Abstract
The use of sodium-glucose co-transporter 2 inhibitors to treat heart failure with preserved ejection fraction (HFpEF) is under investigation in ongoing clinical trials, but the exact mechanism of action is unclear. Here we aimed to use artificial intelligence (AI) to characterize the mechanism of action of empagliflozin in HFpEF at the molecular level. We retrieved information regarding HFpEF pathophysiological motifs and differentially expressed genes/proteins, together with empagliflozin target information and bioflags, from specialized publicly available databases. Artificial neural networks and deep learning AI were used to model the molecular effects of empagliflozin in HFpEF. The model predicted that empagliflozin could reverse 59% of the protein alterations found in HFpEF. The effects of empagliflozin in HFpEF appeared to be predominantly mediated by inhibition of NHE1 (Na+/H+ exchanger 1), with SGLT2 playing a less prominent role. The elucidated molecular mechanism of action had an accuracy of 94%. Empagliflozin’s pharmacological action mainly affected cardiomyocyte oxidative stress modulation, and greatly influenced cardiomyocyte stiffness, myocardial extracellular matrix remodelling, heart concentric hypertrophy, and systemic inflammation. Validation of these in silico data was performed in vivo in patients with HFpEF by measuring the declining plasma concentrations of NOS2, the NLPR3 inflammasome, and TGF-β1 during 12 months of empagliflozin treatment. Using AI modelling, we identified that the main effect of empagliflozin in HFpEF treatment is exerted via NHE1 and is focused on cardiomyocyte oxidative stress modulation. These results support the potential use of empagliflozin in HFpEF.
Highlights
The use of sodium-glucose co-transporter 2 inhibitors to treat heart failure with preserved ejection fraction (HFpEF) is under investigation in ongoing clinical trials, but the exact mechanism of action is unclear
Artificial neural networks (ANNs)-based prediction revealed that the activity of empagliflozin in HFpEF would primarily occur through inhibition of NHE1 (Na+/H+ exchanger 1), with less prominent roles played by the other targets (SGLT2 and NHE3) (Table 1)
We used artificial intelligence (AI) modelling to examine the effects of empagliflozin in HFpEF
Summary
The use of sodium-glucose co-transporter 2 inhibitors to treat heart failure with preserved ejection fraction (HFpEF) is under investigation in ongoing clinical trials, but the exact mechanism of action is unclear. Empagliflozin’s pharmacological action mainly affected cardiomyocyte oxidative stress modulation, and greatly influenced cardiomyocyte stiffness, myocardial extracellular matrix remodelling, heart concentric hypertrophy, and systemic inflammation Validation of these in silico data was performed in vivo in patients with HFpEF by measuring the declining plasma concentrations of NOS2, the NLPR3 inflammasome, and TGF-β1 during 12 months of empagliflozin treatment. Current therapy for HF with reduced ejection fraction (HFrEF) is based on modulation of the neurohormonal activation, i.e. inhibiting deleterious activation of the renin–angiotensin–aldosterone and sympathetic nervous systems and amplifying the protective natriuretic peptide system Such strategies have not been so successful in several landmark trials in HFpEF1–3. Sodium-glucose co-transporter 2 inhibitors (SGLT2i) are a new class of drugs under evaluation for use in HF They have yielded better than anticipated clinical benefit in HFrEF, with and without diabetes, in two expedited landmark clinical trials—namely, the DAPA-HF with d apagliflozin[4] and Emperor-Reduced with e mpagliflozin[5]. Recent evidence suggests that the benefit of SGLT2i in HFrEF may be mediated by the sodium-hydrogen exchanger (NHE)[8,9]
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