Most computational predictive models are specifically trained for a single toxicity endpoint and lack the ability to learn dependencies between endpoints, such as those targeting similar biological pathways. In this study, we compare the performance of 3 multi-label classification (MLC) models, namely Classifier Chains (CC), Label Powersets (LP) and Stacking (SBR), against independent classifiers (Binary Relevance) on Tox21 challenge data. Also, we develop a novel label dependence measure that shows full range of values, even at low prior probabilities, for the purpose of data-driven label partitioning. Using Logistic Regression as the base classifier and random label partitioning (k = 3), CC show statistically significant improvements in model performance using Hamming and multi-label accuracy scores (p<0.05), while SBR show significant improvements in multi-label accuracy scores. The weights in the Logistic Regression and Stacking models are positively associated with label dependencies, suggesting that learning label dependence is a key contributor to improving model performance. An original quantitative measure of label dependency is combined with the Louvain community detection method to learn label partitioning using a data-driven process. The resulting MLCs with learned label partitioning were generally found to be non-inferior to their corresponding random or no label partitioning counterparts. Additionally, using the Random Forest classifier in a 10-fold stratified cross validation Stacking model, we find that the top-performing stacking model out-performs the corresponding base model in 11 out of 12 Tox21 labels. Taken together, these results suggest that MLC models could potentially boost the performance of current single-endpoint predictive models and that label partitioning learning may be used in place of random label partitionings.

BACKGROUND:Some patients receiving a tyrosine kinase inhibitor (TKI) for the first-line treatment of chronic phase chronic myeloid leukemia (CML-CP) experience intolerable adverse events. Management strategies include dose adjustments, interrupting or discontinuing therapy, or switching to an alternative TKI. METHODS:This multicenter, single-arm, Phase IIIb study included CML-CP patients intolerant of, but responsive to, first-line treatment with imatinib or dasatinib. All patients were switched to nilotinib 300 mg bid for up to 24 months. The primary endpoint was achievement of MR4.5 (BCR-ABL transcript level of ≤0.0032% on the International Scale) by 24 months. RESULTS:Twenty patients were enrolled in the study (16 imatinib-intolerant, 4 dasatinib-intolerant); which was halted early because of low recruitment. After the switch to nilotinib 300 mg bid, MR4.5 at any time point up to month 24 was achieved in 10 of 20 patients (50%) in the full analysis set. Of the non-hematological adverse events associated with intolerance to prior imatinib or dasatinib, 74% resolved within 12 weeks of switching to nilotinib 300 mg bid. CONCLUSION:Nilotinib 300 mg bid shows minimal cross intolerance in patients with CML-CP who have prior toxicities to other TKIs and can lead to deep molecular responses.