Multivariate evaluation of pharmacological responses in early clinical trials - a study of rIL-21 in the treatment of patients with metastatic melanoma

center dot Analysis of data from clinical trials is often performed using univariate statistics. center dot In early phases of clinical drug development, interpretation of rare clinical events can be difficult by univariate methods. center dot Principal component analysis has proven successful within related scientific areas such as, for example, genomics and metabonomics, where compression of data and extraction of maximum information are of utmost importance. WHAT THIS STUDY ADDS center dot This study reveals that multivariate chemometric methods coupled with visualization gives a comprehensive overview of early clinical trial data to guide dose and regimen selection and provides additional findings overlooked by traditional univariate methods. center dot This method revealed novel pharmacological patterns in the treatment of metastatic melanoma with recombinant interleukin-21. AIMS Evaluation of the utility of multivariate data analysis in early clinical drug development. METHODS A multivariate chemometric approach was developed and applied for evaluating clinical laboratory parameters and biomarkers obtained from two clinical trials investigating recombinant human interleukin-21 (rIL-21) in the treatment of patients with malignant melanoma. The Phase I trial was an open-label, first-human dose escalation safety and tolerability trial with two separate dosing regimens; six cycles of thrice weekly (3/w) vs. three cycles of daily dosing for 5 days followed by 9 days of rest (5+9) in a total of 29 patients. The Phase II trial investigated efficacy and safety of the '5+9' regimen in 24 patients. RESULTS From the Phase I trial, separate pharmacological patterns were observed for each regimen, clearly reflecting distinct properties of the two regimens. Relations between individual laboratory parameters were visualized and shown to be responsive to rIL-21 dosing. In particular, novel systematic pharmacological effects on liver function parameters as well as a bell-shaped dose-response relationship of the overall pharmacological effects were depicted. In validation of the method, multivariate pharmacological patterns discovered in the Phase I trial could be reproduced by the dataset from the Phase II trial, but not from univariate exploration of the Phase I trial. CONCLUSIONS The new data analytical approach visualized novel correlations between laboratory parameters that points to specific pharmacological properties. This multivariate chemometric data analysis offers a novel robust, comprehensive and intuitive tool to reveal early pharmacological responses and guide selection of dose regimens.