Leiden University Scholarly Publications

Persistent URL of this record https://hdl.handle.net/1887/71808

In Collections

This item can be found in the following collections:

Janssen, A.P.A. (2019)

Inhibitor selectivity: profiling and prediction

Doctoral Thesis
Less than 1 in 10 drug candidates that enter phase 1 clinical trials actually gets approved for human use. The high failure rate is in part due to unforeseen side effects or toxicity. A better understanding of the role of selectivity and a better insight in the off-target activities of drug candidates could greatly aid in preventing candidates to fail for these reasons. This thesis has tried to address some aspects in this challenging part of drug discovery. The use of activity-based protein profiling as presented in Chapters 2 and 3 in drug discovery and hit-to-lead optimization, and in Chapter 5 and 6 for the interaction profiling of a drug candidate, highlights the versatility and importance of this chemical biology technique. Combined with knowledge derived from biochemical assays, such as that developed in Chapter 4, ABPP can greatly aid the medicinal chemist. The recent surge in popularity of machine learning algorithms, backed by exponential growth of the amount of...Show moreLess than 1 in 10 drug candidates that enter phase 1 clinical trials actually gets approved for human use. The high failure rate is in part due to unforeseen side effects or toxicity. A better understanding of the role of selectivity and a better insight in the off-target activities of drug candidates could greatly aid in preventing candidates to fail for these reasons. This thesis has tried to address some aspects in this challenging part of drug discovery. The use of activity-based protein profiling as presented in Chapters 2 and 3 in drug discovery and hit-to-lead optimization, and in Chapter 5 and 6 for the interaction profiling of a drug candidate, highlights the versatility and importance of this chemical biology technique. Combined with knowledge derived from biochemical assays, such as that developed in Chapter 4, ABPP can greatly aid the medicinal chemist. The recent surge in popularity of machine learning algorithms, backed by exponential growth of the amount of biological data available, holds great promise for drug discovery. Chapters 7 and 8 showed the applicability of one such algorithm, which was able to quite reliably predict interaction profiles. The challenges in finding, determining and predicting selectivity are far from solved, but, by incrementally expanding our understanding of the binding of small molecules to their (off-)targets, truly selective inhibitors might at some point become a reality or their necessity might be mitigated.Show less
Activity-based protein profiling
Medicinal chemistry
Endocannabinoid system
Organic synthesis
Biochemistry
Serine hydrolases
All authors
Janssen, A.P.A.
Supervisor
Stelt, M. van der
Co-supervisor
Westen, G.J.P. van
Committee
Overkleeft, H.S.; Boeckel, C.A.A. van; Vlijmen, H. van; IJzerman, A.P.; Kaiser, M.; Dijkgraaf, I.
Qualification
Doctor (dr.)
Awarding Institution
Chemistry, Science, Leiden University
Date
2019-05-01
ISBN (print)
9789402814651