Qualifying a human Liver-Chip for predictive toxicology: Performance assessment and economic implications

Human organ-on-a-chip (Organ-Chip) technology has the potential to disrupt preclinical drug dis-covery and improve success in drug development pipelines as it can recapitulate organ-level patho-physiology and clinical responses. The Innovation and Quality (IQ) consortium formed by multiple pharmaceutical and biotechnology companies, however, systematic and quantitative evaluation of the predictive value of Organ-Chips has not yet been reported. Here, 780 Liver-Chips were analyzed to determine their ability to predict drug-induced liver injury (DILI) caused by small molecules identified as benchmarks by the IQ consortium. The Liver-Chip met the qualification guidelines across a blinded set of 27 known hepatotoxic and non-toxic drugs with a sensitivity of 80% and a specificity of 100%. With this performance, a computational economic value analysis suggests that the Liver-Chip could generate $3 billion annually for the pharmaceutical industry due to increased R&D productivity.

Identifying Protein Features and Pathways Responsible for Toxicity using Machine learning, CANDO, and Tox21 datasets: Implications for Predictive Toxicology

Humans are exposed to numerous compounds daily, some of which have adverse effects on health. Computational approaches for modeling toxicological data in conjunction with machine learning algorithms have gained popularity over the last few years. Machine learning methods have been used to predict toxicity-related biological activities using chemical structure descriptors. However, proteomic features have not been fully investigated. In this study, we construct a computational model using machine learning for selecting the most important proteins representing features in predicting the toxicity of the compounds in the Tox21 dataset using the multiscale Computational Analysis of Novel Drug Opportunities (CANDO) platform for therapeutic discovery. Tox21 is a highly imbalanced dataset consisting of twelve in-vitro assays, seven from the nuclear receptor (NR) signaling pathway and five from the stress response (SR) pathway, for more than 10,000 compounds. For our computational model, we employed a random forest (RF) with the combination of Synthetic Minority Oversampling Technique (SMOTE) and Edited Nearest Neighbor (ENN) method, aka SMOTE+ENN, which is resampling method to balance the activity class distribution. Within the NR and SR pathways, the activity of the aryl hydrocarbon receptor (NR-AhR), toxicity mediating transcription factor, and microchondrial membrane potential (SR-MMP) were two of the top-performing twelve toxicity endpoints with AUROCs of 0.90 and 0.92, respectively. The top extracted features for evaluating compound toxicity were passed into enrichment analysis to highlight the implicated biological pathways and proteins. We validated our enrichment results for the activity of the AhR using a thorough literature search. Our case study showed that the selected enriched pathways and proteins from our computational pipeline are not only correlated with NR-AhR toxicity but also form a cascading upstream/downstream arrangement. Our work elucidates significant relationships between protein and compound interactions computed using CANDO and the associated biological pathways to which the proteins belong, with twelve toxicity endpoints. This novel study uses machine learning not only to predict and understand toxicity but also elucidates therapeutic mechanisms at a proteomic level for a variety of toxicity endpoints.

The Future of Neurotoxicology: A Neuroelectrophysiological Viewpoint

Neuroelectrophysiology is an old science, dating to the 18th century when electrical activity in nerves was discovered. Such discoveries have led to a variety of neurophysiological techniques, ranging from basic neuroscience to clinical applications. These clinical applications allow assessment of complex neurological functions such as (but not limited to) sensory perception (vision, hearing, somatosensory function), and muscle function. The ability to use similar techniques in both humans and animal models increases the ability to perform mechanistic research to investigate neurological problems. Good animal to human homology of many neurophysiological systems facilitates interpretation of data to provide cause-effect linkages to epidemiological findings. Mechanistic cellular research to screen for toxicity often includes gaps between cellular and whole animal/person neurophysiological changes, preventing understanding of the complete function of the nervous system. Building Adverse Outcome Pathways (AOPs) will allow us to begin to identify brain regions, timelines, neurotransmitters, etc. that may be Key Events (KE) in the Adverse Outcomes (AO). This requires an integrated strategy, from in vitro to in vivo (and hypothesis generation, testing, revision). Scientists need to determine intermediate levels of nervous system organization that are related to an AO and work both upstream and downstream using mechanistic approaches. Possibly more than any other organ, the brain will require networks of pathways/AOPs to allow sufficient predictive accuracy. Advancements in neurobiological techniques should be incorporated into these AOP-base neurotoxicological assessments, including interactions between many regions of the brain simultaneously. Coupled with advancements in optogenetic manipulation, complex functions of the nervous system (such as acquisition, attention, sensory perception, etc.) can be examined in real time. The integration of neurophysiological changes with changes in gene/protein expression can begin to provide the mechanistic underpinnings for biological changes. Establishment of linkages between changes in cellular physiology and those at the level of the AO will allow construction of biological pathways (AOPs) and allow development of higher throughput assays to test for changes to critical physiological circuits. To allow mechanistic/predictive toxicology of the nervous system to be protective of human populations, neuroelectrophysiology has a critical role in our future.

ELIXIR and Toxicology: a community in development

Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities.

Human Organoids for Predictive Toxicology Research and Drug Development

Organoids are three-dimensional structures fabricated in vitro from pluripotent stem cells or adult tissue stem cells via a process of self-organization that results in the formation of organ-specific cell types. Human organoids are expected to mimic complex microenvironments and many of the in vivo physiological functions of relevant tissues, thus filling the translational gap between animals and humans and increasing our understanding of the mechanisms underlying disease and developmental processes. In the last decade, organoid research has attracted increasing attention in areas such as disease modeling, drug development, regenerative medicine, toxicology research, and personalized medicine. In particular, in the field of toxicology, where there are various traditional models, human organoids are expected to blaze a new path in future research by overcoming the current limitations, such as those related to differences in drug responses among species. Here, we discuss the potential usefulness, limitations, and future prospects of human liver, heart, kidney, gut, and brain organoids from the viewpoints of predictive toxicology research and drug development, providing cutting edge information on their fabrication methods and functional characteristics.

A Review: In – Silico Approaches in Predictive Toxicology

This article deals with the in – silico techniques for predicting the toxicity of chemical compounds. Toxicology is the branch of biology that deals with the study of adverse effect of chemical substances on the living organisms and the practice of treating and preventing such adverse effects. Predicting toxicity of a new drug to be produced is the first aim of preclinical trials. It is achieved by in-silico methods. There are several in - silico technique softwares which are used for the prediction of ADME and hence toxicity of drugs. In – silico methods involves the use of various softwares to calculate and then predict the toxicity of a compound by first determining its structural and pharmacokinetic and pharmacodynamic properties and then it correlates this information with already existing drugs and molecules and thus gives us conclusion. The article focuses on QSAR and its techniques, HQSAR, several other methods like structural alerts and rule-based models, chemical category and read across model, dose and time response model, virtual ligand screening, docking, 3D pharmacophore mapping, simulation approaches, PKPD models and several other approaches like bioinformatics. After reviewing and studying various in silico techniques the conclusion comes out to be that, in-silico methods of predictive toxicology are more better than in-vitro and in-vivo methods since they are much more safe (as animals are not harmed), economic, fast and accurate w.r.to, results/output in predicting toxicity of compounds by computational methods and hence are widely used in the production of new drug for accessing its toxicity