Forward Engineering™


Forward Engineering™

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.

Competitive Advantage

Competitive Advantage

Forward Engineering™ is able to discover new chemical entities (NCEs) more quickly, at a lower cost, and with less risk than traditional discovery methods. 

Identifying the topological signature responsible for activity via MT based on a few (typically 25-30) structurally diverse compounds is analogous to determining what pieces fit the outer edge of a jigsaw puzzle. With these few compounds, the goal is not to try to populate the entire puzzle with ALL the pieces, just the outer edges. Hence, determining the topological signature and capturing the signature in the model "fills out" the outer edges of the puzzle. Then, "other pieces" can be identified to fill the interior of the puzzle.

Based on previous work performed by Medisyn, the "other pieces" typically include novel compounds in new chemical classes that have the same topological signature as the "pieces" used to form the outer edge. Along with natural and synthesized compounds, MT is able to screen billions of compounds efficiently from the vast domain of virtual space estimated to contain as many as 1063 virtual compounds, whereas high throughput screening is limited to approximately 25 million existing compounds. Furthermore, high throughput screening requires wet chemistry testing of 100,000 to several million compounds to get to useful qualified leads, while MT requires testing of only around 100 compounds to achieve the desired efficacy and ADME/TOX characteristics.   This overall efficiency allows MT to screen millions of molecules in the same amount of time it takes 3-D targeted design methodologies to analyze a few dozen.

This technology is a powerful tool in the discovery process as the training sets can be designed to identify molecules with a wide range of properties. For example, molecular topology has the proven ability to identify compounds based on smell, taste, and mutagenicity (tendency to cause genetic mutations).   Medisyn has developed a diverse range of ADME/Tox filters that are designed to screen compounds in parallel for safety and drug-like effectiveness prior to experimental testing. This unique capability helps mitigate the risk of late-stage failure of lead compounds often encountered during drug development. The design of NCEs can be undertaken using the activity profile captured by the predictive model to yield patentable novel compounds for further development. Thus, the core features of MT are speed, low cost, risk mitigation, and NCE discovery.  

While not every compound identified is necessarily biologically active, a validated model can achieve 50-85% predictive accuracy for useful active molecules (with multiple required properties), versus 0.1% of fuzzy hits with activity using High Throughput Screening techniques. Therefore, MT is not a "fishing expedition" but rather careful selection of "active" molecules based on the same topological signature. Consequently, Medisyn's Forward Engineering™ technology is uniquely equipped to deal with the challenges encountered during discovery of novel compounds.

Selected Publications

Selected Publications

Peer-reviewed publications on the use of Molecular Topology

Charge indexes. New topological descriptors
J. Gálvez, R. Garcia-Domenech, M.T. Salabert-Salvador, R. Soler, Charge indicies. New topological descriptors, J. Chem. Inf. Comput. Sci. 34 (1994) 520-525.

New topological descriptors, namely “charge indexes, are presented in this paper. Their ability for the description of the molecular charge distribution is established by correlating them with the dipole movement of a heterogeneous set to hydrocarbons, as well as with the boiling temperature of alkanes and alcohols and the vaporization enthalpy of alkanes. Moreover, it clearly demonstrated that this ability is higher than that shown by the χ connectivity and Weiner indexes.

On a topological interpretation of electronic and vibrational molecular energies
J. Gálvez, On a topological interpretation of electronic andvibrational molecular engergies, J. Mol. Struct. 429 (1998) 255-264.

Prediction of properties of chiral compounds by molecular topology
J.V. de Julián-Ortiz, C. de Gregorio Alapont, I. Ríos-Santamarina, R.Garcia-Domenech, J. Gálvez, Prediction of properties of chiralcompounds by molecular topology, J. Mol. Graphics and Modeling 16(1998) 14-18.

Departamento de Quimica Fisica, Facultad de Farmacia, Universitat de Valencia, Spain.

A common assumption in chemistry is that chiral behavior is associatedwith 3-D geometry. However, chiral information is related to symmetry,which allows the topological handling of chiral atoms by weightedgraphs and the calculation of new descriptors that give a weight to thecorresponding entry in the main diagonal of the topological matrix. Inthis study, it is demonstrated that, operating in this way, chiraltopological indices are obtained that can differentiate thepharmacological activity between pairs of enantiomers. The 50%inhibitory concentration (IC50) values of the D2 dopamine receptor andthe sigma receptor for a group of 3-hydroxy phenyl piperidines arespecifically predicted. Moreover, the sedative character of a group ofchiral barbiturates can be identified.

Articles on the use of Molecular Topology in Drug Discovery & Design

Topological approach to analgesia
J. Gálvez, R. Garcia-Domenech, J.V. de Julián-Ortiz, R. Soler, Topological approach to analgesia, J. Chem. Inf. Comput. Sci. 34 (1994) 1198-1203.

Department of Physical Chemistry, Faculty of Pharmacy, University of Valencia, Spain.

In this study we show that by making use of the molecular connectivity indicies, including a new index, which we denominate “δt ” and which is obtained from a linear combination of the indicies, as well as from an “ Ε” form index, it is possible to discriminate minor (nonnarcotic) analgesic character with great efficiency, as well as forseeing the analgesic potency of the analyzed compounds, which represents, without a doubt, a powerful tool for the rational design of new analgesic drugs. 

Topological approach to drug design
J. Gálvez, R. Garcia-Domenech, J.V. de Julián-Ortiz, R. Soler, Topological approach to drug design, J. Chem. Inf. Comput. Sci. 35 (1995) 272-284.
Department of Physical Chemistry, Faculty of Pharmacy, University of Valencia, Spain.

In this paper we demonstrated that by an adequate combination of different topological indices it is possible to select and design new active compounds in different therapeutical scopes, with a very high efficiency level. Particularly successful in the search of new "lead drugs", the results show the surprising ability of the topological methods to describe molecular structures. 

New cytostatic agents obtained by molecular topology
J. Gálvez, M.J. Gomez-Lechón, R. Garcia-Domenech, J.V. Castell, New cytostatic agents obtained by molecular topology, Bioorganic & Medicinal Chemistry Letters Vol. 6, No. 19 (1996) 2301-2306. 

Pharmacological distribution diagrams: A tool for de novo drug design
J. Gálvez, R. Garcia-Domenech, J.V. de Julián-Ortiz, L. Popa, Pharmacological distribution diagrams: a tool for de novo drug design, J. Mol. Graphics 14 (1996) 272-276.

Unidad de Investigacion de Diseno de Farmacos y Conectividad Molecular, Facultad de Farmacia, Universidad de Valencia, Burjassot, Spain.

Discriminant analysis applied to SAR studies using topological descriptors allows us to plot frequency distribution diagrams: a function of the number of drugs within an interval of values of discriminant function vs. these values. We make use of these representations, pharmacological distribution diagrams (PDDs), in structurally heterogeneous groups where generally they adopt skewed Gaussian shapes or present several maxima. The maxima afford intervals of discriminant function in which exists a good expectancy to find new active drugs. A set of beta-blockers with contrasted activity has been selected to test the ability of PDDs as a visualizing technique, for the identification of new beta-blocker active compounds.

Virtual combinatorial syntheses and computational screening of new potential anti-herpes compounds
J.V. de Julián-Ortiz, J. de Gálvez, C. Muñoz-Collado, R. Garcia-Domenech, C. Gimeno-Cardona, Virtual combinatorial syntheses and computational screening of new potential anti-herpes compounds, J. Med. Chem. 42 (1999) 3308-3314.

Unidad de Investigacion de Diseno de Farmacos y Conectividad Molecular, Facultat de Farmacia, and Departamento de Microbiologia, Hospital Clinico Universitario, Universitat de Valencia, Spain.

The activity of new anti-HSV-1 chemical structures, designed by virtual combinatorial chemical synthesis and selected by a computational screening, is determined by an in vitro assay. A virtual library of phenol esters and anilides was formed from two databases of building blocks: one with carbonyl fragments and the other containing both substituted phenoxy and phenylamino fragments. The library of virtually assembled compounds was computationally screened, and those compounds which were selected by our mathematical model as active ones were finally synthesized and tested. Our antiviral activity model is a "tandem" of four linear functions of topological graph-theoretical descriptors. A given chemical structure was selected as active if it satisfies every discriminant equation in that model. The final result was that five new structures were selected, synthesized, and tested: all of them demonstrated activity, and three showed appreciable anti-HSV-1 activity, with IC(50) values of 0.9 microM. The same model, applied to a database of known compounds, has identified the anti-herpes activity of the following compounds: 3,5-dimethyl-4-nitroisoxazole, nitrofurantoin, 1-(pyrrolidinocarbonylmethyl)piperazine, nebularine, cordycepin, adipic acid, thymidine, alpha-thymidine, inosine, 2, 4-diamino-6-(hydroxymethyl)pteridine, 7-(carboxymethoxy)-4-methylcoumarin, 5-methylcytidine, and others that showed less activity.

Prediction of quinolone activity against mycobacterium avium by molecular topology and virtual computational screening
R. Gozalbes, M. Brun-Pascaud, R. Garcia-Domenech, J. Gálvez, P.M. Girard, J.P. Doucet, F. Derouin, Prediction of quinolone activity against mycobacterium avium by molecular topology and virtual computational screening, Antimicrobial agents and chemotherapy Vol. 44 No. 10 (2000) 2764-2770.

Optimization of a mathematical topological pattern for the prediction of antihistaminic activity
M.J. Duart, R. Garcia-Domenech, G.M. Antón-Fos, J. Gálvez, Optimization of a mathematical topological pattern for the predication of antihistaminic activity, J. Mol. Graphics and Modeling 15 (2001) 561-572.

Predication of the chemiluminescent behavior of pharmaceuticals and pesticides
L. Lahuerta Zamora, Y. Fuster Mestre, M.J. Duart, G.M. Antón Fos, R. Garcia-Domenech, J. Gálvez Álvarez, J. Martínez Calatayud, Prediction of the chemiluminescent behavior of pharmaceuticals and pesticides, Anal. Chem. 73 (2001) 4301-4306.

General topological patterns of known drugs
J. Gálvez, J.V. de Julián-Ortiz, R. Garcia-Domenech, General topological patterns of known drugs, J. Mol. Graphics and Modeling 20 (2001) 84-94.

Search of a topological pattern to evaluate toxicity of heterogeneous compounds
R. Garcia-Domenech, J.V. de Julián-Ortiz, M. J. Duart, J.M. Garcia-Torrecillas, G.M. Anton-Fos, I. Ríos-Santamarina, C. de Gregorio Alapont, J. Gálvez, Search of a topological pattern to evaluate toxicity of heterogeneous compounds, SAR and QSAR in Environmental Research, 2001, Vol. 12, pp. 237-254

New agents active against Myobacterium avium complex selected by molecular topology: a virtual screening method
Angeles Garcia-Garcia, Jorge Galvez, Jesus-Vicente de Julian-Ortiz, R. Garcia-Domenech, Carlos Munoz, Remedios Guna, and Rafael Borras, New agents active against Myobacterium avium complex selected by molecular topology: a virtual screening method, Journal of Antimicrobal Chemotherapy (2004) 53, 65-73
New hypoglycaemic agents selected by molecular topology
C. Calabuig, G.M. Anton-Fos, J. Galvez, R. Garcia-Domenach, New hypoglycaemic agents selected by molecular topology, International journal of pharmaceutics 278 (2004) 111-118

Getting new bronchodilator compounds from molecular topology
Rios-Santamarina Inmaculada, Garcia-Domenech Ramon, Galvez Jorge, Morcillo Esteban Jesus, Santamaria Pedro, Cortijo Julio, Getting new bronchodilator compounds from molecular topology, European journal of pharmaceutical science (2004) 1120

Prediction of molecular volume and surface of alkanes by molecular topology
Jorge Galvez, rediction of molecular volume and surface of alkanes by molecular topology, J. Chem. Inf. Comput. Sci. 

Structural invariants for the prediction of relative toxicities of polychlorodibenzo-p-dioxins and dibenzofurans
J.M. Luco, J. Galvez, R. Garcia-Domenech, and J.V. de Julian-Ortiz, Structural invariants for the prediction of relative toxicities of polyshlorodibenzo-p-dioxins and dibenzofurans, Molecular Diversity, 8: 331-342, 2004. 

Computational methods in developing quantitative structure-activity relationships (QSAR): a review.
Dudek AZ, Arodz T, Gálvez J. Comb Chem High Throughput Screen. 2006 Mar;9(3):213-28.

Virtual filtering and screening of combinatorial libraries have recently gained attention as methods complementing the high-throughput screening and combinatorial chemistry. These chemoinformatic techniques rely heavily on quantitative structure-activity relationship (QSAR) analysis, a field with established methodology and successful history. In this review, we discuss the computational methods for building QSAR models. We start with outlining their usefulness in high-throughput screening and identifying the general scheme of a QSAR model. Following, we focus on the methodologies in constructing three main components of QSAR model, namely the methods for describing the molecular structure of compounds, for selection of informative descriptors and for activity prediction. We present both the well-established methods as well as techniques recently introduced into the QSAR domain. 

Publications Related to Medisyn Compound Discoveries

Novel Ras pathway inhibitor induces apoptosis and growth inhibition of K-ras-mutates cancer cells in vitro and in vivo.
Jasinski P, Zwolak P, Terai K, Dudek AZ. Transl Res. 2008 Nov;152(5):203-12. Epub 2008 Oct 11.
MT477 is a novel quinoline with potential activity in Ras-mutated cancers. In this study, MT477 preferentially inhibited the proliferation of K-ras-mutated human pulmonary (A549) and pancreatic (MiaPaCa-2) adenocarcinoma cell lines, compared with a non-Ras-mutated human lung squamous carcinoma cell line (H226) and normal human lung fibroblasts. MT477 treatment induced apoptosis in A549 cells and was associated with caspase-3 activation. MT477 also induced sub-G1 cell-cycle arrest in A549 cells. Although we found that MT477 partially inhibited protein kinase C (PKC), it inhibited Ras directly followed in time by inhibition of 2 Ras downstream molecules, Erk1/2 and Ral. MT477 also caused a reorganization of the actin cytoskeleton and formation of filopodias in A549 cells; this event may lead to decreased migration and invasion of tumor cells. In a xenograft mouse model, A549 tumor growth was inhibited significantly by MT477 at a dose of 1 mg/kg (P < 0.05 vs vehicle control). Taken together, these results support the conclusion that MT477 acts as a direct Ras inhibitor. This quinoline, therefore, could potentially be active in Ras-mutated cancers and could be developed extensively as an anticancer molecule with this in mind.

A novel quinoline, MT477: suppresses cell signaling through Ras molecular pathway, inhibits PKC activity, and demonstrates in vivo anti-tumor activity against human carcinoma cell lines.
Jasinski P, Welsh B, Galvez J, Land D, Zwolak P, Ghandi L, Terai K, Dudek AZ. Invest New Drugs. 2008 Jun;26(3):223-32. Epub 2007 Oct 24.
MT477 is a novel thiopyrano[2,3-c]quinoline that has been identified using molecular topology screening as a potential anticancer drug with a high activity against protein kinase C (PKC) isoforms. The objective of the present study was to determine the mechanism of action of MT477 and its activity against human cancer cell lines. MT477 interfered with PKC activity as well as phosphorylation of Ras and ERK1/2 in H226 human lung carcinoma cells. It also induced poly-caspase-dependent apoptosis. MT477 had a dose-dependent (0.006 to 0.2 mM) inhibitory effect on cellular proliferation of H226, MCF-7, U87, LNCaP, A431 and A549 cancer cell lines as determined by in vitro proliferation assays. Two murine xenograft models of human A431 and H226 lung carcinoma were used to evaluate tumor response to intraperitoneal administration of MT477 (33 microg/kg, 100 microg/kg, and 1 mg/kg). Tumor growth was inhibited by 24.5% in A431 and 43.67% in H226 xenografts following MT477 treatment, compared to vehicle controls (p < 0.05). In conclusion, our empirical findings are consistent with molecular modeling of MT477's activity against PKC. We also found, however, that its mechanism of action occurs through suppressing Ras signaling, indicating that its effects on apoptosis and tumor growth in vivo may be mediated by Ras as well as PKC. We propose, therefore, that MT477 warrants further development as an anticancer drug.