High Throughput Read-Across for Screening a Large Inventory of Related Structures by Balancing Artificial Intelligence/Machine Learning and Human Knowledge.

Read-across is an in silico method applied in chemical risk assessment for data-poor chemicals. The read-across outcomes for repeated-dose toxicity end points include the no-observed-adverse-effect level (NOAEL) and estimated uncertainty for a particular category of effects. We have previously developed a new paradigm for estimating NOAELs based on chemoinformatics analysis and experimental study qualities from selected analogues, not relying on quantitative structure-activity relationships (QSARs) or rule-based SAR systems, which are not well-suited to end points for which the underpinning data are weakly grounded in specific chemical-biological interactions. The central hypothesis of this approach is that similar compounds have similar toxicity profiles and, hence, similar NOAEL values. Analogue quality (AQ) quantifies the suitability of an analogue candidate for reading across to the target by considering similarity from structure, physicochemical, ADME (absorption, distribution, metabolism, excretion), and biological perspectives. Biological similarity is based on experimental data; assay vectors derived from aggregations of ToxCast/Tox21 data are used to derive machine learning (ML) hybrid rules that serve as biological fingerprints to capture target-analogue similarity relevant to specific effects of interest, for example, hormone receptors (ER/AR/THR). Once one or more analogues have been qualified for read-across, a decision theory approach is used to estimate confidence bounds for the NOAEL of the target. The confidence interval is dramatically narrowed when analogues are constrained to biologically related profiles. Although this read-across process works well for a single target with several analogues, it can become unmanageable when, for example, screening multiple targets (e.g., virtual screening library) or handling a parent compound having numerous metabolites. To this end, we have established a digitalized framework to enable the assessment of a large number of substances, while still allowing for human decisions for filtering and prioritization. This workflow was developed and validated through a use case of a large set of bisphenols and their metabolites.

A Quasi Universal Matalon-Packter Law for a Periodically Precipitating System of Iron(II) Hydroxide Involving Volumes and Concentrations of the Invading Electrolyte.

The Matalon-Packter (MP) empirical law of periodically precipitating (Liesegang phenomenon) systems under non-equilibrium conditions describes the dependence of the periodicity (spacing coefficient) on the initial concentration of the outer electrolyte. We aim to present the MP law in a more generalized form using a realistic approach wherein mass transfer in the gel column plays a role instead of the initial concentrations. This work is an attempt to make such progress. The Liesegang bands of Fe(OH)2 were studied by varying the reservoir concentrations (c) and volumes (V) of the outer electrolyte (NH4OH). The spacing coefficient was found to be a function of the volume and concentration of the outer electrolyte. It was observed that the amount of chemical substance (cV) and the average molar diffusion flux (Fdiff) of the ions of the outer electrolyte could be a unifying quantity for expressing the MP law instead of the initial electrolyte concentration. We demonstrated that a single model is possible for a system, irrespective of the V value. Three different volumes were employed, and the calculations were performed under small, intermediate, and larger reservoir volume regimes. Interestingly, a single model was observed for the diffusion coefficients for all of the Fdiff values.

Cu(II)-Metallized Three-Layered Cu-8HQ Complex with Hierarchical Crystallite Morphologies Synthesized via Reaction-Diffusion.

Self-organization of regular band patterns of the precipitate via a reaction-diffusion (RD) framework is called Liesegang phenomenon. This non-equilibrium system is emerging as an efficient method for synthesizing materials with unique morphologies that may have desired properties. The formation of continuous precipitation inside a band with poor control over the shape and size of sparingly soluble salts has been well documented. However, only a few reports on forming organic-inorganic bonds are available. In the present work, we demonstrate the formation of 2D frameworks of bis-(8-hydroxyquinoline) copper(II) in the agar gel via RD. The macroscopic particles were dumbbell-shaped, with aspect ratios ranging from 2.7 (inner bands) to 0.7 (outer bands). The particles were composed of ribbon-shaped crystallites at the microscopic level, each with three layers of parallelogram prismatic monoclinic sheets stacked over one another, which could easily be exfoliated. The powder X-ray diffraction patterns at low angles and the surface areas of the crystallites indicated the formation of metal-organic frameworks. It was observed that the sizes of the particles could be tuned by controlling the extent of diffusion using reactant concentrations. Since such heterostructures have energy storage capacity, the cyclic voltammograms of the unexfoliated and exfoliated materials showed that they fall in the pseudocapacitor category with potential application as the electrode material. The frameworks were further characterized by techniques such as optical and electron microscopy, X-ray diffraction, IR spectroscopy, and UV-visible spectrophotometry.

Do Similar Structures Have Similar No Observed Adverse Effect Level (NOAEL) Values? Exploring Chemoinformatics Approaches for Estimating NOAEL Bounds and Uncertainties.

Determination of the no observed adverse effect level (NOAEL) of a substance is an important step in safety and regulatory assessments. Application of conventional in silico strategies, for example, quantitative structure-activity relationship (QSAR) models, to predict NOAEL values is inherently problematic. Whereas QSAR models for well-defined toxicity endpoints such as Ames mutagenicity or skin sensitization can be developed from mechanistic knowledge of molecular initiating events and adverse outcome pathways, QSAR is not appropriate for predicting a NOAEL value, a concentration at which "no effect" is observed. This paper presents a chemoinformatics approach and explores how it can be further refined through the incorporation of toxicity endpoint-specific information to estimate confidence bounds for the NOAEL of a target substance, given experimentally determined NOAEL values for one or more suitable analogues. With a sufficiently large NOAEL database, we analyze how a difference in NOAEL values for pairs of structures depends on their pairwise similarity, where similarity takes both structural features and physicochemical properties into account. The width of the estimate NOAEL confidence interval is proportional to the uncertainty. Using the new threshold of toxicological concern (TTC) database enriched with antimicrobials, examples are presented to illustrate how uncertainty decreases with increasing analogue quality and also how NOAEL bounds estimation can be significantly improved by filtering the full database to include only substances that are in structure categories relevant to the target and analogue.

Precise Evaluation of Spatial Characteristics of Periodically Precipitating Systems via Measurement of RGB (Red, Green, and Blue) Values of Pattern Images.

In the present study, a method is described for precise determination of spatial characteristics of Liesegang bands formed by employing a classical 1D setup using a web-based free resource (https://www.ginifab.com/feeds/pms/color_picker_from_image.php). The method involves the compartmentalization of the information on each pixel into R (red), G (green), or B (blue) values from the pattern images obtained using a simple digital camera. The values can further be converted to absorbance values by using the system blank. Each trough (or peak) in the graph of RGB values (or absorbance values) corresponds to a band in the pattern. The method is employed to determine the spacing and width of the periodically precipitating AgCl, AgBr, and Co(OH)2 in an agar gel. It is observed that AgCl shows revert banding, and AgBr shows revert banding at the top of the tube and then diverges to regular banding at the bottom of the tube, whereas the Co(OH)2 patterns explicitly show regular banding under given experimental conditions. It is also observed that minute instabilities, such as the formation of secondary bands, can also be visualized by the present method.

Characterization of the rat Acetylcholinesterase readthrough (AChE-R) splice variant: Implications for toxicological studies.

Exposure to chemicals and other environmental stressors can differentially impact the expression of Acetylcholinesterase (AChE) splice variants. Surprisingly, despite the widespread use of the rat model in toxicological studies and the wealth of literature on this important biomarker of neurotoxicity, AChE coding exons and splice variants are not yet fully annotated in this species. To address this knowledge gap, a short problematic region of the rat AChE genomic DNA present in GenBank was first re-sequenced. This revised genomic sequence was then aligned to rat AChE RefSeq mRNA and compared to orthologous mammalian sequences, in order to map the coding exon and intron boundaries of the rat AChE gene. Based on these bioinformatics analyses, a sequence was predicted for the yet-unannotated rat Acetylcholinesterase readthrough (AChE-R) splice variant. PCR primers designed to specifically amplify rat AChE-R were used to confirm its expression in rat PC12 cells. Compared to the canonical AChE-S splice variant, AChE-R was expressed at much lower levels but presented distinct regulation patterns in PC12 cells and rat primary cerebral granule cells (CGCs) following exposure to Chlorpyrifos (a well-known neurotoxic organophosphate pesticide). Taken together, these observations point to the evolutionary conservation of the AChE-R splicing event between rodents and human and to the distinct regulation of AChE splice variants in response to toxicological challenges.

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.

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.

Incidence of infection following internal fixation of open and closed tibia fractures in India (INFINITI): a multi-centre observational cohort study.

BACKGROUND: Trauma is a major public health problem, particularly in India due to the country's rapid urbanization. Tibia fractures are a common and often complicated injury that is at risk of infection following surgical fixation. The primary objectives of this cohort study were to determine the incidence of infection within one year of surgery and to describe the distribution of infections by location and time of diagnosis for tibia fractures in India. METHODS: We conducted a multi-center, prospective cohort study. Patients who presented with an open or closed tibia fracture treated with internal fixation to one of the participating hospitals in India were invited to participate in the study. Participants attended follow-up visits at 3, 6, and 12 months post-surgery, where they were assessed for infections, fracture healing, and health-related quality of life as measured by the EurQol-5 Dimensions (EQ-5D). RESULTS: Seven hundred eighty-seven participants were included in the study and 768 participants completed the 12 month follow-up. The overall incidence of infection was 2.9% (23 infections). The incidence of infection was 1.6% (10 infections) in closed and 8.0% (13 infections) in open fractures. There were 7 deep and 16 superficial infections, with 5 being early, 7 being delayed, and 11 being late infections. Intra-operative antibiotics were given to 92.1% of participants and post-operative antibiotics were given to 96.8% of participants. Antibiotics were prescribed for an average of 8.3 days for closed fractures and 9.1 days for open fractures. Infected fractures took significantly longer to heal, and participants who had an infection had significantly lower EQ-5D scores. CONCLUSIONS: The incidence of infection within this cohort is similar to those seen in developed countries. The duration of prophylactic antibiotic use was longer than standard practice in North America, raising concern for the potential development of antibiotic resistant microbes within Indian orthopaedic settings. Future research should aim to identify the best practice for antibiotic use in India to ensure that antibiotic usage patterns do not lead to unnecessary overuse, while maintaining a low incidence of infection. TRIAL REGISTRATION: NCT01691599 , September 17, 2012.

Performance of (Q)SAR models for predicting Ames mutagenicity of aryl azo and benzidine based compounds.

Regulatory agencies worldwide are committed to the objectives of the Strategic Approach to International Chemicals Management to ensure that by 2020 chemicals are used and produced in ways that lead to the minimization of significant adverse effects on human health and the environment. Under the Government of Canada's Chemicals Management Plan, the commitment to address a large number of substances, many with limited data, has highlighted the importance of pursuing alternative hazard assessment methodologies that are able to accommodate chemicals with varying toxicological information. One such method is (Quantitative) Structure Activity Relationships ((Q)SAR) models. The current investigation into the predictivity of 20 (Q)SAR tools designed to model bacterial reverse mutation in Salmonella typhimurium is one of the first of this magnitude to be carried out using an external validation set comprised mainly of industrial chemicals which represent a diverse group of aromatic and benzidine-based azo dyes and pigments. Overall, this study highlights the value in challenging the predictivity of existing models using a small but representative subset of data-rich chemicals. Furthermore, external validation revealed that only a handful of models satisfactorily predicted for the test chemical space. The exercise also provides insight into using the Organisation for Economic Co-operation and Development (Q)SAR Toolbox as a read across tool.

Development and application of consensus in silico models for advancing high-throughput toxicological predictions.

Computational toxicology models have been successfully implemented to prioritize and screen chemicals. There are numerous in silico (quantitative) structure-activity relationship ([Q]SAR) models for the prediction of a range of human-relevant toxicological endpoints, but for a given endpoint and chemical, not all predictions are identical due to differences in their training sets, algorithms, and methodology. This poses an issue for high-throughput screening of a large chemical inventory as it necessitates several models to cover diverse chemistries but will then generate data conflicts. To address this challenge, we developed a consensus modeling strategy to combine predictions obtained from different existing in silico (Q)SAR models into a single predictive value while also expanding chemical space coverage. This study developed consensus models for nine toxicological endpoints relating to estrogen receptor (ER) and androgen receptor (AR) interactions (i.e., binding, agonism, and antagonism) and genotoxicity (i.e., bacterial mutation, in vitro chromosomal aberration, and in vivo micronucleus). Consensus models were created by combining different (Q)SAR models using various weighting schemes. As a multi-objective optimization problem, there is no single best consensus model, and therefore, Pareto fronts were determined for each endpoint to identify the consensus models that optimize the multiple-criterion decisions simultaneously. Accordingly, this work presents sets of solutions for each endpoint that contain the optimal combination, regardless of the trade-off, with the results demonstrating that the consensus models improved both the predictive power and chemical space coverage. These solutions were further analyzed to find trends between the best consensus models and their components. Here, we demonstrate the development of a flexible and adaptable approach for in silico consensus modeling and its application across nine toxicological endpoints related to ER activity, AR activity, and genotoxicity. These consensus models are developed to be integrated into a larger multi-tier NAM-based framework to prioritize chemicals for further investigation and support the transition to a non-animal approach to risk assessment in Canada.

In Silico Models for Developmental Toxicity.

Modeling developmental toxicity has been a challenge for (Q)SAR model developers due to the complexity of the endpoint. Recently, some new in silico methods have been developed introducing the possibility to evaluate the integration of existing methods by taking advantage of various modeling perspectives. It is important that the model user is aware of the underlying basis of the different models in general, as well as the considerations and assumptions relative to the specific predictions that are obtained from these different models for the same chemical. The evaluation on the predictions needs to be done on a case-by-case basis, checking the analogues (possibly using structural, physicochemical, and toxicological information); for this purpose, the assessment of the applicability domain of the models provides further confidence in the model prediction. In this chapter, we present some examples illustrating an approach to combine human-based rules and statistical methods to support the prediction of developmental toxicity; we also discuss assumptions and uncertainties of the methodology.

Implementing in vitro bioactivity data to modernize priority setting of chemical inventories.

Internationally, there are thousands of existing and newly introduced chemicals in commerce, highlighting the ongoing importance of innovative approaches to identify emerging chemicals of concern. For many chemicals, there is a paucity of hazard and exposure data. Thus, there is a crucial need for efficient and robust approaches to address data gaps and support risk-based prioritization. Several studies have demonstrated the utility of in vitro bioactivity data from the ToxCast program in deriving points of departure (PODs). ToxCast contains data for nearly 1,400 endpoints per chemical, and the bioactivity concentrations, indicative of potential adverse outcomes, can be converted to human-equivalent PODs using high-throughput toxicokinetics (HTTK) modeling. However, data gaps need to be addressed for broader application: the limited chemical space of HTTK and quantitative high-throughput screening data. Here we explore the applicability of in silico models to address these data needs. Specifically, we used ADMET predictor for HTTK predictions and a generalized read-across approach to predict ToxCast bioactivity potency. We applied these models to profile 5,801 chemicals on Canada's Domestic Substances List (DSL). To evaluate the approach's performance, bioactivity PODs were compared with in vivo results from the EPA Toxicity Values database for 1,042 DSL chemicals. Comparisons demonstrated that the bioac­tivity PODs, based on ToxCast data or read-across, were conservative for 95% of the chemicals. Comparing bioactivity PODs to human exposure estimates supports the identification of chemicals of potential interest for further work. The bioac­tivity workflow shows promise as a powerful screening tool to support effective triaging of chemical inventories.

Multiresidue analysis of plant growth regulators in grapes by triple quadrupole and quadrupole-time of flight-based liquid chromatography/mass spectrometry.

A selective and sensitive LC-MS/MS method is presented for simultaneous determination of 12 plant growth regulators, viz., indol-3-acetic acid, indol-3-butyric acid, kinetin, zeatin, 6-benzyl aminopurine, gibberellic acid, abscisic acid, chlormequat chloride, forchlorfenuron, paclobutrazole, daminozide, and 2,4-dichlorophenoxy acetic acid, in bud sprouts and grape berries. The sample preparation method involved extraction of homogenized sample (5 g) with 40 mL methanol (80%), and final determination was by LC-MS/MS in the multiple reaction monitoring (MRM) mode with time segmentation for quantification supported by complementary analysis by quadrupole-time of flight (Q-TOF) MS with targeted high-resolution MS/MS scanning for confirmatory identification based on accurate mass measurements. The recovery of the test compounds ranged within 90-107% with precision RSD less than 5% (n = 6). The method could be successfully applied in analyzing incurred residue samples, and the strength of accurate mass analysis could be utilized in identifying the compounds in cases where the qualifier MRM ions were absent or at an S/N less than 3:1 due to low concentrations.

Cement beads and cement spacers: indications, techniques, and clinical results.

Objectives: To report our experience on the use of antibiotic coated nails (ACN) and cement beads for the management of bone infections. Materials and methods: Infected nonunion (INU) cases were classified as: Type I (mild infection with no gap), Type II (moderate with good alignment, severe infection, gap <3 cm, no deformity), Type III (severe infection with gap ≥3 cm, deformity and limb shortening). Treatment involved either the insertion of ACN and cast (Type I), insertion of ACN, beads and external fixator (Type II), or Ilizarov methodology (Type III). A subset of 28 open fractures were admitted with severe contamination or delayed presentation with established infection and treated with debridement, ACN insertion, and antibiotic beads placed in soft tissue dead space areas. Results: Results of 133 cases were classified excellent, good, and poor. Type I INU reported 40 excellent and 22 good results. Type II INU reported 28 (39%) excellent, 30 (43%) good, and 13 (18%) poor results. Poor results were due to uncontrolled infection and knee stiffness. Three patients required knee fusion and 1 required amputation. Fracture union was reported in 68 cases. Four of the 28 Gustilo grade III open fractures treated with ACN developed infected nonunion and had poor function caused by stiff knees. Conclusions: An antibiotic impregnated cement nail (ACN) fills the dead space and elutes high concentrations of antibiotics providing some mechanical stability. We recommend the adjunct use of an ACN for the management of INU cases and for use in select cases of Gustilo grade III open fractures.

4,5,6,7-Tetrabromo-2,3-dihydro-1,1,3-trimethyl-3-(2,3,4,5-tetrabromophenyl)-1H-indene (OBTMPI): Levels in humans and in silico toxicological profiles.

Limited human exposure and toxicity data are currently available for 4,5,6,7-Tetrabromo-2,3-dihydro-1,1,3-trimethyl-3-(2,3,4,5-tetrabromophenyl)-1H-indene (OBTMPI), a flame retardant often used for high temperature application of various polymer materials. Levels of OBTMPI in a cohort population that includes children and their co-residing parents (n = 217) in Canada were determined. Detection frequency of OBTMPI in the samples was 22.6%. OBTMPI levels were in general at sub-to low ng/g lipid weight level with a 95th percentile at 15.6 ng/g lipid weight. Compared to an earlier study conducted in 2008-2009 in the same region, results from this study show an increase in both detection frequency and concentration of OBTMPI. In silico toxicity predictions using Multicase CaseUltra and Leadscope Model Applier suggested that OBTMPI, and its possible metabolites in humans, while unlikely to be carcinogenic or mutagenic, exhibit some estrogen antagonist, androgen antagonist and estrogen binding capability reflective of possible endocrine disrupting properties.

Cr(VI)-Mediated Homogeneous Fenton Oxidation for Decolorization of Methylene Blue Dye: Sludge Free and Pertinent to a Wide pH Range.

Fe(II)-mediated Fenton process is commonly employed for oxidative degradation of recalcitrant pollutants in wastewater. However, the method suffers from limitations like narrow working pH range and iron sludge formation. The present work deals with the degradation of Methylene Blue (MB) dye using Fenton-like oxidation by replacing Fe(II) with Cr(VI), which eliminates the limitations of classical Fenton oxidation. The Fenton-like oxidation of MB is brought about by HO• radicals generated by the disproportionation of chromium-coordinated peroxo complexes. It was observed that the working pH range for the Cr(VI)-mediated Fenton oxidation was 3-10, and no sludge formation takes place up to four cycles as the oxidation remains in the pure solution phase. The complete mineralization of dye was confirmed by observing the decay of MB peaks by a spectrophotometer and cyclic voltammetry. The reaction parameters like pH of the solution, temperature, degradation time, concentrations of H2O2, Cr(VI), and MB were studied for optimal performance of the Cr(VI) as the catalyst. Kinetic studies revealed that the Cr(VI)-mediated Fenton reaction follows pseudo-first-order reaction kinetics and depends on the concentration of HO• radicals. The proposed Cr(VI)-mediated Fenton oxidation in the present work is best suited for the degradation of organic dyes by adding H2O2 as a precursor in chromate-contaminated wastewaters.

Assessment of the predictive capacity of a physiologically based kinetic model using a read-across approach.

With current progress in science, there is growing interest in developing and applying Physiologically Based Kinetic (PBK) models in chemical risk assessment, as knowledge of internal exposure to chemicals is critical to understanding potential effects in vivo. In particular, a new generation of PBK models is being developed in which the model parameters are derived from in silico and in vitro methods. To increase the acceptance and use of these "Next Generation PBK models", there is a need to demonstrate their validity. However, this is challenging in the case of data-poor chemicals that are lacking in kinetic data and for which predictive capacity cannot, therefore, be assessed. The aim of this work is to lay down the fundamental steps in using a read across framework to inform modellers and risk assessors on how to develop, or evaluate, PBK models for chemicals without in vivo kinetic data. The application of a PBK model that takes into account the absorption, distribution, metabolism and excretion characteristics of the chemical reduces the uncertainties in the biokinetics and biotransformation of the chemical of interest. A strategic flow-charting application, proposed herein, allows users to identify the minimum information to perform a read-across from a data-rich chemical to its data-poor analogue(s). The workflow analysis is illustrated by means of a real case study using the alkenylbenzene class of chemicals, showing the reliability and potential of this approach. It was demonstrated that a consistent quantitative relationship between model simulations could be achieved using models for estragole and safrole (source chemicals) when applied to methyleugenol (target chemical). When the PBK model code for the source chemicals was adapted to utilise input values relevant to the target chemical, simulation was consistent between the models. The resulting PBK model for methyleugenol was further evaluated by comparing the results to an existing, published model for methyleugenol, providing further evidence that the approach was successful. This can be considered as a "read-across" approach, enabling a valid PBK model to be derived to aid the assessment of a data poor chemical.

Canadian Regulatory Perspective on Next Generation Risk Assessments for Pest Control Products and Industrial Chemicals.

In 2012, the Council of Canadian Academies published the expert panel on integrated testing of pesticide's report titled: Integrating emerging technologies into chemical safety assessment. This report was prepared for the Government of Canada in response to a request from the Minister of Health and on behalf of the Pest Management Regulatory Agency. It examined the scientific status of the use of integrated testing strategies for the regulatory health risk assessment of pesticides while noting the data-rich/poor dichotomy that exists when comparing pesticide formulations to most industrial chemicals. It also noted that the adoption of integrated approaches to testing and assessment (IATA) strategies may refine and streamline testing of chemicals, as well as improve results in the future. Moreover, the experts expected to see an increase in the use of integrated testing strategies over the next decade, resulting in improved evidence-based decision-making. Subsequent to this report, there has been great advancements in IATA strategies, which includes the incorporation of adverse outcome pathways (AOPs) and new approach methodologies (NAMs). This perspective provides the first Canadian regulatory update on how Health Canada is also advancing the incorporation of alternative, non-animal strategies, using a weight of evidence approach, for the evaluation of pest control products and industrial chemicals. It will include specific initiatives and describe how this work is leading to the creation of next generation risk assessments. It also reflects Health Canada's commitment towards implementing the 3Rs of animal testing: reduce, refine and replace the need for animal studies, whenever possible.

Hip fracture care and national systems: Australia and Asia.

The Asia-Pacific region includes countries with diverse cultural, demographic, and socio-political backgrounds. Countries such as Japan have very high life expectancy and an aged population. China and India, with a combined population over 2.7 billion, will experience a huge wave of ageing population with subsequent osteoporotic injuries. Australia will experience a similar increase in the osteoporotic fracture burden, and is leading the region by establishing a national hip fracture registry with governmental guidelines and outcome monitoring. While it is impossible to compare fragility hip fracture care in every Asia-Pacific country, this review of 4 major nations gives insight into the challenges facing diverse systems. They are united by the pursuit of internationally accepted standards of timely surgery, combined orthogeriatric care, and secondary fracture prevention strategies.