Revealing the Timeline of Structural MRI Changes in Premanifest to Manifest Huntington Disease

Background and ObjectivesLongitudinal measurements of brain atrophy using structural MRI (sMRI) can provide powerful markers for tracking disease progression in neurodegenerative diseases. In this study, we use a disease progression model to learn individual-level disease times and hence reveal a new timeline of sMRI changes in Huntington disease (HD).MethodsWe use data from the 2 largest cohort imaging studies in HD—284 participants from TRACK-HD (100 control, 104 premanifest, and 80 manifest) and 159 participants from PREDICT-HD (36 control and 128 premanifest)—to train and test the model. We longitudinally register T1-weighted sMRI scans from 3 consecutive time points to reduce intraindividual variability and calculate regional brain volumes using an automated segmentation tool with rigorous manual quality control.ResultsOur model reveals, for the first time, the relative magnitude and timescale of subcortical and cortical atrophy changes in HD. We find that the largest (∼20% average change in magnitude) and earliest (∼2 years before average abnormality) changes occur in the subcortex (pallidum, putamen, and caudate), followed by a cascade of changes across other subcortical and cortical regions over a period of ∼11 years. We also show that sMRI, when combined with our disease progression model, provides improved prediction of onset over the current best method (root mean square error = 4.5 years and maximum error = 7.9 years vs root mean square error = 6.6 years and maximum error = 18.2 years).DiscussionOur findings support the use of disease progression modeling to reveal new information from sMRI, which can potentially inform imaging marker selection for clinical trials.

PK/PD Modeling of the PDE7 Inhibitor—GRMS-55 in a Mouse Model of Autoimmune Hepatitis

This study aimed to assess the efficacy and explore the mechanisms of action of a potent phosphodiesterase (PDE)7A and a moderate PDE4B inhibitor GRMS-55 in a mouse model of autoimmune hepatitis (AIH). The concentrations of GRMS-55 and relevant biomarkers were measured in the serum of BALB/c mice with concanavalin A (ConA)-induced hepatitis administered with GRMS-55 at two dose levels. A semi-mechanistic PK/PD/disease progression model describing the time courses of measured biomarkers was developed. The emetogenicity as a potential side effect of the studied compound was evaluated in the α2-adrenoceptor agonist-induced anesthesia model. The results indicate that liver damage observed in mice challenged with ConA was mainly mediated by TNF-α and IFN-γ. GRMS-55 decreased the levels of pro-inflammatory mediators and the transaminase activities in the serum of mice with AIH. The anti-inflammatory properties of GRMS-55, resulting mainly from PDE7A inhibition, led to a high hepatoprotective activity in mice with AIH, which was mediated by an inhibition of pro-inflammatory signaling. GRMS-55 did not induce the emetic-like behavior. The developed PK/PD/disease progression model may be used in future studies to assess the potency and explore the mechanisms of action of new investigational compounds for the treatment of AIH.

Cerebrospinal Fluid Neurofilament Light Chain (NfL) Predicts Disease Aggressiveness in Amyotrophic Lateral Sclerosis: An Application of the D50 Disease Progression Model

Amyotrophic lateral sclerosis (ALS) is a relentlessly progressive neurodegenerative disorder. As previous therapeutic trials in ALS have been severely hampered by patients’ heterogeneity, the identification of biomarkers that reliably reflect disease progression represents a priority in ALS research. Here, we used the D50 disease progression model to investigate correlations between cerebrospinal fluid (CSF) neurofilament light chain (NfL) levels and disease aggressiveness. The D50 model quantifies individual disease trajectories for each ALS patient. The value D50 provides a unified measure of a patient’s overall disease aggressiveness (defined as time taken in months to lose 50% of functionality). The relative D50 (rD50) reflects the individual disease covered and can be calculated for any time point in the disease course. We analyzed clinical data from a well-defined cohort of 156 patients with ALS. The concentration of NfL in CSF samples was measured at two different laboratories using the same procedure. Based on patients’ individual D50 values, we defined subgroups with high (<20), intermediate (20–40), or low (>40) disease aggressiveness. NfL levels were compared between these subgroups via analysis of covariance, using an array of confounding factors: age, gender, clinical phenotype, frontotemporal dementia, rD50-derived disease phase, and analyzing laboratory. We found highly significant differences in NfL concentrations between all three D50 subgroups (p < 0.001), representing an increase of NfL levels with increasing disease aggressiveness. The conducted analysis of covariance showed that this correlation was independent of gender, disease phenotype, and phase; however, age, analyzing laboratory, and dementia significantly influenced NfL concentration. We could show that CSF NfL is independent of patients’ disease covered at the time of sampling. The present study provides strong evidence for the potential of NfL to reflect disease aggressiveness in ALS and in addition proofed to remain at stable levels throughout the disease course. Implementation of CSF NfL as a potential read-out for future therapeutic trials in ALS is currently constrained by its demonstrated susceptibility to (pre-)analytical variations. Here we show that the D50 model enables the discovery of correlations between clinical characteristics and CSF analytes and can be recommended for future studies evaluating potential biomarkers.