From the laboratory to bedside

Drug Candidate

The path connecting discoveries made in basic research with medicines purchasable in a pharmacy is not only a lengthy one, but also one filled with significant hurdles. Researchers developped a new tool that helps identifiying new drug candidates faster.

Chemists, biologists and medical doctors working in basic research at the University of Zurich know really well that understanding the molecular mechanisms underlying diseases and designing compounds that effectively interfere with those do not necessarily translate into medicines impacting patients’ lifes. The last stage of drug development processes preceding commercialization are faced with large attrition rates due to the lack of efficacy in patients, unbearable toxicity or unexpected side effects. While these problems are difficult to foresee and to overcome within the basic research realm, increasing time pressure in drug-discovery campaigns, both in academia and in industry, demands new tools to improve the initial steps of drug development endeavors as much as possible to compensate for the more costly setbacks expected down the road.

In this context, the groups of Profs. Caflisch and Nevado focused their efforts in trying to streamline not only the identification of new hits (molecules capable of interacting with a desired protein target) but also the optimization of these hits into more potent lead compounds which have the potential to become preclinical drug candidates. In this context, it is important to be able to access, in a cost- and time-efficient manner, new areas of chemical space. The drug-like chemical space is estimated at 1060 organic molecules, but only 100 million have been synthesized to date, and an even smaller fraction thereof is commercially available. Available repositories are severely biased towards certain classes of targets. The exploration of chemical space that is not biased towards already investigated targets is decisive not only for the discovery of effective binders for novel protein classes but more importantly, for the development of compounds against protein targets that are hard-to-drug. In this work, the two groups have developed a computational method (AutoCouple) for designing new molecules by coupling easily accessible fragments. The new compounds suggested by the computer program are straightforward to synthesize enabling a faster optimization into drug candidates.

 

Further reading:
Laurent Batiste, Andrea Unzue, Aymeric Dolbois, Fabrice Hassler, Xuan Wang, Nicholas Deerain, Jian Zhu, Dimitrios Spiliotopoulos, Cristina Nevado and Amedeo Caflisch, Chemical Space Expansion of Bromodomain Ligands Guided by in Silico Virtual Couplings (AutoCouple), ACS Central Science,   https://pubs.acs.org/doi/10.1021/acscentsci.7b00401

 

Contact:
University of Zurich
Dept. of Chemistry
Prof. Cristina Nevado
Winterthurerstr. 190
8057 Zurich
Tel. +41 44 635 39 45
cristina.nevado@chem.uzh.ch

 

University of Zurich
Dept. of Biochemistry
Prof. Amedeo Caflisch
Winterthurerstr. 190
8057 Zurich
Tel. +41 44 635 55 21
caflisch@bioc.uzh.ch

 

 

Calista Fischer

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