Towards a 21st-century roadmap for biomedical research and drug discovery: consensus report and recommendations

CKG-IMC: An inductive matrix completion method enhanced by CKG and GNN for Alzheimer's disease compound-protein interactions prediction

Alzheimer's disease (AD) is one of the most prevalent chronic neurodegenerative disorders globally, with a rapidly growing population of AD patients and currently no effective therapeutic interventions available. Consequently, the development of therapeutic anti-AD drugs and the identification of AD targets represent one of the most urgent tasks. In this study, in addition to considering known drugs and targets, we explore compound-protein interactions (CPIs) between compounds and proteins relevant to AD. We propose a deep learning model called CKG-IMC to predict Alzheimer's disease compound-protein interaction relationships. CKG-IMC comprises three modules: a collaborative knowledge graph (CKG), a principal neighborhood aggregation graph neural network (PNA), and an inductive matrix completion (IMC). The collaborative knowledge graph is used to learn semantic associations between entities, PNA is employed to extract structural features of the relationship network, and IMC is utilized for CPIs prediction. Compared with a total of 16 baseline models based on similarities, knowledge graphs, and graph neural networks, our model achieves state-of-the-art performance in experiments of 10-fold cross-validation and independent test. Furthermore, we use CKG-IMC to predict compounds interacting with two confirmed AD targets, 42-amino-acid β-amyloid (Aβ42) protein and microtubule-associated protein tau (tau protein), as well as proteins interacting with five FDA-approved anti-AD drugs. The results indicate that the majority of predictions are supported by literature, and molecular docking experiments demonstrate a strong affinity between the predicted compounds and targets.

Simplifying Data Analysis in Biomedical Research: An Automated, User-Friendly Tool

Robust data normalization and analysis are pivotal in biomedical research to ensure that observed differences in populations are directly attributable to the target variable, rather than disparities between control and study groups. ArsHive addresses this challenge using advanced algorithms to normalize populations (e.g., control and study groups) and perform statistical evaluations between demographic, clinical, and other variables within biomedical datasets, resulting in more balanced and unbiased analyses. The tool's functionality extends to comprehensive data reporting, which elucidates the effects of data processing, while maintaining dataset integrity. Additionally, ArsHive is complemented by A.D.A. (Autonomous Digital Assistant), which employs OpenAI's GPT-4 model to assist researchers with inquiries, enhancing the decision-making process. In this proof-of-concept study, we tested ArsHive on three different datasets derived from proprietary data, demonstrating its effectiveness in managing complex clinical and therapeutic information and highlighting its versatility for diverse research fields.

Transitioning biomedical research toward human-centric methodologies: systems-based strategies

Human-centric methodologies like microphysiological systems and in silico methods have shown promise in addressing the limitations of animal models in understanding human biology and responding to public health priorities. However, the prevailing paradigm based on animal research persists. The article proposes a systemic thinking approach, endorsed by the OECD and the EU, as a tool to leverage innovation to reframe the issue and achieve transformative policies. By identifying the complex factors shaping method selection in basic and biomedical research, a simplified model is presented to illuminate the systemic nature of this decision-making process. The goal is not to prescribe solutions but to offer policymakers a new framework for more-effective strategies, emphasizing collaboration among stakeholders and the need for robust data.

Drug repurposing of FDA-approved anti-viral drugs via computational screening against novel 6M03 SARS-COVID-19

OBJECTIVE

The COVID-19 pandemic has been recognized as severe acute respiratory syndrome, one of the worst and disastrous infectious diseases in human history. Until now, there is no cure to this contagious infection although some multinational pharmaceutical companies have synthesized the vaccines and injecting them into humans, but a drug treatment regimen is yet to come.

AIM

Among the multiple areas of SARS-CoV-2 that can be targeted, protease protein has significant values due to its essential role in viral replication and life. The repurposing of FDA-approved drugs for the treatment of COVID-19 has been a critical strategy during the pandemic due to the urgency of effective therapies. The novelty in this work refers to the innovative use of existing drugs with greater safety, speed, cost-effectiveness, broad availability, and diversity in the mechanism of action that have been approved and developed for other medical conditions.

METHODS

In this research work, we have engaged drug reprofiling or drug repurposing to recognize possible inhibitors of protease protein 6M03 in an instantaneous approach through computational docking studies.

RESULTS

We screened 16 FDA-approved anti-viral drugs that were known for different viral infections to be tested against this contagious novel strain. Through these reprofiling studies, we come up with 5 drugs, namely, Delavirdine, Fosamprenavir, Imiquimod, Stavudine, and Zanamivir, showing excellent results with the negative binding energies in Kcal/mol as - 8.5, - 7.0, - 6.8, - 6.8, and - 6.6, respectively, in the best binding posture. In silico studies allowed us to demonstrate the potential role of these drugs against COVID-19.

CONCLUSION

In our study, we also observed the nucleotide sequence of protease protein consisting of 316 amino acid residues and the influence of these pronouncing drugs over these sequences. The outcome of this research work provides researchers with a track record for carrying out further investigational procedures by applying docking simulations and in vitro and in vivo experimentation with these reprofile drugs so that a better drug can be formulated against coronavirus.

Semi-supervised heterogeneous graph contrastive learning for drug-target interaction prediction

Identification of drug-target interactions (DTIs) is an important step in drug discovery and drug repositioning. In recent years, graph-based methods have attracted great attention and show advantages on predicting potential DTIs. However, these methods face the problem that the known DTIs are very limited and expensive to obtain, which decreases the generalization ability of the methods. Self-supervised contrastive learning is independent of labeled DTIs, which can mitigate the impact of the problem. Therefore, we propose a framework SHGCL-DTI for predicting DTIs, which supplements the classical semi-supervised DTI prediction task with an auxiliary graph contrastive learning module. Specifically, we generate representations for the nodes through the neighbor view and meta-path view, and define positive and negative pairs to maximize the similarity between positive pairs from different views. Subsequently, SHGCL-DTI reconstructs the original heterogeneous network to predict the potential DTIs. The experiments on the public dataset show that SHGCL-DTI has significant improvement in different scenarios, compared with existing state-of-the-art methods. We also demonstrate that the contrastive learning module improves the prediction performance and generalization ability of SHGCL-DTI through ablation study. In addition, we have found several novel predicted DTIs supported by the biological literature. The data and source code are available at: https://github.com/TOJSSE-iData/SHGCL-DTI.

The Use of Interdisciplinary Approaches to Understand the Biology of Campylobacter jejuni

Campylobacter jejuni is a bacterial pathogen recognised as a major cause of foodborne illness worldwide. While Campylobacter jejuni generally does not grow outside its host, it can survive outside of the host long enough to pose a health concern. This review presents an up-to-date description and evaluation of biological, mathematical, and statistical approaches used to understand the behaviour of this foodborne pathogen and suggests future avenues which can be explored. Specifically, the incorporation of mathematical modelling may aid the understanding of C. jejuni biofilm formation both outside and inside the host. Predictive studies may be improved by the introduction of more standardised protocols for assessments of disinfection methods and by assessment of novel physical disinfection strategies as well as assessment of the efficiency of plant extracts on C. jejuni eradication. A full description of the metabolic pathways of C. jejuni, which is needed for the successful application of metabolic models, is yet to be achieved. Finally, a shift from animal models (except for those that are a source of human campylobacteriosis) to human-specific data may be made possible due to recent technological advancements, and this may lead to more accurate predictions of human infections.

Artificial intelligence in microbial natural product drug discovery: current and emerging role

Covering: up to the end of 2022Microorganisms are exceptional sources of a wide array of unique natural products and play a significant role in drug discovery. During the golden era, several life-saving antibiotics and anticancer agents were isolated from microbes; moreover, they are still widely used. However, difficulties in the isolation methods and repeated discoveries of the same molecules have caused a setback in the past. Artificial intelligence (AI) has had a profound impact on various research fields, and its application allows the effective performance of data analyses and predictions. With the advances in omics, it is possible to obtain a wealth of information for the identification, isolation, and target prediction of secondary metabolites. In this review, we discuss drug discovery based on natural products from microorganisms with the help of AI and machine learning.

Towards an artificial human lung: modelling organ-like complexity to aid mechanistic understanding

Respiratory diseases account for over 5 million deaths yearly and are a huge burden to healthcare systems worldwide. Murine models have been of paramount importance to decode human lung biology in vivo, but their genetic, anatomical, physiological and immunological differences with humans significantly hamper successful translation of research into clinical practice. Thus, to clearly understand human lung physiology, development, homeostasis and mechanistic dysregulation that may lead to disease, it is essential to develop models that accurately recreate the extraordinary complexity of the human pulmonary architecture and biology. Recent advances in micro-engineering technology and tissue engineering have allowed the development of more sophisticated models intending to bridge the gap between the native lung and its replicates in vitro Alongside advanced culture techniques, remarkable technological growth in downstream analyses has significantly increased the predictive power of human biology-based in vitro models by allowing capture and quantification of complex signals. Refined integrated multi-omics readouts could lead to an acceleration of the translational pipeline from in vitro experimental settings to drug development and clinical testing in the future. This review highlights the range and complexity of state-of-the-art lung models for different areas of the respiratory system, from nasal to large airways, small airways and alveoli, with consideration of various aspects of disease states and their potential applications, including pre-clinical drug testing. We explore how development of optimised physiologically relevant in vitro human lung models could accelerate the identification of novel therapeutics with increased potential to translate successfully from the bench to the patient's bedside.

Researchers and Their Experimental Models: A Pilot Survey in the Context of the European Union Health and Life Science Research

Simple Summary

Scientists in biomedical research use models and methods to constantly improve health in society. This research heavily relies on animal experimentation, and in recent decades, research and researchers have been questioned by societal stakeholders about their way of conducting research. In order to inform science policy makers, we asked the researchers about the use of their experimental models and their view about the role of external stakeholders in their work.

Abstract

A significant debate is ongoing on the effectiveness of animal experimentation, due to the increasing reports of failure in the translation of results from preclinical animal experiments to human patients. Scientific, ethical, social and economic considerations linked to the use of animals raise concerns in a variety of societal contributors (regulators, policy makers, non-governmental organisations, industry, etc.). The aim of this study was to record researchers’ voices about their vision on this science evolution, to reconstruct as truthful as possible an image of the reality of health and life science research, by using a key instrument in the hands of the researcher: the experimental models. Hence, we surveyed European-based health and life sciences researchers, to reconstruct and decipher the varying orientations and opinions of this community over these large transformations. In the interest of advancing the public debate and more accurately guide the policy of research, it is important that policy makers, society, scientists and all stakeholders (1) mature as comprehensive as possible an understanding of the researchers’ perspectives on the selection and establishment of the experimental models, and (2) that researchers publicly share the research community opinions regarding the external factors influencing their professional work. Our results highlighted a general homogeneity of answers from the 117 respondents. However, some discrepancies on specific key issues and topics were registered in the subgroups. These recorded divergent views might prove useful to policy makers and regulators to calibrate their agenda and shape the future of the European health and life science research. Overall, the results of this pilot study highlight the need of a continuous, open and broad discussion between researchers and science policy stakeholders.

Computational cardiac physiology for new modelers: Origins, foundations, and future

Mathematical models of the cardiovascular system have come a long way since they were first introduced in the early 19th century. Driven by a rapid development of experimental techniques, numerical methods, and computer hardware, detailed models that describe physical scales from the molecular level up to organs and organ systems have been derived and used for physiological research. Mathematical and computational models can be seen as condensed and quantitative formulations of extensive physiological knowledge and are used for formulating and testing hypotheses, interpreting and directing experimental research, and have contributed substantially to our understanding of cardiovascular physiology. However, in spite of the strengths of mathematics to precisely describe complex relationships and the obvious need for the mathematical and computational models to be informed by experimental data, there still exist considerable barriers between experimental and computational physiological research. In this review, we present a historical overview of the development of mathematical and computational models in cardiovascular physiology, including the current state of the art. We further argue why a tighter integration is needed between experimental and computational scientists in physiology, and point out important obstacles and challenges that must be overcome in order to fully realize the synergy of experimental and computational physiological research.

GCHN-DTI: Predicting drug-target interactions by graph convolution on heterogeneous networks

Determining the interaction of drug and target plays a key role in the process of drug development and discovery. The calculation methods can predict new interactions and speed up the process of drug development. In recent studies, the network-based approaches have been proposed to predict drug-target interactions. However, these methods cannot fully utilize the node information from heterogeneous networks. Therefore, we propose a method based on heterogeneous graph convolutional neural network for drug-target interaction prediction, GCHN-DTI (Predicting drug-target interactions by graph convolution on heterogeneous net-works), to predict potential DTIs. GCHN-DTI integrates network information from drug-target interactions, drug-drug interactions, drug-similarities, target-target interactions, and target-similarities. Then, the graph convolution operation is used in the heterogeneous network to obtain the node embedding of the drugs and the targets. Furthermore, we incorporate an attention mechanism between graph convolutional layers to combine node embedding from each layer. Finally, the drug-target interaction score is predicted based on the node embedding of the drugs and the targets. Our model uses fewer network types and achieves higher prediction performance. In addition, the prediction performance of the model will be significantly improved on the dataset with a higher proportion of positive samples. The experimental evaluations show that GCHN-DTI outperforms several state-of-the-art prediction methods.

Building a Network of Adverse Outcome Pathways (AOPs) Incorporating the Tau-Driven AOP Toward Memory Loss (AOP429)

The adverse outcome pathway (AOP) concept was first proposed as a tool for chemical hazard assessment facilitating the regulatory decision-making in toxicology and was more recently recommended during the BioMed21 workshops as a tool for the characterization of crucial endpoints in the human disease development. This AOP framework represents mechanistically based approaches using existing data, more realistic and relevant to human biological systems. In principle, AOPs are described by molecular initiating events (MIEs) which induce key events (KEs) leading to adverse outcomes (AOs). In addition to the individual AOPs, the network of AOPs has been also suggested to beneficially support the understanding and prediction of adverse effects in risk assessment. The AOP-based networks can capture the complexity of biological systems described by different AOPs, in which multiple AOs diverge from a single MIE or multiple MIEs trigger a cascade of KEs that converge to a single AO. Here, an AOP network incorporating a recently proposed tau-driven AOP toward memory loss (AOP429) related to sporadic (late-onset) Alzheimer’s disease is constructed. This proposed AOP network is an attempt to extract useful information for better comprehending the interactions among existing mechanistic data linked to memory loss as an early phase of sporadic Alzheimer’s disease pathology.

IMCHGAN: Inductive Matrix Completion With Heterogeneous Graph Attention Networks for Drug-Target Interactions Prediction

Identification of targets among known drugs plays an important role in drug repurposing and discovery. Computational approaches for prediction of drug-target interactions (DTIs)are highly desired in comparison to traditional biological experiments as its fast and low price. Moreover, recent advances of systems biology approaches have generated large-scale heterogeneous, biological information networks data, which offer opportunities for machine learning-based identification of DTIs. We present a novel Inductive Matrix Completion with Heterogeneous Graph Attention Network approach (IMCHGAN)for predicting DTIs. IMCHGAN first adopts a two-level neural attention mechanism approach to learn drug and target latent feature representations from the DTI heterogeneous network respectively. Then, the learned latent features are fed into the Inductive Matrix Completion (IMC)prediction score model which computes the best projection from drug space onto target space and output DTI score via the inner product of projected drug and target feature representations. IMCHGAN is an end-to-end neural network learning framework where the parameters of both the prediction score model and the feature representation learning model are simultaneously optimized via backpropagation under supervising of the observed known drug-target interactions data. We compare IMCHGAN with other state-of-the-art baselines on two real DTI experimental datasets. The results show that our method is superior to existing methods in term of AUC and AUPR. Moreover, IMCHGAN also shows it has strong predictive power for novel (unknown)DTIs. All datasets and code can be obtained from https://github.com/ljatynu/IMCHGAN/.

Fundamental Biomaterial Considerations in the Development of a 3D Model Representative of Primary Open Angle Glaucoma

Glaucoma is a leading cause of irreversible blindness globally, with primary open angle glaucoma (POAG) being the most common subset. Raised intraocular pressure is an important risk factor for POAG and is caused by a reduction in aqueous humour (AqH) outflow due to dysfunctional cellular and matrix dynamics in the eye’s main drainage site, the trabecular meshwork (TM) and Schlemm’s canal (SC). The TM/SC are highly specialised tissues that regulate AqH outflow; however, their exact mechanisms of AqH outflow control are still not fully understood. Emulating physiologically relevant 3D TM/S in vitro models poses challenges to accurately mimic the complex biophysical and biochemical cues that take place in healthy and glaucomatous TM/SC in vivo. With development of such models still in its infancy, there is a clear need for more well-defined approaches that will accurately contrast the two central regions that become dysfunctional in POAG; the juxtacanalicular tissue (JCT) region of the TM and inner wall endothelia of the Schlemm’s canal (eSC). This review will discuss the unique biological and biomechanical characteristics that are thought to influence AqH outflow and POAG progression. Further consideration into fundamental biomaterial attributes for the formation of a biomimetic POAG/AqH outflow model will also be explored for future success in pre-clinical drug discovery and disease translation.

A Tau-Driven Adverse Outcome Pathway Blueprint Toward Memory Loss in Sporadic (Late-Onset) Alzheimer's Disease with Plausible Molecular Initiating Event Plug-Ins for Environmental Neurotoxicants

The worldwide prevalence of sporadic (late-onset) Alzheimer's disease (sAD) is dramatically increasing. Aging and genetics are important risk factors, but systemic and environmental factors contribute to this risk in a still poorly understood way. Within the frame of BioMed21, the Adverse Outcome Pathway (AOP) concept for toxicology was recommended as a tool for enhancing human disease research and accelerating translation of data into human applications. Its potential to capture biological knowledge and to increase mechanistic understanding about human diseases has been substantiated since. In pursuit of the tau-cascade hypothesis, a tau-driven AOP blueprint toward the adverse outcome of memory loss is proposed. Sequences of key events and plausible key event relationships, triggered by the bidirectional relationship between brain cholesterol and glucose dysmetabolism, and contributing to memory loss are captured. To portray how environmental factors may contribute to sAD progression, information on chemicals and drugs, that experimentally or epidemiologically associate with the risk of AD and mechanistically link to sAD progression, are mapped on this AOP. The evidence suggests that chemicals may accelerate disease progression by plugging into sAD relevant processes. The proposed AOP is a simplified framework of key events and plausible key event relationships representing one specific aspect of sAD pathology, and an attempt to portray chemical interference. Other sAD-related AOPs (e.g., Aβ-driven AOP) and a better understanding of the impact of aging and genetic polymorphism are needed to further expand our mechanistic understanding of early AD pathology and the potential impact of environmental and systemic risk factors.

Scaffold-free 3D cell culture of primary skin fibroblasts induces profound changes of the matrisome

The human skin has a highly developed extracellular matrix (ECM) that is vital for proper skin functioning, its 3D architecture playing a pivotal role in support and guidance of resident and invading cells. To establish relevant in vitro models mimicking the complex design observed in vivo, scaffold-based and scaffold-free 3D cell culture systems have been developed. Here we show that scaffold-free systems are well suited for the analysis of ECM protein regulation. Using quantitative mass spectrometry-based proteomics in combination with magnetic 3D bioprinting we characterize changes in the proteome of skin fibroblasts and squamous cell carcinoma cells. Transferring cells from 2D to 3D without any additional scaffold induces a profound upregulation of matrisome proteins indicating the generation of a complex, tissue-like ECM.

LUNAR :Drug Screening for Novel Coronavirus Based on Representation Learning Graph Convolutional Network

An outbreak of COVID-19 that began in late 2019 was caused by a novel coronavirus(SARS-CoV-2). It has become a global pandemic. As of June 9, 2020, it has infected nearly 7 million people and killed more than 400,000, but there is no specific drug. Therefore, there is an urgent need to find or develop more drugs to suppress the virus. Here, we propose a new nonlinear end-to-end model called LUNAR. It uses graph convolutional neural networks to automatically learn the neighborhood information of complex heterogeneous relational networks and combines the attention mechanism to reflect the importance of the sum of different types of neighborhood information to obtain the representation characteristics of each node. Finally, through the topology reconstruction process, the feature representations of drugs and targets are forcibly extracted to match the observed network as much as possible. Through this reconstruction process, we obtain the strength of the relationship between different nodes and predict drug candidates that may affect the treatment of COVID-19 based on the known targets of COVID-19. These selected candidate drugs can be used as a reference for experimental scientists and accelerate the speed of drug development. LUNAR can well integrate various topological structure information in heterogeneous networks, and skillfully combine attention mechanisms to reflect the importance of neighborhood information of different types of nodes, improving the interpretability of the model. The area under the curve(AUC) of the model is 0.949 and the accurate recall curve (AUPR) is 0.866 using 10-fold cross-validation. These two performance indexes show that the model has superior predictive performance. Besides, some of the drugs screened out by our model have appeared in some clinical studies to further illustrate the effectiveness of the model.

Systematic Organization of COVID-19 Data Supported by the Adverse Outcome Pathway Framework

Adverse Outcome Pathways (AOP) provide structured frameworks for the systematic organization of research data and knowledge. The AOP framework follows a set of key principles that allow for broad application across diverse disciplines related to human health, including toxicology, pharmacology, virology and medical research. The COVID-19 pandemic engages a great number of scientists world-wide and data is increasing with exponential speed. Diligent data management strategies are employed but approaches for systematically organizing the data-derived information and knowledge are lacking. We believe AOPs can play an important role in improving interpretation and efficient application of scientific understanding of COVID-19. Here, we outline a newly initiated effort, the CIAO project (https://www.ciao-covid.net/), to streamline collaboration between scientists across the world toward development of AOPs for COVID-19, and describe the overarching aims of the effort, as well as the expected outcomes and research support that they will provide.

Converging global crises are forcing the rapid adoption of disruptive changes in drug discovery

Spiralling research costs combined with urgent pressures from the Coronavirus 2019 (COVID-19) pandemic and the consequences of climate disruption are forcing changes in drug discovery. Increasing the predictive power of in vitro human assays and using them earlier in discovery would refocus resources on more successful research strategies and reduce animal studies. Increasing laboratory automation enables effective social distancing for researchers, while allowing integrated data capture from remote laboratory networks. Such disruptive changes would not only enable more cost-effective drug discovery, but could also reduce the overall carbon footprint of discovering new drugs.

Multi-omics analysis reveals that co-exposure to phthalates and metals disturbs urea cycle and choline metabolism

This study aimed to evaluate the response of HepaRG cells after co-exposure to phthalates and heavy metals, using a high-dimensional biology paradigm (HDB). Liver is the main metabolism site for the majority of xenobiotics. For this reason, the HepaRG cell line was used as an in vitro model, and cells were exposed to two characteristic mixtures of phthalates and heavy metals containing phthalates (DEHP, DiNP, BBzP) and metals (lead, methylmercury, total mercury) in a concentration-dependent manner. The applied chemical mixtures were selected as the most abundant pollutants in the REPRO_PL and PHIME cohorts, which were studied using the exposome-wide approach in the frame of the EU project HEALS. These studies investigated the environmental causation of neurodevelopmental disorders in neonates and across Europe. The INTEGRA computational platform was used for the calculation of the effective concentrations of the chemicals in the liver through extrapolation from human biomonitoring data and this dose (and a ten-times higher one) was applied to the hepatocyte model. Multi-omics analysis was performed to reveal the genes, proteins, and metabolites affected by the exposure to these chemical mixtures. By extension, we could detect the perturbed metabolic pathways. The generated data were analyzed using advanced bioinformatic tools following the HEALS connectivity paradigm for multi-omics pathway analysis. Co-mapped transcriptomics and proteomics data showed that co-exposure to phthalates and heavy metals leads to perturbations of the urea cycle due to differential expression levels of arginase-1 and -2, argininosuccinate synthase, carbamoyl-phosphate synthase, ornithine carbamoyltransferase, and argininosuccinate lyase. Joint pathway analysis of proteomics and metabolomics data revealed that the detected proteins and metabolites, choline phosphate cytidylyltransferase A, phospholipase D3, group XIIA secretory phospholipase A2, α-phosphatidylcholine, and the a 1,2-diacyl-sn-glycero-3-phosphocholine, are responsible for the homeostasis of the metabolic pathways phosphatidylcholine biosynthesis I, and phospholipases metabolism. The urea, phosphatidylcholine biosynthesis I and phospholipase metabolic pathways are of particular interest since they have been identified also in human samples from the REPRO_PL and PHIME cohorts using untargeted metabolomics analysis and have been associated with impaired psychomotor development in children at the age of two. In conclusion, this study provides the mechanistic evidence that co-exposure to phthalates and metals disturb biochemical processes related to mitochondrial respiration during critical developmental stages, which are clinically linked to neurodevelopmental perturbations.

Links between Nutrition, Infectious Diseases, and Microbiota: Emerging Technologies and Opportunities for Human-Focused Research

The interaction between nutrition and human infectious diseases has always been recognized. With the emergence of molecular tools and post-genomics, high-resolution sequencing technologies, the gut microbiota has been emerging as a key moderator in the complex interplay between nutrients, human body, and infections. Much of the host-microbial and nutrition research is currently based on animals or simplistic in vitro models. Although traditional in vivo and in vitro models have helped to develop mechanistic hypotheses and assess the causality of the host-microbiota interactions, they often fail to faithfully recapitulate the complexity of the human nutrient-microbiome axis in gastrointestinal homeostasis and infections. Over the last decade, remarkable progress in tissue engineering, stem cell biology, microfluidics, sequencing technologies, and computing power has taken place, which has produced a new generation of human-focused, relevant, and predictive tools. These tools, which include patient-derived organoids, organs-on-a-chip, computational analyses, and models, together with multi-omics readouts, represent novel and exciting equipment to advance the research into microbiota, infectious diseases, and nutrition from a human-biology-based perspective. After considering some limitations of the conventional in vivo and in vitro approaches, in this review, we present the main novel available and emerging tools that are suitable for designing human-oriented research.

The Relevance ofIn Silico,In Vitroand Non-human Primate Based Approaches to Clinical Research on Major Depressive Disorder

Major depressive disorder (MDD) is the most severe form of depression and the leading cause of disability worldwide. When considering research approaches aimed at understanding MDD, it is important that their effectiveness is evaluated. Here, we assessed the effectiveness of original studies on MDD by rating their contributions to subsequent medical papers on the subject, and we compared the respective contribution of findings from non-human primate (NHP) studies and from human-based in vitro or in silico research approaches. For each publication, we conducted a quantitative citation analysis and a systematic qualitative analysis of the citations. In the majority of cases, human-based research approaches (both in silico and in vitro) received more citations in subsequent human research papers than did NHP studies. In addition, the human-based approaches were considered to be more relevant to the hypotheses and/or to the methods featured in the citing papers. The results of this study suggest that studies based on in silico and in vitro approaches are taken into account by medical researchers more often than are NHP-based approaches. In addition, these human-based approaches are usually cheaper and less ethically contentious than NHP studies. Therefore, we suggest that the traditional animal-based approach for testing medical hypotheses should be revised, and more opportunities created for further developing human-relevant innovative techniques.

2D- and 3D-cultures of human trabecular meshwork cells: A preliminary assessment of an in vitro model for glaucoma study

A physiologically relevant in vitro human-based model could be the ‘gold standard’ to clarify the pathological steps involved in glaucoma onset. In this regard, human 3D cultures may represent an excellent starting point to achieve this goal. Indeed, the 3D matrix allows to re-create the in vivo-like tissue architecture, maintaining its functionality and cellular behaviour, compared to the 2D model. Thus, we propose a comparison between the 2D and 3D in vitro models of human trabecular meshwork cells in terms of cellular responses after chronic stress exposure. Our results showed that 3D-cells are more sensitive to intracellular reactive oxidative specie production induced by hydrogen peroxide treatment, compared to 2D cultures. Additionally, in 3D cultures a more accurate regulation of the apoptosis trigger and cell adaptation mechanisms was detected than in 2D models. In line with these findings, the 3D-HTMC model shows the ability to better mimic the in vivo cell behaviour in adaptive responses to chronic oxidative stress than 2D.

Mechanically Biomimetic Gelatin-Gellan Gum Hydrogels for 3D Culture of Beating Human Cardiomyocytes

To promote the transition of cell cultures from 2D to 3D, hydrogels are needed to biomimic the extracellular matrix (ECM). One potential material for this purpose is gellan gum (GG), a biocompatible and mechanically tunable hydrogel. However, GG alone does not provide attachment sites for cells to thrive in 3D. One option for biofunctionalization is the introduction of gelatin, a derivative of the abundant ECM protein collagen. Unfortunately, gelatin lacks cross-linking moieties, making the production of self-standing hydrogels difficult under physiological conditions. Here, we explore the functionalization of GG with gelatin at biologically relevant concentrations using semiorthogonal, cytocompatible, and facile chemistry based on hydrazone reaction. These hydrogels exhibit mechanical behavior, especially elasticity, which resembles the cardiac tissue. The use of optical projection tomography for 3D cell microscopy demonstrates good cytocompatibility and elongation of human fibroblasts (WI-38). In addition, human-induced pluripotent stem cell-derived cardiomyocytes attach to the hydrogels and recover their spontaneous beating in 24 h culture. Beating is studied using in-house-built phase contrast video analysis software, and it is comparable with the beating of control cardiomyocytes under regular culture conditions. These hydrogels provide a promising platform to transition cardiac tissue engineering and disease modeling from 2D to 3D.

Gradient Boosting Decision Tree-Based Method for Predicting Interactions Between Target Genes and Drugs

Determining the target genes that interact with drugs—drug–target interactions—plays an important role in drug discovery. Identification of drug–target interactions through biological experiments is time consuming, laborious, and costly. Therefore, using computational approaches to predict candidate targets is a good way to reduce the cost of wet-lab experiments. However, the known interactions (positive samples) and the unknown interactions (negative samples) display a serious class imbalance, which has an adverse effect on the accuracy of the prediction results. To mitigate the impact of class imbalance and completely exploit the negative samples, we proposed a new method, named DTIGBDT, based on gradient boosting decision trees, for predicting candidate drug–target interactions. We constructed a drug–target heterogeneous network that contains the drug similarities based on the chemical structures of drugs, the target similarities based on target sequences, and the known drug–target interactions. The topological information of the network was captured by random walks to update the similarities between drugs or targets. The paths between drugs and targets could be divided into multiple categories, and the features of each category of paths were extracted. We constructed a prediction model based on gradient boosting decision trees. The model establishes multiple decision trees with the extracted features and obtains the interaction scores between drugs and targets. DTIGBDT is a method of ensemble learning, and it effectively reduces the impact of class imbalance. The experimental results indicate that DTIGBDT outperforms several state-of-the-art methods for drug–target interaction prediction. In addition, case studies on Quetiapine, Clozapine, Olanzapine, Aripiprazole, and Ziprasidone demonstrate the ability of DTIGBDT to discover potential drug–target interactions.

A Noninvasive Ocular (Tear) Sampling Method for Genetic Ascertainment of Transgenic Mice and Research Ethics Innovation

Animal models, animal welfare and research ethics are both facilitators and gatekeepers for Big Data generation in genomics and multi-omics R&D. Safeguarding animal welfare is also a research ethics issue that can benefit from technical innovations in biosample collection in particular. Animal welfare draws from the guiding principles of 3R, namely, "Replacement" (methods avoiding the use of animals in research), "Reduction" (methods using fewer animals or derive more information from the same number of animals), and "Refinement" (methods removing or minimizing pain or distress). We report here that noninvasive ocular (tear) sampling for genetic ascertainment of transgenic mice can serve as an innovative ethical safeguard for animal welfare, and as a veritable alternative to the surgical tail biopsies, ear puncture, or blood sampling from the weanling transgenic mice. We compared ocular versus tail biopsy sampling in regard to ascertainment, by genotyping, of apolipoprotein E-deficient (ApoE^-/-) transgenic weanling mice (n = 60) by one-round polymerase chain reaction analysis. We found that ocular sampling compares to the results obtained by tail sampling with the obvious benefit of being noninvasive and improving the 3R, especially for the Refinement principle of animal welfare. To place the importance of this new biosample collection approach into further context, transgenic mice research and animal models are at the epicenter of Big Data translation to health innovation. We suggest that ocular sampling is considered and evaluated further in transgenic mice models, not to mention warrant exploration for applications in other types of animal models that require noninvasive biosample collection.

Exploring new technologies in biomedical research

The Health Law, Policy & Ethics Project at Emory University School of Law and the Human Toxicology Project Consortium of the Humane Society of the United States co-sponsored a symposium on October 23, 2017, to showcase innovations using human-based in silico and in vitro models for drug and device discovery. The goal of the symposium was to introduce researchers and students to exciting new tools and possible future careers that will increase understanding of disease and improve the search for effective therapeutics, while reducing reliance on animal testing. The symposium concluded with a discussion between scientists and lawyers about the legal regulation of new biomedical research technologies.

Brain organoids as a model system for human neurodevelopment and disease

The ability to reproduce early stages of human neurodevelopment in the laboratory is one of the most exciting fields in modern neuroscience. The inaccessibility of the healthy human brain developing in utero has delayed our understanding of the initial steps in the formation of one of the most complex tissues in the body. Animal models, postmortem human tissues and cellular systems have been instrumental in contributing to our understanding of the human brain. However, all model systems have intrinsic limitations. The emerging field of brain organoids, which are three-dimensional self-assembled multicellular structures derived from human pluripotent stem cells, offers a promising complementary cellular model for the study of the human brain. Here, we will discuss the initial experiments that were the foundation for this emerging field, highlight recent uses of the technology and offer our perspective on future directions that might guide further exploratory experimentation to improve the human brain organoid model system.

Sobre uma política de ciência e tecnologia para a saúde

RESUMO Frente ao conjunto de políticas de ciência e tecnologia existentes no Brasil, o texto reivindica um olhar diferenciado sobre a política de pesquisa em saúde. Isso decorre de sua magnitude física, de sua tradição histórica e de sua articulação com uma política pública de saúde na qual a intersetorialidade é valorizada. O texto se divide em três partes, precedidas de uma advertência sobre o impacto da conjuntura atual do País sobre a política geral de ciência e tecnologia. Em primeiro lugar, propõe uma abordagem metodológica para a definição das fronteiras da pesquisa em saúde. Em seguida, reivindica para o campo da saúde coletiva um papel de protagonismo na construção dessa política. Finalmente, apresenta e discute alguns desafios atuais postos para a política.

Evaluation of in vitro neuronal platforms as surrogates for in vivo whole brain systems

Quantitatively benchmarking similarities and differences between the in vivo central nervous system and in vitro neuronal cultures can qualify discrepancies in functional responses and establish the utility of in vitro platforms. In this work, extracellular electrophysiology responses of cortical neurons in awake, freely-moving animals were compared to in vitro cultures of dissociated cortical neurons. After exposure to two well-characterized drugs, atropine and ketamine, a number of key points were observed: (1) significant differences in spontaneous firing activity for in vivo and in vitro systems, (2) similar response trends in single-unit spiking activity after exposure to atropine, and (3) greater sensitivity to the effects of ketamine in vitro. While in vitro cultures of dissociated cortical neurons may be appropriate for many types of pharmacological studies, we demonstrate that for some drugs, such as ketamine, this system may not fully capture the responses observed in vivo. Understanding the functionality associated with neuronal cultures will enhance the relevance of electrophysiology data sets and more accurately frame their conclusions. Comparing in vivo and in vitro rodent systems will provide the critical framework necessary for developing and interpreting in vitro systems using human cells that strive to more closely recapitulate human in vivo function and response.

Harvesting the promise of AOPs: An assessment and recommendations

The Adverse Outcome Pathway (AOP) concept is a knowledge assembly and communication tool to facilitate the transparent translation of mechanistic information into outcomes meaningful to the regulatory assessment of chemicals. The AOP framework and associated knowledgebases (KBs) have received significant attention and use in the regulatory toxicology community. However, it is increasingly apparent that the potential stakeholder community for the AOP concept and AOP KBs is broader than scientists and regulators directly involved in chemical safety assessment. In this paper we identify and describe those stakeholders who currently-or in the future-could benefit from the application of the AOP framework and knowledge to specific problems. We also summarize the challenges faced in implementing pathway-based approaches such as the AOP framework in biological sciences, and provide a series of recommendations to meet critical needs to ensure further progression of the framework as a useful, sustainable and dependable tool supporting assessments of both human health and the environment. Although the AOP concept has the potential to significantly impact the organization and interpretation of biological information in a variety of disciplines/applications, this promise can only be fully realized through the active engagement of, and input from multiple stakeholders, requiring multi-pronged substantive long-term planning and strategies.

The inherent risks associated with newly traded biopharmaceutical firms

Here, we provide a comprehensive study related to the risks of all biopharmaceutical firms going public in the USA between 1996 and 2015. We found 355 firms that met our requirements for being in the sector that focuses on creating drugs for humans. Collectively, these firms spent approximately US$86.9 billion on research and development (R&D) during this time. They also lost approximately US$69.3 billion in combined net income. We also examine the delisting of these firms from a public market, their number of collaborators at the initial public offering (IPO), and estimate the percentage ownership by other biopharmaceutical firms at the IPO.

Human-specific approaches to brain research for the 21st century: a South American perspective

The 21st century paradigm in toxicology, which emphasizes mechanistic understanding and species-relevant modeling of human biology and pathophysiology, is gaining traction in the wider biosciences through a global workshop series organized by the BioMed21 Collaboration. The second of this series, entitled Emerging Technology Toward Pathway-Based Human Brain Research, was held in Brazil in 2017, bringing together leading South American and international scientists, research funders and other stakeholders. The aims were to foster strategic scientific dialogue and identify actionable consensus recommendations as a first step toward a roadmap for 21st century, human-specific health research and funding in the region.

Recommendations toward a human pathway-based approach to disease research

Failures in the current paradigm for drug development have resulted in soaring research and development costs and reduced numbers of new drug approvals. Over 90% of new drug programs fail, the majority terminated at the level of Phase 2/3 clinical trials, largely because of efficacy failures or unexplained toxicity. A recent workshop brought together members from research institutions, regulatory agencies, industry, academia, and nongovernmental organizations to discuss how existing programs could be better applied to understanding human biology and improving drug discovery. Recommendations include increased emphasis on human relevance, better access and curation of data, and improved interdisciplinary and international collaboration.

Powering ex vivo tissue models in microfluidic systems

This Frontiers review analyzes the rapidly growing microfluidic strategies that have been employed in attempts to create physio relevant 'organ-on-chip' models using primary tissue removed from a body (human or animal). Tissue harvested immediately from an organism, and cultured under artificial conditions is referred to as ex vivo tissue. The use of primary (organotypic) tissue offers unique benefits over traditional cell culture experiments, and microfluidic technology can be used to further exploit these advantages. Defining the utility of particular models, determining necessary constituents for acceptable modeling of in vivo physiology, and describing the role of microfluidic systems in tissue modeling processes is paramount to the future of organotypic models ex vivo. Virtually all tissues within the body are characterized by a large diversity of cellular composition, morphology, and blood supply (e.g., nutrient needs including oxygen). Microfluidic technology can provide a means to help maintain tissue in more physiologically relevant environments, for tissue relevant time-frames (e.g., matching the natural rates of cell turnover), and at in vivo oxygen tensions that can be controlled within modern microfluidic culture systems. Models for ex vivo tissues continue to emerge and grow in efficacy as mimics of in vivo physiology. This review addresses developments in microfluidic devices for the study of tissues ex vivo that can serve as an important bridge to translational value.

In silico cancer research towards 3R

BACKGROUND

Improving our understanding of cancer and other complex diseases requires integrating diverse data sets and algorithms. Intertwining in vivo and in vitro data and in silico models are paramount to overcome intrinsic difficulties given by data complexity. Importantly, this approach also helps to uncover underlying molecular mechanisms. Over the years, research has introduced multiple biochemical and computational methods to study the disease, many of which require animal experiments. However, modeling systems and the comparison of cellular processes in both eukaryotes and prokaryotes help to understand specific aspects of uncontrolled cell growth, eventually leading to improved planning of future experiments. According to the principles for humane techniques milestones in alternative animal testing involve in vitro methods such as cell-based models and microfluidic chips, as well as clinical tests of microdosing and imaging. Up-to-date, the range of alternative methods has expanded towards computational approaches, based on the use of information from past in vitro and in vivo experiments. In fact, in silico techniques are often underrated but can be vital to understanding fundamental processes in cancer. They can rival accuracy of biological assays, and they can provide essential focus and direction to reduce experimental cost.

MAIN BODY

We give an overview on in vivo, in vitro and in silico methods used in cancer research. Common models as cell-lines, xenografts, or genetically modified rodents reflect relevant pathological processes to a different degree, but can not replicate the full spectrum of human disease. There is an increasing importance of computational biology, advancing from the task of assisting biological analysis with network biology approaches as the basis for understanding a cell's functional organization up to model building for predictive systems.

CONCLUSION

Underlining and extending the in silico approach with respect to the 3Rs for replacement, reduction and refinement will lead cancer research towards efficient and effective precision medicine. Therefore, we suggest refined translational models and testing methods based on integrative analyses and the incorporation of computational biology within cancer research.

Breast Organotypic Cancer Models

Breast cancer is the most common cancer type diagnosed in women, it represents a critical public health problem worldwide, with 1,671,149 estimated new cases and nearly 571,000 related deaths. Research on breast cancer has mainly been conducted using two-dimensional (2D) cell cultures and animal models. The usefulness of these models is reflected in the vast knowledge accumulated over the past decades. However, considering that animal models are three-dimensional (3D) in nature, the validity of the studies using 2D cell cultures has recently been questioned. Although animal models are important in cancer research, ethical questions arise about their use and usefulness as there is no clear predictivity of human disease outcome and they are very expensive and take too much time to obtain results. The poor performance or failure of most cancer drugs suggests that preclinical research on cancer has been based on an over-dependence on inadequate animal models. For these reasons, in the last few years development of alternative models has been prioritized to study human breast cancer behavior, while maintaining a 3D microenvironment, and to reduce the number of experiments conducted in animals. One way to achieve this is using organotypic cultures, which are being more frequently explored in cancer research because they mimic tissue architecture in vivo. These characteristics make organotypic cultures a valuable tool in cancer research as an alternative to replace animal models and for predicting risk assessment in humans. This chapter describes the cultures of multicellular spheroids, organoids, 3D bioreactors, and tumor slices, which are the most widely used organotypic models in breast cancer research.

Advances in Drug Discovery and Development in Geriatric Psychiatry

PURPOSE OF REVIEW

This article reviews recent advances in drug discovery and development for geriatric psychiatry. Drug discovery for disorders of the central nervous system is a long and challenging process, with a high attrition rate from the preclinical stages through to marketing a compound. Developing drugs for geriatric neuropsychiatric conditions presents additional challenges, due to the complexity of the symptoms, comorbid diagnoses, and the variability of the population. Despite there being limited success over the past two decades, a number of new approaches have identified potential targets for preclinical development and ultimately clinical testing.

RECENT FINDINGS

Recent approaches have tried to address specific mechanisms that relate to the disease progression. These approaches include combining a number of ligands into to multi-target compounds, or targeting specific types of cells such as protein kinases or myeloid cells. In addition, the increased use of induced pluripotent stem cell cultures has enabled new compounds to be tested on disease-specific tissues, increasing the success rate of the lead compounds going through the preclinical stages. New pharmacological agents designed with advanced screening techniques and the shift towards systems pharmacology is changing the landscape of drug discovery in geriatric psychiatry. There is potential for these new agents to produce targeted effects in the framework of disorders that have long been untreatable.

The peroxisome proliferator-activated receptor agonist pioglitazone and 5-lipoxygenase inhibitor zileuton have no effect on lung inflammation in healthy volunteers by positron emission tomography in a single-blind placebo-controlled cohort study

Background

Anti-inflammatory drug development efforts for lung disease have been hampered in part by the lack of noninvasive inflammation biomarkers and the limited ability of animal models to predict efficacy in humans. We used ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET) in a human model of lung inflammation to assess whether pioglitazone, a peroxisome proliferator-activated receptor-γ (PPAR-γ) agonist, and zileuton, a 5-lipoxygenase inhibitor, reduce lung inflammation.

Methods

For this single center, single-blind, placebo-controlled cohort study, we enrolled healthy volunteers sequentially into the following treatment cohorts (N = 6 per cohort): pioglitazone plus placebo, zileuton plus placebo, or dual placebo prior to bronchoscopic endotoxin instillation. ¹⁸F-FDG uptake pre- and post-endotoxin was quantified as the Patlak graphical analysis-determined K_i (primary outcome measure). Secondary outcome measures included the mean standard uptake value (SUV_mean), post-endotoxin bronchoalveolar lavage (BAL) cell counts and differentials and blood adiponectin and urinary leukotriene E₄ (LTE₄) levels, determined by enzyme-linked immunosorbent assay, to verify treatment compliance. One- or two-way analysis of variance assessed for differences among cohorts in the outcome measures (expressed as mean ± standard deviation).

Results

Ten females and eight males (29±6 years of age) completed all study procedures except for one volunteer who did not complete the post-endotoxin BAL. K_i and SUV_mean increased in all cohorts after endotoxin instillation (K_i increased by 0.0021±0.0019, 0.0023±0.0017, and 0.0024±0.0020 and SUV_mean by 0.47±0.14, 0.55±0.15, and 0.54±0.38 in placebo, pioglitazone, and zileuton cohorts, respectively, p<0.001) with no differences among treatment cohorts (p = 0.933). Adiponectin levels increased as expected with pioglitazone treatment but not urinary LTE₄ levels as expected with zileuton treatment. BAL cell counts (p = 0.442) and neutrophil percentage (p = 0.773) were similar among the treatment cohorts.

Conclusions

Endotoxin-induced lung inflammation in humans is not responsive to pioglitazone or zileuton, highlighting the challenge in translating anti-inflammatory drug efficacy results from murine models to humans.

Trial registration

ClinicalTrials.gov NCT01174056.

Future Roadmaps for Precision Medicine Applied to Diabetes: Rising to the Challenge of Heterogeneity

Precision medicine, the concept that specific treatments can be targeted to groups of individuals with specific genetic, cellular, or molecular features, is a key aspect of modern healthcare, and its use is rapidly expanding. In diabetes, the application of precision medicine has been demonstrated in monogenic disease, where sulphonylureas are used to treat patients with neonatal diabetes due to mutations in ATP-dependent potassium (K_ATP) channel genes. However, diabetes is highly heterogeneous, both between and within polygenic and monogenic subtypes. Making the correct diagnosis and using the correct treatment from diagnosis can be challenging for clinicians, but it is crucial to prevent long-term morbidity and mortality. To facilitate precision medicine in diabetes, research is needed to develop a better understanding of disease heterogeneity and its impact on potential treatments for specific subtypes. Animal models have been used in diabetes research, but they are not translatable to humans in the majority of cases. Advances in molecular genetics and functional laboratory techniques and availability and sharing of large population data provide exciting opportunities for human studies. This review will map the key elements of future diabetes research in humans and its potential for clinical translation to promote precision medicine in all diabetes subtypes.

Hurdles in Basic Science Translation

In the past century there have been incredible advances in the field of medical research, but what hinders translation of this knowledge into effective treatment for human disease? There is an increasing focus on the failure of many research breakthroughs to be translated through the clinical trial process and into medical practice. In this mini review, we will consider some of the reasons that findings in basic medical research fail to become translated through clinical trials and into basic medical practices. We focus in particular on the way that human disease is modeled, the understanding we have of how our targets behave in vivo, and also some of the issues surrounding reproducibility of basic research findings. We will also look at some of the ways that have been proposed for overcoming these issues. It appears that there needs to be a cultural shift in the way we fund, publish and recognize quality control in scientific research. Although this is a daunting proposition, we hope that with increasing awareness and focus on research translation and the hurdles that impede it, the field of medical research will continue to inform and improve medical practice across the world.

Adverse outcome pathways: Application to enhance mechanistic understanding of neurotoxicity

Recent developments have prompted the transition of empirically based testing of late stage toxicity in animals for a range of different endpoints including neurotoxicity to more efficient and predictive mechanistically based approaches with greater emphasis on measurable key events early in the progression of disease. The adverse outcome pathway (AOP) has been proposed as a simplified organizational construct to contribute to this transition by linking molecular initiating events and earlier (more predictive) key events at lower levels of biological organization to disease outcomes. As such, AOPs are anticipated to facilitate the compilation of information to increase mechanistic understanding of pathophysiological pathways that are responsible for human disease.

In this review, the sequence of key events resulting in adverse outcome (AO) defined as parkinsonian motor impairment and learning and memory deficit in children, triggered by exposure to environmental chemicals has been briefly described using the AOP framework. These AOPs follow convention adopted in an Organization for Economic Cooperation and Development (OECD) AOP development program, publically available, to permit tailored application of AOPs for a range of different purposes.

Due to the complexity of disease pathways, including neurodegenerative disorders, a specific symptom of the disease (e.g. parkinsonian motor deficit) is considered as the AO in a developed AOP. Though the description is necessarily limited by the extent of current knowledge, additional characterization of involved pathways through description of related AOPs interlinked into networks for the same disease has potential to contribute to more holistic and mechanistic understanding of the pathophysiological pathways involved, possibly leading to the mechanism-based reclassification of diseases, thus facilitating more personalized treatment.