Hepatic toxicity is a key concern for novel pharmaceutical drugs since it is difficult to anticipate in preclinical models, and it can originate from pharmacologically unrelated drug effects, such as ...pathway interference, metabolism, and drug accumulation. Because liver toxicity still ranks among the top reasons for drug attrition, the reliable prediction of adverse hepatic effects is a substantial challenge in drug discovery and development. To this end, more effort needs to be focused on the development of improved predictive in-vitro and in-silico approaches. Current computational models often lack applicability to novel pharmaceutical candidates, typically due to insufficient coverage of the chemical space of interest, which is either imposed by size or diversity of the training data. Hence, there is an urgent need for better computational models to allow for the identification of safe drug candidates and to support experimental design. In this context, a large data set comprising 3712 compounds with liver related toxicity findings in humans and animals was collected from various sources. The complex pathology was clustered into 21 preclinical and human hepatotoxicity endpoints, which were organized into three levels of detail. Support vector machine models were trained for each endpoint, using optimized descriptor sets from chemometrics software. The optimized global human hepatotoxicity model has high sensitivity (68%) and excellent specificity (95%) in an internal validation set of 221 compounds. Models for preclinical endpoints performed similarly. To allow for reliable prediction of “truly external” novel compounds, all predictions are tagged with confidence parameters. These parameters are derived from a statistical analysis of the predictive probability densities. The whole approach was validated for an external validation set of 269 proprietary compounds. The models are fully integrated into our early safety in-silico workflow.
Ochratoxin A (OTA), a mycotoxin produced by several fungi of Aspergillus and Penicillium species, may contaminate agricultural products, resulting in chronic human exposure. In rats, OTA is a potent ...nephrotoxin, and repeated administration of OTA for 2 years to rats in doses up to 0.21 mg/kg of body wt resulted in high incidences of renal tumors arising from the proximal tubular epithelial cells. The mechanism of tumor formation by OTA in the kidney is not well-defined, and controversial results regarding mode of action have been published. The aim of this study was to characterize dose-dependent changes induced by OTA by application of clinical chemistry, biochemical markers, and toxicokinetics for a better conclusion on modes of action. Administration of OTA (0, 0.25, 0.5, 1, and 2 mg/kg of body wt) to male F344 rats (n = 3 per group) by oral gavage for 2 weeks resulted in a dose-dependent increase in OTA plasma concentrations and concentrations of OTA in both liver and kidney. Although oxidative stress has been implicated in OTA carcinogenicity, treatment with OTA did not induce overt lipid peroxidation or an increase in 8-oxo-7,8-dihydro-2‘deoxyguanosine (8-OH-dG) in kidney. In the kidney, OTA-induced pathology was present at all dose levels administered, with a clear increase in severity related to dose. Pathology was restricted to the outer stripe of the outer medulla and consisted of disorganization of the tubule arrangement, frequent apoptotic cells, and abnormally enlarged nuclei scattered through the S3 tubules. Consistent with the histopathology, a dose-dependent increase in the expression of proliferating cell nuclear antigen (PCNA), indicative of cell proliferation, was observed in kidneys, but not in livers of treated animals. The most prominent change in the composition of urine induced by OTA analyzed by 1H NMR and principal component analysis consisted of a major increase in the excretion of trimethylamine N-oxide. However, typical changes observed with other proximal tubular toxins such as increased excretion of glucose were not observed at any of the doses administered. Similarly, treatment with OTA had no clear effects on clinical chemical parameters indicative of nephrotoxicity, although urinary volume was increased at the higher-dose groups. Taken together, the uncommon changes induced by OTA suggest that a unique mechanism may be involved in OTA nephrotoxicity and carcinogenicity.
Ochratoxin B (OTB), a secondary metabolite of
Aspergillus ochraceus, is the nonchlorinated analogue of the mycotoxin ochratoxin A (OTA), which is one of the most potent renal carcinogens in rodents. ...Despite the closely related structure, OTB is considered to be of much lower toxicity. OTA is poorly metabolized and slowly eliminated, and this may play an important role in OTA toxicity, carcinogenicity, and organ specificity. Since little is known regarding biotransformation and renal toxicity of OTB, the aim of this study was to investigate biotransformation of OTB in rats and to characterize the nephrotoxicity and cytotoxicity of OTB. Male F344 rats were administered either a single dose of OTB (10 mg/kg bw) or repeated doses (2 mg/kg bw, 5 days/week for 2 weeks) and euthanized 72 h after the last dosing. In proximal tubule cells of animals treated with a single high dose of OTB, a slight increase in mitotic figures was observed, but no treatment-related changes were evident in clinical chemistry, in renal function, and histopathology after repeated administration. Excretion of OTB and metabolites in urine and feces was analyzed using both HPLC with fluorescence detection and LC-MS/MS. Ochratoxin beta, which results from cleavage of the peptide bond, was the major metabolite excreted in urine in addition to small amounts of 4-hydroxy-OTB. In total, 19% of the administered dose was recovered as OTB and ochratoxin beta in urine and feces within 72 h after a single dose. In contrast to OTA, no tissue-specific retention of OTB was evident after single and repeated administration. In LLC-PK1 cells, a renal cell culture system that retains much of the specific features of the proximal tubule, only minor differences in the extent of cytotoxicity of OTA and OTB were observed. At low concentrations (< 25 μM), treatment with OTA was slightly more toxic, whereas reduction in cell viability was similar at concentrations up to 100 μM. In summary, these data suggest that OTA and OTB have a similar potential to induce cytotoxicity in vitro, but large differences in their potential to induce nephrotoxicity in rodents. OTB is more extensively metabolized and more rapidly eliminated than OTA. The lack of specific retention of OTB in the kidneys and the differences in toxicokinetics may therefore provide an explanation for the lower toxicity of OTB.
NMR and MS Methods for Metabolomics Amberg, Alexander; Riefke, Björn; Schlotterbeck, Götz ...
Methods in molecular biology (Clifton, N.J.),
2017, Letnik:
1641
Journal Article
Metabolomics, also often referred as "metabolic profiling," is the systematic profiling of metabolites in biofluids or tissues of organisms and their temporal changes. In the last decade, ...metabolomics has become more and more popular in drug development, molecular medicine, and other biotechnology fields, since it profiles directly the phenotype and changes thereof in contrast to other "-omics" technologies. The increasing popularity of metabolomics has been possible only due to the enormous development in the technology and bioinformatics fields. In particular, the analytical technologies supporting metabolomics, i.e., NMR, UPLC-MS, and GC-MS, have evolved into sensitive and highly reproducible platforms allowing the determination of hundreds of metabolites in parallel. This chapter describes the best practices of metabolomics as seen today. All important steps of metabolic profiling in drug development and molecular medicine are described in great detail, starting from sample preparation to determining the measurement details of all analytical platforms, and finally to discussing the corresponding specific steps of data analysis.
mRNA vaccines hold tremendous potential in disease control and prevention for their flexibility with respect to production, application, and design. Recent breakthroughs in mRNA vaccination would ...have not been possible without major advances in lipid nanoparticles (LNPs) technologies.
We developed an LNP containing a novel ionizable cationic lipid, Lipid-1, and three well known excipients. An in silico toxicity hazard assessment for genotoxicity, a genotoxicity assessment, and a dose range finding toxicity study were performed to characterize the safety profile of Lipid-1. The in silico toxicity hazard assessment, utilizing two prediction systems DEREK and Leadscope, did not find any structural alert for mutagenicity and clastogenicity, and prediction in the statistical models were all negative. In addition, applying a read-across approach a structurally very similar compound was tested negative in two in vitro assays confirming the low genotoxicity potential of Lipid-1. A dose range finding toxicity study in rabbits, receiving a single intramuscular injection of either different doses of an mRNA encoding Influenza Hemagglutinin H3 antigen encapsulated in the LNP containing Lipid-1 or the empty LNP, evaluated local tolerance and systemic toxicity during a 2-week observation period. Only rabbits exposed to the vaccine were able to develop a specific IgG response, indicating an appropriate vaccine take. The vaccine was well tolerated up to 250 μg mRNA/injection, which was defined as the No Observed Adverse Effect Level (NOAEL).
These results support the use of the LNP containing Lipid-1 as an mRNA delivery system for different vaccine formulations and its deployment into clinical trials.
•Lipid-1 showed no genotoxicity potential using two in silico prediction systems.•Lipid-2, structurally similar to Lipid-1, was tested negative in two in vitro assays.•Lipid-1 as a part of an mRNA vaccine was well tolerated up to 250 μg/injection in rabbits.•Lipid-1 is a safe candidate for the development of new vaccines.
The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop ...standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information.
The ICH M7 guideline describes a consistent approach to identify, categorize, and control DNA reactive, mutagenic, impurities in pharmaceutical products to limit the potential carcinogenic risk ...related to such impurities. This paper outlines a series of principles and procedures to consider when generating (Q)SAR assessments aligned with the ICH M7 guideline to be included in a regulatory submission. In the absence of adequate experimental data, the results from two complementary (Q)SAR methodologies may be combined to support an initial hazard classification. This may be followed by an assessment of additional information that serves as the basis for an expert review to support or refute the predictions. This paper elucidates scenarios where additional expert knowledge may be beneficial, what such an expert review may contain, and how the results and accompanying considerations may be documented. Furthermore, the use of these principles and procedures to yield a consistent and robust (Q)SAR-based argument to support impurity qualification for regulatory purposes is described in this manuscript.
•Summary of sources of existing bacterial mutagenicity and carcinogenicity data.•Description of how to combine (Q)SAR results.•Discussion of when an expert review might be useful.•Outline of expert review considerations.•Discussion of the scope and format for the (Q)SAR documentation.
Sharing legacy data from in vivo toxicity studies offers the opportunity to analyze the variability of control groups stratified for strain, age, duration of study, vehicle, and other experimental ...conditions. Historical animal control group data collected in a repository could be used to construct virtual control groups (VCGs) for toxicity studies. VCGs are an established concept in clinical trials, but the idea of replacing living beings with virtual data sets has so far not been introduced into the design of regulatory animal studies. The use of VCGs has the potential to reduce animal use by 25% by replacing control group animals with existing randomized data sets. Prerequisites for such an approach are the availability of large and well-structured control data sets as well as thorough statistical evaluations. The foundation of data- sharing has been laid within the Innovative Medicines Initiatives projects eTOX and eTRANSAFE. To establish proof of principle, participating companies have started to collect control group data for subacute (4-week) GLP studies with Wistar rats (the strain preferentially used in Europe) and are characterizing these data for its variability. In a second step, the control group data will be shared among the companies, and cross-company variability will be investigated. In a third step, a set of studies will be analyzed to assess whether the use of VCG data would have influenced the outcome of the study compared to the real control group.
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