Combination therapy of glucokinase activators and sodium-glucose cotransporter-2 inhibitors may exhibit potent glucose-lowering effects and improvement of β-cell function and insulin sensitivity in patients with type 2 diabetes mellitus, given their complementary mechanisms of action. However, the safety, pharmacokinetic interactions and pharmacodynamic effects of such combinations remain unexplored. To address this gap, we conduct an open-label, sequential phase I trial (ClinicalTrials.gov, NCT03790787 ) evaluating the dorzagliatin and the sodium-glucose cotransporter-2 inhibitor empagliflozin in patients with type 2 diabetes mellitus and obesity in the US. After strict screening against the inclusion/exclusion criteria, 16 participants are deemed eligible. Participants receive sequential treatments-empagliflozin alone, empagliflozin plus dorzagliatin, and dorzagliatin alone-five days for each regimen, followed by a comprehensive assessment of pharmacokinetic profiles and safety. The primary outcomes included GMR for Cmax and AUC0-24h of empagliflozin and dorzagliatin of pharmacokinetic parameters and the incidence of adverse events, abnormal vital signs, abnormal clinical laboratory findings, and 12-lead electrocardiogram abnormalities of safety profile. A total of 27 adverse events occurs in 9 participants (56.3%), the majority of which are mild, with the exception of one case of moderate constipation. No clinically significant findings or changes attributable to study drug treatment were found in clinical chemistry, hematology and urinary analyses, and observed in vital signs, 12-lead electrocardiogram and physical examination. The geomean ratio and their associated 90% confidence intervals for both maximum plasma concentration (empagliflozin: 98.43% (84.07%, 115.25%); dorzagliatin: 103.94% (90.45%, 119.44%)) and area under the concentration-time curve from 0 to 24 h (empagliflozin: 97.53% (94.35%, 100.82%); dorzagliatin: 99.65% (95.25%, 104.25%)) of empagliflozin and dorzagliatin fall entirely within the conventional bioequivalence range of 80.00% to 125.00%. No pharmacokinetic drug-drug interaction is observed, and the treatment exhibits a favorable safety profile with good overall tolerability.

ABSTRACT Older adults are vulnerable to adverse drug reactions due to multimorbidity, frailty and polypharmacy. In those with cardiovascular‐kidney‐metabolic (CKM) syndrome, CKM‐related multi‐organ burden may further alter drug disposition and elevate risk. Metoprolol exposure varies widely among individuals, and higher plasma concentrations have been associated with falls in prior studies. We aimed to develop a population pharmacokinetic (PopPK) model of metoprolol in older Chinese patients with CKM syndrome using real‐world data, identify key covariates affecting clearance, and perform model‐based dose simulations. The PopPK analysis included sparse real‐world data from 42 older adults (60–93 years) receiving immediate‐release metoprolol tartrate. Candidate covariates included demographics, genetic polymorphism, laboratory variables, comorbidities, frailty phenotype, SARC‐F score, age‐adjusted Charlson Comorbidity Index, CKM stage and CKM2S2‐BAG score. Model‐based simulations were conducted across predefined genotype‐ and disease‐burden strata to evaluate dosing scenarios against literature‐based thresholds. Metoprolol pharmacokinetics were adequately described by a one‐compartment model with first‐order absorption and elimination. The rs1065852 T/T genotype and a high CKM2S2‐BAG score (> 11) were associated with lower apparent clearance (CL/F), resulting in approximately 32% and 30% reductions in CL/F, respectively, and reduced interindividual variability in CL/F from 46.9% to 39.9%. Simulations identified subgroup‐specific dosing regimens that maintained steady‐state trough concentrations within a literature‐derived range. In older Chinese patients with CKM syndrome, rs1065852 and CKM‐related burden influenced metoprolol clearance and accounted for a considerable part of the observed variability between individuals. This study provides a model‐informed framework for exposure‐threshold‐based dose stratification. Prospective validation with clinical outcomes and comprehensive CYP2D6 genotyping is warranted.

Purpose:This study is the first-in-human study to evaluate the safety, tolerability, pharmacokinetics (PK), and pharmacodynamics (PD) profiles of single ascending doses (SAD) and multiple ascending doses (MAD) of JMKX003142 injection in healthy Chinese subjects. Patients and Methods:In this Phase I, randomized, double-blind, placebo-controlled study, 48 subjects in the SAD (0.1-6 mg) study received intravenous injection of JMKX003142 or placebo in ascending dose. Thirty subjects in the MAD (1-4 mg) study received an intravenous injection of JMKX003142 or placebo once a day for five consecutive days. The primary endpoint was the safety and tolerability of JMKX003142 injection, with the secondary and exploratory endpoints focusing on its PK and PD profiles, respectively. Results:The JMKX003142 injection exhibited favourable safety and tolerability, with all treatment-emergent adverse events (TEAEs) being mild. No serious adverse events, deaths or discontinuations due to TEAEs were observed. Following single and multiple intravenous injections of JMKX003142, the maximum concentration (Cmax) and area under plasma concentration-time curve (AUC) of JMKX003142 and its metabolites increased with dose level, with increases in Cmax being dose-proportional. In 1 mg or higher dose group of SAD study, the mean terminal half-life (t1/2) of JMKX003142 was between 5.2 and 12.7 h. Following multiple intravenous injections of JMKX003142, the t1/2 of JMKX003142 was determined to range from 11.1 and 11.8 h. Moreover, JMKX003142 demonstrated favourable PD profiles following both single and multiple intravenous injections. The evaluation of the daily cumulative urine volume indicated that the diuretic effect of the JMKX003142 injection was evident at doses of 1 mg and above, with effects intensifying at higher doses. Conclusion:Overall, both single and multiple intravenous injections of JMKX003142 have been demonstrated to be safe, well tolerated, and to possess excellent PK characteristics as well as significant diuretic activity. Trial Registration:This study was registered with ClinicalTrials.gov (NCT06344533).

Graph Neural Networks (GNNs) in drug repurposing suffer from two limitations: transductive failure in zero shot (cold start) scenarios and popularity bias that misidentifies high-degree nodes as effective drugs. We propose a framework that shifts the optimization objective from graph topology to clinical utility, integrating Knowledge Graph RAG (KG RAG), Supervised Fine Tuning (SFT), and Kahneman Tversky Optimization (KTO) using Phase III clinical trial outcomes as rewards. We evaluated our approach on a rigorous 1:10 negative sampling benchmark derived from MiRAGE, covering Standard, Cold Start, and Degree Matched settings. In Cold Start scenarios where topological signals are absent, traditional GNNs (including TxGNN) collapse (Top 10 Precision < 0.30), whereas DR-SFT model achieves 0.80, demonstrating robust semantic generalization for novel compounds. Crucially, in Degree Matched tests dominated by "popular but ineffective" decoys, the DR-KTO acts as a clinical gatekeeper, achieving 0.90 Top-10 Precision, significantly outperforming DR-SFT (0.70) and GNNs (0.2-0.4) by effectively penalizing hard negatives. Beyond repurposing accuracy, the model achieves state-of-the-art performance on BioASQ, and increased ability in Chemprot. Orthogonal validation via DrugReAlign confirms physical plausibility, yielding significantly lower docking binding energies for KTO-recommended candidates. SPR experiments further corroborate these findings. By aligning LLM reasoning with clinical evidence, our framework successfully bridges the gap between semantic inference, topological structure, and clinical reality. ### Competing Interest Statement The authors have declared no competing interest. the Science and Technology Development Fund of Macau SAR, 0002/2025/NRP, 0049/2024/AGJ University of Macau, https://ror.org/01r4q9n85, MYRG-CRG2023-00007-ICMS-IAS, MYRG-GRG2024-00268-ICMS-UMDF, SHMDF-AI/2026/003 Dr. Stanley Ho Medical Development Foundation, SHMDF-AI/2026/003 the Prevention and Control of Emerging and Major Infectious Diseases-National Science and Technology Major Project, 2025ZD01907900

AIM:Cetirizine is widely used to treat children with allergic rhinitis and urticaria, yet optimal doses are undefined. Limited pharmacokinetic (PK) data and undefined renal P-glycoprotein (P-gp) ontogeny hinder the direct extrapolation of dosing from adults to children. This study aims to develop a paediatric cetirizine physiologically based pharmacokinetic (PBPK) model to explore renal P-gp ontogeny and guide optimal dosing strategies. METHODS:An adult PBPK model for cetirizine was developed to determine drug-specific parameters. Combining the age-dependent physiological parameters embedded in SimCYP® and cetirizine PK from Caucasian children of varying ages to infer renal P-gp ontogeny, the paediatric cetirizine PBPK model was constructed. The ontogeny was validated using PK data from Chinese children across various age groups. The validated model was then employed to evaluate optimal dosing regimens for children in different age groups. RESULTS:The renal P-gp ontogeny equation was successfully inferred and incorporated into the PBPK model, capturing 100% and 87.8% of the observed PK data in Caucasian and Chinese children within the 90% prediction interval, respectively. Simulation results suggest that 5 mg twice daily (BID) for children aged 6-12 years and 0.25 mg/kg BID for children under 6 years are optimal, whereas fixed doses of 5 mg once-daily (QD) for children aged 2-6 years and 2.5 mg BID for children aged 0.5-2 years are also appropriate. CONCLUSION:Utilizing PBPK modelling and simulation, this study elucidated P-gp ontogeny and recommended the optimal cetirizine dosage regimen for children across all age groups.

BackgroundDiabetes patients often exhibit suboptimal efficacy and adverse reactions, accompanied by pharmacokinetic (PK) changes, indicating drug-metabolizing enzymes and transporters (DMET) activities may change. This study aimed to determine whether type 2 diabetes affects DMET activities and quantify impact magnitude. Furthermore, gut microbiome, pharmacogenetic and demographic characteristics were assessed to elucidate the sources of inter-individual variability (IIV) in DMET activities.MethodsThe activities of CYP3A and transporters P-gp, OATP and BCRP were evaluated in type 2 diabetes patients and healthy volunteers (HVs) following a single oral dose of five probe drugs (midazolam, dabigatran etexilate, pitavastatin, rosuvastatin, and atorvastatin). Population pharmacokinetics (PopPK) and an exploratory machine learning (ML) analysis were employed to quantify the impact of type 2 diabetes on DMET activities.ResultsBased on observed PK parameters, compared to HVs, type 2 diabetes patients exhibited increased exposure to midazolam (1.38-fold), dabigatran (1.40-fold), and atorvastatin (1.93-fold), whereas pitavastatin (0.931-fold) and rosuvastatin (1.25-fold) showed no change. PopPK analysis revealed that the mean activities of CYP3A and P-gp were potentially decreased by 23% and 27%, respectively, in type 2 diabetes patients, while no changes for OATP and BCRP. Notably, unlike the direct patient population covariate for P-gp, reduced CYP3A activity was indirectly estimated from lower Salmonella abundance. Additionally, factors such as sex, BCRP genotype, and Clostridium_XlVb partly explained the IIV in probe drugs exposure.ConclusionThe findings regarding altered DMET activities and identified influence factors help to identify specific drug classes that may warrant closer clinical attention in type 2 diabetes patients.

Glomerular filtration rate (GFR) maturation is critical for drug dosing in neonates and children. Current equations face dual limitations: they are primarily derived from Caucasian populations and fail to account for the fundamental physiological differences between neonates and children. This study aimed to develop Chinese-specific GFR equations and to characterize how the key predictors of GFR differ between these two developmental stages. Creatinine clearance as measured GFR (mGFR) in 58 hospitalized neonates (gestational age range, 30.3-41.0 weeks, postnatal age range, 0 to 26 days, mGFR = 3.08 ± 1.86 mL/min) without renal impairment were enrolled. Moreover, a published database of Chinese children (N = 87, age range, 1 to 18 years, mGFR = 97.0 ± 31.9 mL/min/1.73 m2) was applied. Demographic and renal function markers were included to develop equations using the stepwise regression method in allometric form. The GFR prediction equation of serum cystatin C, blood urea nitrogen and postmenstrual age of Chinese neonates was established. In children, GFR is associated with cystatin C, creatinine, weight and age. In an internal comparison with 16 published equations, our newly developed models showed favorable performance within our cohorts, with correlation (R2 of 0.617 and 0.578) and accuracy (P20 of 46.6% and 70.1%, P50 of 93.1% and 100%), respectively. The equations would provide scientific basis for aiding diagnosis of renal function of Chinese neonates and children, and supporting better precision medicine of drugs mainly excreted by kidney.

Background and Purpose Dexamethasone as the first-line treatment for neonatal respiratory distress syndrome (NRDS) has been found to simultaneously promote fetal lung mature and induce offspring neurotoxicity. This model informed study is aim to set the therapeutic window of dexamethasone for NRDS treatment. Experimental Approach Pregnant rats were injected with 0.1 or 0.4 mg/kg dexamethasone to simulate clinically equivalent prenatal-dexamethasone-exposure (PDE). Half rats were anesthetized. Surfactant protein A/B of fetal lung were quantified and pharmacokinetic/pharmacodynamics model was developed to determine the minimal effective concentration. The other half rats delivered naturally and the offspring were underwent open field testing. mRNA-sequencing of fetal hippocampus was performed and concentrations-toxicity assay was conducted in H19-7/IGR-IR cells. These data were integrated with maternal-fetal physiologically-based-pharmacokinetic model to determine the maximum tolerated concentration. Population-pharmacokinetic-model was used to propose optimal clinical dosing regimen. Key Results Pharmacokinetic/pharmacodynamics model well characterized dexamethasone-induced fetal lung maturation, indicating a minimal effective concentration as 1.63 ng/mL. Offspring of PDE rats exhibited significant behavioral alterations. mRNA-sequencing analysis revealed that neurotoxicity may be mediated through the Wnt pathway and renin-angiotensin system, with BDNF and CMA1 identified as potential neurotoxic biomarkers. Cell assays suggested a neurotoxic cut-off concentration of 17 ng/mL. Based on therapeutic window of 1.63-17 ng/mL, a half-dose clinical regimen was proposed as the optimal therapeutic strategy. Conclusion and Implications This model-informed precision dosing strategy offers a rational approach to optimize clinical dexamethasone dosing regimen.

Clinical pharmacokinetic (PK) modelling is constrained by sparse sampling, limited generalisability of single-drug models, and labour-intensive workflows, making it difficult to infer complete drug exposure from limited concentration observations. We present the Pharmacokinetic Foundation Model (PKFM), a grey-box Transformer framework pre-trained across 32 drugs that reconstructs concentration-time profiles from sparse concentration observations, dosing events, molecular descriptors, and physiological covariates while preserving output interpretability. In representative oral PK curves, three sparse input points recovered the principal absorption-elimination trajectory, achieving coefficient of determination (R2) = 0.992 for Midazolam oral and R2 = 0.990 for Verapamil oral. Using reconstructed curves in NONMEM (nonlinear mixed-effects modelling) improved covariance stability and individual prediction accuracy. Contrastive-learning embeddings supported Top-10 physiologically based pharmacokinetic (PBPK) candidate retrieval, with 75.6% of observations within the 2-fold range. A pharmacometrics-informed AI Agent (PM Agent) outperformed general-purpose programming tools in stability and pairwise win rate on a standardised modelling benchmark, with each run requiring human pharmacometrician confirmation before downstream use. These results support cross-drug pre-trained PK models as an information-completion layer for sparse PK evidence and a structured scaffold for the modelling workflow; clinical or regulatory use requires prospective validation, broader external benchmarking, and independent expert assessment.

Background/Objectives: Meropenem pharmacokinetic variability in sepsis often leads to suboptimal exposure and therapeutic failure. Existing covariates like creatinine clearance (CLcr) only partially explain this variability. This study evaluated pyridoxic acid (PDA), an endogenous biomarker of OAT1/3 transporters, as a novel covariate to quantify active tubular secretion and explore pharmacokinetic/pharmacodynamic (PK/PD) linkages with clinical outcomes. Methods: A population PK (PopPK) model was constructed using data from a prospective septic cohort (n = 28). Subsequent exposure-response analysis was conducted in an expanded cohort (n = 49), and Monte Carlo simulations were utilized to evaluate various dosing regimens. Results: The PopPK analysis suggested that PDA may complement CLcr in characterizing meropenem clearance variability. While CLcr explained 10.7% of inter-individual variability (IIV) in clearance, the inclusion of PDA explained an additional 13.7%, reducing total IIV from 50.8% to 26.4%. Achieving a stringent target of 100%fT > 4MIC was significantly associated with a rapid decline in procalcitonin (p = 0.027), establishing a key PD endpoint. Simulations demonstrated that standard dosing (1 g q8h, 1 h infusion) is insufficient for patients with normal or augmented renal function. Target attainment was highly dependent on PDA levels. Conclusions: PDA is a valuable translational biomarker for OAT-mediated clearance. To achieve 100%fT > 4MIC, we recommend (i) 1 g q8h with 3 h infusion for patients with low CLcr and high PDA levels (MIC = 0.5 mg/L), and (ii) an intensified regimen of 2 g q8h with 3 h infusion for patients with normal CLcr and low PDA levels or high resistance risk (MIC ≥ 2 mg/L).

Background: SYHA1805 is a potent farnesoid X receptor (FXR) agonist currently in development for Metabolic Dysfunction-Associated Steatohepatitis (MASH). Methods: To determine the first-in-human (FIH) dose and guide its clinical development, an integrated approach combining in vitro and in vivo ADME and toxicological characterizations, cross-species allometric scaling (AS), and physiologically based pharmacokinetic (PBPK) modeling was employed. Results: Using monkeys and rats as extrapolation species, AS predicted a human intravenous clearance of 20.7 L/h and a steady-state volume of distribution of 15.1 L. Based on body surface area and exposure-based modeling, an FIH dosing regimen for single-dose administration was proposed, ranging from a 30 mg starting dose to a 3000 mg maximum, with an effective dose of 1150 mg. These dosing strategies were further supported by PBPK models, which accurately estimated human systemic exposure. The model simulations were subsequently validated by clinical trial data from a single ascending dose (SAD) study (CTR20202354). Conclusions: These findings establish a robust pharmacokinetic foundation for the continued clinical advancement of SYHA1805.

Abstract Objective: Conventional pharmacodynamic (PD) modeling workflows require manual model selection, repeated equation rewriting, and empirical parameter adjustment, resulting in limited automation, high cross-scenario migration costs, and insufficient reproducibility. This study aims to develop PD Union, a unified, automated, and interpretable framework for mechanistic PD modeling. Methods: PD Union is built upon a unified continuous dynamical skeleton that organizes absorption and systemic exposure module, the receptor module, the drug input module, the first delay module, the primary pharmacodynamic function module, the primary pharmacodynamic state module, the downstream pharmacodynamic state module, the second delay module, the feedback module, the circadian modulation module, the biophase module, the direct effect module, the disease state module, the second PD axis first delay module, the second PD axis primary pharmacodynamic function module, the second PD axis primary pharmacodynamic state module, the second PD axis downstream pharmacodynamic state module, the second PD axis second delay module, and the second PD axis feedback module. A machine learning-based structure identification module is incorporated to recognize drug input modes and mechanism labels from population PK/PD time series, followed by constrained population parameter optimization, forming an integrated pipeline of structure identification, candidate generation, and parameter fitting. Results: Validation was conducted at two levels. In standardized synthetic benchmarking across 14 representative single-endpoint scenarios, the structure identification model achieved an output mode accuracy(NRMSE) of 0.7600 and macro-average F1 of 0.6307; parameter fitting yielded an NRMSE mean of 0.146 and median of 0.117. In the unified reconstruction validation based on 15 population pharmacokinetics/pharmacodynamics (PK/PD) literature data, the mean NRMSE of PDUnion model for PD was 0.261, and the median was 0.228. Among the 15 studies, 14 performed better than the models provided in the original literature. Conclusions: PD Union demonstrates that interpretable mechanistic modularization combined with machine learning-assisted structure identification is feasible for automated PD modeling. The framework provides an executable methodological foundation for unified, reproducible, and extensible mechanistic PD modeling, with potential applicability to multi-endpoint and complex disease-state modeling scenarios.

Although precipitation of poorly soluble drugs in the gastrointestinal (GI) tract is often considered detrimental to bioavailability, the solid-state nature of the precipitate, either crystalline or amorphous, may critically redefine its impact. Contrasting with reported crystalline precipitates of weakly basic drugs, this study investigates XZP8257 (C27H23Cl2N3O4S), a poorly soluble weakly acidic/zwitterionic compound (BCS II), to explore how GI fluid composition governs precipitate formation and its resulting physicochemical properties. Precipitates were generated in simulated intestinal media with varied bile salt concentrations and pH values. Their solid-state forms were characterized using XRD, SEM, FTIR, and DSC, whereas dissolution performance was evaluated via equilibrium solubility and intrinsic dissolution rate (IDR) measurements. Elevated bile salt concentrations induced a molecular structural transition in XZP8257 and facilitated the formation of crystalline-amorphous mixed precipitates, a phenomenon distinct from the crystalline precipitates typically reported. These novel precipitates exhibited a 5.29- to 22.1-fold increase in equilibrium solubility and a 0.7- to 2.35-fold improvement in IDR compared to the crystalline API. This study reveals that for weakly acidic compounds like XZP8257, precipitate formation in the GI tract can lead to high energy, readily redissolving solid forms, potentially enhancing rather than limiting absorption. This challenges the uniform negative view of precipitation and underscores the necessity of compound-specific precipitate characterization to accurately predict oral absorption, especially in physiologically based biopharmaceutics models.

IcoSema is under development as a once-weekly injectable combination therapy of icodec (basal insulin) and semaglutide (glucagon-like peptide 1 receptor agonist). This study assessed the pharmacokinetic characteristics of icodec and semaglutide following IcoSema administration vs. administration of icodec and semaglutide alone in Chinese individuals with type 2 diabetes (T2D). In a randomized, double-blind, three-period crossover study, 20 Chinese individuals with T2D (18–64 years, body mass index 18.5–34.9 kg/m2, glycated hemoglobin ≤ 9.0

Background/Objective: Physiologically based pharmacokinetic (PBPK) modeling is a powerful tool for predicting pharmacokinetics (PK) to support drug development and precision medicine. However, it has not been established for non-renal clearance pathways in patients with end-stage renal disease (ESRD), a population that bears heavy medication burden and is thereby at high risk for drug–drug–disease interactions (DDDIs). Furthermore, the pronounced inter-individual variability in PK observed in ESRD patients highlights the urgent need for individualized PBPK models. Methods: In this study, we developed a PBPK population model for ESRD patients, incorporating functional changes in key drug-metabolizing enzymes and transporters (DMETs), including CYP3A4, OATP1B1/3, P-gp, and BCRP. The model was initially constructed using the recalibrated demographic and physiological parameters of ESRD patients. Then, we used five well-validated substrates (midazolam, dabigatran etexilate, pitavastatin, rosuvastatin, and atorvastatin) and their corresponding PK profiles from ESRD patients taking a microdose cocktail regimen to simultaneously estimate the abundance of all these DMETs. Lastly, machine learning was employed to identify potential factors influencing individual clearance. Results: Our study suggested a significant reduction in hepatic OATP1B1/3 (75%) and intestinal P-gp abundance (34%) in ESRD patients. Ileum BCRP abundance was estimated to increase by 100%, while change in hepatic CYP3A4 abundance is minimal. Notably, simulations of drug combinations revealed potential DDDI risks that were not observed in healthy volunteers. Machine learning further identified Clostridium XVIII and Escherichia genus abundances as significant factors influencing dabigatran clearance. For rosuvastatin, aspartate aminotransferase, total bilirubin, Bacteroides, and Megamonas genus abundances were key influencers. No significant factors were identified for midazolam, pitavastatin, or atorvastatin. Conclusions: Our study proposes a feasible strategy for individualized PK prediction by integrating PBPK modeling with machine learning to support the development and precise use of the aforementioned DMET substrates in ESRD patients.

Background:Chronic inflammation plays a pivotal role in the development of frailty in patients with cardiovascular diseases (CVD). Systemic inflammatory response index (SIRI) has been shown to reflect the overall inflammatory status. This study aimed to investigate the relationship between SIRI and frailty in older patients with CVD, and to develop a nomogram for predicting the risk of frailty in this population. Methods:A total of 234 older patients with CVD were included. Inflammation markers were derived from routine blood tests, and frailty status was assessed using the FRAIL scale. Clinical and laboratory characteristics were compared between patients with or without frailty. Multivariate logistic regression was employed to identify significant variables for inclusion in the nomogram. The performance of the nomogram, including its discrimination and calibration, was rigorously evaluated. Results:A total of 98 cases were assigned to the frailty group and 136 to the non-frailty group. Patients in the non-frailty group were generally younger, more likely to have normal kidney function, and better blood pressure control. Frail patients exhibited a higher degree of systemic inflammation compared to non-frail patients (P < 0.05). Age, LDL-C and SIRI were identified as three independent risk factors with significant potential for predicting frailty in CVD patients. Therefore, we constructed a clinical nomogram model for frailty based on age, LDL-C and SIRI. The nomogram for frailty had considerable discriminative and calibrating abilities. Conclusion:In summary, our study demonstrated a significant association between elevated levels of inflammation markers, particularly SIRI, and an increased risk of frailty. Furthermore, by integrating age, LDL-C and SIRI, we established a nomogram to predict the risk of frailty in older patients with CVD.

Enarodustat is a hypoxia-inducible factor-prolyl hydroxylase inhibitor. We evaluated the pharmacokinetics, pharmacodynamics, and safety profile of domestic enarodustat (SAL-0951) and analyzed the influence of ethnic factors. In this phase I study, healthy Chinese participants received single and multiple oral doses (1, 5, and 15 mg) of SAL-0951 while in a fasted state. We monitored the pharmacokinetics, pharmacodynamics, and safety characteristics and analyzed the impact of ethnicity on pharmacokinetic characteristics. In total, 33 healthy Chinese participants were enrolled; the mean age was 31.2 ± a standard deviation of 5.5 years. After single doses of 1, 5, and 15 mg were administered under fasted conditions, SAL-0951 was rapidly absorbed. Mean maximum plasma concentration and area under the plasma concentration–time curve from time 0 to the last quantifiable concentration increased dose proportionately from 0.14 to 2.54 μg/mL and from 0.63 to 9.50 h × μg/mL, respectively. The elimination half-life was 6.13, 6.32, and 6.74 h, respectively, in these three groups, and the mean value of apparent clearance ranged from 1.64 to 1.89 L/h. SAL-0951 was excreted mostly as the parent compound. It reached a stable concentration after 5 days of multiple-dose administration. We observed no drug accumulation or time-dependent pharmacokinetic characteristics and no significant difference in pharmacokinetic characteristics between Chinese and Japanese participants. SAL-0951 was safe and well tolerated in healthy Chinese participants and had a linear pharmacokinetic profile. We found no ethnic differences in the pharmacokinetic characteristics of the drug between Chinese and Japanese populations. Registered at Chinadrugtrials.org.cn, registration number CTR2020245.

Lopinavir/ritonavir (LPV/r) has been widely used in HIV/HBV co-infected pregnant-women. We aim to characterize the maternal-fetal (m-f) pharmacokinetic (PK) of LPV/r and support the dose optimization and potential drug-drug interaction (DDI) evaluation in this population. Lopinavir PK characteristics in human immunodeficiency virus/hepatitis B virus (HIV/HBV) co-infected pregnant women (n = 35) and fetus were calculated using non-compartmental analysis followed by quantification of maternal PK characteristics using population PK (PopPK) analysis. A maternal-fetal lopinavir physiologically based pharmacokinetic (PBPK) model was developed by incorporating trans-placental transfer, disease- and pregnancy-related physiological changes. This final population PBPK model was applied to simulate different dose regimens of LPV/r and potential DDI risks under different drug combination scenarios. (AUClast) of lopinavir in co-infected pregnancy was first reported to be 34.1 and 31.0 mg/L/h for the 2nd and 3rd trimesters. The PBPK-simulated PK parameters were within 0.75 to 1.16-fold of the observations at different stages of pregnancy. The m-f PBPK model-simulated umbilical vein:maternal plasma (UV:MP) ratio of lopinavir was around 0.16 at late trimester, which is consistent with the PopPK model-simulated individual value of 0.116. Simulated results indicated that a standard dose of LPV/r (400/100 mg Q12 h) might not target the effective therapeutic concentration. Model-simulated DDI results suggested that lopinavir increased dose or shortened dosing interval when co-administered with rifampicin in HIV/HBV co-infected pregnancy. This work successfully applied model-informed approaches to quantitatively assess lopinavir m-f PK and also provided a novel strategy for DDI risk evaluation and dosing optimization for other P-gp substrates in HIV/HBV co-infected pregnant women.

Accumulating evidence highlights the critical role of circadian rhythms in regulating bone turnover. Bone turnover markers, including parathyroid hormone, C-terminal telopeptide of type I collagen, and N-terminal propeptide of type I procollagen, all exhibit distinct diurnal variations. Teriparatide, a recombinant parathyroid hormone analog, demonstrates time-dependent efficacy influenced by these endogenous rhythms. However, the impact of administration timing on bone metabolism remains underexplored. This randomized, open-label, exploratory trial investigates the impact of teriparatide administration timing by comparing subcutaneous injection at 08:00 versus 20:00 on bone turnover markers in postmenopausal women with osteoporosis. Twenty-eight participants (aged 60–70 years, lumbar spine T-score ≤ -3.0) will be randomized in a 1:1 ratio to receive 20 µg/day of teriparatide via subcutaneous injection at either 08:00 or 20:00 for 12 weeks. All participants will receive standardized calcium (1000–1500 mg/day) and cholecalciferol (800–1200 IU/day) supplementation throughout the study period. The primary outcomes are the between-group differences in serum parathyroid hormone, C-terminal telopeptide of type I collagen, and N-terminal propeptide of type I procollagen profiles, which will be assessed at baseline, 4 weeks, and 12 weeks. Secondary outcomes will evaluate the safety profile during the trial. This trial is expected to provide crucial insights into optimizing teriparatide administration timing, potentially guiding personalized dosing strategies to enhance bone formation and reduce fracture risk in osteoporosis. The findings may inform future research on circadian rhythm-aligned therapies. ClinicalTrials.gov ID NCT06951776.

BCS III drugs exhibit high solubility and low permeability, and some excipients were reported to increase their permeability. Although some permeability-enhancing excipients were investigated, permeability-enhancing strategy still need to be improved. Firstly, we established a database and analyzed the possible effects of excipients. Sodium lauryl sulfate (SLS) was found to be the most-used permeability-enhancing excipients. Moreover, the quantitative models for predicting Papp and Peff of BCS III drugs with SLS were developed, and statistically meaningful descriptors include molecular weight (MW), pKa, logP, solubility, hydrogen bond (HB) count, rotatable bond count (RBC), and topological polar surface area. The models demonstrated a good fit and effective predictive capability with all the correlation R2 values over 0.7. Hydrogen bonding remains the most significant factor in enhancing drug permeability with SLS, while hydrophilicity is also vital in this process. It was also found that MW, logP, pKa, and RBC play significant roles in paracellular transport. In summary, current research did the systematic and quantitative analysis of BCS III drugs and their excipients, which may accelerate formulation research on BCS III products.