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The application of support vector regression for prediction of the antiallodynic effect of drug combinations in the mouse model of streptozocin-induced diabetic neuropathy.
Comput Methods Programs Biomed. 2013 Aug; 111(2):330-7.CM

Abstract

Drug interactions are an important issue of efficacious and safe pharmacotherapy. Although the use of drug combinations carries the potential risk of enhanced toxicity, when carefully introduced it enables to optimize the therapy and achieve pharmacological effects at doses lower than those of single agents. In view of the development of novel analgesic compounds for the neuropathic pain treatment little is known about their influence on the efficacy of currently used analgesic drugs. Below we describe the preliminary evaluation of support vector machine in the regression mode (SVR) application for the prediction of maximal antiallodynic effect of a new derivative of dihydrofuran-2-one (LPP1) used in combination with pregabalin (PGB) in the streptozocin-induced neuropathic pain model in mice. Based on SVR the most effective doses of co-administered LPP1 (4mg/kg) and PGB (1mg/kg) were predicted to cause the paw withdrawal threshold at 6.7g in the von Frey test. In vivo for the same combination of doses the paw withdrawal was observed at 6.5g, which confirms good predictive properties of SVR.

Authors+Show Affiliations

Faculty of Production Engineering, Warsaw University of Life Sciences, Nowoursynowska 164, 02-787 Warsaw, Poland. robert_salat@sggw.plNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

23693136

Citation

Sałat, Robert, and Kinga Sałat. "The Application of Support Vector Regression for Prediction of the Antiallodynic Effect of Drug Combinations in the Mouse Model of Streptozocin-induced Diabetic Neuropathy." Computer Methods and Programs in Biomedicine, vol. 111, no. 2, 2013, pp. 330-7.
Sałat R, Sałat K. The application of support vector regression for prediction of the antiallodynic effect of drug combinations in the mouse model of streptozocin-induced diabetic neuropathy. Comput Methods Programs Biomed. 2013;111(2):330-7.
Sałat, R., & Sałat, K. (2013). The application of support vector regression for prediction of the antiallodynic effect of drug combinations in the mouse model of streptozocin-induced diabetic neuropathy. Computer Methods and Programs in Biomedicine, 111(2), 330-7. https://doi.org/10.1016/j.cmpb.2013.04.018
Sałat R, Sałat K. The Application of Support Vector Regression for Prediction of the Antiallodynic Effect of Drug Combinations in the Mouse Model of Streptozocin-induced Diabetic Neuropathy. Comput Methods Programs Biomed. 2013;111(2):330-7. PubMed PMID: 23693136.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - The application of support vector regression for prediction of the antiallodynic effect of drug combinations in the mouse model of streptozocin-induced diabetic neuropathy. AU - Sałat,Robert, AU - Sałat,Kinga, Y1 - 2013/05/18/ PY - 2012/05/12/received PY - 2013/04/16/revised PY - 2013/04/25/accepted PY - 2013/5/23/entrez PY - 2013/5/23/pubmed PY - 2014/2/12/medline KW - Diabetes-induced neuropathic pain KW - Dihydrofuran-2-one KW - Mechanical allodynia KW - Pregabalin KW - Streptozocin KW - Support vector regression SP - 330 EP - 7 JF - Computer methods and programs in biomedicine JO - Comput Methods Programs Biomed VL - 111 IS - 2 N2 - Drug interactions are an important issue of efficacious and safe pharmacotherapy. Although the use of drug combinations carries the potential risk of enhanced toxicity, when carefully introduced it enables to optimize the therapy and achieve pharmacological effects at doses lower than those of single agents. In view of the development of novel analgesic compounds for the neuropathic pain treatment little is known about their influence on the efficacy of currently used analgesic drugs. Below we describe the preliminary evaluation of support vector machine in the regression mode (SVR) application for the prediction of maximal antiallodynic effect of a new derivative of dihydrofuran-2-one (LPP1) used in combination with pregabalin (PGB) in the streptozocin-induced neuropathic pain model in mice. Based on SVR the most effective doses of co-administered LPP1 (4mg/kg) and PGB (1mg/kg) were predicted to cause the paw withdrawal threshold at 6.7g in the von Frey test. In vivo for the same combination of doses the paw withdrawal was observed at 6.5g, which confirms good predictive properties of SVR. SN - 1872-7565 UR - https://www.unboundmedicine.com/medline/citation/23693136/The_application_of_support_vector_regression_for_prediction_of_the_antiallodynic_effect_of_drug_combinations_in_the_mouse_model_of_streptozocin_induced_diabetic_neuropathy_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0169-2607(13)00140-5 DB - PRIME DP - Unbound Medicine ER -