Group Additivity in Ligand Binding Affinity: An Alternative Approach to Ligand Efficiency.

Group additivity is a concept that has been successfully applied to a variety of thermochemical and kinetic properties. This includes drug discovery, where functional group additivity is often assumed in ligand binding. Ligand efficiency can be recast as a special case of group additivity where ΔG/HA is the group equivalent (HA is the number of non-hydrogen atoms in a ligand). Analysis of a large data set of protein-ligand binding affinities (Ki) for diverse targets shows that in general ligand binding is distinctly nonlinear. It is possible to create a group equivalent scheme for ligand binding, but only in the context of closely related proteins, at least with regard to size. This finding has broad implications for drug design from both experimental and computational points of view. It also offers a path forward for a more general scheme to assess the efficiency of ligand binding.

Uncompetitive, adduct-forming SARM1 inhibitors are neuroprotective in preclinical models of nerve injury and disease.

Axon degeneration is an early pathological event in many neurological diseases. The identification of the nicotinamide adenine dinucleotide (NAD) hydrolase SARM1 as a central metabolic sensor and axon executioner presents an exciting opportunity to develop novel neuroprotective therapies that can prevent or halt the degenerative process, yet limited progress has been made on advancing efficacious inhibitors. We describe a class of NAD-dependent active-site SARM1 inhibitors that function by intercepting NAD hydrolysis and undergoing covalent conjugation with the reaction product adenosine diphosphate ribose (ADPR). The resulting small-molecule ADPR adducts are highly potent and confer compelling neuroprotection in preclinical models of neurological injury and disease, validating this mode of inhibition as a viable therapeutic strategy. Additionally, we show that the most potent inhibitor of CD38, a related NAD hydrolase, also functions by the same mechanism, further underscoring the broader applicability of this mechanism in developing therapies against this class of enzymes.

Macrocyclic BACE inhibitors: Optimization of a micromolar hit to nanomolar leads.

We have identified macrocyclic inhibitors of the aspartic protease BACE, implicated in the etiology of Alzheimer's disease. An X-ray structure of screening hit 1 in the BACE active site revealed a hairpin conformation suggesting that constrained macrocyclic derivatives may also bind there. Several of the analogs we prepared were >100x more potent than 1, such as 7 (5 nM K(i)).

Quantum mechanical pairwise decomposition analysis of protein kinase B inhibitors: validating a new tool for guiding drug design.

Quantum mechanical semiempirical comparative binding energy analysis calculations have been carried out for a series of protein kinase B (PKB) inhibitors derived from fragment- and structure-based drug design. These protein-ligand complexes were selected because they represent a consistent set of experimental data that includes both crystal structures and affinities. Seven scoring functions were evaluated based on both the PM3 and the AM1 Hamiltonians. The optimal models obtained by partial least-squares analysis of the aligned poses are predictive as measured by a number of standard statistical criteria and by validation with an external data set. An algorithm has been developed that provides residue-based contributions to the overall binding affinity. These residue-based binding contributions can be plotted in heat maps so as to highlight the most important residues for ligand binding. In the case of these PKB inhibitors, the maps show that Met166, Thr97, Gly43, Glu114, Ala116, and Val50, among other residues, play an important role in determining binding affinity. The interaction energy map makes it easy to identify the residues that have the largest absolute effect on ligand binding. The structure-activity relationship (SAR) map highlights residues that are most critical to discriminating between more and less potent ligands. Taken together the interaction energy and the SAR maps provide useful insights into drug design that would be difficult to garner in any other way.

Ligand binding efficiency: trends, physical basis, and implications.

Ligand efficiency (i.e., potency/size) has emerged as an important metric in drug discovery. In general, smaller, more efficient ligands are believed to have improved prospects for good drug properties (e.g., bioavailability). Our analysis of thousands of ligands across a variety of targets shows that ligand efficiency is dependent on ligand size with smaller ligands having greater efficiencies, on average, than larger ligands. We propose two primary causes for this size dependence: the inevitable reduction in the quality of fit between ligand and receptor as the ligand becomes larger and more complex and the reduction in accessible ligand surface area on a per atom basis as size increases. These results have far-ranging implications for analysis of high-throughput screening hits, fragment-based approaches to drug discovery, and even computational models of potency.

2-Amino-3,4-dihydroquinazolines as inhibitors of BACE-1 (beta-site APP cleaving enzyme): Use of structure based design to convert a micromolar hit into a nanomolar lead.

A new aspartic protease inhibitory chemotype bearing a 2-amino-3,4-dihydroquinazoline ring was identified by high-throughput screening for the inhibition of BACE-1. X-ray crystallography revealed that the exocyclic amino group participated in a hydrogen bonding array with the two catalytic aspartic acids of BACE-1 (Asp(32), Asp(228)). BACE-1 inhibitory potency was increased (0.9 microM to 11 nM K(i)) by substitution into the unoccupied S(1)' pocket.

Linear interaction energy models for beta-secretase (BACE) inhibitors: Role of van der Waals, electrostatic, and continuum-solvation terms.

Computing the binding affinity of a protein-ligand complex is one of the most fundamental and difficult tasks in computer-aided drug design. Many approaches for computing binding affinities can be classified as linear interaction energy (LIE) models as they rely on some type of linear fit of computed interaction energies between ligand and protein. We have examined the computed interaction energies of a series of beta-secretase (BACE) inhibitors in terms of van der Waals, coulombic, and continuum-solvation contributions to ligand binding. We have also systematically examined the effect of different protonation states of the protein and ligands. We find that the binding affinities are relatively insensitive to the protonation state of the protein when neutral ligands are considered. Inclusion of charged ligands leads to large deviations in the coulomb, solvation, and even van der Waals terms. The latter is due to increased repulsive van der Waals interactions in the complex due to the strong coulomb attraction found between oppositely charged functional groups in the protein and ligand. In general, we find that the best models are obtained when the protein is judiciously charged (e.g. Asp32-, Arg235+) and the potentially charged ligands are treated as neutral.

An improved set of mndo parameters for sulfur.

The MNDO parameters for sulfur have been reoptimized. Calculations for a number of sulfur compounds indicate a very significant improvement. Inclusion of d AOs failed to correct the errors for compounds of sulfur in its higher valence states. Since d AOs are not included, the calculations are still confined to compounds of divalent sulfur.

Modeling the protonation states of the catalytic aspartates in beta-secretase.

Beta-secretase (BACE) is a critical enzyme in the production of beta-amyloid, a protein that has been implicated as a potential cause of Alzheimer's disease (AD). There are two aspartic acid residues (Asp 32 and Asp 228) present in the catalytic region of BACE that can adopt multiple protonation states. The protonation state and precise location of the protons for these two residues, particularly in the presence of an inhibitor, are subjects of great interest since they have a direct bearing on the mechanism of aspartyl proteases and efforts to model beta-secretase. We have carried out full liner-scaling quantum mechanical (QM) calculations that include Poisson-Boltzmann solvation in order to identify the preferred protonation state and proton location in the presence and absence of an inhibitor. These calculations favor the monoprotonated state in the presence of ligand, and di-deprotonated state in the absence of ligand. Further the proton in the monoprotonated state is located on the inner oxygen of Asp 228. These results have implications for the catalytic mechanism of BACE and related aspartyl proteases. They also provide a reference state for the protein in structure-based modeling studies of this therapeutically important target.

Calculation of the binding affinity of beta-secretase inhibitors using the linear interaction energy method.

It has been shown that the rate-limiting step in the production of beta-amyloid peptide (Abeta) is the proteolytric cleavage of the membrane-bound beta-amyloid precursor protein (APP) by beta-secretase (BACE). Since the accumulation of Abeta has been implicated as one of the key events in the progression of Alzheimer's disease, BACE has become an important therapeutic target. Recently, two crystal structures of BACE cocrystallized with the inhibitors OM99-2 and OM00-3 were published by Tang and co-workers. In addition, the Ghosh group has published binding data on a series of inhibitors based on their initial lead, OM99-2. Using this set as a basis, we have developed a model for the binding affinity of these ligands to BACE using the linear interaction energy method. The best binding affinity model for the full set of ligands had a RMSD of 1.10 kcal/mol. The best model excluding the two charged ligands had a RMSD of 0.87 kcal/mol.

A Web-based chemoinformatics system for drug discovery.

One of the key questions that must be addressed when implementing a chemoinformatics system is whether the tools will be designed for use by the expert user or by the "bench scientist." This decision can impact not only the style of tools that are rolled out, but is also a factor in terms of how these tools are delivered to the end users. The system that we outline here was designed for use by the non-expert user. As such, the tools that we discuss are in many cases simplified versions of some common algorithms used in chemoinformatics. In addition, the focus is on how to distribute these tools using a web-services interface, which greatly simplifies delivering new protocols to the end user.

Impact of computational structure-based methods on drug discovery.

Structure-based drug design has become an indispensible tool in drug discovery. The emergence of structure-based design is due to gains in structural biology that have provided exponential growth in the number of protein crystal structures, new computational algorithms and approaches for modeling protein-ligand interactions, and the tremendous growth of raw computer power in the last 30 years. Computer modeling and simulation have made major contributions to the discovery of many groundbreaking drugs in recent years. Examples are presented that highlight the evolution of computational structure-based design methodology, and the impact of that methodology on drug discovery.

Protein-ligand cocrystal structures: we can do better.

There is a large body of evidence that many protein-ligand cocrystal structures contain poorly refined ligand geometries. These errors result in bound structures that have nonideal bond lengths and angles, are strained, contain improbable conformations, and have bad protein-ligand contacts. Many of these problems can be greatly reduced with better refinement models.

The role of ligand efficiency metrics in drug discovery.

The judicious application of ligand or binding efficiency metrics, which quantify the molecular properties required to obtain binding affinity for a drug target, is gaining traction in the selection and optimization of fragments, hits and leads. Retrospective analysis of recently marketed oral drugs shows that they frequently have highly optimized ligand efficiency values for their targets. Optimizing ligand efficiency metrics based on both molecular mass and lipophilicity, when set in the context of the specific target, has the potential to ameliorate the inflation of these properties that has been observed in current medicinal chemistry practice, and to increase the quality of drug candidates.

Validity of ligand efficiency metrics.

A recent viewpoint article (Improving the plausibility of success with inefficient metrics. ACS Med. Chem. Lett. 2014, 5, 2-5) argued that the standard definition of ligand efficiency (LE) is mathematically invalid. In this viewpoint, we address this criticism and show categorically that the definition of LE is mathematically valid. LE and other metrics such as lipophilic ligand efficiency (LLE) can be useful during the multiparameter optimization challenge faced by medicinal chemists.

Discovery of 2-[3,5-dichloro-4-(5-isopropyl-6-oxo-1,6-dihydropyridazin-3-yloxy)phenyl]-3,5-dioxo-2,3,4,5-tetrahydro[1,2,4]triazine-6-carbonitrile (MGL-3196), a Highly Selective Thyroid Hormone Receptor ß agonist in clinical trials for the treatment of dyslipidemia.

The beneficial effects of thyroid hormone (TH) on lipid levels are primarily due to its action at the thyroid hormone receptor ß (THR-ß) in the liver, while adverse effects, including cardiac effects, are mediated by thyroid hormone receptor α (THR-α). A pyridazinone series has been identified that is significantly more THR-ß selective than earlier analogues. Optimization of this series by the addition of a cyanoazauracil substituent improved both the potency and selectivity and led to MGL-3196 (53), which is 28-fold selective for THR-ß over THR-α in a functional assay. Compound 53 showed outstanding safety in a rat heart model and was efficacious in a preclinical model at doses that showed no impact on the central thyroid axis. In reported studies in healthy volunteers, 53 exhibited an excellent safety profile and decreased LDL cholesterol (LDL-C) and triglycerides (TG) at once daily oral doses of 50 mg or higher given for 2 weeks.

Thermodynamics of ligand binding and efficiency.

Analysis of the experimental binding thermodynamics for approximately 100 protein-ligand complexes provides important insights into the factors governing ligand affinity and efficiency. The commonly accepted correlation between enthalpy and -TΔS is clearly observed for this relatively diverse data set. It is also clear that affinity (i.e., ΔG) is not generally correlated to either enthalpy or -TΔS. This is a worrisome trend since the vast majority of computational structure-based design is carried out using interaction energies for one, or at most a few, ligand poses. As such, these energies are most closely comparable to enthalpies not free energies. Closer inspection of the data shows that in a few cases the enthalpy (or -TΔS) is correlated with free energy. It is tempting to speculate that this could be an important consideration as to why some targets are readily amenable to modeling and others are not. Additionally, analysis of the enthalpy and -TΔS efficiencies shows that the trends observed for ligand efficiencies with respect to molecular size are primarily a consequence of enthalpic, not entropic, effects.