Preclinical Research Articles

Star Anise (Illicium verum Hook. f.): Dual Therapeutic and Nutritional Potential in Food and Medicine

Star anise (Illicium verum Hook. f., SA) is a well-known culinary seasoning in China and Vietnam. Actually, SA also has been widely used as a traditional Chinese medicine in China with a long history. Phytochemical analysis has revealed that SA contains a high concentration of essential oils, phenols, flavonoids, and other bioactive compounds that contribute to its diverse pharmacological properties. These properties include anti-bacterial, anti-inflammatory and analgesic, anti-oxidation, anti-viral, anti-cancer, anti-diabetes, anti-diarrheal and promoting hair growth. Various preclinical studies have shown that SA extracts and their active constituents may have potential therapeutic applications in preventing and treating various diseases. However, a comprehensive report on the relationship between the active ingredient, biological activity and food characteristics of SA is rare. The medicinal value of SA has not been well valued and developed. This review provides an overview of the botanical chemistry and pharmacological properties of SA, as well as its potential innovative applications in food and personal care products, aiming to provide theoretical support for its further development and utilization. Graphical abstract: http://links.lww.com/AHM/A138

The cellular and molecular mechanisms mediating the protective effects of sodium-glucose linked transporter 2 inhibitors against metabolic dysfunction-associated fatty liver disease.

Metabolic dysfunction-associated fatty liver disease (MAFLD), formerly known as nonalcoholic fatty liver disease (NAFLD), is a common, highly heterogeneous condition that affects about a quarter of the world's population, with no approved drug therapy. Current evidence from preclinical research and a number of small clinical trials indicates that SGLT2 inhibitors could also be effective for MAFLD. MAFLD is associated with a higher risk of chronic liver disease and multiple extrahepatic events, especially cardiovascular disease (CVD) and chronic kidney disease (CKD). MAFLD is considered a more appropriate terminology than NAFLD because it captures the complex bidirectional interplay between fatty liver and metabolic dysfunctions associated with disease progression, such as obesity and type 2 diabetes mellitus (T2DM). SGLT2 inhibitors are antidiabetic drugs that block glucose reabsorption in the kidney proximal tubule. In this article, we reviewed current clinical evidence supporting the potential use of SGLT2 inhibitors as a drug therapy for MAFLD and discussed the possible cellular and molecular mechanisms involved. We also reviewed the clinical benefits of SGLT2 inhibitors against MAFLD-related comorbidities, especially CVD, CKD and cardiovascular-kidney-metabolic syndrome (CKM). The broad beneficial effects of SGLT2 inhibitors support their use, likely in combination with other drugs, as a therapy for MAFLD.

Pregnancy in obese women and mechanisms of increased cardiovascular risk in offspring

Abstract Pregnancy complicated by maternal obesity contributes to an increased cardiovascular risk in offspring, which is increasingly concerning as the rates of obesity and cardiovascular disease are higher than ever before and still growing. There has been much research in humans and preclinical animal models to understand the impact of maternal obesity on offspring health. This review summarizes what is known about the offspring cardiovascular phenotype, describing a mechanistic role for oxidative stress, metabolic inflexibility, and mitochondrial dysfunction in mediating these impairments. It also discusses the impact of secondary postnatal insults, which may reveal latent cardiovascular deficits that originated in utero. Finally, current interventional efforts and gaps of knowledge to limit the developmental origins of cardiovascular dysfunction in offspring of obese pregnancy are highlighted.

Fluorescence-guided tumor visualization of colorectal cancer using tumor-initiating probe yellow in preclinical models.

Fluorescence-guided surgery has emerged as an innovative technique with promising applications in the treatment of various tumors, including colon cancer. Tumor-initiating probe yellow (TiY) has been discovered for identifying tumorigenic cells by unbiased phenotypic screening with thousands of diversity-oriented fluorescence library (DOFL) compounds in a patient-derived lung cancer cell model. This study demonstrated the clinical feasibility of TiY for tumor-specific fluorescence imaging in the tissues of patients with colorectal cancer (CRC). To evaluate the efficacy of TiY in tumor imaging, surgical specimens were obtained, consisting of 36 tissues from 18 patients with CRC, for ex vivo molecular fluorescence imaging, histology, and immunohistochemistry. Orthotopic and chemically induced CRC mice models were administered TiY topically, and distinct tumor lesions were observed in 10min by real-time fluorescence colonoscopy and ex vivo imaging. In a hepatic metastasis mouse model using splenic injection, TiY accumulation was detected in metastatic liver lesions through fluorescence imaging. Correlation analysis between TiY intensity and protein expression, assessed via immunohistochemistry and Western blotting, revealed a positive correlation between TiY and vimentin and Zeb1, which are known as epithelial-mesenchymal transition (EMT) markers of cancers. A comparative analysis of TiY with other FDA-approved fluorescence probes such as ICG revealed greater quantitative differences in TiY fluorescence intensity between tumor and normal tissues than those observed with ICG. Altogether, these results demonstrated that TiY has a strong potential for visualizing CRC by fluorescence imaging in various preclinical models, which can be further translated for clinical use such as fluorescence-guided surgery. Furthermore, our data indicate that TiY is preferentially uptaken by cells with EMT induction and progression, and overexpressing vimentin and Zeb1 in patients with CRC.

Automated acute skin toxicity scoring in a mouse model through deep learning.

This study presents a novel approach to skin toxicity assessment in preclinical radiotherapy trials through an advanced imaging setup and deep learning. Skin reactions, commonly associated with undesirable side effects in radiotherapy, were meticulously evaluated in 160 mice across four studies. A comprehensive dataset containing 7542 images was derived from proton/electron trials with matched manual scoring of the acute toxicity on the right hind leg, which was the target area irradiated in the trials. This dataset was the foundation for the subsequent model training. The two-step deep learning framework incorporated an object detection model for hind leg detection and a classification model for toxicity classification. An observer study involving five experts and the deep learning model, was conducted to analyze the retrospective capabilities and inter-observer variations. The results revealed that the hind leg object detection model exhibited a robust performance, achieving an accuracy of almost 99%. Subsequently, the classification model demonstrated an overall accuracy of about 85%, revealing nuanced challenges in specific toxicity grades. The observer study highlighted high inter-observer agreement and showcased the model's superiority in accuracy and misclassification distance. In conclusion, this study signifies an advancement in objective and reproducible skin toxicity assessment. The imaging and deep learning system not only allows for retrospective toxicity scoring, but also presents a potential for minimizing inter-observer variation and evaluation times, addressing critical gaps in manual scoring methodologies. Future recommendations include refining the system through an expanded training dataset, paving the way for its deployment in preclinical research and radiotherapy trials.

112° field of view high-resolution swept-source OCT angiography for rat retinas

This study introduces an ultra-wide field (UWF) and high-resolution swept-source optical coherence tomographic angiography (OCTA) system for rat retinal imaging. Using an asymmetrical optics design, the system achieves unprecedented details of retinal structures and vascular plexuses over a large field of view (112°) in a single-shot acquisition. Views of single-nerve fiber bundles and single capillary vessels are consistently visible over a 112° field of view. The system has a long imaging range and high penetration and allows a full view of vitreous hyaloid vessels, retina, choroid, sclera, and posterior ciliary arteries, down to sub-sclera connective tissues. In a longitudinal study of oxygen-induced retinopathy (OIR) in rats, the system successfully revealed the progression and regression of OIR-related vascular pathologies, such as ischemia, neovascularization, and tortuosity. To our knowledge, this novel UWF-OCT/OCTA prototype designed for rat retinal imaging will be a vital tool for monitoring disease progression and evaluating therapeutic interventions in preclinical models.

Glucagon-like peptide-1 receptor agonists for treatment of diabetes and obesity: advantage of oral delivery

GLP-1 receptor agonists ((GLP-1 RAs) are currently receiving a lot of attention because of their impact in diabetes, weight loss and other areas. While GLP-1 RAs in injectable form are highly efficacious, further work is required to develop oral versions which can deliver these peptides efficiently without requiring use of excessively high doses. This paper describes the ability of an oral peptide delivery formulation, Axcess™, to enhance uptake of GLP-1 receptor agonists via the intestine, resulting in changes in insulin and glucose blood levels indicative of biopotencies of 9% for exendin-4 and 14.8% for semaglutide in preclinical models. The route of delivery suggests that the peptides will be able to interact with the GLP-1 receptors on the vagal afferents of the intestine, as is the case for native GLP-1 in healthy individuals. GLP-1 receptor agonists administered via this route will be a valuable addition to the therapeutic modalities available for treatment of diabetes and obesity.

Lost in translation: the lack of agreement between surgeons and scientists regarding biomaterials research and innovation for treating bone defects.

With over 2 million grafts performed annually, bone ranks second only to blood in the frequency of transplants. This high demand is primarily driven by the persistent challenges posed by bone defects, particularly following trauma or surgical interventions such as tumour excision. The demand for effective and efficient treatments has increased exponentially in the twenty-first century. Limitations associated with autologous bone grafts drive exploration into replacements, including allografts, synthetic substitutes, and 3D-printed scaffolds. This research aimed to unravel disparities in the knowledge and evaluation of current and future bone defect treatments between surgeons and biomaterial scientists. A prospective cross-sectional survey, pre-registered with the OSF ( https://osf.io/y837m/?view_only=fab29e24df4f4adf897353ac70aa3361 ) and conducted online from October 2022 to March 2023, collected data on surgeons' views (n = 337) and scientists (n = 99) on bone defect treatments. Scientists were significantly more optimistic than surgeons regarding the future replacement of autologous bone grafts with synthetic or tissue-engineered substitutes (p < 0.001). Accordingly, scientists foresee a paradigm shift from autologous bone grafts to biomaterial and tissue-engineered solutions, reflecting their confidence in the ongoing advancements within this field. Furthermore, regulatory trepidations for 3D-printed bone scaffolds were acknowledged, with scientists emphasizing the need for a more significant focus on clinical relevance in preclinical studies and regulatory clarity. In a ranked categorical assessment, witnessing the technology in action was deemed most influential in adopting new bone regeneration methods by both scientists and surgeons. To conclude, this study was conducted through a web-based survey, highlighting a substantial translational gap. It underscores the immediate need ("call to action") for meaningful interdisciplinary collaboration between surgeons and scientists, often referred to as the need to "walk the talk". The findings underscore the critical importance of aligning clinical needs, research outcomes, and regulatory frameworks to improve the development and implementation of biomaterial-based bone graft substitutes that demonstrate efficacy and efficiency in bone defect treatment.

Imaging blood-brain barrier dysfunction in drug-resistant epilepsy: A multi-center feasibility study.

Blood-brain barrier dysfunction (BBBD) has been linked to various neurological disorders, including epilepsy. This study aims to utilize dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to identify and compare brain regions with BBBD in patients with epilepsy (PWE) and healthy individuals. We scanned 50 drug-resistant epilepsy (DRE) patients and 58 control participants from four global specialized epilepsy centers using DCE-MRI. The presence and extent of BBBD were analyzed and compared between PWE and healthy controls. Both greater brain volume and higher number of brain regions with BBBD were significantly present in PWE compared to healthy controls (p < 10-7). No differences in total brain volume with BBBD were observed in patients diagnosed with either focal seizures or generalized epilepsy, despite variations in the affected regions. Overall brain volume with BBBD did not differ in PWE with MRI-visible lesions compared with non-lesional cases. BBBD was observed in brain regions suspected to be related to the onset of seizures in 82% of patients (n = 39) and was typically identified in, adjacent to, and/or in the same hemisphere as the suspected epileptogenic lesion (n = 10). These findings are consistent with pre-clinical studies that highlight the role of BBBD in the development of DRE and identify microvascular stabilization as a potential therapeutic strategy.

Safety and efficacy of Angong Niuhuang Pills in patients with moderate-to-severe acute ischemic stroke (ANGONG TRIAL): A randomized double-blind placebo-controlled pilot clinical trial.

Preclinical studies have indicated that Angong Niuhuang Pills (ANP) reduce cerebral infarct and edema volumes. This study aimed to investigate whether ANP safely reduces cerebral infarct and edema volumes in patients with moderate to severe acute ischemic stroke. This randomized, double-blind, placebo-controlled pilot trial included patients with acute ischemic stroke with National Institutes of Health Stroke Scale (NIHSS) scores ranging from 10 to 20 in 17 centers in China between April 2021 and July 2022. Patients were allocated within 36 h after onset via block randomization to receive ANP or placebo (3 g/day for 5 days). The primary outcomes were changes in cerebral infarct and edema volumes after 14 days of treatment. The primary safety outcome was severe adverse events (SAEs) for 90 days. There were 57 and 60 patients in the ANP and placebo groups finally included respectively for modified intention-to-treat analysis. The median age was 66 years, and the median NIHSS score at baseline was 12. The changes in cerebral infarct volume at day 14 were 0.3mL and 0.4mL in the ANP and placebo groups, respectively (median difference: -7.1mL; interquartile range [IQR]: -18.3 to 2.3 mL, P=0.30). The changes in cerebral edema volume of the ANP and placebo groups on day 14 were 11.4mL and 4.0mL, respectively (median difference: 3.0 mL, IQR: -1.3 to 9.9 mL, P=0.15). The rates of SAE within 90 days were similar in the ANP (3/57, 5%) and placebo (7/60, 12%) groups (P=0.36). Changes in serum mercury and arsenic concentrations were comparable. In patients with large artery atherosclerosis, ANP reduced the cerebral infarct volume at 14 days (median difference: -12.3 mL; IQR: -27.7 to -0.3 mL, P=0.03). ANP showed a similar safety profile to placebo and non-significant tendency to reduce cerebral infarct volume in patients with moderate-to-severe stroke. Further studies are warranted to assess the efficacy of ANP in reducing cerebral infarcts and improving clinical prognosis. Clinicaltrials.gov, No. NCT04475328.

Classification of salivary gland biopsies in Sjögren's syndrome by a convolutional neural network using an auto-machine learning platform.

The histopathological analysis of minor salivary gland biopsies, particularly through the quantification of the Focus Score (FS), is pivotal in the diagnostic workflow for Sjögren's Syndrome (SS). AI-based image recognition using deep learning models has demonstrated potential in enhancing diagnostic accuracy and efficiency in preclinical research. The primary aim of this investigation was to utilize an auto-machine learning (autoML) platform for the automated segmentation and quantification of FS on histopathological slides, aiming to augment diagnostic precision and speed in SS. A cohort comprising 86 patients with sicca syndrome (37 diagnosed with SS based on the 2016 ACR/EULAR Classification Criteria and 49 non-SS) was selected for an in-depth histological examination. A repository of 172 slides (two per patient) was assembled, encompassing 74 slides meeting the classificatory thresholds for SS (FS ≥ 1, indicative of lymphocytic infiltration) and 98 slides showcasing normal salivary gland histology. The autoML platform utilized (Giotto, L2F, Lausanne Switzerland) employed a Convolutional Neural Network (CNN) architecture (ResNet-152) for the training and validation phases, using a dataset of 172 slides. The developed model exhibited a reliability score of 0.88, proficiently distinguishing SS cases, with a sensitivity of 89.47% (95% CI: 66.86% to 98.70%) and a specificity of 88.24% (95% CI: 63.56% to 98.54%). The model found histological slides of suboptimal quality (e.g., those compromised during fixation or staining processes) to be the most challenging for accurate classification. AutoML platforms offer a rapid and flexible approach to developing machine learning models, even with smaller datasets, as demonstrated in this study for SS. These platforms hold significant potential for enhancing diagnostic precision and efficiency in both clinical and research settings. Multicentric studies with larger patient cohorts are essential for thorough evaluation and validation of this innovative diagnostic approach.

Drosophila melanogaster experimental model to test new antimicrobials: a methodological approach

Given the increasing concern about antimicrobial resistance among the microorganisms that cause infections in our society, there is an urgent need for new drug discovery. Currently, this process involves testing many low-quality compounds, resulting from the in vivo testing, on mammal models, which not only wastes time, resources, and money, but also raises ethical questions. In this review, we have discussed the potential of D. melanogaster as an intermediary experimental model in this drug discovery timeline. We have tackled the topic from a methodological perspective, providing recommendations regarding the range of drug concentrations to test based on the mechanism of action of each compound; how to treat D. melanogaster, how to monitor that treatment, and what parameters we should consider when designing a drug screening protocol to maximize the study’s benefits. We also discuss the necessary improvements needed to establish the D. melanogaster model of infection as a standard technique in the drug screening process. Overall, D. melanogaster has been demonstrated to be a manageable model for studying broad-spectrum infection treatment. It allows us to obtain valuable information in a cost-effective manner, which can improve the drug screening process and provide insights into our current major concern. This approach is also in line with the 3R policy in biomedical research, in particular on the replacement and reduce the use of vertebrates in preclinical development.

Revolutionizing new drug discovery: Harnessing AI and machine learning to overcome traditional challenges and accelerate targeted therapies

Designing highly targeted, selective drugs with desirable absorption, distribution, metabolism, excretion, and pharmacokinetic (PK) profiles; single-digit nanomolar efficacy; and a wider therapeutic index are challenging. In the traditional drug discovery process, researchers screen thousands of chemical compounds during pre-clinical development, progressing through hit identification, lead optimization, and candidate selection to shortlist &amp;ndash; potential clinical candidates with favorable PK profiles, high tolerability, and manageable toxicity. The selected candidate must demonstrate sufficient efficacy in treating the target disease in humans. Despite these efforts, the success rate of the pre-clinical candidate to sail through Phase I, Phase II, and Phase III in clinical trials remains exceedingly low. Supported by powerful data-driven tools, artificial intelligence (AI) has transformed this traditional drug discovery process by enabling the analysis of large quantities of omics, phenotypic, and expression data to identify the biological mechanisms of pathological conditions and in turn identify druggable proteins or genes. The generative AI-powered toolbox creates novel compounds from scratch, assists scientists in optimizing druggability attributes, and bridges the differences between animal and human physiology and anatomy to predict potential toxicity in humans with high accuracy. This review discusses the bottlenecks in the traditional drug discovery approach, the impact of AI and machine learning (ML) in drug discovery, and potential challenges associated with AI/ML adoption.

Clinical translation of tissue-engineered oesophageal grafts: are patients ready for us?

We sought to engage with expert patient/carers to understand attitudes towards use of tissue engineering (TE) for long-gap oesophageal atresia (OA). An in-person engagement event for 70 patients/parents was held by the OA patient group, TOFS. Attitudes towards TE were assessed before and after a talk on use of TE oesophagi in a pre-clinical OA model. Perceptions were assessed using a 5-point Likert scale (median [range]) and compared using Mann-Whitney test. 43 attendees responded; 56% parents/caregivers, 21% patients, 7% healthcare workers, 16% unreported. Most (85%) had some awareness of TE but for 15%, it was a new concept. Attendees were receptive to TE; 89% reported no concerns about growth of their/child(s) cells in a lab and 61% reported no concerns about using animal products. Perceptions of TE significantly improved after the presentation from 4 (2-5, n = 32) to 5 (3-5, n = 28) p < 0.0001, and 96% would like to be involved in focus groups on development of a TE product for use in OA. Input from key stakeholders is essential to introduction of TE constructs clinically. The overall response to TE constructs was positive, and informs development of an OA-specific focus group to guide translation.

NET-EN treatment leads to delayed HSV-2 infection, enhanced mucin and T cell functions in the female genital tract when compared to DMPA in a preclinical mouse model

Depot-medroxyprogesterone acetate (DMPA) and Norethisterone Enanthate (NET-EN) are progestin-only injectable contraceptives widely used by women in sub-Sharan Africa, where incidence of HIV-1 and HSV-2 infection remains high. Studies indicate that DMPA usage can increase the risk of HSV-2 infection, but limited data indicate no increased risk with use of NET-EN. We therefore investigated the effects of NET-EN and DMPA on susceptibility to vaginal HSV-2 infection in ovariectomized (OVX) mice and effects on immune responses, particularly in the vaginal tract (VT). OVX mice, when treated with NET-EN and infected intravaginally, had delayed genital pathology, decreased viral shedding, and extended survival compared to DMPA- or untreated OVX mice. CD4+ T cells isolated from VT showed no significant change in frequency with either contraceptive. However, DMPA significantly decreased the total number of VT CD4+ and CD8+ T cells and the number of IFN-γ producing CD4 and CD8 T cells and increased the percentage of CD4 and CD8 T cells producing TNF-α compared to untreated mice. In contrast, NET-EN significantly enhanced percentages of CD8+ T cells compared to DMPA treated mice, and frequencies of IFN-γ+ CD4 and CD8 T cells in the VT compared to untreated mice. Comparative analysis of splenic lymphocytes indicated that DMPA treatment resulted in reduction of CD4+ T cell frequency, but enhanced TNF-α+ CD4 T cells compared to untreated mice. NET-EN enhanced the frequency of CD8 T cells, as well as IFN-γ+ and TNF-α+ CD4, and IFN-γ+ CD8 T cells in the spleen compared to untreated mice. Importantly, we found DMPA treatment that significantly reduced mucin production, whereas NET-EN enhanced expression of cell-associated mucin in VT. High levels of mucin in NET-EN mice were associated with lower levels of HSV-2 virus detected in the vaginal tract. This study provides the first evidence that NET-EN treatment can delay HSV-2 infection compared to DMPA.

Deep functional measurements of Fragile X syndrome human neurons reveal multiparametric electrophysiological disease phenotype.

Fragile X syndrome (FXS) is a neurodevelopmental disorder caused by hypermethylation of expanded CGG repeats (>200) in the FMR1 gene leading to gene silencing and loss of Fragile X Messenger Ribonucleoprotein (FMRP) expression. FMRP plays important roles in neuronal function, and loss of FMRP in mouse and human FXS cell models leads to aberrant synaptic signaling and hyperexcitability. Multiple drug candidates have advanced into clinical trials for FXS, but no efficacious treatment has been identified to date, possibly as a consequence of poor translation from pre-clinical animal models to human. Here, we use a high resolution all-optical electrophysiology platform applied to multiple FXS patient-derived and CRISPR/Cas9-generated isogenic neuronal cell lines to develop a multi-parametric FXS disease phenotype. This neurophysiological phenotype was optimized and validated into a high throughput assay based on the amount of FMRP re-expression and the number of healthy neurons in a mosaic network necessary for functional rescue. The resulting highly sensitive and multiparameter functional assay can now be applied as a discovery platform to explore new therapeutic approaches for the treatment of FXS.

Innovative Glucagon-Based Therapies for Obesity

Abstract Obesity poses a significant global health challenge, with an alarming rise in prevalence rates. Traditional interventions, including lifestyle modifications, often fall short of achieving sustainable weight loss, ultimately leading to surgical interventions, which carry a significant burden and side effects. This necessitates the exploration of effective and relatively tolerable pharmacological alternatives. Among emerging therapeutic avenues, glucagon-based treatments have garnered attention for their potential to modulate metabolic pathways and regulate appetite. This paper discusses current research on the physiological mechanisms underlying obesity and the role of glucagon in energy homeostasis. Glucagon, traditionally recognized for its glycemic control functions, has emerged as a promising target for obesity management due to its multifaceted effects on metabolism, appetite regulation, and energy expenditure. This review focuses on the pharmacological landscape, encompassing single and dual agonist therapies targeting glucagon receptors (GcgRs), glucagon-like peptide-1 receptors (GLP-1Rs), glucose-dependent insulinotropic polypeptide receptors (GIPRs), amylin, triiodothyronine, fibroblast growth factor 21 (FGF21), and peptide tyrosine tyrosine. Moreover, novel triple-agonist therapies that simultaneously target GLP-1R, GIPR, and GcgR show promise in augmenting further metabolic benefits. This review paper tries to summarize key findings from preclinical and clinical studies, elucidating the mechanisms of action, safety profiles, and therapeutic potential of glucagon-based therapies in combating obesity and its comorbidities. Additionally, it explores ongoing research endeavors, including phase III trials, aimed at further validating the efficacy and safety of these innovative treatment modalities.

The emerging role of alternatively activated macrophages to treat acute liver injury.

Acute liver injury (ALI) has a clear requirement for novel therapies. One emerging option is the use of alternatively activated macrophages (AAMs); a distinct subtype of macrophage with a role in liver injury control and repair. In this comprehensive review, we provide an overview of the current limited options for ALI, and the potential advantages offered by AAMs. We describe the evidence supporting their use from in vitro studies, pre-clinical animal studies, and human clinical trials. We suggest why the first evidence for the clinical use of AAMs is likely to be found in acetaminophen toxicity, and discuss the specific evidence for AAM use in this population, as well as potential applications for AAMs in other patient populations. The key domains by which the performance of AAMs for the treatment of ALI will be assessed are identified, and remaining challenges to the successful delivery of AAMs to clinic are explored.

DeepCt: Predicting Pharmacokinetic Concentration-Time Curves and Compartmental Models from Chemical Structure Using Deep Learning.

After initial triaging using in vitro absorption, distribution, metabolism, and excretion (ADME) assays, pharmacokinetic (PK) studies are the first application of promising drug candidates in living mammals. Preclinical PK studies characterize the evolution of the compound's concentration over time, typically in rodents' blood or plasma. From this concentration-time (C-t) profiles, PK parameters such as total exposure or maximum concentration can be subsequently derived. An early estimation of compounds' PK offers the promise of reducing animal studies and cycle times by selecting and designing molecules with increased chances of success at the PK stage. Even though C-t curves are the major readout from a PK study, most machine learning-based prediction efforts have focused on the derived PK parameters instead of C-t profiles, likely due to the lack of approaches to model the underlying ADME mechanisms. Herein, a novel deep learning approach termed DeepCt is proposed for the prediction of C-t curves from the compound structure. Our methodology is based on the prediction of an underlying mechanistic compartmental PK model, which enables further simulations, and predictions of single- and multiple-dose C-t profiles.

Novel factors affecting fibrin clot formation and their clinical implications.

Fibrin formation is pivotal in hemostasis, serving as a temporary barrier to blood loss following vascular injury, while in thrombosis this process is involved in thrombus progression, stability, and recurrence. Growing evidence shows an exceptional complexity of processes that determine fibrin clot structure and function, especially lysability, both in health and disease, which might be relevant in the pathogenesis of arterial and venous thromboembolic diseases. In this overview we summarized available data on novel factors that in recent years have been suggested to contribute to prothrombotic fibrin clot properties, involving formation of compact fibrin networks (reduced clot permeability) displaying impaired susceptibility to lysis (prolonged clot lysis time). The factors discussed in this review encompass elevated levels of factor (F)XI, and its activated form (FXIa), protein carbonylation as the most common type of post-translational modification, neutrophil extracellular traps (NETs) formation, increased levels of circulating lipopolysaccharide (LPS) and zonulin, a marker of gut permeability, along with antithrombin deficiency. These factors have been shown to be not only associated with ischemic stroke, myocardial infarction, pulmonary embolism, and cardiovascular death, but also with unfavorably altered fibrin clot characteristics, which underscores clinical relevance of fibrin clot properties. Given preclinical or ongoing studies aimed at modifying some of these factors, in particular FXI / FXIa inhibitors, recent findings might expand our knowledge on fibrin-related mechanisms of emerging therapeutic agents tested and stimulate further research into new targets for future therapeutic interventions to prevent thromboembolic events.