Unintentional ingestion of putative delta-8 tetrahydrocannabinol by two youth requiring critical care: a case report.

BACKGROUND: Delta-8 tetrahydrocannabinol (THC) is a psychoactive cannabinoid from the cannabis plant that can be synthetically converted from cannabidiol (CBD). Most states permit the full or restricted sale of hemp and hemp-derived CBD products, and therefore, delta-8 THC products are on the rise. Delta-8 THC consumption can cause intoxication. Products are often sold in edible form and occasionally in packaging that appears similar to candy. Clinical presentations for delta-8 THC ingestions are understudied and may differ from those described for delta-9 THC ingestions. CASE PRESENTATION: This case report describes unintentional ingestions of putative delta-8 THC by two pediatric patients that results in admission to the pediatric intensive care unit. The ingestions were of putative delta-8 THC infused product that resembled popular candies. Both patients developed periods of bradypnea with continued intermittent periods of agitation. Medical intervention included observation, noninvasive positive pressure ventilation via high flow nasal cannula, and intubation-but was not needed for both patients. Although family noted ongoing irritability for the patients, both were discharged approximately 45 h after ingestion. Delta-8 THC ingestion is reliant on self-report. CONCLUSIONS: As the availability of delta-8 THC increases, along with associated pediatric exposures, it is imperative for health care providers to quickly recognize and provide adequate treatment. While there is no specific antidote for THC intoxication beyond supportive care, providers can play an important role in prevention by educating parents and guardians on safe cannabis storage and by documenting cases for adverse event monitoring.

Discovery of Highly Selective Inhibitors of the Immunoproteasome Low Molecular Mass Polypeptide 2 (LMP2) Subunit.

Building upon the success of bortezomib (VELCADE) and carfilzomib (KYPROLIS), the design of a next generation of inhibitors targeting specific subunits within the immunoproteasome is of interest for the treatment of autoimmune disease. There are three catalytic subunits within the immunoproteasome (low molecular mass polypeptide-7, -2, and multicatalytic endopeptidase complex subunit-1; LMP7, LMP2, and MECL-1), and a campaign was undertaken to design a potent and selective LMP2 inhibitor with sufficient properties to allow for sustained inhibition in vivo. Screening a focused library of epoxyketones revealed a series of potent dipeptides that were optimized to provide the highly selective inhibitor KZR-504 (12).

Machine learning and docking models for Mycobacterium tuberculosis topoisomerase I.

There is a shortage of compounds that are directed towards new targets apart from those targeted by the FDA approved drugs used against Mycobacterium tuberculosis. Topoisomerase I (Mttopo I) is an essential mycobacterial enzyme and a promising target in this regard. However, it suffers from a shortage of known inhibitors. We have previously used computational approaches such as homology modeling and docking to propose 38 FDA approved drugs for testing and identified several active molecules. To follow on from this, we now describe the in vitro testing of a library of 639 compounds. These data were used to create machine learning models for Mttopo I which were further validated. The combined Mttopo I Bayesian model had a 5 fold cross validation receiver operator characteristic of 0.74 and sensitivity, specificity and concordance values above 0.76 and was used to select commercially available compounds for testing in vitro. The recently described crystal structure of Mttopo I was also compared with the previously described homology model and then used to dock the Mttopo I actives norclomipramine and imipramine. In summary, we describe our efforts to identify small molecule inhibitors of Mttopo I using a combination of machine learning modeling and docking studies in conjunction with screening of the selected molecules for enzyme inhibition. We demonstrate the experimental inhibition of Mttopo I by small molecule inhibitors and show that the enzyme can be readily targeted for lead molecule development.

Structure-activity relationships for the antifungal activity of selective estrogen receptor antagonists related to tamoxifen.

Cryptococcosis is one of the most important invasive fungal infections and is a significant contributor to the mortality associated with HIV/AIDS. As part of our program to repurpose molecules related to the selective estrogen receptor modulator (SERM) tamoxifen as anti-cryptococcal agents, we have explored the structure-activity relationships of a set of structurally diverse SERMs and tamoxifen derivatives. Our data provide the first insights into the structural requirements for the antifungal activity of this scaffold. Three key molecular characteristics affecting anti-cryptococcal activity emerged from our studies: 1) the presence of an alkylamino group tethered to one of the aromatic rings of the triphenylethylene core; 2) an appropriately sized aliphatic substituent at the 2 position of the ethylene moiety; and 3) electronegative substituents on the aromatic rings modestly improved activity. Using a cell-based assay of calmodulin antagonism, we found that the anti-cryptococcal activity of the scaffold correlates with calmodulin inhibition. Finally, we developed a homology model of C. neoformans calmodulin and used it to rationalize the structural basis for the activity of these molecules. Taken together, these data and models provide a basis for the further optimization of this promising anti-cryptococcal scaffold.

An integrated suite of modeling tools that empower scientists in structure- and property-based drug design.

Structure- and property-based drug design is an integral part of modern drug discovery, enabling the design of compounds aimed at improving potency and selectivity. However, building molecules using desktop modeling tools can easily lead to poor designs that appear to form many favorable interactions with the protein's active site. Although a proposed molecule looks good on screen and appears to fit into the protein site X-ray crystal structure or pharmacophore model, doing so might require a high-energy small molecule conformation, which would likely be inactive. To help scientists make better design decisions, we have built integrated, easy-to-use, interactive software tools to perform docking experiments, de novo design, shape and pharmacophore based database searches, small molecule conformational analysis and molecular property calculations. Using a combination of these tools helps scientists in assessing the likelihood that a designed molecule will be active and have desirable drug metabolism and pharmacokinetic properties. Small molecule discovery success requires project teams to rapidly design and synthesize potent molecules with good ADME properties. Empowering scientists to evaluate ideas quickly and make better design decisions with easy-to-access and easy-to-understand software on their desktop is now a key part of our discovery process.

Targeting Mycobacterium tuberculosis topoisomerase I by small-molecule inhibitors.

We describe inhibition of Mycobacterium tuberculosis topoisomerase I (MttopoI), an essential mycobacterial enzyme, by two related compounds, imipramine and norclomipramine, of which imipramine is clinically used as an antidepressant. These molecules showed growth inhibition of both Mycobacterium smegmatis and M. tuberculosis cells. The mechanism of action of these two molecules was investigated by analyzing the individual steps of the topoisomerase I (topoI) reaction cycle. The compounds stimulated cleavage, thereby perturbing the cleavage-religation equilibrium. Consequently, these molecules inhibited the growth of the cells overexpressing topoI at a low MIC. Docking of the molecules on the MttopoI model suggested that they bind near the metal binding site of the enzyme. The DNA relaxation activity of the metal binding mutants harboring mutations in the DxDxE motif was differentially affected by the molecules, suggesting that the metal coordinating residues contribute to the interaction of the enzyme with the drug. Taken together, the results highlight the potential of these small molecules, which poison the M. tuberculosis and M. smegmatis topoisomerase I, as leads for the development of improved molecules to combat mycobacterial infections. Moreover, targeting metal coordination in topoisomerases might be a general strategy to develop new lead molecules.

Inhibition of Mycobacterium tuberculosis topoisomerase I by m-AMSA, a eukaryotic type II topoisomerase poison.

m-AMSA, an established inhibitor of eukaryotic type II topoisomerases, exerts its cidal effect by binding to the enzyme-DNA complex thus inhibiting the DNA religation step. The molecule and its analogues have been successfully used as chemotherapeutic agents against different forms of cancer. After virtual screening using a homology model of the Mycobacterium tuberculosis topoisomerase I, we identified m-AMSA as a high scoring hit. We demonstrate that m-AMSA can inhibit the DNA relaxation activity of topoisomerase I from M. tuberculosis and Mycobacterium smegmatis. In a whole cell assay, m-AMSA inhibited the growth of both the mycobacteria.

Cis-amide isosteric replacement in thienobenzoxepin inhibitors of PI3-kinase.

Substructural class effects surrounding replacement of a 'cis' N-methyl aniline amide within potent and selective thienobenzoxepin PI3-kinase inhibitors are disclosed. While a simple aryl to alkyl switch was not tolerated due to differences in preferred amide conformation, heterocyclic amide isosteres with maintained aryl substitution improved potency and metabolic stability at the cost of physical properties. These gains in potency allowed lipophilic deconstruction of the arene to simple branched alkyl substituents. As such, overall lipophilicity-neutral, MW decreases were realized relative to the aniline amide series. The improved properties for lead compound 21 resulted in high permeability, solubility and bioavailability.

Design of a gene family screening library targeting G-protein coupled receptors.

An iterative process for the design of a G-protein coupled receptor (GPCR) gene family screening library has been developed. A key element of this process is the computational generation of pharmacophore descriptors of known GPCR ligands. Subsequent iterative analysis allows prioritization of scaffolds and sub-libraries for inclusion in the library. The final library, which consisted of 13,769 compounds, displayed a 2.6% hit rate when screened against the micro-opioid receptor.

Performance of 3D-database molecular docking studies into homology models.

The performance of docking studies into protein active sites constructed by homology model building was investigated using CDK2 and factor VIIa screening data sets. When the sequence identity between model and template near the binding site area is greater than approximately 50%, roughly 5 times more active compounds are identified than would be found randomly. This performance is comparable to docking to crystal structures.

Comparing performance of computational tools for combinatorial library design.

In using computational tools for library design it is necessary to understand the performance and limitations of available methods. This letter reports systematic comparisons of applying ligand-based and structure-based tools across therapeutic project-derived data sets. Included are assessments of performance in real-world iterative design applications and the utility of target structural information. The results suggest that combining screening and target structure information is robust; further, a well-designed screening library can compensate for lacking structural information.

Informative library design as an efficient strategy to identify and optimize leads: application to cyclin-dependent kinase 2 antagonists.

The application of an informative, iterative library design strategy is presented for lead identification and optimization. The computational algorithm underlying informative design systematically uses data from both active and inactive compounds and maximizes the information gained from subsequent design-synthesis-screening cycles. Retrospective analysis of a released dataset of 17 550 compounds and corresponding cyclin-dependent kinase-2 activities showed that informative library design yields significant enrichments of active compounds and efficiently discovers novel chemotypes in comparison with commonly used diversity-similarity protocols.

Luddite: an information-theoretic library design tool.

We present an algorithm for the design of either combinatorial or discrete informative libraries. This approach is based on information theoretic techniques used extensively in coding theory. We have extended the information theoretic formalism to include an arbitrary number of property distribution constraints, such as Lipinski "drug-like" distributions. The method is demonstrated by comparing and contrasting a variety of different libraries selected from a single combinatorial source pool of compounds.

Coupling structure-based design with combinatorial chemistry: application of active site derived pharmacophores with informative library design.

Protein structural information is combined with combinatorial library design in the following protocol. Active site maps are generated from protein structures. All possible 2-, 3- and 4-point pharmacophores are enumerated from the active site map and encoded as bit strings. The pharmacophores define a design space that can be used to select compounds using an informative library design tool. The method was evaluated against a collection of compounds assayed previously against a cyclin-dependent kinase target, CDK-2, starting with 23 X-ray co-crystal structures. Performance was assessed based on the number of active scaffolds selected after four rounds of iterative informative library design. The method selects compounds from 12 out of the 15 active scaffolds from the CDK-2 library and outperforms a two-dimensional similarity search and docking calculations.

Evaluation of a novel shape-based computational filter for lead evolution: application to thrombin inhibitors.

A novel shape-feature-based computational method is described and used to rapidly filter compound libraries. The computational model, built using three-dimensional conformations of active and inactive molecules, consists of a collection of whole molecule shapes and chemical feature positions that are ranked according to their correlation with activity. A small ensemble of these shapes and features is used to filter virtual compound libraries. The method is applied to two thrombin data sets and is shown to be efficient in identifying novel scaffolds with enhanced hit rates.