Merck’s journey from flatland

Back in 2009 we highlighted a paper, “Escape from flatland,” suggesting that molecules with higher fractions of sp³-hybridized (ie, non-aromatic) carbons are more likely to become drugs. This hypothesis has been challenged on methodological grounds, and an analysis last year concluded that “Fsp³ may not have been a useful metric to optimize.” Now a new J. Med. Chem. paper by Olivia Garry and colleagues at Merck weighs in on the debate.

 

The researchers calculated Fsp³ (number of sp³-hybridized carbons divided by total carbons) for millions of small molecules (MW < 1200) synthesized at Merck from 2000 through 2024. Interestingly, mean Fsp³ values dropped from 0.34 to 0.31 between 2000 and 2006 or so, coinciding with the increasing popularity of metal-mediated cross coupling reactions during that period. Since then, Fsp³ has risen to 0.40 in 2024, which could be due to the focus on “escaping flatland” as well as the increasing availability of saturated building blocks and new synthetic methodologies.

 

Consistent with the original publication, the average Fsp³ increases as molecules progress through development, from 0.34 overall to 0.36 for those that go into rat pharmacokinetic studies and to 0.38 for preclinical candidates – close to the 0.39 found for drugs approved since 2009 according to the study last year.

 

A key correlation proposed in the original flatland publication was that solubility increased with Fsp³, and this was observed again here. Of 435,280 compounds having Fsp³ between 0.00-0.70 with measured solubility data, 55% of those with more aromatic character had low solubility, while only 15% of those with Fsp³ > 0.65 were poorly soluble. The correlations held for solubility at neutral pH as well as at pH 2. Molecules with lower Fsp³ also tended to have more aromatic rings, and this has previously been shown to lower solubility.

 

In contrast to solubility, lipophilicity (logD) did not correlate with Fsp³ among 503,780 compounds. Apparent permeability showed a complex and noisy correlation for 16,570 compounds, leading the researchers to conclude that “Fsp³ is not a good parameter to optimize P_app.”

 

Inhibition of three cytochrome P450 enzymes, CYP 3A4, 2C8, and 2C9, was examined for 51,760 compounds. Weak trends were observed for the latter two, with highly aromatic compounds being more likely to inhibit the enzymes, but no trend was seen for CYP 3A4.

 

Inhibition of human ether-a-go-go-related gene (hERG) is a major red flag for compound progression, and here there was a correlation, with higher Fsp³ compounds showing lower inhibition. This trend held for both aminergic and nonaminergic molecules, though as with solubility these trends could be driven by aromatic ring count, which has independently been correlated to hERG inhibition.

 

Since the 2009 flatland publication, many solvents have been spilled to replace phenyl rings with more shapely, non-aromatic moieties. The researchers looked at matched molecular pairs for internal compounds to see what effects these changes had on solubility. Simply saturating a phenyl to a cyclohexyl ring tended to decrease solubility, while the best solubility improvements resulted from cyclopropyl or isopropyl substitutions. But these swaps did little for shapeliness: of the eight substitutions for which sufficient data existed, cyclopropyl and isoprproyl had the lowest increase of average Fsp³. As the researchers note, “using Fsp³ without consideration of other properties is not a good strategy to optimize solubility.”

 

Overall this study suggests that leaving the comforts of flatland may be worthwhile. Molecules with higher Fsp³ values are sometimes more challenging to synthesize, but this itself can lead to new intellectual property space.

 

It’s worth emphasizing that the correlations between Fsp³ and solubility, CYP inhibition, and hERG inhibition are limited. In the end, the researchers recommend “considering Fsp³ in combination with other factors in medicinal chemistry optimization as its standalone effect on specific properties is modest.” Just as general risk factors won’t predict whether any given person will contract a disease, metrics won’t predict whether any given molecule will succeed (or fail) as a drug.