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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

Abstract

Purpose

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.

Methods

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.

Results

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.

Conclusions

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

Keywords

ADMET predictor chemotherapy-induced peripheral neuropathy machine learning toxicity QSAR 

Abbreviations

ADMET

Absorption, distribution, metabolism, excretion, and toxicity

ANN

Artificial neural network

CTCAE

Common terminology criteria for adverse events

CIPN

Chemotherapy-induced peripheral neuropathy

EORTC

European Organization for Research and Treatment of Cancer

EMA

European Medicines Agency

FDA

Food and Drug Administration

NCI

National Cancer Institute

PK/PD

Pharmacokinetic and pharmacodynamic

PN

Peripheral neuropathy

QLQ-CIPN20

Quality of Life Questionnaire-CIPN20

QSAR

Quantitative structure-activity relationship

QSTR

Quantitative-structure toxicity relationship

SVM

Support vector machine

Notes

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)

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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

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