PubMed Health⌕ Search

Biomedical subjects

Nandini Raghavan

Publications and source records attributed to Nandini Raghavan.

2 recordsLinked to original sources

Long-term safety and efficacy of ponesimod, an oral S1P1 receptor modulator, in relapsing-remitting multiple sclerosis (RRMS): Results from randomized phase 2b core and extension studies spanning up to 13 years.

BACKGROUND: Ponesimod demonstrated efficacy and safety in relapsing-remitting multiple sclerosis (RRMS) in 24-week phase-2 core study, further confirmed by an interim combined analysis of the core and open-label long-term extension (LTE) studies (NCT01093326; up to 8 years). Current study evaluated safety and efficacy of ponesimod using combined core and LTE studies data for up to 13 years. METHODS: Of 393 participants completing the core study, 353 (90%) entered LTE, having 3 treatment periods (TP). In TP1 (∼1.85 years), participants continued core treatment (ponesimod: 10, 20, or 40 mg QD) whereas placebo-treated participants were re-randomized (1:1:1) to respective ponesimod doses. In TP2 (TP2 and TP3 combined duration: ∼10.4 years), participants on 40 mg were re-randomized (1:1) to 10/20 mg, while others continued same dose; in TP3 all participants received 20 mg. Study outcomes included safety and efficacy (ARR, time-to 24-week confirmed-disability-accumulation [24-week CDA], total T1-weighted Gadolinium-enhanced (T1 Gd+) lesions, new/enlarging T2 lesions and Combined Unique Active Lesions [CUALs]). RESULTS: For 20 mg dose, total 92.4% participants experienced ≥1 TEAEs (73.1% mild/moderate severity) and 19 (13%) participants discontinued study. Mean (95% CI) ARR=0.14 (0.10-0.20); Kaplan-Meier estimate (95% CI) of confirmed relapse and 24-week CDA=52.5% (42.3-63.5) and 31.3% (22.6-42.3). Mean [SD] T1 Gd+ lesions decreased from ponesimod baseline (2.62 [7.06]) to 12.4 years (0.26 [1.48]), mean (95% CI) CUALs per participant/year=3.97 (2.75-5.73). CONCLUSION: Ponesimod treatment for up to 13 years was not associated with new safety concerns. Participants continued to experience low levels of disease activity consistently across clinical and MRI outcomes.

Humans↗

Class prediction in toxicogenomics.

The intent of this article is to discuss some of the complexities of toxicogenomics data and the statistical design and analysis issues that arise in the course of conducting a toxicogenomics study. We also describe a procedure for classifying compounds into various hepatotoxicity classes based on gene expression data. The methodology involves first classifying a compound as toxic or nontoxic and subsequently classifying the toxic compounds into the hepatotoxicity classes, based on votes by binary classifiers. The binary classifiers are constructed by using genes selected to best elicit differences between the two classes. We show that the gene selection strategy improves the misclassification error rates and also delivers gene pathways that exhibit biological relevance.

Algorithms↗