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Developing a drug today currently takes an estimated 12-15 years and costs over $800 million. Much of
this cost is attributed to drugs that have failed along the way. In fact, for every five drugs entering
phase II clinical trials, only one reaches approval, reflecting an 80% failure rate. In silico
technologies have the potential to reduce this failure rate and to shorten the development process by
years, getting new, better, and safer drugs to patients faster.
Entelos delivers in silico R&D using its proprietary PhysioLab technology, unique approach, and
breadth of experience working on problems specific to pharmaceutical R&D. Some of the company’s
achievements to date include:
- Target Identification, Prioritization, and Validation
PhysioLab models provide a common framework for visualizing and testing multiple targets based on the
ultimate criteria, i.e.>, how they affect human physiology and a patient’s disease state. Targets
are identified and evaluated by assessing what impact a change in known or hypothesized pathways has on
patient’s downstream clinical outcomes. In this way, targets with the greatest likelihood of success can
be rapidly identified and ranked.
- Lead Optimization and Candidate Selection
In vitro and animal data are often inaccurate indicators of how a compound will perform in humans.
Using PhysioLab technology provides insights into how species differences may produce unexpected response
to novel treatments in humans. By highlighting these physiological differences, researchers make more
informed decisions and develop more predictive in vitro and in vivo bioassays.
- Clinical Development
PhysioLab technology simulates clinical trials using virtual patients that represent real patient
populations. This approach has successfully identified optimal dose, dose frequency,
responder/non-responder populations, and surrogate markers. It also has been used to understand the
mechanism of action of drugs in development.
- Post-Marketing
The PhysioLab technology has been used to compare competing products, predict competitor clinical trial
results, as well as support new dosing schedules for marketed drugs. The technology has also been used to
assess in-licensing opportunities, and has important applications that could support other areas of
healthcare.
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