Preclinical <i>in vivo</i> longitudinal assessment of KG207-M as a disease-modifying Alzheimer’s disease therapeutic
Bibliographic record
Abstract
<i>In vivo</i> biomarker abnormalities provide measures to monitor therapeutic interventions targeting amyloid-β pathology as well as its effects on downstream processes associated with Alzheimer’s disease pathophysiology. Here, we applied an <i>in vivo</i> longitudinal study design combined with imaging and cerebrospinal fluid biomarkers, mirroring those used in human clinical trials to assess the efficacy of a novel brain-penetrating anti-amyloid fusion protein treatment in the McGill-R-Thy1-APP transgenic rat model. The bi-functional fusion protein consisted of a blood-brain barrier crossing single domain antibody (FC5) fused to an amyloid-β oligomer-binding peptide (ABP) via Fc fragment of mouse IgG (FC5-mFc2a-ABP). A five-week treatment with FC5-mFc2a-ABP (loading dose of 30 mg/Kg/iv followed by 15 mg/Kg/week/iv for four weeks) substantially reduced brain amyloid-β levels as measured by positron emission tomography and increased the cerebrospinal fluid amyloid-β<sub>42/40</sub> ratio. In addition, the 5-week treatment rectified the cerebrospinal fluid neurofilament light chain concentrations, resting-state functional connectivity, and hippocampal atrophy measured using magnetic resonance imaging. Finally, FC5-mFc2a-ABP (referred to as KG207-M) treatment did not induce amyloid-related imaging abnormalities such as microhemorrhage. Together, this study demonstrates the translational values of the designed preclinical studies for the assessment of novel therapies based on the clinical biomarkers providing tangible metrics for designing early-stage clinical trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.082 | 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 teacher head, 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".