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Experimental Systems in Research through Design

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• Gamboa M Baytaş M Hendriks S Ljungblad S (2023) Wisp: Drones as Companions for Breathing Proceedings of the Seventeenth International Conference on Tangible, Embedded, and Embodied Interaction 10.1145/3569009.3572740 (1-16) Online publication date: 26-Feb-2023 https://dl.acm.org/doi/10.1145/3569009.3572740
• Baldursson B Peterson D Gamboa M (2022) Nebula: Artistic Somaesthetic Appreciation with Biosignals in Virtual Reality Adjunct Proceedings of the 2022 Nordic Human-Computer Interaction Conference 10.1145/3547522.3547710 (1-3) Online publication date: 8-Oct-2022 https://dl.acm.org/doi/10.1145/3547522.3547710
• Gamboa M (2022) Living with Drones, Robots, and Young Children: Informing Research through Design with Autoethnography Nordic Human-Computer Interaction Conference 10.1145/3546155.3546658 (1-14) Online publication date: 8-Oct-2022 https://dl.acm.org/doi/10.1145/3546155.3546658
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• design theory
• epistemology
• experimental systems
• research design
• research through design
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• Center for Participatory IT, Aarhus University, Denmark
• Creativity in Blended Interaction Spaces, Aarhus University, Denmark

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• Fedlmeier A Bruijnes M Bos-de Vos M Lemke M Kraal J (2022) Finding what fits: Explorative self-experimentation for health behaviour change Design for Health 10.1080/24735132.2022.2147336 6 :3 (345-366) Online publication date: 29-Nov-2022 https://doi.org/10.1080/24735132.2022.2147336
• Bjørn P Menendez-Blanco M Borsotti V Bjørn P Menendez-Blanco M Borsotti V (2022) GRACE: Designing Sociomaterial Assemblages Unpacking Gender Equity in Computing Diversity in Computer Science 10.1007/978-3-031-13314-5_6 (57-76) Online publication date: 13-Oct-2022 https://doi.org/10.1007/978-3-031-13314-5_6
• Børsting J Culén A (2019) Experiences With a Research Product Rapid Automation 10.4018/978-1-5225-8060-7.ch003 (31-55) Online publication date: 2019 https://doi.org/10.4018/978-1-5225-8060-7.ch003
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MAX-PLANCK-INSTITUT FÜR WISSENSCHAFTSGESCHICHTE Max Planck Institute for the History of Science

Generating experimental knowledge: experimental systems, concept formation and the pivotal role of error.

• Dept. Rheinberger
• Hans-Jörg Rheinberger Israel); Friedrich Steinle (Bergische Universität Wuppertal); Uljana Feest Uljana Feest
• Other Scholars Involved:
• Jutta Schickore (Indiana University, Bloomington)
• Galina Granek (University of Haifa)
• Thomas Dohmen (MPI Berlin)
• Lambert Williams (Harvard University)
• Igal Dotan (MPI Berlin)
• Uljana Feest (TU Berlin – Project Coordinator)

In the past two decades, experimentation, a core procedure of modern science, has received new attention in the history and philosophy of science. While a wealth of new perspectives has opened up, however, one essential feature has remained largely unanalyzed—the very role of the experiment as a knowledge-generating procedure. This is where this project started off. The scholars aimed at developing a broader understanding of how knowledge is gained, shifted, and revised in experimental research, exploring the links and dynamics between three focal issues: experimental systems, concept formation, and the pivotal role of error.

Challenging the clear-cut rationalist picture of experimentation, Ludwik Fleck and others have drawn our attention to the manufacture of scientific facts, arguing that modern scientists usually do not deal with single experiments in the context of a properly delineated theory. Experimental scientists deal with systems of experiments that are usually not well defined and do not provide clear-cut answers. In permanently changing and varying patterns, experimental systems mix up elements that historians and philosophers of science usually wish to have properly separated: research objects, theories, technical arrangements, and instruments as well as disciplinary, institutional, social, and cultural dispositifs. An analysis of the ways in which different experimental systems interact—how they overlap, delimit, exclude, or supplement each other—provideed insights into the developmental dynamics of broader fields of science.

Recent studies have made it clear that in order to account for the great variety of existing epistemic practices, several different levels of theorizing are required. Experiments are only possible by virtue of the fact that scientists rely on certain instruments, procedures, and concepts that are taken as unproblematic. At the same time, experimental practices and scientific conceptualizations are constantly fine-tuned to each other as the experimental process unfolds. Focusing on these processes, a specific type of “ explorative” experimentation becomes visible: Such experiments are not designed to test scientific theories. Nonetheless, they follow distinct guidelines and epistemic principles. In many cases, moreover, they lead to the revision of existing concepts and to the formation of new concepts, which allow for robust characterizations of the experimental results. The study of concept formation in experimental contexts promises new insights into the epistemic dynamics of experimental research. At the same time, it points sharply to the interlocking character of systems of experiments as contrasted with the traditional picture of experiments as single instances of corroboration or refutations of hypotheses.

A claim to knowledge within a certain system of research may be found over time—by various means—to be erroneous. But the variety of notions of what “ error," or more generally, what “ going wrong” might mean, is huge and previously not studied from an epistemological perspective. At the same time, we can gain significant insight into the epistemic dynamics of experiment through the probing of experiments with error. There is a close connection between epistemological framework and methodological approach on the one hand, and detection and characterization of error on the other hand. What counts as an error, moreover, is as much dependent on the singular experiment as on the wider system in which it has been designed and conducted. Again one is directed from the individual experiment to a broader system. To explain the undermining phenomenon of error, the very structure of the system has to be taken into account. Studies of error will demonstrate how the system functions, how it can fail and how it can guard itself against error.

The project combined a set of complementary studies concerning particular experimental systems, historical developments, and systematic conceptual analyses. The project group, working from the two locations of Haifa and Berlin, brought together historians and philosophers of science, PhD students, and postdoctoral and senior researchers.

Publications

Feest, Uljana. “Operationism in Psychology - What the Debate is About, What the Debate Should Be About,” in: Journal for the History of the Behavioral Sciences, XLI 2005, No. 2, pp. 131-150.

Hon, Giora, Jutta Schickore, & Friedrich Steinle (eds.). “Going Amiss” (working title), Dibner Series in History of Science. Cambridge, MA: MIT Press (forthcoming).

Hans-Jörg Rheinberger. Epistemologie des Konkreten. Studien zur Geschichte der modernen Biologie, Frankfurt/M.: Suhrkamp, 2006.

Schickore, Jutta. “’Through Thousands of Errors we Reach the Truth’ – But How? On the Epistemic Roles of Error in Scientific Practice,” in: Studies in History and Philosophy of Science 36 (2005), pp. 539-556.

Schickore, Jutta & Friedrich Steinle (eds.). Revisiting Discovery and Justification. Historical and Philosophical Perspectives on the Context Distinction, New York: Springer, 2006.

Steinle, Friedrich. Explorative Experimente. Ampere, Faraday und die Ursprünge der Elektrodynamik, Boethius 50, Stuttgart: Franz Steiner Verlag, 2005.

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Iterative experimental system development; including consecutive activities and the descriptions they produce. Feedback and possibilities for participatory design are also illustrated.

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Elevating experimental design through automation

Produced by

Tomohisa Hasunuma (standing) at the Autonomous Lab System at Kobe University.

Smart cells — biological organisms, such as microbes, artificially modified using techniques from synthetic biology — hold significant promise as tools in the mass production of raw materials for renewable energy and functional foods, that are difficult to realize through other means.

“They could trigger a technological revolution in a variety of areas, such as pharmaceuticals, foods, new materials, environmental remediation or alternative materials for petrochemical products,” says Tomohisa Hasunuma, director of the Engineering Biology Research Center at Kobe University, Japan.

However, there are significant challenges in the experimental design process — including reliable data acquisition, analysis, and speed — that need to be overcome to move from the design and optimization of these ‘smart cells’ in biofoundries, through to commercial-scale, industrial application. In 2016, to solve these challenges Hasunuma partnered with engineers from a precision instrument manufacturer, Shimadzu Corp, in Kyoto, Japan.

“The stability and robustness of analytical instruments are important in the “test” phase of design-build-test-learn (DBTL) cycles of smart cell development in biofoundries”, says Taichi Tomono, a lead research scientist at Shimadzu.

Automated design

Harnessing smart cells, such as microbes, to produce industrially important compounds involves tinkering with their metabolism by tweaking genes, which is typically a laborious process. Coming up with a system to automate the process and incorporate the power of artificial intelligence is a vital step for making the technology commercially viable.

Microbiologists at Kobe University, in conjunction with Shimadzu, have developed an automated experimental system to optimize all the parameters. It is based on DBTL cycles, in which each of the four steps is automated. “A microbe is designed on a computer, a robot is then used to create the microbe, and the robot is used again to test the performance of the microbe,” says Tomono. “And finally, the results of testing the microbe are fed into machine learning, which is used to enhance the original design.”

A test in progress using Shimadzu's design-build-test-learn (DBTL) system.

This system can greatly accelerate research and development of biofoundries. “We have developed a technology to design metabolic pathways to increase the production by using a computer to perform the design,” says Hasunuma. “It can produce somewhere between 1,000 and 2,000 strains of microorganisms in a single experiment.”

Unique strengths

Hasunuma notes that while other groups are developing DBTL systems, Kobe University can incorporate long snippets of DNA in microbes. “This allows us to quickly create extensive libraries of strains,” he says.

Meanwhile, Shimadzu’s strong point is the state-of-the-art analytic system. “The system contains a world-class mass spectrometer, enabling us to accurately evaluate microorganisms in the biofoundry,” says Hasunuma. “This is important since it is critical to evaluate whether the enzymes are functioning properly in the microbial cells and whether the metabolic pathways are being used correctly.”

Importantly, the liquid chromatography employs robots. “Shimadzu and Kobe University have collaborated to develop the world’s first pretreatment robot, which automatically removes cells from the bioreactor, extracts metabolites from cells, and sends them to the mass spectrometer for analysis,” says Hasunuma.

Accelerating the process

The metabolite-extraction system can analyse 186 metabolites simultaneously, while the high-throughput evaluation system can rapidly find promising smart cell candidates. Combining these two systems will facilitate the mass production of highly functional substances on a much faster time scale than is possible using conventional techniques.

The entire process, from experiment proposal to results management, is completely integrated. Shimadzu engineers designed a dedicated process management app that depicts the entire experiment process flow visually and uses simple and intuitive operations to specify process steps via the cloud.

“Without learning complex programming languages, anyone can easily create and understand experimental protocols,” says Tomono. It also stores information about each sample, such as the containers, instruments, reagents, and analytical techniques used, and manages it together with experimental results in a database to ensure high traceability. All data related to an experiment can be analysed and viewed using the app.

A prototype of Shimadzu's Autonomous Lab System.

Artificial intelligence (AI) is used to propose new experimental conditions based on the previous experimental results. Finally, visual programming and functional modules make it easy to design experimental protocols. “The system, including our mass spectrometers, can be easily reconfigured using visual programming, just like assembling Lego bricks,” says Tomono. “So, for example, if you wanted to test various strains of microorganisms in a biofoundry, you can quickly reconfigure the equipment for testing.”

These distinctive aspects result in a powerful system with the ability to greatly accelerate the development of useful strains of microbes. “Our DBTL system is unique compared to other systems in the world,” says Hasunuma. “It will greatly reduce the time needed to develop a recombinant microorganism requested by a client, making it a very potent tool for commercializing the manufacturing of compounds using microorganisms”

A host of possibilities

The use of the smart cell system for new materials is just the tip of the iceberg when it comes to applications. “By combining robotic, digital, AI, and other technologies, we’ve developed an autonomous laboratory system for developing smart cells, which has the potential for wide application,” says Tomono.

Masahiro Ikegami, a researcher at Shimadzu is also excited about its potential. “Further development of this smart cell technology could see it used in areas such as health care and agriculture,” he says. “For example, it could be used to cultivate new cells for use in gene therapy or produce supplements, biofuels and various materials.”

Like all successful collaborations, the partnership draws on the strengths of both parties. “Kobe University’s strength is that we are able to create various kinds of recombinant microorganisms. We have a lot of experience in making useful microbes,” says Hasunuma. “Shimadzu offers excellent analytical technologies, including system development, automation, data management, and data analysis. These are used to assess the microbes.”

Ultimately, this work is in-line with Shimadzu’ grand vision of an Autonomous Lab. The company envisages a system where a researcher first enters a protocol (experiment procedure) via a cloud service. Then a robot executes the experiment and sends the results back to the researcher. “We want to advance research and development towards a future lab vision where robots and AI can autonomously achieve scientific discoveries,” says Tomono.

For now, the Shimadzu-Kobe team is focusing on verifying the utility of the Autonomous Lab prototype system through, which will contribute to the development of smart cells, and hopefully, early implementation in society, says Tomono. A partnership that leverages the strengths of academia and industry makes Kobe University's DBTL cycle and biofoundry unique. “Biofoundries harnessing smart cells have immense potential,” says Hasunuma.

For more information about biofoundry development please visit Shimadzu .

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• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

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Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

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Research and development (R&D)

Research and experimental development (R&D) comprises creative work undertaken on a systematic basis in order to increase the stock of human knowledge and to devise new applications based upon it. The term R&D covers three activities: basic research, applied research and experimental development. Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundation of phenomena and observable facts, without any particular application or use in view. Applied research is also original investigation undertaken in order to acquire new knowledge. It is, however, directed primarily towards a specific practical aim or objective. Experimental development is systematic work, drawing on existing knowledge gained from research and/or practical experience, which is directed to producing new materials, products or devices, to installing new processes, systems and services, or to improving substantially those already produced or installed.

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Experimental Model Systems Used in the Preclinical Development of Nucleic Acid Therapeutics

Haiyan zhou.

1 Genetics and Genomic Medicine Research and Teaching Department, Great Ormond Street Institute of Child Health, University College London, London, United Kingdom.

2 NIHR Great Ormond Street Hospital Biomedical Research Center, London, United Kingdom.

Virginia Arechavala-Gomeza

3 Nucleic Acid Therapeutics for Rare Disorders (NAT-RD), Biocruces Bizkaia Health Research Institute, Barakaldo, Spain.

4 Ikerbasque, Basque Foundation for Science, Bilbao, Spain.

Alejandro Garanto

5 Department of Pediatrics, Amalia Children's Hospital, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.

6 Department of Human Genetics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.

Associated Data

Preclinical evaluation of nucleic acid therapeutics (NATs) in relevant experimental model systems is essential for NAT drug development. As part of COST Action “DARTER” (Delivery of Antisense RNA ThERapeutics), a network of researchers in the field of RNA therapeutics, we have conducted a survey on the experimental model systems routinely used by our members in preclinical NAT development. The questionnaire focused on both cellular and animal models. Our survey results suggest that skin fibroblast cultures derived from patients is the most commonly used cellular model, while induced pluripotent stem cell-derived models are also highly reported, highlighting the increasing potential of this technology. Splice-switching antisense oligonucleotide is the most frequently investigated RNA molecule, followed by small interfering RNA. Animal models are less prevalent but also widely used among groups in the network, with transgenic mouse models ranking the top. Concerning the research fields represented in our survey, the mostly studied disease area is neuromuscular disorders, followed by neurometabolic diseases and cancers. Brain, skeletal muscle, heart, and liver are the top four tissues of interest reported. We expect that this snapshot of the current preclinical models will facilitate decision making and the share of resources between academics and industry worldwide to facilitate the development of NATs.

Introduction

Nucleic acid therapeutics (NATs) are one of the fastest growing types of drugs. They treat diseases in a target-specific manner and offer great therapeutic potential for a wide range of disorders, applicable not only to common genetic disorders but also to rare diseases and personalized medicine. Novel NAT strategies using various nucleic acid technologies have been successfully developed, with approvals from the United States of America Food and Drug Administration (FDA) or the European Medicines Agency (EMA) for neuromuscular, neurodegenerative, and metabolic disorders, among others [ 1 , 2 ].

Before a successful clinical translation, preclinical evaluation of NATs in suitable experimental systems is essential. Relevant model systems, including cellular and animal models, are used to evaluate their effectiveness on regulating target gene expression [ 3 ], downstream functional readouts [ 4 ], the compounds' uptake and biodistribution [ 5 ], and to perform potential toxicology studies [ 6 ].

Cell-based assays are an essential element of NAT drug discovery. Patient-derived cellular cultures are particularly useful to model human diseases, especially for the mutation-specific NAT approaches [ 4 , 7–10 ]. While relevant disease phenotypes in cellular models pave the way toward high-throughput screening of NAT drugs, maintenance and expansion of human primary cells for large-scale screening remain challenging. Another challenge of using human-derived cells for NAT drug evaluation is the limited cell types available due to the difficulties in tissue accessibility to certain organs. This obstacle can now be overcome by the use of induced pluripotent stem cell (iPSC) technology, which allows subsequent differentiation into diverse cell types [ 11 , 12 ]. This technology has provided the feasibility of allowing NAT drug evaluation in broad type of cells ( Fig. 1 ).

Schematic representation on the selection of the best cellular model when applying NATs. Figure created with Biorender.com . NATs, nucleic acid therapeutics.

For complex tissues, organoids cultured in 3D may approximate to the target tissues more accurately than the 2D cellular system [ 13 ]. Some ex vivo 3D models closely recapitulate tissue architecture and cellular composition of the target organs. iPSC-derived organoids can contain cell types derived from all three germ layers [ 14 ]. Patient-derived organoids have been used in antisense oligonucleotide (AON)-mediated gene knockdown assays in tumors [ 15 , 16 ] and neurological conditions [ 17 , 18 ]. This “disease-in-a-dish” model presents the potential to predict patient response, hence holding great promise for personalized medicine [ 13 , 19–24 ]. Comprehensive NAT drug evaluation in vitro in cellular models enables reliable preliminary screening of potential NAT drugs thus preventing incompetent compounds from entering further validation phase in animal models.

Animal models are also important in NAT drug development. However, as NAT approaches are sequence specific, very often the target sequences in animal models, usually rodents, are different from the human target gene due to sequence variations among species. Therefore, an “animal version” of the NAT drug is usually used for proof-of-concept studies, although this molecule can have different properties compared with the “human version.” Alternatively, a model carrying the human equivalent mutation or a humanized animal model, where the animal gene is completely or partially replaced by the human copy or edited to become more human-like, would be ideal for the in vivo NAT validation [ 25–27 ]. It is important, however, to ensure that the target gene conducts a similar function in the animal model and that the humanization will not affect its function, especially when aiming to mimic disease and assess functional readouts [ 28–31 ].

Most importantly, the use of animal models can provide crucial information on biodistribution, toxicity at specific doses, and the in vivo therapeutic efficacy of NAT drugs. This information is pivotal for the translation of NAT drugs to human clinical trials. In some cases, toxicology studies of NAT drugs in nonhuman primates may also be needed before its translation to human trials [ 32 ].

With the purpose of bringing together the expertise and sharing knowledge in NAT development across Europe and other associated countries, we created the network “Delivery of Antisense RNA ThERapeutics (DARTER)” ( www.antisenserna.eu ), which is supported by the European COST Action Program grant nr. CA17103. The network includes researchers with interests in the specific NAT chemistry and modifications, delivery methodology, and a wide range of disorders and target tissues. It is composed of over 350 members representing academia, industry, health systems, and patient advocacy groups. We aim to join forces to further improve NAT as a viable therapeutic option by studying the best ways to deliver these drugs to different target tissues. Model system is one of the key topics that the DARTER network has been focused on. In the last 4 years, this working group has shared among its members their individual experiences in using different models at DARTER seminars and through shared protocols [ 33 ].

The DARTER network has recently conducted a survey on the model systems routinely used by members directly involved with preclinical NAT development. A significant proportion of members investigate antisense technology as potential treatments, but also other NAT strategies such mRNA delivery or genome editing, as well as nonviral delivery methods (nanoparticles) are represented. We expect that this report will contribute to clarifying various model systems used in NAT development, especially, but not exclusively, for antisense technology, and promote knowledge and resources sharing not only among members of the DARTER network, but also with academics and industry worldwide to facilitate the development of NATs.

The online survey was a Google form distributed in May and June 2021 to the members of DARTER network. The questionnaire consists of questions about the participants (research group and country information), the model systems (three cellular models and three animal models most frequently used in their laboratory), the type of disease(s) investigated, the kind of therapeutic molecules tested, and the read-outs used for evaluation. The blank questionnaire can be found in Supplementary Data S1 .

In total, we received answers from 57 researchers in 15 European countries, Turkey, and the United States ( Fig. 2A ). To avoid overrepresentation of any large research groups, we classified the answers based on the group leader of the research team and we obtained answers from 42 independent research groups in 17 countries ( Fig. 2B ). We then classified the answers by disease groups. Neuromuscular disorders were the most frequently studied diseases within our network (∼30%), followed by neurometabolic diseases (∼16.4%) and cancers (∼12%). In total, 17 groups of diseases were reported ( Fig. 2C ). It is noted that some research groups investigate multiple diseases. There are two answers on general toxicity upon delivery rather than efficacy in a particular disease, which were not related to any disease and marked as “None” ( Fig. 2C ). The survey (100%) indicated that all groups use at least one cellular model. In contrast, only 59% of the groups ( n  = 25) use animal models for their studies ( Fig. 2D ).

(A) Distribution of answers per country ( n  = 57) and (B) distribution of research groups per country ( n  = 42). (C) Classification of diseases investigated within our DARTER COST Action (total of diseases 67). (D) Research groups reporting the use of at least one cellular model ( blue ) or animal model ( orange ). DARTER, Delivery of Antisense RNA ThERapeutics.

In vitro models

The main cellular models used by our network members originated from four different species. The most common type of cells used are of human origin (∼85.72%), followed by mouse, rat, and green monkey (∼11.6%, ∼1.8%, and ∼0.89%, respectively) ( Fig. 3A ). When human cells were reported, ∼54% of the answers mentioned patient/healthy donor-derived cells, while the rest were commercially available cells (∼46%). In this survey, those tumor-derived cell lines, such as HeLa, Neuroblastoma, or WERI-Rb1 have been categorized as commercially available cells ( Fig. 3A ).

Classification of the species origin of the (A) in vitro and (B) in vivo models and whether they are (A) personalized (patient/control-derived) or commercially available cellular models or (B) genetically modified or wild-type animal models.

In total, 113 cellular models were reported, which contained 67 unique entries ( Supplementary Table S1 ). Skin-derived fibroblasts were the most frequently used cell type by respondents to our survey (∼15% of the answers), followed by the kidney cell line HEK293T (human embryonic kidney 293T) reported by ∼6.2% of respondents. Interestingly, six of the cultures reported (∼7.1%) in other categories were differentiated from iPSCs. Regardless of the type of cells obtained, iPSC-derived models accounted for ∼11.6% of all lines (including undifferentiated iPSCs as a model itself), highlighting the potential of this technology in the preclinical development of NAT.

Furthermore, 3D cellular models represented only ∼4.4% of the answers, while as unique entries, this percentage was increased up to ∼7.5%. Finally, we classified the unique entries into the tissue of origin ( Supplementary Fig. S1A ). As expected, this allowed us to reduce the entries to 20. Once all the different cellular models were classified by tissue of origin, muscle cells became the most frequent category with ∼22.4% of the responding laboratories using this model ( Supplementary Fig. S1B ). This translated into ∼19.5% of the total answers, which is supported by the large number of researchers investigating neuromuscular diseases within our network. Skin models, accounting for 17.7% of the answers, was the second most reported model, however, as a unique entry, skin models dropped to the sixth position representing 5.97% of all unique models ( Supplementary Fig. S1 ). This is partly explained by the fact that fibroblasts were counted as a single model system from skin origin. The third category referred to neuronal model systems with ∼10.6% of the answers and 13.43% of the models.

Concerning the purpose behind the use of these models and which type of molecules are routinely assessed for NAT development, 10 types of molecules were reported, from which splice-switching AONs (SS-AON) and small interfering RNA (siRNA) together accounted for >50% of the answers ( Fig. 4A ). Most of these molecules were used to assess efficacy (96.3%), followed by evaluation of delivery (49.5%) or safety/toxicology (22.3%), as shown in Fig. 4B . When the results were segregated by the type of molecule ( Fig. 4C ), it was apparent that efficacy was evaluated in all of them. As expected, for respondents working with nanoparticles, the delivery assessment was almost equally important to the efficacy evaluation in ∼80% of the answers. At the same time, researchers working with nanoparticles showed a high interest in safety and toxicology (>50%), whereas those studying UsnRNA systems (U1 and U7) were barely interested in delivery and safety/toxicology. The very low number of entries mentioning small molecules, translation inhibitors, antago-miRs, and gRNAs (5, 4, 2, and 1, respectively) precluded the identification of any clear trends related to these molecules.

Graphical representation of the percentage of molecules and readouts assessed in cellular models. (A) Percentage of the molecules with respect to all the answers. (B) The study purpose for all the molecules and (C) the specific purposes for efficacy, delivery, and safety/toxicology for every type of therapeutic molecule. (D) Percentage of different readouts from the total amount of answers and (E) the type of readout performed in each line described in the survey, expressed as percentage (eg, in 91.07% of the reported cell lines, a readout at RNA level is conducted).

Finally, we asked our members which readouts were usually used to evaluate efficacy in each of the cell lines they reported. As expected, RNA and protein expression were the two major readouts accounting for ∼85% of all the answers ( Fig. 4D ). When looking at the readouts for each cell line, in almost all cases the major readout is the response at RNA level, regardless of the cell line studied ( Fig. 4E ). Again, the second most common readout is protein assessment by western blot or other methods [ 34 ]. Overall, these results are in line with the current practices that if no effect at RNA level is observed, the molecule is considered not efficacious and therefore further studies are not pursued. Only if lead molecules are effective at RNA level, further validation will be pursued. This could explain the difference between RNA and protein analyses (91% vs. 64%).

In vivo models

In total, 59% of the groups reported the use of at least one animal model. Remarkably, mouse models were the most frequently employed model system within DARTER. The other four models mentioned are zebrafish (4%), chicken embryos (2%), marmoset (2%), and rat (2%). Around 68% of the listed animal models were genetically modified, while ∼32% were wild type ( Fig. 3B ).

Similar to the in vitro models, SS-AONs were the most frequently evaluated molecules (∼46.7%) in vivo ( Fig. 5A ). They were followed by siRNA (∼11.7%), gapmers (∼10%), nanoparticles (∼10%) and U1/U7snRNA systems (∼10%). Other reported molecules included mRNA, small molecules, antago-miRs or gRNAs (CRISPR/Cas9 system). In 96% of the answers, the molecules were assessed in animal models to evaluate the efficacy ( Fig. 5B ). Delivery and safety/toxicology were also highly indicated with 66% and 62% of the models being used for these purposes. Biodistribution only represented 8% of the answers.

Graphical representation of the percentage of molecules and readouts assessed in animal models. (A) Percentage of the molecules with respect to all the answers. (B) The study purpose in each animal model expressed in percentage (eg, 96% of the models were used for efficacy of a therapeutic molecule). (C) Percentage of all readouts with respect to the total amount of answers. (D) The type of readout performed in each animal model described in the survey, expressed as percentage.

Thirteen delivery routes were reported in the in vivo model systems. Intravenous injection was the most preferred administration route with around 32% of respondents, followed by intracerebroventricular and subcutaneous injections (∼18% and ∼11% of answers, respectively). In general, by classifying the answers into local and systemic delivery, the percentages were similar at ∼45% and ∼55%, respectively. When questioned on which tissues/organs were of interest, brain (∼21.7%), muscle (∼19.1%), heart (∼16.5%), and liver (∼14.8%) were the top four answers ( Supplementary Fig. S2 ). This is in line with the distribution of the diseases studied in our network. Compared with cellular models, no specific strain or model was recurrently used over others. However, in general, the majority of the mouse models reported were used to study neuromuscular diseases, in particular associated with Duchenne and Becker muscular dystrophy.

Regarding the type of readouts used in animal models, and similar to the in vitro data, RNA analyses were still the major measurement accounting for ∼30.8% of the answers and applied to ∼96% of the models described. This is followed by (immune)histochemical analysis and protein analysis by western blotting, reported in ∼27.6% and ∼26.3% of the answers and applied to ∼86% and ∼82% of the models, respectively. Other readouts used were functional assessment, tumor growth/survival, and behavioral and physiological analyses ( Fig. 5C, D ).

The procedure for preclinical development of a therapeutic molecule usually involves an initial step with a series of assays performed in cellular model systems, which also applies to NATs. Once the nucleic acid sequences are designed using in silico predictions [ 35–37 ], they are subsequently assessed on efficacy in cellular models [ 33 ]. Thereby, having a suitable cellular model system is crucial not only at initial development stages, but also for lead candidate selection and optimization. The results of the survey are in line with this purpose. As discussed in our previous review, delivery of NATs to target cells and tissues is an important issue in NAT development [ 1 ], and this survey also included reports of nanoparticle evaluations. Furthermore, it is a common practice to exclude molecules that are highly toxic or low efficient in cell culture from further evaluations. In that sense, only the safest and most efficacious molecules will be taken forward to thorough safety and toxicology studies performed in animal models.

In our survey, mice appeared to be the first choice as an in vivo model, probably due to the fact that mice are easy to manipulate genetically, maintain and breed, and while sharing more genetic homologies with human than other commonly used experimental animal models such as fruit fly and zebrafish. It is also necessary to note that many mouse models have already been generated and characterized in the past, and the delivery routes and readouts are also established.

NATs are often directed toward a specific sequence either in patient's DNA, pre-mRNA, or mRNA. Thus, the selection of the in vivo model system to assess efficacy should be determined by the target expression. The most frequently used cell lines reported in the survey are from human origin. Although the commercially available human cell lines offer easy accessibility to models highly accepted in the scientific field, these cell lines lack the patient-specific characteristics, such as the pathogenic variant or the molecular defect. While patient-derived material would be a better model, it is not always easy to obtain. Current implementation of advanced genetic diagnostic techniques using patients' blood (considered a noninvasive approach), has made the requirement of tissue biopsies as diagnostic material redundant and the availability of spare tissue has decreased enormously.

Thus, when choosing the model system to study NATs in a disease, it is important to take into consideration the following criteria: (1) the gene/target of interest is expressed in the particular cell line, (2) the nucleic acid molecule is directed to the same gene/target of the species of origin of the line, (3) the cell line can be cultured, (4) delivery of NAT molecule is feasible, and (5) if the mutation-specific effect is recapitulated in this model.

Two particular cell types were recurrently reported in our survey: HEK293T and skin fibroblasts. As a conventional cell line from human embryonic kidney, HEK293T cells offer the possibility to perform experiments requiring large number of cells in a relatively short period of time. When the gene of interest is not present, vectors containing the target gene can be transfected into HEK293T cells as an experimental cellular model for NAT studies, for example on splice switching or gene silencing [ 3 , 38 , 39 ]. This system however, often relies on the overexpression of part of the gene and lacks the entire gene context (such as introns, splicing enhancers or inhibitors) and, therefore, may lead to different results between the artificial and real situations or even between different cell types [ 11 , 40 , 41 ].

The DARTER network continues the work of a previous European COST action called “Exon skipping” (Number BM1207), which included many researchers on neuromuscular disorders, as many first-in-man studies had been conducted in this field [ 42 ]. This may explain the bias in our current survey toward neuromuscular disorders and skeletal muscle. Among the muscle models reported, myoblasts and myotubes were the cell types most frequently used. In the case of genes only expressed in differentiated muscle cells (myotubes), researchers need to differentiate myoblasts to myotubes. It is hence necessary to report what protocols were used in culture and differentiation, and to compare results between different laboratories. When muscle culture is not available, fibroblasts are sometimes used by researchers as an alternative. Similar situations are also experienced when other organs are studied. This makes fibroblast lines a convenient model widely used among our members, independent of disease pathology.

Dermal fibroblasts generated from skin biopsies used to be part of the routinely performed standard procedure of many biobanks. Fibroblasts allow studies in the precise genetic background of the patient where the pathogenic variant is present. However, as skin-derived cells, dermal fibroblasts do not express genes that are tissue specific in other organs. To circumvent this issue, cell transformation may be performed, for example transdifferentiate dermal fibroblasts to muscle or neuronal cells using MyoD or NGN2 overexpression [ 43 ], or reprogrammed into iPSCs using the four Yamanaka vectors [ 11 , 12 ]. iPSC technology has revolutionized the field and nowadays we can differentiate those cells into almost any cell type of the human body. Although iPSC technology is costly and time-consuming, several groups within our network have shown the potential of these models in assessing NAT treatment, in particular for eye and brain diseases [ 10 , 19 , 23 , 40 , 44 , 45 ].

In addition, gene editing techniques such as CRISPR/Cas9 can insert specific mutations in iPSCs and primary control lines [ 46 ], allowing the study of the direct effect of the mutation and obtaining a line that would mimic the condition of patient [ 47 , 48 ]. This approach has provided a powerful tool for generating specific mutant cell lines for NAT development [ 49 ]. Overall, primary skin fibroblasts cultured from patients with rare diseases are valuable bioresources for NAT development as highlighted in our survey. Therefore, strategies to connect biobanks and researchers are important to continue investigating treatments for rare diseases.

In our survey, the majority of cultures used were in 2D systems, although the target organs and tissues are organized in a more complex 3D structure. iPSC technology enables the generation of 3D models in the form of organoids, with a structure more closely resembling the tissue of interest than the 2D models. However, this is usually a laborious and lengthy procedure that, for example, takes from >80 days to form brain organoids and 200 days for retinal organoids.

Organ-on-chip is a technology aiming to combine different tissues or cell types to study disease and test therapeutics in a complex environment similar to in vivo . Examples include the blood–brain–retinal barriers on chip connected to microfluidic chambers that even allow multiplexing [ 50 , 51 ]. The development of these systems may allow the identification of chemical compounds for systemic delivery able to cross the blood barriers of the brain or the retina, accelerating the development and reducing the number of experimental animals used for this initial identification [ 13 , 20 , 24 ]. The 3D disease modeling system has potential not only in disease mechanism study but also for in vitro drug screening, hence accelerate novel NAT development.

Despite the options of numerous cellular models aforementioned, in many conditions, animal models are still required for pharmacological testing of NAT. The use of the animal models, mainly mice and rats, provides important information on pharmacokinetics, biodistribution, safety and toxicity at specific doses, and therapeutic efficacy. This information is important for the translation of the molecules to clinical trials.

Since NAT molecules are sequence specific, very often the target sequence in the rodent models is different from the human target gene, due to sequence variation between species. To overcome this hurdle, a humanized rodent model where the target gene is replaced or partially replaced by the human counterpart could be used for the in vivo validation of the human NAT sequence. Deep phenotyping is required to assess whether the humanization recapitulates the disease phenotype or maintains the function of the gene [ 26 , 52 , 53 ]. Second to mouse models, zebrafish is also reported in our survey, likely due to the convenient genetic modification, well characterized development, high capacity, and rapid turnaround as a conventional experimental animal model [ 54 ].

Conclusions

In conclusion, both cellular and animal models are required for NAT development. While the conventional cellular models are still widely used by researchers, newly developed model systems, such as iPSC-differentiated cells, CRISPR/Cas9 gene editing-induced disease cellular model and organ-in-chip 3D model are expanding rapidly in this field. These advanced or more complex in vitro model systems may not only overcome the shortage of patient-derived primary cellular models, but also function as a surrogate model to reduce the number of animals used in the subsequent in vivo evaluations.

The selection of a suitable model system should be based on the research question that needs to be answered, and how reliable the model in recapitulating the human condition. Only taking together the results of orthogonal methods and models will provide reliable information about the NAT molecule. Several guidelines have been published on experimental design of NAT studies [ 7 , 55 ]. It is hence important to have standardized protocols for evaluation of NATs in different model systems [ 56 , 57 ]. This requires international efforts to establish guidelines to facilitate the preclinical development of NATs.

The DARTER network is working together with groups worldwide to develop guidelines on how to develop NATs. One of the main efforts is the establishment of standardized protocols and uniformed evaluating systems. However, we acknowledge that with different NAT modalities and variety of target organs, each model needs to be selected specifically based on the research question that needs to be addressed.

Supplementary Material

Acknowledgments.

The authors would like to acknowledge the participants of the DARTER consortium for participating in this survey.

Author Disclosure Statement

Authors disclose being members of the DARTER COST Action. Authors do not disclose anything else related to this work.

Funding Information

This work was facilitated and supported by the European Cooperation of Science and Technology (COST) Action CA17103 (networking grant to V.A.-G.). H.Z. acknowledges funding support from the National Institute of Health Research (NIHR) Biomedical Research Center at Great Ormond Street Hospital and University College London, Muscular Dystrophy UK (17GRO-PG36–0168), Wellcome Trust (215181/Z/19/Z and 204841/Z/16/Z), and Harrington Discovery Institute (Fund for Cures UK). V.A.-G. acknowledges funding from Ikerbasque (Basque Foundation for Science). Research by A.G. is supported by the Foundation Fighting Blindness (PPA-0517-0717-RAD), the Curing Retinal Blindness Foundation, as well as the Landelijke Stichting voor Blinden en Slechtzienden, Stichting Oogfonds Nederland (who contributed through UitZicht 2018-21 and Uitzicht 2019-17), together with the Rotterdamse Stichting Blindenbelangen, Stichting Blindenhulp, and Stichting Dowilvo. The funding organizations had no role in the design or conduct of this research. They provided unrestricted grants.

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Simulation Systems in Experimental Development of a Space Nuclear Power System (Enisei SNPS)

• Published: 16 November 2019
• Volume 127 , pages 19–27, ( 2019 )

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• N. N. Ponomarev-Stepnoi 1 ,
• N. E. Kukharkin 2 ,
• V. V. Skorlygin 2 &
• M. E. Annenkov 2  

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The objectives and problems of modeling systems in developing nuclear thermionic power plants for use in space are discussed. The basic data of the modeling codes and systems developed in the process of creating the Enisei SNPS are presented. The principles of safe integration of a control digital computer into the control circuit of NPP are described and the results of experiments on the development of standard transient regimes in nuclear power tests are presented. The developed multicomputer distributed simulation system is presented, and the results of experimental testing of a scale model of the automatic control system in a wide range of operating modes are given.

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Translated from Atomnaya Énergiya, Vol. 127, No. 1, pp. 18–25, July, 2019.

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Ponomarev-Stepnoi, N.N., Kukharkin, N.E., Skorlygin, V.V. et al. Simulation Systems in Experimental Development of a Space Nuclear Power System (Enisei SNPS). At Energy 127 , 19–27 (2019). https://doi.org/10.1007/s10512-019-00578-2

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DOI : https://doi.org/10.1007/s10512-019-00578-2

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