Drug-induced liver injury (DILI) remains the main reason for drug development attritions largely due to poor mechanistic understanding. Toxicogenomic to interrogate the mechanism of DILI has been... Show moreDrug-induced liver injury (DILI) remains the main reason for drug development attritions largely due to poor mechanistic understanding. Toxicogenomic to interrogate the mechanism of DILI has been broadly performed. Gene co-regulation network-based transcriptome analysis is a bioinformatics approach that potentially contributes to improve mechanistic interpretation of toxicogenomic data. Here we performed an extensive concentration time course response-toxicogenomic study in the HepG2 cell line exposed to 20 DILI compounds, 7 reference compounds for stress response pathways, and 10 agonists for cytokines and growth factor receptors. We performed whole transcriptome targeted RNA sequencing to more than 500 conditions to and applied weighted gene co-regulated network analysis (WGCNA) to the transcriptomics data followed by identification of gene co-regulated networks (modules) that were strongly modulated upon the exposure of DILI compounds. Preservation analysis on the module responses of HepG2 and PHH demonstrated highly preserved adaptive stress response gene co-regulated networks. We correlated gene co-regulated networks with cell death onset and causal relationships of 67 critical target genes of these modules with onset of cell death was evaluated using RNA interference screening. We identified GTPBP2, HSPA1B, IRF1, SIRT1 and TSC22D3 as essential modulators of DILI compound-induced cell death. These genes were also induced by DILI compounds in PHH. Altogether, we demonstrate the application of large transcriptome datasets combined with network-based analysis and biological validation to uncover the candidate determinants of DILI. Show less
Immune checkpoint inhibitors targeting the programmed cell death protein 1 (PD-1)/programmed cell death protein ligand 1 (PD-L1) axis have been remarkably successful in inducing tumor remissions in... Show moreImmune checkpoint inhibitors targeting the programmed cell death protein 1 (PD-1)/programmed cell death protein ligand 1 (PD-L1) axis have been remarkably successful in inducing tumor remissions in several human cancers, yet a substantial number of patients do not respond to treatment. Because this may be partially due to the mechanisms giving rise to high PD-L1 expression within a patient, it is highly relevant to fully understand these mechanisms. In this study, we conduct a bioinformatic analysis to quantify the relative importance of transcription factor (TF) activity, microRNAs (miRNAs) and mutations in determining PD-L1 (CD274) expression at mRNA level based on data from the Cancer Genome Atlas. To predict individual CD274 levels based on TF activity, we developed multiple linear regression models by taking the expression of target genes of the TFs known to directly target PD-L1 as independent variables. This analysis showed that IRF1, STAT1, NFKB and BRD4 are the most important regulators of CD274 expression, explaining its mRNA levels in 90-98% of the patients. Because the remaining patients had high CD274 levels independent of these TFs, we next investigated whether mutations associated with increased CD274 mRNA levels, and low levels of miRNAs associated with negative regulation of CD274 expression could cause high CD274 levels in these patients. We found that mutations or miRNAs offered an explanation for high CD274 levels in 81-100% of the underpredicted patients. Thus, CD274 expression is largely explained by TF activity, and the remaining unexplained cases can largely be explained by mutations or low miRNA abundance. Show less
Discovery and development of Central Nervous System (CNS) drugs is hampered by high attrition rates. One of the reasons is the lack of blood-based biomarkers that represent the interaction between... Show moreDiscovery and development of Central Nervous System (CNS) drugs is hampered by high attrition rates. One of the reasons is the lack of blood-based biomarkers that represent the interaction between the drug and the neurological systems of interest. Here we present a systems-pharmacology approach that combines a multi-biomarker approach (e.g. metabolomics) with pharmacokinetic/pharmacodynamic (PK/PD) modeling to reveal quantitative pharmacological characteristics that are relevant to dopaminergic drug action. Moreover, we set out to identify biomarkers that can be obtained from the blood as non-invasive sampling site. In the first section of this thesis the methodology is introduced in the context of translational CNS drug development. Moreover, a systematic search is performed to available biomarkers of dopaminergic drug action. Then, in the second part, the multi-biomarker PK/PD approach is applied to biomarkers from the neuroendocrine system as connection between brain and blood. In the third section, the methodology is developed using the simultaneous, time-resolved metabolomics response in brain extracellular fluid and plasma. By applying multi-biomarker PK/PD modeling we revealed quantitative pharmacological characteristics of dopaminergic drugs with regard to multiple biological processes. Moreover, we identified potential blood-based biomarkers of dopaminergic effect in the brain. Show less
Brink, W.J. van den; Elassaiss, J.; Gonzalez Amoros, B.; Harms, A.C.; Graaf, P.H. van der; Hankemeier, T.; Lange, E.C.M. de 2017
IntroductionThe Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our... Show moreIntroductionThe Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer's disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance.MethodsFasting serum samples from the Alzheimer's Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted.Results Multivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and α-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for Aβ1–42, tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease.DiscussionMetabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery. Show less
Chronic pain is a significant health problem that greatly impacts the quality of life of individual patients and imparts high costs to society. Despite intense research effort and progress in our... Show moreChronic pain is a significant health problem that greatly impacts the quality of life of individual patients and imparts high costs to society. Despite intense research effort and progress in our understanding of the mechanistic and molecular basis of pain, chronic pain remains a significant clinical problem that has few effective therapies Throughout the various chapters we have highlighted some important conceptual and experimental flaws in the way that pain signalling and pharmacological activity are characterised and translated across species and disease conditions. The common denominator of the work presented here is the requirement for accurate characterisation of exposure-response relationships, without which the dose rationale for the progression of a molecule cannot justified, whether drugs are aimed at symptomatic relief, disease modification or prophylaxis. In addition to a comprehensive review of the mechanisms underlying pain signalling and symptoms, the work developed here focuses on three different aspects of research underpinning the use of pharmacokinetic-pharmacodynamic relationships. First, we have explored the requirements for the characterisation of behavioural measures of pain during the early screening of candidate molecules, shedding light onto the shortcomings of experimental protocols commonly used in preclinical research. Then we introduced the prerequisites for the parameterisation of pain behaviour to ensure accurate translation of the pharmacological properties across species as well as for bridging across different phases of development. Lastly, an attempt was made to model clinical response in chronic inflammatory pain and to establish correlations between symptom improvement and the underlying pharmacological effects using biomarkers. In addition our work showed how clinical trial simulations can be used as a design tool, enabling the evaluation of a variety of scenarios that disentangle the contribution of pharmacology from the confounding effects of placebo and disease dynamics. Show less
