I’m Erika Zehm, Director of Sales and Marketing for Zenobia Fragments but also a San Diego native. In honor of the 10th anniversary of the meeting and its return to San Diego, Zenobia Fragments, a San Diego based fragment library company, has put together the following tips and tricks to make the most out of your visit.
So, you are contemplating your newest great idea for a product and are seeking investment ideally through the SBIR program. To write a strong proposal, there are several key questions to consider beyond technical feasibility.
How have you validated your market? If you plan to partner with pharma, do you know that they will
buy it? Will Angels or Venture Capitalists invest in it? Do you know your Value Proposition? What is your Product-Market fit?
These questions may seem abstract at this point but think about it, do you really want to build something that no one will buy? Even if your goal is to cure a devastating disease, the reality is, unless you are independently wealthy, you will need cash and partners to progress through the clinic and to market. Once on the market, you will need doctors to prescribe your medicine and insurance companies to cover it.
Fortunately, NIH and NSF offer a program, I-Corps, to help you answer these questions! If you have been awarded a Phase I SBIR gran...
It’s that time of year again, we are back from summer vacation and thinking about submissions for the next NIH grant cycle. First up is the SBIR deadline on September 5th. Here at Zenobia, since we are getting ready to submit our own SBIR grant and have products and services to generate preliminary data, we want to share our experience and learnings with the NIH SBIR system. Our last grant was accepted on the first try which is very uncommon for NIH. We attribute that, in part, to learning from past mistakes and developing a grant submission process.
Over the years we have submitted several SBIR's and had very high success-rates with three NIH institutes and no success with a forth. This very different success-rate gives rise to our first suggestion. If you are having difficulties with one institute, you might consider repackaging your project and submitting to a different one! As we discuss below, learn about the institute, talk to program managers, understand your review pan...
In many cases, it is much more efficient to screen as mixtures because it pares down the dataset to a manageable range. One uses screening mixtures by soaking the crystal in a cocktail with multiple ligands. The secret to designing the proper mixture is to ensure that each ligand in the cocktail is differentiable from the other ligands, even at low resolutions. Express-Zen-Core288™ has a very high shape diversity, making it possible to group into shape diverse mixtures that can be distinguished at 2.7Å-3.0Å.
How Does Zenobia Make it's Mixtures?
Zenobia's mixtures for crystallographic screening contain up to 6 compounds that are highly shape diverse permitting identification of the hit from the shape of the electron density map (Nienaber et al., 2000). For those that are not limited by crystals or beamtime, mixtures of 3 or single compound screening kits are also available for crystallography. Sample mixtures of 6 are depicted below.
Crystallographic screening is a method used to detect ligands that bind to a target protein. What makes this metod special is that it also provides structural data about the binding location and interactions between the ligand and protein. The crystal itself has the protein molecules lined up in an ordered array with large solvent channels so that ligands can soak in and bind to sites on the crystallized protein. This method is exceptionally successful in detecting weakly binding ligands because the protein is highly concentrated in the crystal. Once the crystal has been soaked in the cocktail of ligands, diffraction data are collected and used to calculate an electron density map to determine if any of the ligands bound to the protein. If they do bind, one can tell by a visible positive electron density peak in a difference density map between the putative ligand protein complex and apoprotein. The ligand cocktails should be designed so that each lig...
PART 1:Fragments as a Pre-Screen to Assess Target Drugability
This is the first in a series of blogs discussing the concept of using fragment screening to assess target drugability (also termed ligandability in the literature). The idea of using a fragment pre-screen to assess the likelihood of identifying a compound for lead optimization is not a new one. Because simple fragments have a higher probability of binding to targets than more complicated ligands (Hann et al., 2001), they are an ideal test-case for the drugability of a target. Furthermore, because fragment-space (MW < 200) is estimated at 10 orders of magnitude less than drug-size space (MW < 450), a small fragment library will sample chemical space much more efficiently than a library of larger compounds (Edfeldt et al., 2011). Applying this calculation to Zenobia’s Express-Zen-Core-288 compound fragment screen, for example, indicates using it as a drugability pre-screen would be like sampling ~2.88 trillion drug...
This is the last in a three-part series discussing Chemical Diversity in fragment library design. In Part 1, we introduced the concept of diversity in the context of fragment screening libraries. In Part 2, we discussed different measures of diversity and the pros and cons of each in fragment library design.
We view diversity as a measure of the efficiency of the library (hits obtained per compound screen) and the ability to provide of a diverse range of starting points for synthesis. A diverse set of starting points mitigates risk during the optimization process due to scaffold-associated toxicity/pharmacokinetic issues or unforeseen patent conflicts. As such, we view diversity of the compound cores as a primary measure of diversity with slightly different design strategies used for each library.
We define diversity using an internally derived coefficient Zen-DC which is the number of cores in the library divided by the number of unique core...
PART 2:What are the parameters for evaluating diversity?
This is the second in a three-part series discussing chemical diversity. In part 1, we introduced the concept of diversity in the context of fragment screening libraries. In this part, we will discuss common measures of diversity and the pros and cons of each in fragment library design. We view diversity as a measure of the efficiency of the library (hits obtained per compound screen) and the ability to provide of a broad range of starting points for synthesis. A diverse set of starting points mitigates risks during the optimization process that may arise from scaffold-associated toxicity/pharmacokinetic issues or unforeseen patent conflicts.
Chemical property diversity
Chemical property range is a common measure of fragment library diversity and has its historical context in Lipinski’s original Rule of Five and the more recent “Rule of Three” refinement for fragments. This measure includes the distribution of a range of properti...
This is the first in a three-part series discussing chemical diversity.
In Part 1, we introduce the concept of diversity in the context of fragment screening libraries.
How does one understand Chemical Diversity?
Merriam-Webster dictionary defines diversity as: “an instance of being composed of differing elements or qualities".
In the context of screening libraries, we refer to chemical diversity as the diversity of the chemical composition for a set of compounds. However, the approaches and criteria for chemical diversity can be as diverse as the libraries themselves!
Interestingly, despite the strong emphasis placed on library diversity, there is actually no correlation between chemical properties and biological activity reported in the literature (1).
Why is diversity a defining characteristic of chemical libraries?
From our point of view, diversity is directly related to the efficiency of the library. This includes coverage of chemical s...
Over the past decade, a number of rules and calculated metrics have arisen in drug
discovery. In particular, the Rule of Three (RO3) is most commonly used as a filter to generate a "fragment library" from a larger compound collection.
Rules evolve over time to reflect data and experiences
Issues can arise from universally applying rules to all situations
Emerging data has shown that a simple filter of compound libraries to generate a "RO3 fragment library" while a good start, may not yield the most efficient and productive screening results and ultimately the most successful clinical candidate.
Based upon the following evidence, we are proposing a Guideline of Two for fragments (GoT Frags) rather than a Rule of Three:
Guideline 1: MW < 200-250
Emerging data since the RO3 was published shows that the average molecular weight of successful drug candidates is in the range of 350. Recall that lead optimization adds on average about 100 in molecular w...