TY - JOUR
T1 - A Precision Medicine Approach to Treating Alzheimer's Disease Using Rosiglitazone Therapy
T2 - A Biomarker Analysis of the REFLECT Trials
AU - O'Bryant, Sid E.
AU - Zhang, Fan
AU - Petersen, Melissa
AU - Johnson, Leigh
AU - Hall, James
AU - Rissman, Robert A.
N1 - Publisher Copyright:
© 2021-The authors. Published by IOS Press.
PY - 2021
Y1 - 2021
N2 - Background: The REFLECT trials were conducted to examine the treatment of mild-to-moderate Alzheimer's disease utilizing a peroxisome proliferator-activated receptor gamma agonist. Objective: To generate a predictive biomarker indicative of positive treatment response using samples from the previously conducted REFLECT trials. Methods: Data were analyzed on 360 participants spanning multiple negative REFLECT trials, which included treatment with rosiglitazone and rosiglitazone XR. Support vector machine analyses were conducted to generate a predictive biomarker profile. Results: A pre-defined 6-protein predictive biomarker (IL6, IL10, CRP, TNF, FABP-3, and PPY) correctly classified treatment response with 100%accuracy across study arms for REFLECT Phase II trial (AVA100193) and multiple Phase III trials (AVA105640, AV102672, and AVA102670). When the data was combined across all rosiglitazone trial arms, a global RSG-predictive biomarker with the same 6-protein predictive biomarker was able to accurately classify 98%of treatment responders. Conclusion: A predictive biomarker comprising of metabolic and inflammatory markers was highly accurate in identifying those patients most likely to experience positive treatment response across the REFLECT trials. This study provides additional proof-of-concept that a predictive biomarker can be utilized to help with screening and predicting treatment response, which holds tremendous benefit for clinical trials.
AB - Background: The REFLECT trials were conducted to examine the treatment of mild-to-moderate Alzheimer's disease utilizing a peroxisome proliferator-activated receptor gamma agonist. Objective: To generate a predictive biomarker indicative of positive treatment response using samples from the previously conducted REFLECT trials. Methods: Data were analyzed on 360 participants spanning multiple negative REFLECT trials, which included treatment with rosiglitazone and rosiglitazone XR. Support vector machine analyses were conducted to generate a predictive biomarker profile. Results: A pre-defined 6-protein predictive biomarker (IL6, IL10, CRP, TNF, FABP-3, and PPY) correctly classified treatment response with 100%accuracy across study arms for REFLECT Phase II trial (AVA100193) and multiple Phase III trials (AVA105640, AV102672, and AVA102670). When the data was combined across all rosiglitazone trial arms, a global RSG-predictive biomarker with the same 6-protein predictive biomarker was able to accurately classify 98%of treatment responders. Conclusion: A predictive biomarker comprising of metabolic and inflammatory markers was highly accurate in identifying those patients most likely to experience positive treatment response across the REFLECT trials. This study provides additional proof-of-concept that a predictive biomarker can be utilized to help with screening and predicting treatment response, which holds tremendous benefit for clinical trials.
KW - Alzheimer's disease
KW - clinical trial
KW - predictive biomarker
KW - rosiglitazone
UR - http://www.scopus.com/inward/record.url?scp=85106921378&partnerID=8YFLogxK
U2 - 10.3233/jad-201610
DO - 10.3233/jad-201610
M3 - Article
C2 - 33814447
AN - SCOPUS:85106921378
SN - 1387-2877
VL - 81
SP - 557
EP - 568
JO - Journal of Alzheimer's Disease
JF - Journal of Alzheimer's Disease
IS - 2
ER -