Modeling and analysis of an opioid detoxification system for cell-free metabolic engineering

Abstract

Opioids are a class of drugs highly valued for their potent analgesic properties; however, their misuse can lead to addiction, overdose incidents, and death. Although the opioid antagonist naloxone has been used in emergency medicine for over 50 years to reverse the effects of an overdose, access to this life-saving antidote is still limited due to its high cost and restricted availability. To address this issue, we designed a novel biosynthetic pathway as an alternative method for naloxone production. In addition, we formulated a mathematical model to simulate the expression of morphine dehydrogenase, the first enzyme of the proposed pathway. Due to its viability as a point-of-care (POC) solution, we used cell-free protein synthesis (CFPS) as our platform for protein production. However, to make CFPS a mainstream technology for POC manufacturing, the performance of these systems must be optimized. Toward this need, constraint-based approaches have become important for model-driven research. A key issue with such models is the existence of alternate optimal solutions which can result in high uncertainties in metabolic flux estimates. Therefore, in this study, we integrated kinetic parameters, enzyme levels and metabolite data as model constraints to generate accurate flux estimations. Since energy efficiency of CFPS is highly dependent on oxidative phosphorylation activity, we studied the effect of two different inhibitors of oxidative phosphorylation on cell-free metabolism. First, we tested the consistency of flux balance analysis (FBA) simulations with experimental measurements. Next, we used minimization of metabolic adjustment (MOMA) as an alternative to FBA, which removed the assumption of optimality of a specific biological objective. MOMA accurately predicted the overall production of mRNA and protein along with changes in metabolic behavior in the presence of the inhibitors. This modeling approach can be extended to predict the effect of various pathway enzymes involved in the synthesis of naloxone. In addition, it can be used to identify possible negative effectors to improve CFPS yields. Taken together, we have developed an alternate strategy for the production of naloxone and successfully validated MOMA for model guided design and optimization of the proposed platform. Finally, MOMA can be used to engineer strains with improved CFPS performance, thus extending the scope of its application to cell-free metabolic engineering.