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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).)

Enzyme substrate promiscuity has significant implications for metabolic engineering. The ability to predict the space of possible enzymatic side reactions is crucial for elucidating underground metabolic networks in microorganisms, as well as harnessing novel biosynthetic capabilities of enzymes to produce desired chemicals. Reaction rule-based cheminformatics platforms have been implemented to computationally enumerate possible promiscuous reactions, relying on existing knowledge of enzymatic transformations to inform novel reactions. However, past versions of curated reaction rules have been limited by a lack of comprehensiveness in representing all possible transformations, as well as the need to prune rules to enhance computational efficiency in pathway expansion. To this end, we curated a set of 1224 most generalized reaction rules, automatically abstracted from atom-mapped MetaCyc reactions and verified to uniquely cover all common enzymatic transformations. We developed a framework to systematically identify and correct redundancies and errors in the curation process, resulting in a minimal, yet comprehensive, rule set. These reaction rules were capable of reproducing more than 85% of all reactions in the KEGG and BRENDA databases, for which a large fraction of reactions is not present in MetaCyc. Our rules exceed all previously published rule sets for which reproduction was possible in this coverage analysis, which allows for the exploration of a larger space of known enzymatic transformations. By leveraging the entire knowledge of possible metabolic reactions through generalized enzymatic reaction rules, we are able to better utilize underground metabolic pathways and accelerate novel biosynthetic pathway design to enable bioproduction towards a wider range of new molecules., (Copyright © 2021 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

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.)

Biofilms are found within the lungs of patients with chronic pulmonary infections, in particular patients with cystic fibrosis, and are the major cause of morbidity and mortality for these patients. The work presented here is part of a large interdisciplinary effort to develop an effective drug delivery system and treatment strategy to kill biofilms growing in the lung. The treatment strategy exploits silver-based antimicrobials, in particular, silver carbene complexes (SCC). This manuscript presents a mathematical model describing the growth of a biofilm and predicts the response of a biofilm to several basic treatment strategies. The continuum model is composed of a set of reaction-diffusion equations for the transport of soluble components (nutrient and antimicrobial), coupled to a set of reaction-advection equations for the particulate components (living, inert, and persister bacteria, extracellular polymeric substance, and void). We explore the efficacy of delivering SCC both in an aqueous solution and in biodegradable polymer nanoparticles. Minimum bactericidal concentration (MBC) levels of antimicrobial in both free and nanoparticle-encapsulated forms are estimated. Antimicrobial treatment demonstrates a biphasic killing phenomenon, where the active bacterial population is killed quickly followed by a slower killing rate, which indicates the presence of a persister population. Finally, our results suggest that a biofilm with a ready supply of nutrient throughout its depth has fewer persister bacteria and hence may be easier to treat than one with less nutrient., (Copyright © 2013 Elsevier Inc. All rights reserved.)