ABSTRACT
BACKGROUND: Secukinumab, an anti-interleukin-17A monoclonal antibody, has demonstrated rapid and sustained efficacy in phase 3 psoriasis trials. OBJECTIVES: To examine whether partial responders could achieve improved responses with intravenous (IV) secukinumab vs. the same or a higher subcutaneous (SC) dose. METHODS: Forty-three participants with moderate-to-severe psoriasis and partial response [Psoriasis Area and Severity Index (PASI) score improvement of ≥ 50% but < 75%] after 12 weeks of 300 or 150 mg SC secukinumab therapy were randomized 1 : 1 to secukinumab 10 mg kg(-1) IV (baseline, weeks 2 and 4, respectively) or secukinumab 300 mg SC (baseline, week 4). All participants subsequently received secukinumab 300 mg SC every 4 weeks (weeks 8-36). Co-primary end points were PASI 75 and Investigator's Global Assessment [2011 modified version (IGA mod 2011)] 0/1 response rates at week 8 (IV vs. SC). RESULTS: Higher IGA mod 2011 0/1 response rates (66.7% vs. 33.3%; P = 0.03) and a trend towards higher PASI 75 response rates (90.5% vs. 66.7%; P = 0.06) were observed with secukinumab IV vs. SC at week 8. The primary objective was not met, as the difference was not significant for both co-primary end points. Improved responses in both groups were maintained at week 40 in most participants. Safety profiles for IV and SC secukinumab were similar. The trial was underpowered owing to its small sample size. CONCLUSIONS: Improved response may be attained in patients with psoriasis achieving partial response after 12 weeks of SC secukinumab treatment by continued dosing with 300 mg SC or treatment with higher doses.
Subject(s)
Antibodies, Monoclonal/administration & dosage , Dermatologic Agents/administration & dosage , Psoriasis/drug therapy , Antibodies, Monoclonal/adverse effects , Antibodies, Monoclonal, Humanized , Dermatologic Agents/adverse effects , Double-Blind Method , Female , Humans , Infusions, Intravenous , Male , Middle Aged , Treatment OutcomeABSTRACT
MOTIVATION: This paper gives a new and efficient algorithm for the sparse logistic regression problem. The proposed algorithm is based on the Gauss-Seidel method and is asymptotically convergent. It is simple and extremely easy to implement; it neither uses any sophisticated mathematical programming software nor needs any matrix operations. It can be applied to a variety of real-world problems like identifying marker genes and building a classifier in the context of cancer diagnosis using microarray data. RESULTS: The gene selection method suggested in this paper is demonstrated on two real-world data sets and the results were found to be consistent with the literature. AVAILABILITY: The implementation of this algorithm is available at the site http://guppy.mpe.nus.edu.sg/~mpessk/SparseLOGREG.shtml SUPPLEMENTARY INFORMATION: Supplementary material is available at the site http://guppy.mpe.nus.edu.sg/~mpessk/SparseLOGREG.shtml
Subject(s)
Algorithms , Cluster Analysis , Gene Expression Profiling/methods , Genetic Testing/methods , Neoplasms/diagnosis , Neoplasms/genetics , Oligonucleotide Array Sequence Analysis/methods , Regression Analysis , Biomarkers, Tumor/genetics , Breast Neoplasms/classification , Breast Neoplasms/diagnosis , Breast Neoplasms/genetics , Colonic Neoplasms/classification , Colonic Neoplasms/diagnosis , Colonic Neoplasms/genetics , Humans , Neoplasms/classification , Pattern Recognition, Automated , Reproducibility of Results , Sensitivity and SpecificityABSTRACT
This article extends the well-known SMO algorithm of support vector machines (SVMs) to least-squares SVM formulations that include LS-SVM classification, kernel ridge regression, and a particular form of regularized kernel Fisher discriminant. The algorithm is shown to be asymptotically convergent. It is also extremely easy to implement. Computational experiments show that the algorithm is fast and scales efficiently (quadratically) as a function of the number of examples.
Subject(s)
Algorithms , Least-Squares AnalysisABSTRACT
In this paper we give a new fast iterative algorithm for support vector machine (SVM) classifier design. The basic problem treated is one that does not allow classification violations. The problem is converted to a problem of computing the nearest point between two convex polytopes. The suitability of two classical nearest point algorithms, due to Gilbert, and Mitchell et al., is studied. Ideas from both these algorithms are combined and modified to derive our fast algorithm. For problems which require classification violations to be allowed, the violations are quadratically penalized and an idea due to Cortes and Vapnik and Friess is used to convert it to a problem in which there are no classification violations. Comparative computational evaluation of our algorithm against powerful SVM methods such as Platt's sequential minimal optimization shows that our algorithm is very competitive.
ABSTRACT
This paper points out an important source of inefficiency in Smola and Schölkopf's sequential minimal optimization (SMO) algorithm for support vector machine (SVM) regression that is caused by the use of a single threshold value. Using clues from the KKT conditions for the dual problem, two threshold parameters are employed to derive modifications of SMO for regression. These modified algorithms perform significantly faster than the original SMO on the datasets tried.
