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The omnipresence and diversity of allosteric regulation in proteins and protein associations complemented by the potential for the design of allosterically acting biologics and drugs call for the development of a new generation of computational models for the analysis of allostery and rational engineering/design of desired signaling and effector molecules determining it. One of the most important challenges is the consideration of the role of amino acid sequence in forming the protein's allosteric communication, including the mode and strength of the allosteric signal that is communicated to the regulated functional site. Here, we present the network-based model with a sequence dependence added in consideration of allosteric communication by combining the structure-based statistical mechanical model of allostery with the Miyazawa-Jernigan residue–residue potential. Applying the model in the analysis of five classical allosteric proteins, we found that it is necessary to consider the following two major determinants: (i) the free energy exerted by the allosteric site on the regulated one and (ii) the background (average) change in dynamics of the overall structure. We show that working together these two components determine the allosteric modulation, calling one to study their dependence on structures, oligomerization states, and sequence divergence in different proteins. [ABSTRACT FROM AUTHOR]

Aims Methods Results Conclusions Phosphodiesterase 2 (PDE2) regulates intracellular cyclic adenosine monophosphate and guanosine monophosphate (cAMP/cGMP) levels, which contribute to processes crucial for learning and memory. BI 474121, a potent and selective PDE2 inhibitor, is in development for treating cognitive impairment associated with schizophrenia.The effects of BI 474121 on cGMP concentrations were first assessed in rat cerebrospinal fluid (CSF) to demonstrate central nervous system (CNS) and functional target engagement. Next, a Phase I study in healthy participants assessed the pharmacokinetics of BI 474121 in CSF vs. plasma, the pharmacodynamics of BI 474121 by measuring cGMP concentrations in the CSF, and the safety of BI 474121.In rats, BI 474121 was associated with a dose‐dependent increase (71% at the highest dose tested [3.0 mg kg−1]) in cGMP levels in the CSF relative to vehicle (P < 0.001). In healthy participants, the maximum‐measured concentration CSF‐to‐plasma ratio for BI 474121 exposure was similar following single oral doses of BI 474121 2.5, 10, 20 and 40 mg (dose‐adjusted geometric mean: 8.96% overall). BI 474121 2.5–40 mg administration in healthy participants also increased cGMP levels in CSF (maximum exposure‐related change from baseline ratio, BI 474121: 1.44–2.20 vs. placebo: 1.26). The most common treatment‐emergent adverse event (AE) was mild‐to‐moderate post‐lumbar puncture syndrome, which resolved with standard treatment. No AEs of special interest were observed.BI 474121 crosses the blood–brain barrier to inhibit PDE2, supporting cGMP as a translational marker to monitor CNS target engagement. These findings promote further clinical development of BI 474121.ClinicalTrials.gov number (NCT04672954). [ABSTRACT FROM AUTHOR]

Background: Currently, the treatment and prevention of migraine remain highly challenging. Mendelian randomization (MR) has been widely used to explore novel therapeutic targets. Therefore, we performed a systematic druggable genome-wide MR to explore the potential therapeutic targets for migraine. Methods: We obtained data on druggable genes and screened for genes within brain expression quantitative trait locis (eQTLs) and blood eQTLs, which were then subjected to two-sample MR analysis and colocalization analysis with migraine genome-wide association studies data to identify genes highly associated with migraine. In addition, phenome-wide research, enrichment analysis, protein network construction, drug prediction, and molecular docking were performed to provide valuable guidance for the development of more effective and targeted therapeutic drugs. Results: We identified 21 druggable genes significantly associated with migraine (BRPF3, CBFB, CDK4, CHD4, DDIT4, EP300, EPHA5, FGFRL1, FXN, HMGCR, HVCN1, KCNK5, MRGPRE, NLGN2, NR1D1, PLXNB1, TGFB1, TGFB3, THRA, TLN1 and TP53), two of which were significant in both blood and brain (HMGCR and TGFB3). The results of phenome-wide research showed that HMGCR was highly correlated with low-density lipoprotein, and TGFB3 was primarily associated with insulin-like growth factor 1 levels. Conclusions: This study utilized MR and colocalization analysis to identify 21 potential drug targets for migraine, two of which were significant in both blood and brain. These findings provide promising leads for more effective migraine treatments, potentially reducing drug development costs. [ABSTRACT FROM AUTHOR]

Various tests based on different biomarkers have been developed to identify the best candidates for poly(ADP-ribose) polymerase (PARP)-inhibitor therapy. However, due to the absence of harmonization regarding these complex biomarkers, along with various cutoff points and unknown spatial and temporal variations, it is difficult to define the clinical utility of each test and ensure uniformity in treatment decision-making. Here, we propose measures to align biomarker definitions and minimum analytical performance characteristics for diagnostics to ensure equitable and sustainable access to precision medicine. Various tests based on different biomarkers have been developed to identify the best candidates for poly (ADP-ribose) polymerase (PARP)-inhibitor therapy. However, due to the absence of harmonization regarding these complex biomarkers, along with various cutoff points and unknown spatial and temporal variations, it is difficult to define the clinical utility of each test and ensure uniformity in treatment decision-making. Here, we propose measures to align biomarker definitions and minimum analytical performance characteristics for diagnostics to ensure equitable and sustainable access to precision medicine. [ABSTRACT FROM AUTHOR]

In oncology drug development, using biomarkers to select a study population more likely to benefit from a therapeutic effect is critical to increase the efficiency of a clinical trial in demonstrating effectiveness. This perspective delves into therapeutic product approvals that were tested in pivotal trials with all-comers populations, but ultimately received US Food and Drug Administration approval for use within specific patient subgroups identified by biomarkers. Despite initial designs for efficacy and safety assessments in overall populations, a favorable benefit-risk assessment was primarily established in biomarker-positive subgroups. Analyzing these cases, we summarize key considerations pivotal to totality of evidence for regulatory benefit-risk assessments for biomarker-defined subgroup versus all-comers approvals, including biological and clinical rationales, biomarker prevalence, safety data, overall trial design, and subgroup efficacy characterization. Furthermore, a decision tree is proposed to guide optimal clinical trial design, delineating between patient enrichment and stratification, accounting for key factors including biological and clinical rationale, marker type (discreate or continuous), prevalence, assay readiness, and turnaround times for marker assessment. Finally, a recommended approach for subgroup characterization involves prespecifying magnitude of improvement that would be considered clinically meaningful in the biomarker-negative subgroup, which can be supplemented with methodologies such as Bayesian to incorporate evidence from similar studies when available. In summary, this perspective underscores the importance of clinical trial innovations, statistical methodologies and regulatory considerations, to optimize biomarker-driven drug development for patients with cancer. Biomarker-driven oncology trial design and subgroup characterization: Challenges and potential solutions. [ABSTRACT FROM AUTHOR]

Although apoptosis, pyroptosis, and ferroptosis have been implicated in AD, none fully explains the extensive neuronal loss observed in AD brains. Recent evidence shows that necroptosis is abundant in AD, that necroptosis is closely linked to the appearance of Tau pathology, and that necroptosis markers accumulate in granulovacuolar neurodegeneration vesicles (GVD). We review here the neuron-specific activation of the granulovacuolar mediated neuronal-necroptosis pathway, the potential AD-relevant triggers upstream of this pathway, and the interaction of the necrosome with the endo-lysosomal pathway, possibly providing links to Tau pathology. In addition, we underscore the therapeutic potential of inhibiting necroptosis in neurodegenerative diseases such as AD, as this presents a novel avenue for drug development targeting neuronal loss to preserve cognitive abilities. Such an approach seems particularly relevant when combined with amyloid-lowering drugs. [ABSTRACT FROM AUTHOR]

The construction of bioartificial livers, such as liver organoids, offers significant promise for disease modeling, drug development, and regenerative medicine. However, existing methods for generating liver organoids have limitations, including lengthy and complex processes (taking 6–8 weeks or longer), safety concerns associated with pluripotency, limited functionality of pluripotent stem cell‐derived hepatocytes, and small, highly variable sizes (typically ≈50–500 µm in diameter). Prolonged culture also leads to the formation of necrotic cores, further restricting size and function. In this study, a straightforward and time‐efficient approach is developed for creating rapid self‐assembly mini‐livers (RSALs) within 12 h. Additionally, primary hepatocytes are significantly expanded in vitro for use as seeding cells. RSALs exhibit consistent larger sizes (5.5 mm in diameter), improved cell viability (99%), and enhanced liver functionality. Notably, RSALs are functionally vascularized within 2 weeks post‐transplantation into the mesentery of mice. These authentic hepatocyte‐based RSALs effectively protect mice from 90%‐hepatectomy‐induced liver failure, demonstrating the potential of bioartificial liver‐based therapy. [ABSTRACT FROM AUTHOR]

Background: Severe malaria is a life-threatening infection, particularly affecting children under the age of 5 years in Africa. Current treatment with parenteral artemisinin derivatives is highly efficacious. However, artemisinin partial resistance is widespread in Southeast Asia, resulting in delayed parasite clearance after therapy, and has emerged independently in South America, Oceania, and Africa. Hence, new treatments for severe malaria are needed, and it is prudent to define their characteristics now. This manuscript focuses on the target product profile (TPP) for new treatments for severe malaria. It also highlights preparedness when considering ways of protecting the utility of artemisinin-based therapies. Target product profile: Severe malaria treatments must be highly potent, with rapid onset of antiparasitic activity to clear the infection as quickly as possible to prevent complications. They should also have a low potential for drug resistance selection, given the high parasite burden in patients with severe malaria. Combination therapies are needed to deter resistance selection and dissemination. Partner drugs which are approved for uncomplicated malaria treatment would provide the most rapid development pathway for combinations, though new candidate molecules should be considered. Artemisinin combination approaches to severe malaria would extend the lifespan of current therapy, but ideally, completely novel, non-artemisinin-based combination therapies for severe malaria should be developed. These should be advanced to at least phase 2 clinical trials, enabling rapid progression to patient use should current treatment fail clinically. New drug combinations for severe malaria should be available as injectable formulations for rapid and effective treatment, or as rectal formulations for pre-referral intervention in resource-limited settings. Conclusion: Defining the TPP is a key step to align responses across the community to proactively address the potential for clinical failure of artesunate in severe malaria. In the shorter term, artemisinin-based combination therapies should be developed using approved or novel drugs. In the longer term, novel combination treatments should be pursued. Thus, this TPP aims to direct efforts to preserve the efficacy of existing treatments while improving care and outcomes for individuals affected by this life-threatening disease. [ABSTRACT FROM AUTHOR]

Background: Faced with the high cost and limited efficiency of classical randomized controlled trials, researchers are increasingly applying adaptive designs to speed up the development of new drugs. However, the application of adaptive design to drug randomized controlled trials (RCTs) and whether the reporting is adequate are unclear. Thus, this study aimed to summarize the epidemiological characteristics of the relevant trials and assess their reporting quality by the Adaptive designs CONSORT Extension (ACE) checklist. Methods: We searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials (CENTRAL) and ClinicalTrials.gov from inception to January 2020. We included drug RCTs that explicitly claimed to be adaptive trials or used any type of adaptative design. We extracted the epidemiological characteristics of included studies to summarize their adaptive design application. We assessed the reporting quality of the trials by Adaptive designs CONSORT Extension (ACE) checklist. Univariable and multivariable linear regression models were used to the association of four prespecified factors with the quality of reporting. Results: Our survey included 108 adaptive trials. We found that adaptive design has been increasingly applied over the years, and was commonly used in phase II trials (n = 45, 41.7%). The primary reasons for using adaptive design were to speed the trial and facilitate decision-making (n = 24, 22.2%), maximize the benefit of participants (n = 21, 19.4%), and reduce the total sample size (n = 15, 13.9%). Group sequential design (n = 63, 58.3%) was the most frequently applied method, followed by adaptive randomization design (n = 26, 24.1%), and adaptive dose-finding design (n = 24, 22.2%). The proportion of adherence to the ACE checklist of 26 topics ranged from 7.4 to 99.1%, with eight topics being adequately reported (i.e., level of adherence ≥ 80%), and eight others being poorly reported (i.e., level of adherence ≤ 30%). In addition, among the seven items specific for adaptive trials, three were poorly reported: accessibility to statistical analysis plan (n = 8, 7.4%), measures for confidentiality (n = 14, 13.0%), and assessments of similarity between interim stages (n = 25, 23.1%). The mean score of the ACE checklist was 13.9 (standard deviation [SD], 3.5) out of 26. According to our multivariable regression analysis, later published trials (estimated β = 0.14, p < 0.01) and the multicenter trials (estimated β = 2.22, p < 0.01) were associated with better reporting. Conclusion: Adaptive design has shown an increasing use over the years, and was primarily applied to early phase drug trials. However, the reporting quality of adaptive trials is suboptimal, and substantial efforts are needed to improve the reporting. [ABSTRACT FROM AUTHOR]

The prediction of the interaction between a drug and a target is the most critical issue in the fields of drug development and repurposing. However, there are still two challenges in current deep learning research: (i) the structural information of drug molecules is not fully explored in most drug target studies, and the previous drug SMILES does not correspond well to effective drug molecules and (ii) exploration of the potential relationship between drugs and targets is in need of improvement. In this work, we use a new and better representation of the effective molecular graph structure, SELFIES. We propose a hybrid mechanism framework based on convolutional neural network and graph attention network to capture multi‐view feature information of drug and target molecular structures, and we aim to enhance the ability to capture interaction sites between a drug and a target. In this study, our experiments using two different datasets show that the GCARDTI model outperforms a variety of different model algorithms on different metrics. We also demonstrate the accuracy of our model through two case studies. [ABSTRACT FROM AUTHOR]

The rapid emergence of multi-drug-resistant bacteria has raised a serious public health concern. Therefore, new antibiotic developments have been highly desired. Here, we propose a new method to visualize antibiotic actions on translating ribosomes in the cell-free system under macromolecular crowding conditions by cryo-electron microscopy, designated as the DARC method: the Direct visualization of Antibiotic binding on Ribosomes in the Cell-free translation system. This new method allows for acquiring a more comprehensive understanding of the mode of action of antibiotics on the translation inhibition without ribosome purification. Furthermore, with the direct link to biochemical analysis at the same condition as cryo-EM observation, we revealed the evolution of 2-DOS aminoglycosides from dibekacin (DBK) to arbekacin (ABK) by acquiring the synthetic tailored anchoring motif to lead to stronger binding affinity to ribosomes. Our cryo-EM structures of DBK and ABK bound ribosomes in the cell-free environment clearly depicted a synthetic tailored γ-amino-α-hydroxybutyryl (HABA) motif formed additional interactions with the ribosome enhancing antibiotic bindings. This new approach would be valuable for understanding the function of antibiotics for more efficient drug development. [ABSTRACT FROM AUTHOR]

Neurosciences clinical trials continue to have notoriously high failure rates. Appropriate outcomes selection in early clinical trials is key to maximizing the likelihood of identifying new treatments in psychiatry and neurology. The field lacks good standards for designing outcome strategies, therefore The Outcomes Research Group was formed to develop and promote good practices in outcome selection. This article describes the first published guidance on the standardization of the process for clinical outcomes in neuroscience. A minimal step process is defined starting as early as possible, covering key activities for evidence generation in support of content validity, patient-centricity, validity requirements and considerations for regulatory acceptance. Feedback from expert members is provided, regarding the risks of shortening the process and examples supporting the recommended process are summarized. This methodology is now available to researchers in industry, academia or clinics aiming to implement consensus-based standard practices for clinical outcome selection, contributing to maximizing the efficiency of clinical research. [ABSTRACT FROM AUTHOR]

The oncoprotein MYC has been considered 'undruggable' but increasingly direct and indirect methods of targeting are being identified and some compounds are showing strong pharmacological effects. However, responses to indirect targets have lacked efficacy and durability. Combination therapies targeting MYC are being examined with anti-MYC drugs in combination with approved drugs progressing to clinical studies. Combinations co-targeting MYC show strong effects in pre-clinical studies. Dual inhibitors of MYC targets offer the same promise, but with simplified treatment regimens and possibly enhance efficacy compared with combinations. The pharmacokinetics of combinations and dual inhibitors provides an important additional challenge that needs to be addressed in the development of new treatments. Members of the MYC family of proteins are a major target for cancer drug discovery, but the development of drugs that block MYC-driven cancers has not yet been successful. Approaches to achieve success may include the development of combination therapies or dual-acting drugs that target MYC at multiple nodes. Such treatments hold the possibility of additive or synergistic activity, potentially reducing side effect profiles and the emergence of resistance. In this review, we examine the prominent MYC-related targets and highlight those that have been targeted in combination and/or dual-target approaches. Finally, we explore the challenges of combination and dual-target approaches from a drug development perspective. [ABSTRACT FROM AUTHOR]

Atopic dermatitis (AD) represents the most common skin disease characterized by heterogeneous endophenotypes and a high disease burden. In Europe, six new systemic therapies for AD have been approved: the biologics dupilumab (anti‐interleukin‐4 receptor (IL‐4R) α in 2017), tralokinumab (anti‐IL‐13 in 2021), lebrikizumab (anti‐IL‐13 in 2023), and the oral janus kinase (JAK) inhibitors (JAKi) targeting JAK1/2 (baricitinib in 2020 in the EU) or JAK1 (upadacitinib in 2021 and abrocitinib in 2022). Herein, we give an update on new approvals, long‐term safety, and efficacy. Upadacitinib and abrocitinib have the highest short‐term efficacy among the approved systemic therapies. In responders, dupilumab and tralokinumab catch up regarding long‐term efficacy and incremental clinical benefit within continuous use. Recently, the European Medicines Agency has released recommendations for the use of JAKi in patients at risk (cardiovascular and thromboembolic diseases, malignancies, (former) smoking, and age ≥65 years). Furthermore, we give an overview on emerging therapies currently in Phase III trials. Among the topical therapies, tapinarof (aryl hydrocarbon receptor), ruxolitinib (JAK1/2i), delgocitinib (pan‐JAKi), asivatrep (anti‐transient receptor potential vanilloid), and phosphodiesterase‐4‐inhibitors (roflumilast, difamilast) are discussed. Among systemic therapies, current data on cord‐blood‐derived mesenchymal stem cells, CM310 (anti IL‐4Rα), nemolizumab (anti‐IL‐31RA), anti‐OX40/OX40L‐antibodies, neurokinin‐receptor‐1‐antagonists, and difelikefalin (κ‐opioid‐R) are reported. [ABSTRACT FROM AUTHOR]

Protein tyrosine phosphatase 1B (PTP1B), a non-transmembrane phosphatase, has a major role in a variety of signalling pathways, including direct negative regulation of classic insulin and leptin signalling pathways, and is implicated in the pathogenesis of several cardiometabolic diseases and cancers. As such, PTP1B has been a therapeutic target for over two decades, with PTP1B inhibitors identified either from natural sources or developed throughout the years. Some of these inhibitors have reached phase I and/or II clinical trials in humans for the treatment of type 2 diabetes mellitus, obesity and/or metastatic breast cancer. In this Review, we summarize the cellular processes and regulation of PTP1B, discuss evidence from in vivo preclinical and human studies of the association between PTP1B and different disorders, and discuss outcomes of clinical trials. We outline challenges associated with the targeting of this phosphatase (which was, until the past few years, viewed as difficult to target), the current state of the field of PTP1B inhibitors (and dual phosphatase inhibitors) and future directions for manipulating the activity of this key metabolic enzyme. This Review summarizes cellular processes and regulation of protein tyrosine phosphatase 1B (PTP1B), discussing evidence from in vivo preclinical and human studies. PTP1B inhibitors, which are being developed for type 2 diabetes mellitus, obesity, rare diseases (such as Rett syndrome) and some cancers, are also discussed. Key points: Protein tyrosine phosphatase 1B (PTP1B) has a key role in the pathogenesis and development of a variety of diseases. Cellular signalling of PTP1B and its implications have been studied extensively; PTP1B has a major role in the regulation of energy expenditure, insulin and leptin signalling, glucose and lipid homeostasis, immune responses, and cancer. Numerous PTP1B inhibitors have been identified from natural sources or designed and synthesized in the past few decades, several of which have reached clinical trials in humans. The main challenges associated with the drug discovery process and development of effective therapeutic agents against PTP1B activity have been specificity, selectivity and bioavailability. Selective and potent therapeutic inhibitors of PTP1B have been reported and could offer a promising potential treatment option for several diseases, such as type 2 diabetes mellitus, obesity, cancer and Rett syndrome. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for measuring flow rates in microchannels based on electrolytic bubble generation and electrochemical resistance measurement. The method exploits the generation of gas bubbles by electrolysis of ionic fluid at the surface of electrodes positioned within the microchannel. The flow rate is quantified by measuring the electrical resistance of the electrolyte using electrodes in inter-digitated form. By analysing the bubble frequency and size, the variation in the resistance is investigated and as a result the flow rate can be determined with high accuracy and precision. The proposed method is suitable for a wide range of flow rates, and can be easily integrated into microfluidic systems. We demonstrate the feasibility and performance of the method through experiments using 0.1M NaCl over a range of flow rates in a microchannel with different geometries. Flow measurements are made in range 0–200 μl/min with a measurement sensitivity of 3.17 Ω /(μL/min) and RSD < 15%. 3 σ resolution of the sensor is 12.5 μL/min. The rise in fluid temperature is observed to be less than 5 °C at all times. The proposed method has potential applications for on-chip detection in chemical synthesis, drug development and biomedical devices and is easy to implement and scalable. [ABSTRACT FROM AUTHOR]

There is an urgent, persistent, need for better biomarkers in clinical drug development. More informative biomarkers can increase the likelihood of drug advancement or approval, and implementing biomarkers increases the success rate in drug development. Biomarkers may guide decisions and allow resources to be directed to the projects most likely to succeed. However, biomarkers that are validated to high standards are needed, reflecting biological and pathological processes accurately. Such biomarkers are needed to develop treatments faster, and to improve and guide clinical trial design by selecting and de-selecting patients. In this review based on the authors' previous published experience and interaction with pharmaceutical- and biomarker stakeholders, we highlight the use and value of biomarkers in clinical development according to the BEST guidelines. We highlight the value of 3 types of biomarkers that may provide optimal value to stakeholders: diagnostic, prognostic and pharmacodynamic biomarkers. A more appropriate clinical trial design, increasing the ratio between benefits and side effects, may come from a more tailored biomarker-approach identifying suitable molecular endotypes of patients to treat. Biomarkers may guide drug developers in selecting the optimal projects to progress, when designing clinical studies and development paths. Biomarkers may aid in the diagnosis and prognostic assessment of patients and assist in matching the molecular endotype to the selected treatment, which improves the success rate of clinical development progression. The aim of this paper is to provide a comprehensive ideation framework for how to utilize biomarkers in clinical development, with a focus on utility for patients, payers and drug developers. [ABSTRACT FROM AUTHOR]

Food effect (FE) studies characterize food‐drug interactions that may alter the efficacy or safety of a drug, but these studies are not conducted in pediatric patients. Pediatric patients have substantial physiologic, anatomic, and dietary differences from adults, which may result in differences in their FE considerations. Therefore, the objective of this study was to identify oral drug products approved for use in pediatric patients aged <6 years with an FE observed in adults. Additional objectives were to summarize the therapeutic areas, pharmacokinetic effects, and labeling instructions that resulted from these studies. Publicly available data were searched for products studied in pediatric patients and approved for use by the United States Food and Drug Administration (FDA) from 2012 to 2022. Of the 102 oral drug products approved for use in patients aged <6 years, 43 recommended the consideration of food intake in the drug labeling. These included drug products recommended to be taken with food (n = 21, 49%) or without food (n = 14, 33%). Each of the 14 drug products recommended to be taken without food are approved for use in pediatric patients aged <2 years. The products approved for use in pediatric patients aged <2 years comprised the highest proportion with area under the plasma concentration‐time curve extrapolated to infinity (AUCinf, n = 35, 75%) and maximum serum concentration (Cmax, n = 45, 80%) affected by food. Close monitoring is warranted during the postapproval period for products identified as having a significant FE in adults and that are approved for use in pediatric patients aged <6 years. Promising tools for predicting pediatric FE may include physiologically based pharmacokinetic absorption modeling. [ABSTRACT FROM AUTHOR]

[Display omitted] • HIGD1A and ZG16, lower expressed in COAD than in READ, are both associated with poor prognosis in COAD, but not in READ. • Compared with ZG16, HIGD1A showed more efficiency in suppressing tumor growth and metastasis in CRC cells. • miR-501-3p, higher expressed in COAD than in READ, promoted CRC cells proliferation, invasion, and migration through suppressing HIGD1A expression. • High leveled miR-501-3p inhibited HIGD1A expression in COAD, which promoted tumor growth and metastasis, and eventually caused poor prognosis in COAD. • Procyanidin C1 (PCC1), a kind of natural polyphenol has been verified as a potential miR-501-3p inhibitor, which modulated miR-501-3p/HIGD1A axis and inhibited tumor growth and metastasis in vivo and in vitro. Although colon (COAD) and rectal adenocarcinoma (READ) combined to refer to colorectal cancer (CRC), substantial clinical evidence urged that CRC should be treated as two different cancers due to compared with READ, COAD showed higher morbidity and worse 5-year survival. This study has tried to screen for the crucial gene that caused the worse prognosis and investigate its mechanism for mediating tumor growth and metastases in COAD. Meanwhile, the potential anti-COAD compound implicated in this mechanism was identified and testified from 1,855 food-borne chemical kits. This study aims to bring a new perspective to the development of new anti-COAD drugs and personalized medicine for patients with COAD. The survival-related hub genes in COAD and READ were screened out from The Cancer Genome Atlas (TCGA) database and the results showed that HIGD1A , lower expressed in COAD than in READ, was associated with poor prognosis in COAD patients, but not in READ. Over-expressed HIGD1A suppressed CRC cell proliferation, invasion, and migration in vitro and in vivo. Meanwhile, the different expressed microRNA profiles between COAD and READ showed that miR-501-3p was highly expressed in COAD and inhibited HIGD1A expression by targeting 3′UTR of HIGD1A. MiR-501-3p mimics promoted cell proliferation and metastasis in CRC cells. In addition, Procyanidin C1 (PCC1), a kind of natural polyphenol has been verified as a potential miR-501-3p inhibitor. In vitro and in vivo , PCC1 promoted HIGD1A expression by suppressing miR-501-3p and resulted in inhibited tumor growth and metastasis. The present study verified that miR-501-3p/HIGD1A axis mediated tumor growth and metastasis in COAD. PCC1, a flavonoid that riched in food exerts anti-COAD effects by inhibiting miR-501-3p and results in the latter losing the ability to suppress HIGD1A expression. Subsequently, unfettered HIGD1A inhibited tumor growth and metastasis in COAD. [ABSTRACT FROM AUTHOR]

• This study presents the development of several deep-learning models to evaluate different types of compound toxicity. • The models were tailored to specific types of toxicity, each with a unique GCN binary classification. • Evaluation metrics indicate that developed models were reliable in predicting compounds toxicity. • The models were integrated into a virtual screening pipeline to facilitate drug discovery. Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection. [ABSTRACT FROM AUTHOR]

• A deep learning model for drug-target interaction prediction based on a knowledge graph and an intent graph is proposed, • Drug-target interactions be modeled from a more granular perspective, • Using relation path-aware aggregation under the GNN-base paradigm to integrate more information through a larger neighborhood region. Knowledge graph intent graph attention mechanism Predicting drug-target interactions (DTIs) plays a crucial role in drug discovery and drug development. Considering the high cost and risk of biological experiments, developing computational approaches to explore the interactions between drugs and targets can effectively reduce the time and cost of drug development. Recently, many methods have made significant progress in predicting DTIs. However, existing approaches still suffer from the high sparsity of DTI datasets and the cold start problem. In this paper, we develop a new model to predict drug-target interactions via a knowledge graph and intent graph named DTKGIN. Our method can effectively capture biological environment information for targets and drugs by mining their associated relations in the knowledge graph and considering drug-target interactions at a fine-grained level in the intent graph. DTKGIN learns the representation of drugs and targets from the knowledge graph and the intent graph. Then the probabilities of interactions between drugs and targets are obtained through the inner product of the representation of drugs and targets. Experimental results show that our proposed method outperforms other state-of-the-art methods in 10-fold cross-validation, especially in cold-start experimental settings. Furthermore, the case studies demonstrate the effectiveness of DTKGIN in predicting potential drug-target interactions. The code is available on GitHub: https://github.com/Royluoyi123/DTKGIN. [ABSTRACT FROM AUTHOR]

Background: Metabolic syndrome (MetS), which can be induced or exacerbated by the current class of antipsychotic drugs, is highly prevalent in patients with schizophrenia and presents significant challenges to lifetime disease management. Supported by initial clinical results, trace amine‐associated receptor 1 (TAAR1) agonists have emerged as potential novel treatments for schizophrenia. Notably, non‐clinical studies have also shown weight‐lowering and glucoregulatory effects of TAAR1 agonists, including the investigational agent ulotaront. However, the translatability of these findings to humans has not been adequately assessed. Given that delayed gastric emptying (GE) was identified as a potential mechanism contributing to the metabolic benefits of TAAR1 agonists in rodents, the aim of this study was to evaluate the effect of ulotaront on GE in patients with schizophrenia and concurrent MetS with prediabetes. Methods: Patients with schizophrenia were randomized to receive a single oral dose of ulotaront (150 mg) and their previous antipsychotic (PA) in an open‐label, crossover, two‐sequence design (NCT05402111). Eligible participants fulfilled at least three of five MetS criteria and had prediabetes defined by elevated glycated haemoglobin (5.7‐6.4%) and/or fasting homeostatic model assessment of insulin resistance (i.e. ≥2.22). Following an overnight fast and 4 h post‐dose, participants ingested a 99mTc‐sulphur colloid radiolabelled egg meal (320 kcal, 30% fat). GE was measured by scintigraphy over 4 h. Endpoints included GE of solids half‐time (T1/2) and percentage gastric retention at 1, 2 and 4 h. Results: Thirty‐one adults were randomized and 27 completed the study. Ulotaront significantly delayed GE of solids [median GE T1/2 ulotaront at 139 min (119, 182) vs. the participant's PA of 124 min (109, 132), p =.006]. A significant increase in gastric retention was seen in the ulotaront versus the PA group at 1 h (80% vs. 75%, p =.015), 2 h (61% vs. 50%, p =.023) and 4 h (17% vs. 7%, p =.002) post‐meal. Conclusion: Ulotaront delayed the GE of solids in patients with schizophrenia and concurrent MetS with prediabetes. Additional studies are needed to assess whether treatment with TAAR1 agonists is associated with weight loss and glucoregulatory improvement. [ABSTRACT FROM AUTHOR]

Aim: This Phase I study evaluated the safety and early efficacy of an aldosterone synthase inhibitor (BI 690517) in people with diabetes and albuminuric chronic kidney disease. Methods: Double‐blind, placebo‐controlled study (NCT03165240) at 40 sites across Europe. Eligible participants [estimated glomerular filtration rate ≥20 and <75 ml/min/1.73 m2; urine albumin/creatinine ratio (UACR) ≥200 and <3500 mg/g] were randomized 6:1 to receive once‐daily oral BI 690517 3, 10 or 40 mg, or eplerenone 25‐50 mg, or placebo, for 28 days. The primary endpoint was the proportion of participants with drug‐related adverse events (AEs). Secondary endpoints included changes from baseline in the UACR. Results: Fifty‐eight participants were randomized and treated from 27 November 2017 to 16 April 2020 (BI 690517: 3 mg, n = 18; 10 mg, n = 13; 40 mg, n = 14; eplerenone, n = 4; placebo, n = 9) for 28 days. Eight (13.8%) participants experienced drug‐related AEs [BI 690517: 3 mg (two of 18); 10 mg (four of 13); 40 mg (two of 14)], most frequently constipation [10 mg (one of 13); 40 mg (one of 14)] and hyperkalaemia [3 mg (one of 18); 10 mg (one of 13)]. Most AEs were mild to moderate; one participant experienced severe hyperkalaemia (serum potassium 6.9 mmol/L; BI 690517 10 mg). UACR responses [≥20% decrease from baseline (first morning void urine) after 28 days] were observed for 80.0% receiving BI 690517 40 mg (eight of 10) versus 37.5% receiving placebo (three of eight). Aldosterone levels were suppressed by BI 690517, but not eplerenone or placebo. Conclusions: BI 690517 was generally well tolerated, reduced plasma aldosterone and may decrease albuminuria in participants with diabetes and albuminuric chronic kidney disease. [ABSTRACT FROM AUTHOR]

In 2023, seventy novel drugs received market authorization for the first time in either Europe (by the EMA and the MHRA) or in the United States (by the FDA). Confirming a steady recent trend, more than half of these drugs target rare diseases or intractable forms of cancer. Thirty drugs are categorized as "first‐in‐class" (FIC), illustrating the quality of research and innovation that drives new chemical entity discovery and development. We succinctly describe the mechanism of action of most of these FIC drugs and discuss the therapeutic areas covered, as well as the chemical category to which these drugs belong. The 2023 novel drug list also demonstrates an unabated emphasis on polypeptides (recombinant proteins and antibodies), Advanced Therapy Medicinal Products (gene and cell therapies) and RNA therapeutics, including the first‐ever approval of a CRISPR‐Cas9‐based gene‐editing cell therapy. [ABSTRACT FROM AUTHOR]

Drug discovery is an intricate and costly process. Repurposing existing drugs and active compounds offers a viable pathway to develop new therapies for various diseases. By leveraging publicly available biomedical information, it is possible to predict compounds’ activity and identify their potential targets across diverse organisms. In this study, we aimed to assess the antiplasmodial activity of compounds from the Repurposing, Focused Rescue, and Accelerated Medchem (ReFRAME) library using in vitro and bioinformatics approaches. We assessed the in vitro antiplasmodial activity of the compounds using blood-stage and liver-stage drug susceptibility assays. We used protein sequences of known targets of the ReFRAME compounds with high antiplasmodial activity (EC50 < 10 uM) to conduct a protein-pairwise search to identify similar Plasmodium falciparum 3D7 proteins (from PlasmoDB) using NCBI protein BLAST. We further assessed the association between the compounds' in vitro antiplasmodial activity and level of similarity between their known and predicted P. falciparum target proteins using simple linear regression analyses. BLAST analyses revealed 735 P. falciparum proteins that were similar to the 226 known protein targets associated with the ReFRAME compounds. Antiplasmodial activity of the compounds was positively associated with the degree of similarity between the compounds’ known targets and predicted P. falciparum protein targets (percentage identity, E value, and bit score), the number of the predicted P. falciparum targets, and their respective mutagenesis index and fitness scores (R2 between 0.066 and 0.92, P < 0.05). Compounds predicted to target essential P. falciparum proteins or those with a druggability index of 1 showed the highest antiplasmodial activity.Scientific contribution: This is the first study to demonstrate a correlation between in vitro antipathogenic activity of compounds and target similarity across species. Our findings indicate that leveraging protein-target similarity may accelerate the drug repurposing process for many diseases by predicting compounds’ activity and their prospective targets in different organisms. [ABSTRACT FROM AUTHOR]

Preclinical evidence implicating cannabinoid receptor 2 (CB2) in various diseases has led researchers to question whether CB2 genetics influence aetiology or progression. Associations between conditions and genetic loci are often studied via single nucleotide polymorphism (SNP) prevalence in case versus control populations. In the CNR2 coding exon, ~36 SNPs have high overall population prevalence (minor allele frequencies [MAF] ~37%), including non‐synonymous SNP (ns‐SNP) rs2501432 encoding CB2 63Q/R. Interspersed are ~27 lower frequency SNPs, four being ns‐SNPs. CNR2 introns also harbour numerous SNPs. This review summarises CB2 ns‐SNP molecular pharmacology and evaluates evidence from ~70 studies investigating CB2 genetic variants with proposed linkage to disease. Although CNR2 genetic variation has been associated with a wide variety of conditions, including osteoporosis, immune‐related disorders, and mental illnesses, further work is required to robustly validate CNR2 disease links and clarify specific mechanisms linking CNR2 genetic variation to disease pathophysiology and potential drug responses. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) remains a global health challenge with the emergence of drug-resistant Mycobacterium tuberculosis variants, necessitating innovative drug molecules. One potential target is the cell wall synthesis enzyme decaprenylphosphoryl-β-D-ribose 2′-epimerase (DprE1), crucial for virulence and survival. This study employed virtual screening of 111 Protein Data Bank (PDB) database molecules known for their inhibitory biological activity against DprE1 with known IC50 values. Six compounds, PubChem ID: 390820, 86287492, 155294899, 155522922, 162651615, and 162665075, exhibited promising attributes as drug candidates and validated against clinical trial inhibitors BTZ043, TBA-7371, PBTZ169, and OPC-167832. Concurrently, this research focused on DprE1 mutation effects using molecular dynamic simulations. Among the 10 mutations tested, C387N significantly influenced protein behavior, leading to structural alterations observed through root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), and solvent-accessible surface area (SASA) analysis. Ligand 2 (ID: 390820) emerged as a promising candidate through ligand-based pharmacophore analysis, displaying enhanced binding compared with reference inhibitors. Molecular dynamic simulations highlighted ligand 2's interaction with the C387N mutation, reducing fluctuations, augmenting hydrogen bonding, and influencing solvent accessibility. These collective findings emphasize ligand 2's efficacy, particularly against severe mutations, in enhancing protein-ligand complex stability. Integrated computational and pharmacophore methodologies offer valuable insights into drug candidates and their interactions within intricate protein environments. This research lays a strategic foundation for targeted interventions against drug-resistant TB, highlighting ligand 2's potential for advanced drug development strategies. [ABSTRACT FROM AUTHOR]

The departure of pharmaceutical companies from Nigeria — a major source of antimicrobial drug supply in West Africa — increases the risk of an elevated burden of antimicrobial resistance (AMR) in Nigeria and the West Africa subregion. Urgent actions must be taken by relevant actors to address the increased risk of AMR. [ABSTRACT FROM AUTHOR]

Background: One significant challenge in addressing the coronavirus disease 2019 (COVID-19) pandemic is to grasp a comprehensive picture of its infectious mechanisms. We urgently need a consistent framework to capture the intricacies of its complicated viral infectious processes and diverse symptoms. Results: We systematized COVID-19 infectious processes through an ontological approach and provided a unified description framework of causal relationships from the early infectious stage to severe clinical manifestations based on the homeostasis imbalance process ontology (HoIP). HoIP covers a broad range of processes in the body, ranging from normal to abnormal. Moreover, our imbalance model enabled us to distinguish viral functional demands from immune defense processes, thereby supporting the development of new drugs, and our research demonstrates how ontological reasoning contributes to the identification of patients at severe risk. Conclusions: The HoIP organises knowledge of COVID-19 infectious processes and related entities, such as molecules, drugs, and symptoms, with a consistent descriptive framework. HoIP is expected to harmonise the description of various heterogeneous processes and improve the interoperability of COVID-19 knowledge through the COVID-19 ontology harmonisation working group. [ABSTRACT FROM AUTHOR]

Synthesis of bicyclic scaffolds has emerged as an important research topic in modern drug development because they can serve as saturated bioisosters to enhance the physicochemical properties and metabolic profiles of drug candidates. Here we report a remarkably simple silver‐enabled strategy to access polysubstituted 3‐azabicyclo[3.1.1]heptanes in a single operation from readily accessible bicyclobutanes (BCBs) and isocyanides. The process is proposed to involve a formal (3+3)/(3+2)/retro‐(3+2) cycloaddition sequence. This novel protocol allows for rapid generation of molecular complexity from simple starting materials, and the products can be easily derivatized, further enriching the BCB cycloaddition chemistry and the growing set of valuable sp3‐rich bicyclic building blocks. [ABSTRACT FROM AUTHOR]

Proteolysis-targeting chimeras (PROTACs) technology has garnered significant attention over the last 10 years, representing a burgeoning therapeutic approach with the potential to address pathogenic proteins that have historically posed challenges for traditional small-molecule inhibitors. PROTACs exploit the endogenous E3 ubiquitin ligases to facilitate degradation of the proteins of interest (POIs) through the ubiquitin–proteasome system (UPS) in a cyclic catalytic manner. Despite recent endeavors to advance the utilization of PROTACs in clinical settings, the majority of PROTACs fail to progress beyond the preclinical phase of drug development. There are multiple factors impeding the market entry of PROTACs, with the insufficiently precise degradation of favorable POIs standing out as one of the most formidable obstacles. Recently, there has been exploration of new-generation advanced PROTACs, including small-molecule PROTAC prodrugs, biomacromolecule-PROTAC conjugates, and nano-PROTACs, to improve the in vivo efficacy of PROTACs. These improved PROTACs possess the capability to mitigate undesirable physicochemical characteristics inherent in traditional PROTACs, thereby enhancing their targetability and reducing off-target side effects. The new-generation of advanced PROTACs will mark a pivotal turning point in the realm of targeted protein degradation. In this comprehensive review, we have meticulously summarized the state-of-the-art advancements achieved by these cutting-edge PROTACs, elucidated their underlying design principles, deliberated upon the prevailing challenges encountered, and provided an insightful outlook on future prospects within this burgeoning field. [ABSTRACT FROM AUTHOR]

Synthesis of bicyclic scaffolds has emerged as an important research topic in modern drug development because they can serve as saturated bioisosters to enhance the physicochemical properties and metabolic profiles of drug candidates. Here we report a remarkably simple silver‐enabled strategy to access polysubstituted 3‐azabicyclo[3.1.1]heptanes in a single operation from readily accessible bicyclobutanes (BCBs) and isocyanides. The process is proposed to involve a formal (3+3)/(3+2)/retro‐(3+2) cycloaddition sequence. This novel protocol allows for rapid generation of molecular complexity from simple starting materials, and the products can be easily derivatized, further enriching the BCB cycloaddition chemistry and the growing set of valuable sp3‐rich bicyclic building blocks. [ABSTRACT FROM AUTHOR]

Reports from spontaneous reporting systems (SRS) are hypothesis generating. Additional evidence such as more reports is required to determine whether the generated drug-event associations are in fact safety signals. However, underreporting of adverse drug reactions (ADRs) delays signal detection. Through the use of natural language processing, different sources of real-world data can be used to proactively collect additional evidence for potential safety signals. This study aims to explore the feasibility of using Electronic Health Records (EHRs) to identify additional cases based on initial indications from spontaneous ADR reports, with the goal of strengthening the evidence base for potential safety signals. For two confirmed and two potential signals generated by the SRS of the Netherlands Pharmacovigilance Centre Lareb, targeted searches in the EHR of the Leiden University Medical Centre were performed using a text-mining based tool, CTcue. The search for additional cases was done by constructing and running queries in the structured and free-text fields of the EHRs. We identified at least five additional cases for the confirmed signals and one additional case for each potential safety signal. The majority of the identified cases for the confirmed signals were documented in the EHRs before signal detection by the Dutch Medicines Evaluation Board. The identified cases for the potential signals were reported to Lareb as further evidence for signal detection. Our findings highlight the feasibility of performing targeted searches in the EHR based on an underlying hypothesis to provide further evidence for signal generation. [ABSTRACT FROM AUTHOR]

Chronic pain and opioid use disorder (OUD) are major public health problems, with rising opioid‐related overdose deaths linked to increased opioid prescriptions for pain management. Novel treatment approaches for these commonly comorbid disorders are needed. Growing evidence supports a role for glial activation for both chronic pain and substance use disorders, including OUD. This review provides an overview of glial modulators as a novel treatment approach for comorbid pain and OUD. We aim to synthesize clinical studies investigating the efficacy of glial modulators in treating these comorbid disorders. We conducted a literature search of PubMed and Google Scholar databases in October 2023 to identify relevant clinical trials. The included studies varied in terms of patient population, study methodology and outcomes assessed, and were often limited by small sample sizes and other methodological issues. Additionally, several glial modulators have yet to be studied for chronic pain and OUD. Despite these limitations, these studies yielded positive signals that merit further investigation. Both chronic pain and OUD remain significant public health problems, with many treatment challenges. Glial modulators continue to hold promise as novel therapeutics for comorbid pain and OUD, given positive indications that they can improve pain measures, and reduce addiction‐related outcomes. As our understanding of the mechanisms underlying the contributions of glial modulators to pain and addiction behaviours deepens, we will be better equipped to identify more specific therapeutic targets for chronic pain and OUD. [ABSTRACT FROM AUTHOR]

Ten years ago, the second messenger cGAMP was discovered as the activator of the anti-cancer STING pathway. The characterization of cGAMP's paracrine action and dominant extracellular hydrolase ENPP1 cemented cGAMP as an intercellular immunotransmitter that coordinates the innate and adaptive immune systems to fight cancer. In this Perspective, I look back at a decade of discovery of extracellular cGAMP biology and drug development aiming to supply or preserve extracellular cGAMP for cancer treatment. Reviewing our understanding of the cell type-specific regulatory mechanisms of STING agonists, including their transporters and degradation enzymes, I explain on a molecular and cellular level the successes and challenges of direct STING agonists for cancer therapy. Based on what we know now, I propose new ways to stimulate the STING pathway in a manner that is not only cancer specific, but also cell type specific to fully harness the anti-cancer effect of cGAMP while avoiding collateral damage. Li recounts the characterization of the second messenger cGAMP as a key intercellular innate immune transmitter and development of its pharmacological analogs. Cell type-specific cGAMP transporters, hydrolases, and regulators together tightly regulate the paracrine cGAMP-STING signaling that can be harnessed to activate our powerful anti-cancer immune system. [ABSTRACT FROM AUTHOR]

Cyclic peptides are increasingly important structures in drugs but their development can be impeded by difficulties associated with their synthesis. Here, we introduce the 3‐aminoazetidine (3‐AAz) subunit as a new turn‐inducing element for the efficient synthesis of small head‐to‐tail cyclic peptides. Greatly improved cyclizations of tetra‐, penta‐ and hexapeptides (28 examples) under standard reaction conditions are achieved by introduction of this element within the linear peptide precursor. Post‐cyclization deprotection of the amino acid side chains with strong acid is realized without degradation of the strained four‐membered azetidine. A special feature of this chemistry is that further late‐stage modification of the resultant macrocyclic peptides can be achieved via the 3‐AAz unit. This is done by: (i) chemoselective deprotection and substitution at the azetidine nitrogen, or by (ii) a click‐based approach employing a 2‐propynyl carbamate on the azetidine nitrogen. In this way, a range of dye and biotin tagged macrocycles are readily produced. Structural insights gained by XRD analysis of a cyclic tetrapeptide indicate that the azetidine ring encourages access to the less stable, all‐trans conformation. Moreover, introduction of a 3‐AAz into a representative cyclohexapeptide improves stability towards proteases compared to the homodetic macrocycle. [ABSTRACT FROM AUTHOR]

Purpose Health-system pharmacists play a crucial role in monitoring the pharmaceutical pipeline to manage formularies, allocate resources, and optimize clinical programs for new therapies. This article aims to support pharmacists by providing periodic updates on new and anticipated novel drug approvals. Summary Selected drug approvals anticipated in the 12-month period covering the first quarter of 2024 through the fourth quarter of 2024 are reviewed. The analysis emphasizes drugs expected to have significant clinical and financial impact in hospitals and clinics, as selected from 59 novel drugs awaiting US Food and Drug Administration approval. This year's pipeline includes recently added drugs with various indications including oncology, infectious diseases, genetic disorders, and rare diseases. New cellular and gene therapies are rapidly evolving and being studied for several rare diseases and cancers. Conclusion More oncology agents, including gene therapies, oral agents, and monoclonal antibodies, are in the pipeline this year. Additional diseases targeted by new novel drugs, including cellular and gene therapies, are hemophilia, nonalcoholic steatohepatitis, Alzheimer's disease, and rare diseases such as galactosemia and epidermolysis bullosa. [ABSTRACT FROM AUTHOR]

Given the renewed interest in boron neutron capture therapy (BNCT) and the intensified search for improved boron carriers, as well as the difficulties of coherently comparing the carriers described so far, it seems necessary to define a basic set of assays and standardized methods to be used in the early stages of boron carrier development in vitro. The selection of assays and corresponding methods is based on the practical experience of the authors and is certainly not exhaustive, but open to discussion. The proposed tests/characteristics: Solubility, lipophilicity, stability, cytotoxicity, and cellular uptake apply to both low molecular weight (up to 500 Da) and high molecular weight (5000 Da and more) boron carriers. However, the specific methods have been selected primarily for low molecular weight boron carriers; in the case of high molecular weight compounds, some of the methods may need to be adapted. [ABSTRACT FROM AUTHOR]

G protein-coupled receptors (GPCRs) are relevant targets for health and disease as they regulate various aspects of metabolism, proliferation, differentiation, and immune pathways. They are implicated in several disease areas, including cancer, diabetes, cardiovascular diseases, and mental disorders. It is worth noting that about a third of all marketed drugs target GPCRs, making them prime pharmacological targets for drug discovery. Numerous functional assays have been developed to assess GPCR activity and GPCR signaling in living cells. Here, we review the current literature of genetically encoded cell-based assays to measure GPCR activation and downstream signaling at different hierarchical levels of signaling, from the receptor to transcription, via transducers, effectors, and second messengers. Singleplex assay formats provide one data point per experimental condition. Typical examples are bioluminescence resonance energy transfer (BRET) assays and protease cleavage assays (e.g., Tango or split TEV). By contrast, multiplex assay formats allow for the parallel measurement of multiple receptors and pathways and typically use molecular barcodes as transcriptional reporters in barcoded assays. This enables the efficient identification of desired on-target and on-pathway effects as well as detrimental off-target and off-pathway effects. Multiplex assays are anticipated to accelerate drug discovery for GPCRs as they provide a comprehensive and broad identification of compound effects. [ABSTRACT FROM AUTHOR]

Computational drug-repositioning technology is an effective tool for speeding up drug development. As biological data resources continue to grow, it becomes more important to find effective methods to identify potential therapeutic drugs for diseases. The effective use of valuable data has become a more rational and efficient approach to drug repositioning. The disease–drug correlation method (DDCM) proposed in this study is a novel approach that integrates data from multiple sources and different levels to predict potential treatments for diseases, utilizing support-vector regression (SVR). The DDCM approach resulted in potential therapeutic drugs for neoplasms and cardiovascular diseases by constructing a correlation hybrid matrix containing the respective similarities of drugs and diseases, implementing the SVR algorithm to predict the correlation scores, and undergoing a randomized perturbation and stepwise screening pipeline. Some potential therapeutic drugs were predicted by this approach. The potential therapeutic ability of these drugs has been well-validated in terms of the literature, function, drug target, and survival-essential genes. The method's feasibility was confirmed by comparing the predicted results with the classical method and conducting a co-drug analysis of the sub-branch. Our method challenges the conventional approach to studying disease–drug correlations and presents a fresh perspective for understanding the pathogenesis of diseases. [ABSTRACT FROM AUTHOR]

Studying drug-target interactions (DTIs) is the foundational and crucial phase in drug discovery. Biochemical experiments, while being the most reliable method for determining drug-target affinity (DTA), are time-consuming and costly, making it challenging to meet the current demands for swift and efficient drug development. Consequently, computational DTA prediction methods have emerged as indispensable tools for this research. In this article, we propose a novel deep learning algorithm named GRA-DTA, for DTA prediction. Specifically, we introduce Bidirectional Gated Recurrent Unit (BiGRU) combined with a soft attention mechanism to learn target representations. We employ Graph Sample and Aggregate (GraphSAGE) to learn drug representation, especially to distinguish the different features of drug and target representations and their dimensional contributions. We merge drug and target representations by an attention neural network (ANN) to learn drug-target pair representations, which are fed into fully connected layers to yield predictive DTA. The experimental results showed that GRA-DTA achieved mean squared error of 0.142 and 0.225 and concordance index reached 0.897 and 0.890 on the benchmark datasets KIBA and Davis, respectively, surpassing the most state-of-the-art DTA prediction algorithms. [ABSTRACT FROM AUTHOR]

Glioblastoma multiforme, a highly aggressive and lethal brain tumor, is a substantial clinical challenge and a focus of increasing concern globally. Hematological toxicity and drug resistance of first-line drugs underscore the necessity for new anti-glioma drug development. Here, 43 anthracenyl skeleton compounds as p53 activator XI-011 analogs were designed, synthesized, and evaluated for their cytotoxic effects. Five compounds (13d, 13e, 14a, 14b, and 14n) exhibited good anti-glioma activity against U87 cells, with IC50 values lower than 2 μM. Notably, 13e showed the best anti-glioma activity, with an IC50 value up to 0.53 μM, providing a promising lead compound for new anti-glioma drug development. Mechanistic analyses showed that 13e suppressed the MDM4 protein expression, upregulated the p53 protein level, and induced cell cycle arrest at G2/M phase and apoptosis based on Western blot and flow cytometry assays. [ABSTRACT FROM AUTHOR]

The ongoing COVID-19 pandemic still threatens human health around the world. The methyltransferases (MTases) of SARS-CoV-2, specifically nsp14 and nsp16, play crucial roles in the methylation of the N7 and 2′-O positions of viral RNA, making them promising targets for the development of antiviral drugs. In this work, we performed structure-based virtual screening for nsp14 and nsp16 using the screening workflow (HTVS, SP, XP) of Schrödinger 2019 software, and we carried out biochemical assays and molecular dynamics simulation for the identification of potential MTase inhibitors. For nsp14, we screened 239,000 molecules, leading to the identification of three hits A1–A3 showing N7-MTase inhibition rates greater than 60% under a concentration of 50 µM. For the SAM binding and nsp10-16 interface sites of nsp16, the screening of 210,000 and 237,000 molecules, respectively, from ZINC15 led to the discovery of three hit compounds B1–B3 exhibiting more than 45% of 2′-O-MTase inhibition under 50 µM. These six compounds with moderate MTase inhibitory activities could be used as novel candidates for the further development of anti-SARS-CoV-2 drugs. [ABSTRACT FROM AUTHOR]

Simple Summary: B-cell lymphoma (BCL) represents the most common hematological malignancy in dogs and, despite the current chemotherapeutic standard, is generally characterized by poor outcome, highlighting the importance of new drug development. The medical approach to cancer has been revolutionized by the advent of targeted therapies, consisting in targeting specific molecules crucial for malignant cells growth and proliferation. Myc is a transcription factor dysregulated in many cancers, including canine BCL (cBCL). For several reasons, directly targeting Myc remains challenging, but alternative routes have been explored. In our study, we developed and tested on two in vitro models of cBCL a dual approach to indirectly target Myc using two drugs (BI2536 and MZ1). After confirming the specificity of the drugs to their main targets, we observed that both molecules affected the cell viability, individually and in combination. Furthermore, BI2536, alone and in combination with MZ1, induced significant transcriptomic changes in cBCL cell lines, primarily affecting MYC target genes and genes involved in the cell cycle regulation. Our study offers valuable insights into the mechanisms of action of BI2536 and MZ1 in cBCL cell lines and highlights their potential as targeted therapies for this cancer subtype. B-cell lymphomas (BCL) is the most frequent hematological cancer in dogs. Treatment typically consists of chemotherapy, with CHOP-based protocols. However, outcome remains generally poor, urging the exploration of new therapeutic strategies with a targeted approach. Myc transcription factor plays a crucial role in regulating cellular processes, and its dysregulation is implicated in numerous human and canine malignancies, including canine BCL (cBCL). This study aims to evaluate the efficacy of indirectly inhibiting Myc in cBCL using BI2536 and MZ1 compounds in two in vitro models (CLBL-1 and KLR-1201). Both BI2536 and MZ1, alone and combined, affected cell viability in a significant concentration- and time-dependent manner. Western Blot revealed an upregulation of PLK1 expression in both cell lines upon treatment with BI2536, in association with a reduction in c-Myc protein levels. Conversely, MZ1 led to a decrease in its primary target, BRD4, along with a reduction in c-Myc. Furthermore, BI2536, both alone and in combination with MZ1, induced larger transcriptomic changes in cells compared to MZ1 alone, primarily affecting MYC target genes and genes involved in cell cycle regulation. These data underscore the potential role of Myc as therapeutic target in cBCL, providing a novel approach to indirectly modulate this molecule. [ABSTRACT FROM AUTHOR]

Simple Summary: Cancer is one of the leading causes of death worldwide. Among several therapeutic options available for patients, chemotherapy remains the most often used treatment strategy. Anticancer drugs known today are either substances isolated from plants and their derivatives or completely synthetic compounds. Some of them were discovered by accident, others as a result of long-term research. In this review, we present a brief history of the development of the main groups of anticancer drugs, focusing especially on their mode of action. This article also addresses the major side effects and limitations of currently available anticancer drugs and future perspectives in this area. Cancer is considered one of the leading causes of death in the 21st century. The intensive search for new anticancer drugs has been actively pursued by chemists and pharmacologists for decades, focusing either on the isolation of compounds with cytotoxic properties from plants or on screening thousands of synthetic molecules. Compounds that could potentially become candidates for new anticancer drugs must have the ability to inhibit proliferation and/or induce apoptosis in cancer cells without causing too much damage to normal cells. Some anticancer compounds were discovered by accident, others as a result of long-term research. In this review, we have presented a brief history of the development of the most important groups of anticancer drugs, pointing to the fact that they all have many side effects. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a neurodegenerative disease that imposes economic and societal burden. Biomarkers have played a crucial role in the recent approval of aducanumab and lecanemab as disease-modifying therapies which marked a significant milestone for the treatment of AD. The inclusion of biomarkers in AD trials facilitates precise diagnosis, monitors safety, demonstrates target engagement, and supports disease modification. Objective: This study analyzed the utilization state and trends of biomarkers as endpoints in AD trials. Methods: In this retrospective study, trials were collected by searching clinicaltrials.gov using the term "Alzheimer". Primary and secondary outcomes were analyzed separately for each phase. Results: Among the 1,048 analyzed trials, 313 (29.87%) adopted biomarkers as primary endpoints and 364 (34.73%) as secondary endpoints, mainly in phases 1 and 2. The top three biomarkers adopted as primary endpoints in phases 1, 2, and 3 were amyloid-PET, tau-PET, and MRI. The top three biomarkers adopted as secondary endpoints, in phase 1, were cerebrospinal fluid (CSF) amyloid-β (Aβ), blood Aβ and amyloid-PET; in phase 2, they were MRI, CSF Aβ, and CSF phospho-tau; and in phase 3, they were amyloid PET, MRI, and blood Aβ. There was a statistically significant increase in the adoption of biomarkers as primary endpoints in phase 2 trials (p = 0.001) and secondary endpoints in phase 3 trials (p = 0.001). Conclusions: The growing recognition of the importance of biomarkers in AD trial' design and drug development is evident by the significant steady increase in biomarkers' utilization in phases 2 and 3. [ABSTRACT FROM AUTHOR]

Summary: Background: Alpha‐1 antitrypsin liver disease (AATLD) occurs in a subset of patients with alpha‐1 antitrypsin deficiency. Risk factors for disease progression and specific pathophysiologic features are not well known and validated non‐invasive assessments for disease severity are lacking. Currently, there are no approved treatments for AATLD. Aims: To outline existing understanding of AATLD and to identify knowledge gaps critical to improving clinical trial design and development of new treatments. Methods: This report was developed following a multi‐stakeholder forum organised by the Alpha‐1 Antitrypsin Deficiency Related Liver Disease Expert Panel in which experts presented an overview of the available literature on this topic. Results: AATLD results from a 'gain of toxic function' and primarily manifests in those with the homozygous Pi*ZZ genotype. Accumulation of misfolded 'Z' AAT protein in liver cells triggers intracellular hepatocyte injury which may ultimately lead to hepatic fibrosis. Male gender, age over 50 years, persistently elevated liver tests, concomitant hepatitis B or C virus infection, and metabolic syndrome, including obesity and type 2 diabetes mellitus, are known risk factors for adult AATLD. While the gold standard for assessing AATLD disease activity is liver histology, less invasive measures with low intra‐ and inter‐observer variability are needed. Measurement of liver stiffness shows promise; validated thresholds for staging AATLD are in development. Such advances will help patients by enabling risk stratification and personalised surveillance, along with streamlining the development process for novel therapies. Conclusions: This inaugural forum generated a list of recommendations to address unmet needs in the field of AATLD. [ABSTRACT FROM AUTHOR]

Background: The development of resistance by Plasmodium falciparum is a burdening hazard that continues to undermine the strides made to alleviate malaria. As such, there is an increasing need to find new alternative strategies. This study evaluated and validated 2 medicinal plants used in traditional medicine to treat malaria. Methods: Inspired by their ethnobotanical reputation of being effective against malaria, Ziziphus mucronata and Xysmalobium undulutum were collected and sequentially extracted using hexane (HEX), ethyl acetate (ETA), Dichloromethane (DCM) and methanol (MTL). The resulting crude extracts were screened for their anti-malarial and cytotoxic potential using the parasite lactate dehydrogenase (pLDH) and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, respectively. This was followed by isolating the active compounds from the DCM extract of Z. mucronata using silica gel chromatography and structural elucidation using spectroscopic techniques (NMR: 1H, 12C, and DEPT). The active compounds were then targeted against P. falciparum heat shock protein 70–1 (PfHsp70-1) using Autodock Vina, followed by in vitro validation assays using ultraviolet–visible (UV–VIS) spectroscopy and the malate dehydrogenase (MDH) chaperone activity assay. Results: The extracts except those of methanol displayed anti-malarial potential with varying IC50 values, Z. mucronata HEX (11.69 ± 3.84 µg/mL), ETA (7.25 ± 1.41 µg/mL), DCM (5.49 ± 0.03 µg/mL), and X. undulutum HEX (4.9 ± 0.037 µg/mL), ETA (17.46 ± 0.024 µg/mL) and DCM (19.27 ± 0.492 µg/mL). The extracts exhibited minimal cytotoxicity except for the ETA and DCM of Z. mucronata with CC50 values of 10.96 and 10.01 µg/mL, respectively. Isolation and structural characterization of the active compounds from the DCM extracts revealed that betulinic acid (19.95 ± 1.53 µg/mL) and lupeol (7.56 ± 2.03 µg/mL) were responsible for the anti-malarial activity and had no considerable cytotoxicity (CC50 > µg/mL). Molecular docking suggested strong binding between PfHsp70-1, betulinic acid (− 6.8 kcal/mol), and lupeol (− 6.9 kcal/mol). Meanwhile, the in vitro validation assays revealed the disruption of the protein structural elements and chaperone function. Conclusion: This study proves that X undulutum and Z. mucronata have anti-malarial potential and that betulinic acid and lupeol are responsible for the activity seen on Z. mucronata. They also make a case for guided purification of new phytochemicals in the other extracts and support the notion of considering medicinal plants to discover new anti-malarials. [ABSTRACT FROM AUTHOR]