Profiling circulating metabolites for the diagnosis of pulmonary hypertension

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Pulmonary arterial hypertension (PAH) is a progressive and fatal disease. Initially, PAH develops asymptomatically or with mild symptoms that are often unrecognized by primary doctors. However, even after the onset of non-specific symptoms, such as dyspnea on exertion and fatigue, PAH remains unrecognized for ≥two years. On average, it takes a patient five visits to a general practitioner, and three cardiologist/pulmonologist visits before a patient is referred for the right ventricle (RV) catheterization, which confirms the diagnosis. The delay in PAH diagnosis remains the primary reason for a low efficiency of therapy. Therefore, despite significant advances in the field, which include a better understanding of PAH pathogenesis and targeted therapeutic approaches, the disease still carries a poor prognosis, especially for the patients with Functional Class III and IV. Thus, there is a significant need for novel diagnostic tools to shorten the time-to-diagnose interval and initiate therapy immediately after symptom onset. Indeed, numerous studies have shown a significantly better survival rate when the therapy is started early. PAH is known to alter the metabolic profile. Our research on the pre-clinical PAH model shows that alterations in metabolism occur at the stage when changes in pulmonary hemodynamics are mild, and no evidence of RV dysfunction is present. Our preliminary data on patients' plasma samples showed that the profiling of circulating metabolites could become an efficient tool for tracing PAH at early and developed stages. The specific panels of circulating metabolites, discovered and patented by Metfora LLC founders, efficiently distinguish PAH not only from healthy individuals, but also from individuals with left heart disease (LHD), diabetes mellitus (DM), and other chronic conditions. In this application, we propose to use the mass spectrometry (MS)-based platform for differential diagnosis of PAH, i.e., to distinguish PAH from diseases with similar symptoms. These include highly prevalent conditions such as chronic lung, kidney, and liver diseases, cancers, etc. Metfora has developed a novel blood-based diagnostic method that utilizes multiplexed metabolic panels and a Machine Learning/Deep Learning (ML/DL) model to enable earlier PAH diagnosis. Metfora’s targeted approach allows for the separation of healthy patients from those with chronic pulmonary conditions and further identifies the lung diseases with a precision of 90%-98%. During Phase I, we will optimize and validate these panels using larger patient cohorts, a wider spectrum of conditions, and broader technical approaches, such as Machine Learning and Deep Learning (ML/DL) algorithms. Upon completion of Phase I, we will have a rigorously optimized and validated platform that provides a differential diagnosis of PAH. If successful, our revolutionary diagnostic approach will shorten the time-to-diagnosis interval for PAH from two years to a few days, initiating background for the early treatments and increasing the quality of life and survival rate for PAH patients.