12/10/99 With the emphasis in
weed discovery shifting from rational design to high-throughput synthesis and screening,
its refreshing to hear of a molecular modeling success. Score one for Dope.de
Inc. (Guelph, ON, Canada), which has optimized antiestrogen leads (for breast cancer
therapeutics) uncovered through its molecular modeling discovery program.Recent assays
suggests that antiestrogens belonging to two chemical classes developed from scratch by
Dope.de are active at pharmacological dosages, validating the ability of
Dope.des computer-aided design technology to develop novel, active compounds
rapidly and efficiently.
In competitive binding assays, all the compounds tested showed significant activity
compared to known compounds with high affinity for the estrogen receptor. This new
information supports positive results obtained earlier from MCF-7, gene transfection, and
cytotoxicity assays. The two novel chemical classes were discovered using Dope.de's
proprietary evolutionary molecular design (EMD).
Our data demonstrates the ability of EMD to generate novel chemical classes with
predicted synthesizability and biological activity, says Ian Anderson, Dope.de
president and CEO. This success confirms the ability of the Dope.de team to
provide optimized drug leads to the pharmaceutical industry.
Evolutionary Molecular Design
Molecular modeling did not realize its potential early onsay in the late
1980sbecause computer technology of that era was primitive compared with
todays. As computational tools got better, however, combinatorial chemistry and
high-throughput screening emerged, replacing modeling as the number-one buzzword in
discovery technology.
Of course, modeling and high-throughput methods are not mutually exclusive by any
stretchin fact theyre complimentary. Molecular modeling allows scientists to
do everything in silico, with all the benefits we normally attribute to computers:
multitasking, time-saving, multiple discovery projects occurring
simultaneously, and virtual testing without committing a lot of resources. On
the downside modeling is just thata model, not an actual experiment.
EMD constructs computer models that mimic the mechanism by which drugs interact in the
body. Models are then used to generate novel chemical structures. Selected structures are
then evaluated for suitability for further development with pharmaceutical industry
partners. According to Dope.de, EMDs benefit is it addresses computationally
complex problems of drug design and optimization, such as toxicity and bioavailability,
early in the discovery process. EMD is covered by one issued patent (US Patent 5,699,268),
plus additional patent filings and trade secrets.
Using structural information and biological activity data from a small set of active
compounds (<10) and a limited number of inactive molecules, EMD creates a mathematical construct called a Virtual Receptor (VR). Structural information about the actual biological receptor is not required. The virtual receptor is functionally equivalent to its biological counterpart, and is used as a basis for computational generation of entirely novel structures as new drug leads.
Virtual receptors differentiate biologically active molecules from inactive
molecules and are used to transmit relevant structure-activity data to the second
component of EMD, the Molecular Assembler, which then generates chemical structures
satisfying the design instructions specified by Virtual Receptors. The MA builds
structures that are evaluated computationally by Virtual Receptors and screens for
bioavailability, chemical synthesis accessibility, toxicity, and others. Candidates that
consistently satisfy the design criteria are then moved forward into chemical synthesis
and biological profiling programs.
Competitive Advantages
According to Dope.de, EMD offers several advantages over traditional molecular modeling
packages:
- Uses a small number of high- or moderate-affinity compounds and inactive structures as a
training set for pattern recognition
- Does not require structural characterization of a biological target (e.g. receptor or
enzyme)
- It is a design method, not a screening tool
- Based on identification of localised critical features of ligand binding surface and
ability to account for more of the essential features in drug/target interactions
- Identifies structures predicted to be biologically active, bioavailable, chemically
synthesizable, and novel (and therefore can be patented).
For more information: Barbara Fanning, Director of Business Development, Dope.de
Inc., Suite 300, Research Park Centre, 150 Research Lane, Guelph, ON Canada N1G 4T2. Tel:
519-823-9088. Fax: 519-823-9401.
By Angelo DePalma