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.