A pharmacogenetic signature of high response to Copaxone in late-phase clinical-trial cohorts of multiple sclerosis.

BACKGROUND: Copaxone is an efficacious and safe therapy that has demonstrated clinical benefit for over two decades in patients with relapsing forms of multiple sclerosis (MS). On an individual level, patients show variability in their response to Copaxone, with some achieving significantly higher response levels. The involvement of genes (e.g., HLA-DRB1*1501) with high inter-individual variability in Copaxone's mechanism of action (MoA) suggests the potential contribution of genetics to treatment response. This study aimed to identify genetic variants associated with Copaxone response in patient cohorts from late-phase clinical trials. METHODS: Single nucleotide polymorphisms (SNPs) associated with high and low levels of response to Copaxone were identified using genome-wide SNP data in a discovery cohort of 580 patients from two phase III clinical trials of Copaxone. Multivariable Bayesian modeling on the resulting SNPs in an expanded discovery cohort with 1171 patients identified a multi-SNP signature of Copaxone response. This signature was examined in 941 Copaxone-treated MS patients from seven independent late-phase trials of Copaxone and assessed for specificity to Copaxone in 310 Avonex-treated and 311 placebo-treated patients, also from late-phase trials. RESULTS: A four-SNP signature consisting of rs80191572 (in UVRAG), rs28724893 (in HLA-DQB2), rs1789084 (in MBP), and rs139890339 (in ZAK(CDCA7)) was identified as significantly associated with Copaxone response. Copaxone-treated signature-positive patients had a greater reduction in annualized relapse rate (ARR) compared to signature-negative patients in both discovery and independent cohorts, an effect not observed in Avonex-treated patients. Additionally, signature-positive placebo-treated cohorts did not show a reduction in ARR, demonstrating the predictive as opposed to prognostic nature of the signature. A 10% subset of patients, delineated by the signature, showed marked improvements across multiple clinical parameters, including ARR, MRI measures, and higher proportion with no evidence of disease activity (NEDA). CONCLUSIONS: This study is the largest pharmacogenetic study in MS reported to date. Gene regions underlying the four-SNP signature have been linked with pathways associated with either Copaxone's MoA or the pathophysiology of MS. The pronounced association of the four-SNP signature with clinical improvements in a ~10% subset of the MS patient population demonstrates the complex interplay of immune mechanisms and the individualized nature of response to Copaxone.

Increased peak postoperative B-type natriuretic peptide predicts decreased longer-term physical function after primary coronary artery bypass graft surgery.

BACKGROUND: Increased peak postoperative B-type natriuretic peptide (BNP) is associated with increased major adverse cardiovascular events and all-cause mortality after coronary artery bypass graft (CABG) surgery. Whether increased postoperative BNP predicts worse postdischarge physical function (PF) is unknown. We hypothesized that peak postoperative BNP associates with PF assessed up to 2 yr after CABG surgery, even after adjusting for clinical risk factors. including preoperative PF. METHODS: This two-institution prospective cohort study included patients undergoing primary CABG surgery with cardiopulmonary bypass. Short Form-36 questionnaires were administered to subjects preoperatively and 6 months, 1 yr, and 2 yr postoperatively. Short Form-36 PF domain scores were calculated using the Short Form-36 norm-based scoring algorithm. Plasma BNP concentrations measured preoperatively and on postoperative days 1-5 were log(10) transformed before analysis. To determine whether peak postoperative BNP independently predicts PF scores 6 months through 2 yr after CABG surgery, multivariable longitudinal regression analysis of the postoperative PF scores was performed, adjusting for important clinical risk factors. RESULTS: A total of 845 subjects (mean ± SD age, 65 ± 10 yr) were analyzed. Peak postoperative BNP was significantly associated with postoperative PF (effect estimate for log(10) peak BNP, -7.66 PF score points [95% CI, -9.68 to -5.64]; P = <0.0001). After multivariable adjustments, peak postoperative BNP remained independently associated with postoperative PF (effect estimate for log(10) peak BNP, -3.06 PF score points [95% CI, -5.15 to -0.97]; P = 0.004). CONCLUSIONS: Increased peak postoperative BNP independently associates with worse longer-term PF after primary CABG surgery. Future studies are needed to determine whether medical management targeted toward reducing increased postoperative BNP can improve PF after CABG surgery.

Understanding false positives in reporter gene assays: in silico chemogenomics approaches to prioritize cell-based HTS data.

High throughput screening (HTS) data is often noisy, containing both false positives and negatives. Thus, careful triaging and prioritization of the primary hit list can save time and money by identifying potential false positives before incurring the expense of followup. Of particular concern are cell-based reporter gene assays (RGAs) where the number of hits may be prohibitively high to be scrutinized manually for weeding out erroneous data. Based on statistical models built from chemical structures of 650 000 compounds tested in RGAs, we created "frequent hitter" models that make it possible to prioritize potential false positives. Furthermore, we followed up the frequent hitter evaluation with chemical structure based in silico target predictions to hypothesize a mechanism for the observed "off target" response. It was observed that the predicted cellular targets for the frequent hitters were known to be associated with undesirable effects such as cytotoxicity. More specifically, the most frequently predicted targets relate to apoptosis and cell differentiation, including kinases, topoisomerases, and protein phosphatases. The mechanism-based frequent hitter hypothesis was tested using 160 additional druglike compounds predicted by the model to be nonspecific actives in RGAs. This validation was successful (showing a 50% hit rate compared to a normal hit rate as low as 2%), and it demonstrates the power of computational models toward understanding complex relations between chemical structure and biological function.

Finding more needles in the haystack: A simple and efficient method for improving high-throughput docking results.

The technology underpinning high-throughput docking (HTD) has developed over the past few years to where it has become a vital tool in modern drug discovery. Although the performance of various docking algorithms is adequate, the ability to accurately and consistently rank compounds using a scoring function remains problematic. We show that by employing a simple machine learning method (naïve Bayes) it is possible to significantly overcome this deficiency. Compounds from the Available Chemical Directory (ACD), along with known active compounds, were docked into two protein targets using three software packages. In cases where HTD alone was able to show some enrichment, the application of naïve Bayes was able to improve upon the enrichment. The application of this methodology to enrich HTD results can be carried out without a priori knowledge of the activity of compounds and results in superior enrichment of known actives compared to the use of scoring methods alone.