Simulation of clinical trials of oral treprostinil in pulmonary arterial hypertension using a virtual population

• Published in: NPJ systems biology and applications Vol. 11; no. 1; pp. 9 - 11
• Main Authors: Stine, Andrew E., Parmar, Jignesh, Smith, Amy K., Cummins, Zachary, Pillalamarri, Narasimha Rao, Bender, R. Joseph
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 15.01.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/2248; 631/553/2710; Administration, Oral; Antihypertensive Agents - administration & dosage; Antihypertensive Agents - therapeutic use; Bioinformatics; Biology; Biomedical and Life Sciences; Calibration; Cell growth; Clinical trials; Clinical Trials as Topic; Combination therapy; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Computer Simulation; Disease; Drug development; Drug dosages; Epoprostenol - administration & dosage; Epoprostenol - analogs & derivatives; Epoprostenol - pharmacology; Epoprostenol - therapeutic use; Humans; Hypertension; Hypertension, Pulmonary - drug therapy; Kinases; Life Sciences; Lung diseases; Mathematical models; Nitric oxide; Patients; Pharmacology; Population studies; Pulmonary Arterial Hypertension - drug therapy; Pulmonary Arterial Hypertension - physiopathology; Pulmonary hypertension; Smooth muscle; Systems Biology; Trends; Variables; Vascular Resistance - drug effects
• ISSN: 2056-7189; 2056-7189
• DOI: 10.1038/s41540-024-00481-y

Challenges in drug development for rare diseases such as pulmonary arterial hypertension can be addressed through the use of mathematical modeling. In this study, a quantitative systems pharmacology model of pulmonary arterial hypertension pathophysiology and pharmacology was used to predict changes in pulmonary vascular resistance and six-minute walk distance in the context of oral treprostinil clinical studies. We generated a virtual population that spanned the range of clinical observations and then calibrated virtual patient-specific weights to match clinical trials. We then used this virtual population to predict the results of clinical trials on the basis of disease severity, dosing regimen, time since diagnosis, and co-administered background therapies. The virtual population captured the effect of changes in trial design and patient subpopulation on clinical response. We also demonstrated the virtual trial workflow’s potential for enriching populations based on clinical biomarkers to increase likelihood of trial success.