Physiologically Based Precision Dosing Approach for Drug-Drug-Gene Interactions: A Simvastatin Network Analysis

Drug‐drug interactions (DDIs) and drug‐gene interactions (DGIs) are well known mediators for adverse drug reactions (ADRs), which are among the leading causes of death in many countries. Because physiologically based pharmacokinetic (PBPK) modeling has demonstrated to be a valuable tool to improve pharmacotherapy affected by DDIs or DGIs, it might also be useful for precision dosing in extensive interaction network scenarios. The presented work proposes a novel approach to extend the prediction capabilities of PBPK modeling to complex drug‐drug‐gene interaction (DDGI) scenarios. Here, a whole‐body PBPK network of simvastatin was established, including three polymorphisms (SLCO1B1 (rs4149056), ABCG2 (rs2231142), and CYP3A5 (rs776746)) and four perpetrator drugs (clarithromycin, gemfibrozil, itraconazole, and rifampicin). Exhaustive network simulations were performed and ranked to optimize 10,368 DDGI scenarios based on an exposure marker cost function. The derived dose recommendations were translated in a digital decision support system, which is available at simvastatin.precisiondosing.de. Although the network covers only a fraction of possible simvastatin DDGIs, it provides guidance on how PBPK modeling could be used to individualize pharmacotherapy in the future. Furthermore, the network model is easily extendable to cover additional DDGIs. Overall, the presented work is a first step toward a vision on comprehensive precision dosing based on PBPK models in daily clinical practice, where it could drastically reduce the risk of ADRs

Generating a Precision Endoxifen Prediction Algorithm to Advance Personalized Tamoxifen Treatment in Patients with Breast Cancer

Tamoxifen is an endocrine treatment for hormone receptor positive breast cancer. The effectiveness of tamoxifen may be compromised in patients with metabolic resistance, who have insufficient metabolic generation of the active metabolites endoxifen and 4-hydroxy-tamoxifen. This has been challenging to validate due to the lack of measured metabolite concentrations in tamoxifen clinical trials. CYP2D6 activity is the primary determinant of endoxifen concentration. Inconclusive results from studies investigating whether CYP2D6 genotype is associated with tamoxifen efficacy may be due to the imprecision in using CYP2D6 genotype as a surrogate of endoxifen concentration without incorporating the influence of other genetic and clinical variables. This review summarizes the evidence that active metabolite concentrations determine tamoxifen efficacy. We then introduce a novel approach to validate this relationship by generating a precision endoxifen prediction algorithm and comprehensively review the factors that must be incorporated into the algorithm, including genetics of CYP2D6 and other pharmacogenes. A precision endoxifen algorithm could be used to validate metabolic resistance in existing tamoxifen clinical trial cohorts and could then be used to select personalized tamoxifen doses to ensure all patients achieve adequate endoxifen concentrations and maximum benefit from tamoxifen treatment.publishedVersio

Pharmacogenetics of psychotropic drugs and genetic influences on adverse drug reactions

In this thesis, I investigate the impact of genetic variation on adverse drug reactions to psychotropic medications, with a focus on the metabolic and sleep related side effects of psychotropic drugs. In addition to reviewing published literature, I have considered this research topic in three main ways. Chapter one is a systematic review and meta-analysis of the impact of CYP2D6 genetic variation on antipsychotic-induced hyperprolactinaemia and weight gain, which are a relatively common but understudied adverse-drug reactions. Chapters two, three and four are based on data from UK Biobank, where I have conducted a hypothesis-driven analyses of known pharmacogenes and their association with two common adverse drug reactions: increased diabetes risk and sleep disturbance. In working on this thesis, two key limitations became apparent. Firstly, inconsistencies in genotyping and phenotyping make some findings difficult to interpret. Secondly, the nature of my analysis using cross-sectional UK Biobank data makes it difficult to draw firm conclusions on the causal direction of any observations. Chapter five aim to address these limitations. Here, I describe the set-up of a clinical study to assess pharmacogenetic interventions in a psychiatric patient population. Although only pilot data is available, due to a pause in recruitment during the Covid-19 pandemic, I describe the scientific rationale for the study and outline the work conducted to set-up and gain ethical approvals for the study. In addition, I outline my contribution to drafting clinical guidelines for the implementation of pharmacogenetic testing in the NHS

Modelling of steatosis and insulin resistance development in human hepatic 3d spheroids : mechanisms and genetic aspects

Hepatic steatosis and insulin resistance are common disease manifestations seen in metabolic syndrome, non-alcoholic fatty liver disease (NAFLD) and type 2 diabetes (T2D) patients. Aberrant hepatic lipid accumulation leads to both morphological changes and metabolic alterations in liver function and may cause serious end-stage liver disease. There is currently an unmet need for better and more sophisticated human in vitro models that can interrogate these liver conditions, emulate key disease mechanisms and thereby facilitate the development of novel therapeutic drugs. The overall aim of this thesis was to develop and assess the suitability of a human three-dimensional (3D) hepatic in vitro system consisting of primary human hepatocytes (PHH) to mimic fatty liver disease and associated hepatic disease manifestations, including insulin resistance. Moreover, we sought to explore potential disease modifying mechanisms using this novel in vitro model and study the importance of genetic polymorphism related both to liver disease development and drug response. We demonstrate that PHH cultured as 3D microtissues, hepatic spheroids, can emulate important hallmarks of fatty liver disease using pathophysiological concentrations of nutrients in cell media. Following this treatment, hepatic spheroids develop steatosis over the course of several days, accumulate several types of lipids such as triacylglycerols (TAG) and diacylglycerols (DAG) and subsequently become insulin resistant as judged by increased expression of gluconeogenic markers. Induced steatosis was found to be reversible following nutrient deprivation and this reversibility was accelerated by different types of drugs. Interindividual variability in fat accumulation in hepatic spheroids was attributed to genetic polymorphisms associated with risk for NAFLD, such as the TM6SF2 E167K genetic variant. By utilizing sequencing data and a bioinformatics approach, we further demonstrate that rare genetic variants in nuclear receptors, metabolic enzymes and cellular transporters could potentially affect disease susceptibility and drug efficacy. The majority of all studied gene variants were rare and around 30% of the functional variability could be attributed to these variants, highlighting the influence of rare genetic variants for interindividual variability in drug treatment responses. The influence of rare variants must be considered also for genes of importance for NAFLD development. In the last study of this thesis, we also unraveled the beneficial role of inorganic nitrate and nitrite, found in leafy vegetables, in the modulation of hepatic steatosis and cardio-metabolic functions. Boosting of the levels of nitric oxide by nitrate and nitrite ameliorated aberrant hepatic fat accumulation in both hepatic 3D spheroids, HepG2 cells and obese mice. Mechanistically, inorganic nitrate and nitrite treatment decreased oxidative stress derived from NADPH oxidase and stimulated AMP-activated protein kinase with beneficial effects on hepatic lipid accumulation. In conclusion, the versatile hepatic 3D spheroid fatty liver disease model developed and presented in this thesis has the potential to provide new mechanistic insights into liver disease and associated metabolic syndrome disorders and thereby gain future drug development efforts. Furthermore, the insights into the extensive genetic variability in genes potentially influencing drug response and disease development incentivize the adoption of these findings into clinical practice to enable personalized medicine

Artificial Intelligence in Oncology Drug Discovery and Development

There exists a profound conflict at the heart of oncology drug development. The efficiency of the drug development process is falling, leading to higher costs per approved drug, at the same time personalised medicine is limiting the target market of each new medicine. Even as the global economic burden of cancer increases, the current paradigm in drug development is unsustainable. In this book, we discuss the development of techniques in machine learning for improving the efficiency of oncology drug development and delivering cost-effective precision treatment. We consider how to structure data for drug repurposing and target identification, how to improve clinical trials and how patients may view artificial intelligence

Exploring pharmacogenetics in osteosarcoma

Contains fulltext : 252878.pdf (Publisher’s version ) (Open Access)Radboud University, 06 september 2022Promotor : Brunner, H.G. Co-promotores : Coenen, M.J.H., Loo, D.M.W.M. te221 p

A PERSONAL GENOMIC INFORMATION ANALYSIS AND MANAGEMENT SYSTEM FOR HEALTHCARE PURPOSES

Currently, a large amount of personal genomic data can be generated at an affordable price in a short period of time due to the improvement in the DNA sequencing technologies. Abundant research results on genetic diseases have been published in recent years. Therefore, it is eventually possible to integrate multiple types of information together and apply them into genomic-based personalized healthcare. However, this is still a very challenging task for healthcare professionals because the desired information is hidden in highly complex and heterogeneous genomic data sets and spread in various databases, which were typically created for researchers. In this research project, a personal genomic information management and analysis system is created for healthcare professionals, especially physicians. To properly design such a system, an exploratory survey was conducted to identify the current status of physicians in using genomics in their clinical practice and to collect their expectations about the features of a patient genomic information system. The results of this study indicated that physicians have sufficient knowledge in genomics and they are interested in incorporating genomics into their clinical practice. The results also indicated that a well-designed patient genomic information system with desired features can help physicians to incorporate genomics into their clinical practice. Based on the survey findings, a personal genomic information system was created for the purpose of managing and analyzing patient genomic data. In this system, we first created an integrated database, and then developed data analysis algorithms to extract clinical information from patient genetic variation data, including disease-associated genetic variations and pharmacogenomic associations. Physicians can conveniently identify the genetic reasons for diseases and determine personalized treatment options based on the information provided by the system. A usability study was conducted to obtain physicians’ feedback about the system after they use it to finish some tasks such as searching the genetic variations of one patient, determining the patient’s risk of certain diseases, and identifying the corresponding pharmacogenomic results. The results of this study indicated that physicians could easily find the patient information they need and the information can be directly applied in their clinical practice

In Pursuit of Clarity:Understanding the biology of schizophrenia by functional investigations and integrative genomic data analyses

Schizophrenia is a complex and heterogenous illness for which the underlying biology is largely unknown. Using functional investigations and integrative genomic data analyses, this thesis sheds light on the biological causes and consequences of schizophrenia

Gastrointestinal Cancers and Personalized Medicine

Gastrointestinal cancers, such as esophageal and gastric cancers, pancreatic cancers, hepatobiliary cancers, colorectal cancers and gastrointestinal stromal tumors, are frequently diagnosed at an advanced stage and have a dismal prognosis. Even in patients with potentially curative cancer, nearly 50\% will develop recurrent disease despite aggressive treatments. A number of biomarkers currently guide treatment decisions for patients with gastrointestinal neoplasms. Major technological advances in genomics have made it possible to identify critical genetic alterations in cancer, furthering oncology along the path to “personalized cancer medicine”. Future research efforts will focus on the identification of new biomarkers, moving existing biomarkers into earlier lines of therapy and evaluating new combinations of existing biomarkers and therapies.The aim of this Special Issue is to provide an overview of exciting new research in the area of gastrointestinal tumors that may establish innovative personalized management and precision medicine modalities for individualized care