Dissimilarity Measures for Detecting Hepatotoxicity in Clinical Trial Data

In clinical trials, pharmaceutical companies test the efficacy and safety of a new drug for the treatment of a disease by comparing the results from a large number of diseased and healthy patients exposed to either the new drug, an existing drug that treats the disease, or a placebo. One primary concern is liver toxicity, which is usually diagnosed by blood analyte tests. Often, such signals of toxicity lead to the discontinuation of drug development or the withdrawal of the drug from the market. Early detection of liver toxicity can save lives and also save such companies billions of dollars. Existing approaches for detecting liver toxicity typically ignore correlations between blood analyte values, but in this work we present novel dissimilarity measures based on principal component analysis which can be used for detecting liver toxicity and identifying subpopulations who may be susceptible by taking into account the correlations structure of the data. Experimental results on real clinical trial data validate our approach.