Evaluation of Gold’s nomogram for predicting recurrence-free survival in gastrointestinal stromal tumors in Indian patients

Gold described a nomogram for prediction of recurrence-free survival (RFS) after surgery for gastrointestinal stromal tumors (GIST). This retrospective study was intended to evaluate the utility of this nomogram for predicting a 2-year RFS in our patients. Twenty-eight consecutive eligible patients from January 2009 to January 2013 who underwent R0 resection and had histopathologically proven GIST were included in the study. Nomogram predicted RFS was compared with observed RFS in four groups as in the National Institutes of Health (NIH)-Fletcher classification. Calibration was assessed by plotting the predicted probabilities of RFS against the actual outcome. For validation of the nomogram, the graph obtained should be closer to the 45-degree line. The observed overall 2-year RFS was 85.7 % (24 patients). Four patients had recurrence within 2 years. The observed RFS was 87.5 %, 77.8 %, 90 %, and 100 % in the high, intermediate, low, and very low risk groups, respectively. The nomogram predicted the 2-year RFS was 40 %, 84.8 %, 88.6 %, and 90 % for high, intermediate, low, and very low risk groups, respectively. Thus, the predicted probabilities of the 2-year RFS in intermediate, low, and very low risk groups were similar to the observed outcomes. However, for the high risk group, the observed RFS was better than predicted RFS. This variation in the high risk group may be due to the use of adjuvant imatinib in our study.     

High-Throughput In-Use and Stress Size Stability Screening of Protein Therapeutics Using Algorithm-Driven Dynamic Light Scattering

Stability of therapeutic proteins (TPs) is a critical quality attribute that impacts both safety and efficacy of the drug. Size stability is routinely performed during and after biomanufacturing. Dynamic light scattering (DLS) is a commonly used technique to characterize hydrodynamic size of the TPs. Herein, we have developed a novel method to evaluate in-use and thermal stress stability of TPs using algorithm-driven high-throughput DLS. Five marketed TPs were tested under the guidance of customized algorithms. The TPs were evaluated at relevant temperature conditions as well as under dilution and thermal stress for size stability. We found that the TPs were stable under the in-use conditions tested; however, sample loss due to evaporation can lead to large protein aggregates. A combined assessment of autocorrelation function and photos of sample well could be useful in formulation screening. Dilution of TPs also has an impact on the hydrodynamic size. Thermal stress experiments showed the importance of using different data processing methods to access size distribution. Polydispersity index was useful in evaluating sample heterogeneity. Herein, we show that algorithm-driven high-throughput DLS can provide additional supportive information during and after biomanufacturing and the potential to be used in a quality control environment.