Predictiveness of the Human-CYP3A4-Transgenic Mouse Model (Cyp3aXAV) for Human Drug Exposure of CYP3A4-Metabolized Drugs

A comprehensive review on anti-allergic natural bioactive compounds for combating food allergy

Food allergy poses a great challenge to food safety and public health worldwide. Currently, clinical symptoms are primarily managed with medications, which can lead to drug resistance, adverse effects, and disruptions in gut flora balance. As a result, there has been a focus on researching safe and effective anti-allergic natural ingredients. This paper provides a comprehensive overview of food allergy mechanisms, methods of assessment of anti-food allergy studies, and a classification of natural substances with anti-allergic properties. It also examines the anti-allergic effects of these substances on food allergies and investigates gut microbiota changes induced by these natural bioactives, highlighting their significance to food allergies.Natural actives with anti-food allergic properties may alleviate allergic reactions through multiple targets and pathways. These mechanisms include promoting a shift in the Th1/Th2 balance, reducting IgE synthesis, preventing cellular degranulation and reducing the release of allergic mediator. The gut environment is closely related to food allergy and there is a significant interaction between the two. By targeting the intestinal flora, we can adopt dietary interventions to effectively address and control food allergies. This provides valuable insights for the future development of functional foods targeting the alleviation of food allergies.

Early Prediction and Impact Assessment of CYP3A4-Related Drug-Drug Interactions for Small-Molecule Anticancer Drugs Using Human-CYP3A4-Transgenic Mouse Models

Early detection of drug-drug interactions (DDIs) can facilitate timely drug development decisions, prevent unnecessary restrictions on patient enrollment, resulting in clinical study populations that are not representative of the indicated study population, and allow for appropriate dose adjustments to ensure safety in clinical trials. All of these factors contribute to a streamlined drug approval process and enhanced patient safety. Here we describe a new approach for early prediction of the magnitude of change in exposure for cytochrome P450 (P450) CYP3A4-related DDIs of small-molecule anticancer drugs based on the model-based extrapolation of human-CYP3A4-transgenic mice pharmacokinetics to humans. Victim drugs brigatinib and lorlatinib were evaluated with the new approach in combination with the perpetrator drugs itraconazole and rifampicin. Predictions of the magnitude of change in exposure deviated at most 0.99- to 1.31-fold from clinical trial results for inhibition with itraconazole, whereas exposure predictions for the induction with rifampicin were less accurate, with deviations of 0.22- to 0.48-fold. Results for the early prediction of DDIs and their clinical impact appear promising for CYP3A4 inhibition, but validation with more victim and perpetrator drugs is essential to evaluate the performance of the new method. SIGNIFICANCE STATEMENT: The described method offers an alternative for the early detection and assessment of potential clinical impact of CYP3A4-related drug-drug interactions. The model was able to adequately describe the inhibition of CYP3A4 metabolism and the subsequent magnitude of change in exposure. However, it was unable to accurately predict the magnitude of change in exposure of victim drugs in combination with an inducer.

Current trends and future prospects of drug repositioning in gastrointestinal oncology

Gastrointestinal (GI) cancers comprise a significant number of cancer cases worldwide and contribute to a high percentage of cancer-related deaths. To improve survival rates of GI cancer patients, it is important to find and implement more effective therapeutic strategies with better prognoses and fewer side effects. The development of new drugs can be a lengthy and expensive process, often involving clinical trials that may fail in the early stages. One strategy to address these challenges is drug repurposing (DR). Drug repurposing is a developmental strategy that involves using existing drugs approved for other diseases and leveraging their safety and pharmacological data to explore their potential use in treating different diseases. In this paper, we outline the existing therapeutic strategies and challenges associated with GI cancers and explore DR as a promising alternative approach. We have presented an extensive review of different DR methodologies, research efforts and examples of repurposed drugs within various GI cancer types, such as colorectal, pancreatic and liver cancers. Our aim is to provide a comprehensive overview of employing the DR approach in GI cancers to inform future research endeavors and clinical trials in this field.

Predictability of human exposure by human-CYP3A4-transgenic mouse models: A meta-analysis

First-in-human dose predictions are primarily based on no-observed-adverse-effect levels in animal studies. Predictions from these animal models are only as effective as their ability to predict human results. To narrow the gap between human and animals, researchers have, among other things, focused on the replacement of animal cytochrome P450 (CYP) enzymes with their human counterparts (called humanization), especially in mice. Whereas research in humanized mice is extensive, the emphasis has been particularly on qualitative rather than quantitative predictions. Because the CYP3A4 enzyme is most involved in the metabolism of clinically used drugs, most benefit was expected from CYP3A4 models. There are several applications of these mouse models regarding in vivo CYP3A4 functionality, one of which might be their capacity to help improve first-in-human (FIH) dose predictions for CYP3A4-metabolized drugs. To evaluate whether human-CYP3A4-transgenic mouse models are better predictors of human exposure compared to the wild-type mouse model, we performed a meta-analysis comparing both mouse models in their ability to accurately predict human exposure of small-molecule drugs metabolized by CYP3A4. Results showed that, in general, the human-CYP3A4-transgenic mouse model had similar accuracy in the prediction of human exposure compared to the wild-type mouse model, suggesting that there is limited added value in humanization of the mouse Cyp3a enzymes if the primary aim is to acquire more accurate FIH dose predictions. Despite the results of this meta-analysis, corrections for interspecies differences through extension of human-CYP3A4-transgenic mouse models with pharmacokinetic modeling approaches seems a promising contribution to more accurate quantitative predictions of human pharmacokinetics.