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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., Competing Interests: Competing interests: A.E.S. is an employee of United Therapeutics Corporation. J.P. was an employee of United Therapeutics Corporation at the time this study was conducted. A.K.S. is an employee of United Therapeutics Corporation. Z.C. is an employee of United Therapeutics Corporation. N.R.P. is an employee of United Therapeutics Corporation. R.J.B. is an employee of United Therapeutics Corporation., (© 2025. The Author(s).)

Motivation: The urgent need for efficient and sustainable biological production of fuels and high-value chemicals has elicited a wave of in silico techniques for identifying promising novel pathways to these compounds in large putative metabolic networks. To date, these approaches have primarily used general graph search algorithms, which are prohibitively slow as putative metabolic networks may exceed 1 million compounds. To alleviate this limitation, we report two methods--SimIndex (SI) and SimZyme--which use chemical similarity of 2D chemical fingerprints to efficiently navigate large metabolic networks and propose enzymatic connections between the constituent nodes. We also report a Byers-Waterman type pathway search algorithm for further paring down pertinent networks., Results: Benchmarking tests run with SI show it can reduce the number of nodes visited in searching a putative network by 100-fold with a computational time improvement of up to 10(5)-fold. Subsequent Byers-Waterman search application further reduces the number of nodes searched by up to 100-fold, while SimZyme demonstrates ∼ 90% accuracy in matching query substrates with enzymes. Using these modules, we have designed and annotated an alternative to the methylerythritol phosphate pathway to produce isopentenyl pyrophosphate with more favorable thermodynamics than the native pathway. These algorithms will have a significant impact on our ability to use large metabolic networks that lack annotation of promiscuous reactions., Availability and Implementation: Python files will be available for download at http://tyolab.northwestern.edu/tools/., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)