Dimensionless parameter predicts bacterial prodrug success
Abstract Understanding mechanisms of antibiotic failure is foundational to combating the growing threat of multidrug‐resistant bacteria. Prodrugs—which are converted into a pharmacologically active compound after administration—represent a growing class of therapeutics for treating bacterial infecti...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer Nature
2022-01-01
|
| Series: | Molecular Systems Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.15252/msb.202110495 |
| _version_ | 1827014832166010880 |
|---|---|
| author | Brandon Alexander Holt McKenzie Tuttle Yilin Xu Melanie Su Joachim J Røise Xioajian Wang Niren Murthy Gabriel A Kwong |
| author_facet | Brandon Alexander Holt McKenzie Tuttle Yilin Xu Melanie Su Joachim J Røise Xioajian Wang Niren Murthy Gabriel A Kwong |
| author_sort | Brandon Alexander Holt |
| collection | DOAJ |
| description | Abstract Understanding mechanisms of antibiotic failure is foundational to combating the growing threat of multidrug‐resistant bacteria. Prodrugs—which are converted into a pharmacologically active compound after administration—represent a growing class of therapeutics for treating bacterial infections but are understudied in the context of antibiotic failure. We hypothesize that strategies that rely on pathogen‐specific pathways for prodrug conversion are susceptible to competing rates of prodrug activation and bacterial replication, which could lead to treatment escape and failure. Here, we construct a mathematical model of prodrug kinetics to predict rate‐dependent conditions under which bacteria escape prodrug treatment. From this model, we derive a dimensionless parameter we call the Bacterial Advantage Heuristic (BAH) that predicts the transition between prodrug escape and successful treatment across a range of time scales (1–104 h), bacterial carrying capacities (5 × 104–105 CFU/µl), and Michaelis constants (KM = 0.747–7.47 mM). To verify these predictions in vitro, we use two models of bacteria‐prodrug competition: (i) an antimicrobial peptide hairpin that is enzymatically activated by bacterial surface proteases and (ii) a thiomaltose‐conjugated trimethoprim that is internalized by bacterial maltodextrin transporters and hydrolyzed by free thiols. We observe that prodrug failure occurs at BAH values above the same critical threshold predicted by the model. Furthermore, we demonstrate two examples of how failing prodrugs can be rescued by decreasing the BAH below the critical threshold via (i) substrate design and (ii) nutrient control. We envision such dimensionless parameters serving as supportive pharmacokinetic quantities that guide the design and administration of prodrug therapeutics. |
| first_indexed | 2024-03-07T17:59:41Z |
| format | Article |
| id | doaj.art-5d6712bdf18742c38cc6ff861bf63f43 |
| institution | Directory Open Access Journal |
| issn | 1744-4292 |
| language | English |
| last_indexed | 2025-02-18T14:14:15Z |
| publishDate | 2022-01-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj.art-5d6712bdf18742c38cc6ff861bf63f432024-10-28T09:18:51ZengSpringer NatureMolecular Systems Biology1744-42922022-01-0118111310.15252/msb.202110495Dimensionless parameter predicts bacterial prodrug successBrandon Alexander Holt0McKenzie Tuttle1Yilin Xu2Melanie Su3Joachim J Røise4Xioajian Wang5Niren Murthy6Gabriel A Kwong7Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech College of Engineering and Emory School of MedicineWallace H. Coulter Department of Biomedical Engineering, Georgia Tech College of Engineering and Emory School of MedicineWallace H. Coulter Department of Biomedical Engineering, Georgia Tech College of Engineering and Emory School of MedicineWallace H. Coulter Department of Biomedical Engineering, Georgia Tech College of Engineering and Emory School of MedicineDepartment of Bioengineering, Innovative Genomics Institute, University of CaliforniaInstitute of Advanced Synthesis, School of Chemistry and Molecular Engineering, Nanjing Tech UniversityDepartment of Bioengineering, Innovative Genomics Institute, University of CaliforniaWallace H. Coulter Department of Biomedical Engineering, Georgia Tech College of Engineering and Emory School of MedicineAbstract Understanding mechanisms of antibiotic failure is foundational to combating the growing threat of multidrug‐resistant bacteria. Prodrugs—which are converted into a pharmacologically active compound after administration—represent a growing class of therapeutics for treating bacterial infections but are understudied in the context of antibiotic failure. We hypothesize that strategies that rely on pathogen‐specific pathways for prodrug conversion are susceptible to competing rates of prodrug activation and bacterial replication, which could lead to treatment escape and failure. Here, we construct a mathematical model of prodrug kinetics to predict rate‐dependent conditions under which bacteria escape prodrug treatment. From this model, we derive a dimensionless parameter we call the Bacterial Advantage Heuristic (BAH) that predicts the transition between prodrug escape and successful treatment across a range of time scales (1–104 h), bacterial carrying capacities (5 × 104–105 CFU/µl), and Michaelis constants (KM = 0.747–7.47 mM). To verify these predictions in vitro, we use two models of bacteria‐prodrug competition: (i) an antimicrobial peptide hairpin that is enzymatically activated by bacterial surface proteases and (ii) a thiomaltose‐conjugated trimethoprim that is internalized by bacterial maltodextrin transporters and hydrolyzed by free thiols. We observe that prodrug failure occurs at BAH values above the same critical threshold predicted by the model. Furthermore, we demonstrate two examples of how failing prodrugs can be rescued by decreasing the BAH below the critical threshold via (i) substrate design and (ii) nutrient control. We envision such dimensionless parameters serving as supportive pharmacokinetic quantities that guide the design and administration of prodrug therapeutics.https://doi.org/10.15252/msb.202110495antibiotic failurebacteriaenzymesminimum inhibitory concentrationprodrugs |
| spellingShingle | Brandon Alexander Holt McKenzie Tuttle Yilin Xu Melanie Su Joachim J Røise Xioajian Wang Niren Murthy Gabriel A Kwong Dimensionless parameter predicts bacterial prodrug success Molecular Systems Biology antibiotic failure bacteria enzymes minimum inhibitory concentration prodrugs |
| title | Dimensionless parameter predicts bacterial prodrug success |
| title_full | Dimensionless parameter predicts bacterial prodrug success |
| title_fullStr | Dimensionless parameter predicts bacterial prodrug success |
| title_full_unstemmed | Dimensionless parameter predicts bacterial prodrug success |
| title_short | Dimensionless parameter predicts bacterial prodrug success |
| title_sort | dimensionless parameter predicts bacterial prodrug success |
| topic | antibiotic failure bacteria enzymes minimum inhibitory concentration prodrugs |
| url | https://doi.org/10.15252/msb.202110495 |
| work_keys_str_mv | AT brandonalexanderholt dimensionlessparameterpredictsbacterialprodrugsuccess AT mckenzietuttle dimensionlessparameterpredictsbacterialprodrugsuccess AT yilinxu dimensionlessparameterpredictsbacterialprodrugsuccess AT melaniesu dimensionlessparameterpredictsbacterialprodrugsuccess AT joachimjrøise dimensionlessparameterpredictsbacterialprodrugsuccess AT xioajianwang dimensionlessparameterpredictsbacterialprodrugsuccess AT nirenmurthy dimensionlessparameterpredictsbacterialprodrugsuccess AT gabrielakwong dimensionlessparameterpredictsbacterialprodrugsuccess |