Model-Based Meta-Analysis in Ankylosing Spondylitis: A Quantitative Comparison of Biologics and Small Targeted Molecules

Quantitative evaluation of the time-course and efficacy of targeted agents for ulcerative colitis

Background

Targeted agents are widely utilized in the treatment of ulcerative colitis (UC). Hence, a comprehensive understanding of comparative drug efficacy in UC is of great importance for drug development and clinical practice. Our objective was the quantitative evaluation of the comparative efficacy of targeted agents for UC.

Methods

Three mathematical models were developed based on data from randomized controlled trials in patients with moderate-to-severe UC to describe the time-course and dose-response of efficacy defined as clinical remission, clinical response, and endoscopic improvement, as well as the placebo effect. The covariate effects were further evaluated. Model simulation was performed in a hypothetical population to compare the efficacies across different drugs.

Results

The analysis dataset was composed of data from 35 trials of 12 drugs in UC. Time-response relationships were evaluated that indicated a gradual onset of drug efficacy in adalimumab, ozanimod, and Janus kinase (JAK) inhibitors. The dose-response relationships were estimated for each drug respectively. Patient age, disease duration, baseline weight, prior tumor necrosis factor (TNF) inhibitor exposure, and current treatment with corticosteroid showed an impact on efficacy, suggesting that younger patients with shorter UC duration without prior anti-TNF treatment and current corticosteroids therapy tend to display greater treatment effects.

Conclusion

This study developed three longitudinal models for UC to quantitatively describe the efficacy of targeted agents, as well as the influencing factors of efficacy. Infliximab and upadacitinib were determined to be the most effective biological and small targeted molecules, respectively. These findings may provide valuable implications for guiding future decision-making in clinical practice and drug development for UC.

Pan American League of Associations for Rheumatology recommendations for the management of axial spondyloarthritis

Axial spondyloarthritis (axSpA) comprises a spectrum of chronic inflammatory manifestations affecting the axial skeleton and represents a challenge for diagnosis and treatment. Our objective was to generate a set of evidence-based recommendations for the management of axSpA for physicians, health professionals, rheumatologists and policy decision makers in Pan American League of Associations for Rheumatology (PANLAR) countries. Grading of Recommendations, Assessment, Development and Evaluation-ADOLOPMENT methodology was used to adapt existing recommendations after performing an independent systematic search and synthesis of the literature to update the evidence. A working group consisting of rheumatologists, epidemiologists and patient representatives from countries within the Americas prioritized 13 topics relevant to the context of these countries for the management of axSpA. This Evidence-Based Guideline article reports 13 recommendations addressing therapeutic targets, the use of NSAIDs and glucocorticoids, treatment with DMARDs (including conventional synthetic, biologic and targeted synthetic DMARDs), therapeutic failure, optimization of the use of biologic DMARDs, the use of drugs for extra-musculoskeletal manifestations of axSpA, non-pharmacological interventions and the follow-up of patients with axSpA.

A Predictive Disease Risk Model for Ankylosing Spondylitis: Based on Integrated Bioinformatic Analysis and Identification of Potential Biomarkers Most Related to Immunity

Background

The pathogenesis of ankylosing spondylitis (AS) is still not clear, and immune-related genes have not been systematically explored in AS. The purpose of this paper was to identify the potential early biomarkers most related to immunity in AS and develop a predictive disease risk model with bioinformatic methods and the Gene Expression Omnibus database (GEO) to improve diagnostic and therapeutic efficiency.

Methods

To identify differentially expressed genes and create a gene coexpression network between AS and healthy samples, we downloaded the AS-related datasets GSE25101 and GSE73754 from the GEO database and employed weighted gene coexpression network analysis (WGCNA). We used the GSVA, GSEABase, limma, ggpubr, and reshape2 packages to score immune data and investigated the links between immune cells and immunological functions by using single-sample gene set enrichment analysis (ssGSEA). The value of the core gene set and constructed model for early AS diagnosis was investigated by using receiver operating characteristic (ROC) curve analysis.

Results

Biological function and immune score analyses identified central genes related to immunity, key immune cells, key related pathways, gene modules, and the coexpression network in AS. Granulysin (GNLY), Granulysin (GZMK), CX3CR1, IL2RB, dysferlin (DYSF), and S100A12 may participate in AS development through NK cells, CD8⁺ T cells, Th1 cells, and other immune cells and represent potential biomarkers for the early diagnosis of AS occurrence and progression. Furthermore, the T cell coinhibitory pathway may be involved in AS pathogenesis.

Conclusion

The AS disease risk model constructed based on immune-related genes can guide clinical diagnosis and treatment and may help in the development of personalized immunotherapy.

Applications of Model-Based Meta-Analysis in Drug Development

Model-based meta-analysis (MBMA) is a quantitative approach that leverages published summary data along with internal data and can be applied to inform key drug development decisions, including the benefit-risk assessment of a treatment under investigation. These risk-benefit assessments may involve determining an optimal dose compared against historic external comparators of a particular disease indication. MBMA can provide a flexible framework for interpreting aggregated data from historic reference studies and therefore should be a standard tool for the model-informed drug development (MIDD) framework.In addition to pairwise and network meta-analyses, MBMA provides further contributions in the quantitative approaches with its ability to incorporate longitudinal data and the pharmacologic concept of dose-response relationship, as well as to combine individual- and summary-level data and routinely incorporate covariates in the analysis.A common application of MBMA is the selection of optimal dose and dosing regimen of the internal investigational molecule to evaluate external benchmarking and to support comparator selection. Two case studies provided examples in applications of MBMA in biologics (durvalumab + tremelimumab for safety) and small molecule (fenebrutinib for efficacy) to support drug development decision-making in two different but well-studied disease areas, i.e., oncology and rheumatoid arthritis, respectively.Important to the future directions of MBMA include additional recognition and engagement from drug development stakeholders for the MBMA approach, stronger collaboration between pharmacometrics and statistics, expanded data access, and the use of machine learning for database building. Timely, cost-effective, and successful application of MBMA should be part of providing an integrated view of MIDD.

Model-Based Meta-Analysis on the Efficacy of Biologics and Small Targeted Molecules for Crohn’s Disease

Information on comparative drug efficacy is of great importance for drug development as well as clinical practice. Up to now, the relative efficacy of biologics and small targeted molecules for Crohn’s disease (CD) remains unclear. The objective of this study was to quantify the relative efficacy of investigational and approved biological treatments for CD measured in Crohn’s Disease Activity Index (CDAI), Inflammatory Bowel Disease Questionnaire (IBDQ), and C-reactive protein (CRP). The analysis dataset was composed of summary-level data from 46 trials, containing 12,846 patients, with treatment of 24 drugs. Six mathematical models with non-parametric placebo estimations were developed to describe the time course and dose–response of six efficacy measures. The effects of covariate were further evaluated. Time–response relationships were found in outcomes measured in CDAI. The patients’ age, disease duration, baseline CDAI, and CRP showed an impact on the efficacy. Model simulations were performed to compare the efficacies across different drugs. The most achievement in clinical remission (defined as CDAI less than 150) and clinical response (defined as the reduction in CDAI for 100 or 70) was observed in the simulation for PF-04236921 and infliximab, respectively. The most improvement in IBDQ was shown in tofacitinib. In general, tumor necrosis factor (TNF)-α inhibitors were the most effective biologics, and the highest efficacy of small targeted molecules was observed in janus kinase (JAK) inhibitors. These findings have important implications for clinical practice in CD.

Model-Based Meta-Analysis in Psoriasis: A Quantitative Comparison of Biologics and Small Targeted Molecules

Background: The response time-course information of biologics and small targeted molecules for the treatment of moderate to severe plaque psoriasis which helps clinicians to understand the onset of action and maintenance of effect are unclear. Quantitative information about the efficacy comparation of different systemic agents are needed.

Methods: Model-based meta-analysis was conducted and longitudinal models were developed by applying two clinical end points commonly reported in the clinical trials of psoriasis: the proportion of patients achieving ≥75% reduction from baseline Psoriasis Area and Severity Index score (PASI75) and the proportion of patients achieving ≥90% reduction from baseline Psoriasis Area and Severity Index score (PASI90).

Results: A total of 80 trials of thirteen biological agents and four small targeted molecules covering 235 treatment arms and 40323 patients with moderate to severe plaque psoriasis were included in this analysis. The drugs were divided into five classes of biologics and three classes of small molecules. Two longitudinal models of PASI75 and PASI90 were used to describe the time-varying drug effect and dose-effect relationship. The typical response-time courses for PASI75 and PASI90 increased over time and finally reached to the platform. For PASI75 end point at week 12, of all the therapeutic drugs, risankizumab administered as 150 mg at week 0, week 4, and q12w showed the most efficacious with PASI75 was 85.95% (95%CI, 75.71–92.60%), followed by ixekizumab administered as 160 mg at week 0, and q4w with PASI75 was 85.9% (95%CI, 76.12–92.79%). As for PASI90 end point at week 12, ixekizumab 160 mg at week 0, and q4w showed the greatest percentage of person achieved PASI90 (67.2%; 95%CI, 49.91–77.2%), followed by risankizumab 150 mg at week 0, week 4, and q12w (65.5%; 95%CI, 47.8–75.7%). What’s more, the risankizumab provided the highest response of PASI90 at week 16 and week 24.

Conclusions: This study provided a quantitative efficacy comparation of 17 systemic agents for psoriasis in term of efficacy only and that safety was not considered. Risankizumab and ixekizumab showed superiority for both the two end points.