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Background The widespread availability of cannabis raises concerns regarding its effect on driving performance and operation of complex equipment. Currently, there are no established safe driving limits regarding ∆9-tetrahydrocannabinol (THC) concentrations in blood or breath. Daily cannabis users build up a large body burden of THC with residual excretion for days or weeks after the start of abstinence. Therefore, it is critical to have a sensitive and specific analytical assay that quantifies THC, the main psychoactive component of cannabis, and multiple metabolites to improve interpretation of cannabinoids in blood; some analytes may indicate recent use. Methods A liquid chromatography tandem mass spectrometry (LC-MS/MS) method was developed to quantify THC, cannabinol (CBN), cannabidiol (CBD), 11-hydroxy-THC (11-OH-THC), (±)-11-nor-9-carboxy-Δ9-THC (THCCOOH), (+)-11-nor-Δ9-THC-9-carboxylic acid glucuronide (THCCOOH-gluc), cannabigerol (CBG), and tetrahydrocannabivarin (THCV) in whole blood (WB). WB samples were prepared by solid-phase extraction (SPE) and quantified by LC-MS/MS. A rapid and simple method involving methanol elution of THC in breath collected in SensAbues® devices was optimized. Results Lower limits of quantification ranged from 0.5 to 2 μg/L in WB. An LLOQ of 80 pg/pad was achieved for THC concentrations in breath. Calibration curves were linear (R2>0.995) with calibrator concentrations within ±15% of their target and quality control (QC) bias and imprecision ≤15%. No major matrix effects or drug interferences were observed. Conclusions The methods were robust and adequately quantified cannabinoids in biological blood and breath samples. These methods will be used to identify cannabinoid concentrations in an upcoming study of the effects of cannabis on driving.

BackgroundThere is tremendous potential to leverage the value gained from integrating electronic health records (EHR) and population-based cancer registry data for research. Registries provide diagnosis details, tumor characteristics, and treatment summaries, while EHRs contain rich clinical detail. A carefully conducted cancer registry linkage may also be used to improve the internal and external validity of inferences made from EHR-based studies.MethodsWe linked the EHRs of a large, multispecialty, mixed-payer health care system with the statewide cancer registry and assessed the validity of our linked population. For internal validity, we identify patients that might be "missed" in a linkage, threatening the internal validity of an EHR study population. For generalizability, we compared linked cases with all other cancer patients in the 22-county EHR catchment region.ResultsFrom an EHR population of 4.5 million, we identified 306,554 patients with cancer, 26% of the catchment region patients with cancer; 22.7% of linked patients were diagnosed with cancer after they migrated away from our health care system highlighting an advantage of system-wide linkage. We observed demographic differences between EHR patients and non-EHR patients in the surrounding region and demonstrated use of selection probabilities with model-based standardization to improve generalizability.ConclusionsOur experiences set the foundation to encourage and inform researchers interested in working with EHRs for cancer research as well as provide context for leveraging linkages to assess and improve validity and generalizability.ImpactResearchers conducting linkages may benefit from considering one or more of these approaches to establish and evaluate the validity of their EHR-based populations.See all articles in this CEBP Focus section, "Modernizing Population Science."

This white paper presents principles for validating proarrhythmia risk prediction models for regulatory use as discussed at the In Silico Breakout Session of a Cardiac Safety Research Consortium/Health and Environmental Sciences Institute/US Food and Drug Administration-sponsored Think Tank Meeting on May 22, 2018. The meeting was convened to evaluate the progress in the development of a new cardiac safety paradigm, the Comprehensive in Vitro Proarrhythmia Assay (CiPA). The opinions regarding these principles reflect the collective views of those who participated in the discussion of this topic both at and after the breakout session. Although primarily discussed in the context of in silico models, these principles describe the interface between experimental input and model-based interpretation and are intended to be general enough to be applied to other types of nonclinical models for proarrhythmia assessment. This document was developed with the intention of providing a foundation for more consistency and harmonization in developing and validating different models for proarrhythmia risk prediction using the example of the CiPA paradigm.

This white paper presents principles for validating proarrhythmia risk prediction models for regulatory use as discussed at the In Silico Breakout Session of a Cardiac Safety Research Consortium/Health and Environmental Sciences Institute/US Food and Drug Administration-sponsored Think Tank Meeting on May 22, 2018. The meeting was convened to evaluate the progress in the development of a new cardiac safety paradigm, the Comprehensive in Vitro Proarrhythmia Assay (CiPA). The opinions regarding these principles reflect the collective views of those who participated in the discussion of this topic both at and after the breakout session. Although primarily discussed in the context of in silico models, these principles describe the interface between experimental input and model-based interpretation and are intended to be general enough to be applied to other types of nonclinical models for proarrhythmia assessment. This document was developed with the intention of providing a foundation for more consistency and harmonization in developing and validating different models for proarrhythmia risk prediction using the example of the CiPA paradigm.

BackgroundTechnological advancements of remote-monitoring used in clinical-care and research require validation of model updates.ObjectivesTo compare the output of a newer consumer-grade accelerometer to a previous model in people with multiple sclerosis (MS) and to the ActiGraph, a waist-worn device widely used in MS research.MethodsThirty-one individuals with MS participated in a 7-day validation by the Fitbit Flex (Flex), Fitbit Flex-2 (Flex2) and ActiGraph GT3X. Primary outcome was step count. Valid epochs of 5-min block increments, where there was overlap of ≥1 step/min for both devices were compared and summed to give a daily total for analysis.ResultsBland-Altman plots showed no systematic difference between the Flex and Flex2; mean step-count difference of 25 more steps-per-day more recorded by Flex2 (95% confidence intervals (CI) = 2, 48; p = 0.04),interclass correlation coefficient (ICC) = 1.00. Compared to the ActiGraph, Flex2 (and Flex) tended to record more steps (808 steps-per-day more than the ActiGraph (95% CI= -2380, 765; p

Background:Technological advancements of remote-monitoring used in clinical-care and research require validation of model updates. Objectives:To compare the output of a newer consumer-grade accelerometer to a previous model in people with multiple sclerosis (MS) and to the ActiGraph, a waist-worn device widely used in MS research. Methods:Thirty-one individuals with MS participated in a 7-day validation by the Fitbit Flex (Flex), Fitbit Flex-2 (Flex2) and ActiGraph GT3X. Primary outcome was step count. Valid epochs of 5-min block increments, where there was overlap of ≥1 step/min for both devices were compared and summed to give a daily total for analysis. Results:Bland-Altman plots showed no systematic difference between the Flex and Flex2; mean step-count difference of 25 more steps-per-day more recorded by Flex2 (95% confidence intervals (CI) = 2, 48; p = 0.04),interclass correlation coefficient (ICC) = 1.00. Compared to the ActiGraph, Flex2 (and Flex) tended to record more steps (808 steps-per-day more than the ActiGraph (95% CI= -2380, 765; p

Systems neuroscience research is increasingly possible through the use of integrated molecular and circuit-level analyses. These studies depend on the use of animal models and, in many cases, molecular and circuit-level analyses. Associated with genetic, pharmacologic, epigenetic, and other types of environmental manipulations. We illustrate typical pitfalls resulting from poor validation of behavior tests. We describe experimental designs and enumerate controls needed to improve reproducibility in investigating and reporting of behavioral phenotypes.

This article describes the implementation of real-space refinement in the phenix.real_space_refine program from the PHENIX suite. The use of a simplified refinement target function enables very fast calculation, which in turn makes it possible to identify optimal data-restraint weights as part of routine refinements with little runtime cost. Refinement of atomic models against low-resolution data benefits from the inclusion of as much additional information as is available. In addition to standard restraints on covalent geometry, phenix.real_space_refine makes use of extra information such as secondary-structure and rotamer-specific restraints, as well as restraints or constraints on internal molecular symmetry. The re-refinement of 385 cryo-EM-derived models available in the Protein Data Bank at resolutions of 6 Å or better shows significant improvement of the models and of the fit of these models to the target maps.

This article describes the implementation of real-space refinement in the phenix.real_space_refine program from the PHENIX suite. The use of a simplified refinement target function enables very fast calculation, which in turn makes it possible to identify optimal data-restraint weights as part of routine refinements with little runtime cost. Refinement of atomic models against low-resolution data benefits from the inclusion of as much additional information as is available. In addition to standard restraints on covalent geometry, phenix.real_space_refine makes use of extra information such as secondary-structure and rotamer-specific restraints, as well as restraints or constraints on internal molecular symmetry. The re-refinement of 385 cryo-EM-derived models available in the Protein Data Bank at resolutions of 6 Å or better shows significant improvement of the models and of the fit of these models to the target maps.

ObjectiveTo perform patient-specific meta-analysis (MA) of two independent clinical validation studies of a 17-gene biopsy-based genomic assay as a predictor of favorable pathology at radical prostatectomy.Materials and methodsPatient-specific MA was performed on data from 2 studies (732 patients) using the Genomic Prostate Score (GPS; scale 0-100) together with Cancer of the Prostate Risk Assessment (CAPRA) score or National Comprehensive Cancer Network (NCCN) risk group as predictors of the likelihood of favorable pathology (LFP). Risk profile curves associating GPS with LFP by CAPRA score and NCCN risk group were generated. Decision curves and receiver operating characteristic curves were calculated using patient-specific MA risk estimates.ResultsPatient-specific MA-generated risk profiles ensure more precise estimates of LFP with narrower confidence intervals than either study alone. GPS added significant predictive value to each clinical classifier. A model utilizing GPS and CAPRA provided the most risk discrimination. In decision-curve analysis, greater net benefit was shown when combining GPS with each clinical classifier compared with the classifier alone. The area under the receiver operating characteristic curve improved from 0.68 to 0.73 by adding GPS to CAPRA, and 0.64 to 0.70 by adding GPS to NCCN risk group. The proportion of patients with LFP >80% increased from 11% using NCCN risk group alone to 23% using GPS with NCCN. Using GPS with CAPRA identified the highest proportion-31%-of patients with LFP >80%.ConclusionPatient-specific MA provides more precise risk estimates that reflect the complete body of evidence. GPS adds predictive value to 3 widely used clinical classifiers, and identifies a larger proportion of low-risk patients than identified by clinical risk group alone.

ObjectiveTo perform patient-specific meta-analysis (MA) of two independent clinical validation studies of a 17-gene biopsy-based genomic assay as a predictor of favorable pathology at radical prostatectomy.Materials and methodsPatient-specific MA was performed on data from 2 studies (732 patients) using the Genomic Prostate Score (GPS; scale 0-100) together with Cancer of the Prostate Risk Assessment (CAPRA) score or National Comprehensive Cancer Network (NCCN) risk group as predictors of the likelihood of favorable pathology (LFP). Risk profile curves associating GPS with LFP by CAPRA score and NCCN risk group were generated. Decision curves and receiver operating characteristic curves were calculated using patient-specific MA risk estimates.ResultsPatient-specific MA-generated risk profiles ensure more precise estimates of LFP with narrower confidence intervals than either study alone. GPS added significant predictive value to each clinical classifier. A model utilizing GPS and CAPRA provided the most risk discrimination. In decision-curve analysis, greater net benefit was shown when combining GPS with each clinical classifier compared with the classifier alone. The area under the receiver operating characteristic curve improved from 0.68 to 0.73 by adding GPS to CAPRA, and 0.64 to 0.70 by adding GPS to NCCN risk group. The proportion of patients with LFP >80% increased from 11% using NCCN risk group alone to 23% using GPS with NCCN. Using GPS with CAPRA identified the highest proportion-31%-of patients with LFP >80%.ConclusionPatient-specific MA provides more precise risk estimates that reflect the complete body of evidence. GPS adds predictive value to 3 widely used clinical classifiers, and identifies a larger proportion of low-risk patients than identified by clinical risk group alone.

PURPOSE: Sanger sequencing is currently considered the gold standard methodology for clinical molecular diagnostic testing. However, next-generation sequencing has already emerged as a much more efficient means to identify genetic variants within gene panels, the exome, or the genome. We sought to assess the accuracy of next-generation sequencing variant identification in our clinical genomics laboratory with the goal of establishing a quality score threshold for confirmatory Sanger-based testing. METHODS: Confirmation data for reported results from 144 sequential clinical exome-sequencing cases (94 unique variants) and an additional set of 16 variants from comparable research samples were analyzed. RESULTS: Of the 110 total single-nucleotide variants analyzed, 103 variants had a quality score ≥Q500, 103 (100%) of which were confirmed by Sanger sequencing. Of the remaining seven variants with quality scores

The List Sorting Working Memory Test was designed to assess working memory (WM) as part of the NIH Toolbox Cognition Battery. List Sorting is a sequencing task requiring children and adults to sort and sequence stimuli that are presented visually and auditorily. Validation data are presented for 268 participants ages 20 to 85 years. A subset of participants (N=89) was retested 7 to 21 days later. As expected, the List Sorting Test had moderately high correlations with other measures of working memory and executive functioning (convergent validity) but a low correlation with a test of receptive vocabulary (discriminant validity). Furthermore, List Sorting demonstrates expected changes over the age span and has excellent test-retest reliability. Collectively, these results provide initial support for the construct validity of the List Sorting Working Memory Measure as a measure of working memory. However, the relationship between the List Sorting Test and general executive function has yet to be determined.

The objective of this study is to evaluate the construct validity of the NIH Neurobehavioral Toolbox Cognitive Health Battery (NIHTB-CHB) in adults. Confirmatory factor analysis was used to evaluate the dimensional structure underlying the NIHTB-CHB and Gold Standard tests chosen to serve as concurrent validity criteria for the NIHTB-CHB. These results were used to evaluate the convergent and discriminant validity of the NIHTB-CHB in adults ranging from 20 to 85 years of age. Five dimensions were found to explain the correlations among NIHTB-CHB and Gold Standard tests: Vocabulary, Reading, Episodic Memory, Working Memory and Executive Function/Processing Speed. NIHTB-CHB measures and their Gold Standard analogues defined factors in a pattern that broadly supported the convergent and discriminant validity of the NIHTB-CHB tests. This 5-factor structure was found to be invariant across 20-60 year old (N=159) and 65-85 year old (N=109) age groups that were included in the current validity study. Second order Crystallized Abilities (Vocabulary and Reading) and Fluid Abilities (Episodic Memory, Working Memory, Executive/Speed) factors parsimoniously explained correlations among the five first order factors. These results suggest that the NIHTB-CHB will provide both fine-grained and broad characterization of cognition across the adult age span.

Historically disease knowledge development and treatment innovation in osteoarthritis (OA) has been considered to be slow. One of the many reasons purported as responsible for this slow pace has been the alleged lack of valid and responsive biomarkers to ascertain efficacy, which itself has been dependent upon the slow evolution of the understanding of the complex nature of joint tissue biology. This narrative review outlines the rationale for why we need OA biomarkers with regard to biomarker validation and qualification. The main biomarkers in current development for OA are biochemical and imaging markers. We describe an approach to biomarker validation and qualification for OA clinical trials that has recently commenced with the Foundation of NIH OA Biomarkers Consortium study cosponsored by the Osteoarthritis Research Society International (OARSI). With this approach we endeavor to identify, develop, and qualify biological markers (biomarkers) to support new drug development, preventive medicine, and medical diagnostics for osteoarthritis.

Early transplant dysfunction and failure because of immunological and nonimmunological factors still presents a significant clinical problem for transplant recipients. A critical unmet need is the noninvasive detection and prediction of immune injury such that acute injury can be reversed by proactive immunosuppression titration. In this study, we used iTRAQ -based proteomic discovery and targeted ELISA validation to discover and validate candidate urine protein biomarkers from 262 renal allograft recipients with biopsy-confirmed allograft injury. Urine samples were randomly split into a training set of 108 patients and an independent validation set of 154 patients, which comprised the clinical biopsy-confirmed phenotypes of acute rejection (AR) (n = 74), stable graft (STA) (n = 74), chronic allograft injury (CAI) (n = 58), BK virus nephritis (BKVN) (n = 38), nephrotic syndrome (NS) (n = 8), and healthy, normal control (HC) (n = 10). A total of 389 proteins were measured that displayed differential abundances across urine specimens of the injury types (p < 0.05) with a significant finding that SUMO2 (small ubiquitin-related modifier 2) was identified as a "hub" protein for graft injury irrespective of causation. Sixty-nine urine proteins had differences in abundance (p < 0.01) in AR compared with stable graft, of which 12 proteins were up-regulated in AR with a mean fold increase of 2.8. Nine urine proteins were highly specific for AR because of their significant differences (p < 0.01; fold increase >1.5) from all other transplant categories (HLA class II protein HLA-DRB1, KRT14, HIST1H4B, FGG, ACTB, FGB, FGA, KRT7, DPP4). Increased levels of three of these proteins, fibrinogen beta (FGB; p = 0.04), fibrinogen gamma (FGG; p = 0.03), and HLA DRB1 (p = 0.003) were validated by ELISA in AR using an independent sample set. The fibrinogen proteins further segregated AR from BK virus nephritis (FGB p = 0.03, FGG p = 0.02), a finding that supports the utility of monitoring these urinary proteins for the specific and sensitive noninvasive diagnosis of acute renal allograft rejection.

Despite the growing ubiquity of data in the medical domain, it remains difficult to apply results from experimental and observational studies to additional populations suffering from the same disease. Many methods are employed for testing internal validity; yet limited effort is made in testing generalizability, or external validity. The development of disease models often suffers from this lack of validity testing and trained models frequently have worse performance on different populations, rendering them ineffective. In this work, we discuss the use of transportability theory, a causal graphical model examination, as a mechanism for determining what elements of a data resource can be shared or moved between a source and target population. A simplified Bayesian model of glioblastoma multiforme serves as the example for discussion and preliminary analysis. Examination over data collection hospitals from the TCGA dataset demonstrated improvement of prediction in a transported model over a baseline model.

Dementia in Parkinson's disease (PD) is a serious health issue and a major concern for many patients. In most cases mild cognitive impairment (MCI) is considered a transitional stage between normal cognitive functioning and dementia which is of potential importance in the early identification of patients at risk for dementia. Recently, the Movement Disorder Society (MDS) proposed diagnostic criteria for MCI in PD (PD-MCI). These criteria comprise two operationalizations: Level I (based on an abbreviated assessment) and Level II (based on comprehensive neuropsychological evaluation permitting MCI subtyping). These criteria need to be validated. This paper describes a project aiming to validate the MDS PD-MCI criteria by pooling and analyzing cross-sectional and longitudinal neuropsychological databases comprising ≥5,500 PD patients and ≥1,700 controls. After applying the MDS PD-MCI Level I and Level II criteria, rates of conversion to PD-dementia and predictive variables for conversion to PD-dementia will be established. This study will also assist in identifying whether revisions of the PD-MCI criteria are required.

BackgroundPancreatic ductal adenocarcinoma (PDAC) patients demonstrate highly variable survival within each stage of the American Joint Committee on Cancer (AJCC) staging system. We hypothesize that tumor grade is partly responsible for this variation. Recently our group developed a novel tumor, node, metastasis, grade (TNMG) classification system utilizing Surveillance Epidemiology and End Results (SEER) data in which the presence of high tumor grade results in advancement to the next higher AJCC stage. This study's objective was to validate this TNMG staging system utilizing single-institution data.MethodsAll patients with PDAC who underwent resection at UCLA between 1990 and 2009 were identified. Clinicopathologic data reviewed included age, sex, node status, tumor size, grade, and stage. Grade was redefined as a dichotomous variable. The impact of grade on survival was assessed by Cox regression analysis. Disease was restaged into the TNMG system and compared to the AJCC staging system.ResultsWe identified 256 patients who underwent resection for PDAC. Patients with low-grade tumors experienced a 13-month improvement in median survival compared to those with high-grade tumors. On multivariate analysis, tumor grade was the strongest predictor of survival with a hazard ratio of 2.02 (p = 0.0005). Restaging disease according to the novel TNMG staging system resulted in improved survival discrimination between stages compared to the current AJCC system.ConclusionsWe were able to demonstrate that grade is one of the strongest independent prognostic factors in PDAC. Restaging with our novel TNMG system demonstrated improved prognostication. This system offers an effective and convenient way of adding grade to the current AJCC staging system.

BackgroundPancreatic ductal adenocarcinoma (PDAC) patients demonstrate highly variable survival within each stage of the American Joint Committee on Cancer (AJCC) staging system. We hypothesize that tumor grade is partly responsible for this variation. Recently our group developed a novel tumor, node, metastasis, grade (TNMG) classification system utilizing Surveillance Epidemiology and End Results (SEER) data in which the presence of high tumor grade results in advancement to the next higher AJCC stage. This study's objective was to validate this TNMG staging system utilizing single-institution data.MethodsAll patients with PDAC who underwent resection at UCLA between 1990 and 2009 were identified. Clinicopathologic data reviewed included age, sex, node status, tumor size, grade, and stage. Grade was redefined as a dichotomous variable. The impact of grade on survival was assessed by Cox regression analysis. Disease was restaged into the TNMG system and compared to the AJCC staging system.ResultsWe identified 256 patients who underwent resection for PDAC. Patients with low-grade tumors experienced a 13-month improvement in median survival compared to those with high-grade tumors. On multivariate analysis, tumor grade was the strongest predictor of survival with a hazard ratio of 2.02 (p = 0.0005). Restaging disease according to the novel TNMG staging system resulted in improved survival discrimination between stages compared to the current AJCC system.ConclusionsWe were able to demonstrate that grade is one of the strongest independent prognostic factors in PDAC. Restaging with our novel TNMG system demonstrated improved prognostication. This system offers an effective and convenient way of adding grade to the current AJCC staging system.

Prior studies have demonstrated associations between beta-adrenergic receptor (βAR) polymorphisms and left ventricular dysfunction-an important cause of allograft nonutilization for transplantation. We hypothesized that βAR polymorphisms predispose donor hearts to LV dysfunction after brain death. A total of 1043 organ donors managed from 2001-2006 were initially studied. The following βAR single nucleotide polymorphisms were genotyped: β1AR 1165C/G (Arg389Gly), β1AR 145A/G (Ser49Gly), β2AR 46G/A (Gly16Arg) and β2AR 79C/G (Gln27Glu). In multivariable regression analyses, the β2AR46 SNP was significantly associated with LV systolic dysfunction, with each minor allele additively decreasing the odds for LV ejection fraction 10 μg/kg/min of dopamine compared to those with the CC and GG genotypes. However, no significant associations were found between βAR SNPs and cardiac dysfunction in 364 donors managed from 2007-2008, perhaps due to changes in donor management, lack of power in this validation cohort, or the absence of a true association. βAR polymorphisms may be associated with cardiac dysfunction after brain death, but these relationships require further study in independent donor cohorts.

African Americans are disproportionately affected by type 2 diabetes (T2DM) yet few studies have examined T2DM using genome-wide association approaches in this ethnicity. The aim of this study was to identify genes associated with T2DM in the African American population. We performed a Genome Wide Association Study (GWAS) using the Affymetrix 6.0 array in 965 African-American cases with T2DM and end-stage renal disease (T2DM-ESRD) and 1029 population-based controls. The most significant SNPs (n = 550 independent loci) were genotyped in a replication cohort and 122 SNPs (n = 98 independent loci) were further tested through genotyping three additional validation cohorts followed by meta-analysis in all five cohorts totaling 3,132 cases and 3,317 controls. Twelve SNPs had evidence of association in the GWAS (P

African Americans are disproportionately affected by type 2 diabetes (T2DM) yet few studies have examined T2DM using genome-wide association approaches in this ethnicity. The aim of this study was to identify genes associated with T2DM in the African American population. We performed a Genome Wide Association Study (GWAS) using the Affymetrix 6.0 array in 965 African-American cases with T2DM and end-stage renal disease (T2DM-ESRD) and 1029 population-based controls. The most significant SNPs (n = 550 independent loci) were genotyped in a replication cohort and 122 SNPs (n = 98 independent loci) were further tested through genotyping three additional validation cohorts followed by meta-analysis in all five cohorts totaling 3,132 cases and 3,317 controls. Twelve SNPs had evidence of association in the GWAS (P

This is the report of the first workshop on Incorporating In Vitro Alternative Methods for Developmental Neurotoxicity (DNT) Testing into International Hazard and Risk Assessment Strategies, held in Ispra, Italy, on 19-21 April 2005. The workshop was hosted by the European Centre for the Validation of Alternative Methods (ECVAM) and jointly organized by ECVAM, the European Chemical Industry Council, and the Johns Hopkins University Center for Alternatives to Animal Testing. The primary aim of the workshop was to identify and catalog potential methods that could be used to assess how data from in vitro alternative methods could help to predict and identify DNT hazards. Working groups focused on two different aspects: a) details on the science available in the field of DNT, including discussions on the models available to capture the critical DNT mechanisms and processes, and b) policy and strategy aspects to assess the integration of alternative methods in a regulatory framework. This report summarizes these discussions and details the recommendations and priorities for future work.

This is the report of the first workshop on Incorporating In Vitro Alternative Methods for Developmental Neurotoxicity (DNT) Testing into International Hazard and Risk Assessment Strategies, held in Ispra, Italy, on 19-21 April 2005. The workshop was hosted by the European Centre for the Validation of Alternative Methods (ECVAM) and jointly organized by ECVAM, the European Chemical Industry Council, and the Johns Hopkins University Center for Alternatives to Animal Testing. The primary aim of the workshop was to identify and catalog potential methods that could be used to assess how data from in vitro alternative methods could help to predict and identify DNT hazards. Working groups focused on two different aspects: a) details on the science available in the field of DNT, including discussions on the models available to capture the critical DNT mechanisms and processes, and b) policy and strategy aspects to assess the integration of alternative methods in a regulatory framework. This report summarizes these discussions and details the recommendations and priorities for future work.