The Graded Response Model (GRM) is widely used for analyzing polytomous response data from attitude, personality, and character measurement instruments. However, when response data are collected from a range-restricted sample such as selected applicants, screened participants, or high-performing students, GRM discrimination parameters are systematically underestimated. No existing R package implements range-restriction bias correction specifically for GRM discrimination parameters. This paper introduces grmcor, an R package that implements the truncated normal standard deviation ratio correction for GRM discrimination parameters under direct range restriction. The package provides functions for estimating GRM parameters from restricted samples, applying the correction, comparing restricted and corrected estimates, visualizing bias patterns, and validating the correction procedure via Monte Carlo simulation. Monte Carlo results demonstrate that grmcor substantially reduces bias in discrimination parameter estimates, with mean bias reduced from -0.64 to near zero and RMSE reduced by approximately 52% under 50% range restriction. The package is freely available via GitHub.

Hepatic membrane transporters are key determinants of drug disposition, hepatocellular exposure, pharmacodynamic response, drug-drug interactions (DDIs), and hepatobiliary toxicity. Among these, the organic anion transporting polypeptides OATP1B1 and OATP1B3, sodium taurocholate cotransporting polypeptide (NTCP), and bile salt export pump (BSEP) coordinate hepatic uptake and biliary efflux of endogenous compounds, therapeutic agents, and metabolites. Their role in transporter-mediated pharmacokinetics, exposure-response relationships, therapeutic efficacy, DDIs, and drug-induced liver injury (DILI) is critically reviewed. Specific focus is placed on transporter inhibition, competition, saturation, and disease-associated regulation that can lead to clinically relevant changes in hepatic and systemic drug exposure. Pharmacogenomic variation, including variation in SLCO1B1, SLCO1B3, SLC10A1, and ABCB11, is considered together with inflammation and chronic liver disease as determinants of transporter phenotype and interindividual variability. BSEP is described in more detail as a key hepatobiliary safety determinant including impaired bile-acid export, cholestatic liability, and implications for DILI assessment and drug development. Clinical evidence is differentiated from pre-clinical transporter-targeting concepts. NTCP-directed antiviral therapy is an important example of clinical pharmacological translation, while many transporter-guided delivery systems are still experimental. Therefore, selected prodrug, ligand-directed, and nanocarrier strategies are only discussed where transporter engagement is associated with pharmacokinetic, pharmacodynamic, efficacy, safety, or translational consequences. Finally, transporter-informed PBPK modeling, human-relevant models, and biomarker-guided stratification are discussed to improve DDI prediction, risk assessment, and precision dosing. This pharmacology-focused framework defines when transporter modulation or targeting may be therapeutically beneficial and when it may introduce pharmacokinetic or safety liabilities.

Cannabidiol (CBD) inhibits multiple cytochrome P450 (CYP450) enzymes in vitro via reversible and time-dependent mechanisms, raising concerns for metabolic drug-drug interactions (DDIs). Clinical DDI data for CBD are limited, and the translation of in vitro derived inhibition parameters into in vivo DDI predictions remains uncertain. We implemented a stepwise, model-informed framework to evaluate CBD-mediated metabolic DDIs using (i) basic, (ii) mechanistic static (MSM), and (iii) physiologically based pharmacokinetic (PBPK) models. The basic model and MSM incorporated in vitro inhibition parameters for CBD and its primary active metabolite, 7-hydroxycannabidiol (7-OH-CBD), to identify sensitive metabolic pathways. A PBPK model for CBD and 7-OH-CBD was developed and validated against clinical pharmacokinetic data, then used to simulate DDIs. Where necessary, inhibition parameters were refined using the PBPK model to capture clinical observations, and the optimized parameters were used to revisit the basic model and MSM, facilitating comparison across modeling approaches. The MSM predicted 3.6-, 4.1-, and 2.2-fold increases in exposure of sensitive CYP1A2, CYP2C9, and CYP3A4 substrates, respectively, and a strong CYP2C19-mediated interaction. In contrast, the PBPK model predicted lower DDI magnitudes consistent with clinical data, identifying CYP2C19 inhibition as the most clinically relevant liability, with a moderate interaction risk for sensitive substrates, while CYP1A2 inhibition increased sensitive substrate exposure twofold. The validated PBPK model was applied to evaluate exposure in special populations and DDI risk with commonly co-prescribed medications. Overall, this framework reconciles in vitro and clinical DDI data and supports population-specific assessment of CBD-mediated DDIs across diverse clinical scenarios.

With the establishment of additive manufacturing technologies, new dimethacrylate (DMA) -based materials are increasingly being used in dentistry, among others for the fabrication of partial and complete dentures. In this study, six printable DMA-based materials (Freeprint® denture impact (DDI), Freeprint® denture 385 (DD), FotoDent denture (DRE), IMPRIMO® LC Denture (SIL), V-Print dentbase (VPR), and Optiprint Laviva (DOL)) were compared with two millable Polymethylmethacrylat (PMMA) - based reference materials (Vita Vionic Base (VIO), Ivotion Base (IVO)) regarding their mechanical properties (Vickers hardness HV1, Martens hardness HM, Indentation creep CIT, Indentation modulus EIT, Elastic and plastic deformation work Wplast and Welast), surface texture (arithmetical mean height Sa), and optical properties (color change ΔE00, Translucency TP00) before and after a simulated one-year combined aging process. While the printed DMA-based materials (especially DOL, VPR, DD) initially exhibited significantly higher HM, HV1 and EIT, thereby showing superior dimensional stability, the milled PMMA materials (VIO, IVO) were characterized by a more ductile behavior (highest Wplast). After one year of aging, the milled PMMA remained largely stable or tended to show improved properties, whereas most printed DMA-based materials exhibited a decrease in hardness and an increase in roughness. Consequently, the printed systems offer high initial stiffness but require more rigorous hygiene management in the long term. Despite their lower hardness, milled PMMA appears to be prosthetically more predictable due to its higher proportion of plastic deformation and superior long-term chemical stability.

Lung cancer predominantly affects older adults who already experience comorbidities and are at increased risk of drug-drug interactions (DDIs) due to polypharmacy. Traditional DDI assessments often do not fully capture complex co-medication patterns observed in real-world practice due to the risk of introducing confounding factors. This study used U.S. Optum claims data from 2019 to 2024 to characterize co-medication patterns in predominately non-small cell lung cancer (NSCLC) patients and map enzyme- and transporter-mediated liabilities across commonly used medications, chemotherapy agents, and biomarker-driven therapies. Potential DDI liabilities were further prioritized using a semi-quantitative clinical framework based on perpetrator strength, substrate sensitivity, narrow therapeutic index status, and available evidence. Frequently used medications included disease-specific supportive therapies such as ondansetron and dexamethasone, as well as treatments for common U.S. comorbidities such as cardiovascular and endocrine disorders. Many of the co-medications were substrates, inhibitors, and inducers of pathways critical for NSCLC treatments such as CYP3A, CYP2D6, P-gp, and OATP1B and additional risks were identified when giving treatments with acid-reducing agents or QT-prolonging drugs. These findings demonstrate how integrating real-world data (RWD) can strengthen DDI assessment by improving clinical trial design, informing risk-mitigation strategies, and supporting more patient-centric drug development.

Elexacaftor/Tezacaftor/Ivacaftor (ETI) and other highly effective modulator therapies have transformed outcomes for people with cystic fibrosis (pwCF), yielding significant pulmonary and extrapulmonary benefits. Although lung transplant (LTx) rates have declined dramatically in the post ETI era, a substantial number of transplant recipients with CF remain who may benefit from the extrapulmonary effects of these therapies. Unfortunately, available post market data regarding ETI safety, efficacy, and tolerability following LTx are limited and conflicting, raising concerns for Drug-Drug Interactions (DDI) and long-term graft function. In this single center retrospective cohort analysis, we report sustained improvements in sinonasal disease and weight maintenance with preserved spirometric values over a mean follow up of 35.1 months on ETI compared to those not on therapy. We additionally present pharmacogenomic testing in a subset of our cohort as proof-of-concept for predicting DDIs at time of initiation of ETI.

Background: Bovine babesiosis, caused by Babesia bovis, remains a major constraint to cattle production in endemic regions, while the current control relies on only a few drugs with concerns over resistance and residues. Methods: This study screened 9600 chemically diverse compounds from the Drug Discovery Initiative, University of Tokyo, against B. bovis using a 96-well SYBR Green I high-throughput assay at 1 µM. Primary hits were subjected to dose-response testing to determine IC50 values, followed by Madin-Darby bovine kidney cell cytotoxicity assays for selectivity index calculation. Combination effects with diminazene aceturate were assessed using combination indices, whereby three compounds were selected for in silico ADME, drug-likeness and target prediction analyses. DDI-1101229 was further assessed by exploratory docking against BbHT1 and BbCDPK4. Results and Conclusions: Eight compounds showed ≥50% inhibition in the primary screen. DDI-1101229, DDI-8231037 and DDI-15106103 showed sub-micromolar activity (IC50 = 0.27-0.68 µM), selectivity indices > 14.7 and strong synergy with diminazene aceturate (CI < 0.4). Overall, this study identified selective, chemically diverse non-diamidine and non-imidazoline small-molecule inhibitors of B. bovis, highlighting alternative chemotypes with antibabesial activity.

Background/Objectives: Adverse drug reactions (ADRs) caused by drug-drug interactions (DDIs) represent an important problem in pharmacotherapy, especially in patients receiving multiple medications. Most computational approaches to DDI-associated ADR prediction formulate the task as a binary classification, but they do not explicitly consider the severity of adverse reactions. Our study aims to develop structure-based models that predict the severity of ADRs associated with specific drug pairs. Methods: Datasets were generated using DrugMAP as the source of drug pair-ADR associations with annotated severity categories, and TwoSides was used as an additional source to generate conditionally negative examples. The drug pairs were represented using PoSMNA descriptors, which encode pair-specific structural features derived from the molecular structures of both compounds. Predictive models were built using PASS DDI software. Model performance was evaluated using a modified cross-validation procedure that excluded compound-level overlap between the training and test sets, thereby reducing information leakage caused by the repeated occurrence of the same drugs in different pairs. Results: Models were developed for 14 clinically relevant ADR types, including cardiovascular, hepatotoxic, nephrotoxic, hemorrhagic, metabolic, and neurological effects. The unweighted class-level macro-average AUC values ranged from 0.830 for the Major category to 0.911 for the Minor category, while balanced accuracy ranged from 0.776 to 0.857. Predictive performance varied significantly between ADR types and severity categories. Higher accuracy was observed for some ADR types that were better captured by the structure-based descriptors used in this study, whereas complex multifactorial reactions, such as hepatotoxicity, were less accurately predicted. Case-based assessment using clinically documented drug combinations showed that the predicted severity profiles were generally consistent with the expected clinical risk patterns. Conclusions: The proposed approach demonstrates that the PoSMNA descriptors of drug pairs can be used for preliminary prediction of DDI-associated ADR severity. The developed models can help to filter out potentially dangerous drug combinations at an early stage and are implemented in the AdverDDIPred web-application.