Multivalent fragments in the clinic: Muvalaplin

It’s been a couple years since Practical Fragments last updated our “fragments in the clinic” list. Before doing so it makes sense to highlight some of those we’ve missed. Let’s start with an open-access Nature paper from Laura Michael and collaborators at Lilly and Monash University published in 2024. Truth be told I’ve been waiting for a longer discovery paper, but I’ll go with what’s available now.

 

The researchers were interested in lipoprotein(a), or Lp(a), which has been linked to cardiovascular diseases. Lp(a) forms when low-density lipoprotein (LDL) binds to apolipoprotein(a), or apo(a). This is a two step process, in which the ten subtypes of so-called Kringle IV (K_IV) domains in apo(a) bind to lysine residues on LDL, followed by disulfide bond formation between apo(a) and LDL. Blocking the first step in this process should reduce levels of Lp(a).

 

Here's the only description of the initial screen: “Biochemical and biophysical compound screens using purified apo(a) K_IV7-8 protein identified interacting small molecules. Optimization of the initial binding molecules led to…LSN3353871.” Whatever the details, LSN3353871 is unequivocally a rule-of-three compliant fragment. It is also a very ligand-efficient binder with high nanomolar affinity for the K_IV8 domain. LSN3353871 disrupted the formation of Lp(a) in vitro at low micromolar levels and decreased levels of Lp(a) in cynomolgus monkeys when dosed orally.

 

As noted above, the apo(a) protein contains multiple K_IV domains, and a classic method for improving potency is by making dimeric ligands that can bind to two domains simultaneously. The researchers did just this in the form of LSN3441732, which binds to apo(a) and disrupts formation of Lp(a) in vitro at picomolar concentrations.

 

If dimeric ligands are better than monomeric ones, why not go for multimeric ligands? The trimeric molecule LY3473329, or muvalaplin, was synthesized and crystallographically shown to bind to three copies of K_IV8. It blocked formation of Lp(a) in vitro and reduced Lp(a) levels in cynomolgus monkeys.

 

Kringle domains are found not just in apo(a) but also in plasminogen, the zymogen form of plasmin, which is responsible for degrading blood clots. Fortunately, subtle differences between the Kringle domains in apo(a) and human plasminogen provide selectivity for the former protein, especially for multivalent ligands such as muvalaplin, and a phase 1 clinical study showed that Lp(a) could be lowered without affecting plasmin activity.

 

This is a nice application of applying fundamental multivalent principles to develop a potent molecule. It is also another example of a molecule that may not look like a drug but works like one: despite containing four basic nitrogen atoms, three carboxylic acid moieties, and sporting a molecular weight above 700 Da, muvalaplin is orally bioavailable. It is currently in a phase 3 trial in up to 10,450 patients. Cardiovascular disease is the leading cause of death in the developed world, and Practical Fragments wishes luck to everyone involved in these studies.

 

In the meantime, watch for more Practical Fragments posts on new entries to our fragments in the clinic list, which will be updated later this year.

xSAR: Crystallographic SAR from crude reactions

Last year we highlighted an example of crystallographic screening of crude reaction mixtures to find inhibitors against the oncology target PHIP(2). Of 957 molecules tested, 22 showed crystallographic binding in two different orientations: 19 in a “lateral” pose and 3 in a “diving” pose. In a new open-access Chem. Sci. article, Philip Biggin and collaborators at Diamond Light Source and University of Oxford try to extract information from both the binders and the non-binders using crystallographic structure-activity relationships, or xSAR.

 

Chemists often think about SAR in qualitative terms: a methyl group here improves affinity, a chlorine atom there reduces it. In xSAR, the researchers sought to take a more quantitative approach. They converted each molecule into “Morgan fingerprints,” a set of more than 2048 binary bits describing structural features such as atom type, hybridization, and connectivity to other atoms within a certain distance. Some bits were found in all binding compounds, and these were referred to as conserved binding bits (CBB), while conserved nonbinding bits (CNB) were found only in non-binding compounds. These bits were then used to calculate Positive and Negative Binding Scores (PBS and NBS); a compound with a PBS of 1 contains all the CBB. Since there were two separate binding modes, the researchers calculated PBS and NBS values for both lateral and diving poses individually as well as for all binders.

 

As the researchers note, false negatives are a likely issue in crude reaction screening for a variety of reasons. To hunt for these, the PBS and NBS values were calculated for all 957 molecules previoulsy tested. A set of 97 pure compounds having mostly high scores were acquired and tested crystallographically, yielding an additional 23 lateral binders and 3 diving binders, more than doubling the initial yield. PBS was particularly informative in this retrospective exercise to recover false negatives, outperforming both NBS as well as other methods such as Tanimoto similarity scores.

 

The researchers also used PBS and NBS scores to search prospectively for new binders in a virtual set of more than 1.7 billion compounds in the Enamine REAL database. After filtering for high PBS/NBS scoring compounds followed by docking, 93 compounds were acquired and tested crystallographically. Interestingly, this yielded a relatively low hit rate of 9 binders, 6 in the lateral pose and three in somewhat different poses. None of the new compounds bound in the diving pose, which the researchers suggest may be due to the small sample size used to calculate PBS and NBS for this binding mode.

 

The 93 new compounds were also tested for binding using grated-coupled interferometry (GCI), and 13 showed measurable affinity, with most better than 50 µM. Two even showed single-digit micromolar affinity, more than an order of magnitude better than the best compound from the screen we discussed last year, and with better ligand efficiencies too. Surprisingly, these two compounds were not hits in the crystallographic screen.

 

This is an interesting paper with a couple important lessons. First, despite the fact that affinity was not used in calculating PBS and NBS, these metrics were nonetheless useful for identifying molecules with better affinity than those in the original training set, arguing for their utility. But perhaps just as importantly, the molecules with the best affinity were missed by crystallographic screening. If anything, this observation only strengthens my conclusion last year that while “there is a strong case for using crystallography first for finding fragments, I am not yet convinced the same applies for optimizing fragments.”

Fragment merging – and flipping – on the leucine zipper of MITF

Transcription factors can be difficult drug targets, particularly those whose primary structure is a “leucine zipper” in which two α-helices gently coil around each other. Their three-dimensional structure provides few pockets suitable for binding small molecules. In a new (open-access) paper in Nat. Commun., Deborah Castelletti, Wolfgang Jahnke, and a large group of multinational collaborators at Novartis and elsewhere present progress toward one of these, microphthalmia-associated transcription factor (MITF), which has been implicated in melanoma.

 

Most of MITF is believed to be disordered, but the DNA-binding domain (DBD) homodimerizes as a basic helix-loop-helix leucine zipper. Unlike related transcription factors, the helices in MITF contain a small kink that keeps them from heterodimerizing and also creates a small “kink pocket.”

 

The researchers expressed the DNA-binding domain of MITF and screened it using ¹⁹F NMR against the LEF4000 library, which we described here. This yielded just 9 hits that confirmed in protein-observed NMR, a hit rate the researchers note “is amongst the lowest that we have observed across multiple FBS campaigns,” consistent with expectations for a difficult target. Two chemical series, represented by compounds 1 and 2, were prioritized, and analogs from the Novartis compound collection were screened to find more-potent compounds 3 and 4.

 

Crystallography revealed that compounds 3 and 4 both bound in the kink pocket. Excitingly, the binding modes are similar and overlapping, inviting fragment merging. This proved successful, yielding a compound that bound 100-fold more tightly than either fragment. Further optimization ultimately led to compounds 7 and 8, with low or sub-micromolar affinity as assessed by isothermal titration calorimetry (ITC).

 

The bound structures of compounds 7 and 8 were determined by crystallography. Compound 7 (gray, left) superimposes nicely onto compounds 3 (cyan) and 4 (magenta), showing successful fragment merging. Compound 8 (green, right), however, is flipped 180 degrees compared to compound 7, despite having similar structure and affinity. Although surprising, this is not too uncommon; we’ve written about previous flippers here, here, and here.

The MITF homodimer is asymmetric, with one helix kinked and the other straight. NMR experiments and molecular dynamics show that both compounds 7 and 8 slow the interconversion between kinked and straight forms, though it is unclear whether this has functional implications. The compounds do not seem to affect DNA binding, and with at best high nanomolar affinity towards MITF no cell data are reported with the molecules.

 

Nonetheless, the successful identification of ligands against a leucine zipper is exciting. The binding pocket is small; as shown in the figure above, the best compounds already stick out on either side of the helices. Further affinity improvements may be difficult, though perhaps covalent approaches could help. Alternatively, perhaps these molecules could be starting points for induced proximity strategies such as PROTACs. It will be fun to watch this story develop.

How best to assess molecular shapeliness?

The shape of a molecule influences its properties. While this is true on a per-compound level, things get a little more controversial when discussing molecules in general. Back in 2009 researchers argued that “three dimensional” molecules have better drug-like properties, though this assertion has been challenged, repeatedly. But how do you assess the shape of a molecule in the first place? In a recent (open-access) Drug Discov. Today paper, Iwan de Esch and collaborators at Vrije Universiteit Amsterdam compare the main metrics.

 

The researchers focus on three metrics: fraction of sp³-hybridized carbons (F_Csp³), which we wrote about here; plane of best fit (PBF), which we wrote about here; and principal moment of inertia (PMI), which we wrote about here. F_Csp³, which ranges from 0 to 1, is simple to calculate based on the chemical structure alone, while the other two metrics rely on the three-dimensional shape of the molecule, requiring calculations and indeed choices since many molecules can assume multiple conformations. PBF is measured in angstroms with a minimum of 0 Å and no maximum; a protein, for example, could easily have a PBF above 10 Å. PMI is represented by two normalized PMI ratios, and these are often added to give a number (3D Score or ΣNPR) between 1 and 2.

 

The researchers calculated F_Csp³, PBF, and ΣNPR for a set of nearly half a million commercially available fragments which we discussed here; PBF and ΣNPR were calculated based on the single lowest energy conformation for each molecule. As noted above, PBF is somewhat size-dependent. For example, adamantane and buckminsterfullerene have PBF scores of 0.79 and 1.76 Å but identical ΣNPR scores. Nonetheless, the researchers found a correlation between these two metrics, and this correlation increased when PBF was divided by the root of the molecular volume to attempt to normalize for size.

 

In contrast, no correlation was found between F_Csp³ and PMI, making the former “a poor descriptor for predicting 3D molecular shape.” Is there a simple alternative? F_Csp³ only considers carbon atoms, so the researchers proposed F_HAsp³, which includes nitrogen, oxygen, and sulfur atoms. Perhaps not surprisingly, this didn’t improve the correlation.

 

Three years ago we wrote about “spacial scores,” which were developed to assess molecular complexity. The researchers calculated normalized spacial scores (nSPS) for their set of compounds, but these also showed no correlation to PMI.

 

The researchers conclude that, “once corrected for size, PBF captures three-dimensionality similarly to ΣNPR values. However, unlike a PMI analysis, it is not capable of further distinguishing between rod- and disc-shaped molecules, giving PMI a higher resolution in capturing shape diversity.” Interestingly, this is the opposite conclusion of an analysis Teddy wrote about in 2014. My take is that, if you want to assess shapeliness, steer clear of F_Csp³, but both PBF and PMI are fine.

Fragment events in 2026

Lots of interesting events coming up this year - hope to see you at one!

February 17-19:  The Twelfth NovAliX Conference will be held for the first time in San Diego! You can read my impressions of the 2018 Boston event here, the 2017 Strasbourg event here, and Teddy's impressions of the 2013 event here, here, and here. 

 

April 13-16: CHI’s Fragment-Based Drug Discovery turns 21, old enough to legally drink in the US! The longest-running annual fragment event returns as usual to San Diego. This is part of the larger Drug Discovery Chemistry meeting. You can read impressions of the 2025 meeting, the 2024 meeting, the 2023 meeting, the 2022 meeting, the 2021 virtual meeting, the 2020 virtual meeting, the 2019 meeting, the 2018 meeting, the 2017 meeting, the 2016 meeting; the 2015 meeting here, here, and here; the 2014 meeting here and here; the 2013 meeting here and here; the 2012 meeting; the 2011 meeting; and the 2010 meeting. 

May 10-12: Industrial Biostructures of America will be held in Cambridge, MA. The meeting covers all aspects of structural biology including FBLD, membrane proteins, allostery, cryo-EM, machine learning, and more. 

September 14-16: Fragments X, RSC-BMCS Tenth Fragment-based Drug Discovery Meeting, will be held in Cambridge, UK.  You can read my impressions of the 2024 meeting, the 2013 meeting, and the 2009 meeting.

September 28 to October 1: CHI’s Twenty-Fourth Annual Discovery on Target will be held as always in Boston. As the name implies this event is more target-focused than chemistry-focused, but there are always plenty of FBDD-related talks. You can read my impressions of the 2025 meeting, the 2024 meeting, the 2023 meeting, the 2022 meeting, the 2021 meeting, the 2020 virtual meeting, the 2019 meeting, and the 2018 meeting.

 

November 10-12: CHI holds its third Drug Discovery Chemistry Europe in beautiful Barcelona. This will likely include tracks on lead generation, protein-protein interactions, degraders and glues, and machine learning, with multiple fragment talks throughout. 

Know of anything else? Please leave a comment or drop me a note.

A new tool for covalent ligands: kinact/KI made easy with dDRTC

As covalent drug discovery becomes increasingly common, researchers are becoming more rigorous in how they characterize their molecules. The simple IC₅₀ values used for reversible inhibitors are meaningless for irreversible ligands unless the incubation times are also disclosed. And as molecules become more potent during the course of optimization, the incubation time may need to be shortened. An early hit might require treatment overnight to give 50% protein modification, while a potent lead might completely modify the protein in seconds. How do you quantitatively compare these?

 

The most rigorous parameter to characterize irreversible ligands is k_inact/K_I, sometimes called covalent efficiency, which we recently discussed here and here. Unfortunately, determining k_inact/K_I is a pain: it requires running multiple dose-response studies at multiple time points, and is thus typically only done for key compounds. In a new (open-access) Nat. Commun. paper, Robert Everley and colleagues at Frontier Medicines (including yours truly) provide a shortcut.

 

The new method relies on the fact that, especially for low-affinity fragments, much of the data collected in a conventional dose-response time course (DRTC) is redundant, providing little additional value. For example, if a compound at one concentration gives virtually no modification after 8 hours, it also won’t modify after 1 hour. The trick is to collect just the most informative data in a “diagonal” dose-response time course, or dDRTC.

 

I won’t go into the mathematics and full implementation details since the paper is open-access, but suffice it to say that dDRTC lowers the number of required data points by a factor of eight, thus saving both time and reagents – including precious protein.

 

The paper appropriately notes limitations, such as the fact that for compounds with better affinities (K_I < 50 µM), the values derived from dDRTC can underestimate the true k_inact/K_I. However, this situation is uncommon for fragments, and indeed the potencies for even some clinical compounds such as sotorasib and VVD-133214 are largely driven by (specific) k_inact rather than K_I. The paper shows good agreement for k_inact/K_I values determined using dDRTC with those determined using the conventional approach for compounds having k_inact/K_I from 1 to 2000 M^-1s^-1.

 

Perhaps most relevant for this blog, dDRTC is a practical solution for collecting important data. The next time you’re running a covalent program, give it a try!