Natural products data base for antimicrobial resistance solution

The goal of this project is to create data base using different computational tools and methods for combating antimicrobial resistance. To discover new scientific knowledge and specifically new bio-active molecules from different organism sources. Particular attention will be given to endemic organisms from Madagascar and Canada.

Eric Andrianasolo Ph.D. in Medicinal Chemistry - Natural Product Chemistry

College of Pharmacy, Oregon State University ,USA

Thesis: Structure elucidation of bioactive natural products from Madagascar marine algae and cyanobacteria

An increasing threat to global human health is antimicrobial resistance. Extensive antibiotic resistant strains are now being detected; the spread of these strains could greatly reduce medical treatment options available and increase deaths from   previously curable infections. Antibiotic resistance is widespread due in part to clinical overuse and misuse; however, the natural processes of horizontal gene transfer and mutation events that allow genetic exchange within microbial populations have been ongoing since ancient times. By their nature, for example aquaculture systems contain high numbers of diverse bacteria, which exist in combination with the current and past use of antibiotics, probiotics, prebiotics, and other treatment regimens—singularly or in combination.
These systems have been designated as “genetic hotspots” for gene transfer. As our reliance on natural sources grows, it is essential that we identify the sources and sinks of antimicrobial resistance, and monitor and analyze the transfer of antimicrobial resistance between the microbial community, the environment, and the farmed product, in order to better understand the implications to human and environmental health.

The new knowledge stored in the data base will be used by clinicians, experimental biologists, biopharmaceuticals and pharmaceutical company among others. One specific example is the application of the novel NMR-based machine learning tool “Small Molecule Accurate Recognition Technology” (SMART 2.0) for mixture analysis and subsequent accelerated discovery and characterization of new natural products. The concept was applied to the extract of a filamentous marine cyanobacterium known to be a prolific producer of cytotoxic natural products. This environmental Symploca extract was roughly fractionated, and then prioritized and guided by cancer cell cytotoxicity, NMR-based SMART 2.0, and MS2-based molecular networking. This led to the isolation and rapid identification of a new chimeric swinholide-like macrolide, symplocolide A, as well as the identification of swinholide A, samholides A−I, and several new derivatives. The planar structure of symplocolide A was confirmed to be a structural hybrid between swinholide A and luminaolide B by 1D/2D NMR and LC-MS2 analysis. A second example applies SMART 2.0 to the characterization of structurally novel cyclic peptides, and compares this approach to the recently appearing “atomic sort” method. This study exemplifies the revolutionary potential of combined traditional and deep learning-assisted analytical approaches to overcome longstanding challenges in natural products drug discovery.

The  solution will provide a public good in the form of new knowledge to tackle AMR, data base (CANADA NATURA MEDICA).

1- A non-for-profit organization that creates and manages the database CANADA MEDICA NATURA. To manage all data entry and output for potential users (students, researchers, entrepreneurs....). CANADA MEDICA NATURA will freely deliver any information on endemic plants from Canada, Madagascar... 

2-  A for-profit organization will be created in case by case basis at the other end of the process. For example, the creation of company that explores a business in manufacturing new antimicrobial agent from Canadian endemic plants or Madagascar endemic plants.

Market Analysis
A case by case analysis of the market will be on any possible good, medicines or pharmaceuticals from natural products. For example the Colchicine, The global colchicine market size was USD 1,232.00 Million in 2021 and is expected to register a robust Constant Annual Growth Rate of 8.4% during the forecast period. Key factors driving market revenue growth are increasing incidences of gout across the globe and increasing prevalence of other related diseases such as Familial Mediterranean Fever (FMF) and arthritis. Increase in clinical trials for Colchicine is setting a trend that researchers say is encouraging the development of new medicines that is expected to aid pharmaceutical companies and consumers. Colchicine in the treatment of disorders such as Behcet's Disease, Dermatitis Herpetiformis, and others has prompted scientists to seek out more therapeutic options. According to ClinicalTrials.gov, there are over 200 studies in the pipeline, with over 90 of them in Phases 3 and 4. Increase in the number of clinical trials for Colchicine has shown a positive impact on the discovery of therapeutic alternatives for a variety of conditions, including cardiovascular diseases, COVID-19 symptom treatment, and others.

Number of new knowledge stored in the data base.

New antimicrobial sources from natural products.

New technology that can identify fast new molecules stored in the data base...

Financial barrier, CANADA MEDICA NATURA will be designed as a relational database on an apache server. All data will be organized in a publicly available MySQL database as the back end, with a user-friendly web interface based on HTML, CSS, PHP and JavaScript programming languages as the front end. The estimated cost of the database creation is 60,000 USD.
This present project will have an estimated budget of 140,000 CAD including the database creation and human resources costs.
Technological aspects
Several computational tools are applied for natural product discovery for example the novel NMR-based machine learning tool “Small Molecule Accurate Recognition Technology” (SMART 2.0) for mixture analysis and subsequent accelerated discovery and characterization of new natural products. The concept was applied to the extract of a filamentous marine cyanobacterium known to be a prolific producer of cytotoxic natural products. This study exemplifies the revolutionary potential of combined traditional and deep learning-assisted analytical approaches to overcome longstanding challenges in natural products drug discovery.

The goal of the Trinity Challenge is in line of the project and can be helpful to us to overcome the financial barriers.

Pharmaceutical company