Simulate clinical trials on individuals with mutated Uromodulin (UMOD)
Population Selection
Inclusion Criteria Selection
Number of Individuals in Available Sampling Pool
Population Baseline Characteristics
Distribution of SNP Variants
Change Power Curve Settings
Current Settings
Power Curve for UMOD
| eGFR: | Estimated Glomerular Filtration Rate (indexed) |
| eGFR unit: | mL/min/1.73 m2 |
| CKD-EPI: | Chronic Kidney Disease Epidemiology Collaboration equation |
| CKD-EPI equation: | eGFR = 141 × min(Scr/κ, 1)α × max(Scr/κ, 1)-1.209 × 0.993Age × (1.018 if female) × (1.159 if African American) |
| Scr: | Serum creatinine |
| κ | 0.7 for females, 0.9 for males |
| α | -0.241 for females, -0.302 for males |
| SNP: | Single Nucleotide Polymorphism |
| rs429339: | Variant in promoter region for UMOD gene |
| A: | Adenine |
| G: | Guanine |
| DPT50: | The age at which half of the maximum change in eGFR |
| γ1: | Steepness of eGFR decline before DPT50 |
| γ2: | Steepness of eGFR decline after DPT50 |
| Metric 1: | Change in eGFR from baseline to last measure |
| Metric 2: | Last measure of eGFR |
The natural history of disease data presented here was collected by a study conducted at Wake Forest University School of Medicine (WFUHS IRB0000352). The modeling and simulation work was supported by Sail Bio, Wake Forest University School of Medicine, and the University of Florida Center for Pharmacometrics and Systems Pharmacology. The content is solely the responsibility of the authors.
We thank all team members for their valuable feedback and support: Mark Rogge, Ph.D., F.C.P. (University of Florida), Kendrah Kidd, Ph.D. (Wake Forest University School of Medicine), Adrienne Williams, M.A. (Wake Forest University School of Medicine), Julie Roignot, Ph.D. (Sail Bio), Katherine Blakeslee, M.P.A. (Sail Bio), and Anthony J. Bleyer, Sr., M.D. (Wake Forest University School of Medicine) for their contributions to the collection and curation of natural history data, interpretation of disease progression patterns, clinical insights to refine model assumptions, and guidance in contextualizing findings for translational application.
We also acknowledge the contributions of Shyam S. Ramesh, Pharm.D., M.S. (University of Florida), Jongjin Kim, M.S. (University of Florida, current: University of Central Florida), Sanghoon Kang, Ph.D. (University of Florida), Juan Francisco Morales, Ph.D. (University of Florida), Deok Yong Yoon, Pharm.D., Ph.D. (University of Florida, current: Novartis), and Sarah Kim, Ph.D. (University of Florida) to the development of quantitative models and simulation tools.
Additionally, we thank Simulations Plus for the free usage of Simulx, which runs the simulation in the app and we thank David Hemond (University of Florida) for setting up the GUI server.
For inquiries regarding modeling and simulation, please contact Dr. Sarah Kim at sarahkim@cop.ufl.edu
Simulate clinical trials on individuals with mutated Uromodulin (MUC1)
Population Selection
Inclusion Criteria Selection
Number of Individuals in Available Sampling Pool
Population Baseline Characteristics
Change Power Curve Settings
Current Settings
Power Curve for MUC1
| eGFR: | Estimated Glomerular Filtration Rate (indexed) |
| eGFR unit: | mL/min/1.73 m2 |
| CKD-EPI: | Chronic Kidney Disease Epidemiology Collaboration equation |
| CKD-EPI equation: | eGFR = 141 × min(Scr/κ, 1)α × max(Scr/κ, 1)-1.209 × 0.993Age × (1.018 if female) × (1.159 if African American) |
| Scr: | Serum creatinine |
| κ | 0.7 for females, 0.9 for males |
| α | -0.241 for females, -0.302 for males |
| DPT50: | The age at which half of the maximum change in eGFR |
| γ1: | Steepness of eGFR decline before DPT50 |
| γ2: | Steepness of eGFR decline after DPT50 |
| Metric 1: | Change in eGFR from baseline to last measure |
| Metric 2: | Last measure of eGFR |
The natural history of disease data presented here was collected by a study conducted at Wake Forest University School of Medicine (WFUHS IRB0000352). The modeling and simulation work was supported by Sail Bio, Wake Forest University School of Medicine, and the University of Florida Center for Pharmacometrics and Systems Pharmacology. The content is solely the responsibility of the authors.
We thank all team members for their valuable feedback and support: Mark Rogge, Ph.D., F.C.P. (University of Florida), Kendrah Kidd, Ph.D. (Wake Forest University School of Medicine), Adrienne Williams, M.A. (Wake Forest University School of Medicine), Julie Roignot, Ph.D. (Sail Bio), Katherine Blakeslee, M.P.A. (Sail Bio), and Anthony J. Bleyer, Sr., M.D. (Wake Forest University School of Medicine) for their contributions to the collection and curation of natural history data, interpretation of disease progression patterns, clinical insights to refine model assumptions, and guidance in contextualizing findings for translational application.
We also acknowledge the contributions of Shyam S. Ramesh, Pharm.D., M.S. (University of Florida), Jongjin Kim, M.S. (University of Florida, current: University of Central Florida), Sanghoon Kang, Ph.D. (University of Florida), Juan Francisco Morales, Ph.D. (University of Florida), Deok Yong Yoon, Pharm.D., Ph.D. (University of Florida, current: Novartis), and Sarah Kim, Ph.D. (University of Florida) to the development of quantitative models and simulation tools.
Additionally, we thank Simulations Plus for the free usage of Simulx, which runs the simulation in the app and we thank David Hemond (University of Florida) for setting up the GUI server.
For inquiries regarding modeling and simulation, please contact Dr. Sarah Kim at sarahkim@cop.ufl.edu