Poor Translatability of Biomedical Research Using Animals - A Narrative Review

The failure rate for the translation of drugs from animal testing to human treatments remains at over 92%, where it has been for the past few decades. The majority of these failures are due to unexpected toxicity - that is, safety issues revealed in human trials that were not apparent in animal tests - or lack of efficacy. However, the use of more innovative tools, such as organs-on-chips, in the preclinical pipeline for drug testing, has revealed that these tools are more able to predict unexpected safety events prior to clinical trials and so can be used for this, as well as for efficacy testing. Here, we review several disease areas, and consider how the use of animal models has failed to offer effective new treatments. We also make some suggestions as to how the more human-relevant new approach methodologies might be applied to address this.

Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector

Artificial intelligence (AI) has the potential to make substantial progress toward the goal of making healthcare more personalized, predictive, preventative, and interactive. We believe AI will continue its present path and ultimately become a mature and effective tool for the healthcare sector. Besides this AI-based systems raise concerns regarding data security and privacy. Because health records are important and vulnerable, hackers often target them during data breaches. The absence of standard guidelines for the moral use of AI and ML in healthcare has only served to worsen the situation. There is debate about how far artificial intelligence (AI) may be utilized ethically in healthcare settings since there are no universal guidelines for its use. Therefore, maintaining the confidentiality of medical records is crucial. This study enlightens the possible drawbacks of AI in the implementation of healthcare sector and their solutions to overcome these situations.

Graphical Abstract

A Functional Human-on-a-Chip Autoimmune Disease Model of Myasthenia Gravis for Development of Therapeutics

Myasthenia gravis (MG) is a chronic and progressive neuromuscular disease where autoantibodies target essential proteins such as the nicotinic acetylcholine receptor (nAChR) at the neuromuscular junction (NMJ) causing muscle fatigue and weakness. Autoantibodies directed against nAChRs are proposed to work by three main pathological mechanisms of receptor disruption: blocking, receptor internalization, and downregulation. Current in vivo models using experimental autoimmune animal models fail to recapitulate the disease pathology and are limited in clinical translatability due to disproportionate disease severity and high animal death rates. The development of a highly sensitive antibody assay that mimics human disease pathology is desirable for clinical advancement and therapeutic development. To address this lack of relevant models, an NMJ platform derived from human iPSC differentiated motoneurons and primary skeletal muscle was used to investigate the ability of an anti-nAChR antibody to induce clinically relevant MG pathology in the serum-free, spatially organized, functionally mature NMJ platform. Treatment of the NMJ model with the anti-nAChR antibody revealed decreasing NMJ stability as measured by the number of NMJs before and after the synchrony stimulation protocol. This decrease in NMJ stability was dose-dependent over a concentration range of 0.01-20 μg/mL. Immunocytochemical (ICC) analysis was used to distinguish between pathological mechanisms of antibody-mediated receptor disruption including blocking, receptor internalization and downregulation. Antibody treatment also activated the complement cascade as indicated by complement protein 3 deposition near the nAChRs. Additionally, complement cascade activation significantly altered other readouts of NMJ function including the NMJ fidelity parameter as measured by the number of muscle contractions missed in response to increasing motoneuron stimulation frequencies. This synchrony readout mimics the clinical phenotype of neurological blocking that results in failure of muscle contractions despite motoneuron stimulations. Taken together, these data indicate the establishment of a relevant disease model of MG that mimics reduction of functional nAChRs at the NMJ, decreased NMJ stability, complement activation and blocking of neuromuscular transmission. This system is the first functional human in vitro model of MG to be used to simulate three potential disease mechanisms as well as to establish a preclinical platform for evaluation of disease modifying treatments (etiology).

A Molecular Test for Quantifying Functional Notch Signaling Pathway Activity in Human Cancer

Simple Summary

The Notch signal transduction pathway is important for various physiological processes, including immune responses, and plays a role in many diseases, for example cancer. We have developed a new assay to quantitatively measure Notch pathway activity, and we validated it using data from various human cancer cell lines. The assay can be applied across different cell types, and offers numerous possibilities to explore the contribution of the Notch pathway to tumor formation and the stratification of cancer patients. We assessed Notch pathway activity in a cohort of T cell acute lymphoblastic leukemia (T-ALL) patient samples, and found that the pathway activity score more accurately reflects Notch pathway activity than a prediction on the basis of NOTCH1 mutations alone. Finally, we found that patients with low Notch pathway activity had a significantly shorter event-free survival compared to patients who had T-ALL cells with higher activity.

Abstract

Background: The Notch signal transduction pathway is pivotal for various physiological processes, including immune responses, and has been implicated in the pathogenesis of many diseases. The effectiveness of various targeted Notch pathway inhibitors may vary due to variabilities in Notch pathway activity among individual patients. The quantitative measurement of Notch pathway activity is therefore essential to identify patients who could benefit from targeted treatment. Methods: We here describe a new assay that infers a quantitative Notch pathway activity score from the mRNA levels of generally conserved direct NOTCH target genes. Following the calibration and biological validation of our Notch pathway activity model over a wide spectrum of human cancer types, we assessed Notch pathway activity in a cohort of T-ALL patient samples and related it to biological and clinical parameters, including outcome. Results: We developed an assay using 18 select direct target genes and high-grade serous ovarian cancer for calibration. For validation, seven independent human datasets (mostly cancer series) were used to quantify Notch activity in agreement with expectations. For T-ALL, the median Notch pathway activity was highest for samples with strong NOTCH1-activating mutations, and T-ALL patients of the TLX subtype generally had the highest levels of Notch pathway activity. We observed a significant relationship between ICN1 levels and the absence/presence of NOTCH1-activating mutations with Notch pathway activity scores. Patients with the lowest Notch activity scores had the shortest event-free survival compared to other patients. Conclusions: High Notch pathway activity was not limited to T-ALL samples harboring strong NOTCH1 mutations, including juxtamembrane domain mutations or hetero-dimerization combined with PEST-domain or FBXW7 mutations, indicating that additional mechanisms may activate Notch signaling. The measured Notch pathway activity was related to intracellular NOTCH levels, indicating that the pathway activity score more accurately reflects Notch pathway activity than when it is predicted on the basis of NOTCH1 mutations. Importantly, patients with low Notch pathway activity had a significantly shorter event-free survival compared to patients showing higher activity.

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.

Artificial Intelligence in Clinical Health Care Applications: Viewpoint

The idea of artificial intelligence (AI) has a long history. It turned out, however, that reaching intelligence at human levels is more complicated than originally anticipated. Currently, we are experiencing a renewed interest in AI, fueled by an enormous increase in computing power and an even larger increase in data, in combination with improved AI technologies like deep learning. Healthcare is considered the next domain to be revolutionized by artificial intelligence. While AI approaches are excellently suited to develop certain algorithms, for biomedical applications there are specific challenges. We propose six recommendations—the 6Rs—to improve AI projects in the biomedical space, especially clinical health care, and to facilitate communication between AI scientists and medical doctors: (1) Relevant and well-defined clinical question first; (2) Right data (ie, representative and of good quality); (3) Ratio between number of patients and their variables should fit the AI method; (4) Relationship between data and ground truth should be as direct and causal as possible; (5) Regulatory ready; enabling validation; and (6) Right AI method.

Applications of tumor chip technology

Over the past six decades the inflation-adjusted cost to bring a new drug to market has been increasing constantly and doubles every 9 years - now reaching in excess of $2.5 billion. Overall, the likelihood of FDA approval for a drug (any disease indication) that has entered phase I clinical trials is a mere 9.6%, with the approval rate for oncology far below average at only 5.1%. Lack of efficacy or toxicity is often not revealed until the later stages of clinical trials, despite promising preclinical data. This indicates that the current in vitro systems for drug screening need to be improved for better predictability of in vivo outcomes. Microphysiological systems (MPS), or bioengineered 3D microfluidic tissue and organ constructs that mimic physiological and pathological processes in vitro, can be leveraged across preclinical research and clinical trial stages to transform drug development and clinical management for a range of diseases. Here we review the current state-of-the-art in 3D tissue-engineering models developed for cancer research, with a focus on tumor-on-a-chip, or tumor chip, models. From our viewpoint, tumor chip systems can advance innovative medicine to ameliorate the high failure rates in anti-cancer drug development and clinical treatment.

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.

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

Decades of costly failures in translating drug candidates from preclinical disease models to human therapeutic use warrant reconsideration of the priority placed on animal models in biomedical research. Following an international workshop attended by experts from academia, government institutions, research funding bodies, and the corporate and non-governmental organisation (NGO) sectors, in this consensus report, we analyse, as case studies, five disease areas with major unmet needs for new treatments. In view of the scientifically driven transition towards a human pathways-based paradigm in toxicology, a similar paradigm shift appears to be justified in biomedical research. There is a pressing need for an approach that strategically implements advanced, human biology-based models and tools to understand disease pathways at multiple biological scales. We present recommendations to help achieve this.

Lessons from Toxicology: Developing a 21st-Century Paradigm for Medical Research

Biomedical developments in the 21st century provide an unprecedented opportunity to gain a dynamic systems-level and human-specific understanding of the causes and pathophysiologies of disease. This understanding is a vital need, in view of continuing failures in health research, drug discovery, and clinical translation. The full potential of advanced approaches may not be achieved within a 20th-century conceptual framework dominated by animal models. Novel technologies are being integrated into environmental health research and are also applicable to disease research, but these advances need a new medical research and drug discovery paradigm to gain maximal benefits. We suggest a new conceptual framework that repurposes the 21st-century transition underway in toxicology. Human disease should be conceived as resulting from integrated extrinsic and intrinsic causes, with research focused on modern human-specific models to understand disease pathways at multiple biological levels that are analogous to adverse outcome pathways in toxicology. Systems biology tools should be used to integrate and interpret data about disease causation and pathophysiology. Such an approach promises progress in overcoming the current roadblocks to understanding human disease and successful drug discovery and translation. A discourse should begin now to identify and consider the many challenges and questions that need to be solved.