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Abstract Hypertrophic cardiomyopathy (HCM) remains the most common cardiomyopathy in humans and cats with few preclinical pharmacologic interventional studies. Small-molecule sarcomere inhibitors are promising novel therapeutics for the management of obstructive HCM (oHCM) patients and have shown efficacy in left ventricular outflow tract obstruction (LVOTO) relief. The objective of this study was to explore the 6-, 24-, and 48-hour (h) pharmacodynamic effects of the cardiac myosin inhibitor, CK-586, in six purpose-bred cats with naturally occurring oHCM. A blinded, randomized, five-treatment group, crossover preclinical trial was conducted to assess the pharmacodynamic effects of CK-586 in this oHCM model. Dose assessments and select echocardiographic variables were assessed five times over a 48-h period. Treatment with oral CK-586 safely ameliorated LVOTO in oHCM cats. CK-586 treatment dose-dependently eliminated obstruction (reduced LVOTOmaxPG), increased measures of systolic chamber size (LVIDs Sx), and decreased select measures of heart function (LV FS% and LV EF%) in the absence of impact on heart rate. At all tested doses, a single oral CK-586 dose resulted in improved or resolved LVOTO with well-tolerated, dose-dependent, reductions in LV systolic function. The results from this study pave the way for the potential use of CK-586 in both the veterinary and human clinical setting.

Abstract Hypertrophic cardiomyopathy (HCM) is the most prevalent inherited cardiac disease in humans and cats and lacks efficacious pharmacologic interventions in the preclinical phase of disease. LV outflow tract obstruction (LVOTO) is commonly observed in HCM-affected patients and is a primary driver of heart failure symptoms and reduced quality of life. Novel small-molecule cardiac myosin inhibitors target actin-myosin interactions to alleviate overactive protein interactions. A prospective, randomized, controlled cross-over study was performed to evaluate pharmacodynamic effects of two doses (0.3 and 1 mg/kg) of a next-in-class cardiac myosin inhibitor, aficamten (CK-3773274, CK-274), on cardiac function in cats with the A31P MYBPC3 mutation and oHCM. Dose-dependent reductions in LV systolic function, LVOT pressure gradient, and isovolumetric relaxation times compared to baseline were observed. Promising beneficial effects of reduced systolic function warrant further studies of this next-in-class therapeutic to evaluate the benefit of long-term administration in this patient population.

Abstract G protein‐coupled receptors (GPCRs) are valuable therapeutic targets for many diseases. A central question of GPCR drug discovery is to understand what determines the agonism or antagonism of ligands that bind them. Ligands exert their action via the interactions in the ligand binding pocket. We hypothesized that there is a common set of receptor interactions made by ligands of diverse structures that mediate their action and that among a large dataset of different ligands, the functionally important interactions will be over‐represented. We computationally docked ~2700 known β2AR ligands to multiple β2AR structures, generating ca 75 000 docking poses and predicted all atomic interactions between the receptor and the ligand. We used machine learning (ML) techniques to identify specific interactions that correlate with the agonist or antagonist activity of these ligands. We demonstrate with the application of ML methods that it is possible to identify the key interactions associated with agonism or antagonism of ligands. The most representative interactions for agonist ligands involve K972.68×67, F194ECL2, S2035.42×43, S2045.43×44, S2075.46×641, H2966.58×58, and K3057.32×31. Meanwhile, the antagonist ligands made interactions with W2866.48×48 and Y3167.43×42, both residues considered to be important in GPCR activation. The interpretation of ML analysis in human understandable form allowed us to construct an exquisitely detailed structure‐activity relationship that identifies small changes to the ligands that invert their pharmacological activity and thus helps to guide the drug discovery process. This approach can be readily applied to any drug target.