Origin of the TTC values for compounds that are genotoxic and/or carcinogenic and an approach for their revaluation

The threshold of toxicological concern (TTC) approach is a resource-effective de minimismethod for the safety assessment of chemicals, based on distributional analysis of the results of a large number of toxicological studies. It is being increasingly used to screen and prioritise substances with low exposure for which there is little or no toxicological information. The first step in the approach is the identification of substances that may be DNA-reactive mutagens, to which the lowest TTC value is applied. This TTC value was based on analysis of the cancer potency database and involved a number of assumptions that no longer reflect the state-of-the-science and some of which were not as transparent as they could have been. Hence, review and updating of the database is proposed, using inclusion and exclusion criteria reflecting current knowledge. A strategy for the selection of appropriate substances for TTC determination, based on consideration of weight of evidence for genotoxicity and carcinogenicity is outlined. Identification of substances that are carcinogenic by a DNA-reactive mutagenicmode of action and those that clearly act by a non-genotoxic mode of action will enable the protectiveness to be determined of both the TTC for DNA-reactive mutagenicityand that applied by default to substances that may be carcinogenic but are unlikely to be DNA-reactive mutagens (i.e. for Cramer class I-III compounds). Critical to the application of the TTC approach to substances that are likely to be DNA-reactive mutagens is the reliability of the software tools used to identify such compounds. Current methods for this task are reviewed and recommendations made for their application

Low potency toxins reveal dense interaction networks in metabolism

Background The chemicals of metabolism are constructed of a small set of atoms and bonds. This may be because chemical structures outside the chemical space in which life operates are incompatible with biochemistry, or because mechanisms to make or utilize such excluded structures has not evolved. In this paper I address the extent to which biochemistry is restricted to a small fraction of the chemical space of possible chemicals, a restricted subset that I call Biochemical Space. I explore evidence that this restriction is at least in part due to selection again specific structures, and suggest a mechanism by which this occurs. Results Chemicals that contain structures that our outside Biochemical Space (UnBiological groups) are more likely to be toxic to a wide range of organisms, even though they have no specifically toxic groups and no obvious mechanism of toxicity. This correlation of UnBiological with toxicity is stronger for low potency (millimolar) toxins. I relate this to the observation that most chemicals interact with many biological structures at low millimolar toxicity. I hypothesise that life has to select its components not only to have a specific set of functions but also to avoid interactions with all the other components of life that might degrade their function. Conclusions The chemistry of life has to form a dense, self-consistent network of chemical structures, and cannot easily be arbitrarily extended. The toxicity of arbitrary chemicals is a reflection of the disruption to that network occasioned by trying to insert a chemical into it without also selecting all the other components to tolerate that chemical. This suggests new ways to test for the toxicity of chemicals, and that engineering organisms to make high concentrations of materials such as chemical precursors or fuels may require more substantial engineering than just of the synthetic pathways involved

Managing the challenge of drug-induced liver injury: a roadmap for the development and deployment of preclinical predictive models

Drug-induced liver injury (DILI) is a patient-specific, temporal, multifactorial pathophysiological process that cannot yet be recapitulated in a single in vitro model. Current preclinical testing regimes for the detection of human DILI thus remain inadequate. A systematic and concerted research effort is required to address the deficiencies in current models and to present a defined approach towards the development of new or adapted model systems for DILI prediction. This Perspective defines the current status of available models and the mechanistic understanding of DILI, and proposes our vision of a roadmap for the development of predictive preclinical models of human DILI

Novel in vitro and mathematical models for the prediction of chemical toxicity

The focus of much scientific and medical research is directed towards understanding the disease process and defining therapeutic intervention strategies. Whilst the scientific basis of drug safety has received relatively little attention, despite the fact that adverse drug reactions (ADRs) are a major health concern and a serious impediment to development of new medicines. Toxicity issues account for ~21% drug attrition during drug development and safety testing strategies require considerable animal use. Mechanistic relationships between drug plasma levels and molecular/cellular events that culminate in whole organ toxicity underpins development of novel safety assessment strategies. Current in vitro test systems are poorly predictive of toxicity of chemicals entering the systemic circulation, particularly to the liver. Such systems fall short because of 1) the physiological gap between cells currently used & human hepatocytes existing in their native state, 2) the lack of physiological integration with other cells/systems within organs, required to amplify the initial toxicological lesion into overt toxicity, 3) the inability to assess how low level cell damage induced by chemicals may develop into overt organ toxicity in a minority of patients, 4) lack of consideration of systemic effects. Reproduction of centrilobular & periportal hepatocyte phenotypes in in vitro culture is crucial for sensitive detection of cellular stress. Hepatocyte metabolism/phenotype is dependent on cell position along the liver lobule, with corresponding differences in exposure to substrate, oxygen & hormone gradients. Application of bioartificial liver (BAL) technology can encompass in vitro predictive toxicity testing with enhanced sensitivity and improved mechanistic understanding. Combining this technology with mechanistic mathematical models describing intracellular metabolism, fluid-­‐flow, substrate, hormone and nutrient distribution provides the opportunity to design the BAL specifically to mimic the in vivo scenario. Such mathematical models enable theoretical hypothesis testing, will inform the design of in vitro experiments, and will enable both refinement and reduction of in vivo animal trials. In this way, development of novel mathematical modelling tools will help to focus and direct in vitro and in vivo research, and can be used as a framework for other areas of drug safety science

Development of in silico models for the prediction of toxicity incorporating ADME information

Drug discovery is a process that requires a significant investment in both time and resources. Although recent developments have reduced the number of drugs failing at the later stages of development due to poor pharmacokinetic and/or toxicokinetic profiles, late stage attrition of drug candidates remains a problem. Additionally, there is a need to reduce animal testing for toxicological risk assessment for ethical and financial reasons. In silico methods offer an alternative that can address these challenges. A variety of computational approaches have been developed in the last two decades, these must be evaluated to ensure confidence in their use. The research presented in this thesis has assessed a range of existing tools for the prediction of toxicity and absorption, distribution, metabolism and elimination (ADME) parameters with an emphasis on absorption and xenobiotic metabolism. These two ADME properties largely determine bioavailability of a drug and, in turn, also influence toxicity. In vitro (Caco-2 cells and the parallel artificial membrane permeation assay) and in silico approaches, such as various druglikeness filters, can be used to estimate human intestinal absorption; a comparison between different methods was performed to identify relative strengths and weaknesses of the approaches. In terms of xenobiotic metabolism it is not only important to predict metabolites correctly, but it is also crucial to identify those compounds that can be biotransformed into species that can covalently bind to biomolecules. Structural alerts are routinely used to screen for such potential reactive metabolites. The balance between sensitivity and specificity of such reactive metabolite alerts has been discussed in the context of correctly predicting reactive metabolites of pharmaceuticals (using data available from DrugBank). Off-target toxicity, exemplified by human Ether-à-go-go-Related Gene (hERG) channel inhibition, was also explored. A number of novel structural alerts for hERG toxicity were developed based on groups of structurally similar compounds. Finally, the importance of predicting potential ecotoxicological effects of drugs was also considered. The utility of zebrafish embryos to distinguish between baseline and excess toxicity was investigated. In evaluating this selection of existing tools, improvements to the methods have been proposed where possible

Pharmacogenetics of Oral Anticoagulants and Antiplatelets

Thromboembolic disorders are a major cause of morbidity and mortality. Therapeutic intervention with anticoagulants and antiplatelets greatly reduces the risk of arterial and venous thrombosis. However, the observed large interindividual variation in responsiveness to these drugs indicates that subsets of patients are not attaining optimal therapy, resulting in either lack of antithrombotic effect or elevated bleeding risk. Recently, single nucleotide polymorphisms (SNPs) have been linked to the variation observed in efficacy and toxicity for many cardiovascular drugs. Warfarin has been the gold standard anticoagulant for prevention of stroke and thromboembolism in atrial fibrillation (AF) and venous thromboembolism (VTE) patients. SNPs in genes that affect warfarin metabolism (CYP2C9) and response (VKORC1) have an important influence on response and dose, particularly during initiation. Accordingly, we developed and evaluated the clinical utility of a pharmacogenetics-based initiation nomogram in AF and VTE patients which provides safe and optimal anticoagulation therapy irrespective of genetic variation. The new oral anticoagulant (NOAC) rivaroxaban is highly dependent on the kidney for elimination through glomerular filtration and active tubular secretion. Importantly, interindividual variation in exposure and response to rivaroxaban has been reported. Using cell-based and animal models, we demonstrated that rivaroxaban is a dual substrate of the efflux transporters MDR1 and BCRP, which played a synergistic role in modulating rivaroxaban clearance and brain accumulation. The contribution of interindividual variation in transport and metabolism to the efficacy of rivaroxaban as well as other NOACs requires to be addressed in patients. Clopidogrel has been the gold standard antiplatelet for prevention of acute coronary syndromes and stent thrombosis following percutaneous coronary intervention. Two enzymes, CYP2C19 and PON1, have been proposed to affect clopidogrel bioactivation and efficacy. We showed that CYP2C19 but not PON1 is capable of bioactivating clopidogrel to its active metabolite. This is in line our finding that CYP2C19 genetic variation is a predictor of clopidogrel pharmacokinetics and antiplatelet response while PON1 is not. Taken together, these studies demonstrate the contribution of SNPs to the variation in efficacy and toxicity of cardiovascular drugs, enabling personalized medicine for patients, where an individual’s genetic makeup is used to guide drug selection and dosing

Mitochondria and Brain Disorders

The mitochondrion is a unique and ubiquitous organelle that contains its own genome, encoding essential proteins that are major components of the respiratory chain and energy production system. Mitochondria play a dominant role in the life and function of eukaryotic cells including neurons and glia, as their survival and activity depend upon mitochondrial energy production and supply. Besides energy production, mitochondria also play a vital role in calcium homeostasis and may induce apoptosis by excitotoxicity. Mitochondrial dysfunction is related to common neurological diseases, such as Parkinson's disease, Alzheimer's disease, Friedreich's ataxia, Huntington's disease, and Multiple Sclerosis. An efficient treatment of mitochondrial dysfunction would open new horizons in the therapeutic perspectives of a substantial number of inflammatory and degenerative neurological disorders

The anti-obesogenic effects of nitric oxide.

Obesity is a strong risk factor for developing type 2 diabetes and cardiovascular disease and has quickly reached epidemic proportions with few tangible and safe treatment options. While it is generally accepted that the primary cause of obesity is energy imbalance, i.e., more calories are consumed than are utilized, understanding how caloric balance is regulated has proven a challenge. Molecular processes and pathways that directly regulate energy metabolism represent promising targets for therapy. In particular, nitric oxide (NO) is emerging as a central regulator of energy metabolism and body composition. NO bioavailability is decreased in animal models of obesity and in obese and insulin resistant patients, and increasing NO output has remarkable effects on obesity and insulin resistance. Additionally, deletion of eNOS (the source of NO in the vasculature) is associated with adiposity, insulin resistance and impaired fatty acid oxidation. The role of eNOS in regulating metabolism, however, is not well understood. We propose that decreased vascular-derived NO bioavailability during nutrient excess is a critical development that leads to metabolic dysregulation. The studies presented here show that obesity induces severe metabolic changes in adipose tissue including profound decreases in eNOS abundance. Overexpression of eNOS prevents obesity and its related metabolic alterations while causing significant changes in energy expenditure and systemic metabolism. Our findings reveal potent anti-obesogenic effects of NO and demonstrate a significant role for NO in regulating metabolism