Process Development and Scale-Up of a Novel Atypical DAT Inhibitor (S)-CE-123.

Large-scale syntheses of small molecules and kilo laboratories are crucial steps in drug development, especially in advanced stages. (S)-5-((Benzhydrylsulfinyl)methyl)thiazole, (S)-CE-123, a potent, selective, and novel atypical DAT inhibitor, has undergone iterative testing as part of the preclinical evaluation step. This required the process transfer, scale-up, and synthesis of a 1 kg preclinical batch. The Kagan protocol for asymmetric sulfide to sulfoxide oxidation was successfully applied within a four-step synthetic process for the successful upscaling of (S)-CE-123. During the scale-up of the last step, several changes were made to the original synthetic procedure, as with every increase in batch size, new problems had to be overcome. These include, among others, the workup optimization of the last step, the simplification of chromatographic purification, elution modification to improve the purity of the product and saving of workup time. Two washing steps were added to the original procedure to enhance both the yield and the enantiomeric excess value of the final product. The modifications introduced allowed access to a 1 kg (S)-CE-123 batch with a purity >99% and an enantiomeric excess value of 95%.

Reweighting from Molecular Mechanics Force Fields to the ANI-2x Neural Network Potential.

To achieve chemical accuracy in free energy calculations, it is necessary to accurately describe the system's potential energy surface and efficiently sample configurations from its Boltzmann distribution. While neural network potentials (NNPs) have shown significantly higher accuracy than classical molecular mechanics (MM) force fields, they have a limited range of applicability and are considerably slower than MM potentials, often by orders of magnitude. To address this challenge, Rufa et al. [Rufa et al. bioRxiv 2020, 10.1101/2020.07.29.227959.] suggested a two-stage approach that uses a fast and established MM alchemical energy protocol, followed by reweighting the results using NNPs, known as endstate correction or indirect free energy calculation. This study systematically investigates the accuracy and robustness of reweighting from an MM reference to a neural network target potential (ANI-2x) for an established data set in vacuum, using single-step free-energy perturbation (FEP) and nonequilibrium (NEQ) switching simulation. We assess the influence of longer switching lengths and the impact of slow degrees of freedom on outliers in the work distribution and compare the results to those of multistate equilibrium free energy simulations. Our results demonstrate that free energy calculations between NNPs and MM potentials should be preferably performed using NEQ switching simulations to obtain accurate free energy estimates. NEQ switching simulations between the MM potentials and NNPs are efficient, robust, and trivial to implement.

Active Learning Approach for Guiding Site-of-Metabolism Measurement and Annotation.

The ability to determine and predict metabolically labile atom positions in a molecule (also called "sites of metabolism" or "SoMs") is of high interest to the design and optimization of bioactive compounds, such as drugs, agrochemicals, and cosmetics. In recent years, several in silico models for SoM prediction have become available, many of which include a machine-learning component. The bottleneck in advancing these approaches is the coverage of distinct atom environments and rare and complex biotransformation events with high-quality experimental data. Pharmaceutical companies typically have measured metabolism data available for several hundred to several thousand compounds. However, even for metabolism experts, interpreting these data and assigning SoMs are challenging and time-consuming. Therefore, a significant proportion of the potential of the existing metabolism data, particularly in machine learning, remains dormant. Here, we report on the development and validation of an active learning approach that identifies the most informative atoms across molecular data sets for SoM annotation. The active learning approach, built on a highly efficient reimplementation of SoM predictor FAME 3, enables experts to prioritize their SoM experimental measurements and annotation efforts on the most rewarding atom environments. We show that this active learning approach yields competitive SoM predictors while requiring the annotation of only 20% of the atom positions required by FAME 3. The source code of the approach presented in this work is publicly available.

Design, synthesis, and lead optimization of piperazinyl-pyrimidine analogues as potent small molecules targeting the viral capping machinery of Chikungunya virus.

The worldwide re-emerge of the Chikungunya virus (CHIKV), the high morbidity associated with it, and the lack of an available vaccine or antiviral treatment make the development of a potent CHIKV-inhibitor highly desirable. Therefore, an extensive lead optimization was performed based on the previously reported CHVB compound 1b and the reported synthesis route was optimized - improving the overall yield in remarkably shorter synthesis and work-up time. Hundred analogues were designed, synthesized, and investigated for their antiviral activity, physiochemistry, and toxicological profile. An extensive structure-activity relationship study (SAR) was performed, which focused mainly on the combination of scaffold changes and revealed the key chemical features for potent anti-CHIKV inhibition. Further, a thorough ADMET investigation of the compounds was carried out: the compounds were screened for their aqueous solubility, lipophilicity, their toxicity in CaCo-2 cells, and possible hERG channel interactions. Additionally, 55 analogues were assessed for their metabolic stability in human liver microsomes (HLMs), leading to a structure-metabolism relationship study (SMR). The compounds showed an excellent safety profile, favourable physicochemical characteristics, and the required metabolic stability. A cross-resistance study confirmed the viral capping machinery (nsP1) to be the viral target of these compounds. This study identified 31b and 34 as potent, safe, and stable lead compounds for further development as selective CHIKV inhibitors. Finally, the collected insight led to a successful scaffold hop (64b) for future antiviral research studies.

Pharmacokinetics of Novel Dopamine Transporter Inhibitor CE-123 and Modafinil with a Focus on Central Nervous System Distribution.

S-CE-123, a novel dopamine transporter inhibitor, has emerged as a potential candidate for cognitive enhancement. The objective of this study was to compare the tissue distribution profiles, with a specific focus on central nervous system distribution and metabolism, of S-CE-123 and R-modafinil. To address this objective, a precise liquid chromatography-high resolution mass spectrometry method was developed and partially validated. Neuropharmacokinetic parameters were assessed using the Combinatory Mapping Approach. Our findings reveal distinct differences between the two compounds. Notably, S-CE-123 demonstrates a significantly superior extent of transport across the blood-brain barrier (BBB), with an unbound brain-to-plasma concentration ratio (Kp,uu,brain) of 0.5, compared to R-modafinil's Kp,uu,brain of 0.1. A similar pattern was observed for the transport across the blood-spinal cord barrier. Concerning the drug transport across cellular membranes, we observed that S-CE-123 primarily localizes in the brain interstitial space, whereas R-modafinil distributes more evenly across both sides of the plasma membrane of the brain's parenchymal cells (Kp,uu,cell). Furthermore, our study highlights the substantial differences in hepatic metabolic stability, with S-CE-123 having a 9.3-fold faster metabolism compared to R-modafinil. In summary, the combination of improved BBB transport and higher affinity of S-CE-123 to dopamine transporters in comparison to R-modafinil makes S-CE-123 a promising candidate for further testing for the treatment of cognitive decline.

Design, synthesis and preclinical evaluation of muscarine receptor antagonists via a scaffold-hopping approach.

Our research group recently identified a rearrangement product of pirenzepine as starting point for a comprehensive rational drug design approach towards orthosteric muscarinic acetylcholine receptor ligands. Chemical reduction and bioscaffold hop lead to the development of sixteen promising compounds featuring either a benzimidazole or carbamate moiety, all exhibiting comparable pharmacophoric characteristics. The synthesized compounds were characterized by NMR, HR-MS, and RP-HPLC techniques. Subsequent evaluation encompassed binding affinity assessment on CHO-hM1-5 cells, mode of action determination, and analysis of physico-chemical parameters. The CNS MPO score indicated favorable drug-like attributes and potential CNS activity for the antagonistic ligands. The most promising compounds displayed Ki-values within a desirable low nanomolar range, and their structural features allow for potential carbon-11 radiolabeling. Our optimization efforts resulted in compounds with a remarkable 138-fold increase in binding affinity compared to the previously mentioned rearrangement product towards human M5, suggesting their prospective utility in positron emission tomography applications.

Chirality Matters: Fine-Tuning of Novel Monoamine Reuptake Inhibitors Selectivity through Manipulation of Stereochemistry.

The high structural similarity, especially in transmembrane regions, of dopamine, norepinephrine, and serotonin transporters, as well as the lack of all crystal structures of human isoforms, make the specific targeting of individual transporters rather challenging. Ligand design itself is also rather limited, as many chemists, fully aware of the synthetic and analytical challenges, tend to modify lead compounds in a way that reduces the number of chiral centers and hence limits the potential chemical space of synthetic ligands. We have previously shown that increasing molecular complexity by introducing additional chiral centers ultimately leads to more selective and potent dopamine reuptake inhibitors. Herein, we significantly extend our structure-activity relationship of dopamine transporter-selective ligands and further demonstrate how stereoisomers of defined absolute configuration may fine-tune and direct the activity towards distinct targets. From the pool of active compounds, using the examples of stereoisomers 7h and 8h, we further showcase how in vitro activity significantly differs in in vivo drug efficacy experiments, calling for proper validation of individual stereoisomers in animal studies. Furthermore, by generating a large library of compounds with defined absolute configurations, we lay the groundwork for computational chemists to further optimize and rationally design specific monoamine transporter reuptake inhibitors.

Base-mediated homologative rearrangement of nitrogen-oxygen bonds of N-methyl-N-oxyamides.

Due to the well known reactivity of C(O)-N functionalities towards canonical C1-homologating agents (e.g. carbenoids, diazomethane, ylides), resulting in the extrusion of the N-centered fragment en route to carbonyl compounds, formal C1-insertions within N-O bonds still remain obscure. Herein, we document the homologative transformation of N-methyl-N-oxyamides - with high tolerance for diverse O-substituents - into N-acyl-N,O-acetals. Under controlled basic conditions, the N-methyl group of the same starting materials acts as a competent precursor of the methylene synthon required for the homologation. The logic is levered on the formation of an electrophilic iminium ion (via N-O heterolysis) susceptible to nucleophilic attack by the alkoxide previously expulsed. The procedure documents genuine chemocontrol and flexibility, as judged by the diversity of substituents placed on both amide and nitrogen linchpins. The mechanistic rationale was validated through experiments conducted on D-labeled materials which unambiguously attributed the origin of the methylene fragment to the N-methyl group of the starting compounds.

High-Quality Conformer Generation with CONFORGE: Algorithm and Performance Assessment.

Knowledge of the putative bound-state conformation of a molecule is an essential prerequisite for the successful application of many computer-aided drug design methods that aim to assess or predict its capability to bind to a particular target receptor. An established approach to predict bioactive conformers in the absence of receptor structure information is to sample the low-energy conformational space of the investigated molecules and derive representative conformer ensembles that can be expected to comprise members closely resembling possible bound-state ligand conformations. The high relevance of such conformer generation functionality led to the development of a wide panel of dedicated commercial and open-source software tools throughout the last decades. Several published benchmarking studies have shown that open-source tools usually lag behind their commercial competitors in many key aspects. In this work, we introduce the open-source conformer ensemble generator CONFORGE, which aims at delivering state-of-the-art performance for all types of organic molecules in drug-like chemical space. The ability of CONFORGE and several well-known commercial and open-source conformer ensemble generators to reproduce experimental 3D structures as well as their computational efficiency and robustness has been assessed thoroughly for both typical drug-like molecules and macrocyclic structures. For small molecules, CONFORGE clearly outperformed all other tested open-source conformer generators and performed at least equally well as the evaluated commercial generators in terms of both processing speed and accuracy. In the case of macrocyclic structures, CONFORGE achieved the best average accuracy among all benchmarked generators, with RDKit's generator coming close in second place.

Identification of Promising Sulfonamide Chalcones as Inhibitors of SARS-CoV-2 3CLpro through Structure-Based Virtual Screening and Experimental Approaches.

3CLpro is a viable target for developing antiviral therapies against the coronavirus. With the urgent need to find new possible inhibitors, a structure-based virtual screening approach was developed. This study recognized 75 pharmacologically bioactive compounds from our in-house library of 1052 natural product-based compounds that satisfied drug-likeness criteria and exhibited good bioavailability and membrane permeability. Among these compounds, three promising sulfonamide chalcones were identified by combined theoretical and experimental approaches, with SWC423 being the most suitable representative compound due to its competitive inhibition and low cytotoxicity in Vero E6 cells (EC50 = 0.89 ± 0.32 µM; CC50 = 25.54 ± 1.38 µM; SI = 28.70). The binding and stability of SWC423 in the 3CLpro active site were investigated through all-atom molecular dynamics simulation and fragment molecular orbital calculation, indicating its potential as a 3CLpro inhibitor for further SARS-CoV-2 therapeutic research. These findings suggested that inhibiting 3CLpro with a sulfonamide chalcone such as SWC423 may pave the effective way for developing COVID-19 treatments.

Back in Person: Frontiers in Medicinal Chemistry 2023.

The Frontiers in Medicinal Chemistry (FiMC) is the largest international Medicinal Chemistry conference in the German speaking area and took place from April 3rd to 5th 2023 in Vienna (Austria). Fortunately, after being cancelled in 2020 and two years (2021-2022) of entirely virtual meetings, due to the COVID-19 pandemic, the FiMC could be held in a face-to-face format again. Organized by the Division of Medicinal Chemistry of the German Chemical Society (GDCh), the Division of Pharmaceutical and Medicinal Chemistry of the German Pharmaceutical Society (DPhG), together with the Division of Medicinal Chemistry of the Austrian Chemical Society (GÖCH), the Austrian Pharmaceutical Society (ÖPhG), and a local organization committee from the University of Vienna headed by Thierry Langer, the meeting brought together 260 participants from 21 countries. The program included 38 lectures by leading scientists from industry and academia as well as early career investigators. Moreover, 102 posters were presented in two highly interactive poster sessions.

GABAA receptor-mediated seizure liabilities: a mixed-methods screening approach.

GABAA receptors, members of the pentameric ligand-gated ion channel superfamily, are widely expressed in the central nervous system and mediate a broad range of pharmaco-toxicological effects including bidirectional changes to seizure threshold. Thus, detection of GABAA receptor-mediated seizure liabilities is a big, partly unmet need in early preclinical drug development. This is in part due to the plethora of allosteric binding sites that are present on different subtypes of GABAA receptors and the critical lack of screening methods that detect interactions with any of these sites. To improve in silico screening methods, we assembled an inventory of allosteric binding sites based on structural data. Pharmacophore models representing several of the binding sites were constructed. These models from the NeuroDeRisk IL Profiler were used for in silico screening of a compiled collection of drugs with known GABAA receptor interactions to generate testable hypotheses. Amoxapine was one of the hits identified and subjected to an array of in vitro assays to examine molecular and cellular effects on neuronal excitability and in vivo locomotor pattern changes in zebrafish larvae. An additional level of analysis for our compound collection is provided by pharmacovigilance alerts using FAERS data. Inspired by the Adverse Outcome Pathway framework, we postulate several candidate pathways leading from specific binding sites to acute seizure induction. The whole workflow can be utilized for any compound collection and should inform about GABAA receptor-mediated seizure risks more comprehensively compared to standard displacement screens, as it rests chiefly on functional data.

A new set of KNIME nodes implementing the QPhAR algorithm.

Dissemination of novel research methods, especially in the form of chemoinformatics software, depends heavily on their ease of applicability for non-expert users with only a little or no programming skills and knowledge in computer science. Visual programming has become widely popular over the last few years, also enabling researchers without in-depth programming skills to develop tailored data processing pipelines using elements from a repository of predefined standard procedures. In this work, we present the development of a set of nodes for the KNIME platform implementing the QPhAR algorithm. We show how the developed KNIME nodes can be included in a typical workflow for biological activity prediction. Furthermore, we present best-practice guidelines that should be followed to obtain high-quality QPhAR models. Finally, we show a typical workflow to train and optimise a QPhAR model in KNIME for a set of given input compounds, applying the discussed best practices.

Synthesis, analysis of mol-ecular and crystal structures, estimation of inter-molecular inter-actions and biological properties of 1-benzyl-6-fluoro-3-[5-(4-methylcyclohexyl)-1,2,4-oxadiazol-3-yl]-7-(piperidin-1-yl)quinolin-4-one.

The title compound, C30H33N4O2F, can be obtained via a two-step synthetic scheme involving 1-benzyl-6-fluoro-4-oxo-7-(piperidin-1-yl)-1,4-di-hydro-quino-line-3-carbo-nitrile as a starting compound that undergoes substitution with hydroxyl-amine and subsequent cyclization with 4-methyl-cyclo-hexane-1-carb-oxy-lic acid. It crystallizes from 2-propanol in the triclinic space group P with a mol-ecule of the title compound and one of 2-propanol in the asymmetric unit. After the mol-ecular structure was clarified using NMR and LC/MS, the mol-ecular and crystalline arrangements were defined with SC-XRD. A Hirshfeld surface analysis was performed for a better understanding of the inter-molecular inter-actions. One strong (O-H⋯O) and three weak [C-H⋯F (intra-molecular) and two C-H⋯O] hydrogen bonds were found. The contributions of short contacts to the Hirshfeld surface were estimated using two-dimensional fingerprint plots showing that O⋯H/H⋯O, C⋯H/H⋯C and C⋯C contacts are the most significant for the title compound and O⋯H for the 2-propanol. The crystal structure appears to have isotropically packed tetra-mers containing two mol-ecules of the title compound and two mol-ecules of 2-propanol as the building unit according to analysis of the distribution of pairwise inter-action energies. A mol-ecular docking study was carried out to evaluate the inter-actions of the title compound with the active centers of macromolecules corresponding to viral targets, namely, anti-hepatitis B activity [HBV, capsid Y132A mutant (VCID 8772) PDB ID: 5E0I] and anti-COVID-19 main protease activity (PDB ID: 6LU7). The data obtained revealed a noticeable affinity towards them that exceeded that of the reference ligands.

Low-Affinity/High-Selectivity Dopamine Transport Inhibition Sufficient to Rescue Cognitive Functions in the Aging Rat.

The worldwide increase in cognitive decline, both in aging and with psychiatric disorders, warrants a search for pharmacological treatment. Although dopaminergic treatment approaches represent a major step forward, current dopamine transporter (DAT) inhibitors are not sufficiently specific as they also target other transporters and receptors, thus showing unwanted side effects. Herein, we describe an enantiomerically pure, highly specific DAT inhibitor, S-CE-123, synthetized in our laboratory. Following binding studies to DAT, NET and SERT, GPCR and kinome screening, pharmacokinetics and a basic neurotoxic screen, S-CE-123 was tested for its potential to enhance and/or rescue cognitive functions in young and in aged rats in the non-invasive reward-motivated paradigm of a hole-board test for spatial learning. In addition, an open field study with young rats was carried out. We demonstrated that S-CE-123 is a low-affinity but highly selective dopamine reuptake inhibitor with good bioavailability. S-CE-123 did not induce hyperlocomotion or anxiogenic or stereotypic behaviour in young rats. Our compound improved the performance of aged but not young rats in a reward-motivated task. The well-described impairment of the dopaminergic system in aging may underlie the age-specific effect. We propose S-CE-123 as a possible candidate for developing a tentative therapeutic strategy for age-related cognitive decline and cognitive dysfunction in psychiatric disorders.

Apo2ph4: A Versatile Workflow for the Generation of Receptor-based Pharmacophore Models for Virtual Screening.

Pharmacophore models are widely used as efficient virtual screening (VS) filters for the target-directed enrichment of large compound libraries. However, the generation of pharmacophore models that have the power to discriminate between active and inactive molecules traditionally requires structural information about ligand-target complexes or at the very least knowledge of one active ligand. The fact that the discovery of the first known active ligand of a newly investigated target represents a major hurdle at the beginning of every drug discovery project underscores the need for methods that are able to derive high-quality pharmacophore models even without the prior knowledge of any active ligand structures. In this work, we introduce a novel workflow, called apo2ph4, that enables the rapid derivation of pharmacophore models solely from the three-dimensional structure of the target receptor. The utility of this workflow is demonstrated retrospectively for the generation of a pharmacophore model for the M2 muscarinic acetylcholine receptor. Furthermore, in order to show the general applicability of apo2ph4, the workflow was employed for all 15 targets of the recently published LIT-PCBA dataset. Pharmacophore-based VS runs using the apo2ph4-derived models achieved a significant enrichment of actives for 13 targets. In the last presented example, a pharmacophore model derived from the etomidate site of the α1ß2γ2 GABAA receptor was used in VS campaigns. Subsequent in vitro testing of selected hits revealed that 19 out of 20 (95%) tested compounds were able to significantly enhance GABA currents, which impressively demonstrates the applicability of apo2ph4 for real-world drug design projects.

Novel alpha6 preferring GABA-A receptor ligands based on loreclezole.

The family of GABA-A receptors contains nineteen mammalian subunits from which pentameric, GABA gated anion channels are assembled. The subunit encoded by the GABRA6 gene is highly expressed in the cerebellum and the receptors to which it contributes have recently been demonstrated to be a promising candidate as a novel drug target. Here we examined a series of loreclezole derivatives for potentially selective action at α6ß3γ2 receptors with the help of computational methods and functional testing with the two-electrode voltage clamp technique. The synthetic routes to some previously published ligands were improved, and a new derivative was synthesized based on computational docking results. This new loreclezole derivative, [3-(2-chloro-4-methylphenyl)-3-methylbutanenitrile (40)], was shown to display stronger modulatory action in concatenated α6ß3γ2 receptors compared to their α1ß3γ2 counterpart. The hypothetical bound state structure provides valuable guidance for future design of selective therapeutics.

Novel Class of Proteasome Inhibitors: In Silico and In Vitro Evaluation of Diverse Chloro(trifluoromethyl)aziridines.

The ubiquitin-proteasome pathway (UPP) is the major proteolytic system in the cytosol and nucleus of all eukaryotic cells. The role of proteasome inhibitors (PIs) as critical agents for regulating cancer cell death has been established. Aziridine derivatives are well-known alkylating agents employed against cancer. However, to the best of our knowledge, aziridine derivatives showing inhibitory activity towards proteasome have never been described before. Herein we report a new class of selective and nonPIs bearing an aziridine ring as a core structure. In vitro cell-based assays (two leukemia cell lines) also displayed anti-proliferative activity for some compounds. In silico studies indicated non-covalent binding mode and drug-likeness for these derivatives. Taken together, these results are promising for developing more potent PIs.

Pharmacophore-Based Virtual Screening and Experimental Validation of Pyrazolone-Derived Inhibitors toward Janus Kinases.

Janus kinases (JAKs) are nonreceptor protein tyrosine kinases that play a role in a broad range of cell signaling. JAK2 and JAK3 have been involved in the pathogenesis of common lymphoid-derived diseases and leukemia cancer. Thus, inhibition of both JAK2 and JAK3 can be a potent strategy to reduce the risk of these diseases. In the present study, the pharmacophore models built based on the commercial drug tofacitinib and the JAK2/3 proteins derived from molecular dynamics (MD) trajectories were employed to search for a dual potent JAK2/3 inhibitor by a pharmacophore-based virtual screening of 54 synthesized pyrazolone derivatives from an in-house data set. Twelve selected compounds from the virtual screening procedure were then tested for their inhibitory potency against both JAKs in the kinase assay. The in vitro kinase inhibition experiment indicated that compounds 3h, TK4g, and TK4b can inhibit both JAKs in the low nanomolar range. Among them, the compound TK4g showed the highest protein kinase inhibition with the half-maximal inhibitory concentration (IC50) value of 12.61 nM for JAK2 and 15.80 nM for JAK3. From the MD simulations study, it could be found that the sulfonamide group of TK4g can form hydrogen bonds in the hinge region at residues E930 and L932 of JAK2 and E903 and L905 of JAK3, while van der Waals interaction also plays a dominant role in ligand binding. Altogether, TK4g, found by virtual screening and biological tests, could serve as a novel therapeutical lead candidate.

Applications of the Novel Quantitative Pharmacophore Activity Relationship Method QPhAR in Virtual Screening and Lead-Optimisation.

Pharmacophores are an established concept for the modelling of ligand-receptor interactions based on the abstract representations of stereoelectronic molecular features. They became widely popular as filters for the fast virtual screening of large compound libraries. A lot of effort has been put into the development of sophisticated algorithms and strategies to increase the computational efficiency of the screening process. However, hardly any focus has been put on the development of automated procedures that optimise pharmacophores towards higher discriminatory power, which still has to be done manually by a human expert. In the age of machine learning, the researcher has become the decision-maker at the top level, outsourcing analysis tasks and recurrent work to advanced algorithms and automation workflows. Here, we propose an algorithm for the automated selection of features driving pharmacophore model quality using SAR information extracted from validated QPhAR models. By integrating the developed method into an end-to-end workflow, we present a fully automated method that is able to derive best-quality pharmacophores from a given input dataset. Finally, we show how the QPhAR-generated models can be used to guide the researcher with insights regarding (un-)favourable interactions for compounds of interest.