In Preparation for Understanding NASH 700 Topline Results

The study results that Recknor will report will use MRI biomarkers PDFF and cT1 to assess Leronlimab's efficacy in reducing liver fat and fibrosis. Typically, liver biopsy is used and a grading system of the specimen was used to score the level of NASH and fibrosis. This biopsy number was NAS and it ranged from 0 to 8 where 0 was the resolution of NASH and 5 and above was NASH. 1-4 were NAFLD. NASH 700 trial did not use NAS scoring, but used cT1 and PDFF. I read the Frontiers article to try to come to grips with the numbers. I pasted some pertinent points.

I feel the following is pertinent to the 350mg trial:

The coefficient of NAS in the linear regression model suggests that a 2-unit increase in NAS score, has a significant increase in cT1 of 88ms. By way of illustration, an 88ms change in cT1 for a patient moving from NAS 5 to NAS 3 would be equivalent to a drop from 921 to 833 ms.

Predicted cT1 using the effect estimates of the resultant model indicated an average 44-ms difference in cT1 between two stages of NAS when adjusted for PDFF. (if this were implemented on our 350mg trial, Leronlimab would be capable of moving a patient through 4 stages of NAS, say from an NAS of 7 to an NAS of 3.)

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https://tpis.upmc.com/changebody.cfm?url=/tpis/schema/NAFLD2006.jsp

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Scoring interpretation: Total NAS score represents the sum of scores for steatosis, lobular inflammation, and ballooning, and ranges from 0-8. Diagnosis of NASH (or, alternatively, fatty liver not diagnostic of NASH) should be made first, then NAS is used to grade activity. In the reference study, NAS scores of 0-2 occurred in cases largely considered not diagnostic of NASH, scores of 3-4 were evenly divided among those considered not diagnostic, borderline, or positive for NASH. Scores of 5-8 occurred in cases that were largely considered diagnostic of NASH

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https://www.frontiersin.org/articles/10.3389/fendo.2020.575843/full

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Late stage clinical trials in non-alcoholic steatohepatitis (NASH) are currently required by the FDA to use liver biopsy as a primary endpoint.

MRI-derived biomarkers proton density fat fraction (PDFF) and iron-corrected T1 mapping (cT1) are gaining traction as emerging alternatives to biopsy for NASH.

The correlations between cT1 and PDFF with the histopathological hallmarks of NASH demonstrate the potential utility of both cT1 and PDFF as non-invasive biomarkers to detect a pharmacodynamic change in NASH, with cT1 showing superiority for detecting changes in inflammation and fibrosis, rather than liver fat alone.

NASH results when fat accumulation in the liver triggers inflammatory signals and reactive oxygen species that can amplify liver injury and stimulate fibrosis

PDFF is a more robust method of measuring liver fat than histology (7, 15), has been shown to be a repeatable and reproducible metric (16–18) that is sensitive to small changes (15), and as such it is considered to be the most superior non-invasive test for liver fat (19).

cT1 mapping is an indicator of regional tissue water content. Each of the key histopathological features of NASH is known to have an influence on the cT1 signal when measured using the T1 “shMOLLI” sequence (20–22); as such, is it has been reported to correlate with ballooning (23, 24), fibrosis (22, 23, 25, 26), and NAS (24), and has also been shown to predict clinical outcomes (27). Liver cT1 has also been shown to be significantly elevated in patients with clinically significant portal hypertension with low liver fat (28)

evidence of steatosis (PDFF ≥ 5%) or fibro-inflammation (from one of LSM ≥ 7.0 kPa, evidence of fibrosis on MRE, elevated cT1 ≥ 780ms)

The resulting partial correlation between both cT1 and PDFF with ballooning indicated that the correlation with cT1 remained (rs = 0.36, P <.001) but the correlation with PDFF was weaker (rs = 0.21, P = 0.03). After correction for steatosis, the correlation with inflammation remained significant for cT1 (rs = 0.17, P <.05) but the correlation with PDFF was no longer significant (rs = 0.13, P = 0.07). Both remained significantly correlated with NAS, although the correlation with PDFF was weaker than for cT1 (cT1: rs = 0.36, P <.001; PDFF: rs = 0.20, P <.001, respectively). Fibrosis remained moderately correlated with cT1 (rs = 0.33; P <.001).

The coefficient of NAS in the linear regression model suggests that a 2-unit increase in NAS score, has a significant increase in cT1 of 88ms. By way of illustration, an 88ms change in cT1 for a patient moving from NAS 5 to NAS 3 would be equivalent to a drop from 921 to 833 ms.

Predicted cT1 using the effect estimates of the resultant model indicated an average 44-ms difference in cT1 between two stages of NAS when adjusted for PDFF. (if this were implemented on our 350mg trial, Leronlimab would be capable of moving a patient through 4 stages of NAS, say from an NAS of 7 to an NAS of 3.)

a 1 unit increase in ballooning has a significant increase in cT1 of 81 ms. This remained significant, but the estimated coefficient was reduced to 44 ms if the model was adjusted for PDFF.

The coefficient of NAS in the linear regression model suggests that a 2-unit increase in NAS score has a significant relative increase in PDFF of 21.1%. By way of illustration, a 21.1% relative change in PDFF for a patient moving from NAS 5 to NAS 3 would be equivalent to a drop from 15.6% to 12.4%.

The coefficient of ballooning in the linear regression model suggests that a 1 unit increase in ballooning score, has a significant relative increase in PDFF of 16%.

both the correlation analysis and the literature suggests PDFF (15) and cT1 (21) are both sensitive to modulation of liver fat.

cT1 may offer an advantage over PDFF as an endpoint in NASH clinical trials due to the fact that it is also independently associated with inflammation and fibrosis. These are often the features of greatest interest to the physician and healthcare communities because they correlate the most to clinical outcomes, thus are driving research into emerging pharmacotherapies regarding these specific mechanisms of action [e.g., Farnesoid X receptor (FXR) agonists, FG19, and FG21 analogs, THRb and PPARδ agonists]. Thus, combining the information from both PDFF and cT1 is likely to be superior to either on their own for understanding the treatment response dynamics, particularly when interested in more than the movement of fat from the liver.

A cT1 difference of 88 or 81 ms is related to a two-point change in NAS and a one-point decrease in ballooning, respectively. Similarly, a relative difference of 21% in PDFF is related to a two-point change in NAS, and a relative difference of >16% to a one-point change in ballooning. As PDFF is largely dominated by steatosis, cT1 shows superiority when the focus is on changes in inflammation and/or fibrosis, and thus using both in combination may provide more granularity for distinguishing specific treatment effects.