Dual-domain mean-reverting diffusion model-enhanced temporal compressive coherent diffraction imaging.

Temporal compressive coherent diffraction imaging is a lensless imaging technique with the capability to capture fast-moving small objects. However, the accuracy of imaging reconstruction is often hindered by the loss of frequency domain information, a critical factor limiting the quality of the reconstructed images. To improve the quality of these reconstructed images, a method dual-domain mean-reverting diffusion model-enhanced temporal compressive coherent diffraction imaging (DMDTC) has been introduced. DMDTC leverages the mean-reverting diffusion model to acquire prior information in both frequency and spatial domain through sample learning. The frequency domain mean-reverting diffusion model is employed to recover missing information, while hybrid input-output algorithm is carried out to reconstruct the spatial domain image. The spatial domain mean-reverting diffusion model is utilized for denoising and image restoration. DMDTC has demonstrated a significant enhancement in the quality of the reconstructed images. The results indicate that the structural similarity and peak signal-to-noise ratio of images reconstructed by DMDTC surpass those obtained through conventional methods. DMDTC enables high temporal frame rates and high spatial resolution in coherent diffraction imaging.

Pharmacokinetics and Bioequivalence of Vardenafil Hydrochloride in Healthy Chinese Volunteers.

Vardenafil hydrochloride tablet is an inhibitor of phosphodiesterase type 5, primarily for the treatment of erectile dysfunction. This postprandial study evaluated the pharmacokinetics and bioequivalence of the test and reference formulations of vardenafil hydrochloride tablets in healthy Chinese volunteers. An open, randomized, single-center, single-dose, 2-period, 2-sequence bioequivalence test was conducted on 66 healthy subjects under fed conditions. Subjects were randomly assigned to a 20-mg test or reference formulation with a 7-day washout period. Venous blood samples (4 mL) were collected from each subject 25 times spanning predose (0 hour) to 24 hours after dosing. The plasma concentration of vardenafil was determined by high-performance liquid chromatography-tandem mass spectrometry. Sixty-two volunteers completed the study. Under fed conditions, the maximum plasma concentration was 29.1 ng/mL, the area under the concentration-time curve (AUC) from time 0 to the time of the last measurable concentration was 85.3 ng•h/mL, and AUC from time 0 to infinity was 87.1 ng•h/mL. The 90% confidence intervals of the geometric mean ratio of AUC time 0 to the time of the last measurable concentration and AUC from time 0 to infinity were within the bioequivalence acceptance range of 0.80-1.25. The test formulation was a bioequivalent alternative to the reference formulation when taken under fed conditions in healthy Chinese subjects.

CKB Promotes Mitochondrial ATP Production by Suppressing Permeability Transition Pore.

Creatine kinases are essential for maintaining cellular energy balance by facilitating the reversible transfer of a phosphoryl group from ATP to creatine, however, their role in mitochondrial ATP production remains unknown. This study shows creatine kinases, including CKMT1A, CKMT1B, and CKB, are highly expressed in cells relying on the mitochondrial F1F0 ATP synthase for survival. Interestingly, silencing CKB, but not CKMT1A or CKMT1B, leads to a loss of sensitivity to the inhibition of F1F0 ATP synthase in these cells. Mechanistically, CKB promotes mitochondrial ATP but reduces glycolytic ATP production by suppressing mitochondrial calcium (mCa2+) levels, thereby preventing the activation of mitochondrial permeability transition pore (mPTP) and ensuring efficient mitochondrial ATP generation. Further, CKB achieves this regulation by suppressing mCa2+ levels through the inhibition of AKT activity. Notably, the CKB-AKT signaling axis boosts mitochondrial ATP production in cancer cells growing in a mouse tumor model. Moreover, this study also uncovers a decline in CKB expression in peripheral blood mononuclear cells with aging, accompanied by an increase in AKT signaling in these cells. These findings thus shed light on a novel signaling pathway involving CKB that directly regulates mitochondrial ATP production, potentially playing a role in both pathological and physiological conditions.

Genome-wide characterization and functional analysis of the melon TGA gene family in disease resistance through ectopic overexpression in Arabidopsis thaliana.

TGA-binding (TGA) transcription factors, characterized by the basic region/leucine zipper motif (bZIP), have been recognized as pivotal regulators in plant growth, development, and stress responses through their binding to the as-1 element. In this study, the TGA gene families in melon, watermelon, cucumber, pumpkin, and zucchini were comprehensively characterized, encompassing analyses of gene/protein structures, phylogenetic relationships, gene duplication events, and cis-acting elements in gene promoters. Upon transient expression in Nicotiana benthamiana, the melon CmTGAs, with typical bZIP and DOG1 domains, were observed to localize within the nucleus. Biochemical investigation revealed specific interactions between CmTGA2/3/5/8/9 and CmNPR3 or CmNPR4. The CmTGA genes exhibited differential expression patterns in melon plants in response to different hormones like salicylic acid, methyl jasmonate, and ethylene, as well as a fungal pathogen, Stagonosporopsis cucurbitacearum that causes gummy stem blight in melon. The overexpression of CmTGA3, CmTGA8, and CmTGA9 in Arabidopsis plants resulted in the upregulation of AtPR1 and AtPR5 expression, thereby imparting enhanced resistance to Pseudomonas syringae pv. Tomato DC3000. In contrast, the overexpression of CmTGA7 or CmTGA9 resulted in a compromised resistance to Botrytis cinerea, coinciding with a concomitant reduction in the expression levels of AtPDF1.2 and AtMYC2 following infection with B. cinerea. These findings shed light on the important roles of specific CmTGA genes in plant immunity, suggesting that genetic manipulation of these genes could be a promising avenue for enhancing plant immune responses.

Systematic Characterization of DNA Methyltransferases Family in Tumor Progression and Antitumor Immunity.

Objective: DNA methylation is an essential epigenetic marker governed by DNA methyltransferases (DNMTs), which can influence cancer onset and progression. However, few studies have provided an integrated analysis of the relevance of DNMT family genes to cell stemness, the tumor microenvironment (TME), and immunotherapy biomarkers across diverse cancers. Methods: This study investigated the impact of five DNMTs on transcriptional profiles, prognosis, and their association with Ki67 expression, epithelial-mesenchymal transition signatures, stemness scores, the TME, and immunological markers across 31 cancer types from recognized public databases. Results: The results indicated that DNMT1/DNMT3B/DNMT3A expression increased, whereas TRDMT1/DNMT3L expression decreased in most cancer types. DNMT family genes were identified as prognostic risk factors for numerous cancers, as well as being prominently associated with immune, stromal, and ESTIMATE scores, as well as with immune-infiltrating cell levels. Expression of the well-known immune checkpoints, PDCD1 and CILA4, was noticeably related to DNMT1/DNMT3A/DNMT3B expression. Finally, we validated the role of DNMT1 in MCF-7 and HepG2-C3A cell lines through its knockdown, whereafter a decrease in cell proliferation and migration ability in vitro was observed. Conclusion: Our study comprehensively expounded that DNMT family genes not only behave as promising prognostic factors but also have the potential to serve as therapeutic targets in cancer immunotherapy for various types of cancer.

Plasma circulating tumor DNA unveils the efficacy of PD-1 inhibitors and chemotherapy in advanced gastric cancer.

Programmed Death Receptor 1 (PD-1) inhibitors, when combined with chemotherapy, have exhibited notable effectiveness in enhancing the survival outcomes of patients afflicted with advanced gastric cancer. However, it is important to acknowledge that not all patients derive substantial benefits from this therapeutic approach, highlighting the crucial necessity of identifying efficacious biomarkers to inform immunotherapy interventions. In this study, we sought to investigate the predictive utility of circulating tumor DNA (ctDNA) as a biomarker in a cohort of 30 patients diagnosed with advanced gastric cancer, all of whom underwent first-line treatment involving PD-1 inhibitor administration alongside chemotherapy. We procured peripheral blood samples both at baseline and following the completion of two treatment cycles. Additionally, baseline tissue specimens were collected for the purpose of genomic alteration assessment, employing both 47-gene and 737-gene next-generation sequencing panels for plasma and tumor tissue, respectively. We delineated a ctDNA response as the eradication of maximum variant allele frequencies relative to baseline levels. Notably, the objective response rate among individuals exhibiting a ctDNA response proved significantly superior in comparison to non-responders (P = 0.0073). Furthermore, patients who manifested a ctDNA response experienced markedly prolonged progression-free survival (PFS) and overall survival (OS) when juxtaposed with those devoid of a ctDNA response (median PFS: 15.6 vs. 6.0 months, P = 0.003; median OS: not reached [NR] vs. 9.0 months, P = 0.011). In summation, patients with advanced gastric cancer receiving first-line treatment with PD-1 inhibitors and chemotherapy, dynamic changes in ctDNA can serve as a potential biomarker for predicting treatment efficacy and long-term outcomes.

Oridonin attenuates liver ischemia-reperfusion injury by suppressing PKM2/NLRP3-mediated macrophage pyroptosis.

BACKGROUND: The NOD-like receptor protein 3 (NLRP3) mediated pyroptosis of macrophages is closely associated with liver ischemia reperfusion injury (IRI). As a covalent inhibitor of NLRP3, Oridonin (Ori), has strong anti-inflammasome effect, but its effect and mechanisms for liver IRI are still unknown. METHODS: Mice and liver macrophages were treated with Ori, respectively. Co-IP and LC-MS/MS analysis of the interaction between PKM2 and NLRP3 in macrophages. Liver damage was detected using H&E staining. Pyroptosis was detected by WB, TEM, and ELISA. RESULTS: Ori ameliorated liver macrophage pyroptosis and liver IRI. Mechanistically, Ori inhibited the interaction between pyruvate kinase M2 isoform (PKM2) and NLRP3 in hypoxia/reoxygenation（H/R）-induced macrophages, while the inhibition of PKM2/NLRP3 reduced liver macrophage pyroptosis and liver IRI. CONCLUSION: Ori exerted protective effects on liver IRI via suppressing PKM2/NLRP3-mediated liver macrophage pyroptosis, which might become a potential therapeutic target in the clinic.

The effectiveness and feasibility of ball-milled powdered activated carbon (BPAC) for removal of organic pesticides in conventional drinking water treatment process.

In the conventional drinking water treatment process (CDWTP), powdered activated carbon (PAC) is commonly used for removing organic pesticides, or other organic contaminants. However, the hydraulic retention time (HRT) in CDWTP is insufficient for fulfilling PAC adsorption equilibrium to realize its full capacity. This study examined the adsorption kinetics, adsorption thermal dynamics, and removal efficiency for six organic pesticides using the ball-milled PAC (BPAC) with varying particle sizes in CDWTP. Based on the experiments with the pesticides of atrazine, diazinon, dimethoate, fenitrothion, isoproturon and thiometon, the results indicated that as the particle size reduced from around 38 µm for the commercial PAC to 1 µm for the BPAC, the adsorption rates for hydrophobic pesticides increased up to twentyfold. Diffusional adsorption from the bulk solution to the external PAC surface is the most likely predominant mechanism. This could allow a sufficient pesticides' adsorption within the limited HRT and to achieve a great depth removal of these toxic compounds. However, the addition of BPAC with a diameter of 1 µm was observed to significantly increase residual particles in treated water after the conventional treatment process. With a further systematic evaluation of both adsorption rate and particle penetration, a particle size of around 6 µm BPAC was considered a practical compromise between the adsorption rate and particle penetration for real application. Results from five surface waters of different water quality indicated that, compared to commercial PAC, application of 6 µm BPAC could achieve up to a 75% reduction in adsorbent dosage while maintaining around the same pesticide removal efficiencies. Additionally, thermodynamic analyses suggest that adsorption of these pesticides could be enthalpically or entropically driven depending on the degree of pesticide hydrophobicity.

APEX-pHLA: A novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules.

Human leukocyte antigen (HLA) molecules play critically significant role within the realm of immunotherapy due to their capacities to recognize and bind exogenous antigens such as peptides, subsequently delivering them to immune cells. Predicting the binding between peptides and HLA molecules (pHLA) can expedite the screening of immunogenic peptides and facilitate vaccine design. However, traditional experimental methods are time-consuming and inefficient. In this study, an efficient method based on deep learning was developed for predicting peptide-HLA binding, which treated peptide sequences as linguistic entities. It combined the architectures of textCNN and BiLSTM to create a deep neural network model called APEX-pHLA. This model operated without limitations related to HLA class I allele variants and peptide segment lengths, enabling efficient encoding of sequence features for both HLA and peptide segments. On the independent test set, the model achieved Accuracy, ROC_AUC, F1, and MCC is 0.9449, 0.9850, 0.9453, and 0.8899, respectively. Similarly, on an external test set, the results were 0.9803, 0.9574, 0.8835, and 0.7863, respectively. These findings outperformed fifteen methods previously reported in the literature. The accurate prediction capability of the APEX-pHLA model in peptide-HLA binding might provide valuable insights for future HLA vaccine design.

Hinokione: an abietene diterpene with pancreatic ß cells regeneration and hypoglycemic activity, and other derivatives with novel structures from the woods of Agathis dammara.

In this study, 14 abietene and pimarene diterpenoids were isolated from the woods of Agathis dammara. Among them, 4 new compounds, dammarone A-C and dammaric acid A (1-4), were firstly reported, respectively. The structure of the new compounds was determined by HR ESI-MS and 1D/2D NMR spectroscopy, and their absolute configuration was determined by electronic circular dichroism (ECD) exciton chirality method. The hypoglycemic effect of all compounds was evaluated by transgenic zebrafish model, and the structure-activity relationship was discussed. Hinokione (7, HO) has low toxicity and significant hypoglycemic effects on zebrafish, the mechanism is mainly by promoting the differentiation of zebrafish pancreatic endocrine precursor cells (PEP cells) into ß cells, thereby promoting the regeneration of pancreatic ß cells.

Enhancing Predictions of Drug Solubility through Multidimensional Structural Characterization Exploitation.

Solubility is not only a significant physical property of molecules but also a vital factor in smallmolecule drug development. Determining drug solubility demands stringent equipment, controlled environments, and substantial human and material resources. The accurate prediction of drug solubility using computational methods has long been a goal for researchers. In this study, we introduce MSCSol, a solubility prediction model that integrates multidimensional molecular structure information. We incorporate a graph neural network with geometric vector perceptrons (GVP-GNN) to encode 3D molecular structures, representing spatial arrangement and orientation of atoms, as well as atomic sequences and interactions. We also employ Selective Kernel Convolution combined with Global and Local attention mechanisms to capture molecular features context at different scales. Additionally, various descriptors are calculated to enrich the molecular representation. For the 2D and 3D structural data of molecules, we design different data augmentation strategies to enhance generalization ability and prevent the model from learning irrelevant information. Extensive experiments on benchmark and independent datasets demonstrate MSCSol's superior performance. Ablation studies further confirm the effectiveness of different modules. Interpretability analysis highlights the importance of various atomic groups and substructures for solubility and verifies that our model effectively captures functional molecular structures and higher-order knowledge. The source code and datasets are freely available at https://github.com/ZiyuFanCSU/MSCSol.

HighFold: accurately predicting structures of cyclic peptides and complexes with head-to-tail and disulfide bridge constraints.

In recent years, cyclic peptides have emerged as a promising therapeutic modality due to their diverse biological activities. Understanding the structures of these cyclic peptides and their complexes is crucial for unlocking invaluable insights about protein target-cyclic peptide interaction, which can facilitate the development of novel-related drugs. However, conducting experimental observations is time-consuming and expensive. Computer-aided drug design methods are not practical enough in real-world applications. To tackles this challenge, we introduce HighFold, an AlphaFold-derived model in this study. By integrating specific details about the head-to-tail circle and disulfide bridge structures, the HighFold model can accurately predict the structures of cyclic peptides and their complexes. Our model demonstrates superior predictive performance compared to other existing approaches, representing a significant advancement in structure-activity research. The HighFold model is openly accessible at https://github.com/hongliangduan/HighFold.

Ferroptosis: An important mechanism of disease mediated by the gut-liver-brain axis.

AIMS: As a unique iron-dependent non-apoptotic cell death, Ferroptosis is involved in the pathogenesis and development of many human diseases and has become a research hotspot in recent years. However, the regulatory role of ferroptosis in the gut-liver-brain axis has not been elucidated. This paper summarizes the regulatory role of ferroptosis and provides theoretical basis for related research. MATERIALS AND METHODS: We searched PubMed, CNKI and Wed of Science databases on ferroptosis mediated gut-liver-brain axis diseases, summarized the regulatory role of ferroptosis on organ axis, and explained the adverse effects of related regulatory effects on various diseases. KEY FINDINGS: According to our summary, the main way in which ferroptosis mediates the gut-liver-brain axis is oxidative stress, and the key cross-talk of ferroptosis affecting signaling pathway network is Nrf2/HO-1. However, there were no specific marker between different organ axes mediate by ferroptosis. SIGNIFICANCE: Our study illustrates the main ways and key cross-talk of ferroptosis mediating the gut-liver-brain axis, providing a basis for future research.

Differential selection of yield and quality traits has shaped genomic signatures of cowpea domestication and improvement.

Cowpeas (tropical legumes) are important in ensuring food and nutritional security in developing countries, especially in sub-Saharan Africa. Herein, we report two high-quality genome assemblies of grain and vegetable cowpeas and we re-sequenced 344 accessions to characterize the genomic variations landscape. We identified 39 loci for ten important agronomic traits and more than 541 potential loci that underwent selection during cowpea domestication and improvement. In particular, the synchronous selections of the pod-shattering loci and their neighboring stress-relevant loci probably led to the enhancement of pod-shattering resistance and the compromise of stress resistance during the domestication from grain to vegetable cowpeas. Moreover, differential selections on multiple loci associated with pod length, grain number per pod, seed weight, pod and seed soluble sugars, and seed crude proteins shaped the yield and quality diversity in cowpeas. Our findings provide genomic insights into cowpea domestication and improvement footprints, enabling further genome-informed cultivar improvement of cowpeas.

Predicting Drug-Drug Interactions Involving Rifampicin Using a Semi-mechanistic Hepatic Compartmental Model.

AIMS: To develop a semi-mechanistic hepatic compartmental model to predict the effects of rifampicin, a known inducer of CYP3A4 enzyme, on the metabolism of five drugs, in the hope of informing dose adjustments to avoid potential drug-drug interactions. METHODS: A search was conducted for DDI studies on the interactions between rifampicin and CYP substrates that met specific criteria, including the availability of plasma concentration-time profiles, physical and absorption parameters, pharmacokinetic parameters, and the use of healthy subjects at therapeutic doses. The semi-mechanistic model utilized in this study was improved from its predecessors, incorporating additional parameters such as population data (specifically for Chinese and Caucasians), virtual individuals, gender distribution, age range, dosing time points, and coefficients of variation. RESULTS: Optimal parameters were identified for our semi-mechanistic model by validating it with clinical data, resulting in a maximum difference of approximately 2-fold between simulated and observed values. PK data of healthy subjects were used for most CYP3A4 substrates, except for gilteritinib, which showed no significant difference between patients and healthy subjects. Dose adjustment of gilteritinib co-administered with rifampicin required a 3-fold increase of the initial dose, while other substrates were further tuned to achieve the desired drug exposure. CONCLUSIONS: The pharmacokinetic parameters AUCR and CmaxR of drugs metabolized by CYP3A4, when influenced by Rifampicin, were predicted by the semi-mechanistic model to be approximately twice the empirically observed values, which suggests that the semi-mechanistic model was able to reasonably simulate the effect. The doses of four drugs adjusted via simulation to reduce rifampicin interaction.

Multipatterned Chondrocytes' Scaffolds by FL-MOPL with a BSA-GMA Hydrogel to Regulate Chondrocytes' Morphology.

Repairing articular cartilage damage is challenging due to its low regenerative capacity. In vitro, cartilage regeneration is a potential strategy for the functional reconstruction of cartilage defects. A hydrogel is an advanced material for mimicking the extracellular matrix (ECM) due to its hydrophilicity and biocompatibility, which is known as an ideal scaffold for cartilage regeneration. However, chondrocyte culture in vitro tends to dedifferentiate, leading to fibrosis and reduced mechanical properties of the newly formed cartilage tissue. Therefore, it is necessary to understand the mechanism of modulating the chondrocytes' morphology. In this study, we synthesize photo-cross-linkable bovine serum albumin-glycidyl methacrylate (BSA-GMA) with 65% methacrylation. The scaffolds are found to be suitable for chondrocyte growth, which are fabricated by homemade femtosecond laser maskless optical projection lithography (FL-MOPL). The large-area chondrocyte scaffolds have holes with interior angles of triangle (T), quadrilateral (Q), pentagon (P), hexagonal (H), and round (R). The FL-MOPL polymerization mechanism, swelling, degradation, and biocompatibility of the BSA-GMA hydrogel have been investigated. Furthermore, cytoskeleton and nucleus staining reveals that the R-scaffold with larger interior angle is more effective in maintaining chondrocyte morphology and preventing dedifferentiation. The scaffold's ability to maintain the chondrocytes' morphology improves as its shape matches that of the chondrocytes. These results suggest that the BSA-GMA scaffold is a suitable candidate for preventing chondrocyte differentiation and supporting cartilage tissue repair and regeneration. The proposed method for chondrocyte in vitro culture by developing biocompatible materials and flexible fabrication techniques would broaden the potential application of chondrocyte transplants as a viable treatment for cartilage-related diseases.

CCDN-DETR: A Detection Transformer Based on Constrained Contrast Denoising for Multi-Class Synthetic Aperture Radar Object Detection.

The effectiveness of the SAR object detection technique based on Convolutional Neural Networks (CNNs) has been widely proven, and it is increasingly used in the recognition of ship targets. Recently, efforts have been made to integrate transformer structures into SAR detectors to achieve improved target localization. However, existing methods rarely design the transformer itself as a detector, failing to fully leverage the long-range modeling advantages of self-attention. Furthermore, there has been limited research into multi-class SAR target detection. To address these limitations, this study proposes a SAR detector named CCDN-DETR, which builds upon the framework of the detection transformer (DETR). To adapt to the multiscale characteristics of SAR data, cross-scale encoders were introduced to facilitate comprehensive information modeling and fusion across different scales. Simultaneously, we optimized the query selection scheme for the input decoder layers, employing IOU loss to assist in initializing object queries more effectively. Additionally, we introduced constrained contrastive denoising training at the decoder layers to enhance the model's convergence speed and improve the detection of different categories of SAR targets. In the benchmark evaluation on a joint dataset composed of SSDD, HRSID, and SAR-AIRcraft datasets, CCDN-DETR achieves a mean Average Precision (mAP) of 91.9%. Furthermore, it demonstrates significant competitiveness with 83.7% mAP on the multi-class MSAR dataset compared to CNN-based models.

TRPV4 antagonist suppresses retinal ganglion cell apoptosis by regulating the activation of CaMKII and TNF-α expression in a chronic ocular hypertension rat model.

Glaucoma is characterized by a progressive loss of retinal ganglion cells (RGCs), leading to irreversible visual function impairment. Sustained increase in intraocular pressure represents a major risk factor for glaucoma, yet the underlying mechanisms of RGC apoptosis induced by intraocular pressure remains unclear. This study aims to investigate the role of TRPV4 in RGC apoptosis in a rat model of chronic ocular hypertension (COH) and the underlying molecular mechanism. In the COH rat models, we evaluated the visual function, retinal pathological changes and RGC apoptosis. TRPV4 expression and downstream signaling molecules were also detected. We found that RGC density decreased and RGC apoptosis was induced in COH eyes compared with control eyes. TRPV4 expression increased significantly in response to elevated IOP. TRPV4 inhibition by the TRPV4 antagonist HC-067047 (HC-067) suppressed RGC apoptosis and protected visual function. HC-067 treatment upregulated the phosphorylation of CaMKII in both control and COH eyes. Finally, HC-067 treatment suppressed the production of TNF-α induced by ocular hypertension. The TRPV4 antagonist HC-067 might suppress RGC apoptosis by regulating the activation of CaMKII and inhibiting the production of TNF-α in the COH model. This indicated that TRPV4 antagonists may be a potential and novel therapeutic strategy for glaucoma.

Predicting pharmacodynamic effects through early drug discovery with artificial intelligence-physiologically based pharmacokinetic (AI-PBPK) modelling.

A mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model links the concentration-time profile of a drug with its therapeutic effects based on the underlying biological or physiological processes. Clinical endpoints play a pivotal role in drug development. Despite the substantial time and effort invested in screening drugs for favourable pharmacokinetic (PK) properties, they may not consistently yield optimal clinical outcomes. Furthermore, in the virtual compound screening phase, researchers cannot observe clinical outcomes in humans directly. These uncertainties prolong the process of drug development. As incorporation of Artificial Intelligence (AI) into the physiologically based pharmacokinetic/pharmacodynamic (PBPK) model can assist in forecasting pharmacodynamic (PD) effects within the human body, we introduce a methodology for utilizing the AI-PBPK platform to predict the PK and PD outcomes of target compounds in the early drug discovery stage. In this integrated platform, machine learning is used to predict the parameters for the model, and the mechanism-based PD model is used to predict the PD outcome through the PK results. This platform enables researchers to align the PK profile of a drug with desired PD effects at the early drug discovery stage. Case studies are presented to assess and compare five potassium-competitive acid blocker (P-CAB) compounds, after calibration and verification using vonoprazan and revaprazan.

Genetic insights into repurposing statins for hyperthyroidism prevention: a drug-target Mendelian randomization study.

Background: Current therapeutic measures for thyroid dysfunction are limited and often accompanied by adverse effects. The use of lipid-lowering drugs like statins has recently been associated with lower thyroid eye diseases risk. Objective: To investigate the implications of genetically proxied lipid-lowering drugs on thyroid dysfunction. Methods: In this drug-target Mendelian randomization (MR) study, we utilized genetic variants within drug target genes associated with low-density lipoprotein (LDL) or triglyceride (TG), derived from a genome-wide association study (GWAS) meta-analysis (N ≤ 188,577), to simulate lifelong drug interventions. Genetic summary statistics for thyroid dysfunction outcomes were retrieved from GWAS datasets of Thyroid Omics Consortium (N ≤ 54,288) and UK Biobank (N = 484,598). Inverse-variance-weighted MR (IVW-MR) method was performed as primary analysis, followed by validation in colocalization analysis. A subsequent two-step MR analysis was conducted to identify biomarkers mediating the identified drug-outcome association. Results: In IVW-MR analysis, genetic mimicry of 3-hydroxy-3-methylglutarylcoenzyme reductase (HMGCR) inhibitors (e.g. statins) was significantly associated with lower risk of hyperthyroidism in two independent datasets (OR1, 0.417 per 1-mmol/L lower in LDL-C; 95% CI 0.262 to 0.664; P1 = 2.262 × 10-4; OR2 0.996; 95% CI 0.993-0.998; P2 = 0.002). Two-step MR analysis revealed eighteen biomarkers linked to genetic mimicry of HMGCR inhibition, and identified insulin-like growth factor 1 (IGF-1) levels mediating 2.108% of the negative causal relationship between HMGCR inhibition and hyperthyroidism. Conclusion: This study supports HMGCR inhibition as a promising therapeutic strategy for hyperthyroidism and suggests its underlying mechanisms may extend beyond lipid metabolism. Further investigations through laboratory studies and clinical trials are necessary to confirm and elucidate these findings.