Biology subjects

Williamson, D. F. K.

Publications and source records attributed to Williamson, D. F. K..

4 recordsLinked to original sources

bioRxiv2024

AI-driven Discovery of Morphomolecular Signatures in Toxicology

Early identification of drug toxicity is essential yet challenging in drug development. At the preclinical stage, toxicity is assessed with histopathological examination of tissue sections from animal models to detect morphological lesions. To complement this analysis, toxicogenomics is increasingly employed to understand the mechanism of action of the compound and ultimately identify lesion-specific safety biomarkers for which in vitro assays can be designed. However, existing works that aim to identify morphological correlates of expression changes rely on qualitative or semi-quantitative morphological characterization and remain limited in scale or morphological diversity. Artificial intelligence (AI) offers a promising approach for quantitatively modeling this relationship at an unprecedented scale. Here, we introduce GEESE, an AI model designed to impute morphomolecular signatures in toxicology data. Our model was trained to predict 1,536 gene targets on a cohort of 8,231 hematoxylin and eosin-stained liver sections from Rattus norvegicus across 127 preclinical toxicity studies. The model, evaluated on 2,002 tissue sections from 29 held-out studies, can yield pseudo-spatially resolved gene expression maps, which we correlate with six key drug-induced liver injuries (DILI). From the resulting 25 million lesion-expression pairs, we established quantitative relations between up and downregulated genes and lesions. Validation of these signatures against toxicogenomic databases, pathway enrichment analyses, and human hepatocyte cell lines asserted their relevance. Overall, our study introduces new methods for characterizing toxicity at an unprecedented scale and granularity, paving the way for AI-driven discovery of toxicity biomarkers. Live demo: https://mahmoodlab.github.io/tox-discovery-ui/

bioinformatics↗

bioRxiv2024

Deep Learning-based Modeling for Preclinical Drug Safety Assessment

In drug development, assessing the toxicity of candidate compounds is crucial for successfully transitioning from preclinical research to early-stage clinical trials. Drug safety is typically assessed using animal models with a manual histopathological examination of tissue sections to characterize the dose-response relationship of the compound - a timeintensive process prone to inter-observer variability and predominantly involving tedious review of cases without abnormalities. Artificial intelligence (AI) methods in pathology hold promise to accelerate this assessment and enhance reproducibility and objectivity. Here, we introduce TRACE, a model designed for toxicologic liver histopathology assessment capable of tackling a range of diagnostic tasks across multiple scales, including situations where labeled data is limited. TRACE was trained on 15 million histopathology images extracted from 46,734 digitized tissue sections from 157 preclinical studies conducted on Rattus norvegicus. We show that TRACE can perform various downstream toxicology tasks spanning histopathological response assessment, lesion severity scoring, morphological retrieval, and automatic dose-response characterization. In an independent reader study, TRACE was evaluated alongside ten board-certified veterinary pathologists and achieved higher concordance with the consensus opinion than the average of the pathologists. Our study represents a substantial leap over existing computational models in toxicology by offering the first framework for accelerating and automating toxicological pathology assessment, promoting significant progress with faster, more consistent, and reliable diagnostic processes. Live Demo: https://mahmoodlab.github.io/tox-foundation-ui/

bioinformatics↗

bioRxiv2019

A low-cost, open source, self-contained bacterial EVolutionary biorEactor (EVE)

The morbidostat automatically adjusts antibiotic concentration as a bacterial population evolves resistance. Although this device has advanced our understanding of the evolutionary and ecological processes that drive antibiotic resistance, no low-cost and open-source systems are available for educators. Here, we present the EVolutionary biorEactor (EVE), an accessible alternative to other morbidostats for use in low-resource classrooms that requires minimal engineering and programming experience. We first compare our system to others, emphasizing how it differs in design and cost. We then describe how we validated the EVE by evolving replicate Escherichia coli populations under chloramphenicol challenge and comparing our results to those in the published literature. Lastly, we detail how high school students used the EVE to learn about bacterial growth and antibiotic resistance.

bioengineering↗

bioRxiv2018

The immune checkpoint kick start: Optimization of neoadjuvant combination therapy using game theory

An upcoming clinical trial at the Moffitt Cancer Center for women with stage 2/3 ER+breast cancer combines an aromatase inhibitor and a PD-L1 checkpoint inhibitor, and aims to lower a preoperative endocrine prognostic index (PEPI) that correlates with relapse-free survival. PEPI is fundamentally a static index, measured at the end of neoadjuvant therapy before surgery. We develop a mathematical model of the essential components of the PEPI score in order to identify successful combination therapy regimens that minimize both tumor burden and metastatic potential, based on time-dependent trade-offs in the system. We consider two molecular traits, CCR7 and PD-L1 which correlate with treatment response and increased metastatic risk. We use a matrix game model with the four phenotypic strategies to examine the frequency-dependent interactions of cancer cells. This game was embedded into an ecological model of tumor population growth dynamics. The resulting model predicts both evolutionary and ecological dynamics that track with changes in the PEPI score. We consider various treatment regimens based on combinations of the two therapies with drug holidays. By considering the trade off between tumor burden and metastatic potential, the optimal therapy plan was found to be a 1 month kick start of the immune checkpoint inhibitor followed by five months of continuous combination therapy. Relative to a protocol with both therapeutics given together from the start, this delayed regimen results in transient sub-optimal tumor regression while maintaining a phenotypic constitution that is more amenable to fast tumor regression for the final five months of therapy. The mathematical model provides a useful abstraction of clinical intuition, enabling hypothesis generation and testing of clinical assumptions.

cancer biology↗