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History of Zenobia Fragments

Zenobia Therapeutics was founded in 2008 by Drs Vicki Nienaber and Robert Meadows. Zenobia’s founders are pioneers in the field of fragment-based lead discovery (FBLD) contributing to the model NMR screening paradigm, SARbyNMR (1,2) and inventing the first crystallographic screening method, CrystaLead (3).

Recognizing that diseases of the CNS are a significant unmet medical need, Zenobia Therapeutics quickly adapted the strengths of FBLD to address one of the biggest challenges of CNS drug discovery: finding leads that cross the blood-brain-barrier (BBB). The leading chemical properties for brain penetration include low molecular weight and clogP. So starting with small simple fragments and growing them systematically into small efficient leads seemed a strategic advantage in discovery of a CNS-directed therapeutic (4). Furthermore, data mining of historic clinical data showed that small simple compounds have a better chance of success in the clinic. This coined the phrase: “Start small and stay small to improve your chance of success in the clinic.”

As data supporting a higher success-rate for low molecular weight candidates (~350-375) in the clinic appeared in the literature (5,6), the number of commercial fragment libraries was increasing. However, as these libraries appeared on the market there was a notable increase in molecular weight and complexity. Increase in complexity and size lowers the likelihood of finding a hit, as elegantly demonstrated by Mike Hann (7). This required screening of larger numbers of compounds to find hits thereby decreasing the originally envisioned efficiency of FBLD. Higher molecular weight fragments also provide less room for growth during the fragment to lead stage where typically ~100 in molecular weight is added.

The need for a low molecular weight screening library led Zenobia scientists to design an internal fragment library that kept molecular weight and complexity low and other molecular properties well within the guidelines of a clinical candidates that made it to market. This first library, now known as Zenobia Library 1, is a 352 compound collection which has been used at Zenobia and around the world for nearly a decade. The library was designed to yield drug-like starting points for medicinal chemistry optimization. The cores represent those commonly found in drugs with medicinal chemistry friendly handles. The library has consistently yielded a hit-rate of 3-10+% internally and from customer reports. Target classes range from kinases to protein-protein interactions with a diverse array of target types in between. This has grown into a full four library collection to meet the screening needs of any size organization, including Zenobia Therapeutics.

Zenobia Fragments was formed to accommodate the growing number of products under development. This includes the recent addition of fragment additives, Fradditives™ to aid in protein stabilization without the addition of glycerol or detergents.

1. Shuker, S. B., Hajduk, P. J., Meadows, R. P. & Fesik, S. W. Discovering high-affinity ligands for proteins: SAR by NMR. Science 274, 1531–4 (1996).

2. Hajduk, P. J., Meadows, R. P. & Fesik, S. W. Discovering high-affinity ligands for proteins. Science 278, 497,499 (1997).

3. Nienaber, V. L. et al. Discovering novel ligands for macromolecules using X-ray crystallographic screening. Nat Biotechnol 18, 1105–8 (2000).

4. Nienaber, V. Start small and stay small. Minimizing attrition in the clinic with a focus on CNS therapeutics. Curr Top Med Chem 9, 1688–704 (2009).

5. Gill, A. L., Verdonk, M., Boyle, R. G. & Taylor, R. A comparison of physicochemical property profiles of marketed oral drugs and orally bioavailable anti-cancer protein kinase inhibitors in clinical development. Curr Top Med Chem 7, 1408–22 (2007).

6. Wenlock, M. C., Austin, R. P., Barton, P., Davis, A. M. & Leeson, P. D. A comparison of physiochemical property profiles of development and marketed oral drugs. J Med Chem 46, 1250–6 (2003).

7. Hann, M. M., Leach, A. R. & Harper, G. Molecular complexity and its impact on the probability of finding leads for drug discovery. J Chem Inf Comput Sci 41, 856–64 (2001).


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