Category: 3. Business

  • Gold Price in Pakistan Drops Again, Down Nearly Rs. 10,000 Per Tola from Record High

    Gold Price in Pakistan Drops Again, Down Nearly Rs. 10,000 Per Tola from Record High

    The price of 24 karat per tola gold witnessed a decrease of Rs. 1,400 on Wednesday and was sold at Rs. 355,200 against its sale at Rs. 356,600 on the previous trading day, All Pakistan Sarafa Gems and Jewelers Association reported.

    The prices of 10 grams of 24 karat also decreased by Rs. 1,201 to Rs. 304,526 from Rs. 305,727, whereas the price of 10 grams of 22 Karat went down by Rs. 1,101 to Rs. 279,158 from Rs. 280,259.

    The rates of per tola and ten-gram silver decreased by Rs. 96 and Rs. 82, and were traded at Rs. 3,935 and Rs. 3,373, respectively.

    The price of gold in the international market decreased by $14 to $3,325 from $3,339, whereas silver decreased by $0.96 to $37.02 from $37.98, the Association reported.


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  • Vietnam: New 2025 Atomic Energy Law – Safe, secure and sustainable nuclear development

    Vietnam: New 2025 Atomic Energy Law – Safe, secure and sustainable nuclear development

    In brief

    On 27 June 2025, Vietnam’s National Assembly enacted Law on Atomic Energy No. 94/2025/QH15 (“Atomic Energy Law“). It establishes a robust legal framework for the peaceful development and application of atomic energy.

    The Atomic Energy Law applies to domestic and foreign organizations and individuals and international organizations engaged in atomic energy activities.

    The Atomic Energy Law regulates the following key areas:

    • Development of nuclear power plants and research nuclear reactors
    • Licensing of nuclear-related activities
    • Management of radioactive waste and spent nuclear fuel
    • Nuclear and radiation safety and security
    • Response to radiation and nuclear incidents
    • Compensation mechanisms for radiation and nuclear-related damage

    Effective from 1 January 2026, the Atomic Energy Law replaces Law on Atomic Energy No. 18/2008/QH12, strengthening nuclear governance and aligning with international standards and the guidelines of the International Atomic Energy Agency (IAEA), paving the way for upcoming projects such as Ninh Thuan’s nuclear power plants.


    Click here to read the full alert

    * * * * *

    Hoang Anh Vu, Trainee Solicitor, has contributed to this legal update.


    Author
    Oanh H. K. Nguyen

    Oanh Nguyen is a partner in Baker McKenzie Vietnam and has been practising capital markets, banking and finance, M&A, and commercial law for more than 25 years. Knowledgeable about all aspects of investments, she advises on all types of transactions, ranging from investment structures to project structures and their related financing. She has focused on public M&A matters, including major IPOs and projects finance.

    Oanh is a respected presenter in the areas of finance and capital markets. In addition, she has lectured at the Ho Chi Minh City Bar Association. She also serves as a legal advisor to the Capital Market Committee of Ho Chi Minh City American Chamber of Commerce.

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  • NIO Inc. to Report Unaudited Second Quarter 2025 Financial Results on Tuesday, September 2, 2025 – NIO – Home

    1. NIO Inc. to Report Unaudited Second Quarter 2025 Financial Results on Tuesday, September 2, 2025  NIO – Home
    2. Nio CEO Says EV Maker Entered ‘Harvest Period,’ Reaffirms Profitability Goal  Yahoo Finance
    3. Global EV Leader NIO Announces Q2 2025 Earnings Date: What to Expect from the Chinese Tesla Rival  Stock Titan
    4. Nio to report Q2 2025 earnings on Sept 2  CnEVPost

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  • Time series forecasting of infant mortality rate in India using Bayesian ARIMA models | BMC Public Health

    Time series forecasting of infant mortality rate in India using Bayesian ARIMA models | BMC Public Health

    Some suggested ARIMA models and Bayes estimates for real-world time series data sets are numerically illustrated in this section. The example serves as a key demonstration of how our models work in finding a true state of the system by showcasing the method’s practical utility and relevance to real-life problems. In addition to the analysis, we have also mentioned the forecast for future purposes.

    Data source

    We have taken a real data set of the IMR for India over the period of 73 years from 1950 to 2023 annually. The data set is given in the form of a time series from World Population Prospects. World Population Prospects is the twenty-seventh edition of official United Nations population estimates and projections that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat. It presents population estimates from 1950 to the present for 237 countries or areas underpinned by analyses of historical demographic trends. This latest assessment considers the results of 1,758 national population censuses conducted between 1950 and 2023, as well as information from vital registration systems and 2,890 nationally representative sample surveys (UN-WPP). Table 2 shows the IMR values, and Table 3 shows the IMR growth rate in percentage.

    Table 2 Infant mortality rate in India from 1950 to 2023 (values decrease from left to right)
    Table 3 Infant mortality growth rate in India (in percentage), 1951–2023

    After understanding the dataset, we have drawn the time series plot of IMR growth rate data and differenced IMR growth rate. These plots are given in Fig. 1.

    Fig. 1

    Time series plots showing IMR growth and differenced IMR growth rate of India from 1951-2023

    After plotting the IMR growth data, it can be observed that it is not stationary (see Fig. 1a). However, after differencing it once, we obtain stationarity in Fig. 1b. This shows that we can set (d=1). The ADF test also shows that unit root is not present for the first difference. The p-value (=0.31) is also greater than 0.05.

    ACF and PACF plots for the given data

    Selecting the appropriate values for p and q is crucial in building an effective ARIMA model for a given time series [7]. To determine p and q, we have drawn the ACF and PACF plots as mentioned in Determining the order section. This plotting involves computing the autocorrelation function (ACF) and partial autocorrelation function (PACF) plots of the time series data. ACF is a plot of the correlation of a series with its own lagged values. PACF plot is a plot of the partial correlation between a series and its lagged values, regressed the values of the time series at all shorter lags. ACF and PACF plots of the data are given in Fig. 2.

    Fig. 2
    figure 2

    ACF and PACF plots of the IMR growth dataset after first differencing

    The above ACF (see in Fig. 2a) and PACF plots (see in Fig. 2b) are shown with details. Significant autocorrelation spikes at specific lags may indicate periodic behaviour or a strong dependence on past values, as seen at lag 5, which is the highest. Significant spikes at multiple lags may suggest a mix of autoregressive and moving average components, indicating a more complex time series structure in the partial autocorrelation at lags 5 and 10, respectively. Furthermore, these plots provide valuable insights into the temporal dependencies within a time series, aiding in model selection and forecasting. Using all the nearest possible combinations of AR lag, fixing the difference at one time, and other nearest lag possible combinations of MA order, we go for the likelihood estimation and Bayesian estimation as well in the next section.

    Classical analysis

    The primary aim of the study is to emphasise Bayesian analysis, a crucial aspect of establishing initial values to compute the MLE using the Newton-Raphson method. This initial value helps us to run the algorithm 3.4. In this study, the ARIMA model results from the specified combinations of (p, d, q), namely (5,1,0), (5,1,1), (5,1,2),(5,1,3),(5,1,4),(5,1,5), (0,1,5), (1,1,5), (2,1,5),(3,1,5), (4,1,5) and (5,1,5). Since we have stationarity at the first lag, we have selected d = 1. Although the ACF and PACF plots suggest a lag of 5, we are not very sure about it. Therefore, we have selected these combinations of p and q. We have computed the MLE for the mentioned models, along with their respective standard errors (SE), for the above-selected combinations of p and q in the ARIMA (pdq) model, and their AIC and BIC values. The results are shown in Table 4.

    Table 4 MLEs, SE, and model comparison (AIC and BIC) for various ARIMA(p,1,q) model configurations applied to IMR growth data

    From Table 4, it can be seen that the ARIMA models generally show consistent parameter estimates across different parameter specifications, with varying degrees of uncertainty (as indicated by the standard error, SE). Notably, the order (5,1,0) and (0,1,5) models have more stable parameter estimates with relatively lower standard errors compared to higher-order models like (5,1,3), (5,1,4), (5,1,5), where standard errors are larger, indicating less precise estimates. Additionally, the ARIMA models with moving average terms (e.g., order (5,1,1) and (5,1,2)) show slightly higher parameter variability, suggesting increased model complexity that may lead to poor precision.

    Besides classical estimates, Table 4 presents AIC and BIC values. Based on these values, we can say that ARIMA models with orders (5,1,0), (5,1,1), (0,1,5) and (1,1,5) are performing better than the others. Also, it is well known that Bayesian analysis is computationally costly, due to the need for repeated likelihood evaluations and high-dimensional sampling using Markov Chain Monte Carlo (MCMC) methods. Each iteration of the Random Walk Metropolis algorithm requires evaluating the full likelihood via the Kalman filter, which increases computational load significantly. Therefore, to ensure tractability and focus on the most promising configurations, we restrict the Bayesian analysis to four models that showed the best performance in the classical model selection phase. Therefore, we plan to perform a Bayesian analysis for these four models only. In the next subsection, we will provide the Bayesian analysis of these four models.

    Bayesian analysis

    To conduct Bayesian analysis, the initial step involves determining the prior hyperparameters. Since the Bayesian framework relies heavily on prior information, carefully selecting priors is crucial to avoid misleading results. As suggested in Prior distribution section, the most appropriate prior for both the (phi) and (theta) parameters is the Multivariate Normal (MVN) distribution. We choose the hyperparameters as follows: The MLE (hat{phi }) of ({phi }) is considered as the prior mean for the respective AR models. The diagonal elements of the prior variance-covariance matrix (Sigma) is 2(times) abs [({phi _1}), ({phi _2}),…, ({phi _5})]. The non-diagonal elements of (Sigma) are considered to be zero. In the same way, we choose the MLE and diagonal elements of the prior variance-covariance matrix for the (theta) parameter of the MA model (details mention in 3.2).

    We now proceed to run the RWM algorithm, as discussed in Random walk Metropolis (RWM) algorithm section. The proposal scale, (sigma), has been chosen in the algorithm to keep the acceptance rate optimal. The initial values of the chain are set to the corresponding MLE. The algorithm has been run for 5e5 iterations. Under the aforementioned conditions, the optimal acceptance rate ranged from (10%) to (60%), indicating a low rejection rate of the algorithm.

    Table 5 presents estimated posterior characteristics for different configurations of the models, which have been chosen according to the minimum AIC and BIC values. So, we have chosen four models of order (5,1,0). (5,1,1), (0,1,5), (1,1,5). The posterior summary includes the posterior mean, median, mode, and highest posterior density (HPD) intervals with a 0.95 probability.

    Table 5 Posterior summaries for selected ARIMA(p,1,q) models based on IMR growth data

    By varying the hyperparameter of prior distributions and comparing the resulting posterior summaries, we observed that the estimates remained largely consistent, indicating robustness of the Bayesian inference. Across the models, the parameter estimates indicate variability in both magnitude and uncertainty. In general, the fifth lag of the AR or MA terms shows relatively larger means and wider HPD intervals, suggesting a stronger or more uncertain contribution at that lag. Comparing models, the ARIMA(5,1,1) and ARIMA(1,1,5) appear to capture richer dynamics due to the inclusion of both AR and MA terms, although some parameters show wide HPD intervals, implying caution in their interpretation.

    From Fig. 3 shows the trace plots from 5e5 MCMC iterations show well-mixed, stationary chains for all five parameters, with no visible trends or drifts. The rapid fluctuations indicate low autocorrelation, suggesting good convergence and reliable posterior sampling. This analysis helps identify the most suitable model structure for forecasting while highlighting parameter uncertainty. Further details of this combination of models has been discussed in the next subsection.

    Fig. 3
    figure 3

    Trace plot of the parameters for order (5,1,1)

    Bayesian model selection

    In order to proceed with model selection, which is to make a comparison among models, and wish to know the best one among four models considered for Bayesian Analysis. We have used the AIC score, BIC score for model selection as discussed in Bayesian Model selection section. Also, K-fold cross-validation (CV) is used to assess the predictive performance of the selected models on simulated time series data. For practical compatibility, K is commonly set to 5 or 10; this study chose K = 10, following [34]. The results are shown in Table 6.

    Table 6 Model comparison based on AIC, BIC, and forecast error metrics

    The Table 6 presents a comparative analysis of four Bayesian ARIMA model configurations (5,1,0), (5,1,1), (0,1,5), and (1,1,5)—based on evaluation metrics such as AIC, BIC, MSE, RMSE, and MAE. Among them, the ARIMA(5,1,0) model demonstrates the best performance, having the lowest AIC (18.68), BIC (30.14), and the most favorable error values (MSE = 4.72, RMSE = 2.17, MAE = 1.66). To provide a more comprehensive evaluation of model performance, we also computed Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) for each selected ARIMA model. These metrics offer additional insight into forecast accuracy, with RMSE being sensitive to large errors and MAE providing a more robust view of average forecast deviations. The ARIMA(5,1,0) model outperforms the others across all four criteria—AIC, BIC, RMSE, and MAE—reinforcing its status as the most reliable model for forecasting India’s IMR growth.

    However, ARIMA(5,1,0) offers several additional advantages that justify its selection. First, it has a simpler structure with fewer parameters than ARIMA(1,1,5) or ARIMA(0,1,5), which reduces the risk of overfitting and enhances model interpretability. Second, in the Bayesian estimation process, the ARIMA(5,1,0) model demonstrated better convergence diagnostics (e.g., well-mixed trace plots and stable posterior distributions), indicating numerical stability and robustness. These practical and computational considerations, along with its marginally better predictive accuracy, make ARIMA(5,1,0) the most appropriate model for forecasting India’s infant mortality rate in this study.

    Retrospective study

    This retrospective study examines the trend in interval estimates over the period 2015 to 2023. The intervals represent credible intervals, reflecting changes observed year-over-year. By systematically analysing these intervals, the study aims to understand the longitudinal behaviour of IMR growth rate, offering insights that may support future forecasting or decision-making.

    The Table 7 presents forecasted IMR growth rates from 2015 to 2023, each accompanied by a 95% confidence interval. While the predicted values consistently show a decline in IMR growth( in %) over time, the widening confidence intervals (CI), especially in later years, indicate increasing uncertainty in the forecasts. This suggests that although the model anticipates continued improvement, the reliability of long-term predictions decreases as the forecast horizon extends.

    Table 7 Retrospective study of IMR growth rate (in %) for the period 2015 to 2023

    Again, for validating the model’s accuracy and reliability we are comparing the forecasted IMR growth rates and their confidence intervals with actual observed data is essential. It shows how well the model reflects real trends, whether its uncertainty estimates are appropriate, and helps identify over or underestimations. This comparison also supports model refinement and builds credibility, making the forecasts more meaningful for evidence-based decision-making.

    The Table 8 compares actual and forecasted IMR values from 2015 to 2023, along with the absolute error for each year. The results show that the model consistently overestimated IMR across all years, with forecasted values slightly higher than actual figures. While the forecast closely matched actual IMR in 2015 (smallest error: 0.22), the accuracy gradually declined over time, reaching the highest discrepancy in 2023 (1.51). This pattern suggests the model performs better for short-term predictions, but its accuracy diminishes as the forecast horizon extends. Overall, the model demonstrates a reasonable fit, though its tendency to over-predict should be noted for future refinement.

    Table 8 Comparison between actual historical IMR values and forecasted values during the retrospective period (2015–2023)

    Comparing predictive performance

    As the purpose of this article is to forecast the IMR growth data using a Bayesian ARIMA model. But for the simplicity of this study, we can go for the Autoregressive(AR) model to predict the same dataset. And also make a comparison of the predictive performance between the common forecasting model and the Bayesian ARIMA model. Since, AR models are ideal for small, stationary datasets, capturing temporal dependence through past values. They’re simple, interpretable, and effective for short-term forecasts, requiring no external inputs.

    Bayesian ARIMA provides probability distributions for forecasts, and also requires a more iterative process to find the estimates. Bayesian ARIMA offers enhanced precision by incorporating uncertainty and adaptability. The choice between the two depends on the complexity of the dataset and the need for probabilistic forecasting. To evaluate the comparative performance of the two models, we present Table 9, which reports the IMR values and their growth rates for the same year, along with the corresponding 95% CI for both the AR model and the Bayesian ARIMA model.

    Table 9 Comparison of predictive performance of IMR (per 1,000 live births) and growth rate data for the period of 2015 to 2023

    The Bayesian ARIMA model demonstrates superior forecasting performance compared to the AR model, as shown by significantly lower MSE values averaging 4.3 across 10 folds versus 18.5 for the AR model. Also, its ability to provide probabilistic confidence intervals enhances its reliability in uncertain environments. Compared to the AR model, Bayesian ARIMA delivers more accurate and informative forecasts, especially when accounting for uncertainty and evolving data patterns.

    Forecasting

    To fulfil the second objective of our study, we have generated forecasts for the subsequent periods based on the fitted time series model. The model captured the underlying patterns and trends in the historical data, and the forecasted values provide an estimate of the expected behaviour moving forward. The result of the point forecast for IMR and IMR growth (in %) with their respective credible interval are summarised in Table 10 for the next decade.

    Table 10 Forecast of IMR (per 1,000 live births) data for the period of 2024 to 2033

    To obtain these patterns, we initially simulated the corresponding posterior and obtained a posterior sample of size 1e5 for the ARIMA(5,1,0) model using available data values. Subsequently, we simulated predictive samples for the remaining unobserved datasets for each value in the simulated posterior sample. The predictive estimates are provided as the corresponding posterior modes based on 100 predictive samples. These samples are used to apply the Kalman filter to predict future observations by using the model’s estimated parameters and current state information.

    The forecasted IMR from 2024 to 2033 in Table 10 indicates a consistent downward trend, highlighting gradual improvements in infant mortality rates (IMR). Starting from an IMR of 25.21 in 2024, the rate steadily declines to 15.68 by 2033. The year-over-year growth rate remains negative throughout the period, with the highest reduction observed in 2033 (-5.81%). The output typically includes forecasted values along with their associated uncertainties, which helps with short-term time series prediction.

    From Fig. 4 shows that the trend is promising as this persistent decline reflects the potential impact of public health interventions, improved healthcare services, and socio-economic development. The model effectively captures this trend, offering valuable projections for health policy planning and evaluation.

    Fig. 4
    figure 4

    Forecasted IMR growth trend with credible intervals

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  • Bank Indonesia front-loads easing amid growth concerns | articles

    Bank Indonesia front-loads easing amid growth concerns | articles

    Bank Indonesia has lowered its policy rate by 25 basis points to 5.00%, marking a second consecutive surprise cut. While we had expected BI to hold off on further easing until the fourth quarter – given the recent strength in GDP and CPI data, as well as weak transmission to lending rates – the move signals BI’s increasing concern over the growth outlook.

    The decision also suggests that BI is taking advantage of periods of Indonesian rupiah (IDR) strength to ease policy without risking currency instability. Despite headline inflation ticking higher, it remains well below BI’s upper target of 3.5%, giving the central bank room to act pre-emptively to support domestic demand.

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  • Discovery of N-acyl Tryptophan Derivatives as Promising P2Y14R Antagon

    Discovery of N-acyl Tryptophan Derivatives as Promising P2Y14R Antagon

    Introduction

    When the body is under pressure or injured, specific tissues or organs can release various important intracellular molecules into the extracellular fluid.1,2 These extracellular nucleotides can interact with at least 15 different cell surface receptors to regulate cellular signaling and physiological responses.3 These receptors have been identified as purinergic receptors and are mainly divided into adenosine (P1) and nucleotide (P2) receptors.4 The P2 family is further divided into two subfamilies: P2X and P2Y receptors.5,6 As a subtype of P2Y receptors, the P2Y14 receptor (P2Y14R) is a G protein-coupled receptor (GPCR) that can be activated by extracellular UDPG, P2Y14R is expressed in a variety of tissues, particularly in epithelial and immune cells. Its activation is closely linked to intracellular signaling pathways and plays a significant role in modulating immune responses and inflammatory processes.7–9 Mechanistically, P2Y14R activation triggers Gi-mediated inhibition of adenylate cyclase (AC), thereby suppressing intracellular cAMP production and modulating downstream signaling cascades.10–12 This receptor demonstrates pathological significance across multiple inflammatory disorders: In acute gouty arthritis, it exacerbates disease progression by promoting NLRP3 inflammasome-dependent macrophage pyroptosis.13,14 In ulcerative colitis, P2Y14R overexpression in intestinal epithelium drives disease severity through programmed necroptosis, with genetic ablation ameliorating colitis symptoms in preclinical models.15 Moreover, P2Y14R exerts key regulatory functions in a variety of pathological processes, including diabetes,16 cystic fibrosis,17 and allergic diseases.18 Recent studies have shown that the activation of P2Y14R by endogenous UDPG leads to neutrophil recruitment and increased inflammation, whereas P2Y14R antagonists reduce inflammation in renal intercalated cells.19,20 In addition, P2Y14R exists in the central nervous system, inhibiting the release of matrix metalloproteinase-9 (MMP-9) and tumor necrosis factor (TNF) from astrocytes.21 It’s worth noting that P2Y14R antagonists significantly reduce lung inflammation and tissue damage in acute lung injury (ALI) by inhibiting the activation of NLRP3 inflammatory body signaling pathway and reducing the release of inflammatory factors and immune cell infiltration.22 Therefore, P2Y14R is a potential target for the treatment of inflammation. The LPS-induced ALI model holds significant value in anti-inflammatory research, it simulates the acute inflammatory phase of ARDS related to sepsis or COVID-19, offering substantial translational medicine value. Therefore, the ALI model was selected in this study to verify the in vivo efficacy of P2Y14R antagonists.22 To date, only a few P2Y14R antagonists with different skeletal structures have been reported. Initially, the Black group reported a series of weak P2Y14R antagonists containing dihydropyridopyrimidine structures, and compound 1 showed the best antagonistic activity among them.23 However, further research has not been conducted on dihydropyridopyrimidine derivatives because they have serious side effects in the human body. Subsequently, the Black group reported 7-phenyl-2-naphthoic acid derivative 2, which is an effective, high-affinity, competitive, and highly selective P2Y14R antagonist, well-known as PPTN.24,25 Jacobson and Hu further proposed bioisosteric alternatives to hydrophobic and rigid naphthalene rings, namely, compounds 3, 4, and.26,27 However, these compounds have poor solubility and low oral bioavailability because of their high lipophilicity and zwitterionic properties (Please refer to Table 1 for the explanation of professional vocabulary), which make their application in medicine difficult. In recent years, Jacobson group has focused on modifying the piperidine ring with zwitterionic characteristics on the basis of PPTN, that is, enhancing the spatial constraints on the piperidine ring and bridging the piperidine ring, compound 6 is one example as shown in Figure 1.28 In 2020, Hu et al reported a series of hit compounds of P2Y14R with novel scaffolds using a Glide-docking-based virtual screening (VS) strategy. Compound 7 eliminated the zwitterionic characteristics and had considerable antagonistic activity compared to other hit compounds.29 Hu et al further modified compound 5 to obtain compound 8, which eliminated the zwitterionic characteristics and significantly improved the solubility and bioavailability of P2Y14R antagonists.14 Afterwards, Jiang et al designed and synthesized a series of 5-amide-1H-pyrazole-3-carboxyl derivatives and obtained the novel, easily synthesized, and potent P2Y14R antagonist compound 9.30 Recently, different studies employed computational methods to investigate novel skeleton compounds and molecular interactions. Common methods include docking, MD, and MM-PBSA, while enhanced sampling (umbrella/steered MD) addresses specific binding/unbinding dynamics. GROMACS and MM-PBSA are central across studies, with tailored approaches (HADDOCK, DFT optimization) for system-specific challenges. These workflows highlight integrative computational strategies for drug design and biophysical analysis.31–33

    Table 1 Glossary

    Figure 1 Representative chemical structures of reported P2Y14R antagonists.

    Although many new P2Y14R antagonists have been reported in recent years, there have been no reports of P2Y14R antagonists entering clinical trial. Compound 7 exhibited significant antagonistic activity, but its poor bioavailability (F = 6%) limited its clinical application. Therefore, Based on molecular docking insights, we hypothesized that modifying the carboxylic acid moiety and introducing ester prodrugs could eliminate zwitterionic interactions and enhance solubility. This rationale guided the design of three series of N-acyl tryptophan derivatives, aiming to balance antagonistic potency with druggability. We aimed to obtain P2Y14R antagonists with high antagonistic activity and druggability for further investigation into anti-inflammatory drugs.29 This not only continues the previous achievements of Jacobson, Hu and other teams, but also provides compelling evidence for the critical pro-inflammatory role of P2Y14R in ALI. This study is the first to combine single-cell profiling to analyze the spatial distribution and function of P2Y14R in ALI, revealing that II-3, as a P2Y14R antagonist, alleviates the therapeutic effect of ALI through dual mechanisms (inhibition of immune cell infiltration and the NLRP3 pathway).

    Materials and Methods

    Chemical Synthesis

    All chemical reagents and solvents were commercially available and used without further purification. All reactions were monitored by thin-layer chromatography (TLC) using 254 nm fluorescent indicators. The silica gel used for column chromatography (CC) was 200–300 mesh, produced by Shandong Xinnuo New Material Technology Co., Ltd. Bruker 400 MHz or 600 MHz NMR instruments (1H = 400 MHz and 600 MHz, 13C = 101MHz) were used to determine the hydrogen and carbon spectra of, using CDCl3d or DMSO-d6 as solvents (TMS was the internal standard at 25 °C). High-resolution mass spectroscopy (HRMS) data were recorded on an Orbitrap Exploris 120 mass spectrometer. Melting points were detected using an XT4A micro-melting point apparatus (Beijing Scientific Instruments Electrooptic Instrument Factory).

    General Procedure for the Synthesis of 26 (Method A)

    Compound 25 (200 mg, 0.9792 mmol, 1.0 equiv) was dissolved in 15mL methanol. After the addition of SOCl2 (278 μL, 3.9168 mmol, 4.0 eq), the ice bath was removed and the reaction was stirred at high temperature under reflux conditions. When the reaction was complete, the solvent and remaining SOCl2 were dried under reduced pressure to obtain intermediate 26. The product was directly added to the subsequent reaction step. 27 and 28 were synthesized using the same procedure (Figure 2).

    Figure 2 General procedure for the synthesis of 26 (Method A).

    Procedure for the Synthesis of II-1 (Method B)

    2-(p-Tolyloxy)acetic acid (100 mg, 0.5993 mmol, 1.0 equiv) was dissolved in CH2Cl2 (10 mL). EDCI (144 mg, 0.7192 mmol, 1.2 equiv) and HOBt (91 mg, 0.7192 mmol, 1.2 equiv) were added. The mixture was then stirred at ambient temperature for 20 min. Et3N (166 μL, 1.1986 mmol, 2.0 equiv) and 26 (170 mg, 0.5993 mmol, 1.0 equiv) were added. The reaction was monitored using thin-layer chromatography (TLC). After the completion of the reaction was complete, 5 mL CH2Cl2 was added. The mixture was then washed with 20 mL of distilled water. The separated aqueous phase was extracted with CH2Cl2 (2×25 mL). The combined organic phases were washed with brine (3×10 mL), dried over anhydrous magnesium sulfate, filtered, and concentrated under reduced pressure. The residue was purified using silica gel column chromatography (petroleum ether/ethyl acetate = 3:1) to yield II-1. II-2~II-3, III-1~III-22 and 10~24 were synthesized using the same procedure (Figure 3).

    Figure 3 Procedure for the synthesis of II-1 (Method B).

    Procedure for the Synthesis of the Hit Compound 7 (Method C)

    Compound II-1 (100 mg, 0.2760 mmol, 1.0 equiv) was dissolved in methanol, and 2M NaOH solution (567μL, 1.1040 mmol, 4.0 eq) was added to the reaction system. The mixture was heated to 65 °C. After the reaction was complete, 1 M HCl was added until a pH of 2–3 was the precipitated precipitate was filtered. The filter cake was washed successively with water (5 mL) and CH2Cl2 (2 mL), and then dried to obtain compound 7. I-1~I-15 were synthesized using the same procedure (Figure 4).

    Figure 4 Procedure for the synthesis of the hit compound 7 (Method C).

    Procedure for the Synthesis of II-4 (Method D)

    Compound 7 (100 mg, 0.2838 mmol, 1.0 equiv) was dissolved in CH2Cl2 (5 mL). DCC (72 mg, 0.3406 mmol, 1.2 equiv), DMAP (46 mg, 0.3406 mmol, 1.2 equiv) and benzyl alcohol (30 μL, 0.2838 mmol, 1.0 equiv) were added. The reaction mixture was stirred at ambient temperature until completion as monitored by TLC. 25 mL CH2Cl2 was added to dilute the solution. The system was then washed once with 20 mL of distilled water, and the aqueous phase was extracted twice with 25 mL of CH2Cl2. The organic phases were collected and washed three times with 10 mL of saturated salt water, dried with anhydrous magnesium sulfate, concentrated under reduced pressure, and subjected to silica gel column chromatography (petroleum ether/ethyl acetate = 2:1) to obtain II-4. Compound II-5 was synthesized using the same procedure (Figure 5).

    Figure 5 Procedure for the synthesis of II-4 (Method D).

    (2-Phenoxyacetyl)tryptophan (I-1)

    Method C: White solid; yield: 85.3%. HPLC purity: 100%. m.p. 154.3–155.7 °C. 1H NMR (400 MHz, DMSO-d6) δ 12.82 (s, 1H), 10.89 (s, 1H), 8.21–8.06 (m, 1H), 7.61–7.51 (m, 1H), 7.40–7.33 (m, 1H), 7.32–7.21 (m, 2H), 7.17–7.11 (m, 1H), 7.11–7.03 (m, 1H), 7.03–6.92 (m, 2H), 6.90–6.79 (m, 2H), 4.63–4.52 (m, 1H), 4.47 (s, 2H), 3.29–3.11 (m, 2H). 13C NMR (101 MHz, DMSO-d6) δ 172.99, 167.53, 157.52, 136.05, 129.40, 127.16, 123.67, 121.10, 120.91, 118.39, 118.12, 114.58, 111.38, 109.52, 66.47, 52.55, 26.74. HRMS (ESI+) m/z calculated for C19H18N2O4 [M + H] + 339.1339, found 339.1339.

    (2-(4-Methoxyphenoxy)acetyl)tryptophan (I-2)

    Method C: White solid; yield: 84.9%. HPLC purity: 100%. m.p. 160.1–161.5 °C. 1H NMR (400 MHz, DMSO-d6) δ 12.84 (s, 1H), 10.92 (s, 1H), 8.17–8.00 (m, 1H), 7.63–7.48 (m, 1H), 7.42–7.29 (m, 1H), 7.15 (s, 1H), 7.13–7.05 (m, 1H), 7.05–6.93 (m, 1H), 6.89–6.71 (m, 4H), 4.67–4.50 (m, 1H), 4.50–4.28 (m, 2H), 3.70 (s, 3H), 3.30–3.07 (m, 2H). 13C NMR (101 MHz, DMSO-d6) δ 172.94, 167.72, 153.75, 151.60, 136.08, 127.20, 123.72, 120.91, 118.40, 118.15, 115.64, 114.47, 111.35, 109.52, 67.44, 55.37, 52.51, 26.75. HRMS (ESI+) m/z calculated for C20H20N2O5 [M + H] + was 369.1445 and 369.1446.

    (2-(4-Fluorophenoxy)acetyl)tryptophan (I-3)

    Method C: White solid, 80.5% yield. HPLC purity: 100%. m.p. 159.1–160.7 °C. 1H NMR (400 MHz, DMSO-d6) δ 12.83 (s, 1H), 10.91 (s, 1H), 8.17 (d, J = 8.0 Hz, 1H), 7.56 (d, J = 7.9 Hz, 1H), 7.39–7.30 (m, 1H), 7.19–7.12 (m, 1H), 7.12–7.04 (m, 3H), 7.03–6.93 (m, 1H), 6.90–6.80 (m, 2H), 4.62–4.52 (m, 1H), 4.45 (s, 2H), 3.30–3.07 (m, 2H). 13C NMR (101 MHz, DMSO-d6) δ 172.93, 167.41, 156.75 (d, JF-C = 237.2 Hz), 153.93, 136.07, 127.18, 123.71, 120.91, 118.39, 118.10, 115.99 (d, JF-C = 8.1 Hz), 115.7 (d, JF-C = 22.2 Hz), 111.34, 109.56, 67.18, 52.60, 26.76. HRMS (ESI+) m/z values calculated for C19H17FN2O4 [M + H] + 357.1245, 357.1247.

    (2-(4-Chlorophenoxy)acetyl)tryptophan (I-4)

    Method C: White solid; yield: 84.6%. HPLC purity: 100%. m.p. 138.7–139.6 °C. 1H NMR (400 MHz, DMSO-d6) δ 12.83 (s, 1H), 10.89 (s, 1H), 8.20 (d, J = 8.0 Hz, 1H), 7.56 (d, J = 7.9 Hz, 1H), 7.39–7.33 (m, 1H), 7.33–7.23 (m, 2H), 7.19–7.13 (m, 1H), 7.12–7.03 (m, 1H), 7.02–6.93 (m, 1H), 6.90–6.80 (m, 2H), 4.64–4.53 (m, 1H), 4.48 (s, 2H), 3.29–3.05 (m, 2H). 13C NMR (101 MHz, DMSO-d6) δ 172.98, 167.23, 156.44, 136.05, 129.10, 127.15, 124.76, 123.67, 120.91, 118.40, 118.13, 116.35, 111.37, 109.59, 66.71, 52.62, 26.76. HRMS (ESI+) m/z calculated for C19H17ClN2O4 [M + H] + 373.0950, 373.0948.

    (2-(4-Bromophenoxy)acetyl)tryptophan (I-5)

    Method C: White solid; yield: 83.9%. HPLC purity: 96.40%. m.p. 165.7–166.9 °C. 1H NMR (400 MHz, DMSO-d6) δ 12.96 (s, 1H), 10.89 (s, 1H), 8.19 (d, J = 7.7 Hz, 1H), 7.56 (d, J = 7.7 Hz, 1H), 7.45–7.30 (m, 3H), 7.15 (s, 1H), 7.12–7.03 (m, 1H), 7.03–6.90 (m, 1H), 6.86–6.74 (m, 2H), 4.65–4.50 (m, 1H), 4.48 (s, 2H), 3.31–3.07 (m, 2H). 13C NMR (101 MHz, DMSO-d6) δ 172.98, 167.12, 156.92, 136.06, 132.01, 127.23, 123.69, 120.89, 118.37, 118.12, 116.90, 112.53, 111.35, 109.66, 66.75, 52.75, 26.82. The HRMS (ESI+) m/z calculated for C19H17BrN2O4 [M + H] + 417.0444 was found to be 417.0447.

    (2-(4-Formylphenoxy)acetyl)tryptophan (I-6)

    Method C: White solid; yield: 73.4%. HPLC purity: 100%. m.p. 160.4–161.9 °C. 1H NMR (400 MHz, DMSO-d6) δ 12.86 (s, 1H), 10.89 (s, 1H), 9.87 (s, 1H), 8.32 (d, J = 8.0 Hz, 1H), 7.86–7.76 (m, 2H), 7.57 (d, J = 7.9 Hz, 1H), 7.39–7.33 (m, 1H), 7.16 (s, 1H), 7.12–7.04 (m, 1H), 7.05–6.94 (m, 3H), 4.63 (s, 2H), 4.61–4.54 (m, 1H), 3.32–3.08 (m, 2H). 13C NMR (101 MHz, DMSO-d6) δ 191.24, 172.93, 166.87, 162.48, 136.08, 131.59, 130.01, 127.18, 123.70, 120.91, 118.40, 118.10, 115.04, 111.38, 109.59, 66.59, 52.71, 26.83. HRMS (ESI+) m/z calculated for C20H18N2O5 [M + H] + 367.1288, 367.1289.

    (2-(4-(Trifluoromethyl)phenoxy)acetyl)tryptophan (I-7)

    Method C: White solid; yield: 70.6%. HPLC purity: 100%. m.p. 175.4–176.3 °C. 1H NMR (400 MHz, DMSO-d6) δ 12.86 (s, 1H), 10.89 (s, 1H), 8.30 (d, J = 8.0 Hz, 1H), 7.62–7.52 (m, 3H), 7.35 (d, J = 8.1 Hz, 1H), 7.19–7.13 (m, 1H), 7.11–7.03 (m, 1H), 7.02–6.93 (m, 3H), 4.58 (s, 2H), 4.57–4.52 (m, 1H), 3.28–3.08 (m, 2H). 13C NMR (101 MHz, DMSO-d6) δ 172.99, 166.92, 160.44, 136.08, 127.21, 126.77, 124.44 (q, JF-C = 272.1 Hz), 123.72, 121.58 (q, JF-C = 32.2 Hz), 120.88, 118.38, 118.12, 115.09, 111.34, 109.67, 66.55, 52.74, 26.86. HRMS (ESI+) m/z values calculated for C20H17F3N2O4 [M + H] + 407.1213, 407.1215.

    (Furan-2-Carbonyl)tryptophan (I-8)

    Method C: white solid; yield: 78.0%. HPLC purity: 98.66%. m.p. 169.9–171.2 °C. 1H NMR (400 MHz, DMSO-d6) δ 12.73 (s, 1H), 10.81 (s, 1H), 8.37 (d, J = 8.1 Hz, 1H), 7.81 (s, 1H), 7.56 (d, J = 7.9 Hz, 1H), 7.35–7.29 (m, 1H), 7.18–7.14 (m, 1H), 7.14–7.10 (m, 1H), 7.09–7.01 (m, 1H), 6.70–6.92 (m, 1H), 6.63–6.57 (m, 1H), 4.68–4.59 (m, 1H), 3.29–3.16 (m, 2H). 13C NMR (101 MHz, DMSO-d6) δ 173.27, 157.63, 147.35, 145.13, 136.03, 127.06, 123.53, 120.91, 118.34, 118.10, 113.79, 111.82, 111.39, 110.12, 52.76, 26.53. HRMS (ESI+) m/z calculated for C16H14N2O4 [M + H] + 299.1026, 299.1028.

    (Thiophene-2-Carbonyl)tryptophan (I-9)

    Method C: White solid; yield: 75.9%. HPLC purity: 98.56%. m.p. 168.1–169.4 °C. 1H NMR (400 MHz, DMSO-d6) δ 12.71 (s, 1H), 10.80 (s, 1H), 8.67 (d, J = 8.1 Hz, 1H), 7.85–7.80 (m, 1H), 7.77–7.71 (m, 1H), 7.59 (d, J = 7.8 Hz, 1H), 7.35–7.29 (m, 1H), 7.20–7.16 (m, 1H), 7.16–7.11 (m, 1H), 7.09–7.02 (m, 1H), 7.01–6.94 (m, 1H), 4.70–4.55 (m, 1H), 3.29–3.11 (m, 2H). 13C NMR (101 MHz, DMSO-d6) δ 173.40, 161.14, 139.39, 136.06, 130.96, 128.52, 127.86, 127.09, 123.54, 120.94, 118.39, 118.12, 111.41, 110.30, 53.53, 26.72. HRMS (ESI+) m/z calculated for C16H14N2O3S [M + H] + 315.0798; observed: 315.0800.

    4.1.10. (thiazole-4-carbonyl)tryptophan (I-10). Method C: White solid; yield: 81.5%. HPLC purity: 97.76%. m.p. 169.2–170.3 °C. 1H NMR (400 MHz, DMSO-d6) δ 12.91 (s, 1H), 10.85 (s, 1H), 9.14 (s, 1H), 8.38–8.29 (m, 1H), 8.22 (d, J = 8.0 Hz, 1H), 7.52 (d, J = 7.9 Hz, 1H), 7.36–8.28 (m, 1H), 7.19–7.10 (m, 1H), 7.09–7.01 (m, 1H), 6.98–6.89 (m, 1H), 4.81–4.66 (m, 1H), 3.31 (s, 2H). 13C NMR (101 MHz, DMSO-d6) δ 172.94, 160.07, 155.03, 150.14, 136.07, 127.23, 124.47, 123.57, 120.94, 118.35, 111.36, 109.52, 52.67, 26.73. HRMS (ESI+) m/z calculated for C15H13N3O3S [M + H] + 316.0750 found that 316.0752.

    (Benzo[B]thiophene-2-Carbonyl)tryptophan (I-11)

    Method C: White solid; yield: 76.4%. HPLC purity: 98.93%. m.p. 148.1–149.3 °C. 1H NMR (400 MHz, DMSO-d6) δ 12.74 (s, 1H), 10.81 (s, 1H), 8.97 (d, J = 8.0 Hz, 1H), 8.18 (s, 1H), 8.03–7.98 (m, 1H), 7.97–7.91 (m, 1H), 7.61 (d, J = 7.8 Hz, 1H), 7.50–7.39 (m, 2H), 7.35–7.28 (m, 1H), 7.24–7.18 (m, 1H), 7.09–7.02 (m, 1H), 7.02–6.95 (m, 1H), 4.71–4.59 (m, 1H), 3.38–3.15 (m, 2H). 13C NMR (101 MHz, DMSO-d6) δ 173.30, 161.52, 140.17, 139.38, 139.06, 136.04, 127.03, 126.24, 125.20, 124.90, 123.57, 122.77, 120.92, 118.08, 111.40, 118.37, 110.21, 53.75, 26.66. HRMS (ESI+) m/z calculated for C20H16N2O3S [M + H] + 365.0954, 365.0957.

    (Benzo[D][1,3]dioxole-5-Carbonyl)tryptophan (I-12)

    Method C: White solid; yield: 77.0%. HPLC purity: 100%. m.p. 146.1–147.5 °C. 1H NMR (400 MHz, DMSO-d6) δ 12.63 (s, 1H), 10.80 (s, 1H), 8.46 (d, J = 7.8 Hz, 1H), 7.58 (d, J = 7.8 Hz, 1H), 7.42 (dd, J = 8.2, 1.5 Hz, 1H), 7.36 (d, J = 1.4 Hz, 1H), 7.32 (d, J = 8.0 Hz, 1H), 7.21–7.16 (m, 1H), 7.09–7.02 (m, 1H), 7.01–6.94 (m, 2H), 6.08 (s, 2H), 4.66–4.56 (m, 1H), 3.29–3.14 (m, 2H). 13C NMR (101 MHz, DMSO-d6) δ 173.61, 165.40, 149.73, 147.21, 136.06, 127.87, 127.10, 123.55, 122.40, 120.91, 118.35, 118.07, 111.40, 110.43, 107.72, 107.35, 101.62, 53.71, 26.64. HRMS (ESI+) m/z calculated for C19H16N2O5 [M + H] + 353.1132 and 353.1133.

    (1H-Indole-3-Carbonyl)tryptophan (I-13)

    Method C: White solid; yield: 85.3%. HPLC purity: 97.09%. m.p. 163.7–164.8 °C. 1H NMR (400 MHz, DMSO-d6) δ 12.53 (s, 1H), 11.56 (s, 1H), 10.81 (s, 1H), 8.12–8.06 (m, 1H), 8.02 (d, J = 7.9 Hz, 1H), 7.97 (d, J = 7.9 Hz, 1H), 7.67–7.58 (m, 1H), 7.41 (d, J = 8.0 Hz, 1H), 7.32 (d, J = 8.0 Hz, 1H), 7.24–7.18 (m, 1H), 7.18–7.10 (m, 1H), 7.09–7.02 (m, 2H), 7.02–6.94 (m, 1H), 4.77–4.64 (m, 1H), 3.29–3.12 (m, 2H). 13C NMR (101 MHz, DMSO-d6) δ 170.68, 150.58, 150.50, 136.04, 128.88, 128.67, 128.31, 123.63, 122.92, 120.66, 120.14, 119.93, 118.45, 118.11, 115.97, 111.19, 110.59, 54.29, 44.38, 27.49. HRMS (ESI+) m/z calculated for C20H17N3O3 [M + H] + 348.1343, 348.1346.

    (9H-Xanthene-9-Carbonyl)tryptophan (I-14)

    Method C: White solid; yield: 83.6%. HPLC purity: 100%. m.p. 157.4–158.6 °C. 1H NMR (400 MHz, DMSO-d6) δ 10.82 (s, 1H), 8.49 (s, 1H), 7.56 (d, J = 7.8 Hz, 1H), 7.34 (d, J = 8.0 Hz, 1H), 7.30–7.15 (m, 3H), 7.14–6.99 (m, 5H), 6.99–6.92 (m, 1H), 6.90–6.82 (m, 1H), 6.81–6.74 (m, 1H), 5.01 (s, 1H), 4.34 (s, 1H), 3.13–2.99 (m, 2H). 13C NMR (101 MHz, DMSO-d6) δ 174.16, 164.52, 136.06, 128.24, 127.24, 126.01, 123.55, 121.84, 120.84, 120.36, 118.34, 111.80, 111.37, 110.49, 110.06, 52.89, 27.05. HRMS (ESI+) m/z values calculated for C25H20N2O4 [M + H] + were 413.1496, 413.1498.

    (1-Methyl-1H-Indole-3-Carbonyl)tryptophan (I-15)

    Method C: White solid; yield: 83.7%. HPLC purity: 100%. m.p. 172.5–173.6 °C. 1H NMR (400 MHz, DMSO-d6) δ 12.59 (s, 1H), 10.81 (s, 1H), 8.07 (s, 1H), 8.03 (d, J = 7.9 Hz, 1H), 7.94 (d, J = 7.9 Hz, 1H), 7.65–7.56 (m, 1H), 7.51–7.44 (m, 1H), 7.36–7.28 (m, 1H), 7.24–7.16 (m, 2H), 7.15–7.09 (m, 1H), 7.08–7.02 (m, 1H), 7.02–6.95 (m, 1H), 4.76–4.66 (m, 1H), 3.82 (s, 3H), 3.30–3.12 (m, 2H). 13C NMR (101 MHz, DMSO-d6) δ 173.56, 167.97, 158.04, 136.55, 129.90, 127.73, 124.17, 121.61, 121.39, 118.88, 118.65, 115.10, 111.86, 110.11, 67.03, 53.18, 27.28. HRMS (ESI+) m/z calculated for C21H19N3O3 [M + H] + 362.1499, found 362.1504.

    Methyl (2-(p-Tolyloxy)acetyl)tryptophanate (II-1)

    Method B: White solid, yield: 80.1%. yield HPLC purity: 100%. m.p. 113.3–114.2 °C. 1H NMR (400 MHz, CDCl3) δ 8.14 (s, 1H), 7.57 (d, J = 7.9 Hz, 1H), 7.38 (d, J = 8.1 Hz, 1H), 7.26–7.17 (m, 1H), 7.18–7.03 (m, 4H), 6.94–6.86 (m, 1H), 6.73–6.62 (m, 2H), 5.10–4.91 (m, 1H), 4.53–4.40 (m, 2H), 3.71 (s, 3H), 3.44–3.28 (m, 2H), 2.31 (s, 3H). 13C NMR (101 MHz, CDCl3) δ 171.90, 168.32, 155.11, 136.14, 131.37, 130.06, 127.48, 122.80, 122.27, 119.80, 118.51, 114.62, 111.27, 109.71, 67.48, 52.43, 52.42, 27.63, 20.50. HRMS (ESI+) m/z calculated for C21H22N2O4 [M + H] + 367.1652, 367.1649.

    Ethyl (2-(p-Tolyloxy)acetyl)tryptophanate (II-2)

    Method B: White solid, yield: 85.4%. yield HPLC purity: 100%. m.p. 128.9–129.9 °C. 1H NMR (400 MHz, CDCl3) δ 8.18 (s, 1H), 7.59 (d, J = 7.9 Hz, 1H), 7.37 (d, J = 8.1 Hz, 1H), 7.25–7.14 (m, 2H), 7.14–7.03 (m, 3H), 6.96–6.87 (m, 1H), 6.76–6.63 (m, 2H), 5.07–4.91 (m, 1H), 4.53–4.39 (m, 2H), 4.24–4.05 (m, 2H), 3.46–3.27 (m, 2H), 2.31 (s, 3H), 1.29–1.14 (m, 3H). 13C NMR (101 MHz, CDCl3) δ 171.57, 168.50, 155.15, 136.27, 131.37, 130.07, 127.58, 123.07, 122.15, 119.65, 118.46, 114.68, 111.43, 109.51, 67.53, 61.64, 52.76, 27.69, 20.49, 14.09. HRMS (ESI+) m/z calculated for C22H24N2O4 [M + H] + 381.1809, found 381.1809.

    Isopropyl (2-(p-Tolyloxy)acetyl)tryptophanate (II-3)

    Method B: White solid; yield: 81.9%. HPLC purity: 100%. m.p. 107.3–108.4 °C. 1H NMR (400 MHz, CDCl3) δ 8.17 (s, 1H), 7.60 (d, J = 7.9 Hz, 1H), 7.37 (d, J = 8.1 Hz, 1H), 7.24–7.18 (m, 1H), 7.18–7.14 (m, 1H), 7.14–7.08 (m, 1H), 7.07 (d, J = 8.7 Hz, 2H), 6.99–6.91 (m, 1H), 6.69 (d, J = 8.6 Hz, 2H), 5.07–4.88 (m, 2H), 4.53–4.37 (m, 2H), 3.41–3.27 (m, 2H), 2.31 (s, 3H), 1.21–1.14 (m, 6H). 13C NMR (101 MHz, CDCl3) δ 171.00, 168.23, 155.15, 136.08, 131.32, 130.05, 127.64, 122.70, 122.23, 119.72, 118.70, 114.62, 111.15, 109.99, 69.37, 67.53, 52.66, 27.72, 21.68, 20.50. HRMS (ESI+) m/z calculated for C23H26N2O4 [M + H] + 395.1965, found 395.1962.

    Benzyl (2-(p-Tolyloxy)acetyl)tryptophanate (II-4)

    Method D: pale yellow solid, yield: 78.2%. HPLC purity: 100%. m.p. 133.1–134.6 °C. 1H NMR (400 MHz, CDCl3) δ 8.13 (s, 1H), 7.56–7.48 (m, 1H), 7.37–7.28 (m, 4H), 7.26–7.12 (m, 4H), 7.10–7.03 (m, 1H), 7.01 (d, J = 8.4 Hz, 2H), 6.69 (s, 1H), 6.62 (d, J = 8.4 Hz, 2H), 5.13–5.06 (m, 2H), 5.06–5.99 (m, 1H), 4.41 (s, 2H), 3.41–3.27 (m, 2H), 2.27 (s, 3H). 13C NMR (101 MHz, CDCl3) δ 171.30, 168.42, 155.11, 136.18, 135.26, 131.35, 130.08, 128.59, 128.42, 127.54, 123.02, 122.98, 122.20, 119.79, 118.52, 114.65, 111.33, 109.44, 67.51, 67.27, 52.69, 27.71, 20.48. HRMS (ESI+) m/z calculated for C27H26N2O4 [M + H] + 443.1965, found 443.1963.

    Cyclohexyl (2-(p-Tolyloxy)acetyl)tryptophanate (II-5)

    Method D: pale yellow solid; yield: 81.4%. HPLC purity: 100%. m.p. 119.0–120.1 °C. 1H NMR (400 MHz, CDCl3) δ 8.24 (s, 1H), 7.57 (d, J = 7.9 Hz, 1H), 7.32 (d, J = 8.1 Hz, 1H), 7.16 (q, J = 7.3 Hz, 2H), 7.07 (t, J = 7.5 Hz, 1H), 7.02 (d, J = 8.3 Hz, 2H), 6.93–6.88 (m, 1H), 6.64 (d, J = 8.4 Hz, 2H), 5.03–4.90 (m, 1H), 4.82–4.66 (m, 1H), 4.50–4.30 (m, 2H), 3.42–3.23 (m, 2H), 2.27 (s, 3H), 1.72–1.22 (m, 10H). 13C NMR (101 MHz, CDCl3) δ 170.89, 168.28, 155.16, 136.15, 131.31, 130.03, 127.66, 122.82, 122.17, 119.67, 118.66, 114.63, 111.21, 109.88, 74.05, 67.55, 52.83, 31.33, 27.83, 25.27, 23.51, 20.46. HRMS (ESI+) m/z calculated for C26H30N2O4 [M + H] + 435.2278, 435.2276.

    Isopropyl (2-Phenoxyacetyl)tryptophanate (III-1)

    Method B: White solid, 82.7% yield. HPLC purity: 100%. m.p. 144.2–145.3 °C. 1H NMR (400 MHz, CDCl3) δ 8.05 (s, 1H), 7.58 (d, J = 7.9 Hz, 1H), 7.34 (d, J = 8.1 Hz, 1H), 7.29–7.22 (m, 1H), 7.21–7.16 (m, 1H), 7.15–7.05 (m, 2H), 7.02–6.95 (m, 1H), 6.94–6.87 (m, 1H), 6.82–6.73 (m, 2H), 5.03–4.90 (m, 2H), 4.52–4.40 (m, 2H), 3.42–3.26 (m, 2H), 1.16 (t, J = 6.7 Hz, 6H). 13C NMR (101 MHz, CDCl3) δ 170.98, 168.03, 157.13, 136.05, 129.66, 127.61, 122.73, 122.24, 122.20, 119.73, 118.67, 114.74, 111.19, 109.90, 69.42, 67.22, 52.67, 27.67, 21.65. HRMS (ESI+) m/z calculated for C22H24N2O4 [M + H] + 381.1809, found 381.1808.

    Isopropyl (2-(4-Methoxyphenoxy)acetyl)tryptophanate (III-2)

    Method B: White solid; yield: 74.5%. HPLC purity: 100%. m.p. 158.2–159.4 °C. 1H NMR (400 MHz, CDCl3) δ 8.07 (s, 1H), 7.50 (d, J = 7.9 Hz, 1H), 7.26 (d, J = 8.1 Hz, 1H), 7.15–7.08 (m, 1H), 7.08–7.03 (m, 1H), 7.03–6.97 (m, 1H), 6.88–6.81 (m, 1H), 6.69 (d, J = 9.1 Hz, 2H), 6.61 (d, J = 9.1 Hz, 2H), 4.96–4.82 (m, 2H), 4.39–4.25 (m, 2H), 3.68 (s, 3H), 3.31–3.22 (m, 2H), 1.13–1.05 (m, 6H). 13C NMR (101 MHz, CDCl3) δ 171.07, 168.43, 154.66, 151.41, 136.17, 127.65, 122.88, 122.15, 119.64, 118.60, 115.87, 114.73, 111.28, 109.76, 69.44, 68.21, 55.65, 52.78, 27.70, 21.72. HRMS (ESI+) m/z calculated for C23H26N2O5 [M + H] + 411.1914, 411.1911.

    Isopropyl (2-(4-Isopropylphenoxy)acetyl)tryptophanate (III-3)

    Method B: White solid; yield: 77.2%. HPLC purity: 100%. m.p. 162.4–163.7 °C. 1H NMR (400 MHz, CDCl3) δ 8.05 (s, 1H), 7.58 (d, J = 7.9 Hz, 1H), 7.34 (d, J = 8.1 Hz, 1H), 7.12–7.16 (m, 1H), 7.16–7.04 (m, 4H), 6.95–6.87 (m, 1H), 6.71 (d, J = 8.6 Hz, 2H), 5.08–4.85 (m, 2H), 4.51–4.34 (m, 2H), 3.42–3.26 (m, 2H), 2.94–2.78 (m, 1H), 1.22 (d, J = 6.9 Hz, 6H), 1.14 (t, J = 6.4 Hz, 6H). 13C NMR (101 MHz, CDCl3) δ 171.07, 168.41, 155.35, 142.54, 136.22, 127.67, 127.44, 122.99, 122.14, 119.65, 118.62, 114.70, 111.34, 109.69, 69.45, 67.55, 52.86, 33.34, 27.76, 24.18, 21.67. HRMS (ESI+) m/z calculated for C25H30N2O4 [M + H] + was 423.2278, found to be 423.2278.

    Isopropyl (2-(4-Fluorophenoxy)acetyl)tryptophanate (III-4)

    Method B: White solid; yield: 81.4%. HPLC purity: 100%. m.p. 185.1–186.4 °C. 1H NMR (400 MHz, CDCl3) δ 8.07 (s, 1H), 7.57 (d, J = 7.9 Hz, 1H), 7.35 (d, J = 8.1 Hz, 1H), 7.24–7.15 (m, 1H), 7.13–7.00 (m, 2H), 6.98–6.86 (m, 3H), 6.73–6.61 (m, 2H), 5.07–4.85 (m, 2H), 4.49–4.31 (m, 2H), 3.41–6.26 (m, 2H), 1.24–1.11 (m, 6H). 13C NMR (101 MHz, CDCl3) δ 171.00, 167.90, 157.87 (d, JF-C = 240.9 Hz), 153.30 (d, JF-C = 2.2 Hz), 136.11, 127.67, 122.73, 122.24, 119.72, 118.57, 116.01 (d, JF-C = 22.4 Hz), 115.94 (d, JF-C = 8.2 Hz), 111.25, 109.84, 69.48, 68.03, 52.80, 27.59, 21.67. HRMS (ESI+) m/z calculated for C22H23FN2O4 [M + H] + 399.1715, 399.1712.

    Isopropyl (2-(4-Chlorophenoxy)acetyl)tryptophanate (III-5)

    Method B: White solid, 86.1% yield HPLC purity: 100%. m.p. 166.4–167.9 °C. 1H NMR (400 MHz, CDCl3) δ 8.12 (s, 1H), 7.56 (d, J = 7.9 Hz, 1H), 7.35 (d, J = 8.1 Hz, 1H), 7.23–7.14 (m, 3H), 7.11–7.05 (m, 1H), 7.05–7.98 (m, 1H), 6.95–6.87 (m, 1H), 6.69–6.58 (m, 2H), 5.04–4.96 (m, 1H), 4.96–4.89 (m, 1H), 4.47–4.30 (m, 2H), 3.44–3.22 (m, 2H), 1.24–1.09 (m, 6H). 13C NMR (101 MHz, CDCl3) δ 171.00, 167.70, 155.72, 136.13, 129.51, 127.64, 126.96, 122.77, 122.24, 119.73, 118.52, 116.05, 111.30, 109.73, 69.48, 67.56, 52.84, 27.54, 21.69. HRMS (ESI+) m/z calculated for C22H23ClN2O4 [M + H] + 415.1419, found, 415.1419.

    Isopropyl (2-(4-Bromophenoxy)acetyl)tryptophanate (III-6)

    Method B: White solid; yield: 84.8%. HPLC purity: 95.74%. m.p. 181.5–182.4 °C. 1H NMR (400 MHz, CDCl3) δ 8.15 (s, 1H), 7.59 (d, J = 7.9 Hz, 1H), 7.38 (d, J = 8.1 Hz, 1H), 7.33 (d, J = 9.0 Hz, 2H), 7.25–7.18 (m, 1H), 7.14–7.07 (m, 1H), 7.07–6.99 (m, 1H), 6.98–6.89 (m, 1H), 6.61 (d, J = 9.0 Hz, 2H), 5.08–4.98 (m, 1H), 4.98–4.90 (m, 1H), 4.50–4.36 (m, 2H), 3.43–3.27 (m, 2H), 1.27–1.17 (m, 6H). 13C NMR (101 MHz, CDCl3) δ 170.95, 167.60, 156.12, 136.03, 132.38, 127.55, 122.70, 122.21, 119.69, 118.49, 116.45, 114.24, 111.26, 109.63, 69.48, 67.35, 52.75, 27.45, 21.69. HRMS (ESI+) m/z values calculated for C22H23BrN2O4 [M + H] + 459.0914, 459.0913.

    Isopropyl (2-(4-Formylphenoxy)acetyl)tryptophanate (III-7)

    Method B: White solid; yield: 85.1%. HPLC purity: 100%. m.p. 174.6–175.9 °C. 1H NMR (400 MHz, CDCl3) δ 9.90 (s, 1H), 8.17 (s, 1H), 7.79 (d, J = 8.6 Hz, 2H), 7.58 (d, J = 7.9 Hz, 1H), 7.37 (d, J = 8.2 Hz, 1H), 7.26–7.17 (m, 1H), 7.12–7.06 (m, 1H), 7.06–6.99 (m, 1H), 6.99–6.94 (m, 1H), 6.83 (d, J = 8.7 Hz, 2H), 5.10–4.99 (m, 1H), 5.00–4.92 (m, 1H), 4.62–4.46 (m, 2H), 3.38 (s, 2H), 1.27–1.13 (m, 6H). 13C NMR (101 MHz, CDCl3) δ 189.73, 169.91, 166.05, 160.68, 135.12, 130.89, 129.83, 126.59, 121.82, 121.16, 118.66, 117.38, 113.97, 110.35, 108.49, 68.50, 66.12, 51.84, 26.40, 20.66. HRMS (ESI+) m/z calculated for C23H24N2O5 [M + H] + 409.1758, 409.1755.

    Isopropyl (2-(4-(Trifluoromethyl)phenoxy)acetyl)tryptophanate (III-8)

    Method B: White solid; 79.3% yield. HPLC purity: 100%. m.p. 183.0–184.6 °C. 1H NMR (400 MHz, CDCl3) δ 8.04 (s, 1H), 7.56 (d, J = 8.0 Hz, 1H), 7.48 (d, J = 8.6 Hz, 2H), 7.35 (d, J = 8.2 Hz, 1H), 7.24–7.15 (m, 1H), 7.13–7.04 (m, 1H), 7.03–6.96 (m, 1H), 6.95–6.89 (m, 1H), 6.77 (d, J = 8.6 Hz, 2H), 5.05–4.97 (m, 1H), 4.97–4.89 (m, 1H), 4.55–4.41 (m, 2H), 3.44–3.23 (m, 2H), 1.24–1.11 (m, 6H). 13C NMR (101 MHz, CDCl3) δ 170.91, 167.23, 159.42, 136.10, 127.65, 127.06, 124.21 (q, JF-C = 32.8 Hz), 124.16 (q, JF-C = 272.3 Hz), 122.59, 122.33, 119.81, 118.51, 114.73, 111.26, 109.81, 69.49, 67.27, 52.85, 27.46, 21.68. HRMS (ESI+) m/z calculated for C23H23F3N2O4 [M + H] + 449.1683, found 449.1682.

    Isopropyl (Furan-2-Carbonyl)tryptophanate (III-9)

    Method B: White solid, yield: 78.1%. HPLC purity: 99.66%. m.p. 186.3–187.5 °C. 1H NMR (600 MHz, CDCl3) δ 8.38 (s, 1H), 7.58 (d, J = 7.9 Hz, 1H), 7.40–7.34 (m, 1H), 7.32 (d, J = 8.1 Hz, 1H), 7.19–7.12 (m, 1H), 7.12–7.03 (m, 2H), 7.03–6.99 (m, 1H), 6.96–6.85 (m, 1H), 6.48–6.39 (m, 1H), 5.11–5.02 (m, 1H), 5.02–4.90 (m, 1H), 3.55–3.24 (m, 2H), 1.25–1.01 (m, 6H). 13C NMR (101 MHz, CDCl3) δ 171.33, 157.92, 147.51, 144.23, 136.11, 127.67, 122.84, 122.15, 119.53, 118.81, 114.61, 112.08, 111.19, 110.04, 69.46, 52.78, 28.07, 21.66. HRMS (ESI+) m/z calculated for C19H20N2O4 [M + H] + 341.1496, found 341.1494.

    Isopropyl (Thiophene-2-Carbonyl)tryptophanate (III-10)

    Method B: White solid; yield: 79.8%. HPLC purity: 99.71%. m.p. 176.1–177.8 °C. 1H NMR (600 MHz, CDCl3) δ 8.30 (s, 1H), 7.63–7.53 (m, 1H), 7.47–7.30 (m, 1H), 7.38–7.30 (m, 2H), 7.22–7.12 (m, 1H), 7.11–7.03 (m, 1H), 7.03–6.95 (m, 2H), 6.63–6.46 (m, 1H), 5.10–5.03 (m, 1H), 5.04–4.94 (m, 1H), 3.47–3.30 (m, 2H), 1.23–1.15 (m, 6H). 13C NMR (101 MHz, CDCl3) δ 171.40, 161.56, 138.43, 136.20, 130.32, 128.47, 127.76, 122.99, 122.18, 119.63, 118.72, 111.37, 109.85, 69.54, 53.63, 27.81, 21.74. HRMS (ESI+) m/z values calculated for C19H20N2O3S [M + H] + 357.1267, 357.1263.

    Isopropyl Picolinoyltryptophanate (III-11)

    Method B: White solid; yield: 81.6%. HPLC purity: 100%. m.p. 145.9–150.7 °C. 1H NMR (600 MHz, CDCl3) δ 8.63–8.47 (m, 1H), 8.46–8.37 (m, 1H), 8.34 (s, 1H), 8.18–7.95 (m, 1H), 7.76–7.63 (m, 1H), 7.58–7.45 (m, 1H), 7.39–7.14 (m, 2H), 7.13–7.02 (m, 1H), 7.01–6.81 (m, 2H), 5.12–4.96 (m, 1H), 4.94–4.76 (m, 1H), 3.46–3.14 (m, 2H), 1.17–0.93 (m, 6H). 13C NMR (101 MHz, CDCl3) δ 171.51, 164.20, 149.40, 148.24, 137.23, 136.23, 127.61, 126.27, 123.02, 122.17, 121.95, 119.31, 118.79, 111.26, 110.03, 69.29, 53.32, 28.23, 21.74. HRMS (ESI+) m/z calculated for C20H21N3O3 [M + H] + 352.1656, found 352.1654.

    Isopropyl (Thiazole-4-Carbonyl)tryptophanate (III-12)

    Method B: White solid, 80.5% yield. HPLC purity: 100%. m.p. 150.6–151.8 °C. 1H NMR (600 MHz, CDCl3) δ 8.67 (s, 1H), 8.28 (s, 1H), 8.14 (s, 1H), 7.93 (d, J = 8.0 Hz, 1H), 7.60 (d, J = 7.9 Hz, 1H), 7.36–7.27 (m, 1H), 7.19–7.10 (m, 1H), 7.10–6.97 (m, 2H), 5.10–5.03 (m, 1H), 5.02–4.93 (m, 1H), 3.55–3.18 (m, 2H), 1.22–1.07 (m, 6H). 13C NMR (101 MHz, CDCl3) δ 171.34, 160.68, 152.80, 150.59, 136.22, 127.59, 123.54, 122.92, 122.02, 119.39, 118.75, 111.28, 109.97, 69.39, 53.18, 28.17, 21.73. HRMS (ESI+) m/z values calculated for C18H19N3O3S [M + H] + 358.1220, 358.1217.

    Isopropyl (Benzo[B]thiophene-2-Carbonyl)tryptophanate (III-13)

    Method B: White solid; yield: 81.9%. HPLC purity: 100%. m.p. 133.5–134.9 °C. 1H NMR (600 MHz, CDCl3) δ 8.50 (s, 1H), 7.76 (d, J = 8.0 Hz, 1H), 7.68 (d, J = 7.8 Hz, 1H), 7.64–7.56 (m, 1H), 7.52 (s, 1H), 7.41–7.28 (m, 3H), 7.21–7.11 (m, 1H), 7.10–7.03 (m, 1H), 7.02–6.94 (m, 1H), 6.85–6.70 (m, 1H), 5.14–5.05 (m, 1H), 5.04–4.93 (m, 1H), 3.50–3.32 (m, 2H), 1.24–1.11 (m, 6H). 13C NMR (101 MHz, CDCl3) δ 171.28, 161.93, 141.03, 139.03, 138.04, 136.19, 127.80, 126.38, 125.51, 125.10, 124.86, 123.06, 122.67, 122.24, 119.71, 118.79, 111.36, 109.88, 69.61, 53.82, 27.77, 21.79. HRMS (ESI+) m/z values calculated for C23H22N2O3S [M + H] + 407.1424, 407.1420.

    Isopropyl (Benzo[D][1,3]dioxole-5-Carbonyl)tryptophanate (III-14)

    Method B: White solid; yield: 82.4%. HPLC purity: 99.71%. m.p. 144.8–145.7 °C. 1H NMR (600 MHz, CDCl3) δ 8.38 (s, 1H), 7.56 (d, J = 7.9 Hz, 1H), 7.32 (d, J = 8.1 Hz, 1H), 7.22–7.13 (m, 3H), 7.10–7.02 (m, 1H), 7.01–6.94 (m, 1H), 6.80–6.66 (m, 1H), 6.63–6.52 (m, 1H), 5.97 (s, 2H), 5.09–5.03 (m, 1H), 5.03–4.92 (m, 1H), 3.50–3.25 (m, 2H), 1.23–1.13 (m, 6H). 13C NMR (101 MHz, CDCl3) δ 171.55, 166.16, 150.40, 147.86, 136.03, 128.17, 127.78, 122.74, 122.23, 121.78, 119.63, 118.85, 111.19, 110.22, 107.92, 107.67, 101.64, 69.38, 53.61, 27.66, 21.72. HRMS (ESI+) m/z calculated for C22H22N2O5 [M + H] + 395.1601, found 395.1601.

    Isopropyl (2,3-Dihydrobenzo[B][1,4]dioxine-2-Carbonyl)tryptophanate (III-15)

    Method B: White solid; 79.1% yield HPLC purity: 100%. m.p. 137.6–138.7 °C. 1H NMR (600 MHz, CDCl3) δ 8.42–8.04 (m, 1H), 7.68–7.45 (m, 1H), 7.39–7.25 (m, 1H), 7.23–7.00 (m, 3H), 6.99–6.76 (m, 4H), 6.75–6.59 (m, 1H), 5.00–4.91 (m, 1H), 4.90–4.83 (m, 1H), 4.66–4.57 (m, 1H), 4.51–4.30 (m, 1H), 4.16–4.02 (m, 1H), 3.40–3.20 (m, 2H), 1.21–1.07 (m, 6H). 13C NMR (101 MHz, CDCl3) δ 170.87 and 170.60, 166.96 and 166.89, 143.34 and 143.12, 141.67 and 141.52, 136.11 and 135.91, 127.68 and 127.50, 122.73 and 122.70, 122.38 and 122.28, 122.23 and 122.21, 121.93 and 121.82, 119.82 and 119.60, 118.73 and 118.64, 117.61 and 117.52, 117.37 and 117.21, 111.26 and 111.11, 109.93 and 109.63, 73.16 and 73.06, 69.65 and 69.40, 65.19 and 64.87, 52.98 and 52.81, 27.57 and 27.45, 21.71 and 21.66. HRMS (ESI+) m/z calculated for C23H24N2O5 [M + H] + 409.1758, 409.1755.

    Isopropyl (1H-Indole-3-Carbonyl)tryptophanate (III-16)

    Method B: Gray solid, yield: 78.2%. HPLC purity: 100%. m.p. 154.9–156.1 °C. 1H NMR (600 MHz, DMSO-d6) δ 11.60 (s, 1H), 10.85 (s, 1H), 8.18–8.09 (m, 2H), 8.05 (d, J = 8.0 Hz, 1H), 7.66–7.53 (m, 1H), 7.43 (d, J = 8.1 Hz, 1H), 7.35 (d, J = 8.1 Hz, 1H), 7.27–7.19 (m, 1H), 7.15 (t, J = 8.0 Hz, 1H), 7.13–7.04 (m, 2H), 7.04–6.96 (m, 1H), 4.93–4.83 (m, 1H), 4.75–4.66 (m, 1H), 3.32–3.15 (m, 2H), 1.21–1.03 (m, 6H). 13C NMR (101 MHz, DMSO-d6) δ 172.19, 164.52, 136.03, 128.29, 127.16, 126.03, 123.64, 123.57, 121.85, 120.86, 120.36, 118.34, 118.05, 111.78, 111.39, 109.89, 67.66, 53.20, 27.08, 26.32, 21.55, 21.28. HRMS (ESI+) m/z calculated for C23H23N3O3 [M + H] + 390.1812, found 390.1810.

    Isopropyl (Quinoline-3-Carbonyl)tryptophanate (III-17)

    Method B: White solid, yield: 84.0%. HPLC purity: 100%. m.p. 157.7–158.8 °C. 1H NMR (600 MHz, CDCl3) δ 9.15 (s, 1H), 8.52 (s, 1H), 8.32 (s, 1H), 8.18–8.01 (m, 1H), 7.86–7.69 (m, 2H), 7.65–7.47 (m, 2H), 7.40–7.28 (m, 1H), 7.23–7.13 (m, 1H), 7.12–7.04 (m, 1H), 7.04–7.00 (m, 1H), 6.98–6.83 (m, 1H), 5.21–5.11 (m, 1H), 5.11–4.98 (m, 1H), 3.58–3.35 (m, 2H), 1.32–1.20 (m, 6H). 13C NMR (101 MHz, CDCl3) δ 171.40, 165.28, 149.16, 148.30, 136.20, 135.61, 131.28, 129.21, 128.76, 127.43, 126.74, 126.51, 122.93, 122.86, 122.36, 119.77, 118.60, 111.43, 109.90, 69.68, 53.89, 27.55, 21.82. HRMS (ESI+) m/z calculated for C24H23N3O3 [M + H] + 402.1812; found: 402.1812.

    Isopropyl (Quinoline-2-Carbonyl)tryptophanate (III-18)

    Method B: White solid; yield: 82.9%. HPLC purity: 100%. m.p. 166.4–167.5 °C. 1H NMR (400 MHz, CDCl3) δ 8.82 (d, J = 8.2 Hz, 1H), 8.37 (s, 1H), 8.28–8.17 (m, 2H), 7.98 (d, J = 8.5 Hz, 1H), 7.81 (d, J = 8.1 Hz, 1H), 7.75–7.63 (m, 2H), 7.56 (t, J = 7.5 Hz, 1H), 7.31 (d, J = 8.1 Hz, 1H), 7.15 (t, J = 7.5 Hz, 1H), 7.11–7.01 (m, 2H), 5.18–5.07 (m, 1H), 5.07–4.93 (m, 1H), 3.51–3.39 (m, 2H), 1.22–1.09 (m, 6H). 13C NMR (101 MHz, CDCl3) δ 171.52, 164.36, 149.20, 146.50, 137.36, 136.23, 129.95, 129.31, 127.94, 127.70, 127.61, 123.03, 122.96, 122.05, 119.49, 118.93, 118.68, 111.25, 110.20, 69.27, 53.58, 28.19, 21.61. HRMS (ESI+) m/z values calculated for C24H23N3O3 [M + H] + 402.1812, 402.1810.

    Isopropyl (Quinoline-6-Carbonyl)tryptophanate (III-19)

    Method B: pale yellow solid, yield: 78.3%. HPLC purity: 100%. m.p. 161.2–162.4 °C. 1H NMR (600 MHz, DMSO-d6) δ 10.86 (s, 1H), 9.09–9.01 (m, 1H), 9.01–8.95 (m, 1H), 8.54–8.49 (m, 1H), 8.49–8.43 (m, 1H), 8.21–8.12 (m, 1H), 8.12–8.04 (m, 1H), 7.67–7.53 (m, 2H), 7.38–7.31 (m, 1H), 7.30–7.21 (m, 1H), 7.12–7.04 (m, 1H), 7.03–7.96 (m, 1H), 5.00–4.82 (m, 1H), 4.79–4.59 (m, 1H), 3.32–3.23 (m, 2H), 1.31–0.97 (m, 6H). 13C NMR (101 MHz, DMSO-d6) δ 171.51, 166.04, 152.10, 148.68, 137.03, 136.12, 131.61, 128.94, 128.22, 127.77, 127.13, 127.00, 123.71, 122.16, 120.96, 118.39, 118.06, 111.43, 109.88, 67.92, 54.27, 26.73, 21.56, 21.30. HRMS (ESI+) m/z values calculated for C24H23N3O3 [M + H] + 402.1812, 402.1810.

    Isopropyl (Quinoxaline-2-Carbonyl)tryptophanate (III-20)

    Method B: Yellow solid; yield: 77.5%. HPLC purity: 100%. m.p. 159.4–160.2 °C. 1H NMR (400 MHz, DMSO-d6) δ 10.92 (s, 1H), 9.45 (s, 1H), 9.05–8.93 (m, 1H), 8.27–8.10 (m, 2H), 8.06–7.91 (m, 2H), 7.60 (d, J = 7.9 Hz, 1H), 7.36 (d, J = 8.1 Hz, 1H), 7.29–7.18 (m, 1H), 7.14–7.03 (m, 1H), 7.03–6.92 (m, 1H), 4.99–4.89 (m, 1H), 4.88–4.80 (m, 1H), 3.47–3.39 (m, 2H), 1.23–1.11 (m, 6H). 13C NMR (101 MHz, DMSO-d6) δ 170.75, 162.84, 143.43, 143.05, 139.61, 136.12, 132.06, 131.35, 129.38, 129.06, 127.21, 123.92, 123.85, 121.01, 118.42, 118.14, 111.44, 109.08, 68.46, 53.52, 26.72, 21.31. HRMS (ESI+) m/z calculated for C23H22N4O3 [M + H] + 403.1765, 403.1766.

    Isopropyl (Quinoxaline-6-Carbonyl)tryptophanate (III-21)

    Method B: White solid; yield: 74.9%. HPLC purity: 100%. m.p. 176.4–177.5 °C. 1H NMR (600 MHz, DMSO-d6) δ 10.87 (s, 1H), 9.23 (d, J = 7.4 Hz, 1H), 9.10–8.92 (m, 2H), 8.70–8.55 (m, 1H), 8.24 (dd, J = 8.7, 1.9 Hz, 1H), 8.18 (d, J = 8.7 Hz, 1H), 7.61 (d, J = 7.9 Hz, 1H), 7.35 (d, J = 8.1 Hz, 1H), 7.27 (d, J = 2.1 Hz, 1H), 7.13–7.03 (m, 1H), 7.03–6.95 (m, 1H), 4.96–4.87 (m, 1H), 4.77–4.66 (m, 1H), 3.34–3.25 (m, 2H), 1.26–1.03 (m, 6H). 13C NMR (101 MHz, DMSO-d6) δ 171.38, 165.54, 147.02, 146.63, 143.38, 141.49, 136.12, 134.89, 129.31, 128.59, 127.08, 123.73, 123.66, 120.95, 118.39, 118.02, 111.42, 109.89, 67.97, 54.31, 26.62, 21.27. HRMS (ESI+) m/z calculated for C23H22N4O3 [M + H] + 403.1765, 403.1763.

    Isopropyl (9H-Xanthene-9-Carbonyl)tryptophanate (III-22)

    Method B: White solid, yield: 80.7%. yield HPLC purity: 100%. m.p. 154.8–155.7 °C. 1H NMR (400 MHz, CDCl3) δ 7.92 (s, 1H), 7.44–7.37 (m, 1H), 7.31–7.25 (m, 3H), 7.24–7.19 (m, 2H), 7.19–7.13 (m, 1H), 7.13–7.09 (m, 1H), 7.08–6.95 (m, 4H), 6.52–6.44 (m, 1H), 5.94–5.77 (m, 1H), 4.89–4.75 (m, 2H), 4.76–4.64 (m, 1H), 3.21–3.08 (m, 2H), 1.05–0.92 (m, 6H). 13C NMR (101 MHz, DMSO-d6) δ 171.05, 150.56, 136.15, 128.53, 127.16, 123.84, 123.77, 123.05, 120.96, 119.79, 119.57, 118.40, 118.10, 116.25, 111.40, 109.43, 68.03, 53.40, 44.23, 26.99, 21.26. HRMS (ESI+) m/z calculated for C28H26N2O4 [M + H] + 455.1965, found 455.1965.

    MD Simulation

    Gromacs2023 was selected as the kinetic simulation software, and Amber99sb-ildn was selected as the protein and small molecule force field. TIP3P water was added to the system using the TIP3P model to establish a water box with a size of 10*10*10 nm3 (the edge of the water box was at least 1.2 nm away from the protein edge), and an automatic ion equilibrium system was added.

    Particle-mesh Ewald (PME) was used to handle electrostatic interactions, and energy minimization was used for the maximum number of steps (50,000 steps) using the steepest descent method. The cut-off distance of the Coulomb force and the van der Waals radius were both 1 nm, and the canonical system (NVT) and the isothermal and isobaric system (NPT) were adopted to balance the system. Then, the MD simulation of 100ns was performed at room temperature and pressure. The non-bonding interaction cut-off value was set to 10 Å. The simulated temperature was controlled at 300 K using the V-rescale temperature coupling method and the pressure at 1 bar using the Berendsen method.

    The built-in analysis module of Gromacs2023 was used to analyze the simulated trajectories. RMSD (root mean square deviation) was used to observe the overall conformational change of the protein relative to the initial structure during the simulation. Rg (radius of gyration) was used to evaluate the tightness of the architecture. RMSF (root mean square function) was used to observe the structural fluctuations of local amino acid residue sites in the system during the simulation process. Gmx_MMPBSA was used to calculate the binding free energy. Binding free energy calculations were performed by subtracting the free energies of the isolated receptor (ΔGreceptor) and ligand (ΔGligand) from that of the bound complex (ΔGcomplex), as expressed by the fundamental equation ΔGbind = ΔGcomplex – ΔGreceptor – ΔGligand.

    Cell Lines

    HEK293 cells were obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). Cells were grown in corresponding medium containing 10% fetal bovine serum (FBS) at 37 °C and 5% CO2.

    P2Y14R Inhibitory Activity Screening

    HEK293-hP2Y14R cells were seeded in each well of a 96-well plate. Cells were treated with the tested compounds and then stimulated with the intracellular cAMP inducer, forskolin (TargetMol, Shanghai, China). cAMP levels were detected using a cAMP-Glo Assay Kit (Promega, Madison, Wisconsin, USA). This is a common method used to evaluate adenylate cyclase activity, which is closely related to P2Y14R function.10–12 Prism was used to analyze the data, and the curve-fitting equation was “log(antagonist) vs response-variable slope”.

    Selectivity of II-3 on P2Y1, P2Y2, P2Y6, and P2Y12 Receptors

    The selectivity of II-3 for different P2Y receptors was determined using an IP3 production assay (P2Y1,2,4,6R) or cAMP production assay (P2Y12R). HEK293 cells stably expressing hP2Y1R (P2Y1R-HEK293 cells), hP2Y2R (P2Y2R-HEK293 cells), hP2Y4R (P2Y4R-HEK293 cells), hP2Y6R (P2Y6R-HEK293 cells), or hP2Y12R (P2Y12R-HEK293 cells) were grown to 85–90% confluence prior to assays in DMEM supplemented with 10% FBS. The cells were treatment for 0.5 h with various concentrations of II-3 and then stimulated with the physiological ligand at a concentration that corresponds to its EC50:500 nM ADP for P2Y1R, 500 nM UTP for P2Y2R and P2Y4R, 750 nM UDP for P2Y6R, and 320 nM 2-MeSADP for the P2Y12R receptor. IP3 and cAMP levels were then detected using an IP3 (Inositol Triphosphate) ELISA Kit (Elabscience, Wuhan, China) or cAMP-GloTM Assay (Promega, WI, USA).

    Cytochrome P450 Inhibition

    To further explore the druggability of II-3, We conducted Cytochrome P450 Inhibition test. Human liver microsomes are a well-established in vitro model that mimics the metabolic environment of the human liver. They contain the major drug-metabolizing enzymes, making them suitable for predicting the metabolic fate of II-3 in humans. Human liver microsomes (Sigma-Aldrich, St. Louis, America) were stored in a −80 °C freezer. All samples were incubated in a 37 °C water bath. A total of 98 μL of microsome working solution (0.5 mg/kg) and 2 μL of the test product were added and incubated in a water bath for 10 min. Different substrates were added to the solution containing each CYP enzyme. CYP1A2 substrate: Phenacetin (75 μM). CYP2C9 substrate: diclofenac (10 μM). CYP2C19 substrate: toin (10 μM). CYP2D6 substrate: Dextromethorphan (10 μM). CYP3A4 substrate: midazolam (2 μM). After 30 min of incubation, metabolites were analyzed by LC/MS.

    Microsomal Stability Assay

    To further explore the druggability of II-3, liver microsomal incubation was conducted in triplicate. The adaptability of this method refers to its suitability and effectiveness in assessing the metabolic stability and potential drug interactions of II-3. Test II-3 (20 mM) was preincubated with human liver microsomes (0.2 mg/mL) and cofactor NADPH (1 mM) in a total volume of 400 mL of potassium phosphate buffer (0.5 mM NADP+, 5 mM MgCl2, 10 mM glucose 6-phosphate, and 1 unit/mL G6PDH) at 37 °C. After 5 min of pre-incubation, 50 mL of the sample mixture was collected at 0, 5, 10, 15, 30, and 45 min and terminated with 100 μL EtOAc containing the internal standard. The resulting mixture was centrifuged and the supernatant was subjected to LC‒MS analysis. The natural log of the amount of remaining parent compound was plotted against time to calculate the rate of disappearance and half-life of the tested compound.

    hERG K+ Channel Inhibition Assay

    To further explore the druggability of II-3, Whole-cell recordings were performed by using an automated QPatch system (Sophion, Denmark). hERG-HEK293 cells were harvested and adjusted to 2–5 million/mL for the automated QPatch 16X experiments. The cells were voltage-clamped at a holding potential of −80 mV. The hERG current was activated by depolarizing at +20 mV for 5 s, after which the current was returned to −50 mV for 5 s to remove inactivation and observe the deactivating tail current. The maximum tail current is used to determine the amplitude of the hERG current. It is crucial because hERG inhibition can lead to potentially fatal cardiac arrhythmias. Testing for hERG liability is a standard safety assessment in drug development.

    Pharmacokinetic Studies

    To further explore the druggability of II-3, Pharmacokinetic studies were performed using male SD mice aged 6–8 weeks (200–220 g, n = 3 per group) in compliance with the Guide for the Care and Use of Laboratory Animals. Compound II-3 was prepared as a solution containing 5% DMSO and 95% sodium carboxymethyl cellulose (CMC). The intragastric administration group was administered 20 mg/kg and the tail vein injection group was administered 10 mg/kg. The collection time points after the administration of the test compounds were 0.25, 0.5, 1, 2, 4, 8, and 24 h before and after the administration of the test substances. Blood samples were collected and placed on ice, and plasma was centrifuged for separation within 1 h. The blood sample was centrifuged to obtain the plasma, which was stored at −80 °C before analysis. The ratio of the sample peak area to the internal standard peak area was used as an indicator, and pharmacokinetic parameters were analyzed using the Phoenix WinNonlin v6.3 noncompartment model. The experimental design involving oral and intravenous administration allows for the evaluation of the pharmacokinetic characteristics of II-3 under different routes of administration. This helps to understand its bioavailability and distribution, providing crucial pharmacokinetic data to support the drug development of II-3.

    LPS-Induced ALI in Mice

    C57BL/6 mice (male; 6–8 weeks) were purchased from Skobes Biotechnology Co., Ltd. (Henan, China) and kept in a quarantine room for one week to adapt to the environment. This procedure was in strict compliance with the protocol endorsed by the National Institutional Animal Care and Ethical Committee at Zhengzhou University. To further explore the pro-inflammatory mechanisms of P2Y14R in ALI. Mice were divided into five groups: control, LPS, II-3-treated (2 and 6 mg/kg), and DXMS-treated (2 mg/kg). Each group consisted of six mice. Compound II-3 (2 and 6 mg/kg) and dexmedetomidine (2 mg/kg) were administered by gavage once daily for seven consecutive days. One hour after the last administration, acute lung injury models were established in all groups, except for the control group. After anesthetization, endotoxin (8 mg/kg) was injected intratracheally. The mice were then placed in a vertical position and shaken slowly for 1 min to ensure that the drug was evenly distributed in the lungs. The wound was sutured and LPS (10 mg/kg) was injected intraperitoneally. The control and LPS groups were administered 0.5% CMC via oral gavage. Twenty-four hours after LPS administration, the animals were euthanized by isoflurane inhalation, after which BALF and lung tissue samples were collected.

    Histopathologic Examination of Lung Tissues

    For histopathological analysis, the superior lobe of the right lung tissue was first excised and fixed in a 4% paraformaldehyde solution for 24 h at 4 °C to preserve tissue structure. After fixation, the tissues were washed in phosphate-buffered saline (PBS) to remove any residual fixative. The tissues were then subjected to a graded series of ethanol solutions for dehydration, starting with 70% ethanol and increasing to 100%. After dehydration, tissues were cleared in xylene and infiltrated with molten paraffin wax. Paraffin-embedded tissues were oriented and sectioned at a thickness of 5 μm using a microtome.

    The resulting sections were mounted onto glass slides, and any excess wax was removed by baking the slides at 60 °C for 1 h. Sections were deparaffinized in xylene and rehydrated using a descending alcohol series in water. For staining, the sections were immersed in hematoxylin for 5 min to stain the nuclei, followed by a quick rinse in running tap water. Afterward, the sections were stained with eosin for 1–2 minutes to color the cytoplasm and the extracellular matrix. The slides were then dehydrated again using an ethanol series, cleared in xylene, and coverslipped with resinous mounting medium.

    Finally, the stained sections were examined under a Nikon Ni-U microscope (Nikon, Tokyo, Japan) to visualize and assess histopathological changes, including cellular infiltration, tissue damage, and structural alterations, indicative of inflammation.

    Immunofluorescence

    We utilized the TSA-based multiple immunofluorescencemethod. After fixing and permeabilizing the lung tissue sections, we blocked nonspecific-binding sites with blockingbuffer. We then incubated the sections with a cocktail of primary antibodies specific for the proteins of interest, including F4/80 (Abways, Beijing, China) and P2Y14R (Abclone, Wuhan, China). Following a thorough wash to remove unbound primary antibodies, we applied fluorophore-conjugated secondary antibodies and proceeded with the TSA amplification step. This involved the use of tyramide conjugated to a fluorophore, which was selectively bound to the secondary antibodies through horseradish peroxidase-catalyzed reactions. After quenching the reaction and washing away excess reagents, the slides were mounted with a fluorescence-preserving medium and analyzed using a confocal microscope.

    Analysis of BALF

    After euthanasia, the BALF was collected by inserting a retention needle into the trachea and securing it with a surgical suture to ensure a closed airway. To standardize the volume of each lavage, 1 mL physiological saline was instilled into the lungs. The chest cavity was gently massaged to facilitate saline distribution and promote equilibration with the bronchoalveolar lining fluid. Saline, containing cellular and soluble components from the lungs, was carefully aspirated into a collection tube. The collected BALF samples were centrifuged at 1000 rpm for 10 min at 4 °C. The supernatant of the samples was collected and used to assess protein and cytokine concentrations.

    ELISA

    The protein levels of cytokines, including IL1β (R&D Systems, Minneapolis, MN, USA), IL-6 (Boster, Wuhan, China), and TNF-α (Boster, Wuhan, China) in the BALF supernatant, and MPO (Thermo Fisher, Waltham, MA) in the lung tissue were measured using enzyme-linked immunosorbent assay (ELISA) kits according to the manufacturer’s instructions.

    Statistical Analysis

    Statistical analyses were performed using GraphPad Prism version 8.0. Data are shown as the mean ± standard deviation from at least three independent experiments. For multiple comparisons, One-way analysis of variance (ANOVA) was performed after the assessment of normal distribution and homogeneity of variance test. Differences were considered statistically significant at p < 0.05.

    Results and Discussion

    Molecule Design

    In 2020, Hu et al reported a series of hit compounds of P2Y14R with novel scaffolds using a Glide-docking-based virtual screening (VS) strategy. Compound 7 is one of the hit compounds, which has exhibited moderate antagonistic activity with IC50 value of 35.4 nM.29

    In this study, the protein structure used for molecular docking is hP2Y14R protein structure reported by the Jacobson group.26 As shown in Figure 6A and B, the binding mode and interactions between compound 7 and hP2Y14R were displayed, which proved the key effects of the antagonistic activity of compound 7: (1) the indole moiety directed to the extracellular solvent area and formed π-H interactions with Lys160 and Cys161, H-bonding with Gln249, (2) the 4-methylbenzene moiety entered into the hydrophobic pocket and interacted with Tyr91 via π–π interactions, Arg242 and Val88 via π-H interactions, and (3) the carboxylic acid moiety did not form any interactions with the surrounding residues, and there was a part of the unoccupied space below it. These results suggest that the above three parts can be modified to improve the antagonistic activity and druggability. This year, our group reported an article on modifying the indole moiety of compound 7.34 Therefore, the indole moiety was not considered in the process of structural modification. Most currently reported P2Y14R antagonists contain amide bonds, and Figure 6 shows that the amide bond of compound 7 forms H-bonds with Lys66 of hP2Y14R. Thus, the amide bond remains in the novel antagonists as a tool to connect different pharmacophores.

    Figure 6 (A) The binding site of hit compound 7 in the active pocket of hP2Y14R. (B) The interactions between hit compound 7 and the residues of hP2Y14R.

    The overlap modes of PPTN and 7 in hP2Y14R are shown in Figure 7. It can be seen that the carboxylic acid part of compound 7 had no interaction with hP2Y14R, but the carboxylic acid part of PPTN formed H-bonding with Lys66. Therefore, modifying the carboxylic acid portion of 7 to interact with P2Y14R and enhance its antagonistic activity of the compound 7 was used in this study. Based on these findings, three series of novel P2Y14R antagonists were designed. In series I, the methyl groups in benzene and p-methylphenoxymethylene groups were optimized based on compound 7, hoping that the compound could penetrate deeper into the pocket, bind to the receptor more firmly, and achieve the effect of increase antagonistic activity. Considering that the 4-methylbenzene moiety entered the hydrophobic pocket, which has been reported by different groups,27–29 the bioactivity of the orthotopic substituents on the benzene ring is relatively good relative to the ortho- and meta-substituents. Therefore, in the process of modifying the substituents on the benzene ring, we mainly changed the type of para-substituents. We also selected aromatic heterocycles to replace the p-methylphenoxymethylene group and directly connect them to the amide bond to further explore whether aromatic heterocycles have better biological activity than benzene rings. After Series 1 was explored, some of the active substituents were retained. In series II, the use of an ester pro-drug to cap the acid moiety was recognized as an effective method to eliminate zwitterionic characteristics and improve the oral biological activity.24 Thus, ester groups of different sizes are introduced here. In series III, after selecting the optimal ester group in series II, the methyl groups in benzene and p-methylphenoxymethylene groups were optimized based on series I.

    Figure 7 The overlap mode of PPTN and hit compound 7 in hP2Y14R.

    In summary, by improving the structure–activity relationship through the design of the three series of antagonists, we expect to find compounds with better binding ability and druggability with P2Y14R and simultaneously provide a theoretical basis for the discovery and optimal design of other skeletal types of P2Y14R antagonists.

    Chemistry

    Series I was synthesized from 26, which was reacted with different commercially available 2-(4-substitutedphenoxy)acetic acids or aryl carboxylic acids to obtain ester intermediates, which yielded target compounds I-1~I-15, as shown in Scheme 1.

    Scheme 1 Access to target compounds with diverse R functionality. R1: Substituting Group of I Series Compounds. R2: Substituting Group of II Series Compounds. R3: Substituting Group of III Series Compounds. Reagents and conditions: (a) R2OH, SOCl2, 0 °C-reflux; (b) EDCI, HOBt, Et3N, CH2Cl2, rt; (c) CH3OH, NaOH, 65 °C, then HCl, H2O. (d) R2OH, DCC, DMAP, CH2Cl2, rt.

    In Series II, the carboxyl group of tryptophan was activated by thionyl chloride and reacted with methanol, ethanol, and isopropanol to obtain intermediates 26~28. The reaction of the intermediates with 2-(p-tolyloxy)acetic acid, using EDCI and HOBt as condensation agents, provided compounds II-1~II-3. In addition, owing to the low yields of benzyl alcohol and cyclohexanol, the conventional esterification method was not applicable. Thus, compound II-1 was first hydrolyzed to obtain hit compound 7, which was then reacted with the above-mentioned alcohols in the presence of DCC as an ester-condensation agent to access the final products II-4 and II-5. Subsequently, the classical amide condensation reaction was used to obtain compounds III-1~III-22 because the initial substrate was 28.

    Vitro P2Y14R Inhibition and Structure–Activity Relationship

    To evaluate the activity of the three series of compounds that we designed and synthesized, we conducted tests to determine the inhibitory effects of these compounds on cAMP production induced by the intracellular cAMP inducer forskolin (30 mM) in HEK293 cells stably expressing P2Y14R. The inhibitory activities of these compounds were tested at a relatively high single inhibitor concentration (100 nM) to determine the most effective P2Y14R antagonist. The IC50 values of antagonists with more than 70% inhibition at 100 nM were further tested in the functional assay, and the results are presented in Tables 2–4, respectively. Structure–activity relationships (SAR) of these compounds were investigated.

    Table 2 In vitro P2Y14R Antagonistic Activity and cLogP Values of Compounds I-1~I-15

    Table 3 In vitro P2Y14R Antagonistic Activity and cLogP Values of Compounds II-1~II-5

    Table 4 In vitro P2Y14R Antagonistic Activity and cLogP Values of Compounds III-1~III-22

    In series I, the methyl group on the benzene ring of compound 7 was replaced with common electron-withdrawing and electron-donating substituents to obtain compounds I-1~I-7. As shown in Table 2, while compound I-1 (inhibition = 82.67%, IC50 = 18.9 nM) with a hydrogen substitute had an inhibitory activity as hit compound 7, the inhibitory activity of the other six compounds decreased to varying degrees (inhibition activity was between 34% and 64%), especially when an electron-withdrawing substituent was attached (inhibition of I-2 with a methoxy group was 63.15% and I-7 with a trifluoromethyl group was 34.81%). This could be due to changes in the electron density on the benzene ring, which prevented the new compounds from interacting with the surrounding residues through π–H and π–π interactions, as seen with compound 7 in Figure 6B. Subsequently, I-8~I-15 were prepared by replacing the benzene ring with a heterocyclic ring. The introduction of heterocyclic or benzoheterocyclic rings did not significantly improve the antagonistic activities of the compounds. I-8, which has a furan ring (IC50 = 23.4 nM), showed slightly better activity than compound 7. However, compared to PPTN, these compounds did not exhibit significantly higher antagonistic activity. To advance our research on P2Y14R antagonists, there is an urgent need to identify compounds that demonstrate antagonistic activity significantly superior to that of PPTN.

    In series II, five diverse alcohols were used to cap the acid moiety of the hit compound 7 to access compounds II-1~II-5. As shown in Table 3, the inhibition activity almost completely disappeared when R2 was methyl (II-1), ethyl (II-2), or cyclohexyl (II-5), and the inhibition activity decreased by half compared to that of hit compound 7 when R2 was benzyl (II-4). However, compound II-3 with R2 as isopropyl, showed potent antagonistic activity, with an IC50 value of 1.2 nM. The results indicated that the size of the ester group had a significant effect on the antagonistic activity.

    The mechanisms by which esterification and benzene ring substitution affect binding affinity and bioavailability at the molecular level are complex and multidimensional. Esterification modification may reduce hydrogen bonding or electrostatic interaction with the target protein by introducing ester groups to change the polarity and spatial configuration of the molecule, thereby affecting the binding affinity. At the same time, esterification modifications usually improve the lipid solubility of molecules, enhance their transmembrane ability and improve bioavailability. Benzene ring substitution changes the electronic distribution and configuration of molecules through electronic and spatial effects, affecting the π-π accumulation or cation-π interaction with the target protein, thereby regulating the binding affinity. In addition, the nature of the substituents significantly affects the solubility and metabolic stability of the molecule, thus determining its bioavailability. Taken together, the synergistic effect of esterification modification and benzene ring substitution can play a key role in optimizing molecular efficacy and pharmacokinetic properties.

    Since II-3 exhibited potent inhibitory activity, it was selected as the lead compound to optimize series III. Thus, by maintaining the isopropyl group at R2 and modifying R3 substituents, compounds III-1~III-22 were prepared. The methyl group on the benzene ring of compound II-3 was replaced with common electron-withdrawing and electron-donating substituents to obtain compounds III-1~III-8. As shown in Table 4, no compounds in III-1~III-8 showed better antagonistic activity than II-3. When the methyl group was replaced by hydrogen (III-1, IC50 = 4.4 nM) or methoxy (III-2, IC50 = 15.6 nM) groups, the compounds retained a certain degree of antagonistic activity. However, compared to II-3, the effect decreased several to dozens of times. When other electron-donating substituents, such as isopropyl (III-3), were introduced, the inhibitory activity obviously decreased (inhibition < 50%). In addition, when electron-withdrawing substituents, such as halogen (III-4~III-6) or formyl (III-7), were introduced, the inhibition activity also decreased significantly (inhibition < 40%), but the inhibition activity was retained when the methyl group was replaced by trifluoromethyl (III-8, inhibition = 68.24%). The above results confirmed that changing the electron cloud density on the benzene ring had no positive influence on antagonistic activity. This may be due to the electron-withdrawing substituents decreasing the electron density of the benzene ring through induction and conjugation effects, weakening its key interactions with the target protein (hydrogen bonding, π-π packing and cation-π interactions), thereby decreasing binding affinity. At the same time, electron-withdrawing substituents may introduce steric hindrance, which prevents the molecule from entering the target-binding pocket or interferes with its interaction with key residues. Subsequently, common heterocyclic and benzoheterocyclic rings with various biological activities were introduced into the system to replace the benzene ring. As expected, when R3 was furan (III-9, IC50 = 21.6 nM), thiophene (III-10, IC50 = 45.8 nM), pyridine (III-11, IC50 = 10.3 nM), or indole (III-16, inhibition = 67.57%), the antagonistic activity still remained though declined to some extent compared to II-3. However, when thiazole (III-12), benzo[b]thiophene (III-13), benzo[d][1,3]dioxole (III-14), or 2,3-dihydrobenzo[b][1,4]dioxine (III-15) were introduced, the antagonistic activity was significantly reduced, which may have been influenced by the conjugated π-electron systems. Interestingly, when quinoline was introduced at different positions in the system, the change in the antagonistic activity of III-17~III-19 was significant. The main performance was 2-position < 3-position < 6-position, indicating that the system connected directly to benzene was favorable for improving the antagonistic activity. The antagonistic activity of compound III-21 with a 6-substituted quinoxaline structure was better than that of compound III-20 with 2-substituted quinoxaline, and III-22 with 9-substituted-9H-xanthene lost its inhibitory activity, which further confirmed the importance of the benzene ring. However, we found that after the structural optimization of the III series, the antagonistic activity of the compounds obtained in III series was not as good as that of the lead compound II-3.

    After the optimization and structure–activity relationship discussion of three series of novel P2Y14R antagonists, we found that compound II-3 had the strongest antagonistic activity against P2Y14R, which was better than PPTN and lead compound 7. In some cases, the introduction of an ester group may optimize the spatial configuration of the molecule or enhance the hydrophobic interaction, which in turn improves the binding capacity to the target. In terms of druggability, esterification modification usually significantly improves the lipid solubility of molecules, enhances their transmembrane ability and oral absorption, thereby improving bioavailability. In addition, esterification may also reduce the toxicity and irritation of the molecule, and improve its formulation stability. Therefore, compound II-3 was selected for further study.

    Molecular Docking

    During the optimization process, compound II-3 was identified as a P2Y14R antagonist with high antagonistic activity and as a new scaffold. Molecular docking was employed to illuminate the prominent antagonistic activity of compound 7 with the novel scaffold; compound II-3 was used as an example, and PPTN was used as a control. The protein structure used for molecular docking was the hP2Y14R structure reported by Jacobson et al.

    The binding mode and interactions between compound II-3 and hP2Y14R through molecular docking are shown in Figure 8A. It can be clearly seen that compound II-3 can be well fixed in the hydrophobic pocket of hP2Y14R. The indole moiety of II-3 was directed to the extracellular solvent area and formed H-bonds with Thr157 at a distance of 3.02 Å. The 4-methylbenzene moiety entered the hydrophobic pocket and interacted with Ile162 via π–H interactions at a distance of 4.28 Å. It can also be seen that the amide bond of compound II-3 formed H-bonds with Cys161 in P2Y14R at a distance of 3.19 Å, which indicates that the amide bond will remain in novel antagonists not only as a tool to connect different pharmacophores but also play a key role in improving the antagonistic activity of new compounds. As a linker, the amide bond maintains the spatial arrangement of the pharmacophore through its rigidity and planarity, ensuring that it is precisely matched to the active site of the target protein, thereby enhancing the binding affinity. Secondly, the amide bond itself is polar, and its carbonyl oxygen and amino hydrogen can act as hydrogen bond acceptors and donors, respectively, to form stable hydrogen bond interactions with residues in the target protein, further consolidating the binding of the molecule to the target. In addition, the resonance effect of amide bonds gives it partial double bond properties, which enhances the conformational stability of the molecule and reduces the distribution of inactive conformations. In addition, the carboxyl group of compound II-3 formed H-bonds with Arg263 at a distance of 2.89 Å, whereas compound 7 did not form any interactions with the residues surrounding P2Y14R, indicating that the carboxyl group is essential for binding, which is consistent with the results of the SAR studies. This further confirmed that the introduction of ester groups can optimize the spatial configuration and electron distribution of the molecule, so that it can better bind to the binding pocket of the target protein, and ester group modifications may also play a role through the prodrug strategy, and be metabolized into a more active carboxylic acid form in vivo, thereby enhancing antagonistic activity.

    Figure 8 Binding modes of hP2Y14R antagonists and their interactions. (A) 2D binding model and the interactions between compound II-3 and hP2Y14R. (B) 3D binding model and the interactions between compound II-3 (purple-colored moiety) and hP2Y14R. (C) Docking pose overlay of PPTN (green-colored moiety) and II-3 (purple-colored moiety) in hP2Y14R.

    A 3D docking model of compounds II-3 and hP2Y14R, as well as the crystallographic overlap between II-3 and PPTN, is displayed in Figure 8B and C. Compounds II-3 (purple carbons) and PPTN (green carbons) were docked on the same active site through an analogous binding pattern, and the degree of crystallographic overlap between the two compounds was considerable.

    Binding Mode Analysis

    We conducted molecular dynamics simulations of the complex, as shown in Figure 9A–E. In the 100 ns simulation, the Rg fluctuation of the complex fluctuated slightly due to the system equilibrium in the early stage of the simulation, and then the amplitude gradually decreased. At 100 ns, the Rg value of the complex was 1.855 nm. Similarly, the RMSD value of the complex fluctuated in the early stage of the simulation. With the extension of the simulation time, the fluctuation amplitude of the RMSD value decreased, and finally the RMSD value was 0.824 nm. RMSF reflects the flexibility of amino acid sites. As shown in Figure 9C, except for the fluctuations of the residues at the N-terminal and C-terminal, the RMSF of most residues is less than 0.4 nm. The residues show overall stability, with only a few residues showing fluctuations.

    Figure 9 Binding modes of hP2Y14R antagonists and their interactions. (A) The Rg results of complex; (B) The RMSD results of complex; (C) The RMSF results of complex; (D) The H-Bonds Number results of complex; (E) The SASA results of complex.

    In addition, we analyzed and obtained the hydrogen bond information formed between the protein and the ligand. As shown in Figure 9D, in the 100 ns molecular dynamics simulation, the ligand and the protein can form hydrogen bond interactions, and the number of hydrogen bonds formed between the protein and the ligand ranges from 0 to 5. In 100 ns, the average number of hydrogen bonds is 0.69, indicating a relatively limited number of hydrogen bonds. SASA can reflect the surface area of the protein exposed to the solvent and also the stability of the protein structure. As shown in Figure 9E, combined with the SASA graph, in the middle and later stages of the simulation, the SASA value was maintained at around 115 nm2, and the protein structure was basically stable.

    Selectivity versus Other P2Y Receptor Subtypes

    To gain insight into the selectivity of II-3, we assessed II-3 for receptor subtypes that share the greatest sequence homology with P2Y14R, including P2Y1R, P2Y2R, P2Y4R, P2Y6R, and P2Y12R.29,30,35 The receptors exhibit a certain degree of structural similarity at the molecular level, primarily characterized by the presence of seven transmembrane α-helical regions (TM1–TM7), which together form a binding pocket for the ligands. Moreover, there is a high degree of sequence conservation in key transmembrane segments such as TM3, TM6, and TM7.36 As shown in Table 5, II-3 showed a particularly high selectivity compared to P2Y1R, P2Y2R, P2Y4R, P2Y6R, and P2Y12R, suggesting that II-3 displays P2Y14R selectivity. The high selectivity of II-3 makes it a promising anti-inflammatory agent that can modulate the immune system more accurately and with a lower risk of side effects.

    Table 5 The Selectivity of II-3 to Different P2Y Receptors

    In vitro ADMET Study of II-3

    Experimental studies on the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of II-3 were conducted and are summarized in Table 6. II-3 showed IC50 values of >25 µM for all five CYPs tested. It was stable in simulated intestinal and gastric fluids and demonstrated excellent stability in human liver microsomes with a half-life of 352 min. The stability of the compound across various simulated environments and its low potential for CYP-mediated drug interactions suggest good metabolic stability and reduced risk of adverse effects. Assays for the inhibition of hERG channels indicated that II-3 had a few cardiotoxic side effects (IC50 > 50 μM). Thus, these attributes position II-3 as are promising candidates for further development.

    Table 6 In vitro ADMET Profile of II-3

    PK/PD Evaluation of II-3

    The pharmacokinetic profiles of II-3 in mice were comprehensively evaluated and are summarized in Table 7. Pharmacokinetic evaluation of II-3 in Sprague-Dawley (SD) mice revealed robust absorption and distribution characteristics. Oral dosing at 20 mg/kg yielded a peak concentration (Cmax) of 2561 ng/mL, indicative of efficient absorption. The time to reach Cmax (Tmax) was 72 min, and the area under the curve from 0 to 24 hours (AUC0-24h) was 3258 μg/(L∙h), reflecting the exposure of the compound over the initial dosing interval. The half-life (T1/2) of 6.1 hours suggests that II-3 was cleared at a moderate rate. A moderate bioavailability (F) of 39% implies that a significant portion of the oral dose is available in systemic circulation, which is beneficial for ensuring therapeutic plasma levels. Upon intravenous administration at 10 mg/kg, II-3 demonstrated a slightly lower Cmax (1845 ng/mL), Tmax (48 min), and AUC0-24h (2456 μg/(L∙h)), suggesting rapid distribution and elimination. The shorter half-life (2.2 hours via the intravenous route underscores the rapid clearance of the compound from the systemic circulation.

    Table 7 Pharmacokinetic Profiles of II-3

    In vivo Efficacy of II-3 in the LPS-Induced Mouse ALI Model

    Based on in vitro results, an LPS-induced ALI mouse model was used to investigate the therapeutic potential of II-3 in vivo. At present, the treatment of ALI mainly involves supportive therapies (respiratory and hemodynamic support) and drug treatments (anti-inflammatory drugs, antioxidants, and cytoprotective agents), but there are no approved drugs specifically for ALI.37 Dexamethasone (DXMS), a clinical glucocorticoid, was used as an anti-inflammatory agent for positive comparison. The anti-inflammatory effects of P2Y14R have been demonstrated in a range of inflammatory diseases (such as AGA and IBD) highlighting its potential for the treatment of ALI.15,34 However, the pro-inflammatory mechanisms of P2Y14R in ALI have not been fully elucidated. In this study, we utilized single-cell sequencing technology to obtain a single-cell atlas of lung tissue from both normal mice and LPS-induced ALI mice. Our observations revealed an increase in immune cells within the lung tissue of mice under disease conditions, along with an increased co-localization of P2Y14R and immune cells, with macrophages showing a particularly noticeable co-localization with P2Y14R (Figure 10A). This suggests that P2Y14R may play a crucial role in the inflammatory response during ALI. Consequently, we employed immunofluorescence technology to co-localize macrophages and P2Y14R in lung tissue (Figure 10B), and our findings were consistent with those obtained from single-cell sequencing. This provides a visual and direct evidence supporting the role of P2Y14R in mediating inflammation. In summary, our research has provided critical insights into the pathophysiology of ALI and has suggested that targeting P2Y14R could represent a promising strategy for the development of novel therapeutic interventions. Our findings underscore the potential of modulating P2Y14R signaling as a means to mitigate the inflammatory cascade in ALI, paving the way for future studies aimed at exploring this pathway’s role and the efficacy of related treatments.

    Figure 10 II-3 attenuated LPS-induced lung inflammation in mice. (A) Comparison of the differential expression of P2Y14R in monocytes/macrophages between normal mouse (Control) and LPS-induced ALI mouse (LPS) in lung tissue, with data derived from our self-built single-cell database. (B) Co-localization of P2Y14R and macrophages under different disease conditions (scale bar = 100 μm). (C) Immunofluorescence analysis of F4/80 in lung tissue sections (scale bar = 100 μm). (D) H&E staining of lung sections. Red arrows: intravascular congestion; black arrows: pulmonary interstitial hemorrhage; blue arrows:neutrophilic infiltration. (EH) Protein levels of (E) IL-1β, (F) IL-6, and (G) TNF-α in the BALF and (H) MPO in lung tissue after various treatments (n = 6). (IK) Western blotting analysis of NLRP3 inflammasome pathway proteins. Each bar represents the mean ± SEM of three independent experiments. One-way analysis of variance (ANOVA) was performed after the assessment of normal distribution and homogeneity of variance test. ***p < 0.001 compared with the control group. ##p < 0.01 and ###p < 0.001 compared with the LPS only group.

    Next, we evaluated the efficacy of the selected P2Y14R antagonists, By immunofluorescence, we observed an improvement in pulmonary macrophage infiltration following drug administration (Figure 10C). Additionally, H&E staining revealed pronounced pathological changes in the LPS-induced group, including intravascular congestion (red arrows), pulmonary interstitial hemorrhage (black arrows), and neutrophilic infiltration (blue arrows). DXMS treatment (2 mg/kg) mitigated these effects, resulting in a reduction in inflammatory cell infiltration and less severe hemorrhage and congestion. Remarkably, II-3 demonstrated a superior efficacy against DXMS. At a low dose of 2 mg/kg, II-3 significantly reduced immune cell infiltration with minimal parenchymal lesions. A high dose of 6 mg/kg further decreased inflammatory cell infiltration and preserved the alveolar structure more effectively than did DXMS (Figure 10D).

    Macrophages and neutrophils participate in the development of ALI, and activated macrophages can release inflammatory mediators and recruit neutrophils to amplify the inflammatory response.19,20 To evaluate the anti-inflammatory effects of II-3, we measured the BALF levels of the pro-inflammatory cytokines IL-1β, IL-6, and TNF-α, which were significantly upregulated in the ALI model (Figure 10E–G). Pretreatment with II-3 notably reversed the LPS-induced increase in cytokine levels, with outcomes surpassing those of the DXMS group. Additionally, MPO is a key neutrophil enzyme that induces inflammation and is released by neutrophils.38 We measured MPO levels in the lung tissue to assess II-3’s impact on neutrophil activity. The results showed that II-3 significantly lowered MPO levels triggered by LPS (Figure 10H). Collectively, these results indicate that II-3 plays a protective role in lung inflammation by reducing immune cell infiltration, inflammatory cytokine levels, and pulmonary pathological changes in the LPS-induced ALI model.

    It is noteworthy that recent studies have reported that P2Y14R antagonists significantly reduce pulmonary inflammation and tissue damage in ALI by inhibiting the activation of the NLRP3 inflammasome signaling pathway, decreasing the release of inflammatory factors, and reducing immune cell infiltration. The NLRP3 inflammasome, a multiprotein complex, plays a crucial role in cellular inflammatory responses and cell death. P2Y14R antagonists may offer protective effects against ALI by influencing this signaling pathway. Additionally, the activation of P2Y14R may be involved in modulating immune-inflammatory stress responses. In this study, we further investigated the inhibitory effect of P2Y14R antagonists on the NLRP3 inflammasome pathway. Our observations revealed that P2Y14R antagonists significantly reduced the activation of the NLRP3 inflammasome and the release of related inflammatory factors, thereby alleviating the inflammatory response (Figure 10I–K). These results are consistent with previous studies, indicating that P2Y14R plays a significant role in regulating inflammatory responses, and its antagonists may exert anti-inflammatory effects by inhibiting the activation of the NLRP3 inflammasome signaling pathway. Moreover, our findings further support the potential of P2Y14R as a therapeutic target for ALI.

    Conclusion

    In conclusion, we have designed a series of N-acyl tryptophan derivatives that have been identified as novel and potent P2Y14R antagonists based on compound 7. Compound II-3 showed the most promising antagonistic activity. The solubility and bioavailability of compound II-3 were better than PPTN owing to the elimination of zwitterionic characteristics, and its druggability was significantly improved. Optimized compound II-3 (IC50 = 1.2 nM) also displayed a more potent P2Y14R antagonistic activity than PPTN (IC50 = 2.0 nM). The high selectivity of compound II-3 against P2Y-type receptors homologous to P2Y14R, the high selectivity of II-3 for P2Y14R was further confirmed. In the pharmacokinetic experiments, compound II-3 showed excellent bioavailability (F = 39%). Good plasma clearance and half-life indicated that compound II-3 was remarkably stable in vitro and vivo. Assays for the inhibition of the cytochrome P450 and hERG channels indicated that compound II-3 had few cardiotoxic side effects in vivo. Using single-cell sequencing and immunofluorescence techniques, this study for the first time reveals the specific high expression of P2Y14R in macrophages of ALI lung tissue, and confirms that II-3 effectively targets P2Y14R, inhibiting NLRP3 inflammasome activation and curbing the release of key inflammatory cytokines like IL-1β, IL-6, and TNF-α. This action results in a dual anti-inflammatory effect, both reducing immune cell infiltration and inflammatory signaling. Importantly, II-3 was found to notably ameliorate lung pathology and lower pro-inflammatory cytokine levels in an ALI model, outperforming dexamethasone (DXMS). Overall, this study innovatively combines single-cell spatial profiling to analyze target distribution, providing a new direction for the development of specific anti-ALI drugs, compound II-3 has clear targets, excellent anti-inflammatory activity in vivo, druggability, and low liver toxicity. In addition, the synthesis route and post-treatment of compound II-3 were relatively simple and convenient for mass production. Compound II-3 with potent P2Y14R antagonistic activity, provides a reliable therapeutic strategy for efficient treatment of inflammatory diseases and is a promising candidate for further research.

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  • Climate spillover effects for reinsurance should concern regulators, say experts – Green Central Banking

    1. Climate spillover effects for reinsurance should concern regulators, say experts  Green Central Banking
    2. Nature and Insurance: An Untapped Partnership to Mitigate Rising Risks  Environmental Defense Fund
    3. The impacts of climate change on your home insurance  WUNC
    4. How Insurance Can Meet the Challenge of Climate Change  Sustainable Brands
    5. Green insurance gains ground as climate risks intensify  Insurance Business America

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  • Chinese Government Bond Yields Set to Fall – The Wall Street Journal

    1. Chinese Government Bond Yields Set to Fall  The Wall Street Journal
    2. Danske Bank: The upward pressure on the 30-year government bond yields in the eurozone intensifies, with the dual auction of German bonds attracting attention.  富途牛牛
    3. Eurozone Long-Term Yields Under Pressure as Debt Supply Builds  Investing.com South Africa
    4. Germany’s Borrowing Rises As Interest Rates Hit Turning Point  Finimize
    5. German 30-year government bond yields hit highest since 2011  Mint

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  • Police warning after £2.1m Bitcoin scam

    Police warning after £2.1m Bitcoin scam

    A police force has issued a warning to cryptocurrency holders after a victim lost £2.1 million in Bitcoin to a “sophisticated scam”.

    North Wales Police said a person posing as a senior UK officer tricked the victim into entering their password on a fake site by using a false story about a security breach.

    The case highlights a “disturbing new trend”, the force said, with scammers “crafting sophisticated social engineering schemes to trick even the most diligent holders”.

    It has urged people to remain vigilant and trust their instincts, and has issued guidance to help prevent similar attacks.

    Police said those targeted may have been identified through a data breach “making this a highly targeted and advanced scam”.

    It involved the victim being told a “fabricated story” that police had arrested an individual whose phone contained the victim’s personal identification documents.

    Exploiting “fear” and “urgency”, they urged the victim to “secure their assets” by logging in via a fake website link – and, believing it was legitimate, the victim entered their password.

    This gave them access to rebuild the victim’s wallet and steal £2.1 million within a “matter of moments”.

    The force said it was working to trace the funds but warned the case “serves as a reminder that scammers are constantly evolving their tactics”.

    It also issued the following advice:

    • Police will never call you unexpectedly about your crypto or ask you to use your cold storage device – this is a big red flag.
    • If unsure, hang up and call 101 to check if the contact was real.
    • Never share or enter your password anywhere except directly on your cold storage device during setup or recovery.
    • No legitimate company or police officer will ever ask for your seed phrase.

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  • Feasibility and efficacy of real-time teleresistance exercise programs for physical function in elderly patients after hip fracture surgery: a randomized controlled trial | BMC Geriatrics

    Feasibility and efficacy of real-time teleresistance exercise programs for physical function in elderly patients after hip fracture surgery: a randomized controlled trial | BMC Geriatrics

    Impact of telerehabilitation on physical function

    Immobilization after major surgery and during hospitalization can substantially decrease muscle strength and function. Physical training has been shown to improve strength and functional performance in patients recovering from hip fractures [23]. This study demonstrated that telerehabilitation programs, specifically tele-resistance exercises, can significantly enhance physical function in elderly patients following hip fracture surgery. At 12 weeks postintervention, the improvements were particularly notable compared with those achieved with traditional exercise booklets. This is the first study to implement such a program for fragility hip fractures in Thailand, contributing important evidence from a low-resource context.

    Advantages of real-time video conferencing

    The intervention employed real-time video conferencing through the LINE application to deliver exercises and provide immediate feedback from physiotherapists. This method differs from other telerehabilitation approaches, which typically use prerecorded videos or less interactive platforms [15]. The ability to offer real-time feedback allowed for personalized adjustments, likely contributing to the observed improvements in physical function. Additionally, participants could use smartphones or tablets with a standard application, making this telerehabilitation approach more accessible and cost-effective than systems relying on complex technology.

    Challenges with traditional rehabilitation

    Rehabilitation after surgery primarily aims to restore mobility. In the standard approach, older adults with hip fractures and their caregivers typically receive training on home exercise programs upon discharge, supplemented by an exercise booklet. However, clinical observations have shown that some patients struggle to follow and progress with these exercises, leading to delayed mobility recovery. Furthermore, mobility issues often prevent patients from receiving outpatient therapy, as they rely on caregiver assistance and face transportation challenges. These barriers can exacerbate inequities in healthcare access, particularly for those in remote or underserved areas. The trial addressed these limitations by introducing a more structured and accessible alternative the 12-week tele-resistance exercise program which allowed patients to receive rehabilitation remotely. This approach not only enhances accessibility but also potentially reduces inequities in healthcare access, aligning with the sustainable development goals [24].

    Safety considerations in telerehabilitation

    Safety is paramount in remote exercise programs. Therefore, 100 patients who were considered unsafe for telerehabilitation were excluded from the study. There were 31 participants with extreme ages and 69 conditions that could prohibit active exercise were excluded from the study. A meta-analysis reported that physiotherapist-led, exercise-based telerehabilitation is noninferior to face-to-face rehabilitation and superior to no intervention for older adults with musculoskeletal conditions [25]. Systematic reviews have also indicated that progressive resistance exercises following hip fracture surgery improve mobility, activities of daily living, balance, lower-limb strength, and performance in various tasks [26, 27]. Therefore, tele-resistance exercise was selected as the intervention. Tele-resistance exercise showed an adherence rate of 70%, demonstrating its superior effectiveness compared to using exercise booklets demonstrating its effectiveness compared to exercise booklets.

    Primary outcome: improvements in SPPB scores

    The Short Physical Performance Battery (SPPB) served as the primary outcome measure, evaluating balance, gait speed, and lower limb strength. At 12 weeks, the intervention group demonstrated a median improvement of 3.5 points—exceeding the threshold for substantial clinical relevance in older adults [15, 28]. This result aligns with previous meta-analyses supporting the efficacy of home-based digital interventions in enhancing physical function among elderly populations [15].

    The use of real-time telerehabilitation, which provided personalized instruction and immediate feedback, likely contributed to these superior outcomes. Unlike conventional home programs that rely on static materials, the interactive nature of this approach allowed for progressive, individualized resistance training. This supports existing evidence indicating that supervised exercise produces greater functional gains than unsupervised programs in older adults [29].

    Analysis of individual SPPB components revealed significant improvements across all domains within the intervention group. Notably, the chair stand test, which reflects lower limb strength, showed marked improvement as early as six weeks—a finding consistent with Vikberg et al., who reported similar early responses to resistance training. [30] Gait speed improved progressively in both groups, but significantly more in the intervention group by week 12. This is consistent with literature suggesting that resistance, multimodal, and coordination-focused training effectively enhance gait performance in older individuals [31, 32]. The early gains observed may have encouraged greater voluntary activity, thereby reinforcing ongoing improvements.

    In contrast, while balance scores increased gradually over time, there was no significant between-group difference. Given that postural control involves multiple physiological systems, a multicomponent approach incorporating proprioceptive, aerobic, and neuromuscular training may be necessary to elicit more pronounced improvements in this domain [33].

    Overall, the findings indicate that real-time tele-resistance exercise was effective in improving overall physical performance, particularly in total SPPB scores and gait speed, when compared to traditional unsupervised home rehabilitation.

    Secondary outcomes

    2-Minute walk test (2MWT)

    The 2MWT revealed that both groups improved significantly from baseline at 6 and 12 weeks. Notably, the intervention group showed greater improvement at 6 weeks, though this difference did not remain statistically significant at 12 weeks. Despite this, the absolute gain in walking distance remained higher in the intervention group at both follow-up points. The mean increase of 21.4 m surpassed the minimal detectable change in older adults, suggesting clinically meaningful improvement in ambulatory capacity [19].

    This finding aligns with evidence linking 2MWT performance to aerobic capacity during rehabilitation after hip fracture. [34] However, variability in the 12-week results may reflect natural recovery trajectories or increasing physical activity in the control group. Some studies have also suggested that endurance gains in this context may stem primarily from increased muscle strength [35, 36].

    Knee extension strength

    Contrary to expectations, no significant between-group differences were observed in knee extension strength on the fractured side. This contrasts with prior research showing strength improvements with resistance training. The limited impact may be due to the low intensity and volume of resistance used (0.5–1 kg), which may be insufficient for inducing measurable hypertrophy or strength gains, particularly in frail or sarcopenic populations [37].

    Additionally, the control group had lower baseline strength, which may have motivated more self-directed exercise. Once participants regained mobility, reduced adherence may have further attenuated strength gains. While adherence to tele-resistance training was approximately 70%, no data were available for adherence to unsupervised exercises. The reduction in supervised sessions from twice weekly to once weekly after week 6 may have also affected training consistency and outcomes [38].

    Anxiety scores and sociocultural factors

    Improvements in anxiety were observed in both groups, though no significant between-group differences emerged. This contrasts with findings from Wu et al., who reported reduced anxiety with telerehabilitation [39]. Nevertheless, our findings are consistent with studies showing that physical activity can positively influence anxiety in older adults [40].

    In the Thai context, strong familial caregiving support may have contributed to generally low baseline anxiety and steady improvements over time. Cultural values emphasizing elder care may mitigate psychological distress associated with physical decline, especially when combined with functional recovery. Additionally, greater mobility limitations and comorbidities in the control group may have been associated with higher fear of falling, which can influence anxiety scores [41, 42].

    Safety and adverse events

    Importantly, our study did not report any adverse effects or deaths related to the tele-resistance exercise program. One fall occurred in the intervention group; however, it was unrelated to the exercise program and did not result in serious complications. This study underscores the effectiveness of home-based digital health interventions involving communication, feedback, education, and telerehabilitation, which enhance functional outcomes among older patients recovering from hip fractures postsurgery [15].

    Limitations

    Several limitations must be acknowledged in this study. First, a significant number of patients were excluded due to safety concerns about remote exercise. Since this study was conducted in a tertiary, university-based medical school, the participants may have had more severe health conditions and a higher prevalence of comorbidities compared to those in community-based hospitals. Consequently, the findings may not be applicable to patients in such settings. Second, the relatively small sample size limits the generalizability of the results. This small sample size was partly due to recruitment challenges toward the end of 2022. During this period, many caregivers who were proficient in using smart devices and the LINE video call application had to resume on-site work, reducing their availability to support patients in the telerehabilitation program. Then some of the participants were institutionalized during this time, further limiting the pool of eligible participants. Increasing the sample size in future research could enhance the robustness of the findings. Additionally, the current study employed a conventional approach that included an exercise booklet and a home exercise program provided prior to discharge. This approach resulted in reduced therapist interaction for the control group, which may have negatively influenced their physical outcomes. Moreover, the participants in the control group were older and utilized gait aids more frequently compared to those in the intervention group. Previous research has established that older age and reduced walking abilities were associated with diminished functional recovery following hip fractures [6, 43]. Therefore, it is possible that the control group experienced poorer recovery outcomes than the intervention group. Finally, investigating the long-term effects of telerehabilitation is crucial for evaluating the sustainability of the observed benefits.

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