Drug Development Research Articles

Co-culture of human AT2 cells with fibroblasts reveals a MUC5B phenotype: insights from an organoid model

Impaired interaction of fibroblasts with pneumocytes contributes to the progression of chronic lung disease such as idiopathic pulmonary fibrosis (IPF). Mucin 5B (MUC5B) is associated with IPF. Here we analyzed the interaction of primary fibroblasts and alveolar type 2 (AT2) pneumocytes in the organoid model. Single-cell analysis, histology, and qRT-PCR revealed that fibroblasts expressing high levels of fibrosis markers regulate STAT3 signaling in AT2 cells, which is accompanied by cystic organoid growth and MUC5B expression. Cystic growth and MUC5B expression were also caused by the cytokine IL-6. The PI3K-Akt signaling pathway was activated in fibroblasts. The drug dasatinib prevented the formation of MUC5B-expressing cystic organoids. MUC5B associated with AT2 cells in samples obtained from IPF patients. Our model shows that fibrotic primary fibroblasts induce impaired differentiation of AT2 cells via STAT3 signaling pathways, as observed in IPF patients. It can be used for mechanistic studies and drug development.

Conserved glucokinase regulation in zebrafish confirms therapeutic utility for pharmacologic modulation in diabetes

Glucokinase (GCK) is an essential enzyme for blood glucose homeostasis. Because of its importance in glucose metabolism, GCK is considered an attractive target for the development of antidiabetic drugs. However, a viable therapeutic agent has still to emerge, prompting efforts to improve understanding of the complex regulation and biological effects of GCK. Using the vertebrate organism zebrafish, an attractive model to study metabolic diseases and pharmacological responses, we dissected the complexities of gck regulation and unraveled effects of Gck modulation. We found that while gck expression in zebrafish islet cells is constitutive, gck expression in the liver is regulated by nutritional status, confirming similarity to the mammalian system. A combination of transgenic gck reporter lines and our diabetes model, the pdx1 mutant, allowed monitoring of gck expression under pathological conditions, revealing reduced gck expression and activity in the liver, which was unresponsive to nutrient stimulation, and decreased expression in the islet due to the reduced number of β-cells. Gck activation substantially ameliorated hyperglycemia in pdx1 mutants, without inducing oxidative stress responses in liver or islet. In-depth characterization of Gck activity and regulation at the cellular level in a whole-organism diabetes model clarifies its applicability as a drug target for therapies.

Design, synthesis, and biological evaluation of novel molecules as potent inhibitors of indoleamine 2,3-dioxygenase 1.

Tumoral immune escape is an obstacle to successful cancer therapy. Tryptophan catabolism mediated by indoleamine 2,3-dioxygenase (IDO1) is an important mechanism of peripheral immune tolerance contributing to tumoral immune resistance, and IDO1 inhibition is an active area of drug development. Several classes of small molecule-based IDO1 inhibitors have already been reported. Still, only a few compounds are currently being evaluated in various stages of clinical trials as adjuvants or in combination with chemo- and radiotherapies. In this study, a novel series of 1,2,5-oxadiazole-3-carboximidamide derivatives were designed, synthesized, and evaluated for inhibitory activities against IDO1, and their structure-activity relationship was investigated. Notably, several compounds (11c, 11j, 11o, and 11u) showed powerful anti-tumor effects in the low micromolar range. Among them, compound 11u exhibited excellent inhibitory potency against hIDO1 (IC50 = 42.2 ± 2.23nM) and in Hela cells expressing hIDO1 (IC50 = 4.35 ± 0.13nM). Combined with favorable in vitro potency, pharmacokinetic profile, and in vivo efficacy, the promising antitumor drug candidate 11u has subsequently advanced into preclinical research. These compounds provide valuable ideas and information for developing new cancer immunotherapy.

AI neurotechnology compatibility with Alzheimers Disease Treatment: A Review

Alzheimers Disease (AD) is one of the most ordinary kind of dementia, yet no definitive cause or cure for it is found. Currently, there are only developments in drugs or technology that can slowly help to minimize the symptoms of AD through treatment or aid in the diagnosis of this disease. The main problem is that the treatment and diagnosis of AD administered through medical professionals are prone to errors. This has created a need for methods that make the process of diagnosing AD and concluding the most effective treatment both efficient and highly accurate. This brings focus to studies in AD, more specifically neurotechnology that uses Artificial Intelligence (AI) to aid in diagnosis and process of treatment. AI has the potential to diagnose and can even work hand in hand with different AD treatments, as it is expected to analyze the best approach for individual patients for course of treatment which could lead to the cure of Alzheimers. Studies on Neurotechnologies that use AI, such as systems like Machine Learning, Deep Learning, and have yielded promising results of the potential accuracy of these systems, proving its ability to diagnose AD with high accuracy while being more efficient. With the wide variety of potential treatments of AD, it is in these situations where AI neuro technology can play a role in the deciding factor of treatment. This paper will be a comparison on these diverse types of AI systems, and which may be the best in terms of diagnosis and treatment of AD.

A landscape review to identify what matters to patients with thrombotic cardiovascular diseases and patient-reported outcome instruments which can be used to capture the patient experience.

Thrombotic cardiovascular diseases profoundly impact patients' health-related quality of life (HRQoL). However, patient-reported outcome (PRO) instruments that are disease-specific or antithrombotic-treatment focused, developed according to US Food and Drug Administration (FDA) guidance on PROs, and can be used in clinical trials, are lacking. The aim of this study was to understand concepts important to patients diagnosed with coronary artery disease (CAD) or acute coronary syndrome (ACS), atrial fibrillation (AF), or stroke who require antithrombotic treatment for reducing risk of future thrombotic events (indications being evaluated for an investigational new drug), identify PROs that measure relevant symptoms and impacts, and determine acceptability of PROs from a health technology assessment (HTA) perspective. A landscape review, conducted between January 2009 and October 2020, included a search of qualitative literature (OVID), a review of PRO instruments using multiple sources (e.g., OVID and clinical trials databases), and a survey of HTA decisions for antithrombotic medications. The qualitative literature review identified 27 publications used to develop a high-level conceptual summary of symptoms and HRQoL impacts reported by patients. The instrument landscape review indicated that generic PROs have been utilized for thrombotic indications, but disease-specific, fit-for-purpose instruments are lacking, and the HTA review revealed that although HTA agencies discussed PRO instruments, evidence of specific recommendations was not found. To ensure patients' experiences, perspectives, and priorities are incorporated into drug development and evaluation, a core set of PROs for thrombotic indications that meet health authority guidance and are acceptable to HTA agencies is needed.

In Silico Clinical Trial for Osteoporosis Treatments to Prevent Hip Fractures: Simulation of the Placebo Arm.

Osteoporosis represents a major healthcare concern. The development of novel treatments presents challenges due to the limited cost-effectiveness of clinical trials and ethical concerns associated with placebo-controlled trials. Computational models for the design and assessment of biomedical products (In Silico Trials) are emerging as a promising alternative. In this study, a novel In Silico Trial technology (BoneStrength) was applied to replicate the placebo arms of two concluded clinical trials and its accuracy in predicting hip fracture incidence was evaluated. Two virtual cohorts (N = 1238 and 1226, respectively) were generated by sampling a statistical anatomy atlas based on CT scans of proximal femurs. Baseline characteristics were equivalent to those reported for the clinical cohorts. Fall events were sampled from a Poisson distribution. A multiscale stochastic model was implemented to estimate the impact force associated to each fall. Finite Element models were used to predict femur strength. Fracture incidence in 3years follow-up was computed with a Markov chain approach; a patient was considered fractured if the impact force associated with a fall exceeded femur strength. Ten realizations of the stochastic process were run to reach convergence. Each realization required approximately 2500 FE simulations, solved using High-Performance Computing infrastructures. Predicted number of fractures was 12 ± 2 and 18 ± 4 for the two cohorts, respectively. The predicted incidence range consistently included the reported clinical data, although on average fracture incidence was overestimated. These findings highlight the potential of BoneStrength for future applications in drug development and assessment.

Medical treatment of primary sclerosing cholangitis: What have we learned and where are we going?

It has proven difficult to establish robust evidence for significant clinical benefits of medical treatment in primary sclerosing cholangitis (PSC). For ursodeoxycholic acid, clinical practice guidelines only offer vague recommendations, leading to a situation of variable prescription rates depending on local reimbursement policies and physician preference. The difficulty in drug development in PSC is partly related to poor understanding of critical disease processes with failure to identify relevant mechanisms of action of putative drugs. The variable disease course, both intra-individually and between individuals, and lack of robust definitions of what success looks like for clinical trials in PSC have also contributed to the negative outcomes of trials performed. In this review article we will discuss these uncertainties and challenges, building on key previous and ongoing clinical trials. Despite the lack of consensus for an ideal phase II and phase III study design, several trials for diverse compounds are currently ongoing, indicating a shift from therapeutic nihilism towards hope for people with PSC. While waiting for robust efficacy data for drugs currently being tested, the current lack of effective interventions should not motivate prescription of compounds to people with PSC based on low-quality evidence.

Exploring 3D Printing in Drug Development: Assessing the Potential of Advanced Melt Drop Deposition Technology for Solubility Enhancement by Creation of Amorphous Solid Dispersions

Exploring 3D Printing in Drug Development: Assessing the Potential of Advanced Melt Drop Deposition Technology for Solubility Enhancement by Creation of Amorphous Solid Dispersions

Incretin hormones and obesity.

The incretin hormones glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) play critical roles in co-ordinating postprandial metabolism, including modulation of insulin secretion and food intake. They are secreted from enteroendocrine cells in the intestinal epithelium following food ingestion, and act at multiple target sites including pancreatic islets and the brain. With the recent development of agonists targeting GLP-1 and GIP receptors for the treatment of type 2 diabetes and obesity, and the ongoing development of new incretin-based drugs with improved efficacy, there is great interest in understanding the physiology and pharmacology of these hormones.

Cloning of the Thermus thermophilus methionine aminopeptidase gene and confirmation of the enzyme functional activity

Background. Methionine aminopeptidases (MAPs) are a class of enzymes that catalyze the removal of the N-terminal initiator methionine from a polypeptide chain. Bacterial MAPs are considered as targets for the development of broad-spectrum antibacterial drugs, and using MAPs in biotechnology necessitates the search for new MAPs and the study of their functioning and inhibition mechanisms.The aim of the study. To identify methionine aminopeptidase in the Thermus thermophilus genome (Tt-MAP) and to confirm its functional activity.Materials and methods. To identify Tt-MAP, we analyzed the Thermus thermophilus genome in the GeneBank database. Modern genetic engineering techniques (polymerase chain reaction, restriction, transformation, heterologous expression) were used to clone the putative open reading frame (ORF) encoding Tt-MAP in the pHUE vector. Various chromatography techniques (affinity, ion exchange, and size-exclusion) were used to obtain a purified enzyme preparation. The fluorogenic substrate L-methionine 7-amino-4-methylcoumarin (Met-AMC) was used to confirm the specific functional aminopeptidase activity of the enzyme.Results. An ORF encoding MAP was identified in the Thermus thermophilus bacterium genome. Oligonucleotide primers were designed based on the nucleotide sequence. The ORF was cloned in the vector, and the recombinant enzyme was produced in E. coli cells. A method for purifying the enzyme to a homogeneous state was developed using a series of sequential chromatographies, allowing up to 30 mg to be obtained from 1 liter of culture. Using the fluorogenic substrate Met-AMC, the specific functional activity of the enzyme was demonstrated (the enzyme cleaves methionine from the substrate).Conclusion. We have identified the Thermus thermophilus MAP and tested its functional activity. It has been shown that the ORF product TTHA1670 encodes a methionine-specific aminopeptidase, i. e. methionine aminopeptidase. The enzyme can be used in various fields of biotechnology and scientific research.

Research Progress on Natural Products Alleviating Liver Inflammation and Fibrosis via NF-κB Pathway.

Liver fibrosis is a key pathological process in chronic liver diseases, regulated by various cytokines and signaling pathways. Among these, the NF-κB signaling pathway plays a significant role in the initiation and progression of liver fibrosis. Recently, natural products have garnered attention as potential anti-fibrotic agents. This review highlights recent studies on how natural products, including flavonoids, terpenoids, polysaccharides, phenols, alkaloids, quinones, phenylpropanoids, steroids, and nitrogen compounds, mitigate liver fibrosis by modulating the NF-κB signaling pathway. Specifically, it examines how these natural products influence NF-κB activation, nuclear translocation, and downstream signaling, thereby inhibiting inflammatory responses, reducing apoptosis, and regulating hepatic stellate cell (HSC) activity, ultimately achieving therapeutic effects against liver fibrosis. A deeper understanding of the mechanisms by which natural products regulate the NF-κB signaling pathway can provide crucial theoretical foundations and valuable insights for the development of novel anti-fibrotic drugs.

Advances in Artificially Designed Antibacterial Active Antimicrobial Peptides.

Antibacterial resistance has emerged as a significant global concern, necessitating the urgent development of new antibacterial drugs. Antimicrobial peptides (AMPs) are naturally occurring peptides found in various organisms. Coupled with a wide range of antibacterial activity, AMPs are less likely to develop drug resistance and can act as potential agents for treating bacterial infections. However, their characteristics, such as low activity, instability, and toxicity, hinder their clinical application. Consequently, researchers are inclined towards artificial design and optimization based on natural AMPs. This review discusses the research advancements in the field of artificial designing and optimization of various AMPs. Moreover, it highlights various strategies for designing such peptides, aiming to provide valuable insights for developing novel AMPs.

The Mechanism of Propofol in Treating Depression-like Behaviors Based on Network Pharmacology and Experimental Validation

Objective: To explore the potential mechanism of propofol in improving antidepressant-like behavior through network pharmacology and animal experimental validation, providing theoretical and experimental support for the development of novel antidepressant drugs based on propofol. Methods: 1. The targets of propofol and the disease targets of depression were screened through Swiss Target prediction, Super-pred, SEA, Drugbank, CTD, GeneCards, OMIM, and TTD databases. A protein-protein interaction (PPI) network was constructed, and the core targets were screened and visualized using CytoScape 3.10.2 software. Additionally, GO and KEGG enrichment analyses of the intersection targets were performed using the Metascape database. 2. Thirty male C57BL/6 mice were randomly divided into the CON group, LH model group, and PRO treatment group, with 10 mice in each group. The sucrose preference test, forced swim test, and tail suspension test were conducted to determine whether the depression model was successfully established and to assess the improvement effect of propofol on antidepressant-like behavior. 3. The pathological morphological changes of hippocampal neurons in the three groups of mice were observed through HE staining experiments. The expression of mGluR5 protein in the hippocampus was analyzed by Western blot. Results: 1. Network pharmacology analysis indicated that propofol may exert antidepressant effects by acting on core genes such as ALB, ESR1, NFKB1, HSP90AB1, and EGFR. These core target genes were mainly enriched in biological processes such as synaptic transmission and membrane potential regulation; they involved cellular components such as GABA receptor complexes and synaptic membranes; and they included molecular functions such as neurotransmitter receptor activity. Additionally, propofol may exert antidepressant effects through signaling pathways such as neuroactive ligand-receptor interaction, morphine addiction, and GABAergic synapse. 2. Compared with the CON group, the sucrose preference rate of mice in the LH model group significantly decreased, while the immobility time in the forced swim test and tail suspension test significantly increased. Compared with the LH model group, the sucrose preference rate of mice in the PRO treatment group significantly increased, and the immobility time significantly decreased. 3. The HE results showed that compared with the CON group, the number of neurons in the LH group decreased, with loose arrangement, pycnotic and deeply stained nuclei with blurred boundaries. However, the number of necrotic neurons in the PRO group significantly decreased. The Western blot results showed that compared with the CON group, the expression level of mGluR5 protein in the LH model group significantly increased, while it significantly decreased in the PRO treatment group compared with the LH model group. Conclusion: Propofol may exert antidepressant effects by regulating the expression of mGluR5, improving antidepressant-like behavior, and reducing hippocampal neuronal necrosis.

Investigating the inhibitory potential of natural bioactive compounds against cyclin-dependent kinase 13: virtual high throughput screening and MD simulation studies to target CDK signaling

Cyclin-dependent kinase 13 (CDK13) belongs to the cyclin-dependent kinase (CDK) family that is actively involved in transcription regulation and RNA splicing. CDK13 binds with its partner, cyclin K, to regulate several biological processes. CDK13 and cyclin K complex phosphorylates RNA pol II carboxyl-terminal domain (CTD) at several serine residues, creating transcription elongation. The upregulation of the kinase contributes to tumor growth and cell proliferation, and is highly associated with various cancers, including skin, stomach, and ovarian. Thus, it can be considered an efficient therapeutic target for the development of drugs against cancer. In this work, a virtual high throughput screening (vHTS) of the ZINC library was carried out to elucidate the initial potent compounds. Further, filters were applied to identify the hit compounds among the ∼90,000 compound library. Based on the docking scores and binding affinity, the top 100 hits were elucidated, and they were further narrowed down to 50 compounds based on ADMET and Lipinski’s RO5 filter. Finally, 10 compounds were chosen that showed appreciable biological activity. Among them, ZINC02136558 was selected as a potent lead compound that showed strong interaction with the amino acid residues of active and binding sites of CDK13. Furthermore, the all-atom molecular dynamic simulation was performed at 200 ns to explore the dynamic evolution of the system. Finally, the results showed that the ZINC02136558 may be considered as a potential lead molecule to inhibit CDK13 and implicated in therapeutic management of cancer and associated diseases.

Exploring STK3 in melanoma: a systematic review of signaling networks and therapeutic opportunities.

Melanoma is an aggressive cancer that disregards both the MAPK and Hippo signaling pathways. This systematic review explores STK3 function in the Hippo pathway to regulate networks and its therapeutic potential in melanoma. From 1991 to 2024, we studied how STK3 interacts with the MAPK/ERK pathway to promote apoptosis and inhibit tumor growth. STK3 controls cell growth, apoptosis, and metastasis via the Hippo and MAPK pathways. It is a melanoma tumor suppressor. Some ways to target STK3 are to directly activate it, stop downstream effectors like YAP/TAZ from working, or use existing BRAF inhibitors together with other methods. Despite advancements, challenges in STK3 drug development persist, warranting further investigation. This review examined the role of STK3 in the development of melanoma and identified potential vulnerabilities for therapeutic intervention.

Lp(a): A Rapidly Evolving Therapeutic Landscape.

Elevated lipoprotein(a) (Lp[a]) is a genetically determined cardiovascular risk factor, causally linked to both atherosclerotic coronary artery disease and aortic stenosis. Elevated Lp(a) is widely prevalent, and several cardiovascular societies now recommend performing Lp(a) screening at least once in all adults. However, there are currently no approved drugs aimed specifically at lowering Lp(a). In this review, we describe several promising Lp(a)-lowering therapies in the drug development pipeline and outline what role these may have in future clinical practice. Pelacarsen and olpasiran are two novel RNA-based injectable therapies which are being studied in ongoing phase 3 clinical trials, with the earliest of these to be concluded in 2025. These drugs act by degrading transcribed LPA mRNA, which would normally yield the apolipoprotein(a) constituent of Lp(a). Other candidate drugs, such as Lepodisiran, Zerlasiran, and Muvalaplin, are also in early-stage development. While there are presently no Lp(a)-lowering drugs available for routine clinical use, several promising candidates are currently under investigation. If these prove to be effective in randomized clinical trials, they will expand the cardiovascular care armamentarium and will allow clinicians to treat a presently unmitigated cardiovascular risk factor.

Improved Prediction of Ligand-Protein Binding Affinities by Meta-modeling.

The accurate screening of candidate drug ligands against target proteins through computational approaches is of prime interest to drug development efforts. Such virtual screening depends in part on methods to predict the binding affinity between ligands and proteins. Many computational models for binding affinity prediction have been developed, but with varying results across targets. Given that ensembling or meta-modeling approaches have shown great promise in reducing model-specific biases, we develop a framework to integrate published force-field-based empirical docking and sequence-based deep learning models. In building this framework, we evaluate many combinations of individual base models, training databases, and several meta-modeling approaches. We show that many of our meta-models significantly improve affinity predictions over base models. Our best meta-models achieve comparable performance to state-of-the-art deep learning tools exclusively based on 3D structures while allowing for improved database scalability and flexibility through the explicit inclusion of features such as physicochemical properties or molecular descriptors. We further demonstrate improved generalization capability by our models using a large-scale benchmark of affinity prediction as well as a virtual screening application benchmark. Overall, we demonstrate that diverse modeling approaches can be ensembled together to gain meaningful improvement in binding affinity prediction.

SGLT2 Inhibitors in Patients with Heart Failure: A Model-Based Meta-Analysis.

This study aimed to quantify the effects of sodium-glucose co-transporter 2 (SGLT2) inhibitors on N-terminal pro-B-type natriuretic peptide (NT-proBNP) as a therapeutic approach for heart failure. A systematic literature review was conducted to collect pharmacokinetics (PK) and pharmacodynamics (PD) data on empagliflozin, dapagliflozin, and canagliflozin. Population pharmacokinetic models were developed separately for each drug, along with PK/PD turnover models for SGLT2 inhibitors, to describe the time course of NT-proBNP and simulate its changes over 52 weeks. A total of 42 publications were included in this study. The results showed that baseline NT-proBNP levels, estimated glomerular filtration rate levels, and body weight significantly influenced the therapeutic effects of SGLT2 inhibitors. Among the studied drugs, canagliflozin demonstrated a greater reduction in NT-proBNP at comparable baseline levels. Baseline NT-proBNP concentration, renal function, and body weight were covariates affecting the efficacy of SGLT2 inhibitors in reducing NT-proBNP. Canagliflozin showed the most favorable treatment outcomes at similar baseline levels. This model-based meta-analysis approach may support further drug development for SGLT2 inhibitors.

Mechanistic Modeling of Spatial Heterogeneity of Drug Penetration and Exposure in the Human Central Nervous System and Brain Tumors.

Direct measurement of spatial-temporal drug penetration and exposure in the human central nervous system (CNS) and brain tumors is difficult or infeasible. This study aimed to develop an innovative mechanistic modeling platform for quantitative prediction of spatial pharmacokinetics of systemically administered drugs in the human CNS and brain tumors. A nine-compartment CNS (9-CNS) physiologically-based pharmacokinetic model was developed to account for general anatomical structure and pathophysiological heterogeneity of the human CNS and brain tumors. Drug distribution into and within the CNS and tumors is driven by plasma concentration-time profiles and governed by drug properties and CNS pathophysiology. The model was validated by comparisons of model predictions and clinically observed data of six drugs (abemaciclib, ribociclib, pamiparib, olaparib, temuterkib, and ceritinib) in glioblastoma patients. As rigorously validated, the 9-CNS model allows reliable prediction of spatial pharmacokinetics in different regions of the brain parenchyma (i.e., parenchyma adjacent to CSF and deep parenchyma), tumors (i.e., tumor rim, bulk tumor, and tumor core), and CSF (i.e., ventricular CSF, cranial and spinal subarachnoid CSF). By considering inter-individual plasma pharmacokinetic variability and CNS/tumor heterogeneity, the model well predicts the inter-individual variability and spatial heterogeneity of drug exposure in the CNS and tumors as observed for all six drugs in glioblastoma patients. The 9-CNS model is a first-of-its kind, mechanism-based computational modeling platform that enables early reliable prediction of spatial CNS and tumor pharmacokinetics based on plasma concentration-time profiles. It provides a valuable tool to assist rational drug development and treatment for brain cancer.

Progress in PGK1 in Prostate Cancer

Phosphoglycerol kinase 1 (PGK1) is the first key metabolic enzyme that produces ATP during glycolysis and is involved in the glycolytic pathway of prostate cancer. PGK 1 can not only act as a metabolic enzyme to affect tumor growth, but also affect the gene expression, energy metabolism, molecular regulation and other processes of tumor cells through its non-metabolic enzyme function, and then mediate the growth, migration and invasion of tumors, and aggravate the biological characteristics of malignant cancer cells. In this review, we reviewed the structure and function of PGK1 and its relationship with prostate cancer to clarify the important role of PGK1 in the progression of advanced adenoma and provide a theoretical basis for targeting PGK1 for drug development.