A Bioinformatic Algorithm based on Pulmonary Endoarterial Biopsy for Targeted Pulmonary Arterial Hypertension Therapy.

Background:Optimal pharmacological therapy for pulmonary arterial hypertension (PAH) remains unclear, as pathophysiological heterogeneity may affect therapeutic outcomes. A ranking methodology based on pulmonary vascular genetic expression analysis could assist in medication selection and potentially lead to improved prognosis. Objective:To describe a bioinformatics approach for ranking currently approved pulmonary arterial antihypertensive agents based on gene expression data derived from percutaneous endoarterial biopsies in an animal model of pulmonary hypertension. Methods:We created a chronic PAH model in Micro Yucatan female swine by surgical anastomosis of the left pulmonary artery to the descending aorta. A baseline catheterization, angiography and pulmonary endoarterial biopsy were performed. We obtained pulmonary vascular biopsy samples by passing a biopsy catheter through a long 8 French sheath, introduced via the carotid artery, into 2- to 3-mm peripheral pulmonary arteries. Serial procedures were performed on days 7, 21, 60, and 180 after surgical anastomosis. RNA microarray studies were performed on the biopsy samples. Results:Utilizing the medical literature, we developed a list of PAH therapeutic agents, along with a tabulation of genes affected by these agents. The effect on gene expression from pharmacogenomic interactions was used to rank PAH medications at each time point. The ranking process allowed the identification of a theoretical optimum three-medication regimen. Conclusion:We describe a new potential paradigm in the therapy for PAH, which would include endoarterial biopsy, molecular analysis and tailored pharmacological therapy for patients with PAH.