Phase I study of saracatinib (AZD0530) in combination with paclitaxel and/or carboplatin in patients with solid tumours.

BACKGROUND: As a prelude to combination studies aimed at resistance reversal, this dose-escalation/dose-expansion study investigated the selective Src kinase inhibitor saracatinib (AZD0530) in combination with carboplatin and/or paclitaxel. METHODS: Patients with advanced solid tumours received saracatinib once-daily oral tablets in combination with either carboplatin AUC 5 every 3 weeks (q3w), paclitaxel 175 mg m(-2) q3w, paclitaxel 80 mg m(-2) every 1 week (q1w), or carboplatin AUC 5 plus paclitaxel 175 mg m(-2) q3w. The primary endpoint was safety/tolerability. RESULTS: A total of 116 patients received saracatinib 125 (N=20), 175 (N=44), 225 (N=40), 250 (N=9), or 300 mg (N=3). There were no clear dose-related trends within each chemotherapy regimen group in number or severity of adverse events (AEs). However, combining all groups, the occurrence of grade ≥3 asthenic AEs (all causality) was dose-related (125 mg, 10%; 175 mg, 20%; ≥225 mg, 33%), and grade ≥3 neutropenia occurred more commonly at doses ≥225 mg. There was no evidence that saracatinib affected exposure to carboplatin or paclitaxel, or vice versa. Objective responses were seen in 5 out of 44 patients (11%) receiving carboplatin plus paclitaxel q3w, and 5 out of 24 (21%) receiving paclitaxel q1w. CONCLUSION: Saracatinib doses up to 175 mg with paclitaxel with/without carboplatin showed acceptable toxicity in most patients, and are suitable for further trials.

A case-acquisition and decision-support system for the analysis of group-average lactation curves.

A case-acquisition and decision-support system was developed to support the analysis of group-average lactation curves and to acquire example cases from domain specialists. This software was developed through several iterations of a three-step approach involving 1) problem analysis and formulation in consultation with two dairy nutrition specialists; 2) development of a case-acquisition and decision-support prototype by the system developer; and 3) use of the prototype by the domain specialists to analyze and classify milk-recording data from example herds. The overall problem was decomposed into three subproblems: removal of outlier tests and lactation curves of individual cows; interpretation of group-average lactation curves; and diagnosis of detected abnormalities at the herd level through the identification of potential management deficiencies. For each subproblem, a software module was developed allowing the user to analyze both graphical and numerical performance representations and classify these representations using predefined linguistic descriptors. The example-based method for the development of the program proved to be very useful, facilitating the communication between system developer and domain specialists, and allowing the specialists to explore the appropriateness of the various prototypes developed. The resulting software represents a formalization of the approach to group-average lactation curve analysis, elicited from the two domain specialists. In future research, the case-acquisition and decision-support system will be complemented with knowledge to automate identified classification tasks, which will be captured through the application of machine-learning techniques to example cases, acquired from domain specialists using the software.