RNAi inhibition of angiopoietin-like protein 3 (ANGPTL3) with ARO-ANG3 mimics the lipid and lipoprotein profile of familial combined hypolipidemia

Background

Elevated LDL-C and triglyceride rich lipoproteins (TRLs) are independent risk factors for cardiovascular disease (CVD). Genetic deficiency of angiopoietin-like protein 3 (ANGPTL3) is associated with reduced circulating levels of LDL-C, triglycerides (TGs), VLDL-C, HDL-C and reduced CVD risk, with no described adverse phenotype. ARO-ANG3 is a RNA interference drug designed to silence expression of ANGPTL3. Single doses of ARO-ANG3 have been shown to reduce ANGPTL3, TGs, VLDL-C and LDL-C in healthy volunteers (HVs, AHA 2019). We report the effects of multiple doses of ARO-ANG3 in HVs with a focus on the duration of action.

Methods

ARO-ANG3 was administered subcutaneously to HVs on days 1 and 29 at doses of 100, 200 or 300 mg (n=4 per group). Measured parameters included ANGPTL3, LDL-C, TGs, VLDL-C and HDL-C. Follow up is ongoing.

Results

All HVs have received both doses and follow-up is currently through week 16 (12 weeks after second dose). Mean nadir for ANGPTL3 levels occurred 2 weeks after the second dose (−83–93%) with minimal change for 200 and 300 mg but 16% recovery for 100 mg at week 16. Mean TGs and VLDL-C reached nadir earlier (3 wks, −61–65%) without apparent dose response and minimal change for any dose at wk 16. LDL-C nadir occurred 4–6 wks after the second dose (−45–54%), again with minimal evidence for dose response or change through wk 16. HDL-C was reduced 14–37% at wk 16. ARO-ANG3 was well tolerated without serious or severe adverse events or dropouts related to drug. The most common adverse events have been headache and upper respiratory infections.

Conclusions

Genetic deficiency of ANGPTL3 is a cause of familial combined hypolipemia and is associated with a decreased risk of CVD. Using RNAi to selectively suppress ANGPTL3 production reproduces these genetic effects with a duration of at least 12 weeks following a second dose and with good tolerability over 16 wks. ANGPTL3 inhibition results in lowering of LDL-C and TRLs which may confer protection against CVD in patients with atherogenic mixed dyslipidemia.

Funding Acknowledgement

Type of funding source: Private company. Main funding source(s): Arrowhead Pharmaceuticals

P1926Artificial intelligence in echocardiography for standard clinical metrics

Introduction

A full transthoracic echocardiogram (TTE) study usually takes 40 - 60 mins to perform and report. Our aim was to validate an artificial intelligence (AI) which automatically calculates measurements with manual standard clinical metrics.

Methods

41 patients with heart failure (HF) and 19 controls were enrolled retrospectively. A shortened 5-minute TTE exam was performed. Studies were exported from the hospital database in a DICOM format and fed to an AI pipeline to classify, segment and analyse each image. A convolutional neural network (CNN) was used to label each view into one of 23 classes. Views of interest (Apical 2-, 4-Chamber and Parasternal Short/Long Axis) were individually segmented using a segmentation CNN. View classification was trained on 4,000 labelled studies, segmentation models were trained for each view with 72 manually segmented images for PSAX, 128 for PLAX, 168 for A4C and 198 for A2C. The area-length formula was used to calculate left-ventricular volumes (LVEDV/LVESV), ejection fraction (AI-LVEF). Indexed LA volume (LAVOLI) LV mass (LVMI) were also compared. LVEDV, LVESV, LVEF and LVMI were averaged over multiple videos.

Results

Mean manual LVEF (M-LVEF) in HF patients was 39±10% vs 57±5% in controls. Compute time using was between 4 to 7 mins for classification, segmentation and analysis using a single Graphics Processing Unit (GPU). 11 (18%) non-physiological AI-ESV and associated AI-LVEF were excluded vs 2 (3%) M-LVEF (×2 7 95% CI 3 to 27%, p=0.008). AI generated measurements correlated well with manual measures LVEDV r=0.77, LVESV r=0.8, LVEF r=0.71, LAVOLI r=0.71, LVMI r=0.6, p<0.005. Mean absolute error of M-LVEF vs AI-LVEF was 7.4±6.6%. AI-LVEF, M-LVEF and other HF biomarkers had a similar discrimination for HF (AUC M-LVEF 0.93 vs AI-LVEF 0.88, 95% CI-0.03 to 0.15, p=0.19).

AI vs Manual, Correlation Matrix and ROC

Conclusion

AI with minimal human input is approaching the accuracy required for clinical utility. AI has the ability to distinguish LV systolic dysfunction, and chamber volumes which could be applied to handheld ultrasound in real-time.

Acknowledgement/Funding

Health Research Council of New Zealand, Auckland Bioengineering Institute