Trends in covalent drug discovery: a 2020-23 patent landscape analysis focused on select covalent reacting groups (CRGs) found in FDA-approved drugs.

INTRODUCTION: Covalent drugs contain electrophilic groups that can react with nucleophilic amino acids located in the active sites of proteins, particularly enzymes. Recently, there has been considerable interest in using covalent drugs to target non-catalytic amino acids in proteins to modulate difficult targets (i.e. targeted covalent inhibitors). Covalent compounds contain a wide variety of covalent reacting groups (CRGs), but only a few of these CRGs are present in FDA-approved covalent drugs. AREAS COVERED: This review summarizes a 2020-23 patent landscape analysis that examined trends in the field of covalent drug discovery around targets and organizations. The analysis focused on patent applications that were submitted to the World International Patent Organization and selected using a combination of keywords and structural searches based on CRGs present in FDA-approved drugs. EXPERT OPINION: A total of 707 patent applications from >300 organizations were identified, disclosing compounds that acted at 71 targets. Patent application counts for five targets accounted for ~63% of the total counts (i.e. BTK, EGFR, FGFR, KRAS, and SARS-CoV-2 Mpro). The organization with the largest number of patent counts was an academic institution (Dana-Farber Cancer Institute). For one target, KRAS G12C, the discovery of new drugs was highly competitive (>100 organizations, 186 patent applications).

AI/ML Models to Predict the Severity of Drug-Induced Liver Injury for Small Molecules.

Drug-induced liver injury (DILI), believed to be a multifactorial toxicity, has been a leading cause of attrition of small molecules during discovery, clinical development, and postmarketing. Identification of DILI risk early reduces the costs and cycle times associated with drug development. In recent years, several groups have reported predictive models that use physicochemical properties or in vitro and in vivo assay endpoints; however, these approaches have not accounted for liver-expressed proteins and drug molecules. To address this gap, we have developed an integrated artificial intelligence/machine learning (AI/ML) model to predict DILI severity for small molecules using a combination of physicochemical properties and off-target interactions predicted in silico. We compiled a data set of 603 diverse compounds from public databases. Among them, 164 were categorized as Most DILI (M-DILI), 245 as Less DILI (L-DILI), and 194 as No DILI (N-DILI) by the FDA. Six machine learning methods were used to create a consensus model for predicting the DILI potential. These methods include k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), Naïve Bayes (NB), artificial neural network (ANN), logistic regression (LR), weighted average ensemble learning (WA) and penalized logistic regression (PLR). Among the analyzed ML methods, SVM, RF, LR, WA, and PLR identified M-DILI and N-DILI compounds, achieving a receiver operating characteristic area under the curve of 0.88, sensitivity of 0.73, and specificity of 0.9. Approximately 43 off-targets, along with physicochemical properties (fsp3, log S, basicity, reactive functional groups, and predicted metabolites), were identified as significant factors in distinguishing between M-DILI and N-DILI compounds. The key off-targets that we identified include: PTGS1, PTGS2, SLC22A12, PPARγ, RXRA, CYP2C9, AKR1C3, MGLL, RET, AR, and ABCC4. The present AI/ML computational approach therefore demonstrates that the integration of physicochemical properties and predicted on- and off-target biological interactions can significantly improve DILI predictivity compared to chemical properties alone.

Bicyclo((aryl)methyl)benzamides as inhibitors of GlyT1.

A series of isoquinuclidine benzamides as glycine uptake inhibitors for the treatment of schizophrenia are described. Potency, lipophilicity, and intrinsic human microsomal clearance were parameters for optimization. Potency correlated with the nature of the ortho substituents of the benzamide ring, and reductions in lipophilicity could be achieved through heteroatom incorporation in the benzamide and pendant phenyl moieties. Improvements in human CLint were achieved through changes in ring size and the N-alkyl group of the isoquinuclidine itself, with des-alkyl derivatives (40-41, 44) demonstrating the most robust microsomal stability. Dimethylbenzamide 9 was tested in a mouse MK801 LMA assay and had a statistically significant attenuation of locomotor activity at 3 and 10 µmol/kg compared to control.

2,6-Disubstituted pyrazines and related analogs as NR2B site antagonists of the NMDA receptor with anti-depressant activity.

Herein we describe the discovery of compounds that are competitive antagonists of the CP101-606 binding site within the NR2B subtype of the NMDA receptor. The compounds identified do not possess phenolic functional groups such as those in ifenprodil and related analogs. Initial identification of hits in this series focused on a basic, secondary amine side chain which led to good potency, but also presented a hERG liability. Further modifications led to examples of non-basic replacements which demonstrated much less liability in this regard. Finally, one compound in the series, 6a, was tested in the mouse forced swim depression assay and found to show activity (s.c. 60 mg/kg).

Development and SAR of functionally selective allosteric modulators of GABAA receptors.

Positive modulators at the benzodiazepine site of α2- and α3-containing GABA(A) receptors are believed to be anxiolytic. Through oocyte voltage clamp studies, we have discovered two series of compounds that are positive modulators at α2-/α3-containing GABA(A) receptors and that show no functional activity at α1-containing GABA(A) receptors. We report studies to improve this functional selectivity and ultimately deliver clinical candidates. The functional SAR of cinnolines and quinolines that are positive allosteric modulators of the α2- and α3-containing GABA(A) receptors, while simultaneously neutral antagonists at α1-containing GABA(A) receptors, is described. Such functionally selective modulators of GABA(A) receptors are expected to be useful in the treatment of anxiety and other psychiatric illnesses.

Identification of N-(2-(azepan-1-yl)-2-phenylethyl)-benzenesulfonamides as novel inhibitors of GlyT1.

A novel series of glycine transporter 1 (GlyT1) inhibitors is described. Scoping of the heterocycle moiety of hit 4-chlorobenzenesulfonamide 1 led to replacement of the piperidine with an azepane for a modest increase in potency. Phenyl sulfonamides proved superior to alkyl and non-phenyl aromatic sulfonamides, while subsequent ortho substitution of the 2-(azepan-1-yl)-2-phenylethanamine aromatic ring yielded 39 (IC(50) 37 nM, solubility 14 microM), the most potent GlyT1 inhibitor in this series. Favorable brain-plasma ratios were observed for select compounds in pharmacokinetic studies to evaluate CNS penetration.

Hydride transfer catalyzed by xylose isomerase: mechanism and quantum effects.

We have applied molecular dynamics umbrella-sampling simulation and ensemble-averaged variational transition state theory with multidimensional tunneling (EA-VTST/MT) to calculate the reaction rate of xylose-to- xylulose isomerization catalyzed by xylose isomerase in the presence of two Mg2+ ions. The calculations include determination of the free energy of activation profile and ensemble averaging in the transmission coefficient. The potential energy function is approximated by a combined QM/MM/SVB method involving PM3 for the quantum mechanical (QM) subsystem, CHARMM22 and TIP3P for the molecular mechanical (MM) environment, and a simple valence bond (SVB) local function of two bond distances for the hydride transfer reaction. The simulation confirms the essential features of a mechanism postulated on the basis of kinetics and X-ray data by Whitlow et al. (Whitlow, M.; Howard, A. J.; Finzel, B. C.; Poulos, T. L.; Winborne, E.; Gilliland, G. L. Proteins 1991, 9, 153) and Ringe, Petsko, and coworkers (Labie, A.; Allen, K.-N.; Petsko, G. A.; Ringe, D. Biochemistry 1994, 33, 5469). This mechanism involves a rate-determining 1,2-hydride shift with prior and post proton transfers. Inclusion of quantum mechanical vibrational energy is important for computing the free energy of activation, and quantum mechanical tunneling effects are essential for computing kinetic isotope effects (KIEs). It is found that 85% of the reaction proceeds by tunneling and 15% by overbarrier events. The computed KIE for the ratio of hydride to deuteride transfer is in good agreement with the experimental results. The molecular dynamics simulations reveal that proton and hydride transfer reactions are assisted by breathing motions of the mobile Mg2+ ion in the active site, providing evidence for concerted motion of Mg2+ during the hydride transfer step.

Quantum dynamics of hydride transfer catalyzed by bimetallic electrophilic catalysis: synchronous motion of Mg(2+) and H(-) in xylose isomerase.

Xylose isomerase exhibits a bridged-bimetallic active-site motif in which the substrate is bound to two metals connected by a glutamate bridge, and X-ray crystallographic studies suggest that metal movement is involved in the hydride transfer rate-controlling catalytic step. Here we report classical/quantal dynamical simulations of this step that provide new insight into the metal motion. The potential energy surface is calculated by treating xylose with semiempirical molecular orbital theory augmented by a simple valence bond potential and the rest of the system by molecular mechanics. The rate constant for the hydride-transfer step was calculated by ensemble-averaged dynamical simulations including both variational transition-state theory for determination of the statistically averaged dynamical bottleneck and optimized multidimensional tunneling calculations. The dynamics calculations include 25 317 atoms, with quantized vibrational free energy in 89 active-site degrees of freedom, and with 32 atoms moving through static secondary zone transition-state configurations in the quantum tunneling simulation. Our simulations show that the average Mg-Mg distance R increases monotonically as a function of the hydride-transfer progress variable z. The range of the average R along the reaction path is consistent with the X-ray structure, thus providing a dynamical demonstration of the postulated role of Mg in catalysis. We also predicted the primary deuterium kinetic isotope effect (KIE) for the chemical step. We calculated a KIE of 3.8 for xylose at 298 K, which is consistent with somewhat smaller experimentally observed KIEs for glucose substrate at higher temperatures. More than half of our KIE is due to tunneling; neglecting quantum effects on the reaction coordinate reduces the calculated KIE to 1.8.

The incorporation of quantum effects in enzyme kinetics modeling.

We present an overview of new procedures for including quantum mechanical effects in enzyme kinetics. Quantum effects are included in three ways: (1) The electronic structure of the atoms in the catalytic center is treated quantum mechanically in order to calculate a realistic potential energy surface for the bond rearrangement process. (2) The discrete nature of quantum mechanical vibrational energies is incorporated in the treatment of nuclear motion for computing the potential of mean force. (3) Multidimensional tunneling contributions are included. These procedures are illustrated by applications to proton abstractions catalyzed by enolase and methylamine dehydrogenase and hydride-transfer reactions by alcohol dehydrogenase and xylose isomerase.