Construction of a predictive model for evaluating multiple organ toxicity

A multi-label learning model for predicting drug-induced pathology in multi-organ based on toxicogenomics data

Drug-induced toxicity damages the health and is one of the key factors causing drug withdrawal from the market. It is of great significance to identify drug-induced target-organ toxicity, especially the detailed pathological findings, which are crucial for toxicity assessment, in the early stage of drug development process. A large variety of studies have devoted to identify drug toxicity. However, most of them are limited to single organ or only binary toxicity. Here we proposed a novel multi-label learning model named Att-RethinkNet, for predicting drug-induced pathological findings targeted on liver and kidney based on toxicogenomics data. The Att-RethinkNet is equipped with a memory structure and can effectively use the label association information. Besides, attention mechanism is embedded to focus on the important features and obtain better feature presentation. Our Att-RethinkNet is applicable in multiple organs and takes account the compound type, dose, and administration time, so it is more comprehensive and generalized. And more importantly, it predicts multiple pathological findings at the same time, instead of predicting each pathology separately as the previous model did. To demonstrate the effectiveness of the proposed model, we compared the proposed method with a series of state-of-the-arts methods. Our model shows competitive performance and can predict potential hepatotoxicity and nephrotoxicity in a more accurate and reliable way. The implementation of the proposed method is available at https://github.com/RanSuLab/Drug-Toxicity-Prediction-MultiLabel.

Investigation of Ifosfamide Toxicity Induces Common Upstream Regulator in Liver and Kidney

Ifosfamide is an alkylating agent, a synthetic analogue of cyclophosphamide, used to treat various solid cancers. In this study, the toxicity of ifosfamide was evaluated using single-and multiple-dose intraperitoneal administration in rats under Good Laboratory Practice guidelines, and an additional microarray experiment was followed to support toxicological findings. A single dose of ifosfamide (50 mg/kg) did not induce any pathological changes. Meanwhile, severe renal toxicity was observed in the 7 and 28 days consecutively administered groups, with significant increases in blood urea nitrogen and creatinine levels. In the tox-list analysis, cholesterol synthesis-related genes were mostly affected in the liver and renal failure-related genes were affected in the kidney after ifosfamide administration. Moreover, interferon regulatory factor 7 was selected as the main upstream regulator that changed in both the liver and kidney, and was found to interact with other target genes, such as ubiquitin specific peptidase 18, radical S-adenosyl methionine domain containing 2, and interferon-stimulated gene 15, which was further confirmed by real-time RT-PCR analysis. In conclusion, we confirmed kidney-biased ifosfamide organ toxicity and identified identically altered genes in both the liver and kidney. Further comprehensive toxicogenomic studies are required to reveal the exact relationship between ifosfamide-induced genes and organ toxicity.

In silico approaches in organ toxicity hazard assessment: current status and future needs in predicting liver toxicity

Hepatotoxicity is one of the most frequently observed adverse effects resulting from exposure to a xenobiotic. For example, in pharmaceutical research and development it is one of the major reasons for drug withdrawals, clinical failures, and discontinuation of drug candidates. The development of faster and cheaper methods to assess hepatotoxicity that are both more sustainable and more informative is critically needed. The biological mechanisms and processes underpinning hepatotoxicity are summarized and experimental approaches to support the prediction of hepatotoxicity are described, including toxicokinetic considerations. The paper describes the increasingly important role of in silico approaches and highlights challenges to the adoption of these methods including the lack of a commonly agreed upon protocol for performing such an assessment and the need for in silico solutions that take dose into consideration. A proposed framework for the integration of in silico and experimental information is provided along with a case study describing how computational methods have been used to successfully respond to a regulatory question concerning non-genotoxic impurities in chemically synthesized pharmaceuticals.

Microfluidic-Based Multi-Organ Platforms for Drug Discovery

Development of predictive multi-organ models before implementing costly clinical trials is central for screening the toxicity, efficacy, and side effects of new therapeutic agents. Despite significant efforts that have been recently made to develop biomimetic in vitro tissue models, the clinical application of such platforms is still far from reality. Recent advances in physiologically-based pharmacokinetic and pharmacodynamic (PBPK-PD) modeling, micro- and nanotechnology, and in silico modeling have enabled single- and multi-organ platforms for investigation of new chemical agents and tissue-tissue interactions. This review provides an overview of the principles of designing microfluidic-based organ-on-chip models for drug testing and highlights current state-of-the-art in developing predictive multi-organ models for studying the cross-talk of interconnected organs. We further discuss the challenges associated with establishing a predictive body-on-chip (BOC) model such as the scaling, cell types, the common medium, and principles of the study design for characterizing the interaction of drugs with multiple targets.