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Effectiveness of CASE Ultra Expert System in Evaluating Adverse Effects of Drugs

Authors

  • Roustem Saiakhov,

    Corresponding author
    1. Multicase Inc. 23811 Chagrin Boulevard, Suite 305, Beachwood, OH 44122, USA phone: +1-216-831-3740; fax: +1-216-831-3742
    • Multicase Inc. 23811 Chagrin Boulevard, Suite 305, Beachwood, OH 44122, USA phone: +1-216-831-3740; fax: +1-216-831-3742
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  • Suman Chakravarti,

    1. Multicase Inc. 23811 Chagrin Boulevard, Suite 305, Beachwood, OH 44122, USA phone: +1-216-831-3740; fax: +1-216-831-3742
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  • Gilles Klopman

    1. Multicase Inc. 23811 Chagrin Boulevard, Suite 305, Beachwood, OH 44122, USA phone: +1-216-831-3740; fax: +1-216-831-3742
    2. Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA
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Abstract

Purpose of this pilot study is to test the QSAR expert system CASE Ultra for adverse effect prediction of drugs. 870 drugs from the SIDER adverse effect dataset were tested using CASE Ultra for carcinogenicity, genetic, liver, cardiac, renal and reproductive toxicity. 47 drugs that were withdrawn from market since the 1950s were also evaluated for potential risks using CASE Ultra and compared them with the actual reasons for which the drugs were recalled. For the whole SIDER test set (n=870), sensitivity and specificity of the carcinogenicity predictions are 66.67 % and 82.17 % respectively; for liver toxicity: 78.95 %, 78.50 %; cardiotoxicity: 69.07 %, 57.57 %; renal toxicity: 46.88 %, 67.90 %; and reproductive toxicity: 100.00 %, 61.10 %. For the SIDER test chemicals not present in the training sets of the models, sensitivity and specificity of carcinogenicity predictions are 100.00 % and 88.89 % respectively (n=404); for liver toxicity: 100.00 %, 51.33 % (n=115); cardiotoxicity: 100.00 %, 20.45 % (n=94); renal toxicity: 100.00 %, 45.54 % (n=115); and reproductive toxicity: 100.00 %, 48.57 % (n=246). CASE Ultra correctly recognized the relevant toxic effects in 43 out of the 47 withdrawn drugs. It predicted all 9 drugs that were not part of the training set of the models, as unsafe.

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