Am J Respir Cell Mol Biol. 2025 Apr 30. doi: 10.1165/rcmb.2024-0595LE. Online ahead of print.

NO ABSTRACT

PMID:40305670 | DOI:10.1165/rcmb.2024-0595LE

Biomolecules. 2025 Apr 5;15(4):535. doi: 10.3390/biom15040535.

ABSTRACT

Pharmacogenomics is revolutionizing precision medicine by enabling tailored therapeutic strategies based on an individual genetic and molecular profile. Circular RNAs (circRNAs), a distinct subclass of endogenous non-coding RNAs, have recently emerged as key regulators of drug resistance, tumor progression, and therapeutic responses. Their covalently closed circular structure provides exceptional stability and resistance to exonuclease degradation, positioning them as reliable biomarkers and novel therapeutic targets in cancer management. This review provides a comprehensive analysis of the interplay between circRNAs and pharmacogenomics, focusing on their role in modulating drug metabolism, therapeutic efficacy, and toxicity profiles. We examine how circRNA-mediated regulatory networks influence chemotherapy resistance, alter targeted therapy responses, and impact immunotherapy outcomes. Additionally, we discuss emerging experimental tools and bioinformatics techniques for studying circRNAs, including multi-omics integration, machine learning-driven biomarker discovery, and high-throughput sequencing technologies. Beyond their diagnostic potential, circRNAs are being actively explored as therapeutic agents and drug delivery vehicles. Recent advancements in circRNA-based vaccines, engineered CAR-T cells, and synthetic circRNA therapeutics highlight their transformative potential in oncology. Furthermore, we address the challenges of standardization, reproducibility, and clinical translation, emphasizing the need for rigorous biomarker validation and regulatory frameworks to facilitate their integration into clinical practice. By incorporating circRNA profiling into pharmacogenomic strategies, this review underscores a paradigm shift toward highly personalized cancer therapies. circRNAs hold immense potential to overcome drug resistance, enhance treatment efficacy, and optimize patient outcomes, marking a significant advancement in precision oncology.

PMID:40305280 | DOI:10.3390/biom15040535

Sci Adv. 2025 May 2;11(18):eadr1576. doi: 10.1126/sciadv.adr1576. Epub 2025 Apr 30.

ABSTRACT

Accurately predicting pathological complete response (pCR) before neoadjuvant chemotherapy (NAC) is crucial for patients with breast cancer. In this study, we developed a multimodal integrated fully automated pipeline system (MIFAPS) in forecasting pCR to NAC, using a multicenter and prospective dataset of 1004 patients with locally advanced breast cancer, incorporating pretreatment magnetic resonance imaging, whole slide image, and clinical risk factors. The results demonstrated that MIFAPS offered a favorable predictive performance in both the pooled external test set [area under the curve (AUC) = 0.882] and the prospective test set (AUC = 0.909). In addition, MIFAPS significantly outperformed single-modality models (P < 0.05). Furthermore, the high deep learning scores were associated with immune-related pathways and the promotion of antitumor cells in the microenvironment during biological basis exploration. Overall, our study demonstrates a promising approach for improving the prediction of pCR to NAC in patients with breast cancer through the integration of multimodal data.

PMID:40305609 | DOI:10.1126/sciadv.adr1576

Sci Adv. 2025 May 2;11(18):eadt5111. doi: 10.1126/sciadv.adt5111. Epub 2025 Apr 30.

ABSTRACT

Protein aggregation is a pathological hallmark of more than 50 human diseases and a major problem for biotechnology. Methods have been proposed to predict aggregation from sequence, but these have been trained and evaluated on small and biased experimental datasets. Here we directly address this data shortage by experimentally quantifying the aggregation of >100,000 protein sequences. This unprecedented dataset reveals the limited performance of existing computational methods and allows us to train CANYA, a convolution-attention hybrid neural network that accurately predicts aggregation from sequence. We adapt genomic neural network interpretability analyses to reveal CANYA's decision-making process and learned grammar. Our results illustrate the power of massive experimental analysis of random sequence-spaces and provide an interpretable and robust neural network model to predict aggregation.

PMID:40305601 | DOI:10.1126/sciadv.adt5111

J Med Internet Res. 2025 Apr 30. doi: 10.2196/75340. Online ahead of print.

ABSTRACT

BACKGROUND: Implementing machine learning models to identify clinical deterioration on the wards is associated with decreased morbidity and mortality. However, these models have high false positive rates and only use structured data.

OBJECTIVE: We aim to compare models with and without information from clinical notes for predicting deterioration.

METHODS: Adults admitted to the wards at the University of Chicago (development cohort) and University of Wisconsin-Madison (external validation cohort) were included. Predictors consisted of structured and unstructured variables extracted from notes as Concept Unique Identifiers (CUIs). We parameterized CUIs in five ways: Standard Tokenization (ST), ICD Rollup using Tokenization (ICDR-T), ICD Rollup using Binary Variables (ICDR-BV), CUIs as SapBERT Embeddings (SE), and CUI Clustering using SapBERT embeddings (CC). Each parameterization method combined with structured data and structured data-only were compared for predicting intensive care unit transfer or death in the next 24 hours using deep recurrent neural networks.

RESULTS: The development (UC) cohort included 284,302 patients, while the external validation (UW) cohort included 248,055. In total, 4.9% (N=26,281) of patients experienced the outcome. The SE model achieved the highest AUPRC (0.208), followed by CC (0.199) and the structured-only model (0.199), ICDR-BV (0.194), ICDR-T (0.166), and ST (0.158). The CC and structured-only models achieved the highest AUROC (0.870), followed by ICDR-T (0.867), ICDR-BV (0.866), ST (0.860), and SE (0.859). In terms of sensitivity and positive predictive value, the CC model achieved the greatest positive predictive value (12.53%) and sensitivity (52.15%) at the cutoff that flagged 5% of the observations in the test set. At the 15% cutoff, the ICDR-T, CC, and ICDR-BV models tied for the highest positive predictive value at 5.67%, while their sensitivities were 70.95%, 70.92%, and 70.86%, respectively. All models were well calibrated, achieving Brier scores in the range of 0.011-0.012. The modified IG method revealed that CUIs corresponding to terms such as "NPO - Nothing by mouth", "Chemotherapy", "Transplanted tissue", and "Dialysis procedure" were most predictive of deterioration.

CONCLUSIONS: A multimodal model combining structured data with embeddings using SapBERT had the highest AUPRC, but performance was similar between models with and without CUIs. Although the addition of CUIs from notes to structured data did not meaningfully improve model performance for predicting clinical deterioration, models using CUIs could provide clinicians with relevant information and additional clinical context for supporting decision-making.

PMID:40305429 | DOI:10.2196/75340

Biomolecules. 2025 Apr 16;15(4):589. doi: 10.3390/biom15040589.

ABSTRACT

Immune checkpoint inhibitors (ICIs) have transformed melanoma treatment; however, predicting patient responses remains a significant challenge. This study reviews the potential of artificial intelligence (AI) to optimize ICI therapy in melanoma by integrating various diagnostic tools. Through a comprehensive literature review, we analyzed studies on AI applications in melanoma immunotherapy, focusing on predictive modeling, biomarker identification, and treatment response prediction. Key findings highlight the efficacy of AI in improving ICI outcomes. Machine learning models successfully identified prognostic cytokine signatures linked to nivolumab clearance. The combination of AI with RNAseq analysis had the potential for the development of personalized treatment with ICIs. A machine learning-based approach was able to assess the risk-benefit ratio for the prediction of immune-related adverse events (irAEs) using the electronic health record (EHR) data. Deep learning algorithms demonstrated high accuracy in tumor microenvironment analysis, including tumor region identification and lymphocyte detection. AI-assisted quantification of tumor-infiltrating lymphocytes (TILs) proved prognostically valuable in primary melanoma and predictive of anti-PD-1 therapy response in metastatic cases. Integrating multiple diagnostic modalities, such as CT imaging and laboratory data, modestly enhanced predictive performance for 1-year survival in advanced cancers treated with immunotherapy. These findings underscore the potential of AI-driven approaches to refine biomarker identification, treatment prediction, and patient stratification in melanoma immunotherapy. While promising, clinical validation and implementation challenges remain.

PMID:40305346 | DOI:10.3390/biom15040589

IEEE Trans Neural Netw Learn Syst. 2025 Apr 30;PP. doi: 10.1109/TNNLS.2025.3561930. Online ahead of print.

ABSTRACT

How to learn and accurately distinguish new concepts from few samples, as humans do, is a long-standing concern in artificial intelligence (AI). Studies in brain science and neuroscience have shown that human brain perception is based on nonlinear manifolds, and high-dimensional manifolds can facilitate concept learning in neural circuits. Based on this inspiration, in this paper, we propose a heterogeneous Riemannian few-shot learning network (HRFL-Net), which is the first few-shot learning method to perform end-to-end deep learning on heterogeneous Riemannian manifolds. Specifically, to enhance the geometric invariance of the image representation, the image features are projected into three heterogeneous Riemannian manifold spaces. Then, the implicit Riemannian kernel function maps the manifolds to the separable high-dimensional reproducing Hilbert space. It is assumed that the embedded kernel features of the complementary manifolds are mapped to the same common subspace. Thus, a novel neural network-based Riemannian metric learning method is designed to solve the subspace feature vectors by imposing orthogonal normalized projection, which overcomes the data extension limitation of the Riemannian metric. Finally, with the optimization objective of increasing the interclass distance and decreasing the intraclass distance in Hilbert space, the HRFL-Net is trained with end-to-end stochastic optimization, and the optimal aggregation subspace is learned during the gradient descent process. Thus, the proposed HRFL-Net can be easily generalized to challenging nonconvex data. The evaluation of four public datasets shows that the proposed HRFL-Net has significant superiority and also achieves competitive results compared with the state-of-the-art methods.

PMID:40305249 | DOI:10.1109/TNNLS.2025.3561930

IEEE Trans Med Imaging. 2025 Apr 30;PP. doi: 10.1109/TMI.2025.3565514. Online ahead of print.

ABSTRACT

Rib fractures are a common and potentially severe injury that can be challenging and labor-intensive to detect in CT scans. While there have been efforts to address this field, the lack of large-scale annotated datasets and evaluation benchmarks has hindered the development and validation of deep learning algorithms. To address this issue, the RibFrac Challenge was introduced, providing a benchmark dataset of over 5,000 rib fractures from 660 CT scans, with voxel-level instance mask annotations and diagnosis labels for four clinical categories (buckle, nondisplaced, displaced, or segmental). The challenge includes two tracks: a detection (instance segmentation) track evaluated by an FROC-style metric and a classification track evaluated by an F1-style metric. During the MICCAI 2020 challenge period, 243 results were evaluated, and seven teams were invited to participate in the challenge summary. The analysis revealed that several top rib fracture detection solutions achieved performance comparable or even better than human experts. Nevertheless, the current rib fracture classification solutions are hardly clinically applicable, which can be an interesting area in the future. As an active benchmark and research resource, the data and online evaluation of the RibFrac Challenge are available at the challenge website (https://ribfrac. grand-challenge.org/). In addition, we further analyzed the impact of two post-challenge advancements-largescale pretraining and rib segmentation-based on our internal baseline for rib fracture detection. These findings lay a foundation for future research and development in AI-assisted rib fracture diagnosis.

PMID:40305244 | DOI:10.1109/TMI.2025.3565514

Biomolecules. 2025 Apr 3;15(4):524. doi: 10.3390/biom15040524.

ABSTRACT

Molecular modelling is a vital tool in the discovery and characterisation of bioactive peptides, providing insights into their structural properties and interactions with biological targets. Many models predicting bioactive peptide function or structure rely on their intrinsic properties, including the influence of amino acid composition, sequence, and chain length, which impact stability, folding, aggregation, and target interaction. Homology modelling predicts peptide structures based on known templates. Peptide-protein interactions can be explored using molecular docking techniques, but there are challenges related to the inherent flexibility of peptides, which can be addressed by more computationally intensive approaches that consider their movement over time, called molecular dynamics (MD). Virtual screening of many peptides, usually against a single target, enables rapid identification of potential bioactive peptides from large libraries, typically using docking approaches. The integration of artificial intelligence (AI) has transformed peptide discovery by leveraging large amounts of data. AlphaFold is a general protein structure prediction tool based on deep learning that has greatly improved the predictions of peptide conformations and interactions, in addition to providing estimates of model accuracy at each residue which greatly guide interpretation. Peptide function and structure prediction are being further enhanced using Protein Language Models (PLMs), which are large deep-learning-derived statistical models that learn computer representations useful to identify fundamental patterns of proteins. Recent methodological developments are discussed in the context of canonical peptides, as well as those with modifications and cyclisations. In designing potential peptide therapeutics, the main outstanding challenge for these methods is the incorporation of diverse non-canonical amino acids and cyclisations.

PMID:40305228 | DOI:10.3390/biom15040524

J Vet Cardiol. 2025 Apr 5;59:126-132. doi: 10.1016/j.jvc.2025.04.001. Online ahead of print.

ABSTRACT

A 10-week-old female Oriental shorthair was referred due to stunted growth, weight loss, dyspnea, and reduced activity levels compared to her littermates. Thoracic radiography revealed a markedly enlarged cardiac silhouette and a diffuse unstructured interstitial pulmonary pattern, presumably due to cardiogenic pulmonary edema. Echocardiography showed marked left- and right-sided ventricular dilation, decreased contractility, and enlargement of both atria, without any identifiable congenital defects. Pleural and peritoneal effusion were also present. Based on these findings, a presumptive diagnosis of both left- and right-sided congestive heart failure due to a dilated cardiomyopathy phenotype was made. Cardiovascular pathological examination confirmed the echocardiographic findings. Additionally, mild interstitial myocardial fibrosis was present in the left ventricle, both atria, the interventricular septum, and, to a minimal extent, in the right ventricle. Moderate endocardial fibrosis was observed in the left atrium and left atrial appendage, while mild endocardial fibrosis was present in the left ventricle. Both antemortem and postmortem evaluations did not provide clear evidence of the underlying cause. Therefore, we consider this a rare case of feline juvenile idiopathic dilated cardiomyopathy with secondary reactive endocardial and myocardial fibrosis.

PMID:40305901 | DOI:10.1016/j.jvc.2025.04.001

Redox Biol. 2025 Apr 12;83:103635. doi: 10.1016/j.redox.2025.103635. Online ahead of print.

ABSTRACT

Inhibiting ferroptosis represents a promising strategy to combat ferroptosis-related diseases. Here we show that 428, a selenide-containing noscapine derivative, effectively inhibits ferroptosis in various cell lines by enhancing the stability and activity of GPX4. TRIM41 was identified as a novel E3 ubiquitin ligase of GPX4 and 428 was demonstrated to bind to the selenocysteine residue Sec46 of GPX4 via the formation of a transient and reversible Se-Se bond, thereby blocking the interaction between GPX4 and TRIM41, stabilizing GPX4 and enhancing its activity. This unique dynamic covalent binding mode was preliminarily validated by structure-activity relationship analysis and molecular docking studies. Importantly, we demonstrated that 428 treatment alleviates bleomycin-induced pulmonary fibrosis in vivo by inhibiting ferroptosis. Overall, our studies identified a novel stabilizer and activator of GPX4, offering a potential therapeutic approach for the treatment of ferroptosis-related diseases and uncovering a new mechanism for regulating GPX4 degradation.

PMID:40305884 | DOI:10.1016/j.redox.2025.103635

Ann Am Thorac Soc. 2025 Apr 30. doi: 10.1513/AnnalsATS.202503-361ED. Online ahead of print.

NO ABSTRACT

PMID:40305677 | DOI:10.1513/AnnalsATS.202503-361ED

Biomolecules. 2025 Apr 1;15(4):509. doi: 10.3390/biom15040509.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a disease with a poor prognosis and no curative therapies. Fibroblast activation by transforming growth factor β1 (TGFβ1) and disrupted metabolic pathways, including the arginine-polyamine pathway, play crucial roles in IPF development. Polyamines are agonists of the calcium/cation-sensing receptor (CaSR), activation of which is detrimental for asthma and pulmonary hypertension, but its role in IPF is unknown. To address this question, we evaluated polyamine abundance using metabolomic analysis of IPF patient saliva. Furthermore, we examined CaSR functional expression in human lung fibroblasts (HLFs), assessed the anti-fibrotic effects of a CaSR antagonist, NPS2143, in TGFβ1-activated normal and IPF HLFs by RNA sequencing and immunofluorescence imaging, respectively; and NPS2143 effects on polyamine synthesis in HLFs by immunoassays. Our results demonstrate that polyamine metabolites are increased in IPF patient saliva. Polyamines activate fibroblast CaSR in vitro, elevating intracellular calcium concentration. CaSR inhibition reduced TGFβ1-induced polyamine and pro-fibrotic factor expression in normal and IPF HLFs. TGFβ1 directly stimulated polyamine release by HLFs, an effect that was blocked by NPS2143. This suggests that TGFβ1 promotes CaSR activation through increased polyamine expression, driving a pro-fibrotic response. By halting some polyamine-induced pro-fibrotic changes, CaSR antagonists exhibit disease-modifying potential in IPF onset and development.

PMID:40305220 | DOI:10.3390/biom15040509

Biomolecules. 2025 Mar 26;15(4):485. doi: 10.3390/biom15040485.

ABSTRACT

Calcium is a ubiquitous second messenger and plays a major role in a variety of cellular functions, both within the same cell and between different cells [...].

PMID:40305225 | DOI:10.3390/biom15040485

Cell Biochem Biophys. 2025 Apr 30. doi: 10.1007/s12013-025-01762-x. Online ahead of print.

ABSTRACT

Acute Respiratory Distress Syndrome (ARDS) is a severe lung disease with a high fatality rate and few treatment options. Targeting certain signalling pathways, notably the Transforming Growth Factor-beta (TGF-beta) signalling pathway, has emerged as a promising option for ARDS therapy. We identified TGF-beta Receptor 1 (TGFBR1) as a major target for ARDS treatment using the STRING and KEGG databases and validated TGFBR1's critical function in the TGF-beta signalling pathway, which is important in ARDS pathogenesis. To find prospective TGFBR1 inhibitors, we selected two FDA-approved medicines, Galunisertib and Vactosertib, which are established pharmacological profiles in cancer and fibrotic illnesses. Furthermore, the SwissSimilarity platform's ligand-based virtual screening revealed structurally related drugs in the DrugBank and ChEMBL databases. Among these, seven candidates were selected for further consideration. Molecular docking experiments found that DB08387 and CHEMBL14297639 had the strongest affinity for TGFBR1, creating strong hydrogen bonds at key sites. These findings point to their potential as TGFBR1 inhibitors in ARDS treatment. The pharmacokinetic screening revealed that most of the chosen compounds had favourable ADME features, with CHEMBL14297639 standing out for its low gastrointestinal absorption and limited cytochrome P450 inhibition. This study demonstrates the possibility of targeting TGFBR1 with Galunisertib, Vactosertib, and other prospective ARDS treatments. The findings lay the groundwork for additional experimental validation and the development of innovative therapeutics aimed at reducing ARDS severity.

PMID:40304856 | DOI:10.1007/s12013-025-01762-x

Clin Transl Sci. 2025 May;18(5):e70237. doi: 10.1111/cts.70237.

ABSTRACT

Clinical trials with investigator sponsors at academic sites have increased, in part due to studies involving drug repurposing, the process of identifying new uses for existing drugs that are initially conducted in patients rather than healthy participants. In contrast to industry- or government-sponsored trials, investigator-sponsored clinical studies, also known as investigator-initiated trials, are typically conducted at one or several academic centers and are resource-limited by finances and patient numbers. These studies can serve as crucial pilot studies to inform the design of larger, more definitive clinical trials. Drawing from the experience of working with clinical researchers in academic settings, this tutorial presents guidelines for writing clinical protocols for resource-limited investigator-sponsored studies that meet international standards and optimize the detection of meaningful signals or outcomes that can lead to investigation in larger well-controlled trials.

PMID:40304394 | DOI:10.1111/cts.70237

J Clin Res Pediatr Endocrinol. 2025 Apr 30. doi: 10.4274/jcrpe.galenos.2025.2024-11-11. Online ahead of print.

ABSTRACT

This systematic review and meta-analysis aim to synthesize the existing literature to clarify the role of amino acids as potential indicators or contributors to childhood obesity. The study follows the PRISMA 2020 guidelines. A comprehensive search was conducted across multiple electronic databases, including PubMed, Cochrane Library, Embase, Web of Science, Google Scholar, Semantic Scholar, and ResearchRabbit, using relevant keywords such as "childhood obesity," "amino acids," and "branched-chain amino acids (BCAAs)."Heterogeneity among studies was assessed using the chi-square test and the I² statistic. Publication bias was evaluated using funnel plots and Egger's test. Five studies involving a total of 1,229 participants met the inclusion criteria. A significant association was observed between amino acid levels and obesity in children. Specifically, glutamine was inversely associated with obesity (SMD = -0.48, 95% CI: -0.85 to -0.11), while leucine (SMD = 0.79, 95% CI: 0.20 to 1.38) and valine (SMD = 0.67, 95% CI: 0.18 to 1.15) were positively associated. Additionally, odds ratio analysis indicated that higher glutamine levels were associated with 56% lower odds of obesity (OR = 0.44, 95% CI: 0.21-0.94, P < .01), suggesting a potential protective role. Elevated levels of specific amino acids, particularly BCAAs, were consistently linked to increased body mass index (BMI) and other obesity-related indicators in children. Future research should focus on longitudinal and interventional studies to better understand these associations and explore targeted strategies involving amino acid metabolism to help prevent and manage childhood obesity.

PMID:40304146 | DOI:10.4274/jcrpe.galenos.2025.2024-11-11

Andrology. 2025 Apr 30. doi: 10.1111/andr.70053. Online ahead of print.

ABSTRACT

Male factor infertility (MFI) is involved in half of the cases of couple infertility. The follicle-stimulating hormone (FSH) therapy is considered efficient to improve semen parameters and pregnancy rate in patients with idiopathic MFI, following the lesson learned from hypogonadotropic hypogonadism. However, while in patients with hypogonadotropic hypogonadism FSH therapy, in combination with human chorionic gonadotropin (hCG), is a well-established treatment, in patients with MFI the effects of the FSH therapy are variable and unpredictable. The FSH therapy in MFI should be a personalized treatment, tailored on the characteristics of the male patient and the couple. The pivotal aspect is the accurate identification of patients who might benefit from such treatment (responders) from those who might not (nonresponders). To date, selection of patients to be treated is based on history, physical examination, semen analysis, and hormonal assessment. However, these parameters cannot adequately identify a priori responder patients. Furthermore, tailored management should include pharmacological adaptation (dosage and duration of the therapy), as happens during ovarian hyperstimulation in assisted reproductive technologies. In a fully personalized therapy, pharmacogenetic factors must be considered. In this paper, we describe the evidence dealing with the pharmacogenetics of the FSH therapy in MFI, presenting the physiological and physiopathological basis and the pharmacogenetics studies dealing with effects of polymorphisms in the beta-subunit of FSH (FSHB) and the FSH receptor (FSHR) gene. According to the evidence so far available, genetic evaluation of FSHB and FSHR is recommended only for research purposes, since the data are not conclusive and even contrasting. Furthermore, the evidence so far is derived from quite small studies with different endpoints considered and relatively few cases. Better studies that consider the combined effect of several FSHB and FSHR gene polymorphisms, together with clinical, biochemical, seminal and testicular cytology, are necessary to develop an algorithm that might predict the response to the FSH treatment.

PMID:40304702 | DOI:10.1111/andr.70053

Clin Pharmacol Ther. 2025 Apr 30. doi: 10.1002/cpt.3702. Online ahead of print.

ABSTRACT

Older adults (65 years and over) frequently manage complex medication regimens and are vulnerable to adverse drug reactions and treatment inefficacies, some of which could be preventable with pharmacogenetics (PGx)-guided prescribing. This study examined the prevalence of actionable PGx genotypes (i.e., those linked to a guideline that recommends a change to standard prescribing), the use of cautionary medications (i.e., those associated with an actionable PGx genotype), polypharmacy (i.e., ≥ 5 medications simultaneously), and cytochrome P450 enzyme inhibitor and inducer use among 13,670 older adults enrolled in the ASPirin in Reducing Events in the Elderly (ASPREE) trial. Genotyping was conducted for 10 pharmacogenes with actionable PGx-based prescribing guidelines. Medication data were collected annually and assessed to identify cautionary medication use in the cohort. Most participants (98.8%) carried at least one actionable PGx genotype, with an average of three actionable genotypes per participant. VKORC1 (61.1%) and CYP2C19 (59.6%) were the most frequently observed genes with actionable genotypes. Statins (29.3%), nonsteroidal anti-inflammatory drugs (14.2%), and proton-pump inhibitors (7.9%) were the most used cautionary medications, with 27.5% of participants taking at least one medication for which PGx guidelines recommended a deviation from standard prescribing. Most (83.9%) participants reported taking a polypharmacy regimen, and 68.2% reported use of at least one cytochrome P450 enzyme inhibitor or inducer during the trial. Our findings underscore the high prevalence of actionable PGx genotypes, polypharmacy, and use of inhibitors and inducers in older adults, which collectively have the potential to inform safer and more effective prescribing practices.

PMID:40304392 | DOI:10.1002/cpt.3702

Methods Mol Biol. 2025;2908:51-64. doi: 10.1007/978-1-0716-4434-8_4.

ABSTRACT

The cystic fibrosis transmembrane conductance regulator (CFTR), a chloride channel residing primarily at the apical membrane of epithelial cells, plays a major role in fluid secretion and the maintenance of epithelial surface hydration. Mutations in the CFTR gene lead to the fatal disease known as cystic fibrosis (CF). Drugs that improve mutant CFTR protein folding and channel function have dramatically improved CF patient outcomes. However, the current regimen only restores the function of the most common mutant, ΔF508, to ~62% of wildtype (WT). Notably, ~10% of patients harboring hundreds of less common CFTR mutations are not eligible or do not respond at all to treatment with current CFTR modulators. Better characterizing the WT and mutant CFTR protein interactomes could provide critical insight into how to treat patients with rarer mutations and thereby improve the druggability of this devastating disease. Here we describe how BioID (proximity-dependent biotin identification) can be used to map the CFTR interactome in a human airway model-bronchial epithelial cells grown at the air-liquid interface. Approximately 26% (>5500) of all human protein-coding genes are predicted to code for membrane proteins, which together account for ~30% of the druggable proteome. The methods described here could thus also be applied to improve our understanding of many additional respiratory, autoimmune, and metabolic diseases.

PMID:40304902 | DOI:10.1007/978-1-0716-4434-8_4