Plasma p‐tau181 and nfl as surrogates biomarkers in clinical trials targeting preclinical stage of Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background The preclinical stage of Alzheimer’s disease (AD) is one of the main focuses of clinical trials. Plasma and positron emission tomography (PET) biomarkers have already been proposed to monitor participants’ disease progression in clinical trials targeting cognitively unimpaired (CU) individuals. Although longitudinal changes in plasma phosphorylated tau 181 (p‐tau181) and neurofilament light chain(NfL) correlate with AD progression, it is unknown whether these changes can be used to monitor drug effects in preventive clinical trials. Here, we aimed to evaluate the utility of using changes in plasma p‐tau181 and NfL as surrogate biomarkers for clinical trials targeting preclinical AD. Method We access 257 CU older individuals that had available Aβ‐PET at baseline, as well as the baseline, up to 24‐month plasma p‐tau181 and NfL measures from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We calculate the estimated sample size needed to test a 25% drug effect with 80% of power at a 0.05 level on reducing changes in plasma markers. Result We demonstrated that therapeutical clinical trials of 24‐month follow‐up using p‐tau181 and NfL would require 78% and 63% smaller sample sizes compared with a 12‐month follow‐up(Figure 1). The use of intermediate levels of Aβ(CL20‐40), rather than merely Aβ positivity, as an enrichment strategy reduced the sample size by 73% for p‐tau181 and 59% for NfL over 24 months. As demonstrated in Figure 2A the estimated cost of a clinical trial using only plasma biomarkers is lower than using neuroimaging biomarkers for surrogacy(Figure 2A). However, as showed by Figure 2B a trial including all Aβ positive individuals, the total estimated cost when considering surrogate biomarkers plus other related assessments are numerically higher using plasma than neuroimaging biomarkers; while trials including only individuals with intermediate Aβ levels, the cost was similar using plasma and neuroimaging biomarkers for surrogacy. Conclusion Our results suggest that to monitor large scale large‐scale population interventions in CU Aβ positive individuals’ plasma p‐tau181/ NfL could potentially be used. Furthermore, using a strategy that has been proposed in recent clinical trials, we demonstrated that the use of intermediate levels of Aβ is more cost‐effective than trials using Aβ‐positive individuals in clinical.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".