Challenging the pipeline

Microphysiological pancreas-on-chip platform with integrated sensors to model endocrine function and metabolism

Pancreatic in vitro research is of major importance to advance mechanistic understanding and development of treatment options for diseases such as diabetes mellitus. We present a thermoplastic-based microphysiological system aiming to model the complex microphysiological structure and function of the endocrine pancreas with concurrent real-time read-out capabilities. The specifically tailored platform enables self-guided trapping of single islets at defined locations: β-cells are assembled to pseudo-islets and injected into the tissue chamber using hydrostatic pressure-driven flow. The pseudo-islets can further be embedded in an ECM-like hydrogel mimicking the native microenvironment of pancreatic islets in vivo. Non-invasive real-time monitoring of the oxygen levels on-chip is realized by the integration of luminescence-based optical sensors to the platform. To monitor insulin secretion kinetics in response to glucose stimulation in a time-resolved manner, an automated cycling of different glucose conditions is implemented. The model's response to glucose stimulation can be monitored via offline analysis of insulin secretion and via specific changes in oxygen consumption due to higher metabolic activity of pseudo-islets at high glucose levels. To demonstrate applicability for drug testing, the effects of antidiabetic medications are assessed and changes in dynamic insulin secretion are observed in line with the respective mechanism of action. Finally, by integrating human pancreatic islet microtissues, we highlight the flexibility of the platform and demonstrate the preservation of long-term functionality of human endocrine pancreatic tissue.

Recommendations for robust and reproducible preclinical research in personalised medicine

BACKGROUND

Personalised medicine is a medical model that aims to provide tailor-made prevention and treatment strategies for defined groups of individuals. The concept brings new challenges to the translational step, both in clinical relevance and validity of models. We have developed a set of recommendations aimed at improving the robustness of preclinical methods in translational research for personalised medicine.

METHODS

These recommendations have been developed following four main steps: (1) a scoping review of the literature with a gap analysis, (2) working sessions with a wide range of experts in the field, (3) a consensus workshop, and (4) preparation of the final set of recommendations.

RESULTS

Despite the progress in developing innovative and complex preclinical model systems, to date there are fundamental deficits in translational methods that prevent the further development of personalised medicine. The literature review highlighted five main gaps, relating to the relevance of experimental models, quality assessment practices, reporting, regulation, and a gap between preclinical and clinical research. We identified five points of focus for the recommendations, based on the consensus reached during the consultation meetings: (1) clinically relevant translational research, (2) robust model development, (3) transparency and education, (4) revised regulation, and (5) interaction with clinical research and patient engagement. Here, we present a set of 15 recommendations aimed at improving the robustness of preclinical methods in translational research for personalised medicine.

CONCLUSIONS

Appropriate preclinical models should be an integral contributor to interventional clinical trial success rates, and predictive translational models are a fundamental requirement to realise the dream of personalised medicine. The implementation of these guidelines is ambitious, and it is only through the active involvement of all relevant stakeholders in this field that we will be able to make an impact and effectuate a change which will facilitate improved translation of personalised medicine in the future.

Design of an Integrated Microvascularized Human Skin-on-a-Chip Tissue Equivalent Model

Tissue-engineered skin constructs have been under development since the 1980s as a replacement for human skin tissues and animal models for therapeutics and cosmetic testing. These have evolved from simple single-cell assays to increasingly complex models with integrated dermal equivalents and multiple cell types including a dermis, epidermis, and vasculature. The development of micro-engineered platforms and biomaterials has enabled scientists to better recreate and capture the tissue microenvironment in vitro, including the vascularization of tissue models and their integration into microfluidic chips. However, to date, microvascularized human skin equivalents in a microfluidic context have not been reported. Here, we present the design of a novel skin-on-a-chip model integrating human-derived primary and immortalized cells in a full-thickness skin equivalent. The model is housed in a microfluidic device, in which a microvasculature was previously established. We characterize the impact of our chip design on the quality of the microvascular networks formed and evidence that this enables the formation of more homogenous networks. We developed a methodology to harvest tissues from embedded chips, after 14 days of culture, and characterize the impact of culture conditions and vascularization (including with pericyte co-cultures) on the stratification of the epidermis in the resulting skin equivalents. Our results indicate that vascularization enhances stratification and differentiation (thickness, architecture, and expression of terminal differentiation markers such as involucrin and transglutaminase 1), allowing the formation of more mature skin equivalents in microfluidic chips. The skin-on-a-chip tissue equivalents developed, because of their realistic microvasculature, may find applications for testing efficacy and safety of therapeutics delivered systemically, in a human context.

Developer's Guide to an Organ-on-Chip Model

Over the past decade, organ-on-chip research has been one of the most prolific areas of the entire field of tissue engineering. The development of organ-on-chip models requires an integrated interdisciplinary approach merging technologies and concepts from several different disciplines, including microfabrication, microfluidics, biomaterials, stem cell science, pharma-/toxicology, and medicine. In this perspective, we follow the journey of an organ-on-chip through its many different stages, from (i) the initial idea/specific scientific question to (ii) the design/concept phase, (iii) the engineering (fabrication and materials, sensor/actuator integration) and (iv) biology considerations (cell sources, biomaterials/scaffold), (v) the cell injection and tissue assembly process, (vi) the assay development, and (vii) the functional validation, all the way to (viii) the final applications. By summarizing some of the key learnings and findings from a developer's perspective and identifying suitable introductory reviews, this perspective strives to provide a conceptual, stepwise guide for the holistic development of an organ-on-chip model.

Studying metabolism with multi-organ chips: new tools for disease modelling, pharmacokinetics and pharmacodynamics

Non-clinical models to study metabolism including animal models and cell assays are often limited in terms of species translatability and predictability of human biology. This field urgently requires a push towards more physiologically accurate recapitulations of drug interactions and disease progression in the body. Organ-on-chip systems, specifically multi-organ chips (MOCs), are an emerging technology that is well suited to providing a species-specific platform to study the various types of metabolism (glucose, lipid, protein and drug) by recreating organ-level function. This review provides a resource for scientists aiming to study human metabolism by providing an overview of MOCs recapitulating aspects of metabolism, by addressing the technical aspects of MOC development and by providing guidelines for correlation with in silico models. The current state and challenges are presented for two application areas: (i) disease modelling and (ii) pharmacokinetics/pharmacodynamics. Additionally, the guidelines to integrate the MOC data into in silico models could strengthen the predictive power of the technology. Finally, the translational aspects of metabolizing MOCs are addressed, including adoption for personalized medicine and prospects for the clinic. Predictive MOCs could enable a significantly reduced dependence on animal models and open doors towards economical non-clinical testing and understanding of disease mechanisms.