Systems toxicology: modelling biomarkers of glutathione homeostasis and paracetamol metabolism

Pharmacotherapeutic advances in fibromyalgia: what's new on the horizon?

INTRODUCTION

This review delves into Fibromyalgia Syndrome (FMS), a chronic pain condition demanding thorough understanding for precise diagnosis and treatment. Yet, a definitive pharmacological solution for FMS remains elusive.

AREAS COVERED

In this article, we systematically analyze various pharmacotherapeutic prospects for FMS treatment, organized into sections based on the stage of drug development and approval. We begin with an overview of FDA-approved drugs, discussing their efficacy in FMS treatment. Next, we delve into other medications currently used for FMS but still undergoing further study, including opioids and muscle relaxants. Further, we evaluate the evidence behind medications that are currently under study, such as cannabinoids and naltrexone. Lastly, we explore new drugs that are in phase II trials. Our research involved a thorough search on PUBMED, Google Scholar, and clinicaltrials.gov. We also discuss the action mechanisms of these drugs and their potential use in specific patient groups.

EXPERT OPINION

A focus on symptom-driven, combination therapy is crucial in managing FMS. There is also a need for ongoing research into drugs that target neuroinflammation, immunomodulation, and the endocannabinoid system. Bridging the gap between benchside research and clinical application is challenging, but it holds potential for more targeted and effective treatment strategies.

Machine learning-based biomarkers identification from toxicogenomics - Bridging to regulatory relevant phenotypic endpoints

One of the major challenges in realization and implementations of the Tox21 vision is the urgent need to establish quantitative link between in-vitro assay molecular endpoint and in-vivo regulatory-relevant phenotypic toxicity endpoint. Current toxicomics approach still mostly rely on large number of redundant markers without pre-selection or ranking, therefore, selection of relevant biomarkers with minimal redundancy would reduce the number of markers to be monitored and reduce the cost, time, and complexity of the toxicity screening and risk monitoring. Here, we demonstrated that, using time series toxicomics in-vitro assay along with machine learning-based feature selection (maximum relevance and minimum redundancy (MRMR)) and classification method (support vector machine (SVM)), an "optimal" number of biomarkers with minimum redundancy can be identified for prediction of phenotypic toxicity endpoints with good accuracy. We included two case studies for in-vivo carcinogenicity and Ames genotoxicity prediction, using 20 selected chemicals including model genotoxic chemicals and negative controls, respectively. The results suggested that, employing the adverse outcome pathway (AOP) concept, molecular endpoints based on a relatively small number of properly selected biomarker-ensemble involved in the conserved DNA-damage and repair pathways among eukaryotes, were able to predict both Ames genotoxicity endpoints and in-vivo carcinogenicity in rats. A prediction accuracy of 76% with AUC = 0.81 was achieved while predicting in-vivo carcinogenicity with the top-ranked five biomarkers. For Ames genotoxicity prediction, the top-ranked five biomarkers were able to achieve prediction accuracy of 70% with AUC = 0.75. However, the specific biomarkers identified as the top-ranked five biomarkers are different for the two different phenotypic genotoxicity assays. The top-ranked biomarkers for the in-vivo carcinogenicity prediction mainly focused on double strand break repair and DNA recombination, whereas the selected top-ranked biomarkers for Ames genotoxicity prediction are associated with base- and nucleotide-excision repair The method developed in this study will help to fill in the knowledge gap in phenotypic anchoring and predictive toxicology, and contribute to the progress in the implementation of tox 21 vision for environmental and health applications.

Paracetamol-Induced Glutathione Consumption: Is There a Link With Severe COVID-19 Illness?

COVID-19 pandemic is posing an unprecedented sanitary threat: antiviral and host-directed medications to treat the disease are urgently needed. A great effort has been paid to find drugs and treatments for hospitalized, severely ill patients. However, medications used for the domiciliary management of early symptoms, notwithstanding their importance, have not been and are not presently regarded with the same attention and seriousness. In analogy with other airways viral infections, COVID-19 patients in the early phase require specific antivirals (still lacking) and non-etiotropic drugs to lower pain, fever, and control inflammation. Non-steroidal anti-inflammatory drugs (NSAIDs) and paracetamol (PAC) are widely used as non-etiotropic agents in common airways viral infections and hence are both theoretically repurposable for COVID-19. However, a warning from some research reports and National Authorities raised NSAIDs safety concerns because of the supposed induction of angiotensin-converting enzyme 2 (ACE2) levels (the receptor used by SARS-CoV2 to enter host airways cells), the increased risk of bacterial superinfections and masking of disease symptoms. As a consequence, the use of NSAIDs was, and is still, discouraged while the alternative adoption of paracetamol is still preferred. On the basis of novel data and hypothesis on the possible role of scarce glutathione (GSH) levels in the exacerbation of COVID-19 and of the GSH depleting activity of PAC, this commentary raises the question of whether PAC may be the better choice.

Harvesting the promise of AOPs: An assessment and recommendations

The Adverse Outcome Pathway (AOP) concept is a knowledge assembly and communication tool to facilitate the transparent translation of mechanistic information into outcomes meaningful to the regulatory assessment of chemicals. The AOP framework and associated knowledgebases (KBs) have received significant attention and use in the regulatory toxicology community. However, it is increasingly apparent that the potential stakeholder community for the AOP concept and AOP KBs is broader than scientists and regulators directly involved in chemical safety assessment. In this paper we identify and describe those stakeholders who currently-or in the future-could benefit from the application of the AOP framework and knowledge to specific problems. We also summarize the challenges faced in implementing pathway-based approaches such as the AOP framework in biological sciences, and provide a series of recommendations to meet critical needs to ensure further progression of the framework as a useful, sustainable and dependable tool supporting assessments of both human health and the environment. Although the AOP concept has the potential to significantly impact the organization and interpretation of biological information in a variety of disciplines/applications, this promise can only be fully realized through the active engagement of, and input from multiple stakeholders, requiring multi-pronged substantive long-term planning and strategies.

A Liver-Centric Multiscale Modeling Framework for Xenobiotics

We describe a multi-scale, liver-centric in silico modeling framework for acetaminophen pharmacology and metabolism. We focus on a computational model to characterize whole body uptake and clearance, liver transport and phase I and phase II metabolism. We do this by incorporating sub-models that span three scales; Physiologically Based Pharmacokinetic (PBPK) modeling of acetaminophen uptake and distribution at the whole body level, cell and blood flow modeling at the tissue/organ level and metabolism at the sub-cellular level. We have used standard modeling modalities at each of the three scales. In particular, we have used the Systems Biology Markup Language (SBML) to create both the whole-body and sub-cellular scales. Our modeling approach allows us to run the individual sub-models separately and allows us to easily exchange models at a particular scale without the need to extensively rework the sub-models at other scales. In addition, the use of SBML greatly facilitates the inclusion of biological annotations directly in the model code. The model was calibrated using human in vivo data for acetaminophen and its sulfate and glucuronate metabolites. We then carried out extensive parameter sensitivity studies including the pairwise interaction of parameters. We also simulated population variation of exposure and sensitivity to acetaminophen. Our modeling framework can be extended to the prediction of liver toxicity following acetaminophen overdose, or used as a general purpose pharmacokinetic model for xenobiotics.