Identification of alkaline phosphatase as a putative biomarker of anti-NGF treatment-associated arthropathies: Machine learning-assisted analyses of clinical trial data
Abstract
Objective: Nerve growth factor (NGF) inhibitors have been shown to provide pain relief in patients with osteoarthritis but are associated with adjudicated arthropathies (AAs). Exploratory analyses were performed to identify whether peripheral biomarkers routinely collected in trials can predict AAs, independent of known clinical covariates.
Methods: Clinical and biomarker data from seven phase 2/3 fasinumab trials were pooled, and 33 laboratory baseline and week 16 change variables were assessed. Individuals with AA were identified and propensity score matched 1:1 to non-AA controls, creating four unique sets of non-AA individuals. Random forest machine learning models were used. Variables with >30 % missing data were excluded. The training/validation set included 75 % of the available dataset; 10 % formed the working validation test set, and 15 % a held-back test set. Area under the curve of the receiving operating characteristic (AUROC) and ranked feature importance were assessed across models using peripheral biomarkers to predict AA vs non-AA individuals.
Results: Of the final dataset (n = 11,490), 911 AA individuals were compared with four unique sets of non-AA individuals (n = 878-908). The AUROC was 0.51-0.57 for biomarkers at baseline and 0.54-0.62 for biomarker changes at week 16. Change in alkaline phosphatase (ALP) from baseline to week 16 was the only important variable identified consistently across models. ALP was also elevated by several points on average in…
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