Disease trajectory of SLE clinical endpoints and covariates affecting disease severity and probability of response: Analysis of pooled patient‐level placebo (Standard‐of‐Care ) data to enable model‐informed drug development
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· 2022
· Open Access
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· DOI: https://doi.org/10.1002/psp4.12888
Systemic lupus erythematosus (SLE) is an autoimmune disease affecting multiple organ systems. Many investigational agents have failed or shown only modest effects when added to standard of care (SoC) therapy in placebo‐controlled trials, and only two therapies have been approved for SLE in the last 60 years. Clinical trial outcomes have shown discordance in drug effects between clinical endpoints. Herein, we characterized longitudinal disease activity in the SLE population and the sources of variability by developing a latent disease trajectory model for SLE component endpoints (Systemic Lupus Erythematosus Disease Activity Index [SLEDAI], Physician's Global Assessment [PGA], British Isles Lupus Assessment Group Index [BILAG]) and composite endpoints (Systemic Lupus Erythematosus Responder Index [SRI], BILAG‐based Composite Lupus Assessment [BICLA], and Lupus Low Disease Activity State [LLDAS]) using patient‐level historical SoC data from nine phase II and III studies. Across all endpoints, in predictions up to 52 weeks from the final disease trajectory model, the following baseline covariates were associated with a greater decrease in SLE disease activity and higher response to placebo + SoC: Hispanic ethnicity from Central/South America, absence of hypocomplementemia, recent SLE diagnosis, and high baseline disease activity score using SLEDAI and BILAG separately. No discernible differences were observed in the trajectory of response to placebo + SoC across different SoC medications (antimalarial and immunosuppressant such as mycophenolate, methotrexate, and azathioprine). Across all endpoints, disease trajectory showed no difference in Asian versus non‐Asian patients, supporting Asia‐inclusive global SLE drug development. These results describe the first population approach to support a model‐informed drug development framework in SLE.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/psp4.12888
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/psp4.12888
- OA Status
- gold
- Cited By
- 8
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4308611954
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4308611954Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1002/psp4.12888Digital Object Identifier
- Title
-
Disease trajectory of
SLE clinical endpoints and covariates affecting disease severity and probability of response: Analysis of pooledpatient‐level placebo (Standard‐of‐Care ) data to enablemodel‐informed drug developmentWork title - Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-09Full publication date if available
- Authors
-
Kosalaram Goteti, Jonathan French, Ramon Garcia, Ying Li, Florence Casset‐Semanaz, Aida Aydemir, Robert Townsend, Cristina Vazquez Mateo, Matthew Studham, O Guenther, Amy Kao, Marc R. Gastonguay, Pascal Girard, Lisa J. Benincosa, Karthik VenkatakrishnanList of authors in order
- Landing page
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https://doi.org/10.1002/psp4.12888Publisher landing page
- PDF URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/psp4.12888Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/psp4.12888Direct OA link when available
- Concepts
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Medicine, Systemic lupus erythematosus, Placebo, Internal medicine, Population, Azathioprine, Clinical endpoint, Clinical trial, Disease, Pharmacodynamics, Immunology, Pathology, Alternative medicine, Environmental health, PharmacokineticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 3, 2023: 3, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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44Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.SoC | 128, 208, 211 |
| abstract_inverted_index.all | 138, 223 |
| abstract_inverted_index.and | 34, 70, 104, 118, 134, 166, 184, 192, 214, 220 |
| abstract_inverted_index.for | 41, 82 |
| abstract_inverted_index.the | 44, 67, 71, 147, 152, 201, 244 |
| abstract_inverted_index.two | 36 |
| abstract_inverted_index.Many | 13 |
| abstract_inverted_index.SLE. | 256 |
| abstract_inverted_index.SoC: | 172 |
| abstract_inverted_index.been | 39 |
| abstract_inverted_index.care | 28 |
| abstract_inverted_index.data | 129 |
| abstract_inverted_index.drug | 55, 239, 252 |
| abstract_inverted_index.from | 130, 146, 175 |
| abstract_inverted_index.have | 16, 38, 51 |
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| abstract_inverted_index.last | 45 |
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| abstract_inverted_index.only | 20, 35 |
| abstract_inverted_index.such | 216 |
| abstract_inverted_index.were | 156, 198 |
| abstract_inverted_index.when | 23 |
| abstract_inverted_index.with | 158 |
| abstract_inverted_index.(SLE) | 4 |
| abstract_inverted_index.(SoC) | 29 |
| abstract_inverted_index.Asian | 231 |
| abstract_inverted_index.BILAG | 193 |
| abstract_inverted_index.Group | 101 |
| abstract_inverted_index.Index | 91, 102, 111 |
| abstract_inverted_index.Isles | 98 |
| abstract_inverted_index.Lupus | 87, 99, 108, 115, 119 |
| abstract_inverted_index.State | 123 |
| abstract_inverted_index.These | 241 |
| abstract_inverted_index.added | 24 |
| abstract_inverted_index.final | 148 |
| abstract_inverted_index.first | 245 |
| abstract_inverted_index.lupus | 2 |
| abstract_inverted_index.model | 81 |
| abstract_inverted_index.organ | 11 |
| abstract_inverted_index.phase | 132 |
| abstract_inverted_index.score | 189 |
| abstract_inverted_index.shown | 19, 52 |
| abstract_inverted_index.trial | 49 |
| abstract_inverted_index.using | 125, 190 |
| abstract_inverted_index.weeks | 145 |
| abstract_inverted_index.Across | 137, 222 |
| abstract_inverted_index.Global | 94 |
| abstract_inverted_index.SLEDAI | 191 |
| abstract_inverted_index.[PGA], | 96 |
| abstract_inverted_index.[SRI], | 112 |
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| abstract_inverted_index.agents | 15 |
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| abstract_inverted_index.global | 237 |
| abstract_inverted_index.higher | 167 |
| abstract_inverted_index.latent | 78 |
| abstract_inverted_index.model, | 151 |
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| abstract_inverted_index.recent | 181 |
| abstract_inverted_index.showed | 227 |
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| abstract_inverted_index.British | 97 |
| abstract_inverted_index.Disease | 89, 121 |
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| abstract_inverted_index.disease | 8, 64, 79, 149, 164, 187, 225 |
| abstract_inverted_index.effects | 22, 56 |
| abstract_inverted_index.greater | 160 |
| abstract_inverted_index.placebo | 170, 206 |
| abstract_inverted_index.results | 242 |
| abstract_inverted_index.sources | 72 |
| abstract_inverted_index.support | 249 |
| abstract_inverted_index.therapy | 30 |
| abstract_inverted_index.trials, | 33 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Activity | 90, 122 |
| abstract_inverted_index.America, | 177 |
| abstract_inverted_index.Clinical | 48 |
| abstract_inverted_index.Hispanic | 173 |
| abstract_inverted_index.Systemic | 1 |
| abstract_inverted_index.[BICLA], | 117 |
| abstract_inverted_index.[BILAG]) | 103 |
| abstract_inverted_index.[LLDAS]) | 124 |
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| abstract_inverted_index.approach | 247 |
| abstract_inverted_index.approved | 40 |
| abstract_inverted_index.baseline | 154, 186 |
| abstract_inverted_index.clinical | 58 |
| abstract_inverted_index.decrease | 161 |
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| abstract_inverted_index.multiple | 10 |
| abstract_inverted_index.observed | 199 |
| abstract_inverted_index.outcomes | 50 |
| abstract_inverted_index.response | 168, 204 |
| abstract_inverted_index.standard | 26 |
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| abstract_inverted_index.affecting | 9 |
| abstract_inverted_index.component | 84 |
| abstract_inverted_index.composite | 105 |
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| abstract_inverted_index.following | 153 |
| abstract_inverted_index.framework | 254 |
| abstract_inverted_index.patients, | 234 |
| abstract_inverted_index.therapies | 37 |
| abstract_inverted_index.Assessment | 95, 100, 116 |
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| abstract_inverted_index.autoimmune | 7 |
| abstract_inverted_index.covariates | 155 |
| abstract_inverted_index.developing | 76 |
| abstract_inverted_index.diagnosis, | 183 |
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| abstract_inverted_index.endpoints. | 59 |
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| abstract_inverted_index.development | 253 |
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| abstract_inverted_index.erythematosus | 3 |
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| abstract_inverted_index.azathioprine). | 221 |
| abstract_inverted_index.mycophenolate, | 218 |
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| corresponding_author_ids | https://openalex.org/A5071883134 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 15 |
| corresponding_institution_ids | https://openalex.org/I164928964 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.6899999976158142 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.82469476 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |