ACS Pharmacology and Translational Science最新文献

ACKR3 is a class A G protein-coupled receptor that is considered as an atypical chemokine receptor. It does not activate G proteins but efficiently recruits β-arrestin and mediates ligand internalization and was thus proposed as a scavenger receptor. Besides chemokines, ACKR3 internalizes a variety of endogenous opioid peptides, including adrenorphin and dynorphin A. By reducing their availability to the classical opioid receptors, ACKR3 is proposed to participate in the endogenous pain management system, suggesting it as a new potential target for a new class of analgesics. Available structural data for ACKR3 are focused on the binding of chemokines (e.g., CXCL12), but how opioid peptides bind at ACKR3 remains enigmatic. Here, we structurally modeled opioid peptide binding at ACKR3 with a focus on adrenorphin, its ACKR3 selective variant LIH383, and dynorphin A. By combining molecular dynamics simulations with pharmacophore analysis, we analyze the opioid peptides’ binding modes and compare them with binding to classical opioid receptors (MOR, KOR, and DOR). We apply our model to rationally explain previously reported structure–activity relationships for adrenorphin derivatives, which also supports the model’s validation. Moreover, we include in vitro ACKR3 mutational experiments on both the receptor and LIH383 to further strengthen our structural model. Taken together, we systematically combine in silico observations and in vitro readouts to contribute to the understanding of ACKR3's ligand binding profile and set the basis for further ACKR3 ligand development.

Although animal models offer the physiology of the entire organism, various cell populations, and circuit-level behaviors, their predictive ability for polygenic neuropsychiatric disorders may be limited by species-specific neurodevelopment and genetics. Consequently, despite decades of neuropharmacological research, many CNS-targeted drug candidates still fail in late-stage clinical trials. This review summarizes how neuronal-engineering platforms, especially patient-derived induced pluripotent stem-cell (iPSC) organoids and neuron-glia cocultures, enable high-throughput screening (HTS) pipelines with greater clinical fidelity. This review focuses explicitly on neuropsychiatric disorders such as major depressive disorder, schizophrenia, bipolar disorder, and anxiety, and emphasizes human cell-derived organoid and neuron-glia coculture models tailored to their circuit-level pathophysiology. Organoid-enabled HTS couples human genetics with automated phenotyping, accelerating identification of circuit-level drug effects while reducing animal use. The remaining issues are integrating multiomics data, vascularization, and batch variability. These gaps will be filled, and precision psychiatry will become attainable with the continued advancements in biomaterials, single-cell analytics, and machine learning, by highlighting how human iPSC-derived organoids and advanced neuronal engineering recapitulate pathology and enable scalable drug screening. This review addresses a critical bottleneck in psychiatric drug development and outlines how these innovations can help close the bench-to-bedside gap in neuropsychiatric drug discovery.

High-throughput analysis has become a critical component in chemical biology and analytical chemistry due to the large libraries of compounds that are screened every day for drug development. Mass spectrometry (MS)-based proteomics is the methodology of choice for large-scale identification and quantification of protein modifications, both chemically deposited and biological post-translational modifications (PTMs). With the advent of antibody drug conjugates (ADCs) and other novel protein-based conjugates, the demand for such an analysis has skyrocketed. Here, we present a new protocol that achieves quantitative data for modified peptides in approximately 30 s of MS acquisition time. This platform includes a direct injection MS approach coupled with new software named iFishMass to extract targeted signals from hundreds of runs. iFishMass automatically generates plots and statistics. This platform will enable a faster analysis of synthetic modifications installed on monoclonal antibodies to create ADCs, and it is potentially scalable to biological PTMs. Sample preparation can be parallelized for 384 samples by using multichannel pipettes and 96-well plates, paving the way to an inexpensive but effective platform for high-throughput screening of conjugation sites on proteins.

The Hamm laboratory recently published a cohort of PAR4 antagonists that were effective against the tethered ligand activation of PAR4. These compounds were generated from an ultralarge virtual screen using a homology model of PAR4. Upon further investigation, it appears the protease-activated receptor antagonists highlighted in this work have some thrombin liability. The Hamm laboratory further characterized the activity of these compounds using various methods, including a fluorescent thrombin activity assay, a chromogenic thrombin activity assay, and flow cytometry assays. We conclude that they do indeed antagonize PAR4, but thrombin is an additional target.

Prediction of monoclonal antibody (mAb) pharmacokinetics (PK) in drug development remains challenging due to the lack of a standardized method for predicting elimination based on mechanistic pathways. Among the processes implemented in the physiologically based pharmacokinetic (PBPK) models for large molecules, FcRn-mediated recycling constitutes the predominant mechanism influencing the elimination of mAbs. In the present study, we assessed the predictivity of a generic value for the dissociation constant (Kd) for FcRn (Kd_FcRn) in humans, identified based on clinical data, to provide means for mechanism-based PK projections for mAbs in first-in-human (FIH) trials. We compiled a database of digitalized linear PK profiles for 50 mAbs administered intravenously in humans. Subsequently, the database was randomly divided into a training and a test data set, using a 7:3 ratio. For each drug in the training data set, a generic PBPK model was set up in PK-Sim, and a drug-specific Kd_FcRn parameter was estimated through data fitting. The median of estimated drug-specific Kd_FcRn was 1.05 μM and was used for naïve predictions of the PK of the drugs in the test data set. Plasma exposure (AUC) and terminal half-life were accurately predicted for 80% and 60% of the drugs in the test data set, respectively, with a prediction error within the 0.80–1.25-fold range. Additionally, 100% of the test data set showed prediction errors within the 0.50–2.00-fold range for both plasma exposure and half-life. The median of the estimated drug-specific Kd_FcRn determined using the whole database with 50 mAbs was 1.07 μM and was retained after evaluation as a more accurate default Kd_FcRn value. The reported results provide a large database of mAbs PBPK models with estimated Kd_FcRn values using PK-Sim, and a validated default Kd_FcRn value of 1.07 μM to perform naïve predictions of mAbs linear PK in the context of FIH trials.

Fructose-1,6-bisphosphatase (FBPase) is a rate-limiting enzyme in gluconeogenesis, and its inhibition has the potential to improve glucose homeostasis. We characterize Cpd96, a novel inhibitor of FBPase, and demonstrate its multifaceted antidiabetic effects. In vivo, Cpd96 significantly improved glucose tolerance, enhanced insulin sensitivity, and promoted insulin secretion in type 2 diabetic (db/db and KKAy) mice. In vitro, Cpd96 potentiated insulin secretion in MIN6 cells and primary pancreatic islets by facilitating glucose uptake, elevating the ATP/ADP ratio, and activating the cAMP and AMPK/mTORC1/S6K signaling pathways. Notably, the insulinotropic effect of Cpd96 was FBPase-dependent, as it failed to promote insulin secretion in primary islets from β-cell-specific FBPase knockout mice. These findings suggest that Cpd96 improves insulin secretion through the metabolic reprogramming of β-cells and highlight its potential as a novel therapeutic strategy for diabetes treatment.

CD73 generates immunosuppressive adenosine in the tumor microenvironment and is a promising target for cancer immunotherapy. We have designed and systematically studied diverse 2-substituted 7-deazapurine ribonucleoside 5′-O-bisphosphonates bearing a variety of (het)aryl groups at position 6 and discovered their highly potent and selective CD73 inhibition activity. The most active compounds (with single-digit picomolar K_i) contained bicyclic (het)aryl groups at position 6 in combination with chlorine at position 2. Further optimization of pharmacokinetic properties identified inhibitors with low clearance, long half-life, high solubility, and excellent selectivity over CD39 and NTPDase3. They effectively suppressed adenosine formation in MDA-MB-231 cells, rescued CD8⁺ T cell activation, and were nontoxic to human fibroblasts. Overall, their profile compares favorably with AB680, a CD73 inhibitor currently in phase I/II clinical trials.

Vesicular monoamine transporter 2 (VMAT2) is an internal membrane protein found predominantly in the central nervous system that plays an integral role in the transport of biogenic monoamines (e.g., dopamine, serotonin, and norepinephrine) into synaptic vesicles for storage within the neuron. While multiple drugs that inhibit VMAT2 have been approved by the US Food and Drug Administration (FDA) for the treatment of hyperkinetic movement disorders, it has been reported that off-target interaction with VMAT2 may lead to neuropsychiatric consequences. In the present study an in vitro analysis was conducted for 257 chemically diverse compounds, most of which were FDA-approved drugs, to calculate the IC₅₀ values for inhibition of dopamine uptake at the VMAT2. The results of this study revealed that a total of 55 chemicals have strong inhibitory activities on dopamine uptake (IC₅₀ < 1 μM), some of which were not previously reported. Furthermore, 69 chemicals exhibited weak inhibitory activity on dopamine uptake between 1 and 10 μM, while 133 showed minimal to no impact on dopamine uptake (IC₅₀ > 10 μM). The IC₅₀ values and resulting inhibition categories were compared to the reported neurologic adverse events including deliria, Parkinson’s-related symptoms, dyskinesia, and suicidal ideation in the FDA Adverse Event Reporting System (FAERS) and drug labeling; however, no correlation was established between adverse events and VMAT2 inhibition. Additional analysis indicated that many of the compounds that inhibited dopamine uptake at VMAT2 were frequently known to interact with serotonin, dopamine, or adrenergic receptors; therefore, it is possible that a synergistic interaction between VMAT2 and one or more additional targets may be responsible for previously reported neurological adverse events.

Estrogen-related receptors (ERRs) are orphan nuclear receptors critical to the regulation of energy metabolism, mitochondrial biogenesis, and tissue-specific transcriptional programs. This review provides a comprehensive structural analysis of ERR isoforms (ERRα, ERRβ, and ERRγ), emphasizing insights from X-ray crystallography and NMR studies. We discuss the ligand-binding domains (LBDs), coactivator and corepressor interactions, and the molecular mechanisms underlying ligand-induced agonism or antagonism. Structural comparisons with estrogen receptors (ERs) reveal key amino acid determinants for ligand selectivity and functional activity. Furthermore, we highlight the development of isoform-selective synthetic ligands, including inverse agonists such as GSK5182, DN200434, and DN201000, with therapeutic potential in metabolic, neurodegenerative, and oncologic diseases. This synthesis of structural data provides a framework for rational drug design targeting ERRs, supporting the development of selective modulators to manipulate ERR signaling in a tissue- and disease-specific manner.

Acanthamoeba keratitis is a rare, vision-threatening corneal infection that remains difficult to diagnose and treat, with therapy often extending for many months. Despite recent advances, the management of Acanthamoeba keratitis still depends largely on empirical regimens combining biguanides, diamidines, and azoles. Outcomes vary widely, reflecting differences in pathogen virulence, drug penetration, host response, and timing of diagnosis. It is proposed that digital-twin technology offers a powerful new framework for studying and managing this disease. Digital twin is a data-driven computational approach that creates continuously updating virtual replicas of biological systems. By integrating multimodal clinical, imaging, and molecular data, digital twins could simulate corneal infection dynamics, drug diffusion, and cyst reactivation, providing clinicians with predictive insight rather than retrospective interpretation. Here, it is discussed how digital-twin models could be constructed for Acanthamoeba keratitis, challenges to implementation, and implications for precision ophthalmology.