Acceptable intakes (AIs) for 11 small molecule N-nitrosamines (NAs).

Low levels of N-nitrosamines (NAs) were detected in pharmaceuticals and, as a result, health authorities (HAs) have published acceptable intakes (AIs) in pharmaceuticals to limit potential carcinogenic risk. The rationales behind the AIs have not been provided to understand the process for selecting a TD50 or read-across analog. In this manuscript we evaluated the toxicity data for eleven common NAs in a comprehensive and transparent process consistent with ICH M7. This evaluation included substances which had datasets that were robust, limited but sufficient, and substances with insufficient experimental animal carcinogenicity data. In the case of robust or limited but sufficient carcinogenicity information, AIs were calculated based on published or derived TD50s from the most sensitive organ site. In the case of insufficient carcinogenicity information, available carcinogenicity data and structure activity relationships (SARs) were applied to categorical-based AIs of 1500 ng/day, 150 ng/day or 18 ng/day; however additional data (such as biological or additional computational modelling) could inform an alternative AI. This approach advances the methodology used to derive AIs for NAs.

Setting impurity limits for endogenous substances: Recommendations for a harmonized procedure and an example using fatty acids.

Endogenous substances, such as fatty, amino, and nucleic acids, are often purposefully used in parenterally pharmaceuticals, but may be present as impurities. Currently, no consensus guidance exists on setting impurity limits for these substances. Specific procedures are needed, as the amount and types of toxicity data available for endogenous substances are typically far less than those for other chemical impurities. Additionally, the parenteral route of administration of these substances is inherently non-physiological, resulting in potentially different or increased severity of toxicity. Risk Assessment Process Maps (RAPMAPs) are proposed as a model to facilitate the development of health-based exposure limits (HBELs) for endogenous substances. This yielded a framework that was applied to derive HBELs for several fatty acids commonly used in parenteral pharmaceuticals. This approach was used to derive HBELs with further vetting based on anticipated perturbations in physiological serum levels, impacts of dose-rate, and consideration of intermittent dosing. Parenteral HBELs of 100-500 mg/day were generated for several fatty acids, and a proposed class-based limit of 50 mg/day to be used in the absence of chemical-specific data. This default limit is consistent with the low toxicity of this chemical class and ICH Q3C value for Class 3 solvents.

An inherited night blindness in Wiltshire sheep.

Twelve cases of adult-onset blindness were identified in a flock of 130 polled Wiltshire sheep in New Zealand over a 3-year period. Affected sheep developed night blindness between 2 and 3 years of age, which progressed to complete blindness by 4 to 5 years of age. Fundic examination findings included progressive tapetal hyperreflectivity and attenuation of retinal blood vessels. Histologically, the retinas had a selective loss of rod photoreceptors with initial preservation of cone photoreceptors. Retinal degeneration was not accompanied by any other ocular or central nervous system abnormalities, and pedigree analysis suggested an inherited basis for the disease. Mating an affected Wiltshire ram to 2 affected Wiltshire ewes resulted in 6 progeny that all developed retinal degeneration by 2 years of age, while mating of the same affected ram to 6 unaffected ewes resulted in 8 unaffected progeny, consistent with autosomal recessive inheritance. Homozygosity mapping of 5 affected Wiltshire sheep and 1 unaffected Wiltshire sheep using an OvineSNP50 Genotyping BeadChip revealed an identical-by-descent region on chromosome 5, but none of the genes within this region were considered plausible candidate genes. Whole-genome sequencing of 2 affected sheep did not reveal any significant mutations in any of the genes associated with retinitis pigmentosa in humans or progressive retinal atrophy in dogs. Inherited progressive retinal degeneration affecting rod photoreceptors has not been previously reported in sheep, but this disease has several similarities to inherited retinal dystrophies in other species.

Principles and Procedures for Assessment of Acute Toxicity Incorporating In Silico Methods.

Acute toxicity in silico models are being used to support an increasing number of application areas including (1) product research and development, (2) product approval and registration as well as (3) the transport, storage and handling of chemicals. The adoption of such models is being hindered, in part, because of a lack of guidance describing how to perform and document an in silico analysis. To address this issue, a framework for an acute toxicity hazard assessment is proposed. This framework combines results from different sources including in silico methods and in vitro or in vivo experiments. In silico methods that can assist the prediction of in vivo outcomes (i.e., LD50) are analyzed concluding that predictions obtained using in silico approaches are now well-suited for reliably supporting assessment of LD50-based acute toxicity for the purpose of GHS classification. A general overview is provided of the endpoints from in vitro studies commonly evaluated for predicting acute toxicity (e.g., cytotoxicity/cytolethality as well as assays targeting specific mechanisms). The increased understanding of pathways and key triggering mechanisms underlying toxicity and the increased availability of in vitro data allow for a shift away from assessments solely based on endpoints such as LD50, to mechanism-based endpoints that can be accurately assessed in vitro or by using in silico prediction models. This paper also highlights the importance of an expert review of all available information using weight-of-evidence considerations and illustrates, using a series of diverse practical use cases, how in silico approaches support the assessment of acute toxicity.

MUCOPOLYSACCHARIDOSIS II (MPS II) IN A FREE-LIVING KAKA (NESTOR MERIDIONALIS) IN NEW ZEALAND.

A lysosomal storage disease, identified as a mucopolysaccharidosis (MPS), was diagnosed in a free-living Kaka (Nestor meridionalis), an endemic New Zealand parrot, which exhibited weakness, incoordination, and seizures. Histopathology showed typical colloid-like cytoplasmic inclusions in Purkinje cells and many other neurons throughout the brain. Electron microscopy revealed that storage bodies contained a variety of linear, curved, or circular membranous profiles and electron-dense bodies. Because the bird came from a small isolated population of Kaka in the northern South Island, a genetic cause was deemed likely. Tandem mass spectrometry revealed increased levels of heparan sulfate-derived disaccharides in the brain and liver compared with tissues from controls. Enzymatic assays documented low levels of iduronate-2-sulfatase activity, which causes a lysosomal storage disorder called MPS type II or Hunter syndrome. A captive breeding program is currently in progress, and the possibility of detecting carriers of this disorder warrants further investigation.

A community effort for automatic detection of postictal generalized EEG suppression in epilepsy.

Applying machine learning to healthcare sheds light on evidence-based decision making and has shown promises to improve healthcare by combining clinical knowledge and biomedical data. However, medicine and data science are not synchronized. Oftentimes, researchers with a strong data science background do not understand the clinical challenges, while on the other hand, physicians do not know the capacity and limitation of state-of-the-art machine learning methods. The difficulty boils down to the lack of a common interface between two highly intelligent communities due to the privacy concerns and the disciplinary gap. The School of Biomedical Informatics (SBMI) at UTHealth is a pilot in connecting both worlds to promote interdisciplinary research. Recently, the Center for Secure Artificial Intelligence For hEalthcare (SAFE) at SBMI is organizing a series of machine learning healthcare hackathons for real-world clinical challenges. We hosted our first Hackathon themed centered around Sudden Unexpected Death in Epilepsy and finding ways to recognize the warning signs. This community effort demonstrated that interdisciplinary discussion and productive competition has significantly increased the accuracy of warning sign detection compared to the previous work, and ultimately showing a potential of this hackathon as a platform to connect the two communities of data science and medicine.

Skin sensitization in silico protocol.

The assessment of skin sensitization has evolved over the past few years to include in vitro assessments of key events along the adverse outcome pathway and opportunistically capitalize on the strengths of in silico methods to support a weight of evidence assessment without conducting a test in animals. While in silico methods vary greatly in their purpose and format; there is a need to standardize the underlying principles on which such models are developed and to make transparent the implications for the uncertainty in the overall assessment. In this contribution, the relationship between skin sensitization relevant effects, mechanisms, and endpoints are built into a hazard assessment framework. Based on the relevance of the mechanisms and effects as well as the strengths and limitations of the experimental systems used to identify them, rules and principles are defined for deriving skin sensitization in silico assessments. Further, the assignments of reliability and confidence scores that reflect the overall strength of the assessment are discussed. This skin sensitization protocol supports the implementation and acceptance of in silico approaches for the prediction of skin sensitization.

In Vivo Mutagenicity Testing of Arylboronic Acids and Esters.

Arylboronic acids and esters (referred to collectively as arylboronic compounds) are commonly used intermediates in the synthesis of pharmaceuticals but pose a challenge for chemical syntheses because they are often positive for bacterial mutagenicity in vitro. As such, arylboronic compounds are then typically controlled to levels that are acceptable for mutagenic impurities, that is, the threshold of toxicological concern (TTC). This study used ICH M7 guidance to design and conduct a testing strategy to investigate the in vivo relevance of the in vitro positive findings of arylboronic compounds. Eight arylboronic compounds representing a variety of chemical scaffolds were tested in Sprague Dawley and/or Wistar rats in the in vivo Pig-a (peripheral blood reticulocytes and mature red blood cells) and/or comet assays (duodenum and/or liver). Five of the eight compounds were also tested in the micronucleus (peripheral blood) assay. The arylboronic compounds tested orally demonstrated high systemic exposure; thus the blood and bone marrow were adequately exposed to test article. One compound was administered intravenously due to formulation stability issues. This investigation showed that arylboronic compounds that were mutagenic in vitro were not found to be mutagenic in the corresponding in vivo assays. Therefore, arylboronic compounds similar to the scaffolds tested in this article may be considered non-mutagenic and managed in accordance with the ICH Q3A/Q3B guidelines. Environ. Mol. Mutagen. 2019. © 2019 Wiley Periodicals, Inc.

Genetic toxicology in silico protocol.

In silico toxicology (IST) approaches to rapidly assess chemical hazard, and usage of such methods is increasing in all applications but especially for regulatory submissions, such as for assessing chemicals under REACH as well as the ICH M7 guideline for drug impurities. There are a number of obstacles to performing an IST assessment, including uncertainty in how such an assessment and associated expert review should be performed or what is fit for purpose, as well as a lack of confidence that the results will be accepted by colleagues, collaborators and regulatory authorities. To address this, a project to develop a series of IST protocols for different hazard endpoints has been initiated and this paper describes the genetic toxicity in silico (GIST) protocol. The protocol outlines a hazard assessment framework including key effects/mechanisms and their relationships to endpoints such as gene mutation and clastogenicity. IST models and data are reviewed that support the assessment of these effects/mechanisms along with defined approaches for combining the information and evaluating the confidence in the assessment. This protocol has been developed through a consortium of toxicologists, computational scientists, and regulatory scientists across several industries to support the implementation and acceptance of in silico approaches.

ß-Mannosidosis in German Shepherd Dogs.

A neurological disease was investigated in 3 German Shepherd pups from the same litter that failed to grow normally, appeared stiff, were reluctant to move, and were deaf. They developed intermittent seizures and ataxia and had proprioceptive defects. Histopathology showed severe vacuolation of neurons, astrocytes in nervous tissue, renal tubular epithelial cells, and macrophages in nervous tissue, spleen, and liver. Vacuoles appeared empty with no storage material stained by periodic acid-Schiff (PAS) or Sudan black stains, leading to a diagnosis of a lysosomal storage disease and in particular an oligosaccharidosis. Biochemical and genomic studies showed that this was ß-mannosidosis, not previously diagnosed in dogs. A c.560T>A transition in exon 4 of the MANBA gene was found, which segregated in these and other family members in a manner consistent with it being the causative mutation of an autosomal recessive disease. This mutation led to substitution of isoleucine to asparagine at position 187 of the 885 amino acid enzyme, a change expected to have functional significance.

Evaluation of Disease Lesions in the Developing Canine MPS IIIA Brain.

Mucopolysaccharidosis IIIA (MPS IIIA) is an inherited neurodegenerative disease of childhood that results in early death. Post-mortem studies have been carried out on human MPS IIIA brain, but little is known about early disease development. Here, we utilised the Huntaway dog model of MPS IIIA to evaluate disease lesion development from 2 to 24 weeks of age. A significant elevation in primarily stored heparan sulphate was observed in all brain regions assessed in MPS IIIA pups ≤9.5 weeks of age. There was a significant elevation in secondarily stored ganglioside (GM3 36:1) in ≤9.5-week-old MPS IIIA pup cerebellum, and other brain regions also exhibited accumulation of this lipid with time. The number of neural stem cells and neuronal precursor cells was essentially unchanged in MPS IIIA dog brain (c.f. unaffected) over the time course assessed, a finding corroborated by neuron cell counts. We observed early neuroinflammatory changes in young MPS IIIA pup brain, with significantly increased numbers of activated microglia recorded in all but one brain region in MPS IIIA pups ≤9.5 weeks of age (c.f. age-matched unaffected pups). In conclusion, infant-paediatric-stage MPS IIIA canine brain exhibits substantial and progressive primary and secondary substrate accumulation, coupled with early and robust microgliosis. Whilst early initiation of treatment is likely to be required to maintain optimal neurological function, the brain's neurodevelopmental potential appears largely unaffected by the disease process; further investigations confirming this are warranted.

Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: is aromatic N-oxide a structural alert for predicting DNA-reactive mutagenicity?

(Quantitative) structure-activity relationship or (Q)SAR predictions of DNA-reactive mutagenicity are important to support both the design of new chemicals and the assessment of impurities, degradants, metabolites, extractables and leachables, as well as existing chemicals. Aromatic N-oxides represent a class of compounds that are often considered alerting for mutagenicity yet the scientific rationale of this structural alert is not clear and has been questioned. Because aromatic N-oxide-containing compounds may be encountered as impurities, degradants and metabolites, it is important to accurately predict mutagenicity of this chemical class. This article analysed a series of publicly available aromatic N-oxide data in search of supporting information. The article also used a previously developed structure-activity relationship (SAR) fingerprint methodology where a series of aromatic N-oxide substructures was generated and matched against public and proprietary databases, including pharmaceutical data. An assessment of the number of mutagenic and non-mutagenic compounds matching each substructure across all sources was used to understand whether the general class or any specific subclasses appear to lead to mutagenicity. This analysis resulted in a downgrade of the general aromatic N-oxide alert. However, it was determined there were enough public and proprietary data to assign the quindioxin and related chemicals as well as benzo[c][1,2,5]oxadiazole 1-oxide subclasses as alerts. The overall results of this analysis were incorporated into Leadscope's expert-rule-based model to enhance its predictive accuracy.

Principles and procedures for handling out-of-domain and indeterminate results as part of ICH M7 recommended (Q)SAR analyses.

The International Council for Harmonization (ICH) M7 guideline describes a hazard assessment process for impurities that have the potential to be present in a drug substance or drug product. In the absence of adequate experimental bacterial mutagenicity data, (Q)SAR analysis may be used as a test to predict impurities' DNA reactive (mutagenic) potential. However, in certain situations, (Q)SAR software is unable to generate a positive or negative prediction either because of conflicting information or because the impurity is outside the applicability domain of the model. Such results present challenges in generating an overall mutagenicity prediction and highlight the importance of performing a thorough expert review. The following paper reviews pharmaceutical and regulatory experiences handling such situations. The paper also presents an analysis of proprietary data to help understand the likelihood of misclassifying a mutagenic impurity as non-mutagenic based on different combinations of (Q)SAR results. This information may be taken into consideration when supporting the (Q)SAR results with an expert review, especially when out-of-domain results are generated during a (Q)SAR evaluation.

In silico toxicology protocols.

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.

Slow, continuous enzyme replacement via spinal CSF in dogs with the paediatric-onset neurodegenerative disease, MPS IIIA.

Intra-cerebrospinal fluid (CSF) injection of recombinant human lysosomal enzyme is a potential treatment strategy for several neurodegenerative lysosomal storage disorders including Sanfilippo syndrome (Mucopolysaccharidosis type IIIA; MPS IIIA). Here we have utilised the MPS IIIA Huntaway dog model to compare the effectiveness of the repeated intermittent bolus injection strategy being used in the trials with an alternate approach; slow, continual infusion of replacement enzyme (recombinant human sulphamidase; rhSGSH) into the spinal CSF using a SynchroMed II® pump attached to a spinal infusion cannula. The ability of each enzyme delivery strategy to ameliorate lesions in MPS IIIA brain was determined in animals treated from ∼three- to six-months of age. Controls received buffer or no treatment. Significant reductions in heparan sulphate (primary substrate) were observed in brain samples from dogs treated via either cisternal or lumbar spinal CSF bolus injection methods and also in slow intra-spinal CSF infusion-treated dogs. The extent of the reduction differed regionally. Pump-delivered rhSGSH was less effective in reducing secondary substrate (GM3 ganglioside) in deeper aspects of cerebral cortex, and although near-amelioration of microglial activation was seen in superficial (but not deep) layers of cerebral cortex in both bolus enzyme-treated groups, pump-infusion of rhSGSH had little impact on microgliosis. While continual low-dose infusion of rhSGSH into MPS IIIA dog CSF reduces disease-based lesions in brain, it was not as efficacious as repeated cisternal or spinal CSF bolus infusion of rhSGSH over the time-frame of these experiments.

A harmonization effort for acceptable daily exposure application to pharmaceutical manufacturing - Operational considerations.

A European Union (EU) regulatory guideline came into effect for all new pharmaceutical products on June 1st, 2015, and for all existing pharmaceutical products on December 1st, 2015. This guideline centers around the use of the Acceptable Daily Exposure (ADE) [synonymous with the Permitted Daily Exposure (PDE)] and operational considerations associated with implementation are outlined here. The EU guidance states that all active pharmaceutical ingredients (API) require an ADE; however, other substances such as starting materials, process intermediates, and cleaning agents may benefit from an ADE. Problems in setting ADEs for these additional substances typically relate to toxicological data limitations precluding the ability to establish a formal ADE. Established methodologies such as occupational exposure limits or bands (OELs or OEBs) and the threshold of toxicological concern (TTC) can be used or adjusted for use as interim ADEs when only limited data are available and until a more formal ADE can be established. Once formal ADEs are derived, it is important that the documents are routinely updated and that these updates are communicated to appropriate stakeholders. Another key operational consideration related to data-poor substances includes the use of maximum daily dose (MDD) in setting cross-contamination limits. The MDD is an important part of the maximum allowable/safe concentration (MAC/MSC) calculation and there are important considerations for its use and definition. Finally, other considerations discussed include operational aspects of setting ADEs for pediatrics, considerations for large molecules, and risk management in shared facilities.

Issues and approaches for ensuring effective communication on acceptable daily exposure (ADE) values applied to pharmaceutical cleaning.

This manuscript centers on communication with key stakeholders of the concepts and program goals involved in the application of health-based pharmaceutical cleaning limits. Implementation of health-based cleaning limits, as distinct from other standards such as 1/1000th of the lowest clinical dose, is a concept recently introduced into regulatory domains. While there is a great deal of technical detail in the written framework underpinning the use of Acceptable Daily Exposures (ADEs) in cleaning (for example ISPE, 2010; Sargent et al., 2013), little is available to explain how to practically create a program which meets regulatory needs while also fulfilling good manufacturing practice (GMP) and other expectations. The lack of a harmonized approach for program implementation and communication across stakeholders can ultimately foster inappropriate application of these concepts. Thus, this period in time (2014-2017) could be considered transitional with respect to influencing best practice related to establishing health-based cleaning limits. Suggestions offered in this manuscript are intended to encourage full and accurate communication regarding both scientific and administrative elements of health-based ADE values used in pharmaceutical cleaning practice. This is a large and complex effort that requires: 1) clearly explaining key terms and definitions, 2) identification of stakeholders, 3) assessment of stakeholders' subject matter knowledge, 4) formulation of key messages fit to stakeholder needs, 5) identification of effective and timely means for communication, and 6) allocation of time, energy, and motivation for initiating and carrying through with communications.

Assessing Concordance of Drug-Induced Transcriptional Response in Rodent Liver and Cultured Hepatocytes.

The effect of drugs, disease and other perturbations on mRNA levels are studied using gene expression microarrays or RNA-seq, with the goal of understanding molecular effects arising from the perturbation. Previous comparisons of reproducibility across laboratories have been limited in scale and focused on a single model. The use of model systems, such as cultured primary cells or cancer cell lines, assumes that mechanistic insights derived from the models would have been observed via in vivo studies. We examined the concordance of compound-induced transcriptional changes using data from several sources: rat liver and rat primary hepatocytes (RPH) from Drug Matrix (DM) and open TG-GATEs (TG), human primary hepatocytes (HPH) from TG, and mouse liver/HepG2 results from the Gene Expression Omnibus (GEO) repository. Gene expression changes for treatments were normalized to controls and analyzed with three methods: 1) gene level for 9071 high expression genes in rat liver, 2) gene set analysis (GSA) using canonical pathways and gene ontology sets, 3) weighted gene co-expression network analysis (WGCNA). Co-expression networks performed better than genes or GSA when comparing treatment effects within rat liver and rat vs. mouse liver. Genes and modules performed similarly at Connectivity Map-style analyses, where success at identifying similar treatments among a collection of reference profiles is the goal. Comparisons between rat liver and RPH, and those between RPH, HPH and HepG2 cells reveal lower concordance for all methods. We observe that the baseline state of untreated cultured cells relative to untreated rat liver shows striking similarity with toxicant-exposed cells in vivo, indicating that gross systems level perturbation in the underlying networks in culture may contribute to the low concordance.

Principles and procedures for implementation of ICH M7 recommended (Q)SAR analyses.

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.

Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity.

Statistical-based and expert rule-based models built using public domain mutagenicity knowledge and data are routinely used for computational (Q)SAR assessments of pharmaceutical impurities in line with the approach recommended in the ICH M7 guideline. Knowledge from proprietary corporate mutagenicity databases could be used to increase the predictive performance for selected chemical classes as well as expand the applicability domain of these (Q)SAR models. This paper outlines a mechanism for sharing knowledge without the release of proprietary data. Primary aromatic amine mutagenicity was selected as a case study because this chemical class is often encountered in pharmaceutical impurity analysis and mutagenicity of aromatic amines is currently difficult to predict. As part of this analysis, a series of aromatic amine substructures were defined and the number of mutagenic and non-mutagenic examples for each chemical substructure calculated across a series of public and proprietary mutagenicity databases. This information was pooled across all sources to identify structural classes that activate or deactivate aromatic amine mutagenicity. This structure activity knowledge, in combination with newly released primary aromatic amine data, was incorporated into Leadscope's expert rule-based and statistical-based (Q)SAR models where increased predictive performance was demonstrated.