Proprioceptive and Visual Feedback Responses in Macaques Exploit Goal Redundancy.

A common problem in motor control concerns how to generate patterns of muscle activity when there are redundant solutions to attain a behavioral goal. Optimal feedback control is a theory that has guided many behavioral studies exploring how the motor system incorporates task redundancy. This theory predicts that kinematic errors that deviate the limb should not be corrected if one can still attain the behavioral goal. Studies in humans demonstrate that the motor system can flexibly integrate visual and proprioceptive feedback of the limb with goal redundancy within 90 ms and 70 ms, respectively. Here, we show monkeys (Macaca mulatta) demonstrate similar abilities to exploit goal redundancy. We trained four male monkeys to reach for a goal that was either a narrow square or a wide, spatially redundant rectangle. Monkeys exhibited greater trial-by-trial variability when reaching to the wide goal consistent with exploiting goal redundancy. On random trials we jumped the visual feedback of the hand and found monkeys corrected for the jump when reaching to the narrow goal and largely ignored the jump when reaching for the wide goal. In a separate set of experiments, we applied mechanical loads to the arm of the monkey and found similar corrective responses based on goal shape. Muscle activity reflecting these different corrective responses were detected for the visual and mechanical perturbations starting at ∼90 and ∼70 ms, respectively. Thus, rapid motor responses in macaques can exploit goal redundancy similar to humans, creating a paradigm to study the neural basis of goal-directed motor action and motor redundancy.SIGNIFICANCE STATEMENT Moving in the world requires selecting from an infinite set of possible motor commands. Theories predict that motor commands are selected that exploit redundancies. Corrective responses in humans to either visual or proprioceptive disturbances of the limb can rapidly exploit redundant trajectories to a goal in <100 ms after a disturbance. However, uncovering the neural correlates generating these rapid motor corrections has been hampered by the absence of an animal model. We developed a behavioral paradigm in monkeys that incorporates redundancy in the form of the shape of the goal. Critically, monkeys exhibit corrective responses and timings similar to humans performing the same task. Our paradigm provides a model for investigating the neural correlates of sophisticated rapid motor corrections.

Integration of proprioceptive and visual feedback during online control of reaching.

Visual and proprioceptive feedback both contribute to perceptual decisions, but it remains unknown how these feedback signals are integrated together or consider factors such as delays and variance during online control. We investigated this question by having participants reach to a target with randomly applied mechanical and/or visual disturbances. We observed that the presence of visual feedback during a mechanical disturbance did not increase the size of the muscle response significantly but did decrease variance, consistent with a dynamic Bayesian integration model. In a control experiment, we verified that vision had a potent influence when mechanical and visual disturbances were both present but opposite in sign. These results highlight a complex process for multisensory integration, where visual feedback has a relatively modest influence when the limb is mechanically disturbed, but a substantial influence when visual feedback becomes misaligned with the limb.NEW & NOTEWORTHY Visual feedback is more accurate, but proprioceptive feedback is faster. How should you integrate these sources of feedback to guide limb movement? As predicted by dynamic Bayesian models, the size of the muscle response to a mechanical disturbance was essentially the same whether visual feedback was present or not. Only under artificial conditions, such as when shifting the position of a cursor representing hand position, can one observe a muscle response from visual feedback.

Maintained Representations of the Ipsilateral and Contralateral Limbs during Bimanual Control in Primary Motor Cortex.

Primary motor cortex (M1) almost exclusively controls the contralateral side of the body. However, M1 activity is also modulated during ipsilateral body movements. Previous work has shown that M1 activity related to the ipsilateral arm is independent of the M1 activity related to the contralateral arm. How do these patterns of activity interact when both arms move simultaneously? We explored this problem by training 2 monkeys (male, Macaca mulatta) in a postural perturbation task while recording from M1. Loads were applied to one arm at a time (unimanual) or both arms simultaneously (bimanual). We found 83% of neurons (n = 236) were responsive to both the unimanual and bimanual loads. We also observed a small reduction in activity magnitude during the bimanual loads for both limbs (25%). Across the unimanual and bimanual loads, neurons largely maintained their preferred load directions. However, there was a larger change in the preferred loads for the ipsilateral limb (∼25%) than the contralateral limb (∼9%). Lastly, we identified the contralateral and ipsilateral subspaces during the unimanual loads and found they captured a significant amount of the variance during the bimanual loads. However, the subspace captured more of the bimanual variance related to the contralateral limb (97%) than the ipsilateral limb (66%). Our results highlight that, even during bimanual motor actions, M1 largely retains its representations of the contralateral and ipsilateral limbs.SIGNIFICANCE STATEMENT Previous work has shown that primary motor cortex (M1) represents information related to the contralateral limb, its downstream target, but also reflects information related to the ipsilateral limb. Can M1 still represent both sources of information when performing simultaneous movements of the limbs? Here we record from M1 during a postural perturbation task. We show that activity related to the contralateral limb is maintained between unimanual and bimanual motor actions, whereas the activity related to the ipsilateral limb undergoes a small change between unimanual and bimanual motor actions. Our results indicate that two independent representations can be maintained and expressed simultaneously in M1.

Visual Feedback Processing of the Limb Involves Two Distinct Phases.

Muscle responses to mechanical disturbances exhibit two distinct phases: a response starting at ~20 ms that is fairly stereotyped, and a response starting at ~60 ms modulated by many behavioral contexts including goal-redundancy and environmental obstacles. Muscle responses to disturbances of visual feedback of the hand arise within ~90 ms. However, little is known whether these muscle responses are sensitive to behavioral contexts. We had 49 human participants (27 male) execute goal-directed reaches with visual feedback of their hand presented as a cursor. On random trials, the cursor jumped laterally to the reach direction, and thus, required a correction to attain the goal. The first experiment demonstrated that the response amplitude starting at 90 ms scaled with jump magnitude, but only for jumps <2 cm. For larger jumps, the duration of the muscle response scaled with the jump size starting after 120 ms. The second experiment demonstrated that the early response was sensitive to goal redundancy as wider targets evoked a smaller corrective response. The third experiment demonstrated that the early response did not consider the presence of obstacles, as this response routinely drove participants directly to the goal even though this path was blocked by an obstacle. Instead, the appropriate muscle response to navigate around the obstacle started after 120 ms. Our findings highlight that visual feedback of the limb involves two distinct phases: a response starting at 90 ms with limited sensitivity to jump magnitude and sensitive to goal-redundancy, and a response starting at 120 ms with increased sensitivity to jump magnitude and environmental factors.SIGNIFICANCE STATEMENT The motor system can integrate proprioceptive feedback to guide an ongoing action in ~60 ms and is flexible to a broad range of behavioral contexts. In contrast, the present study identified that the motor response to a visual disturbance exhibits two distinct phases: an early response starting at 90 ms with limited scaling with disturbance size and sensitivity to goal-redundancy, and a slower response starting after 120 ms with increased sensitivity to disturbance size and sensitive to environmental obstacles. These data suggest visual feedback of the hand is processed through two distinct feedback processes.

Development of improved QSAR models for predicting the outcome of the in vivo micronucleus genetic toxicity assay.

All drugs entering clinical trials are expected to undergo a series of in vitro and in vivo genotoxicity tests as outlined in the International Council on Harmonization (ICH) S2 (R1) guidance. Among the standard battery of genotoxicity tests used for pharmaceuticals, the in vivo micronucleus assay, which measures the frequency of micronucleated cells mostly from blood or bone marrow, is recommended for detecting clastogens and aneugens. (Quantitative) structure-activity relationship [(Q)SAR] models may be used as early screening tools by pharmaceutical companies to assess genetic toxicity risk during drug candidate selection. Models can also provide decision support information during regulatory review as part of the weight-of-evidence when experimental data are insufficient. In the present study, two commercial (Q)SAR platforms were used to construct in vivo micronucleus models from a recently enhanced in-house database of non-proprietary study findings in mice. Cross-validated performance statistics for the new models showed sensitivity of up to 74% and negative predictivity of up to 86%. In addition, the models demonstrated cross-validated specificity of up to 77% and coverage of up to 94%. These new models will provide more reliable predictions and offer an investigational approach for drug safety assessment with regards to identifying potentially genotoxic compounds.

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.

Correlations Between Primary Motor Cortex Activity with Recent Past and Future Limb Motion During Unperturbed Reaching.

Many studies highlight that human movements are highly successful yet display a surprising amount of variability from trial to trial. There is a consistent pattern of variability throughout movement: initial motor errors are corrected by the end of movement, suggesting the presence of a powerful online control process. Here, we analyze the trial-by-trial variability of goal-directed reaching in nonhuman primates (five male Rhesus monkeys) and demonstrate that they display a similar pattern of variability during reaching, including a strong negative correlation between initial and late hand motion. We then demonstrate that trial-to-trial neural variability of primary motor cortex (M1) is positively correlated with variability of future hand motion (τ = ∼160 ms) during reaching. Furthermore, the variability of M1 activity is also correlated with variability of past hand motion (τ = ∼90 ms), but in the opposite polarity (i.e., negative correlation). Partial correlation analysis demonstrated that M1 activity independently reflects the variability of both past and future hand motions. These findings provide support for the hypothesis that M1 activity is involved in online feedback control of motor actions.SIGNIFICANCE STATEMENT Previous studies highlight that primary motor cortex (M1) rapidly responds to either visual or mechanical disturbances, suggesting its involvement in online feedback control. However, these studies required external disturbances to the motor system and it is not clear whether a similar feedback process addresses internal noise/errors generated by the motor system itself. Here, we introduce a novel analysis that evaluates how variations in the activity of M1 neurons covary with variations in hand motion on a trial-to-trial basis. The analyses demonstrate that M1 activity is correlated with hand motion in both the near future and the recent past, but with opposite polarity. These results suggest that M1 is involved in online feedback motor control to address errors/noise within the motor system.

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.

Improvement of quantitative structure-activity relationship (QSAR) tools for predicting Ames mutagenicity: outcomes of the Ames/QSAR International Challenge Project.

The International Conference on Harmonization (ICH) M7 guideline allows the use of in silico approaches for predicting Ames mutagenicity for the initial assessment of impurities in pharmaceuticals. This is the first international guideline that addresses the use of quantitative structure-activity relationship (QSAR) models in lieu of actual toxicological studies for human health assessment. Therefore, QSAR models for Ames mutagenicity now require higher predictive power for identifying mutagenic chemicals. To increase the predictive power of QSAR models, larger experimental datasets from reliable sources are required. The Division of Genetics and Mutagenesis, National Institute of Health Sciences (DGM/NIHS) of Japan recently established a unique proprietary Ames mutagenicity database containing 12140 new chemicals that have not been previously used for developing QSAR models. The DGM/NIHS provided this Ames database to QSAR vendors to validate and improve their QSAR tools. The Ames/QSAR International Challenge Project was initiated in 2014 with 12 QSAR vendors testing 17 QSAR tools against these compounds in three phases. We now present the final results. All tools were considerably improved by participation in this project. Most tools achieved >50% sensitivity (positive prediction among all Ames positives) and predictive power (accuracy) was as high as 80%, almost equivalent to the inter-laboratory reproducibility of Ames tests. To further increase the predictive power of QSAR tools, accumulation of additional Ames test data is required as well as re-evaluation of some previous Ames test results. Indeed, some Ames-positive or Ames-negative chemicals may have previously been incorrectly classified because of methodological weakness, resulting in false-positive or false-negative predictions by QSAR tools. These incorrect data hamper prediction and are a source of noise in the development of QSAR models. It is thus essential to establish a large benchmark database consisting only of well-validated Ames test results to build more accurate QSAR models.

Transitioning to composite bacterial mutagenicity models in ICH M7 (Q)SAR analyses.

The International Council on Harmonisation (ICH) M7(R1) guideline describes the use of complementary (quantitative) structure-activity relationship ((Q)SAR) models to assess the mutagenic potential of drug impurities in new and generic drugs. Historically, the CASE Ultra and Leadscope software platforms used two different statistical-based models to predict mutations at G-C (guanine-cytosine) and A-T (adenine-thymine) sites, to comprehensively assess bacterial mutagenesis. In the present study, composite bacterial mutagenicity models covering multiple mutation types were developed. These new models contain more than double the number of chemicals (n = 9,254 and n = 13,514) than the corresponding non-composite models and show better toxicophore coverage. Additionally, the use of a single composite bacterial mutagenicity model simplifies impurity analysis in an ICH M7 (Q)SAR workflow by reducing the number of model outputs requiring review. An external validation set of 388 drug impurities representing proprietary pharmaceutical chemical space showed performance statistics ranging from of 66-82% in sensitivity, 91-95% in negative predictivity and 96% in coverage. This effort represents a major enhancement to these (Q)SAR models and their use under ICH M7(R1), leading to improved patient safety through greater predictive accuracy, applicability, and efficiency when assessing the bacterial mutagenic potential of drug impurities.

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.

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.

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.

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.

Rapid Online Corrections for Proprioceptive and Visual Perturbations Recruit Similar Circuits in Primary Motor Cortex.

An important aspect of motor function is our ability to rapidly generate goal-directed corrections for disturbances to the limb or behavioral goal. The primary motor cortex (M1) is a key region involved in processing feedback for rapid motor corrections, yet we know little about how M1 circuits are recruited by different sources of sensory feedback to make rapid corrections. We trained two male monkeys (Macaca mulatta) to make goal-directed reaches and on random trials introduced different sensory errors by either jumping the visual location of the goal (goal jump), jumping the visual location of the hand (cursor jump), or applying a mechanical load to displace the hand (proprioceptive feedback). Sensory perturbations evoked a broad response in M1 with ∼73% of neurons (n = 257) responding to at least one of the sensory perturbations. Feedback responses were also similar as response ranges between the goal and cursor jumps were highly correlated (range of r = [0.91, 0.97]) as were the response ranges between the mechanical loads and the visual perturbations (range of r = [0.68, 0.86]). Lastly, we identified the neural subspace each perturbation response resided in and found a strong overlap between the two visual perturbations (range of overlap index, 0.73-0.89) and between the mechanical loads and visual perturbations (range of overlap index, 0.36-0.47) indicating each perturbation evoked similar structure of activity at the population level. Collectively, our results indicate rapid responses to errors from different sensory sources target similar overlapping circuits in M1.

In Silico Assessment of Biomolecule Reactivity with Leachables.

Leachables in pharmaceutical products may react with biomolecule active pharmaceutical ingredients (APIs), for example, monoclonal antibodies (mAb), peptides, and ribonucleic acids (RNA), potentially compromising product safety and efficacy or impacting quality attributes. This investigation explored a series of in silico models to screen extractables and leachables to assess their possible reactivity with biomolecules. These in silico models were applied to collections of known leachables to identify functional and structural chemical classes likely to be flagged by these in silico approaches. Flagged leachable functional classes included antimicrobials, colorants, and film-forming agents, whereas specific chemical classes included epoxides, acrylates, and quinones. In addition, a dataset of 22 leachables with experimental data indicating their interaction with insulin glargine was used to evaluate whether one or more in silico methods are fit-for-purpose as a preliminary screen for assessing this biomolecule reactivity. Analysis of the data showed that the sensitivity of an in silico screen using multiple methodologies was 80%-90% and the specificity was 58%-92%. A workflow supporting the use of in silico methods in this field is proposed based on both the results from this assessment and best practices in the field of computational modeling and quality risk management.

Analysis of chemical structures and mutations detected by Salmonella TA98 and TA100.

As part of an analysis performed under the auspices of the International Workshop on Genotoxicity Testing (IWGT) in 2017, we and others showed that Salmonella frameshift strain TA98 and base-substitution strain TA100 together + /- S9 detected 93% of the mutagens detected by all the bacterial strains recommended by OECD TG471 (Williams et al., Mutation Res. 848:503081, 2019). We have extended this analysis by identifying the numbers and chemical classes of chemicals detected by these two strains either alone or in combination, including the role of S9. Using the Leadscope 2021 SAR Genetox database containing > 21,900 compounds, our dataset containing 7170 compounds tested in both TA98 and TA100. Together, TA98 and TA100 detected 94% (3733/3981) of the mutagens detected using all the TG471-recommended bacterial strains; 39% were mutagenic in one or both strains. TA100 detected 77% of all of these mutagens and TA98 70%. Considering the overlap of detection by both strains, 12% of these mutagens were detected only by TA98 and 19% only by TA100. In the absence of S9, sensitivity dropped by 31% for TA98 and 29% for TA100. Overall, 32% of the mutagens required S9 for detection by either strain; 9% were detected only without S9. Using the 2021 Leadscope Genetox Expert Alerts, TA100 detected 18 mutagenic alerting chemical classes with better sensitivity than TA98, whereas TA98 detected 10 classes better than TA100. TA100 detected more chemical classes than did TA98, especially hydrazines, azides, various di- and tri-halides, various nitrosamines, epoxides, aziridines, difurans, and half-mustards; TA98 especially detected polycyclic primary amines, various aromatic amines, polycyclic aromatic hydrocarbons, triazines, and dibenzo-furans. Model compounds with these structures induce primarily G to T mutations in TA100 and/or a hotspot GC deletion in TA98. Both TA98 and TA100 + /- S9 are needed for adequate mutagenicity screening with the Salmonella (Ames) assay.

Characterization and validation of an in silico toxicology model to predict the mutagenic potential of drug impurities.

Control and minimization of human exposure to potential genotoxic impurities found in drug substances and products is an important part of preclinical safety assessments of new drug products. The FDA's 2008 draft guidance on genotoxic and carcinogenic impurities in drug substances and products allows use of computational quantitative structure-activity relationships (QSAR) to identify structural alerts for known and expected impurities present at levels below qualified thresholds. This study provides the information necessary to establish the practical use of a new in silico toxicology model for predicting Salmonella t. mutagenicity (Ames assay outcome) of drug impurities and other chemicals. We describe the model's chemical content and toxicity fingerprint in terms of compound space, molecular and structural toxicophores, and have rigorously tested its predictive power using both cross-validation and external validation experiments, as well as case studies. Consistent with desired regulatory use, the model performs with high sensitivity (81%) and high negative predictivity (81%) based on external validation with 2368 compounds foreign to the model and having known mutagenicity. A database of drug impurities was created from proprietary FDA submissions and the public literature which found significant overlap between the structural features of drug impurities and training set chemicals in the QSAR model. Overall, the model's predictive performance was found to be acceptable for screening drug impurities for Salmonella mutagenicity.

Implementation of in silico toxicology protocols within a visual and interactive hazard assessment platform.

Mechanistically-driven alternative approaches to hazard assessment invariably require a battery of tests, including both in silico models and experimental data. The decision-making process, from selection of the methods to combining the information based on the weight-of-evidence, is ideally described in published guidelines or protocols. This ensures that the application of such approaches is defendable to reviewers within regulatory agencies and across the industry. Examples include the ICH M7 pharmaceutical impurities guideline and the published in silico toxicology protocols. To support an efficient, transparent, consistent and fully documented implementation of these protocols, a new and novel interactive software solution is described to perform such an integrated hazard assessment based on public and proprietary information.