Efficacy and safety of candidate biosimilar CT-P41 versus reference denosumab: a double-blind, randomized, active-controlled, Phase 3 trial in postmenopausal women with osteoporosis.

This 78-week (18-month) study conducted in 479 postmenopausal women with osteoporosis evaluated the efficacy, pharmacodynamics, pharmacokinetics, safety, and immunogenicity of candidate biosimilar CT-P41 relative to US reference denosumab. CT-P41 had equivalent efficacy and pharmacodynamics to US-denosumab, with similar pharmacokinetics and comparable safety and immunogenicity profiles. PURPOSE: To demonstrate equivalence of candidate biosimilar CT-P41 and US reference denosumab (US-denosumab) in postmenopausal women with osteoporosis. METHODS: This 78-week (18-month), double-blind, randomized, active-controlled Phase 3 study (NCT04757376) comprised two treatment periods (TPs). In TPI, patients (N = 479) were randomized 1:1 to 60 mg subcutaneous CT-P41 or US-denosumab. At Week 52, those who had received CT-P41 in TPI continued to do so. Those who had received US-denosumab were randomized (1:1) to continue treatment or switch to CT-P41 in TPII. The primary efficacy endpoint was percent change from baseline in lumbar spine bone mineral density at Week 52. Efficacy equivalence was concluded if associated 95% confidence intervals (CI) for least squares (LS) mean group differences fell within ± 1.503%. The primary pharmacodynamic (PD) endpoint was area under the effect curve for serum carboxy-terminal cross-linking telopeptide of type I collagen through the first 26 weeks, with an equivalence margin of 80-125% (for 95% CIs associated with geometric LS mean ratios). RESULTS: Equivalence was demonstrated for CT-P41 and US-denosumab with respect to primary efficacy (LS mean difference [95% CI]: - 0.139 [- 0.826, 0.548] in the full analysis set and - 0.280 [- 0.973, 0.414] in the per-protocol set) and PD (geometric LS mean ratio [95% CI]: 94.94 [90.75, 99.32]) endpoints. Secondary efficacy, PD, pharmacokinetics, and safety results were comparable among all groups up to Week 78, including after transitioning to CT-P41 from US-denosumab. CONCLUSIONS: CT-P41 was equivalent to US-denosumab in women with postmenopausal osteoporosis, with respect to primary efficacy and PD endpoints.

The Association between Inefficient Repair of DNA Double-Strand Breaks and Common Polymorphisms of the HRR and NHEJ Repair Genes in Patients with Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation affecting up to 2.0% of adults around the world. The molecular background of RA has not yet been fully elucidated, but RA is classified as a disease in which the genetic background is one of the most significant risk factors. One hallmark of RA is impaired DNA repair observed in patient-derived peripheral blood mononuclear cells (PBMCs). The aim of this study was to correlate the phenotype defined as the efficiency of DNA double-strand break (DSB) repair with the genotype limited to a single-nucleotide polymorphism (SNP) of DSB repair genes. We also analyzed the expression level of key DSB repair genes. The study population contained 45 RA patients and 45 healthy controls. We used a comet assay to study DSB repair after in vitro exposure to bleomycin in PBMCs from patients with rheumatoid arthritis. TaqMan SNP Genotyping Assays were used to determine the distribution of SNPs and the Taq Man gene expression assay was used to assess the RNA expression of DSB repair-related genes. PBMCs from patients with RA had significantly lower bleomycin-induced DNA lesion repair efficiency and we identified more subjects with inefficient DNA repair in RA compared with the control (84.5% vs. 24.4%; OR 41.4, 95% CI, 4.8-355.01). Furthermore, SNPs located within the RAD50 gene (rs1801321 and rs1801320) increased the OR to 53.5 (95% CI, 4.7-613.21) while rs963917 and rs3784099 (RAD51B) to 73.4 (95% CI, 5.3-1011.05). These results were confirmed by decision tree (DT) analysis (accuracy 0.84; precision 0.87, and specificity 0.86). We also found elevated expression of RAD51B, BRCA1, and BRCA2 in PBMCs isolated from RA patients. The findings indicated that impaired DSB repair in RA may be related to genetic variations in DSB repair genes as well as their expression levels. However, the mechanism of this relation, and whether it is direct or indirect, needs to be elucidated.

Machine learning-based prediction of rheumatoid arthritis with development of ACPA autoantibodies in the presence of non-HLA genes polymorphisms.

Machine learning (ML) algorithms can handle complex genomic data and identify predictive patterns that may not be apparent through traditional statistical methods. They become popular tools for medical applications including prediction, diagnosis or treatment of complex diseases like rheumatoid arthritis (RA). RA is an autoimmune disease in which genetic factors play a major role. Among the most important genetic factors predisposing to the development of this disease and serving as genetic markers are HLA-DRB and non-HLA genes single nucleotide polymorphisms (SNPs). Another marker of RA is the presence of anticitrullinated peptide antibodies (ACPA) which is correlated with severity of RA. We use genetic data of SNPs in four non-HLA genes (PTPN22, STAT4, TRAF1, CD40 and PADI4) to predict the occurrence of ACPA positive RA in the Polish population. This work is a comprehensive comparative analysis, wherein we assess and juxtapose various ML classifiers. Our evaluation encompasses a range of models, including logistic regression, k-nearest neighbors, naïve Bayes, decision tree, boosted trees, multilayer perceptron, and support vector machines. The top-performing models demonstrated closely matched levels of accuracy, each distinguished by its particular strengths. Among these, we highly recommend the use of a decision tree as the foremost choice, given its exceptional performance and interpretability. The sensitivity and specificity of the ML models is about 70% that are satisfying. In addition, we introduce a novel feature importance estimation method characterized by its transparent interpretability and global optimality. This method allows us to thoroughly explore all conceivable combinations of polymorphisms, enabling us to pinpoint those possessing the highest predictive power. Taken together, these findings suggest that non-HLA SNPs allow to determine the group of individuals more prone to develop RA rheumatoid arthritis and further implement more precise preventive approach.