Biology subjects

Harvey, J. S.

Publications and source records attributed to Harvey, J. S..

5 recordsLinked to original sources

bioRxiv2026

Mutagenic and carcinogenic potency determinations for NDMA support the cumulative dose assumption underpinning the less-than-lifetime Threshold of Toxicological Concern

The management of N-nitrosamine impurities challenges pharmaceutical development and regulation worldwide. Because most medicinal exposures are shorter than lifetime and absolute impurity exclusion is impossible, reliable approaches to define duration-specific intake limits are essential. On the premise that carcinogenic risk is proportional to cumulative dose, the Less-Than-Lifetime (LTL) Threshold of Toxicological Concern (TTC) framework defines progressively lower intake limits for mutagenic impurities over longer exposures. However, N-nitrosamines are currently treated as a cohort of concern, necessitating compound-specific evaluation placing reliance on in vivo mutagenicity assays for impurity qualifications. To better understand durational potency relationships and the application domain of the LTL-TTC, we apply benchmark dose (BMD) modelling to cumulative-dose-scaled transgenic rodent (TGR), error-corrected sequencing and rodent carcinogenicity datasets for N-nitrosodimethylamine (NDMA) obtained from the published literature. For TGR, cumulative-dose scaling better resolved liver as the most sensitive organ and reduced interstudy variability: liver BMDs spanned [~]80-fold in daily-dose units but only [~]20-fold when scaled to cumulative dose. Among closely-matched mouse liver gavage studies, cumulative-dose BMDs only varied by [~]2.5-fold across 1 to 28-day treatment regimens. Error-corrected sequencing also demonstrated parity, with acute-dose regimens producing mutation burdens near-identical (< 1.2-fold) to those cumulated from 28-day repeat-dose regimens. Comparable results were obtained from carcinogenicity datasets confirming proportionality-of-effect to cumulative dose. These findings empirically support the validity of the LTL-TTC concept. More broadly, they demonstrate that short-term in vivo mutagenicity assays can serve as reliable surrogates for lifetime carcinogenicity studies, strengthening the scientific and regulatory basis for duration-adjusted acceptable intakes for N-nitrosamine impurities.

pharmacology and toxicology↗

bioRxiv2025

Order code in the olfactory system.

The ability to recognize odor identity across a wide range of concentrations is essential for natural olfactory behaviors. However, how odor identity is represented in the early olfactory system remains an open question. One theory proposes that glomeruli in the olfactory bulb, along with their associated odorant receptors (ORs), are activated by odorants in a temporal order that conveys information about odor identity. This order code is relatively robust to concentration changes, as the rank order of glomerular activation by a given odorant remains similar across concentrations. Alternatively, the primacy coding theory suggests that odor identity is defined by the most sensitive ORs, those that respond first, collectively referred to as the primacy set. To test these theories, we measured glomerular responses to a large set of odorants in the mouse olfactory bulb using calcium imaging. We found that receptor affinities can be embedded in a low-dimensional space (D = 10) with minimal loss of information. Within this space, we identified two clusters of glomeruli with distinct tuning properties that form independent odor representations. These clusters may correspond to the two phylogenetic classes of ORs, as revealed by both their functional characteristics and anatomical locations. In the OR affinity space, odorants evoke orderly activation waves whose directions can be used to define odor identities in the order coding model. Finally, we compared the order code and primacy model in predicting odor identity both across concentrations and across animals. Despite containing less information overall, the primacy model performed comparably to the order code. We confirmed the primacy models prediction that each receptor tends to contribute to the primacy set of at least one odorant. Analysis of binary odor mixtures revealed that mixture responses lie along geodesic lines connecting their component odors. Together, these findings suggest that odor information in the olfactory bulb may be represented through two complementary coding strategies: the primacy and order codes.

neuroscience↗

bioRxiv2024

Data Science in Olfaction

Advances in neural sensing technology are making it possible to observe the olfactory process in great detail. In this paper, we conceptualize smell from a Data Science and AI perspective, that relates the properties of odorants to how they are sensed and analyzed in the olfactory system from the nose to the brain. Drawing distinctions to color vision, we argue that smell presents unique measurement challenges, including the complexity of stimuli, the high dimensionality of the sensory apparatus, as well as what constitutes ground truth. In the face of these challenges, we argue for the centrality of odorant-receptor interactions in developing a theory of olfaction. Such a theory is likely to find widespread industrial applications, and enhance our understanding of smell, and in the longer-term, how it relates to other senses and language. As an initial use case of the data, we present results using machine learning-based classification of neural responses to odors as they are recorded in the mouse olfactory bulb with calcium imaging. Our larger objective is to create the equivalent of an "MNIST database for olfaction," which we call oMNIST, so that researchers are able to work from a standard dataset to further the state of the art, similar to how the availability of standard datasets catalyzed research in computer vision.

neuroscience↗

bioRxiv2023

Utilizing gene co-expression networks with the rat kidney TXG-MAPr tool to enhance safety assessment, biomarker identification and human translation

Toxicogenomic data represent a valuable source of biological information at molecular and cellular level to understand unanticipated organ toxicities. Weighted gene co-expression networks analysis can reduce the complexity of gene-level transcriptomic data to a set of biological response-networks useful for providing insights into mechanisms of drug-induced adverse outcomes. In this study, we have built co-regulated gene networks (modules) from the TG-GATEs and DrugMatrix rat kidney datasets consisting of time- and dose-response data for 180 compounds, including nephrotoxicants. Data from the 347 modules were incorporated into the rat kidney TXG-MAPr web tool, a user-friendly interface that enables visualization and analysis of module perturbations, quantified by a module eigengene score (EGS) for each treatment condition. Several modules annotated for cellular stress, renal injury and inflammation were statistically associated with concurrent renal pathologies, including modules that contain both well-known and novel renal biomarker genes. In addition, many rat kidney modules contain well annotated, robust gene networks that are preserved across transcriptome datasets, suggesting that these biological networks translate to other (drug-induced) kidney injury cases. Moreover, preservation analysis of human kidney transcriptomic data provided a quantitative metric to assess the likelihood that rat kidney modules, and the associated biological interpretation, translate from non-clinical species to human. In conclusion, the rat kidney TXG-MAPr enables uploading and analysis of kidney gene expression data in the context of rat kidney co-expression networks, which could identify possible safety liabilities and/or mechanisms that can lead to adversity for chemical or drug candidates. Translational StatementGene co-expression networks (modules) were generated using rat kidney toxicogenomics data, which reduced data complexity and retained quantitative mechanisms to enhance safety assessment. Several stress, injury and inflammation modules were statistically associated with renal pathologies, useful for biomarker identification. Moreover, many rat kidney modules contained well-annotated, robust gene-networks that were preserved in human patients transcriptome data after renal transplantation, suggesting that these biological networks translate to human relevant kidney-injury. So, the rat kidney TXG-MAPr tool enables transcriptome analysis in the context of kidney co-expression networks, which could identify chemical-induced safety liabilities and/or mechanisms leading to adversity, relevant for human risk-assessment.

pharmacology and toxicology↗

bioRxiv2021

Modeling metallicity: low level visual features support robust material perception

The human visual system is able to rapidly and accurately infer the material properties of objects and surfaces in the world. Yet an inverse optics approach--estimating the bi-directional reflectance distribution function of a surface, given its geometry and environment, and relating this to the optical properties of materials--is both intractable and computationally unaffordable. Rather, previous studies have found that the visual system may exploit low-level spatio-chromatic statistics as heuristics for material judgment. Here, we present results from psychophysics and modelling that supports the use of image statistics heuristics in the judgement of metallicity--the quality of appearance that suggests an object is made from metal. Using computer graphics, we generated stimuli that varied along two physical dimensions: the smoothness of a metal object, and the evenness of its transparent coating. This allowed for the manipulation of low-level image statistics, whilst ensuring that each stimulus was a naturalistic, physically plausible image. A conjoint-measurement task decoupled the contributions of these dimensions to the perception of metallicity. Low-level image features, as represented in the activations of oriented linear filters at different spatial scales, were found to correlate with the dimensions of the stimulus space, and decision-making models using these activations replicated observer performance in judging metal smoothness, coating bumpiness, and metallicity. Importantly, the performance of these models did not deteriorate when objects were rotated within their simulated scene, with corresponding changes in image properties. We therefore conclude that low-level image features may provide reliable cues for the robust perception of metallicity.

neuroscience↗