Introduction: Adverse drug reactions (ADRs) are among the leading causes of death, and frequently associated with drug-gene interactions (DGIs). In addition to pharmacogenomic programs for implementation of genetic preemptive testing into clinical practice, mathematical modeling can help to understand, quantify and predict the effects of DGIs . Moreover, modeling can contribute to optimize prospective clinical drug trial activities and to reduce DGI-related ADRs.
Areas Covered: Approaches and challenges of mechanistical DGI implementation and model parameterization are discussed for population pharmacokinetic and physiologically based pharmacokinetic models. The broad spectrum of published DGI models and their applications is presented, focusing on the investigation of DGI effects on pharmacology and model-based dose adaptations.
Expert Opinion: Mathematical modeling provides an opportunity to investigate complex DGI scenarios and can facilitate the development process of safe and efficient personalized dosing regimens. However, reliable DGI model input data from and measurements are crucial. For this, collaboration among pharmacometricians, laboratory scientists and clinicians is important to provide homogeneous datasets and unambiguous model parameters. For a broad adaptation of validated DGI models in clinical practice, interdisciplinary cooperation should be promoted and qualification toolchains must be established.
Background: There are currently no reliable diagnostic biomarkers or treatments for lupus nephritis (LN), a complication of systemic lupus erythematosus. Objective: We aimed to explore gene networks and potential biomarkers for LN by analyzing the GSE32591 and GSE113342 datasets from the Gene Expression Omnibus database, focusing on and -related genes.
Methods: We used differential expression analysis, functional enrichment, protein-protein interaction (PPI) network construction, and the CIBERSORT algorithm for immune infiltration assessment.
We explored the biological mechanisms by which curcumin (Cur) confronts osteosarcoma (OS) tumorigenesis and potential drug gene targets based on network pharmacology and in vitro cell experiments. Cur has been recognized for its significant role in combating various types of tumors. However, the intrinsic molecular mechanisms through which it affects OS remain uncharted.
Background: The etiopathogenesis of hepatic stellate cells (HSC) activation has yet to be completely comprehended, and there has been broad concern about the interplay between amino acid transporter and cell proliferation. This study proposed exploring the molecular mechanism from amino acid transport-related genes in HSC activation by bioinformatic methods, seeking to identify the potentially crucial biomarkers.
Methods: GSE68000, the mRNA expression profile dataset of activated HSC, was applied as the training dataset, and GSE67664 as the validation dataset.
The evaluation of drug-gene-disease interactions is key for the identification of drugs effective against disease. However, at present, drugs that are effective against genes that are critical for disease are difficult to identify. Following a disease-centric approach, there is a need to identify genes critical to disease function and find drugs that are effective against them.
Hematology, Oncology and Stem Cell Transplantation Research Center, Research Institute for Oncology, Hematology and Cell Therapy, Tehran University of Medical Sciences, Tehran, Iran.
Background: Gastrointestinal cancers (GICs) continue to dominate in terms of both incidence and mortality worldwide. Due to the absence of efficient and accurate prognostic biomarkers, the prognosis and treatment outcomes of many GICs are poor. Identifying biomarkers to predict individual clinical outcomes efficiently is a fundamental challenge in clinical oncology.
