Medisyn’s platform enables discovery of new chemical entities and bioactives in new chemical classes not previously known to possess the desired properties. It is the extrapolation from known chemical space to novel chemical space (synthetic and natural) that is so powerful.
— David Land, President, Medisyn Technologies

Medisyn's proprietary technology, called Forward Engineering™, is a unique in silico prediction platform capable of quickly designing new or improved patentable molecules from pre-specified properties.   The core competency of our disruptive technology has the ability to identify novel lead candidates that are structurally very different from the starting point and that belong to diverse new chemical classes. The technology can identify molecules that possess desired properties (such as efficacy in treating a disease) and accurately assess the drug-like characteristics (e.g., safety, efficacy, ADME/Toxicity, potency) of novel compounds.   With this technology, Medisyn can do in weeks what traditionally takes years to accomplish.   Medisyn's unique methodology and these supporting data have been published in over 100 peer reviewed journal articles.

Medisyn's discovery process works by creating a "digital fingerprint" of therapeutic activity for a specific disease which is then used to search chemical libraries for compounds with similar or identical fingerprints.   Once a match has been made, the compounds are submitted for lab testing to confirm activity. Medisyn has identified potent compounds in as little as eight weeks and currently has 40 active projects in 22 therapeutic areas including cancer, AIDS and Alzheimer's.  

  • Discovered Over 100 New Compounds Identified in 40 different therapeutic classes
  • Predictive Technology up to 85% accuracy in bioactive compound selection 
  • Discovery of New Chemical Entities which Offer Broad Patent Protection 
  • Successfully Implemented Forward Engineering™ With Big Pharma, Biotech, Animal Health, and Nutraceutical Companies

Medisyn's core technology is based on application of mathematical topology to chemical characterization and prediction.   A training set of molecules is used to construct a model for the attributes of interest, and the model then predicts attributes of other chemical structures based on topological similarities.   The approach is distinct from, but complementary with, standard computational chemistry, structure-based drug design, and medicinal chemistry methods.   The key differentiating benefit is the technology's core ability to discover New Chemical Entities (NCEs). NCEs are drug leads in new chemical classes offering potentially greater disease treating benefits and "first-in-class" market opportunities. This is an extraordinarily difficult task with conventional approaches. The mathematics underlying Forward Engineering™, topology, is also the basis for string theory in physics, electrical engineering applications, and other fields.  

How it Works

Forward Engineering™ uses a mathematical discipline called Molecular Topology (MT) and has applied it to chemistry. MT is capable of generating an activity signature for a broad range of properties. The molecular topological signature that describes the biological properties in an active compound can be derived solely using MT. Topology is a large branch of mathematics which focuses on the interconnectivity of objects rather than the size or shape of the connected objects. MT is used to describe the topological signature of a molecule and directly relate this signature to a biological property. MT is a purely mathematical approach that is completely independent and not hierarchically behind quantum mechanics. The topological signature is abstract, and ultimately allows for identification of drug leads that are not recognizable analogs of known active compounds.

When applying MT, the topological signature is obtained by analyzing a biological property through a numerical description. Active compounds or a group of active compounds are used to create a training set which allows Forward Engineering™ to understand what these compounds have in common with each other.

Typically, training sets based on the desired activity profile are characterized in terms of (1) the number of atoms, (2) the number of connections for each atom, and (3) whether the atoms are connected to form a straight chain with branches, rings, or combinations thereof.

Once the training set has been created, the molecular signature of a compound is identified using indices. These indices are derived from procedures (algorithms) for converting the topological structures of a molecule into a single characteristic number. The results of this analysis are displayed in terms of a digital topological signature or a digital fingerprint that is captured in each in silico model. After determining the digital fingerprint for a group of active compounds, the digital fingerprint is stored in the in silico model and is compared to other digital fingerprints belonging to a chemical database of compounds, like the Available Chemical Database (ACD), and the Screening Chemical Database (SCD). The various databases used include natural and synthetic compounds as well as virtual compounds for de novo design. Based on this comparison, if two different compounds have the same digital fingerprint, both compounds would be expected to have the same pharmacological characteristics and the newly identified compound is submitted for in vitro testing.