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Clinic Exploratory research Preclinical

Artificial intelligence against bacterial infections: the case of bacteriophages

« If we fail to act, we are looking at an almost unthinkable scenario where antibiotics no longer work and we are cast back into the dark ages of medicine » – David Cameron, former UK Prime Minister

Hundreds of millions of lives are at stake. The WHO has made antibiotic resistance its number one global priority, showing that antibiotic resistance could lead to more than 100 million deaths per year by 2050, and that it already causes around 700,000 deaths per year, including 33,000 in Europe. Among the various therapeutic strategies that can be implemented, there is the use of bacteriophages, an old and neglected alternative approach that Artificial Intelligence could bring it back. Explanations.

Strategies that can be put in place to fight antibiotic resistance

The first pillar of the fight against antibiotic resistance is the indispensable public health actions and recommendations aimed at reducing the overall use of antibiotics. For example :

  • The continuation of communication campaigns aimed at combating the excessive prescription and consumption of antibiotics (in France a famous slogan is: “Antibiotics are not automatic”?)
  • Improving sanitary conditions to reduce the transmission of infections and therefore the need for antibiotics. This measure concerns many developing countries, whose inadequate drinking water supply causes, among other things, many cases of childhood diarrhea.
  • Reducing the use of antibiotics in animal husbandry, by banning the addition of certain antibiotics to the feed of food-producing animals.
  • Reducing environmental pollution with antibiotic molecules, particularly in establishing more stringent anti-pollution standards for manufacturing sites in the pharmaceutical industry.
  • The improvement and establishment of comprehensive structures, for monitoring human and animal consumption of antibiotics and the emergence of multi-drug resistant bacterial strains.
  • More frequent use of diagnostic tests, to limit the use of antibiotics and to select more precisely which molecule is needed.
  • Increased use of vaccination

The second pillar of the fight is innovative therapeutic strategies, to combat multi-drug resistant bacterial strains against which conventional antibiotics are powerless. We can mention :

  • Phage therapy: the use of bacteriophages, natural predatory viruses of bacteria. Phages can be used in therapeutic cases where they can be put directly in contact with bacteria (in the case of infected wounds, burns, etc.) but not in cases where they should be injected into the body, as they would be destroyed by the patient’s immune system.
  • The use of enzybiotics: enzymes, mainly from bacteriophages like lysine, that can be used to destroy bacteria. At the time of writing, this approach is still at an experimental stage.
  • Immunotherapy, including the use of antibodies: Many anti-infective monoclonal antibodies – specifically targeting a viral or bacterial antigen – are in development. Palivizumab directed against the F protein of the respiratory syncytial virus was approved by the FDA in 1998. The synergistic use of anti-infective antibodies and antibiotic molecules is also being studied.

Each of the proposed strategies – therapeutic or public health – can be implemented and their effect increased tenfold with the help of technology. One of the most original uses of Artificial Intelligence concerns the automation of the design of new bacteriophages.

Introduction to bacteriophages

Bacteriophages are capsid viruses that only infect bacteria. They are naturally distributed throughout the biosphere and their genetic material can be DNA, in the vast majority of cases, or RNA. Their discovery is not recent and their therapeutic use has a long history, in fact, they started to be used as early as the 1920s in Human and Animal medicine. Their use was gradually abandoned in Western countries, mainly because of the ease of use of antibiotics and the fact that relatively few clinical trials were conducted on phages, their use being essentially based on empiricism. In other countries of the world, such as Russia and the former USSR, the culture of using phages in human and animal health has remained very strong: they are often available without prescription and used as a first-line treatment.

The mechanism of bacterial destruction by lytic bacteriophages

There are two main types of bacteriophages:

  • On the one hand, lytic phages, which are the only ones used in therapeutics and those we will focus on, destroy the bacteria by hijacking the bacterial machinery in order to replicate.
  • On the other hand, temperate phages, which are not used therapeutically but are useful experimentally because they add genomic elements to the bacteria, potentially allowing it to modulate its virulence. The phage cycle is called lysogenic.

The diagram below shows the life cycle of a lytic phage:

This is what makes lytic phages so powerful, they are in a “host-parasite” relationship with bacteria, they need to infect and destroy them in order to multiply. Thus, the evolution of bacteria will select mainly resistant strains, as in the case of antibiotic resistance, however, unlike antibiotics, which do not evolve – or rather “evolve” slowly, in step with the scientific discoveries of the human species – phages will also be able to adapt in order to survive and continue to infect bacteria, in a kind of evolutionary race between the bacteria and the phages.

The possible use of Artificial Intelligence

One of the particularities of phages is that, unlike some broad-spectrum antibiotics, they are usually very specific to a bacterial strain. . Thus, when one wishes to create or find appropriate phages for a patient, a complex and often relatively long process must be followed, even though a race against time is usually engaged for the survival of the patient: the bacteria must be identified, which implies sample cultivation from the patient, characterizing the bacterial genome and then determining which phage will be the most likely to fight the infection. Until recently, this stage was an iterative process of in-vivo testing, which was very time-consuming, but as Greg Merril, CEO of the start-up Adaptive Phage Therapeutics (a company which is developing a phage selection algorithm based on bacterial genomes), points out: “When a patient is severely affected by an infection, every minute is important.”

Indeed, to make phage therapy applicable on a very large scale, it is necessary to determine quickly and at a lower cost which phage will be the most effective. This is what the combination of two technologies already allows and will increasingly allow: high frequency sequencing and machine learning. The latter makes it possible to process the masses of data generated by genetic sequencing (the genome of the bacteriophage or the bacterial strain) and to detect patterns in relation to an experimental database indicating that a phage with a genome X was effective against a bacterium with a genome Y.  The algorithm is then able to determine the chances of success of a whole library of phages on a given bacterium and determine which will be the best without performing long iterative tests. As with every test-and-learn domain, phage selection can be automated.

In addition to the determination of the best host for a given bacteriophage (and vice versa) discussed below, the main use cases described for artificial intelligence in the use of phages are

  • Classification of bacteriophages: The body in charge of classification is the International Committee on Taxonomy of Viruses (ICTV). More than 5000 different bacteriophages are described and the main family is the Caudovirales. Traditional approaches to the classification of bacteriophages are based on the morphology of the virion protein that is used to inject the genetic material into the target bacterium. These approaches are mainly based on electron microscopy techniques. A growing body of scientific literature suggests that Machine Learning is a relevant alternative for a more functional classification of bacteriophages.
  • Predicting the functionality of bacteriophage proteins: Machine Learning can be useful to elucidate the precise mechanisms of the PVP (Phage Virion Protein), involved, as mentioned above, in the injection of genetic material into the bacterium.
  • Determining the life cycle of bacteriophages: As discussed earlier in this article, there are two categories of phages: lytic and temperate. Traditionally, the determination of whether a phage belongs to one of these two families was determined by culture and in-vitro The task is more difficult than one might think because under certain stress conditions and in the presence of certain hosts, temperate phages have the ability to survive by performing lytic cycles. At present, PhageAI algorithms are able to determine 99% of the phage category.

It is also possible, as illustrated in the diagram below, for rare and particularly resistant bacteria, to combine the techniques seen above with synthetic biology and bio-engineering techniques in order to rapidly create “tailor-made” phages. In this particular use case, Artificial Intelligence offersits full potential in the development of an ultra-personalised medicine.

Conclusion

Despite its usefulness, phage therapy is still complicated to implement in many Western countries. In France, this therapy is possible within the framework of a Temporary Authorisation for Use under the conditions that the patient’s life is engaged or that his functional prognosis is threatened, that the patient is in a therapeutic impasse and that he or she is the subject of a mono-microbial infection. The use of the therapy must also be validated by a Temporary Specialised Scientific Committee on Phagotherapy of the ANSM and a phagogram – an in vitro test that studies the sensitivity of a bacterial strain to bacteriophages, in the manner of antibiograms – must be presented before treatment is started. Faced with these multiple difficulties, many patient associations are mobilizing to campaign for simplified access to phagotherapy. With the help of Artificial Intelligence, more and more phagotherapies can be developed, as illustrated in this article, and given the urgency and scale of the problem of antibiotic resistance, it is essential to prepare the regulatory framework within which patients will be able to access the various alternative treatments, including bacteriophages. The battle is not yet lost, and Artificial Intelligence will be a main key ally.

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Clinic Exploratory research Preclinical

3D printing and artificial intelligence: the future of galenics?

“Ten years from now, no patient will take the same thing as another million people. And no doctor will prescribe the same thing to two patients.”

Fred Paretti from the 3D drug printing startup Multiply Labs.

3D printing – also known as additive manufacturing – is one of the technologies capable of transforming pharmaceutical development, and will certainly play a role in the digitalization of the drug manufacturing sector. This short article will attempt to provide an overview of how 3D printing works, its various use cases in the manufacture of personalized medicines, the current regulatory framework for this innovative technology, and the synergies that may exist with Artificial Intelligence.

3D printing, where do we stand?

The principle of 3D printing, developed since the early 2000s and now used in a large number of industrial fields, consists of superimposing layers of material in accordance with coordinates distributed along three axes (in three dimensions) following a digital file. This 3D file is cut into horizontal slices and sent to the 3D printer, allowing it to print one slice after another. The terminology “3D printing” brings together techniques that are very different from each other:

  • The deposition of molten wire or extrusion: a plastic wire is heated until it melts and deposited at points of interest, in successive layers, which are bound together by the plastic solidifying as it cools. This is the most common technique used by consumer printers.
  • The photopolymerization of the resin: a photosensitive resin is solidified with the help of a laser or a very concentrated light source, layer by layer. This is one of the techniques that allows a very high level of detail.
  • Sintering or powder fusion: a laser is used to agglomerate the powder particles with the energy it releases. This technique is used to produce metal or ceramic objects.

In the pharmaceutical industry, 3D printing is used in several ways, the main ones being :

  • The realization of medical devices, using the classic techniques of printing plastic or metallic compounds or more particular techniques allowing medical devices to acquire original properties, like the prostheses of the start-up Lattice Medical allowing adipose tissue to regenerate.
  • Bio-printing, allowing, by printing with human cells, to reconstitute organs such as skin or heart patches, like what is done by another French start-up: Poietis
  • Finally, and this is what will be discussed in this article, 3D printing also has a role to play in galenics by making it possible to print, from a mixture of excipient(s) and active substance(s), an orally administered drug.

What are the uses of 3D printing of medicines? 

3D printing brings an essential feature to drug manufacturing: flexibility. This flexibility is important for:

  • Manufacturing small clinical batches: clinical phases I and II often require small batches of experimental drugs for which 3D printing is useful: it is sometimes economically risky to make large investments in drug manufacturing at this stage. Moreover, it is often necessary to modify the active ingredient content of the drugs used, and 3D printing would enable these batches to be adapted in real time. Finally, 3D printing can also be useful for offering patients placebos that are as similar as possible to their usual treatments.
  • Advancing towards personalized medicine: 3D printing of drugs allows the creation of “à la carte” drugs by mixing several active ingredients with different contents for each patient. In the case of patients whose weight and absorption capacities vary over time (children or the elderly who are malnourished, for example), 3D printing could also adapt their treatments in real time according to changes in their weight, particularly in terms of dosage and speed of dissolution.

To address these issues, most major pharmaceutical companies are increasingly interested in 3D printing of drugs. They are investing massively in this field or setting up partnerships, like Merck, which is cooperating with the company AMCM in order to set up a printing system that complies with good manufacturing practices. The implementation of this solution has the potential to disrupt the traditional manufacturing scheme, as illustrated in the diagram below.

Figure 1 – Modification of the manufacturing steps of a tablet by implementing 3D printing (Source : Merck)

Regulation

The first commercialized 3D printed drug was approved by the FDA in 2015. Its active ingredient is levetiracetam. The goal of using 3D printing for this drug was to achieve a more porous tablet that dissolves more easily and is more suitable for patients with swallowing disorders. Despite these initial approvals and market accesses, the regulatory environment has yet to be built, as it is still necessary to assess the changes in best practices that 3D printing technology may impose and determine what types of tests and controls should be implemented. Destructive quality controls are not particularly well suited to the small batches produced by the 3D printer technique. To our knowledge, there are currently no GMP-approved 3D printers for the manufacture of drugs.

Will the future of drug 3D printing involve artificial intelligence? 

A growing number of authors believe that 3D printing of drugs will only be able to move out of the laboratory and become a mainstream technology in industry if artificial intelligence is integrated. Indeed, as things stand at present, because of the great flexibility mentioned above, the use of 3D printing requires a long iterative phase: it is necessary to test thousands of factors concerning in particular the excipients used, but also the parameters of the printer and the printing technique to be selected. The choice of these different factors is currently made by the galenics team according to its objectives and constraints: what is the best combination of factors to meet a given pharmacokinetic criterion? Which ones allow to minimize the production costs? Which ones allow to respect a possible regulatory framework? Which ones allow for rapid production? This iterative phase is extremely time-consuming and capital-intensive, which contributes to making 3D printing of drugs incompatible with the imperatives of pharmaceutical development for the moment. Artificial Intelligence seems to be the easiest way to overcome this challenge and to make the multidimensional choice of parameters to be implemented according to the objectives “evidence-based”. Artificial Intelligence could also be involved in the quality control of the batches thus manufactured.

The use of Artificial Intelligence to design new drugs opens up the prospect of new technical challenges, particularly with regard to the availability of the data required for these Machine Learning models, which are often kept secret by pharmaceutical laboratories.  We can imagine that databases can be built by text-mining scientific articles and patents dealing with different galenic forms and different types of excipients and then completed experimentally, which will require a significant amount of time. In addition to these technical challenges, it will also be necessary to ask more ethical questions, particularly with regard to the disruption of responsibilities caused by the implementation of these new technologies: who would be responsible in the event of a non-compliant batch being released? The manufacturer of the 3D printer? The developer of the algorithm that designed the drug? The developer of the algorithm that validated the quality control? Or the pharmacist in charge of the laboratory?

All in all, we can conclude that 3D printing of medicines is a technology that is already well mastered, whose market is growing by 7% each year to reach a projected market of 440 million dollars in 2025, but whose usefulness is so far limited to certain cases of use, but which could tomorrow, due to the unlocking of its potential through the combination of Artificial Intelligence, allow us to achieve a fully automated and optimized galenic development and manufacturing of oral forms, finally adapted to the ultra-customized medicine that is coming.

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To go further:

  • Moe Elbadawi, Laura E. McCoubrey, Francesca K.H. Gavins, Jun J. Ong, Alvaro Goyanes, Simon Gaisford, and Abdul W. Basit ; Disrupting 3D Printing of medicines with machine learning ; Trends in Pharmacological Sciences, September 2021, Vol 42, No.9
  • Moe Elbadawi, Brais Muñiz Castro, Francesca K H Gavins, Jun Jie Ong, Simon Gaisford, Gilberto Pérez , Abdul W Basit , Pedro Cabalar , Alvaro Goyanes ; M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines ; Int J Pharm. 2020 Nov 30;590:119837
  • Brais Muñiz Castro, Moe Elbadawi, Jun Jie Ong, Thomas Pollard, Zhe Song, Simon Gaisford, Gilberto Pérez, Abdul W Basit, Pedro Cabalar, Alvaro Goyanes ; Machine learning predicts 3D printing performance of over 900 drug delivery systems ; J Control Release. 2021 Sep 10;337:530-545. doi: 10.1016/j.jconrel.2021.07.046
  • Les médicaments imprimés en 3D sont-ils l’avenir de la médecine personnalisée ? ; 3D Natives, le média de l’impression 3D ; https://www.3dnatives.com/medicaments-imprimes-en-3d-14052020/#!
  • Les médicaments de demain seront-ils imprimés en 3D ? ; Le mag’ Lab santé Sanofi ; https://www.sanofi.fr/fr/labsante/les-medicaments-de-demain-seront-ils-imprimes-en-3D
  • Press Releases – Merck and AMCM / EOS Cooperate in 3D Printing of Tablets ; https://www.merckgroup.com/en/news/3d-printing-of-tablets-27-02-2020.html

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Why are we still conducting meta-analyses by hand?

« It is necessary, while formulating the problems of which in our further advance we are to find solutions, to call into council the views of those of our predecessors who have declared an opinion on the subject, in order that we may profit by whatever is sound in their suggestions and avoid their errors. »

Aristotle, De anima, Book 1, Chapter 2

Systematic literature reviews and meta-analyses are essential tools for synthesizing existing knowledge and generating new scientific knowledge. Their use in the pharmaceutical industry is varied and will continue to diversify. However, they are particularly limited by the lack of scalability of their current methodologies, which are extremely time-consuming and prohibitively expensive. At a time when scientific articles are available in digital format and when Natural Language Processing algorithms make it possible to automate the reading of texts, should we not invent meta-analyses 2.0? Are meta-analyses boosted by artificial intelligence, faster and cheaper, allowing more data to be exploited, in a more qualitative way and for different purposes, an achievable goal in the short term or an unrealistic dream?

Meta-analysis: methods and presentation

A meta-analysis is basically a statistical analysis that combines the results of many studies. Meta-analysis, when done properly, is the gold standard for generating scientific and clinical evidence, as the aggregation of samples and information provides significant statistical power. However, the way in which the meta-analysis is carried out can profoundly affect the results obtained.

Conducting a meta-analysis therefore follows a very precise methodology consisting of different stages:

  • Firstly, a search protocol will be established in order to determine the question to be answered by the study and the inclusion and exclusion criteria for the articles to be selected. It is also at this stage of the project that the search algorithm is determined and tested.
  • In a second step, the search is carried out using the search algorithm on article databases. The results are exported.
  • Articles are selected on the basis of titles and abstracts. The reasons for exclusion of an article are mentioned and will be recorded in the final report of the meta-analysis.
  • The validity of the selected studies is then assessed on the basis of the characteristics of the subjects, the diagnosis, and the treatment.
  • The various biases are controlled for in order to avoid selection bias, data extraction bias, conflict of interest bias and funding source bias.
  • A homogeneity test will be performed to ensure that the variable being evaluated is the same for each study. It will also be necessary to check that the data collection characteristics of the clinical studies are similar.
  • A statistical analysis as well as a sensitivity analysis are conducted.
  • Finally, the results are presented from a quantitative and/or non-quantitative perspective in a meta-analysis report or publication. The conclusions are discussed.

The systematic literature review (SLR), unlike the meta-analysis, with which it shares a certain number of methodological steps, does not have a quantitative dimension but aims solely to organize and describe a field of knowledge precisely.

The scalability problem of a powerful tool

The scalability problem is simple to put into equation and will only get worse over time: the increase in the volume of data generated by clinical trials to be processed in literature reviews is exponential while the methods used for extracting and processing these data have evolved little and remain essentially manual. The intellectual limits of humans are what they are, and humans cannot disrupt themselves.

As mentioned in the introduction to this article, meta-analyses are relatively costly in terms of human time. It is estimated that a minimum of 1000 hours of highly qualified human labor are required for a simple literature review and that 67 weeks are needed between the start of the work and its publication. Thus, meta-analyses are tools with a high degree of inertia and their temporality is not currently adapted to certain uses, such as strategic decision-making, which sometimes requires certain data to be available quickly. Publications illustrate the completion of full literature reviews in 2 weeks and 60 working hours using automation tools using artificial intelligence.

“Time is money”, they say. Academics have calculated that, on average, each meta-analysis costs about $141,000. The team also determined that the 10 largest pharmaceutical companies each spend about $19 million per year on meta-analyses. While this may not seem like a lot of money compared to the various other expenses of generating clinical evidence, it is not insignificant and it is conceivable that a lower cost could allow more meta-analyses to be conducted, which would in turn explore the possibility of conducting meta-analyses of pre-clinical data and potentially reduce the failure rate of clinical trials – currently 90% of compounds entering clinical trials fail to demonstrate sufficient efficacy and safety to reach the market.

Reducing the problem of scalability in the methodology of literature reviews and meta-analyses would make it easier to work with data from pre-clinical trials. These data present a certain number of specificities that make their use in systematic literature reviews and meta-analyses more complex: the volumes of data are extremely large and evolve particularly rapidly, the designs of pre-clinical studies as well as the form of reports and articles are very variable and make the analyses and the evaluation of the quality of the studies particularly complex. However, systematic literature reviews and other meta-analyses of pre-clinical data have different uses: they can identify gaps in knowledge and guide future research, inform the choice of a study design, a model, an endpoint or the relevance or not of starting a clinical trial. Different methodologies for exploiting preclinical data have been developed by academic groups and each of them relies heavily on automation techniques involving text-mining and artificial intelligence in general.

Another recurring problem with meta-analyses is that they are conducted at a point in time and can become obsolete very quickly after publication, when new data have been published and new clinical trials completed. So much time and energy is spent, in some cases after only a few months or weeks, to present inaccurate or partially false conclusions. We can imagine that the automated performance of meta-analyses would allow their results to be updated in real time.

Finally, we can think that the automation of meta-analyses would contribute to a more uniform assessment of the quality of the clinical studies included in the analyses. Indeed, many publications show that the quality of the selected studies, as well as the biases that may affect them, are rarely evaluated and that when they are, it is done according to various scores that take few parameters into account – for example, the Jadad Score only takes into account 3 methodological characteristics – and this is quite normal: the collection of information, even when it is not numerous, requires additional data extraction and processing efforts.

Given these scalability problems, what are the existing or possible solutions?

Many tools already developed

The automation of the various stages of meta-analyses is a field of research for many academic groups and some tools have been developed. Without taking any offence to these tools, some examples of which are given below, it is questionable why they are not currently used more widely. Is the market not maturing enough? Are the tools, which are very fragmented in their value proposition, not suitable for carrying out a complete meta-analysis? Do these tools, developed by research laboratories, have sufficient marketing? Do they have sufficiently user-friendly interfaces?

As mentioned above, most of the tools and prototypes developed focus on a specific task in the meta-analysis methodology. Examples include Abstrackr, which specialises in article screening, ExaCT, which focuses on data extraction, and RobotReviewer, which is designed to automatically assess bias in reports of randomised controlled trials.

Conclusion: improvement through automation?

When we take into account the burgeoning field of academic exploration concerning automated meta-analysis as well as the various entrepreneurial initiatives in this field (we can mention in particular the very young start-up: Silvi.ai), we can only acquire the strong conviction that more and more, meta-analysis will become a task dedicated to robots and that the role of humans will be limited to defining the research protocol, assisted by software that will allow us to make the best possible choices in terms of scope and search algorithms. Thus, apart from the direct savings that will be made by automating meta-analyses, many indirect savings will be considered, particularly those that will be made possible by the best decisions that will be taken, such as whether or not to start a clinical trial. All in all, the automation of meta-analyses will contribute to more efficient and faster drug invention.

Resolving Pharma, whose project is to link reflection and action, will invest in the coming months in the concrete development of meta-analysis automation solutions.

Would you like to discuss the subject? Would you like to take part in writing articles for the Newsletter? Would you like to participate in an entrepreneurial project related to PharmaTech?

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To go further:
  • Marshall, I.J., Wallace, B.C. Toward systematic review automation: a practical guide to using machine learning tools in research synthesis. Syst Rev 8, 163 (2019). https://doi.org/10.1186/s13643-019-1074-9
  • Clark J, Glasziou P, Del Mar C, Bannach-Brown A, Stehlik P, Scott AM. A full systematic review was completed in 2 weeks using automation tools: a case study. J Clin Epidemiol. 2020 May;121:81-90. doi: 10.1016/j.jclinepi.2020.01.008. Epub 2020 Jan 28. PMID: 32004673.
  • Beller, E., Clark, J., Tsafnat, G. et al. Making progress with the automation of systematic reviews: principles of the International Collaboration for the Automation of Systematic Reviews (ICASR). Syst Rev 7, 77 (2018). https://doi.org/10.1186/s13643-018-0740-7
  • Lise Gauthier, L’élaboration d’une méta-analyse : un processus complexe ! ; Pharmactuel, Vol.35 NO5. (2002) ; https://pharmactuel.com/index.php/pharmactuel/article/view/431
  • Nadia Soliman, Andrew S.C. Rice, Jan Vollert ; A practical guide to preclinical systematic review and meta-analysis; Pain September 2020, volume 161, Number 9, http://dx.doi.org/10.1097/j.pain.0000000000001974
  • Matthew Michelson, Katja Reuter, The significant cost of systematic reviews and meta-analyses: A call for greater involvement of machine learning to assess the promise of clinical trials, Contemporary Clinical Trials Communications, Volume 16, 2019, 100443, ISSN 2451-8654, https://doi.org/10.1016/j.conctc.2019.100443
  • Vance W. Berger, Sunny Y. Alperson, A general framework for the evaluation of clinical trial quality; Rev Recent Clin Trials. 2009 May ; 4(2): 79–88.
  • A start-up specializing in meta-analysis enhanced by Artificial Intelligence: https://www.silvi.ai/
  • And finally, the absolute bible of meta-analysis: The handbook of research synthesis and meta-analysis, Harris Cooper, Larry V. Hedges et Jefferey C. Valentine

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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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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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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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