Identification of an Orally Bioavailable, Brain-Penetrant Compound with Selectivity for the Cannabinoid Type 2 Receptor

Modulation of the endocannabinoid system (ECS) is of great interest for its therapeutic relevance in several pathophysiological processes. The CB2 subtype is largely localized to immune effectors, including microglia within the central nervous system, where it promotes anti-inflammation. Recently, a rational drug design toward precise modulation of the CB2 active site revealed the novelty of Pyrrolo[2,1-c][1,4]benzodiazepines tricyclic chemotype with a high conformational similarity in comparison to the existing leads. These compounds are structurally unique, confirming their chemotype novelty. In our continuing search for new chemotypes as selective CB2 regulatory molecules, following SAR approaches, a total of 17 selected (S,E)-11-[2-(arylmethylene)hydrazono]-PBD analogs were synthesized and tested for their ability to bind to the CB1 and CB2 receptor orthosteric sites. A competitive [3H]CP-55,940 binding screen revealed five compounds that exhibited >60% displacement at 10 μM concentration. Further concentration-response analysis revealed two compounds, 4k and 4q, as potent and selective CB2 ligands with sub-micromolar activities (Ki = 146 nM and 137 nM, respectively). In order to support the potential efficacy and safety of the analogs, the oral and intravenous pharmacokinetic properties of compound 4k were sought. Compound 4k was orally bioavailable, reaching maximum brain concentrations of 602 ± 162 ng/g (p.o.) with an elimination half-life of 22.9 ± 3.73 h. Whether administered via the oral or intravenous route, the elimination half-lives ranged between 9.3 and 16.7 h in the liver and kidneys. These compounds represent novel chemotypes, which can be further optimized for improved affinity and selectivity toward the CB2 receptor.

Structural insights into inhibitor regulation of the DNA repair protein DNA-PKcs

The DNA-dependent protein kinase catalytic subunit (DNA-PKcs) has a central role in non-homologous end joining, one of the two main pathways that detect and repair DNA double-strand breaks (DSBs) in humans1,2. DNA-PKcs is of great importance in repairing pathological DSBs, making DNA-PKcs inhibitors attractive therapeutic agents for cancer in combination with DSB-inducing radiotherapy and chemotherapy3. Many of the selective inhibitors of DNA-PKcs that have been developed exhibit potential as treatment for various cancers4. Here we report cryo-electron microscopy (cryo-EM) structures of human DNA-PKcs natively purified from HeLa cell nuclear extracts, in complex with adenosine-5′-(γ-thio)-triphosphate (ATPγS) and four inhibitors (wortmannin, NU7441, AZD7648 and M3814), including drug candidates undergoing clinical trials. The structures reveal molecular details of ATP binding at the active site before catalysis and provide insights into the modes of action and specificities of the competitive inhibitors. Of note, binding of the ligands causes movement of the PIKK regulatory domain (PRD), revealing a connection between the p-loop and PRD conformations. Electrophoretic mobility shift assay and cryo-EM studies on the DNA-dependent protein kinase holoenzyme further show that ligand binding does not have a negative allosteric or inhibitory effect on assembly of the holoenzyme complex and that inhibitors function through direct competition with ATP. Overall, the structures described in this study should greatly assist future efforts in rational drug design targeting DNA-PKcs, demonstrating the potential of cryo-EM in structure-guided drug development for large and challenging targets.

An overview of in silico methods used in the design of VEGFR-2 inhibitors as anticancer agents

VEGFR-2 enzyme known for physiological functioning of the cell also involves in pathological angiogenesis and tumor progression. Recently VEGFR-2 has gained the interest of researchers all around the world as a promising target for the drug design and discovery of new anticancer agents. VEGFR2 inhibitors are a major class of anticancer agents used for clinical purposes. In silico methods like virtual screening, molecular docking, molecular dynamics, pharmacophore modeling, and other computational approaches help extensively in identifying the main molecular interactions necessary for the binding of the small molecules with the respective protein target to obtain the expected pharmacological potency. In this chapter, we discussed some representative case studies of in silico techniques used to determine molecular interactions and rational drug design of VEGFR-2 inhibitors as anticancer agents.

Ins and outs of AlphaFold2 transmembrane protein structure predictions

Transmembrane (TM) proteins are major drug targets, but their structure determination, a prerequisite for rational drug design, remains challenging. Recently, the DeepMind’s AlphaFold2 machine learning method greatly expanded the structural coverage of sequences with high accuracy. Since the employed algorithm did not take specific properties of TM proteins into account, the reliability of the generated TM structures should be assessed. Therefore, we quantitatively investigated the quality of structures at genome scales, at the level of ABC protein superfamily folds and for specific membrane proteins (e.g. dimer modeling and stability in molecular dynamics simulations). We tested template-free structure prediction with a challenging TM CASP14 target and several TM protein structures published after AlphaFold2 training. Our results suggest that AlphaFold2 performs well in the case of TM proteins and its neural network is not overfitted. We conclude that cautious applications of AlphaFold2 structural models will advance TM protein-associated studies at an unexpected level.

Evaluation of Virtual Screening Strategies for the Identification of γ-Secretase Inhibitors and Modulators

γ-Secretase is an intramembrane aspartyl protease that is important in regulating normal cell physiology via cleavage of over 100 transmembrane proteins, including Amyloid Precursor Protein (APP) and Notch family receptors. However, aberrant proteolysis of substrates has implications in the progression of disease pathologies, including Alzheimer’s disease (AD), cancers, and skin disorders. While several γ-secretase inhibitors have been identified, there has been toxicity observed in clinical trials associated with non-selective enzyme inhibition. To address this, γ-secretase modulators have been identified and pursued as more selective agents. Recent structural evidence has provided an insight into how γ-secretase inhibitors and modulators are recognized by γ-secretase, providing a platform for rational drug design targeting this protease. In this study, docking- and pharmacophore-based screening approaches were evaluated for their ability to identify, from libraries of known inhibitors and modulators with decoys with similar physicochemical properties, γ-secretase inhibitors and modulators. Using these libraries, we defined strategies for identifying both γ-secretase inhibitors and modulators incorporating an initial pharmacophore-based screen followed by a docking-based screen, with each strategy employing distinct γ-secretase structures. Furthermore, known γ-secretase inhibitors and modulators were able to be identified from an external set of bioactive molecules following application of the derived screening strategies. The approaches described herein will inform the discovery of novel small molecules targeting γ-secretase.

Mechanistic Insights into Passive Membrane Permeability of Drug-Like Molecules from a Weighted Ensemble of Trajectories

Passive permeability of a drug-like molecule is a critical assay early in a drug discovery campaign that informs a medicinal chemist how well a compound can traverse biological membranes, such as gastrointestinal epithelial or restrictive organ barriers, so it can perform a specific therapeutic function. However, the challenge that remains is the development of a method, experimental or computational, which can both determine the permeation rate and provide mechanistic insights into the transport process to help with the rational design for any given molecule. Typically, one of three methods are used to measure membrane permeability: (1) experimental permeation assays acting on either artificial or natural membranes; (2) quantitative structure-permeability relationship (QSPR) models that rely on experimental values of permeability or related pharmacokinetic properties of a range of molecules to infer those for new molecules; (3) estimates of permeability from the Smoluchowski equation, where free energy and diffusion profiles along the membrane normal are taken as input from large-scale molecular dynamics simulations. While all these methods provide estimates of permeation coefficients, they provide very little information for guiding rational drug design. In this study, we employ a highly parallelizable weighted ensemble (WE) path sampling strategy, empowered by cloud computing techniques, to generate unbiased permeation pathways and permeability coefficients for a set of drug-like molecules across a neat 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylcholine (POPC) membrane bilayer. Our WE method predicts permeability coefficients that compare well to experimental values from an MDCK-LE cell line and PAMPA assays for a set of drug-like amines of varying size, shape, and flexibility. Our method also yields a series of continuous permeation pathways weighted and ranked by their associated probabilities. Taken together, the ensemble of reactive permeation pathways, along with the estimate of the permeability coefficient, provides a clearer picture of the microscopic underpinnings of small molecule membrane permeation.

Leveraging nonstructural data to predict structures and affinities of protein–ligand complexes

Over the past five decades, tremendous effort has been devoted to computational methods for predicting properties of ligands—i.e., molecules that bind macromolecular targets. Such methods, which are critical to rational drug design, fall into two categories: physics-based methods, which directly model ligand interactions with the target given the target’s three-dimensional (3D) structure, and ligand-based methods, which predict ligand properties given experimental measurements for similar ligands. Here, we present a rigorous statistical framework to combine these two sources of information. We develop a method to predict a ligand’s pose—the 3D structure of the ligand bound to its target—that leverages a widely available source of information: a list of other ligands that are known to bind the same target but for which no 3D structure is available. This combination of physics-based and ligand-based modeling improves pose prediction accuracy across all major families of drug targets. Using the same framework, we develop a method for virtual screening of drug candidates, which outperforms standard physics-based and ligand-based virtual screening methods. Our results suggest broad opportunities to improve prediction of various ligand properties by combining diverse sources of information through customized machine-learning approaches.

Peptide Inhibitors of Kv1.5: An Option for the Treatment of Atrial Fibrillation

The human voltage gated potassium channel Kv1.5 that conducts the IKur current is a key determinant of the atrial action potential. Its mutations have been linked to hereditary forms of atrial fibrillation (AF), and the channel is an attractive target for the management of AF. The development of IKur blockers to treat AF resulted in small molecule Kv1.5 inhibitors. The selectivity of the blocker for the target channel plays an important role in the potential therapeutic application of the drug candidate: the higher the selectivity, the lower the risk of side effects. In this respect, small molecule inhibitors of Kv1.5 are compromised due to their limited selectivity. A wide range of peptide toxins from venomous animals are targeting ion channels, including mammalian channels. These peptides usually have a much larger interacting surface with the ion channel compared to small molecule inhibitors and thus, generally confer higher selectivity to the peptide blockers. We found two peptides in the literature, which inhibited IKur: Ts6 and Osu1. Their affinity and selectivity for Kv1.5 can be improved by rational drug design in which their amino acid sequences could be modified in a targeted way guided by in silico docking experiments.