Month: January 2022

Categories
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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Towards virtual clinical trials?

Quentin Vicentini 16 January 2022
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
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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Categories

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Categories
Entrepreneurship Entrevues

Interview – Molecule, the start-up that wants to revolutionise the financing of drug development with the Blockchain

The Resolving Pharma team is pleased to inaugurate a series of interviews with start-ups creating the pharmaceutical world of tomorrow with this interview with Molecule, a young and ambitious German company willing to change the rules of Drug Development by using Blockchain technology in a new way.

We would like to thank the Molecule team for this exchange and especially Heinrich Tessendorf.

Some of the terms used in this interview are technical and very specific to the field of Blockchain, in order to facilitate the understanding of the Molecule.to project, a glossary has been added at the end of the interview. Do not hesitate to contact us if you have any questions or wish to discuss the subject. Have a good read!

This interview was conducted by Alexandre Demailly and Quentin Vicentini.

Resolving Pharma: With Molecule, you are trying to reinvent, among other things, the financing of pharmaceutical research and development. Can you explain how your platform works?

Molecule: Our platform is a marketplace that moves early-stage IP into web3 via NFTs. This is coupled with frameworks to build biotech DAOs and communities coming together to fund research in specific therapeutic areas. These communities consist of patients, researchers, and enthusiasts.

Practically, all of this comes together when researchers upload a project on our website. From here on forward, other researchers, investors or patient communities can discover these (and other) projects and decide where to invest. Once these role players have decided where to invest, they can connect their web3 wallet (e.g. Metamask) and fund the project by purchasing it as an IP-NFT. IP rights could immediately be transferred to the purchaser and funds could be transferred to the researcher at the exact same time.

Resolving Pharma: What are your company’s goals? What is your vision?

Molecule: Our vision is simple – we see patient, researcher, and investor communities forming to fund and govern end-to-end drug development. We enable this by making IP a highly liquid, data-driven asset class.

Over the next 2+ years, our goal is that our protocol will fund as much R&D as a mid-sized pharma company. With this, we’re ambitious to double our team, launch Molecule V2 and the Molecule DAO, see the first asset out licensed to Pharma, and realize the first patient-led use cases just to name a few.

Our hope is that decentralised biotech will do for access to therapies and medicine what FinTech and Decentralised finance did for how we manage and get access to financial services.

Resolving Pharma: How are submitted projects selected and evaluated? 

Molecule: Projects can be submitted on Molecule’s Discover App or VitaDAO’s Project Submission Form.

On Molecule’s Discover App, any researcher can upload their project and investors can discover them. Currently we have over 300 projects listed on this platform. We, as Molecule, don’t evaluate these projects – it’s up to investors to decide what projects they want to invest in. 

On VitaDAO’s Project Submission Form you can submit your longevity-focused project, but the concept is different in that there, you apply for funding for your project from VitaDAO. We do due diligence in ways similar to how the biopharmaceutical industry currently operates. Namely, they evaluate assets and research as a business opportunity where they’ll take into account market size, competition, team, etc. However, VitaDAO wants to pursue more high-risk and earlier stage projects than those in which traditional funding mechanisms show interest. Also, they want to focus on projects that promote longevity/healthspan/lifespan per se. This is notable because aging isn’t recognized as a disease by government agencies such as the FDA. Therefore, its market can’t be estimated traditionally. They accept this risk and have strategies including pursuing clinical trials in countries with favorable legal framework and/or countries willing to work together to design clinical trials with biomarkers that are relevant to longevity/healthspan/lifespan per se.

Projects submitted for funding through VitaDAO are evaluated by VitaDAO’s scientific evaluation board. They’ll then come up with a suggestion for or against funding. Evaluation is independent of the final decision for funding. If a project qualifies for funding it moves over to an on-chain funding proposal and VitaDAO token holders eventually vote for or against funding the project.

Resolving Pharma: How to invest in a research project using your platform?

Molecule: Currently, every investor needs to be a verified user on Molecule to invest in research projects. To enable you to directly invest in a research project, we need some information from the investor. Our platform is web3 enabled, so once investors have been whitelisted and selected a project they would like to fund it would be similar to how you would purchase an NFT on OpenSea.  Practically, the steps would look like this:

  1. Create an investor account on discover.molecule.to

  2. Explore research projects in your field of interest. If you want to get in touch with specific researchers that have no contact information listed, feel free to reach out to us via info@molecule.to

  3. Get whitelisted for IP-NFT sales: To participate in IP-NFT sales and make binding offers to researchers, Molecule needs to collect certain information from investors. This information will be used primarily to enable investors to sign the underlying legal agreements connected to IP-NFTs. To trigger the whitelisting process, please get in touch with info@molecule.to

  4. Bid on IP-NFTs: You are now ready to make offers for new research projects or existing IP-NFTs. We will keep you informed of new funding opportunities arising on Molecule Discovery. If you are interested in funding research projects which are not listed on Molecule yet, feel free to put the researcher in touch with the Molecule team.

  5. Transfer of funds and receiving the IP-NFT: After your bid has been accepted by a researcher, you will be asked to transfer the funds to an escrow account. As soon as the funds are received, the escrow contract will release the IP-NFT to the origin address of the funds.

  6. Manage your IP-NFT: After you have received the IP-NFT, you are now able to manage it on the molecule platform. View the IP-NFT, make selling offers, or review the underlying legal agreement and data (to be added) via the Molecule platform.

Resolving Pharma: How can individual investors choose between different projects?

Molecule: Individual investors will need to do their own due-diligence (DYOR) and consult a scientific advisor. Individuals will most likely choose projects that interest them personally, e.g. someone with a family member living with a certain disease. A lot of the information they require will be on the project page, but they can reach out to individual researchers through the project page on our Discovery app to ask further questions.

In the case where a DAO (e.g. VitaDAO) funds a project, the DAO has a group of subject matter experts (the scientific evaluation board) which advise the DAO on which projects to fund. The decision is then formalised by a governance proposal which is put up to a vote and the final decision is made by all token holders ($Vita in this case). Token holders then vote on these proposals through a simple yes or no vote.

Resolving Pharma: What are the advantages of decentralizing drug development?

Molecule: If IP is siloed and owned by individual companies, these companies could have a very strong bias towards only publishing positive data and this leads to information asymmetry. That’s not how science is supposed to be done. The research community could achieve desired outcomes much faster if research were done more openly and collaboratively. Learning can be done much faster and costs saved by reducing the duplication work through failed experiments. One thing which can help facilitate this is getting attention on research projects through a global public marketplace.

Resolving Pharma: How does your model differ from that offered by crowdfunding platforms?

Molecule: Molecule’s platform is different from crowdfunding, because novel approaches to democratised ownership mean stakeholders can directly co-own the therapies that affect them. Imagine a world where a new insulin treatment is collectively owned by diabetics – what would that do to access and pricing? What if patients could have a direct impact and say in the drugs developed for them? Communities help bring drugs to market through crowd intelligence and curation markets, not just funding, but co-owning.

Resolving Pharma: Can you explain the concept of IP-NFT? How is it secured from a legal point of view?

Molecule: The IP-NFT is a new NFT standard that we’ve developed. IP-NFTs represent the full legal intellectual property rights and provide data access to biopharma research. Think of the IP-NFT as a unique token on the Ethereum blockchain. This token will link to a legal agreement that the researcher will have concluded with investors. Through fractionalization, frictionless transfer, and collateralisation of IP in decentralised financial (DeFi) systems, it unlocks new value in biopharma IP. Fundamentally, IP-NFT enables funding, liquidity and valuation of the IP and research. 

From a legal perspective, the IP-NFT transacts real-world legal rights/licences of the IP. It does this by means of a legal contract and a smart contract that cross-references one another. The legal contract is an IP license with language referencing blockchain transactions, addresses, and signatures. The smart contract is an NFT with code referencing the IP licensing agreement, obfuscating certain data components and storing them on decentralised file storage networks. Combined, the legal contract and the smart contract create the IP-NFT. This gives secure access control to the IP and data to buyers and in the process speeds up due diligence and saves costs. You can learn more about the technical and legal setup of an IP-NFT in this Medium article.

Resolving Pharma: How are decisions made regarding the management of the project’s intellectual property? What is the role of the DAO?

Molecule: VitaDAO is governed by its members. All decisions undergo a pre-defined decision-making process that is inclusive and transparent to all members. Smaller decisions are made informally on VitaDAO’s Discourse forum or Discord, but can be escalated to require an on-chain vote where anyone who owns Vita tokens can vote. Decisions that are contested, have a notable impact on VitaDAO’s stakeholders, affect processes in a fundamental way, or involve a significant use of funds, always undergo an on-chain vote and require a relative majority of token holders to agree.

Resolving Pharma: In this regard, can you introduce us to VitaDAO? How could this project extend human life expectancy?

Molecule: VitaDAO is a decentralised organisation funding longevity research and governing biotech IP and data via IP-NFTs. Think about VitaDAO as the vehicle towards the democratization of access to therapeutics in the biotech world in order to make these assets widely accessible to people across the globe. 

Considering the project’s role in extending human life expectancy, VitaDAO funds early stage research, and could, for example, turn these research projects into biotech companies. As an example, the first project that VitaDAO funded is seeking to validate longevity observations through a series of wet lab experiments and if successful, this work could potentially result in the repurposing of several FDA-approved therapeutics to extend human lifespan, at a lower cost and over faster timelines than conceivably possible with de novo drug discovery.

Resolving Pharma: If our readers want to help you and participate in your projects, what can they do?

Molecule: The best way is to join our Discord, introduce yourself and talk to us there. You can also reach out to our community manager via email at heinrich@molecule.to  

If you wish to learn more about the project, you can refer to

  • The company’s website: https://www.molecule.to/
  • The company’s Medium blog: https://medium.com/molecule-blog
  • As well as the various talks and conferences given by Tyler and Paul, the two co-founders of Molecule: https ://youtube.com/playlist?list=PLeOXpfDM0Oy7aIg7wIfFRxiTADbBbUyLC

Glossary :

  • Web3: “Web3 refers to a third generation of the Internet where online services and platforms move to a model based on blockchains and cryptocurrencies. In theory, this means that infrastructures are decentralised and anyone who has a token associated with that infrastructure has some control over it. This model of the web represents a financialised vision of the internet.”
  • NFT for Non-Fungible Token: “An NFT refers to a digital file to which a digital certificate of authenticity has been attached. More precisely, the NFT is a cryptographic token stored on a blockchain. The digital file alone is fungible, whether it is a photo, video or other, the associated NFT is non-fungible.”
  • DAOs: “A DAO (Decentralized Autonomous Organization) is an entity powered by a computer program that provides automated governance rules to a community. The DAO is a complex, smart contract deployed on the Ethereum blockchain, similar to a decentralised venture capital fund. These rules are immutably and transparently written into a blockchain, a secure information storage and transmission technology that operates without a central controlling body. A DAO differs, in theory, from a traditional entity in three ways: it cannot be stopped or closed, no one or no organisation can control it (and thus manipulate its numbers) and, finally, everything is transparent and auditable, all within a supranational framework. A DAO is based on computer code: its operating rules are public and it is not based on any jurisdiction.
  • WhiteList: “The term whitelist defines, in the context of Blockchain projects, a set of people who are assigned a maximum level of freedom or trust in a particular system.
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Categories

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