
  51. 51.
    Sodhi JK, Wong S, Kirkpatrick DS, Liu L, Khojasteh SC, Hop CE, et al. A novel reaction mediated by human aldehyde oxidase: amide hydrolysis of GDC-0834. Drug Metab Dispos. 2015;43(6):908–15.PubMedPubMedCentralCrossRefGoogle Scholar
  52. 52.
    Vogl DT, Martin TG, Vij R, Hari P, Mikhael JR, Siegel D, et al. Phase I/II study of the novel proteasome inhibitor delanzomib (CEP-18770) for relapsed and refractory multiple myeloma. Leuk Lymphoma. 2017;58(8):1872–9.PubMedCrossRefGoogle Scholar
  53. 53.
    Wang XM, Lehky TJ, Brell JM, Dorsey SG. Discovering cytokines as targets for chemotherapy-induced painful peripheral neuropathy. Cytokine. 2012;59(1):3–9.PubMedPubMedCentralCrossRefGoogle Scholar
  54. 54.
    Head KA. Peripheral neuropathy: pathogenic mechanisms and alternative therapies. Altern Med Rev. 2006;11(4):294–329.PubMedGoogle Scholar
  55. 55.
    Kamerman PR, Moss PJ, Weber J, Wallace VC, Rice AS, Huang W. Pathogenesis of HIV-associated sensory neuropathy: evidence from in vivo and in vitro experimental models. J Peripher Nerv Syst. 2012;17(1):19–31.PubMedCrossRefGoogle Scholar
  56. 56.
    Postma TJ, Heimans JJ, Muller MJ, Ossenkoppele GJ, Vermorken JB, Aaronson NK. Pitfalls in grading severity of chemotherapy-induced peripheral neuropathy. Ann Oncol. 1998;9(7):739–44.PubMedCrossRefGoogle Scholar
  57. 57.
    Cavaletti G, Frigeni B, Lanzani F, Mattavelli L, Susani E, Alberti P, et al. Chemotherapy-induced peripheral neurotoxicity assessment: a critical revision of the currently available tools. Eur J Cancer. 2010;46(3):479–94.PubMedCrossRefGoogle Scholar
  58. 58.
    Cohen JS. Peripheral neuropathy associated with fluoroquinolones. Ann Pharmacother. 2001;35(12):1540–7.PubMedCrossRefGoogle Scholar
  59. 59.
    Tierney EF, Thurman DJ, Beckles GL, Cadwell BL. Association of statin use with peripheral neuropathy in the U.S. population 40 years of age or older. J Diabetes. 2013;5(2):207–15.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical SciencesUniversity at Buffalo, The State University of New YorkBuffaloUSA

Personalised recommendations