The year ends, and with it the
awkward teenage phase of the twenty-first century. As we have done since 2012,
we're using this last post of the year to highlight conferences and reviews
over the previous twelve months.
There were some good events, including
CHI’s Fourteenth Annual Fragment-based Drug Discovery meeting in San Diego in
April, their Discovery on Target meeting in Boston in September, and the third
Fragment-based Drug Design Down Under 2019 in Melbourne in November, which also
saw the launch of the Centre for Fragment-Based Design. Our updated schedule of
2020 events will publish next week.
Turning to FBLD reviews, Martin
Empting (Helmholtz-Institute for Pharmaceutical Research Saarland) and collaborators
published a general overview in Molecules. This is a nice up-to-date
summary, covering library design, methods to find, confirm, and rank fragments,
and optimization approaches. It’s also open access so you can read it anywhere.
Targets
Protein-protein interactions can
be particularly challenging drug targets, and these are covered in a Eur. J.
Med. Chem. review by Dimitrios Tzalis (Taros Chemicals), Christian Ottmann
(Technische Universiteit Eindhoven) and colleagues. The focus is on clinical compounds,
and several of these – including venetoclax, ASTX660, mivebresib, onalespib –
are discussed in detail. The article is particularly useful in discussing
late-stage optimization of pharmacokinetic and pharmacodynamic properties. It
also provides a nice summary of physicochemical properties for fragment hits
and derived candidates.
Target selectivity is always
important, and this is the focus of a review in Exp. Opin. Drug Disc. by
Rainer Riedl and collaborators at the Zurich University of Applied Sciences and
the Università degli Studi dell’Insubria. Although the broader topic is de novo
drug design, fragment-based methods are prominent, and include case studies we’ve
discussed on nNOS, pantothenate synthetase, and MMP-13.
In terms of specific targets,
Fubao Huang, Kai Wang, and Jianhua Shen at the Shanghai Institute of Materia Medica
provide an extensive review of lipoprotein-associated phospholipase A2 (Lp-PLA2)
in Med. Res. Rev. This serine hydrolase has been studied for four decades
but – as the researchers note – “divergence seems to be ubiquitous among Lp-PLA2
studies.” At least this is not for lack of good chemical tools, fragment-derived
(see here, here, and here) and otherwise.
Methods
Although NMR has fallen behind crystallography
in our latest poll, that is certainly not reflected in terms of reviews. In particular,
19F NMR is covered in three papers. CongBao Kang (A*STAR) manages to
pack a lot (including 261 references!) into a concise review in Curr. Med.
Chem. Topics include protein-observed 19F NMR, in which one or
more fluorine atoms are introduced into a protein genetically, enzymatically,
or chemically, as well as ligand-observed methods, in which fluorine-containing
small molecules are directly observed or used as probes that are displaced by
non-fluorine-containing molecules.
Protein-observed 19F
NMR (PrOF NMR) is covered in Acc. Chem. Res. by William
Pomerantz and colleagues at the University of Minnesota. Although the first
example was published 45 years ago, only in the past few years has the
technique been used for studying protein-ligand interactions. The researchers
note that introducing fluorines into aromatic residues is ideal because they
are relatively rare, simplifying interpretation, and overrepresented at
protein-protein interactions, maximizing utility. Several case studies are
described, and even proteins as large as 180 kDa are amenable to the technique.
Ligand-based fluorine NMR
screening is simpler and more common than techniques that focus on proteins,
and this topic is thoroughly reviewed by Claudio Dalvit (Lavis) and Anna
Vulpetti (Novartis) in J. Med. Chem. After a section on theory, the
researchers discuss library design, including a long section on quality control
(which involves assessing solubility, purity, and aggregation
of the molecule in a SPAM filter). Direct and competition-based screening
approaches are covered in detail; for the latter, a new method for determining binding
constants is provided. The paper concludes with more than a dozen case studies.
Clearly much has changed in the ten years since I wondered “why
fluorine-labeled fragments are not used more widely.” This perspective is a definitive
guide to the topic.
Moving to less common methods for
characterizing fragments, György Ferenczy and György Keserű (Research Center
for Natural Sciences, Budapest) cover thermodynamic profiling in Expert
Opin. Drug Disc. After discussing several case studies, they conclude that “thermodynamic
quantities are not suitable endpoints for medicinal chemistry optimizations”
due to the complexity of contributing factors. This is consistent with another recent
paper on the subject (see here), though the information provided is still
interesting for understanding molecular interactions.
And although you might have
thought the 2017 VAPID publication was the last word on the limitations of ligand
efficiency (LE), Pete Kenny has published a splenetic jeremiad on the topic in J.
Cheminform. (see also his blog post on the topic, which includes a sea serpent).
This is largely a retread of a 2014 article on the same topic (reviewed by
Teddy in his inimitable manner here). Pete also describes a more complicated
alternative to LE involving residuals, though unfortunately he provides no
evidence that it provides more useful information. Pete is of course correct to
remind us that metrics have limitations, but assertions that LE “should not
even be considered to be a metric” are overwrought.
Chemistry
Two articles discuss virtual
chemical libraries. In J. Med. Chem., W. Patrick Walters (Relay
Therapeutics) describes efforts to measure, enumerate, and explore chemical
space. He notes that false positives could quickly overwhelm a virtual screen
of a hundred million molecules, but as we saw earlier this year, progress is
being made. Indeed, Torsten Hoffmann (Taros Chemicals) and Marcus Gastreich
(BioSolveIT) focus on navigating the vastness of chemical space in Drug
Disc. Today. They note that the Enamine REAL Space is up to 3.8 billion
commercially accessible compounds, more than double the number of stars in the
Milky Way. But this pales in comparison to the 1020 potential compounds
in Merck’s MASSIV space. Just storing the chemical structures of these in
compressed format would require 200,000 terabytes – and searching them exhaustively
is beyond current technology.
Ratmir Derda and Simon Ng (University
of Alberta) discuss “genetically encoded fragment-based discovery” in Curr.
Opin. Chem. Biol. This involves starting with a known fragment that is then
coupled to a library of peptides and screened to find tighter binders. The researchers
provide a number of case studies, though adding even a small peptide to a fragment
will generally have deleterious effects on ligand efficiency. And – Rybelsus
not withstanding – oral delivery of peptides is challenging.
Finally, Vasanthanathan
Poongavanam, Xinyong Liu, and Peng Zhang, and collaborators at Shandong
University, University of Bonn, University of Southern Denmark, and K.U. Leuven
review “recent strategic advances in medicinal chemistry” in J. Med. Chem.
Among a wide range of topics from drug repurposing to antibody-recruiting
molecules is a nice, up-to-date section on target-guided synthesis. As I opined
a couple years ago, I still doubt whether this will ever be generally
practical, but from an intellectual standpoint I’m happy to see work continue
on the approach.
And with that, Practical
Fragments says goodbye to the teens and wishes you all a happy new year. Thanks
for reading and commenting. May 2020 bring wisdom, and progress.
3 comments:
Happy New Year, Dan, and I’m greatly honored to have my LE perspective included in your review of fragment stuff from 2019. My motivation for writing it was that I actually think that ligand efficiency is a good concept (even though the commonly used metrics stink) that provides an alternative to descriptor-based multivariate modelling (some machine learning methods fit into this category) for taking account of physicochemical properties in drug design.
Given that LE metrics have been used to sell fragment-based approaches to the drug discovery community, I can certainly understand (and even sympathize with to some extent) the sensitivity of the FBDD establishment to criticism of ligand efficiency metrics. My reason for asserting that LE should not be described as a metric is that perception of efficiency changes with the units in which affinity is expressed. Given that metrics are used to effectively quantify perception, this would actually be a very serious criticism of any metric. I’m guessing that, if you were able to provide a coherent counter argument, you would have done so rather than simply dismissing the assertion as “overwrought”.
Hi Pete,
Happy new year to you too, and thanks for commenting!
Regarding a “coherent counter argument,” I think our differences are clearly expressed in the 2017 VAPID post and the comments to this 2015 post, in which you agreed that changing the definition of standard state can change the sign of ΔG˚. Despite all the tumult throughout the world, the laws of thermodynamics still seem to hold, so I did not feel the need to rehash them.
The argument that LE should not be considered to a metric actually differs from the criticism of LE on thermodynamic grounds. The essence of the thermodynamic argument is that if your conclusions change when you change the definition of the standard state then you’re not actually doing thermodynamics. The ‘not a metric’ argument is tied to units and would also apply if, for example, you were scaling the logarithm of an inactivation rate constant by number of non-hydrogen atoms. You can actually define standard states without units (e.g. in terms of mole fractions) but if you’re claiming to be doing thermodynamics then your conclusions cannot change with the definition of the standard state. Put more bluntly, to demonstrate that perception changes in response to a change in a unit or a change in the definition of the standard state is a most excellent bullshit detector.
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