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Clinic

Towards virtual clinical trials?

Clinical trials are among the most critical and expensive steps in drug development. They are highly regulated by the various international health agencies, and for good reason: the molecule or new medical procedure being tested can potentially harm the patients. To date, randomized clinical trials are the most valuable to health authorities. However, even though studies are designed to generate as much data as possible while limiting bias and respecting patient safety, they are limited in terms of the parameters tested. For example, some molecules are designed to treat diseases affecting small numbers of patients, so it is very complicated and costly for clinical trial sponsors to recruit enough patients and the statistical power generated is sometimes too low to be interpreted with confidence.

Could a mathematical, computerized model of a patient totally replace, or at least supplement, the data generated by humans in a clinical trial?

This short article will try to develop the concept of in silico clinical trials through some notions and examples from the scientific literature. We hope that it can teach you more about this exciting field.

In silico clinical trials use virtual patients, i.e. mathematical models generated by an algorithm, mimicking our physiology and capable of reproducing, for example, the pharmacokinetics of a drug X 1 or their associated toxicity 2. They have many advantages, such as generating more confidence in the molecule being tested before any animal and/or human experiments. Increasing the statistical power of trials carried out on small populations; such as when a molecule is tested in orphan diseases. Eventually, this technology follows the 3 Rs rule of limiting the use of laboratory animals: Replace, Reduce, Refine.

Working with this concept, Sarrami-Foroushan et al 1 modelled the therapeutic effect of stenting in the treatment of intracranial aneurysms. The first step of the project was to check whether it was possible to replicate the data from existing studies and, secondly, to explore certain situations that would have required a more complicated set of patients to pool.

Based on “virtual” carotid anatomies (but modelled from real patients), the researchers were able to apply a set of models to reproduce the different physical mechanisms (fluid dynamics for the blood, for example) involved in the evolution of the aneurysm, and to observe the effect of the prosthesis on the diseased vessel (in this case, its occlusion). The aim was also to generate a model capable of comparing the effect of the prosthesis in a normotensive patient and in a hypertensive patient.

The predicted score was comparable to results already published in the literature, and allowed the exploration of new scenarios where, for example, the aneurysm has a more complex morphology and some patients are more difficult to recruit.

This example illustrates the strength that in silico technology will represent in the coming decades. Various health authorities, such as the FDA 2, are placing increasing emphasis on these predictions, as they reduce the cost and duration of clinical trials.

Another case study is that developed by Gutiérrez-Casares et al. 3 in the treatment of ADHD, using two different small molecules, lisdexamfetamine and methylphenidate.

The team first had to characterize the pathology and the drugs tested at the molecular level: in ADHD, the expression of certain proteins is altered, and the two molecules have a different mode of action. Sensitivity and efficacy may therefore be different in a patient depending on the molecule studied. The activity of these proteins was then correlated to clinical efficacy criteria. They generated a virtual population, demographically similar to the populations observed in the pathology, describing different protein profiles according to the “healthy” or “sick” status of the patient. Finally, the team used this virtual population to generate their pharmacokinetic profiles and simulate the concentration that the drug would have in their body.

Based on their protein profiles and by cross-referencing the generated efficacy data, the researchers were able to find the key proteins in the mechanism of action of both drugs. It is not only efficacy and safety data that can be generated via in silico testing, but also data fundamental to the drug’s mode of action that can be inferred.

To date, it is still complicated to adopt a holistic approach to the simulation of Human physiology. As the article by Gutiérrez-Casares et al. shows, the reliability of models is limited to what is already known.  The notion of digital twins is applicable to many fields, but may never be applicable to Health. However, with ever-increasing computing power and evolving clinical databases, models will come closer and closer to plausible outcomes. Where a phase 3 trial requires numerous patients, could in silico trials reduce this number and speed up the approval of new drugs on the market?

On the public side, initiatives such as the VPH Institute 5 and Avicenna Alliance 6 promote the use of in silico and contain multiple resources available to all to democratize the technology.

On the private side, there are companies such as InSilicoTrials 7, Novadiscovery 8 or the InClinico tool from the company InSilico 9 that offer platforms accessible to the various players in the health sector, and provide them with “ready-to-use” tools to initiate their own simulations.

Is it possible to imagine a future where these tools allow small and medium-sized biotechs to access phase 3 clinical trials without the financial means of a BigPharma? The ecosystem of the healthcare industries would then be more favourable to innovative and “risky” ideas, and not only to the historical players, capable of absorbing the heavy failure of a phase 3.


To go further:

  1. Sarrami-Foroushani, A. et al. In-silico trial of intracranial flow diverters replicates and expands insights from conventional clinical trials. Nat. Commun. 12, 3861 (2021).
  2. AltaThera Pharmaceuticals Announces FDA Approval for New Indications of Sotalol IV: A New and Faster Way to Initiate Sotalol Therapy for Atrial Fibrillation (AFib) Patients. 
  3. Gutiérrez-Casares, J. R. et al. Methods to Develop an in silico Clinical Trial: Computational Head-to-Head Comparison of Lisdexamfetamine and Methylphenidate. Front. Psychiatry 12, 1902 (2021).
  4. Marr, B. 7 Amazing Examples of Digital Twin Technology In Practice. Forbes 
  5. VPH Institute | Virtual Physiological Human – International non-profit organisation. 
  6. AVICENNA ALLIANCE. 
  7. InSilicoTrials – Modeling and simulation in drug development. InSilicoTrials 
  8. Novadiscovery. 
  9. InClinico | Insilico Medicine. 

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Introduction to DeSci

How Science of the Future is being born before our eyes « [DeSci] transformed my research impact from a low-impact virology article every other year to saving the lives and…
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Towards virtual clinical trials?

Clinical trials are among the most critical and expensive steps in drug development. They are highly regulated by the various international health agencies, and for good reason: the molecule or…

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Categories
Exploratory research Generalities Preclinical

Oligonucleotides and Machine Learning Tools

Today, oligonucleotides – short DNA or RNA molecules – are essential tools in molecular biology projects, but also in therapeutics and diagnostics. In 2021, ten or so antisense therapies are authorised on the market, and much more are under clinical trials.

The recent Covid-19 crisis has also brought PCR tests to the public’s knowledge, these tests use small sequences of about 20 nucleotides to amplify and detect genetic material. Oligos have been so successful that, since their synthesis was automated, their market share has grown steadily. It is estimated that it will reach $14 billion by 2026.

Oligonucleotides have an elegance in their simplicity. It was in the 1950s that Watson and Crick described the double helix that makes up our genetic code, and the way in which the bases Adenine/Thymine and Cytosine/Guanine pair up. Thanks to this property, antisense therapies can virtually target our entire genome, and regulate its expression. Diseases that are difficult to treat, such as Spinal Dystrophy Disorder or Duchenne’s disease, are now benefiting some therapeutic support (1).

This article does not aim to restate the history of oligonucleotides used in clinic (many reviews are already available in the literature (2), (3), (4)), but to provide a quick overview of what has been developed in this area, with a Machine Learning tint.

We hope that the article will inspire some researchers, and that others may find new ideas of research and exploration. At a time when Artificial Intelligence has reached a certain maturity, it is particularly interesting to exploit it and to streamline all decision making in R&D projects.

This list is not exhaustive, and if you have a project or article to share with us, please contact us at hello@resolving-pharma.com. We will be happy to discuss it and include it in this article.

Using Deep Learning to design PCR primers

As the Covid-19 health crisis has shown, diagnosing the population is essential to control and evaluate a pandemic. Thanks to two primers of about twenty nucleotides, a specific sequence can be amplified and detected, even at a very low level (PCR technique is technically capable of detecting up to 3 copies of a sequence of interest (5)).

A group from Utrecht University in the Netherlands (6) has developed a CNN (for Convolutional Neural Network, a type of neural network particularly effective in image recognition) capable of revealing areas of exclusivity in a genome. This allows the development of highly specific primers for the target of interest. In their case, they analysed more than 500 genomes of viruses from the Coronavirus family in order to train the algorithm to sort the different genomes. The primers designed by the model showed similar efficiency to the sequences used in practice. This tool could be used to develop PCR diagnostic tools with greater efficiency and speed.

Predicting the penetration power of an oligonucleotide

There are many peptides that improve the penetration of oligonucleotides into cells. These are called CPPs for Cell Penetrating Peptides, small sequences of less than 30 amino acids. Using a random decision tree, a team from MIT (7) was able to predict the activity of CPPs for oligonucleotides, modified by morpholino phosphorodiamidates (MO). Although the use of this model is limited (there are many chemical modifications to date and MOs cover only a small fraction of them), it is still possible to develop it for larger chemical families. For example, the model was able to predict experimentally whether a CPP would improve the penetration of an oligonucleotide into cells by a factor of three.

Optimising therapeutic oligonucleotides

Although oligonucleotides are known to be little immunogenic (8), they do not escape the toxicity associated with all therapies. “Everything is poison, nothing is poison: it is the dose that makes the poison. “- Paracelsus

This last parameter is key in the future of a drug during its development. A Danish group (9) has developed a prediction model capable of estimating the hepatotoxicity of a nucleotide sequence in mouse models. Again, here “only” unmodified and LNA (Locked Nucleic Acid, a chemical modification that stabilises the hybridisation of the therapeutic oligonucleotide to its target) modified oligonucleotides were analysed. It would be interesting to increase the chemical space studied and thus extend the possibilities of the algorithm. However, it is this type of model that will eventually reduce attrition in the development of new drugs. From another perspective (10), a model has been developped for optimising the structure of LNAs using oligonucleotides as gapmers. Gapmers are hybrid oligonucleotide sequences that have two chemically modified ends, that are resistant to degrading enzymes, and an unmodified central part that can be degraded once hybridised to its target. It is this final ‘break’ that will generate the desired therapeutic effect. Using their model, the researchers were able to predict the gapmer design that has the best pharmacological profile.

Accelerating the discovery of new aptamers

Also known as “chemical antibodies”, aptamers are DNA or RNA sequences capable of recognising and binding to a particular target with the same affinity as a monoclonal antibody. Excellent reviews on the subject are available here (11) or here (12). In clinic, pegatinib is the first aptamer to be approved for use. The compound is indicated for certain forms of AMD.

Current research methods, based on SELEX (Systematic Evolution of Ligands by Exponential Enrichment), have made it possible to generate aptamers directed against targets of therapeutic and diagnostic interest, such as nucleolin or thrombin. Although the potential of the technology is attractive, it is difficult and time-consuming to discover new pairs of sequence/target. To boost the search of new candidates, an American team (13) was able to train an algorithm to optimise an aptamer and reduce the size of its sequence, while maintaining or even increasing its affinity to its target. They were able to prove experimentally that the aptamer generated by the algorithm had more affinity than the reference candidate, while being 70% shorter. The interest here is to keep the experimental part (the SELEX part), and to combine it with these in silico tools in order to accelerate the optimisation of new candidates.

There is no doubt that the future of oligonucleotides is promising, and their versatility is such that they can be found in completely different fields, ranging from DNA-based nanotechnology to CRISPR/Cas technology. The latter two areas alone could be the subject of individual articles, as their research horizons are so important and exciting.

In our case, we hope that this short article has given you some new ideas and concepts, and inspired you to learn more about oligonucleotides and machine learning.


Bibliography:
  1. Bizot F, Vulin A, Goyenvalle A. Current Status of Antisense Oligonucleotide-Based Therapy in Neuromuscular Disorders. Drugs. 2020 Sep;80(14):1397–415.
  2. Roberts TC, Langer R, Wood MJA. Advances in oligonucleotide drug delivery. Nat Rev Drug Discov. 2020 Oct;19(10):673–94.
  3. Shen X, Corey DR. Chemistry, mechanism and clinical status of antisense oligonucleotides and duplex RNAs. Nucleic Acids Res. 2018 Feb 28;46(4):1584–600.
  4. Crooke ST, Liang X-H, Baker BF, Crooke RM. Antisense technology: A review. J Biol Chem [Internet]. 2021 Jan 1 [cited 2021 Jun 28];296. Available from: https://www.jbc.org/article/S0021-9258(21)00189-7/abstract
  5. Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, et al. The MIQE Guidelines: Minimum Information for Publication of Quantitative Real-Time PCR Experiments. Clin Chem. 2009 Apr 1;55(4):611–22.
  6. Lopez-Rincon A, Tonda A, Mendoza-Maldonado L, Mulders DGJC, Molenkamp R, Perez-Romero CA, et al. Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning. Sci Rep. 2021 Jan 13;11(1):947.
  7. Wolfe JM, Fadzen CM, Choo Z-N, Holden RL, Yao M, Hanson GJ, et al. Machine Learning To Predict Cell-Penetrating Peptides for Antisense Delivery. ACS Cent Sci. 2018 Apr 25;4(4):512–20.
  8. Stebbins CC, Petrillo M, Stevenson LF. Immunogenicity for antisense oligonucleotides: a risk-based assessment. Bioanalysis. 2019 Nov 1;11(21):1913–6.
  9. Hagedorn PH, Yakimov V, Ottosen S, Kammler S, Nielsen NF, Høg AM, et al. Hepatotoxic Potential of Therapeutic Oligonucleotides Can Be Predicted from Their Sequence and Modification Pattern. Nucleic Acid Ther. 2013 Oct 1;23(5):302–10.
  10. Papargyri N, Pontoppidan M, Andersen MR, Koch T, Hagedorn PH. Chemical Diversity of Locked Nucleic Acid-Modified Antisense Oligonucleotides Allows Optimization of Pharmaceutical Properties. Mol Ther – Nucleic Acids. 2020 Mar 6;19:706–17.
  11. Zhou J, Rossi J. Aptamers as targeted therapeutics: current potential and challenges. Nat Rev Drug Discov. 2017 Mar;16(3):181–202.
  12. Recent Progress in Aptamer Discoveries and Modifications for Therapeutic Applications | ACS Applied Materials & Interfaces [Internet]. [cited 2021 Jul 25]. Available from: https://pubs-acs-org.ressources-electroniques.univ-lille.fr/doi/10.1021/acsami.0c05750
  13. Bashir A, Yang Q, Wang J, Hoyer S, Chou W, McLean C, et al. Machine learning guided aptamer refinement and discovery. Nat Commun. 2021 Apr 22;12(1):2366.

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Vitalik_Buterin_Scientist_Landscape

Introduction to DeSci

How Science of the Future is being born before our eyes « [DeSci] transformed my research impact from a low-impact virology article every other year to saving the lives and…
Illustration In Silico

Towards virtual clinical trials?

Clinical trials are among the most critical and expensive steps in drug development. They are highly regulated by the various international health agencies, and for good reason: the molecule or…

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Categories
Exploratory research Preclinical

Robots in the lab: will tomorrow’s researchers also be roboticists?

Frequently, there are very repetitive and laborious tasks in a laboratory that could be automated. Whether it’s compiling data on a spreadsheet or preparing a gel for electrophoresis, the ratio Value generated / Time used is not often high. Derek Lowe, a chemist and author of the Into The Pipeline blog (1), humorously recalls a time when a simple chromatography took an enormous amount of time to perform (a time now almost over), and correctly notes that the goal of automation is not to push the researcher out of the lab, but to reduce all those laborious tasks and support the scientist’s intellectual input to the maximum.

In Chemistry or Biology, many groups are trying to imagine the laboratory of the future, one that could carry out end-to-end the synthesis and the testing of a molecule. However, from a technical point of view, the range of actions required to reproduce the work of a researcher by a robot is far too wide to be effective today, but the various projects presented below are promising for the future.

***

In the field of diagnostics, it is becoming increasingly vital to automate tests, in order to meet the demands of patients and clinicians. To give you an idea, in the UK, almost one million PCR tests are performed every day for covid-19 alone (2).

In clinical microbiology the use of automated systems is particularly interesting, where protocols require a lot of time and attention from microbiologists. The WASP robot, designed by Copan (3), combines robotics and software and is capable of performing culture operations, bacterial isolation, and monitoring whether the growth is done correctly thanks to a small camera installed in the robot. There is also Roche’s Cobas (4), which is capable of performing various molecular biology tests such as qPCR. The versatility of these robots allow them to be easily adapted for other diagnostic purposes.

***

In a more chemical context, a Liverpudlian group led by Prof. Andrew Cooper (5) has designed a robot capable of physically moving around the laboratory in order to optimise hydrogen production by photocatalysis. The advantage of this robot is that it is human-sized and can operate and move freely in any room. Although it took some time to set up such a system, it is estimated that the robot was 1000 times faster than if the work was done manually. The video of the robot is available below:

The artificial intelligence implemented in most robots works by iterations: the results of each experiment are evaluated by the algorithm and allow it to design the next experiment.

Figure 1: The combination of Artificial Intelligence and robotics allows the creation of an iterative circuit, where each cycle analyses the results of the previous one, and adapts the parameters to optimise the process defined by the researcher

In the field of Materials Chemistry, Alán Aspuru-Guzik et al. (6) have developed an automated and autonomous platform capable of working with a large number of parameters in order to discover new materials, useful for solar panels or electronic consumables. In Organic Chemistry, Coley et al (7) have used AI and robotics to synthesize small molecules by Flow Chemistry. All the chemist has to do is to indicate the molecule one want to obtain, and the AI will carry out its own retro synthesis pathway and try to synthesize the compound. The automaton was able to synthesise 15 small therapeutic molecules, ranging from aspirin to warfarin.

Other initiatives can be noted, particularly from Big Pharmas, such as AstraZeneca and its iLab (8), which aims to automate the discovery of therapeutic molecules via an iterative circuit of Design, Make, Test, Analyse. In Medicinal Chemistry, combinatorial chemistry methods enable the chemical space of a target to be explored very rapidly, thanks to controlled and optimised reactions. These projects witness the progress towards totally autonomous synthesis systems.

***

It is probably fair to note that some researchers are wary of using robots, and sometimes feel threatened by being replaced by a machine. Myself, as an apprentice chemist and exploring the subject, have said to myself on several occasions “Hey, but this robot could work for me! I remember the weeks I would spend trying to optimise a reaction, chain testing different catalysts, a job that an automaton (or a monkey!) could have done for me much more quickly and certainly more efficiently. Robotics has this enormous potential to improve the productivity of researchers, and to reduce tedious tasks that ultimately require little or even no intellectual thought at all.

There are also tools that help the researcher to develop research designs to optimise an X or Y process in the most efficient way. For example, the EDA tool developed by NC3Rs (9) is useful for in vivo research projects, where one tries to obtain statistically powerful data while reducing the number of animals used. Other tools have also been developed using Bayensian or Montecarlo Research Tree models (10), and lead to optimal experimental designs. In the same vein, Aldeghi et al. have developed Golem (11), an open-source tool available on GitHub (12).

Cloud technologies (i.e. access to a service via the internet) also hold great promise for the laboratory of the future. They will allow researchers to carry out their research entirely from home, thanks to “a few” lines of code. Projects such as Strateos have initiated this practice and already allow researchers to program Chemistry, Biochemistry and Biology experiments from home. Once the protocol is defined, the researcher simply launches the experiment from one’s computer and the robot located thousands of kilometres away carries out the operation for one’s. In a few years’ time, if the service becomes more widely adopted in the scientific community, everyone will have easy access to it for a reasonable price.

Figure 2: Cloub Lab principle. 1) The researcher sends his or her research protocol to the automaton, located on the other side of the world. 2) The automaton carries out the experiment designed by the researcher and 3) returns the results to the researcher as soon as the experiment is completed.

***

Between progress and doubts, it is probably only a matter of time before the scientific community adopts a different mentality. There was a time when telephone switchboards were run entirely by people, until the day everything was replaced by automatons. This short documentary by David Hoffman crystallised this transition and the reaction of users when they hear a robotic voice for the very first time. Although some of them were reluctant at first, the implementation of voice recognition has made the service much more efficient and less expensive for consumers. Won’t tomorrow’s researchers all be roboticists to some extent?


Bibliography

  1. Lab! Of! The! Future! | In the Pipeline [Internet]. 2021 [cited 2021 Jun 9]. Available from: //blogs-sciencemag-org.ressources-electroniques.univ-lille.fr/pipeline/archives/2021/03/31/lab-of-the-future
  2. Testing in the UK | Coronavirus in the UK [Internet]. [cited 2021 Jun 13]. Available from: https://coronavirus.data.gov.uk/details/testing
  3. Copan WASP DT: Walk-Away Specimen Processor [Internet]. [cited 2021 Jun 9]. Available from: https://www.beckmancoulter.com/products/microbiology/copan-wasp-dt-walk-away-specimen-processor
  4. Automation in Molecular Diagnostic Testing [Internet]. Diagnostics. [cited 2021 Jun 13]. Available from: https://diagnostics.roche.com/global/en/article-listing/automation-in-molecular-diagnostic-testing.html
  5. Burger B, Maffettone PM, Gusev VV, Aitchison CM, Bai Y, Wang X, et al. A mobile robotic chemist. Nature. 2020 Jul;583(7815):237–41.
  6. MacLeod BP, Parlane FGL, Morrissey TD, Häse F, Roch LM, Dettelbach KE, et al. Self-driving laboratory for accelerated discovery of thin-film materials. Sci Adv. 2020 May 1;6(20):eaaz8867.
  7. Coley CW, Thomas DA, Lummiss JAM, Jaworski JN, Breen CP, Schultz V, et al. A robotic platform for flow synthesis of organic compounds informed by AI planning. Science [Internet]. 2019 Aug 9 [cited 2021 Jun 3];365(6453). Available from: http://science.sciencemag.org/content/365/6453/eaax1566
  8. The AstraZeneca iLab [Internet]. [cited 2021 Jun 27]. Available from: https://www.astrazeneca.com/r-d/our-technologies/ilab.html
  9. du Sert NP, Bamsey I, Bate ST, Berdoy M, Clark RA, Cuthill IC, et al. The Experimental Design Assistant. Nat Methods. 2017 Nov;14(11):1024–5.
  10. Dieb TM, Tsuda K. Machine Learning-Based Experimental Design in Materials Science. In: Tanaka I, editor. Nanoinformatics [Internet]. Singapore: Springer; 2018 [cited 2021 Jun 6]. p. 65–74. Available from: https://doi.org/10.1007/978-981-10-7617-6_4
  11. Aldeghi M, Häse F, Hickman RJ, Tamblyn I, Aspuru-Guzik A. Golem: An algorithm for robust experiment and process optimization. ArXiv210303716 Phys [Internet]. 2021 Mar 5 [cited 2021 Jun 9]; Available from: http://arxiv.org/abs/2103.03716
  12. aspuru-guzik-group/golem [Internet]. Aspuru-Guzik group repo; 2021 [cited 2021 Jun 9]. Available from: https://github.com/aspuru-guzik-group/golem

 

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Vitalik_Buterin_Scientist_Landscape

Introduction to DeSci

How Science of the Future is being born before our eyes « [DeSci] transformed my research impact from a low-impact virology article every other year to saving the lives and…
Illustration In Silico

Towards virtual clinical trials?

Clinical trials are among the most critical and expensive steps in drug development. They are highly regulated by the various international health agencies, and for good reason: the molecule or…

To subscribe free of charge to the monthly Newsletter, click here.

Would you like to take part in the writing of Newsletter articles ? Would you like to take part in an entrepreneurial project on these topics ?

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Categories
Exploratory research Preclinical

Organ-On-Chip: Towards totally miniaturized assays?

Before testing a new molecule in Humans, it is necessary to make toxicological and pharmacokinetic predictions with various preclinical models. Researchers try to reconstruct as best as they can what would happen in a specific tissue or organ. Among the most commonly used techniques are cell cultures, which, although effective, cannot fully simulate the dynamics of an organ or a pathology. There are also in vivo models, which are often more relevant, but are not adapted to high-throughput data generation. First, ethically, the models must be sacrificed and what is observed in animals is not always observed in Humans. Of the compounds that fail in the clinic, it is estimated that 60% of the causes are related to lack of efficacy in Humans, and 30% to unexpected toxicity [1]. Clearly, new biological models are needed.

***

Paradoxically, chemical libraries are growing, but the number of outgoing drugs is thinning. Therefore, the scientific community must rethink its models permanently to generate reliable information quicker. It is from this problem that the genesis of the Organs-On-Chips (OOC) begins. It was in 1995 that Michael L. Schuler was the first to propose a prototype of cell culture analogue, connecting several compartments of different cells 2. It is when these compartments were connected by microchannels that the term “organ-on-a-chip” appeared.

OOCs are devices the size of a USB flash drive. This is made possible thanks to the microchannel technology that harnesses volumes of the order of nanoliter and below. OOCs have three characteristics that allow them to better model a tissue or an organ:

  1. The control of the 3D distribution of cells
  2. The integration of different cell populations
  3. And the possibility of generating and controlling biomechanical forces.

This allows physiological conditions to be transcribed much more faithfully, compared to a static two-dimensional cell culture on a flat surface. There is no single design for OOC, but perhaps the easiest example to visualize is the lung OOC mimicking the air-alveolus interface. (see Figure 1).

Figure 1 : Illustration of an OOC mimicking the air-lung interface. A semi-permeable membrane separates the external environment from the pulmonary cells. The vacuum chamber makes it possible to mimic the diaphragm.

To date, different OOC have been designed, ranging from the liver to chronic obstructive bronchopneumopathy. Riahi et al. have developed a liver OOC, capable of assessing the chronic toxicity of a molecule by quantifying the evolution of certain biomarkers 3. Compared to 2D cultures, the OOC is more sustainable and generates data that could have only been observed in vivo. Another interesting model was developed by Zhang et al. and focuses on the heart and its cardiomyocytes 4. By integrating electrodes on the chip, the researchers were able to assess cell contraction, and evaluate the effectiveness and cardiotoxicity of certain drugs. If the adoption of the technology is successful, the OOC will be used as a complement to cellular tests and animal models, and may completely replace them.

Impressively, the versatility of the concept will allow clinicians to evaluate the response of our own cells to a specific treatment. By implementing, for instance, a tumor extract from a patient in an OOC, it will be possible to observe and optimize the therapeutic response to a molecule X, and transcribe these observations in clinic 5. This is a first step of the OOC towards personalized medicine.

***

Eventually, the different OOC models can be combined in order to group several organs and simulate an entire organism. This last idea, also known as “body-on-a-chip”, is extremely powerful and could capture both the effect of a drug and its associated toxicity on the various organs. Some models, such as Skardal et al.’, have allowed to study the migration of tumour cells from a colon OOC to a liver OOC 6. Edington et al. were able to connect up to 10 different OOCs, capturing some of their physiological functions. It consisted of the liver, lungs, intestines, endometrium, brain, heart, pancreas, kidneys, skin and skeletal muscles. The system was functional for four weeks 7. Even if such systems are not optimal yet, their exploration will enable the generation of much more relevant data, much faster, to boost Drug Discovery projects.


To go further :

https://wyss.harvard.edu/technology/human-organs-on-chips/

Excellent reviews on the subject are available:

Bibliography

  1. Waring, M. J. et al. An analysis of the attrition of drug candidates from four major pharmaceutical companies. Nat. Rev. Drug Discov. 14, 475–486 (2015).
  2. Sweeney, L. M., Shuler, M. L., Babish, J. G. & Ghanem, A. A cell culture analogue of rodent physiology: Application to naphthalene toxicology. Toxicol. In Vitro 9, 307–316 (1995).
  3. Riahi, R. et al. Automated microfluidic platform of bead-based electrochemical immunosensor integrated with bioreactor for continual monitoring of cell secreted biomarkers. Sci. Rep. 6, 24598 (2016).
  4. Zhang, X., Wang, T., Wang, P. & Hu, N. High-Throughput Assessment of Drug Cardiac Safety Using a High-Speed Impedance Detection Technology-Based Heart-on-a-Chip. Micromachines 7, 122 (2016).
  5. Shirure, V. S. et al. Tumor-on-a-chip platform to investigate progression and drug sensitivity in cell lines and patient-derived organoids. Lab. Chip 18, 3687–3702 (2018).
  6. Skardal, A., Devarasetty, M., Forsythe, S., Atala, A. & Soker, S. A Reductionist Metastasis-on-a-Chip Platform for In Vitro Tumor Progression Modeling and Drug Screening. Biotechnol. Bioeng. 113, 2020–2032 (2016).
  7. Edington, C. D. et al. Interconnected Microphysiological Systems for Quantitative Biology and Pharmacology Studies. Sci. Rep. 8, 4530 (2018).

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Illustration In Silico

Towards virtual clinical trials?

Clinical trials are among the most critical and expensive steps in drug development. They are highly regulated by the various international health agencies, and for good reason: the molecule or…
Vitalik_Buterin_Scientist_Landscape

Introduction to DeSci

How Science of the Future is being born before our eyes « [DeSci] transformed my research impact from a low-impact virology article every other year to saving the lives and…

To subscribe free of charge to the monthly Newsletter, click here.

Would you like to take part in the writing of Newsletter articles ? Would you like to take part in an entrepreneurial project on these topics ?

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