David Edward Korenchan

Portable low-field MRI is expanding access to imaging, but its diagnostic impact will depend in part on developing quantitative contrast mechanisms that remain informative at ultra-low magnetic field strengths. Magnetization transfer provides one such opportunity by probing interactions between free water and semisolid macromolecular pools, potentially adding molecular specificity to low-field MRI. Quantification of semisolid tissue parameters can further improve disease specificity but requires validated mathematical models applicable to ultra-low magnetic field strengths. Using custom semisolid samples that mimic in vivo brain tissue, we perform some of the first studies of quantitative magnetization transfer spectroscopy at an ultra-low field strength of 6.5 mT, combining saturation-based Z-spectroscopy methods with dynamic water-selective inversion-recovery and simultaneous model fitting to improve semisolid parameter isolation. Our results strongly suggest that the quantitative models developed for high-field magnetic resonance are also applicable at ultra-low field, laying the groundwork for molecularly informed quantitative imaging on future low-field portable MRI systems.

Medicine is evolving beyond therapy largely predicated on anatomical information and towards incorporating patient-specific molecular biomarkers of disease for more accurate diagnosis and effective treatment. The complementary combination of hyperpolarization by spin-lock induced crossing signal amplification by reversible exchange (SLIC SABRE) and low field magnetic resonance imaging (MRI) can enable accessible metabolic imaging to advance personalized medicine. Hyperpolarized ¹³C-enriched pyruvate has demonstrated promise for imaging metabolism in cancer, heart disease and neurodegenerative disorders; however, broader clinical adoption awaits validated clinical indications, and is further constrained by the cost and limited availability of current hyperpolarization technology. Parahydrogen-based polarization techniques, paired with low-cost high-performance MRI at millitesla fields, offer a means of broadening the reach of metabolic imaging. Here we show results demonstrating in situ hyperpolarization of pyruvate at 6.5 mT by SLIC SABRE, followed by immediate readout without field cycling or sample shuttling. We achieve ¹³C signal enhancements several million times above thermal equilibrium at 6.5 mT, corresponding to polarization levels of approximately 3%. Leveraging this enhancement, we perform ¹³C MRI and acquire NMR spectra with resolution sufficient to distinguish chemical shifts between pyruvate isotopomers. These results show a viable pathway towards accessible metabolic imaging with hyperpolarized ¹³C MRI at ultra-low field.

Nucleic acid‐based therapeutics, such as oncolytic virotherapy or gene therapy, would benefit greatly from a reporter gene that induces endogenous production of a protein biomarker to noninvasively track the delivery, persistence, and spread with imaging. Several chemical exchange saturation transfer (CEST) reporter proteins detectable by magnetic resonance imaging (MRI) have been demonstrated to have high sensitivity. However, to date none can provide strong CEST contrast at a distinct resonance from that of endogenous proteins, limiting their specificity. We investigated proteins and peptides containing tyrosine (Tyr), tryptophan (Trp), and lysine (Lys) residues that demonstrate CEST contrast shifted far downfield (4–10 ppm) from water. Although Tyr, Trp, and Lys exchangeable protons are typically not detectable under physiological conditions, those in our tested molecules are, having exchange rates of 400–2500 s⁻¹. The large chemical shift dispersion and rapid exchange rates are attributed to unique hydrogen bonding and cation‐π network interactions. These discoveries set the stage for designing a stable reporter protein with high detection specificity and sensitivity that can facilitate the in vivo monitoring of viral and gene therapies using MRI.

Magnesium (Mg²⁺) is the most abundant divalent cation in the cell and is essential to nearly every biochemical reaction involving adenosine triphosphate (ATP) and its lower energy counterpart, adenosine diphosphate (ADP). In this work, we examine the solution dynamics of ADP at different concentrations and record the changes thereof due to the presence of Mg²⁺ ions. Relaxation and diffusion experiments were performed on a range of ADP solutions with increasing magnesium concentration. The most significant changes of both relaxation and diffusion behaviors are observed when adding Mg²⁺ up to 0.5 ADP equivalent (eq), with most of the changes complete at 1 eq. Molecular dynamics simulations also show a significant structure introduced by Mg²⁺ with very stable pyramidal coordination with the phosphate oxygens. A more extended structure found in the presence of Mg²⁺ is consistent with the experimental slowing of diffusion and an increase in the spin-lattice relaxation rate. We do not observe direct evidence of aggregation in solution, although translational diffusion is slowed down significantly at higher concentrations (while solvent diffusion remains constant).

The development of peptides for therapeutic targets or biomarkers for disease diagnosis is a challenging task in protein engineering. Current approaches are tedious, often time-consuming and require complex laboratory data due to the vast search spaces that need to be considered. In silico methods can accelerate research and substantially reduce costs. Evolutionary algorithms are a promising approach for exploring large search spaces and can facilitate the discovery of new peptides. This study presents the development and use of a new variant of the genetic-programming-based POET algorithm, called POET_Regex, where individuals are represented by a list of regular expressions. This algorithm was trained on a small curated dataset and employed to generate new peptides improving the sensitivity of peptides in magnetic resonance imaging with chemical exchange saturation transfer (CEST). The resulting model achieves a performance gain of 20% over the initial POET models and is able to predict a candidate peptide with a 58% performance increase compared to the gold-standard peptide. By combining the power of genetic programming with the flexibility of regular expressions, new peptide targets were identified that improve the sensitivity of detection by CEST. This approach provides a promising research direction for the efficient identification of peptides with therapeutic or diagnostic potential.

Solid tumors such as prostate cancer (PCa) commonly develop an acidic microenvironment with pH 6.5-7.2, owing to heterogeneous perfusion, high metabolic activity, and rapid cell proliferation. In preclinical prostate cancer models, disease progression is associated with a decrease in tumor extracellular pH, suggesting that pH imaging may reflect an imaging biomarker to detect aggressive and high-risk disease. Therefore, we developed a hyperpolarized carbon-13 MRI method to image the tumor extracellular pH (pH_e) and prepared it for clinical translation for detection and risk stratification of PCa. This method relies on the rapid breakdown of hyperpolarized (HP) 1,2-glycerol carbonate (carbonyl-¹³C) via base-catalyzed hydrolysis to produce HP ¹³CO₃^2-, which is neutralized and converted to HP H¹³CO₃^-. After injection, HP H¹³CO₃^- equilibrates with HP ¹³CO₂ in vivo and enables the imaging of pH_e. Using insights gleaned from mechanistic studies performed in the hyperpolarized state, we solved issues of polarization loss during preparation in a clinical polarizer system. We successfully customized a reaction apparatus suitable for clinical application, developed clinical standard operating procedures, and validated the radiofrequency pulse sequence and imaging data acquisition with a wide range of animal models. The results demonstrated that we can routinely produce a highly polarized and safe HP H¹³CO₃^- contrast agent suitable for human injection. Preclinical imaging studies validated the reliability and accuracy of measuring acidification in healthy kidney and prostate tumor tissue. These methods were used to support an Investigational New Drug application to the U.S. Food and Drug Administration. This methodology is now ready to be implemented in human trials, with the ultimate goal of improving the management of PCa.

Background The development of peptides for therapeutic targets or biomarkers for disease diagnosis is a challenging task in protein engineering. Current approaches are tedious, often time-consuming and require complex laboratory data due to the vast search space. In silico methods can accelerate research and substantially reduce costs. Evolutionary algorithms are a promising approach for exploring large search spaces and facilitating the discovery of new peptides. Results This study presents the development and use of a variant of the initial POET algorithm, called POET_Regex, which is based on genetic programming, where individuals are represented by a list of regular expressions. The program was trained on a small curated dataset and employed to predict new peptides that can improve the problem of sensitivity in detecting peptides through magnetic resonance imaging using chemical exchange saturation transfer (CEST). The resulting model achieves a performance gain of 20% over the initial POET variant and is able to predict a candidate peptide with a 58% performance increase compared to the gold-standard peptide. Conclusions By combining the power of genetic programming with the flexibility of regular expressions, new potential peptide targets were identified to improve the sensitivity of detection by CEST. This approach provides a promising research direction for the efficient identification of peptides with therapeutic or diagnostic potential.

Here we develop a mechanism of protein optimization using a computational approach known as “genetic programming”. We developed an algorithm called Protein Optimization Engineering Tool (POET). Starting from a small library of literature values, the use of this tool allowed us to develop proteins that produce four times more MRI contrast than what was previously state-of-the-art. Interestingly, many of the peptides produced using POET were dramatically different with respect to their sequence and chemical environment than existing CEST producing peptides, and challenge prior understandings of how those peptides function. While existing algorithms for protein engineering rely on divergent evolution, POET relies on convergent evolution and consequently allows discovery of peptides with completely different sequences that perform the same function with as good or even better efficiency. Thus, this novel approach can be expanded beyond developing imaging agents and can be used widely in protein engineering.