Discovery and Characterization of VU0542270, the First Selective Inhibitor of Vascular Kir6.1/SUR2B KATP Channels.

Vascular smooth muscle KATP channels critically regulate blood flow and blood pressure by modulating vascular tone and therefore represent attractive drug targets for treating several cardiovascular disorders. However, the lack of potent inhibitors that can selectively inhibit Kir6.1/SUR2B (vascular KATP) over Kir6.2/SUR1 (pancreatic KATP) has eluded discovery despite decades of intensive research. We therefore screened 47,872 chemically diverse compounds for novel inhibitors of heterologously expressed Kir6.1/SUR2B channels. The most potent inhibitor identified in the screen was an N-aryl-N'-benzyl urea compound termed VU0542270. VU0542270 inhibits Kir6.1/SUR2B with an IC50 of approximately 100 nM but has no apparent activity toward Kir6.2/SUR1 or several other members of the Kir channel family at doses up to 30 µM (>300-fold selectivity). By expressing different combinations of Kir6.1 or Kir6.2 with SUR1, SUR2A, or SUR2B, the VU0542270 binding site was localized to SUR2. Initial structure-activity relationship exploration around VU0542270 revealed basic texture related to structural elements that are required for Kir6.1/SUR2B inhibition. Analysis of the pharmacokinetic properties of VU0542270 showed that it has a short in vivo half-life due to extensive metabolism. In pressure myography experiments on isolated mouse ductus arteriosus vessels, VU0542270 induced ductus arteriosus constriction in a dose-dependent manner similar to that of the nonspecific KATP channel inhibitor glibenclamide. The discovery of VU0542270 provides conceptual proof that SUR2-specific KATP channel inhibitors can be developed using a molecular target-based approach and offers hope for developing cardiovascular therapeutics targeting Kir6.1/SUR2B. SIGNIFICANCE STATEMENT: Small-molecule inhibitors of vascular smooth muscle KATP channels might represent novel therapeutics for patent ductus arteriosus, migraine headache, and sepsis; however, the lack of selective channel inhibitors has slowed progress in these therapeutic areas. Here, this study describes the discovery and characterization of the first vascular-specific KATP channel inhibitor, VU0542270.

Redefining Health Care Data Interoperability: Empirical Exploration of Large Language Models in Information Exchange.

BACKGROUND: Efficient data exchange and health care interoperability are impeded by medical records often being in nonstandardized or unstructured natural language format. Advanced language models, such as large language models (LLMs), may help overcome current challenges in information exchange. OBJECTIVE: This study aims to evaluate the capability of LLMs in transforming and transferring health care data to support interoperability. METHODS: Using data from the Medical Information Mart for Intensive Care III and UK Biobank, the study conducted 3 experiments. Experiment 1 assessed the accuracy of transforming structured laboratory results into unstructured format. Experiment 2 explored the conversion of diagnostic codes between the coding frameworks of the ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification), and Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) using a traditional mapping table and a text-based approach facilitated by the LLM ChatGPT. Experiment 3 focused on extracting targeted information from unstructured records that included comprehensive clinical information (discharge notes). RESULTS: The text-based approach showed a high conversion accuracy in transforming laboratory results (experiment 1) and an enhanced consistency in diagnostic code conversion, particularly for frequently used diagnostic names, compared with the traditional mapping approach (experiment 2). In experiment 3, the LLM showed a positive predictive value of 87.2% in extracting generic drug names. CONCLUSIONS: This study highlighted the potential role of LLMs in significantly improving health care data interoperability, demonstrated by their high accuracy and efficiency in data transformation and exchange. The LLMs hold vast potential for enhancing medical data exchange without complex standardization for medical terms and data structure.

Synthesis and SAR of a novel Kir6.2/SUR1 channel opener scaffold identified by HTS.

Kir6.2/SUR1 is an ATP-regulated potassium channel that acts as an intracellular metabolic sensor, controlling insulin and appetite-stimulatory neuropeptides secretion. In this Letter, we present the SAR around a novel Kir6.2/SUR1 channel opener scaffold derived from an HTS screening campaign. New series of compounds with tractable SAR trends and favorable potencies are reported.

Changes in the Circadian Rhythm of High-Frequency Heart Rate Variability Associated With Depression.

BACKGROUND: Heart rate variability (HRV) extracted from electrocardiogram measured for a short period during a resting state is clinically used as a bio-signal reflecting the emotional state. However, as interest in wearable devices increases, greater attention is being paid to HRV extracted from long-term electrocardiogram, which may contain additional clinical information. The purpose of this study was to examine the characteristics of HRV parameters extracted through long-term electrocardiogram and explore the differences between participants with and without depression and anxiety symptoms. METHODS: Long-term electrocardiogram was acquired from 354 adults with no psychiatric history who underwent Holter monitoring. Evening and nighttime HRV and the ratio of nighttime-to-evening HRV were compared between 127 participants with depressive symptoms and 227 participants without depressive symptoms. Comparisons were also made between participants with and without anxiety symptoms. RESULTS: Absolute values of HRV parameters did not differ between groups based on the presence of depressive or anxiety symptoms. Overall, HRV parameters increased at nighttime compared to evening. Participants with depressive symptoms showed a significantly higher nighttime-to-evening ratio of high-frequency HRV than participants without depressive symptoms. The nighttime-to-evening ratio of HRV parameters did not show a significant difference depending on the presence of anxiety symptoms. CONCLUSION: HRV extracted through long-term electrocardiogram showed circadian rhythm. Depression may be associated with changes in the circadian rhythm of parasympathetic tone.

VU6036720: The First Potent and Selective In Vitro Inhibitor of Heteromeric Kir4.1/5.1 Inward Rectifier Potassium Channels.

Heteromeric Kir4.1/Kir5.1 (KCNJ10/KCNJ16) inward rectifier potassium (Kir) channels play key roles in the brain and kidney, but pharmacological tools for probing their physiology and therapeutic potential have not been developed. Here, we report the discovery, in a high-throughput screening of 80,475 compounds, of the moderately potent and selective inhibitor VU0493690, which we selected for characterization and chemical optimization. VU0493690 concentration-dependently inhibits Kir4.1/5.1 with an IC50 of 0.96 µM and exhibits at least 10-fold selectivity over Kir4.1 and ten other Kir channels. Multidimensional chemical optimization of VU0493690 led to the development of VU6036720, the most potent (IC50 = 0.24 µM) and selective (>40-fold over Kir4.1) Kir4.1/5.1 inhibitor reported to date. Cell-attached patch single-channel recordings revealed that VU6036720 inhibits Kir4.1/5.1 activity through a reduction of channel open-state probability and single-channel current amplitude. Elevating extracellular potassium ion by 20 mM shifted the IC50 6.8-fold, suggesting that VU6036720 is a pore blocker that binds in the ion-conduction pathway. Mutation of the "rectification controller" asparagine 161 to glutamate (N161E), which is equivalent to small-molecule binding sites in other Kir channels, led to a strong reduction of inhibition by VU6036720. Renal clearance studies in mice failed to show a diuretic response that would be consistent with inhibition of Kir4.1/5.1 in the renal tubule. Drug metabolism and pharmacokinetics profiling revealed that high VU6036720 clearance and plasma protein binding may prevent target engagement in vivo. In conclusion, VU6036720 represents the current state-of-the-art Kir4.1/5.1 inhibitor that should be useful for probing the functions of Kir4.1/5.1 in vitro and ex vivo. SIGNIFICANCE STATEMENT: Heteromeric inward rectifier potassium (Kir) channels comprising Kir4.1 and Kir5.1 subunits play important roles in renal and neural physiology and may represent inhibitory drug targets for hypertension and edema. Herein, we employ high-throughput compound library screening, patch clamp electrophysiology, and medicinal chemistry to develop and characterize the first potent and specific in vitro inhibitor of Kir4.1/5.1, VU6036720, which provides proof-of-concept that drug-like inhibitors of this channel may be developed.

Discovery of a potent M5 antagonist with improved clearance profile. Part 2: Pyrrolidine amide-based antagonists.

This Letter describes our ongoing effort to improve the clearance of selective M5 antagonists. Herein, we report the replacement of the previously disclosed piperidine amide (4, disclosed in Part 1) with a pyrrolidine amide core. Several compounds within this series provided good potency, subtype selectivity, and low to moderate clearance profiles. Interestingly, the left-hand side SAR for this series diverged from our earlier efforts.

Discovery of a potent M5 antagonist with improved clearance profile. Part 1: Piperidine amide-based antagonists.

The lack of potent and selective tool compounds with pharmaceutically favorable properties limits the in vivo understanding of muscarinic acetylcholine receptor subtype 5 (M5) biology. Previously, we presented a highly potent and selective M5 antagonist VU6019650 with a suboptimal clearance profile as our second-generation tool compound. Herein, we disclose our ongoing efforts to generate next-generation M5 antagonists with improved clearance profiles. A mix and match approach between VU6019650 (lead) and VU0500325 (HTS hit) generated a piperidine amide-based novel M5 antagonist series. Several analogs within this series, including 29f, provided good on-target potency with improved clearance profiles, though room for improvement remains.

Automated Detection of Acute Myocardial Infarction Using Asynchronous Electrocardiogram Signals-Preview of Implementing Artificial Intelligence With Multichannel Electrocardiographs Obtained From Smartwatches: Retrospective Study.

BACKGROUND: When using a smartwatch to obtain electrocardiogram (ECG) signals from multiple leads, the device has to be placed on different parts of the body sequentially. The ECG signals measured from different leads are asynchronous. Artificial intelligence (AI) models for asynchronous ECG signals have barely been explored. OBJECTIVE: We aimed to develop an AI model for detecting acute myocardial infarction using asynchronous ECGs and compare its performance with that of the automatic ECG interpretations provided by a commercial ECG analysis software. We sought to evaluate the feasibility of implementing multiple lead-based AI-enabled ECG algorithms on smartwatches. Moreover, we aimed to determine the optimal number of leads for sufficient diagnostic power. METHODS: We extracted ECGs recorded within 24 hours from each visit to the emergency room of Ajou University Medical Center between June 1994 and January 2018 from patients aged 20 years or older. The ECGs were labeled on the basis of whether a diagnostic code corresponding to acute myocardial infarction was entered. We derived asynchronous ECG lead sets from standard 12-lead ECG reports and simulated a situation similar to the sequential recording of ECG leads via smartwatches. We constructed an AI model based on residual networks and self-attention mechanisms by randomly masking each lead channel during the training phase and then testing the model using various targeting lead sets with the remaining lead channels masked. RESULTS: The performance of lead sets with 3 or more leads compared favorably with that of the automatic ECG interpretations provided by a commercial ECG analysis software, with 8.1%-13.9% gain in sensitivity when the specificity was matched. Our results indicate that multiple lead-based AI-enabled ECG algorithms can be implemented on smartwatches. Model performance generally increased as the number of leads increased (12-lead sets: area under the receiver operating characteristic curve [AUROC] 0.880; 4-lead sets: AUROC 0.858, SD 0.008; 3-lead sets: AUROC 0.845, SD 0.011; 2-lead sets: AUROC 0.813, SD 0.018; single-lead sets: AUROC 0.768, SD 0.001). Considering the short amount of time needed to measure additional leads, measuring at least 3 leads-ideally more than 4 leads-is necessary for minimizing the risk of failing to detect acute myocardial infarction occurring in a certain spatial location or direction. CONCLUSIONS: By developing an AI model for detecting acute myocardial infarction with asynchronous ECG lead sets, we demonstrated the feasibility of multiple lead-based AI-enabled ECG algorithms on smartwatches for automated diagnosis of cardiac disorders. We also demonstrated the necessity of measuring at least 3 leads for accurate detection. Our results can be used as reference for the development of other AI models using sequentially measured asynchronous ECG leads via smartwatches for detecting various cardiac disorders.

Controlling the Cleavage of Carbon-Carbon Bonds To Generate α,α-Difluorobenzyl Carbanions for the Construction of Difluoromethylbenzenes.

Controlling the cleavage of carbon-carbon bonds during a chemical reaction is a substantial challenge; however, synthetic methods that accomplish this objective produce valuable and often unexplored reactivity. We have designed a mild process to generate α,α-difluorobenzyl carbanions in the presence of potassium carbonate by exploiting the cleavage of C-C bonds during the release of trifluoroacetate. The initiating reagent is potassium carbonate, which represents an improvement over existing protocols that require a strong base. Fragmentation studies across substituted arenes and heteroarenes were conducted along with computational analyses to elucidate reactivity trends. Furthermore, the mildly generated α,α-difluorobenzyl carbanions from electron-deficient aromatics and heteroaromatic rings can react with aldehydes to create derivatives of difluoromethylbenzenes, which are valuable synthetic targets.

SAR inspired by aldehyde oxidase (AO) metabolism: Discovery of novel, CNS penetrant tricyclic M4 PAMs.

This letter describes progress towards an M4 PAM preclinical candidate inspired by an unexpected aldehyde oxidase (AO) metabolite of a novel, CNS penetrant thieno[2,3-c]pyridine core to an equipotent, non-CNS penetrant thieno[2,3-c]pyrdin-7(6H)-one core. Medicinal chemistry design efforts yielded two novel tricyclic cores that enhanced M4 PAM potency, regained CNS penetration, displayed favorable DMPK properties and afforded robust in vivo efficacy in reversing amphetamine-induced hyperlocomotion in rats.

Optimization of M4 positive allosteric modulators (PAMs): The discovery of VU0476406, a non-human primate in vivo tool compound for translational pharmacology.

This letter describes the further chemical optimization of the 5-amino-thieno[2,3-c]pyridazine series (VU0467154/VU0467485) of M4 positive allosteric modulators (PAMs), developed via iterative parallel synthesis, culminating in the discovery of the non-human primate (NHP) in vivo tool compound, VU0476406 (8p). VU0476406 is an important in vivo tool compound to enable translation of pharmacodynamics from rodent to NHP, and while data related to a Parkinson's disease model has been reported with 8p, this is the first disclosure of the optimization and discovery of VU0476406, as well as detailed pharmacology and DMPK properties.

Conversion of Methyl Ketones and Methyl Sulfones into α-Deutero-α,α-Difluoromethyl Ketones and α-Deutero-α,α-Difluoromethyl Sulfones in Three Synthetic Steps.

Deuterodifluoromethyl ketones and sulfones were assembled in three synthetic steps from methyl ketones and sulfones, respectively. The key synthetic transformation is the deuteration of the difluorocarbanion generated by the release of trifluoroacetate from highly α-fluorinated gem-diols. High levels of deuterium on the "CF2D" group were routinely observed. This strategy is mild and versatile and it can be applied to both ketones and sulfones without additional concerns of over- or under-fluorination. Additional examples address issues of over-deuteration when compounds with other acidic protons are subjected to the reaction conditions. This process not only demonstrates a new method to install a "CF2D" group but also extends the scope of trifluoroacetate release to sulfones.

Discovery and optimization of a novel series of highly CNS penetrant M4 PAMs based on a 5,6-dimethyl-4-(piperidin-1-yl)thieno[2,3-d]pyrimidine core.

This Letter describes the chemical optimization of a novel series of M4 positive allosteric modulators (PAMs) based on a 5,6-dimethyl-4-(piperidin-1-yl)thieno[2,3-d]pyrimidine core, identified from an MLPCN functional high-throughput screen. The HTS hit was potent and selective, but not CNS penetrant. Potency was maintained, while CNS penetration was improved (rat brain:plasma Kp=0.74), within the original core after several rounds of optimization; however, the thieno[2,3-d]pyrimidine core was subject to extensive oxidative metabolism. Ultimately, we identified a 6-fluoroquinazoline core replacement that afforded good M4 PAM potency, muscarinic receptor subtype selectivity and CNS penetration (rat brain:plasma Kp>10). Moreover, this campaign provided fundamentally distinct M4 PAM chemotypes, greatly expanding the available structural diversity for this exciting CNS target.

Gentle Sampling of Submicrometer Airborne Virus Particles using a Personal Electrostatic Particle Concentrator.

Measurements of airborne viruses via sampling have been critical issues. Most electrostatic samplers have been assessed for bacterial aerosols or micrometer-sized viral particles; however, sampling of submicrometer-sized airborne viruses is necessary, especially because of the high probability of their staying airborne and their deposition in the lower respiratory tract. Here, we present a novel personal electrostatic particle concentrator (EPC) for gentle sampling of submicrometer airborne virus particles. Owing to the enhanced electric field designed in this EPC, the collection efficiencies reached values as high as 99.3-99.8% for 0.05-2 µm diameter polystyrene particles at a flow rate of 1.2 L/min. Submicrometer-sized MS2 and T3 virus particles were also collected in the EPC, and the concentrations relative to their respective initial suspensions were more than 10 times higher than those in the SKC BioSampler. Moreover, the recovery rate of T3 was 982 times higher in the EPC (-2 kV) than in the BioSampler at 12.5 L/min because of the gentle sampling of the EPC. Gentle sampling is desirable because many bioaerosols suffer from significant viability losses during sampling. The influence of ozone generated, applied electrostatic field, and the flow rate on the viability of the viruses will also be discussed.

Progress towards small molecule menin-mixed lineage leukemia (MLL) interaction inhibitors with in vivo utility.

A series of substituted hydroxymethyl piperidine small molecule inhibitors of the protein-protein interaction between menin and mixed lineage leukemia 1 (MLL1) are described. Initial members of the series showed good inhibitory disruption of the menin-MLL1 interaction but demonstrated poor physicochemical and DMPK properties. Utilizing a structure-guided and iterative optimization approach key substituents were optimized leading to inhibitors with cell-based activity, improved in vitro DMPK parameters, and improved half-lives in rodent PK studies leading to MLPCN probe ML399. Ancillary off-target activity remains a parameter for further optimization.

Discovery and SAR of muscarinic receptor subtype 1 (M1) allosteric activators from a molecular libraries high throughput screen. Part 1: 2,5-dibenzyl-2H-pyrazolo[4,3-c]quinolin-3(5H)-ones as positive allosteric modulators.

Results from a 2012 high-throughput screen of the NIH Molecular Libraries Small Molecule Repository (MLSMR) against the human muscarinic receptor subtype 1 (M1) for positive allosteric modulators is reported. A content-rich screen utilizing an intracellular calcium mobilization triple-addition protocol allowed for assessment of all three modes of pharmacology at M1, including agonist, positive allosteric modulator, and antagonist activities in a single screening platform. We disclose a dibenzyl-2H-pyrazolo[4,3-c]quinolin-3(5H)-one hit (DBPQ, CID 915409) and examine N-benzyl pharmacophore/SAR relationships versus previously reported quinolin-3(5H)-ones and isatins, including ML137. SAR and consideration of recently reported crystal structures, homology modeling, and structure-function relationships using point mutations suggests a shared binding mode orientation at the putative common allosteric binding site directed by the pendant N-benzyl substructure.

An Explainable Artificial Intelligence-enabled ECG Framework for the Prediction of Subclinical Coronary Atherosclerosis.

Coronary artery calcium (CAC) as assessed by computed tomography (CT) is a marker of subclinical coronary atherosclerosis. However, routine application of CAC scoring via CT is limited by high costs and accessibility. An electrocardiogram (ECG) is a widely-used, sensitive, cost-effective, non-invasive, and radiation-free diagnostic tool. Considering this, if artificial intelligence (AI)-enabled electrocardiograms (ECGs) could opportunistically detect CAC, it would be particularly beneficial for the asymptomatic or subclinical populations, acting as an initial screening measure, paving the way for further confirmatory tests and preventive strategies, a step ahead of conventional practices. With this aim, we developed an AI-enabled ECG framework that not only predicts a CAC score ≥400 but also offers a visual explanation of the associated potential morphological ECG changes, and tested its efficacy on individuals undergoing health checkups, a group primarily comprising healthy or subclinical individuals. To ensure broader applicability, we performed external validation at a separate institution.

Bloodletting for Acute Stroke Recovery: A Systematic Review and Meta-Analysis.

BACKGROUND: Bloodletting is a non-pharmacological treatment commonly used for acute stroke in traditional East Asian medicine. This study evaluated the efficacy and safety of bloodletting in acute stroke recovery. METHODS: We conducted a comprehensive search of eight electronic databases up to 4 June 2024 to identify relevant randomized controlled trials (RCTs). Review Manager 5.4 was used for the meta-analysis, with methodological quality assessed using the Cochrane Risk of Bias 2 tool and the GRADE approach. RESULTS: Seventeen RCTs were included in this meta-analysis. The bloodletting group showed statistically significant improvements in neurological deficits compared to the non-bloodletting group, as measured using the National Institutes of Health Stroke Scale (mean difference [MD]: -2.08, 95% confidence interval [CI]: -3.13 to -1.02) and the treatment effective rate (risk ratio: 1.17, 95% CI: 1.11 to 1.22). Motor function also improved significantly in both upper (Fugl-Meyer Assessment, MD: 12.20, 95% CI: 9.67 to 14.73) and lower extremities (MD: 3.86, 95% CI: 2.16 to 5.56). The effect on daily living activities was not significant overall, but benefits were observed in patients treated within three days of stroke onset (Barthel Index, standardized MD: 0.85, 95% CI: 0.01 to 1.69). No significant differences in the frequency of adverse events were observed between the groups. CONCLUSION: Bloodletting may be an effective and safe adjunctive therapy for patients with acute stroke receiving conventional Western medical treatment. However, further research is necessary because of the small sample sizes and low quality of the included studies.

Machine Learning with Clinical and Intraoperative Biosignal Data for Predicting Cardiac Surgery-Associated Acute Kidney Injury.

Early identification of patients at high risk of cardiac surgery-associated acute kidney injury (CSA-AKI) is crucial for its prevention. We aimed to leverage perioperative clinical and intraoperative biosignal data to develop machine learning models that predict CSA-AKI. We introduced a novel approach for extracting relevant features from high-resolution intraoperative biosignals to reflect the patient's baseline status, the extent of unfavorable conditions encountered intraoperatively, and data variability. We developed XGBoost models from 2,003 patients across three consecutive perioperative phases using: 1) only preoperative, 2) pre- and intraoperative, and 3) pre-, intra-, and postoperative variables. The predictive performance progressively improved throughout the three consecutive perioperative phases (e.g., AUROC of 0.767 to 0.797 and 0.840), all surpassing the Thakar Score's performance. According to the SHAP method, intraoperative perfusion pressure was most important in the prediction, highlighting the importance of intraoperative patient management and the use of high-resolution biosignal data in predictive modeling to analyze hemodynamic fluctuations during surgery. Early postoperative biomarkers were also important predictors, highlighting the importance of intensified monitoring early after surgery.

Evaluation of GPT-4 for 10-year cardiovascular risk prediction: Insights from the UK Biobank and KoGES data.

Cardiovascular disease (CVD) remains a pressing global health concern. While traditional risk prediction methods such as the Framingham and American College of Cardiology/American Heart Association (ACC/AHA) risk scores have been widely used in the practice, artificial intelligence (AI), especially GPT-4, offers new opportunities. Utilizing large scale of multi-center data from 47,468 UK Biobank participants and 5,718 KoGES participants, this study quantitatively evaluated the predictive capabilities of GPT-4 in comparison with traditional models. Our results suggest that the GPT-based score showed commendably comparable performance in CVD prediction when compared to traditional models (AUROC on UKB: 0.725 for GPT-4, 0.733 for ACC/AHA, 0.728 for Framingham; KoGES: 0.664 for GPT-4, 0.674 for ACC/AHA, 0.675 for Framingham). Even with omission of certain variables, GPT-4's performance was robust, demonstrating its adaptability to data-scarce situations. In conclusion, this study emphasizes the promising role of GPT-4 in predicting CVD risks across varied ethnic datasets, pointing toward its expansive future applications in the medical practice.