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Enabling Technologies for Field Monitoring of Phthalates

University of Miami Superfund Research Program

Plastic contaminants, specifically phthalates, commonly used as plasticizers, fall under the category of Superfund chemicals. Phthalates are recognized as endocrine disruptors, with documented adverse effects on human reproduction, development, and immune function.

The levels of various phthalates found in Superfund sites in South Florida, particularly at the Homestead Air Force Base, are alarmingly high. Superfund sites pose a significant risk of dispersing phthalates into the surrounding areas, thus presenting a serious health hazard to the communities residing near these sites. Consequently, there is an urgent need for technologies capable of detecting phthalates in environmental and biological samples. This is essential for preventing health risks, unraveling the mechanisms of phthalate toxicity, and determining their potential connection to observed health effects. Currently, various methods are employed for phthalate detection, including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), and various biosensing techniques. Although these methods are effective for laboratory-based analysis and offer high sensitivity in detection, they are not yet optimized for field analysis. They lack miniaturization and require extensive sample pretreatment. Consequently, there is a need for on-site monitoring technologies capable of rapid, selective, and sensitive detection of multiple types of phthalates without the extensive sample pretreatment presently necessary.

The overall goal of this work is the development of novel enabling technologies for field monitoring of phthalates based on the integration of : (1) Rapid design of selective peptide binders for phthalates; (2) Incorporation of unnatural amino acids on the peptide structure or modification of the peptides to yield self-reporting reagentless biosensing elements; (3) Use of magnetic ionic liquids as direct extractants of phthalates from the environmental samples; and (4) Portable, cost-effective biosensor construction in a paper electrochemical platform. To accomplish our goal, we will employ a bioinformatics platform for the in-silico design of the peptides aided by artificial intelligence (AI) that yields rapid identification of peptides that bind target phthalates with high binding affinity and can be used as biorecognition elements in the development of biosensors. The combined technology cuts the design time of peptides by ten-fold when compared to molecular docking alone. Peptide binders afford several advantages such as cost- effective and reproducible synthesis, stability against varying temperature and solvents, and small size yielding enhanced specificity. To design a portable biosensor platform that allows direct environmental sample analysis while avoiding any background phthalates contamination from plastic materials used for analysis, we chose electrochemical detection principles. Electrochemical biosensors can be constructed for analysis of various phthalates by immobilizing the peptide binders on carbon/metal electrodes screen printed on a plastic-free paper. Electrochemical biosensor design affords simplicity, selectivity, plastic-free analysis, and low cost. To achieve analysis on-site we opted for magnetic ionic liquid-based extraction methods allowing us to directly apply ionic liquids with extracted phthalates from environmental samples onto the electrode surface avoiding any background contamination. Integration of our peptide binder identification platform with electrochemical biosensors, and on-site extraction method offers an excellent solution to address current challenges in environmental phthalate detection.

Our goal will be achieved through the following Specific Aims

Specific Aim 1. Design of peptide binders for phthalates employing a bioinformatics and AI platform

Small peptides with high affinity binding to phthalates will be designed by employing computational modeling in tandem with AI, specifically machine learning. These peptides can be designed such that upon binding the target, the resulting conformational change in the peptide can form the basis for target detection.

Specific Aim 2. Peptide synthesis, physicochemical characterization, and development of biosensors for phthalates

We will synthesize the peptides and incorporate an electroactive moiety through unnatural amino acids or chemical conjugation. We will characterize the peptides identified in Aim 1 in terms of their physicochemical characteristics, and binding to target phthalates. The optimal peptides in terms of affinity and selectivity for the phthalate will be utilized in the construction of an electrochemical biosensor for target phthalates by deposition of the peptides on carbon/metal electrodes screen printed on a plastic-free paper.

Specific Aim 3. Validation of biosensors, analysis of environmental samples in the laboratory and on-site

Electrochemical biosensors will be optimized and validated using UPLC-MS/MS for detection in environmental and biological samples, specifically from Superfund contaminated sites.

This technology is anticipated to play a significant role in achieving the goals of UM-SRP Program, which is to investigate the impact of phthalates on the environment and human health. We will employ biosensors to assess samples provided by Projects 1,2, 4, and validate with the RSC. We will also provide our portable biosensors to CEC and RETCC and work with DMAC to manage data. We will work with the ART to disseminate the results.