Background: Pharmacokinetic drug–drug interactions (DDIs) can be caused by the effect of a pharmaceutical compound on the activity of one or more subtypes of the Cytochrome P450 (CYP) family, UDP-glucuronosyltransferases (UGTs), and/or transporters. As the number of therapeutic areas with polypharmacy has increased, interest has grown in assessing the risk of DDIs during the early phases of drug development. Various lines of research have led to improved mathematical models to predict DDIs, culminating in the Food and Drug Administration’s (FDA) guidelines on evaluating pharmacokinetic DDI risks. However, the recommended static models are highly conservative and often result in false positive predictions. The current research aims to improve the workflow for assessing CYP-mediated DDI risk using Boehringer Ingelheim (BI) proprietary compounds. Methods: The Drug–drug Interaction Risk Calculator (PharmaPendium) was used to evaluate the mechanistic static model, and predictions were correlated with human pharmacokinetic studies from Phase I clinical trials. Results: The results demonstrated that the FDA formula performed well in predicting DDIs for BI proprietary compounds. Furthermore, the integration of either human renal excretion or preclinical species total excretion data into the mechanistic static model enhanced the predictive performance for candidate drugs as victims in DDIs. Conclusions: The basic static models (BSMs) for drug interactions should be used in early drug discovery to “rule out” DDI risks because of the minimal inputs required and the low rate of false negative predictions. Mechanistic static models (MSMs) can then be implemented for compounds that require additional evaluation.

We are delighted to share with you our twelfth Journal Club and highlight some of the most interesting papers published recently

Background/Objectives: Long non-coding RNAs (lncRNAs) play significant roles in tissue development and disease progression and have emerged as crucial regulators of gene expression. The hepatocyte nuclear factor alpha antisense RNA 1 (HNF1A-AS1) lncRNA is a particularly intriguing regulatory molecule in liver biology that is involved in the regulation of cytochrome P450 enzymes via epigenetic mechanisms. Despite the growing recognition of lncRNAs in liver disease, the comprehensive role of HNF1A-AS1 in liver function remains unclear. This study aimed to investigate the roles of the mouse homolog of the human HNF1A-AS1 lncRNA HNF1A opposite strand 1 (Hnf1aos1) in liver function, gene expression, and cellular processes using a mouse model to identify potential therapeutic targets for liver disorders. Methods: The knockdown of Hnf1aos1 was performed in in vitro mouse liver cell lines using siRNA and in vivo livers of AAV-shRNA complexes. Changes in the global expression landscapes of mRNA and proteins were revealed using RNA-seq and proteomics, respectively. Changes in the selected genes were further validated via real-time quantitative polymerase chain reaction (RT-qPCR). Phenotypic changes were assessed via histological and absorbance-based assays. Results: After the knockdown of Hnf1aos1, RNA-seq and proteomics analysis revealed the differential gene expression of the mRNAs and proteins involved in the processes of molecular transport, liver regeneration, and immune signaling pathways. The downregulation of ABCA1 and SREBF1 indicates their role in cholesterol transport and fatty acid and triglyceride synthesis. Additionally, significant reductions in hepatic triglyceride levels were observed in the Hnf1aos1-knockdown group, underscoring the impact on lipid regulation. Notably, the knockdown of Hnf1aos1 also led to an almost complete depletion of CYP7A1, the rate-limiting enzyme in bile acid synthesis, highlighting its role in cholesterol homeostasis and hepatotoxicity. Histological assessments confirmed these molecular findings, with increased hepatic inflammation, hepatocyte swelling, and disrupted liver architecture observed in the Hnf1aos1-knockdown mice. Conclusions: This study illustrated that Hnf1aos1 is a critical regulator of liver health, influencing both lipid metabolism and immune pathways. It maintains hepatic lipid homeostasis, modulates lipid-induced inflammatory responses, and contributes to viral immunity, indirectly affecting glucose and lipid metabolic balance.

The field of non-coding RNA research is advancing at a breathtaking pace, continually uncovering new layers of regulatory complexity and functional diversity

Background: Long noncoding RNAs (lncRNAs) have emerged as critical regulators of hepatic metabolism and disease progression. The hepatocyte nuclear factor 1 alpha antisense 1 (HNF1A-AS1) lncRNA modulates liver-specific transcription factors; however, its physiological role in diet-dependent lipid homeostasis remains poorly defined. Methods: In this study, we investigated the mouse ortholog, Hnf1a opposite strand 1 (Hnf1aos1), using AAV-mediated knockdown in C57BL/6J mice fed either a chow diet (10% kcal from fat) or a high-fat diet (HFD; 60% kcal from fat) for 12 weeks. Metabolic phenotyping included hepatic lipid quantification, histological analysis, serum biochemistry, and quantitative gene expression profiling. Results: Loss of Hnf1aos1 produced distinct, diet-dependent alterations in hepatic lipid handling. Under chow conditions, knockdown mice exhibited selective hepatic cholesterol accumulation (6.10 ± 2.9 mg/g tissue vs. 3.51 ± 1.1 mg/g in controls), accompanied by dysregulation of cholesterol clearance pathways. In contrast, under HFD conditions, knockdown precipitated severe macrovesicular degeneration, with hepatic triglyceride levels approximately doubled relative to HFD-fed controls (51.72 ± 19.8 mg/g vs. 26.34 ± 11.9 mg/g) and a numerically elevated triglyceride-to-cholesterol ratio (TG:TC ≈ 6.1:1; p = 0.0621, trend). Chow/Kd mice gained significantly less weight than chow-fed controls, whereas HFD/Kd mice exhibited weight gain comparable to HFD controls despite severe hepatic steatosis. This paradoxical phenotype suggests impaired metabolic feedback at the post-transcriptional level, in which compensatory upregulation of Hnf1a mRNA is insufficient to suppress lipid-associated genes such as Cd36, despite profound lipid overload; however, HNF1A protein levels were not directly measured in this study. Conclusion: Collectively, these findings identify Hnf1aos1 as a regulator of hepatic lipid homeostasis whose loss produces a phenotype consistent with inappropriate lipid accumulation during nutrient excess, without defining the underlying molecular mechanism. Our results support a role for Hnf1aos1 in shaping hepatic metabolic plasticity and provide insight into lncRNA-associated MASLD phenotypes.