Assessment of Drug-Induced Liver Injury through Cell Morphology and Gene Expression Analysis

Abstract

Drug-induced liver injury (DILI) is a significant concern in drug development, often leading to drug withdrawal. Although many studies aim to identify biomarkers and gene/pathway signatures related to liver toxicity and aim to predict DILI compounds, this remains a challenge in drug discovery. With a strong development of high-content screening/imaging (HCS/HCI) for phenotypic screening, we explored the morphological cell perturbations induced by DILI compounds. In the first step, cell morphological signatures were associated with two datasets of DILI chemicals (DILIRank and eTox). The mechanisms of action were then analyzed for chemicals having transcriptomics data and sharing similar morphological perturbations. Signaling pathways associated with liver toxicity (cell cycle, cell growth, apoptosis, ...) were then captured, and a hypothetical relation between cell morphological perturbations and gene deregulation was illustrated within our analysis. Finally, using the cell morphological signatures, machine learning approaches were developed to predict chemicals with a potential risk of DILI. Some models showed relevant performance with validation set balanced accuracies between 0.645 and 0.739. Overall, our findings demonstrate the utility of combining HCI with transcriptomics data to identify the morphological and gene expression signatures related to DILI chemicals. Moreover, our protocol could be extended to other toxicity end points, offering a promising avenue for comprehensive toxicity assessment in drug discovery.

Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction

“…Two other approaches with the potential to contribute to DILI prediction were released in 2023: one based on in vivo studies that capture mechanistic and phenotypic DILI information more reliably and accurately than other data sets, using in vivo liver histopathology nomenclature to provide a more informative and reliable data set for ML algorithms, and another using cell painting and transcriptomic data on compounds. This demonstrates the advantages of high-content imaging using transcriptomics data . These studies highlight the importance of combining different modalities for comprehensive toxicity assessments in drug discovery, such as integration of chemical and biological data, as mentioned by Liu et al .…”

“…This demonstrates the advantages of high-content imaging using transcriptomics data. 57 These studies highlight the importance of combining different modalities for comprehensive toxicity assessments in drug discovery, such as integration of chemical and biological data, as mentioned by Liu et al. 58 This multifaceted approach has the potential to lead to more robust predictive models, ultimately improving the safety and efficacy of new therapeutics.…”

Unleashing the potential of cell painting assays for compound activities and hazards prediction

“…These datasets are valuable for diverse applications, including studies on 1) the compound MoA as illustrated by Lapins and Spjuth ( Lapins and Spjuth, 2019 ), Schneidewind et al ( Schneidewind et al, 2020 ), Wong et al ( Wong et al, 2023 ), Tian et al ( Tian et al, 2023 ), 2) target identification investigated by Akbarzadeh et al ( Akbarzadeh et al, 2022 ) and 3) linking cell morphology to disease studied by Cerisier et al ( Cerisier et al, 2023 ), Lejal et al ( Lejal et al, 2023 ). Moshkov et al ( Moshkov et al, 2023 ) demonstrated success in assessing gene function and Seal et al ( Seal et al, 2021 ; Seal et al, 2022 ; Seal et al, 2023a ) in evaluating environmental toxicants, which will be discussed in more detail throughout this review.…”

Attention-based deep learning for accurate cell image analysis

“…Cellular morphology is a potentially rich data source for interrogating biological perturbations, especially at a large scale [9][10][11] . For example, Cellular morphological pro ling of compounds has been used to determine their mechanism of action 8,12,13 , identify their targets 14,15 , discover relationships with genes 16,17 , and characterize cellular heterogeneity 18 . Genes have been analyzed by creating pro les of cell populations in which the gene is perturbed by CRISRP and RNA interference (RNAi); these pro les have been used to represents the functional landscape of essential human genes [19][20][21] and identify genetic interactions 22,23 , or characterize cellular heterogeneity 24 .…”