PubMed · 1389913
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T A Connors. 1992. Alkylating agents.. https://pubmed.ncbi.nlm.nih.gov/1389913/
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Described here are neural networks capable of predicting a drug's mechanism of action from its pattern of activity against a panel of 60 malignant cell lines in the National Cancer Institute's drug screening program. Given six possible classes of mechanism, the network misses the correct category for only 12 out of 141 agents (8.5 percent), whereas linear discriminant analysis, a standard statistical technique, misses 20 out of 141 (14.2 percent). The success of the neural net indicates several things. (i) The cell line response patterns are rich in information about mechanism. (ii) Appropriately designed neural networks can make effective use of that information. (iii) Trained networks can be used to classify prospectively the more than 10,000 agents per year tested by the screening program. Related networks, in combination with classical statistical tools, will help in a variety of ways to move new anticancer agents through the pipeline from in vitro studies to clinical application.
We have previously reported that several biological agents, when given simultaneously with cytosine arabinoside or cytoxan, will protect from cytosine arabinoside-induced but not from cytoxan-induced alopecia. In the present study we used the secosteroid 1,25-dihydroxyvitamin D3 in a different timing schedule to protect from chemotherapy-induced alopecia. In three separate experiments, 0.2 microgram of topical, 1,25-dihydroxyvitamin D3 protected rats from alopecia induced by etoposide, cytoxan, and an Adriamycin-cytoxan combination. In another experiment, 0.1 microgram protected rats from etoposide-induced alopecia at the site of application. 1,25-Dihydroxyvitamin D3 may offer a new and exciting approach to the prevention of chemotherapy-induced alopecia.
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