Chemical modification patterns for microRNA therapeutic mimics: a structure-activity relationship (SAR) case-study on miR-200c.

microRNA (miRNA) mimics are an emerging class of oligonucleotide therapeutics, with a few compounds already in clinical stages. Synthetic miRNAs are able to restore downregulated levels of intrinsic miRNAs, allowing for parallel regulation of multiple genes involved in a particular disease. In this work, we examined the influence of chemical modifications patterns in miR-200c mimics, assessing the regulation of a selection of target messenger RNAs (mRNA) and, subsequently, of the whole transcriptome in A549 cells. We have probed 37 mimics and provided an initial set of instructions for designing miRNA mimics with potency and selectivity similar to an unmodified miRNA duplex. Additionally, we have examined the stability of selected mimics in serum. Finally, the selected two modification patterns were translated to two other miRNAs, miR-34a and miR-155. To differing degrees, these designs acted on target mRNAs in a similar manner to the unmodified mimic. Here, for the first time, we describe a structured overview of 'miRNA mimics modification templates' that are chemically stabilised and optimised for use in an in vitro set up and highlight the need of further sequence specific optimization when mimics are to be used beyond in vitro tool experiments.

Sequence-based prediction of the intrinsic solubility of peptides containing non-natural amino acids.

Non-natural amino acids are increasingly used as building blocks in the development of peptide-based drugs as they expand the available chemical space to tailor function, half-life and other key properties. However, while the chemical space of modified amino acids (mAAs) such as residues containing post-translational modifications (PTMs) is potentially vast, experimental methods for measuring the developability properties of mAA-containing peptides are expensive and time consuming. To facilitate developability programs through computational methods, we present CamSol-PTM, a method that enables the fast and reliable sequence-based prediction of the intrinsic solubility of mAA-containing peptides in aqueous solution at room temperature. From a computational screening of 50,000 mAA-containing variants of three peptides, we selected five different small-size mAAs for a total number of 37 peptide variants for experimental validation. We demonstrate the accuracy of the predictions by comparing the calculated and experimental solubility values. Our results indicate that the computational screening of mAA-containing peptides can extend by over four orders of magnitude the ability to explore the solubility chemical space of peptides and confirm that our method can accurately assess the solubility of peptides containing mAAs. This method is available as a web server at https://www-cohsoftware.ch.cam.ac.uk/index.php/camsolptm .

Strategies for predictive modeling of overloaded oligonucleotide elution profiles in ion-pair chromatography.

Due to their potential for gene regulation, oligonucleotides have moved into focus as one of the preferred modalities modulating currently undruggable disease-associated targets. In the course of synthesis and storage of oligonucleotides a significant number of compound-related impurities can be generated. Purification protocols and analytical methods have become crucial for the therapeutic application of any oligonucleotides, be they antisense oligonucleotides (ASOs), small interfering ribonucleic acids (siRNAs) or conjugates. Ion-pair chromatography is currently the standard method for separating and analyzing therapeutic oligonucleotides. Although mathematical modeling can improve the accuracy and efficiency of ion-pair chromatography, its application remains challenging. Simple models may not be suitable to treat advanced single molecules, while complex models are still inefficient for industrial oligonucleotide optimization processes. Therefore, fundamental research to improve the accuracy and simplicity of mathematical models in ion-pair chromatography is still a necessity. In this study, we predict overloaded concentration profiles of oligonucleotides in ion-pair chromatography and compare relatively simple and more advanced predictive models. The experimental system consists of a traditional C18 column using (dibutyl)amine as the ion-pair reagent and acetonitrile as organic modifier. The models were built and tested based on three crude 16-mer oligonucleotides with varying degrees of phosphorothioation, as well as their respective n - 1 and (P = O)1 impurities. In short, the proposed models were suitable to predict the overloaded concentration profiles for different slopes of the organic modifier gradient and column load.

Gymnotic uptake of AntimiRs alter microRNA-34a levels in 2D and 3D epithelial cell culture.

MicroRNAs are attractive therapeutic targets in many diseases, including chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis. Among microRNA inhibitors antimiRs have been proven successful in lowering aberrant microRNA levels in the clinic. We present a set of antimiRs targeting miR-34a, which has been shown to be dysregulated in chronic lung diseases. The tool compounds were taken up by a bronchial epithelial cell line and primary human bronchial epithelial cells, followed by efficient knockdown of miR-34a. Similar results were observed in 3D differentiated primary human bronchial epithelial cells cultured at the air-liquid interface. Varying chemical properties of antimiRs had significant impact on cellular uptake and potency, resulting in effective tool compounds for use in lung-relevant cellular systems. This report demonstrates gymnotic antimiR uptake and activity in 3D epithelial cell culture after apical administration, mimicking inhalation conditions.

European Medicinal Chemistry Leaders in Industry (EMCL) - On the Status and Future of Medicinal Chemistry Research in Europe.

The status of industrial Medicinal Chemistry was discussed with European Medicinal Chemistry Leaders from large to mid-sized pharma and CRO companies as well as biotechs. The chemical modality space has expanded recently from small molecules to address new challenging targets. Besides the classical SAR/SPR optimization of drug molecules also their 'greenness' has increasing importance. The entire pharma discovery ecosystem has developed significantly. Beyond pharma and academia new key players such as Biotech and integrated CROs as well as Digital companies have appeared and are now to a large extend fueled by VC money. Digitalization is happening everywhere but surprisingly did not change speed and success rates of projects so far. Future Medicinal Chemists will still have to be excellent synthetic chemists but in addition they must be knowledgeable in new computational areas such as data sciences. Their ability to collaborate and to work in teams is key.

Ionic Liquid-Mediated Approach for the Synthesis of Site-Specific Thioether Conjugates.

Site-specific conjugation approaches are of great importance in drug discovery, notably for the synthesis of biochemical probes or molecular conjugates for targeted delivery. Herein, we report a mild ionic liquid (IL)-mediated thiolation technique that relies on the use of 1,3-ethyl-methyl imidazolium acetate, [C2 mim][OAc] as a solvent and precursor to generate activated IL, as well as a solvent for the conjugation reaction. First, a focused library of active ILs was prepared for functionalizing/conjugating cysteine-containing small molecules and unprotected peptides. Interestingly, a bifunctional active IL could also be successfully employed as a linker for the conjugation of peptides lacking Cys. This study sets the ground for further investigation of the use of active ILs for modifying, labeling or conjugating larger and more complex therapeutic modalities such as proteins and antibodies.

Analyzing proteolytic stability and metabolic hotspots of therapeutic peptides in two rodent pulmonary fluids.

Peptides and peptide drug conjugates are emerging modalities to treat pulmonary diseases. Peptides are susceptible to proteolytic cleavage. Expression levels of specific proteases in the lung can be significantly increased in disease state and may lead to exaggerated peptide proteolysis. To support optimization of peptides for inhaled administration, we have recently reported a streamlined high-throughput LC-HRMS protocol to determine enzymatic protease stability of peptides. This method has now been complemented with profiling of peptide metabolic stability in two respiratory fluids, a lung supernatant (lung S9) and a bronchioalveolar lavage fluid (BALF) taken from rats. We have tested a set of 28 peptides with high structural diversity, analyzed the whole data set for formed metabolites, and identified the differences of cleavage pattern in the two test fluids. Comparison of our experimental results and literature-derived cleavage site estimates based on e.g. MEROPS show significant differences for a number of peptides. This indicates the need for an experimental workflow using both protease panels and testing of metabolic stability in lung fluid (BALF) to guide peptide optimization and selection of peptides for inhaled in vivo PK/PD studies in our drug discovery projects.

Machine learning/molecular dynamic protein structure prediction approach to investigate the protein conformational ensemble.

Proteins exist in several different conformations. These structural changes are often associated with fluctuations at the residue level. Recent findings show that co-evolutionary analysis coupled with machine-learning techniques improves the precision by providing quantitative distance predictions between pairs of residues. The predicted statistical distance distribution from Multi Sequence Analysis reveals the presence of different local maxima suggesting the flexibility of key residue pairs. Here we investigate the ability of the residue-residue distance prediction to provide insights into the protein conformational ensemble. We combine deep learning approaches with mechanistic modeling to a set of proteins that experimentally showed conformational changes. The predicted protein models were filtered based on energy scores, RMSD clustering, and the centroids selected as the lowest energy structure per cluster. These models were compared to the experimental-Molecular Dynamics (MD) relaxed structure by analyzing the backbone residue torsional distribution and the sidechain orientations. Our pipeline allows to retrieve the experimental structural dynamics experimentally represented by different X-ray conformations for the same sequence as well the conformational space observed with the MD simulations. We show the potential correlation between the experimental structure dynamics and the predicted model ensemble demonstrating the susceptibility of the current state-of-the-art methods in protein folding and dynamics prediction and pointing out the areas of improvement.

Advances in the design of new types of inhaled medicines.

Inhalation of small molecule drugs has proven very efficacious for the treatment of respiratory diseases due to enhanced efficacy and a favourable therapeutic index compared with other dosing routes. It enables targeted delivery to the lung with rapid onset of therapeutic action, low systemic drug exposure, and thereby reduced systemic side effects. An increasing number of pharmaceutical companies and biotechs are investing in new modalities-for this review defined as therapeutic molecules with a molecular weight >800Da and therefore beyond usual inhaled small molecule drug-like space. However, our experience with inhaled administration of PROTACs, peptides, oligonucleotides (antisense oligonucleotides, siRNAs, miRs and antagomirs), diverse protein scaffolds, antibodies and antibody fragments is still limited. Investigating the retention and metabolism of these types of molecules in lung tissue and fluid will contribute to understanding which are best suited for inhalation. Nonetheless, the first such therapeutic molecules have already reached the clinic. This review will provide information on the physiology of healthy and diseased lungs and their capacity for drug metabolism. It will outline the stability, aggregation and immunogenicity aspects of new modalities, as well as recap on formulation and delivery aspects. It concludes by summarising clinical trial outcomes with inhaled new modalities based on information available at the end of 2021.

Transformer-based molecular optimization beyond matched molecular pairs.

Molecular optimization aims to improve the drug profile of a starting molecule. It is a fundamental problem in drug discovery but challenging due to (i) the requirement of simultaneous optimization of multiple properties and (ii) the large chemical space to explore. Recently, deep learning methods have been proposed to solve this task by mimicking the chemist's intuition in terms of matched molecular pairs (MMPs). Although MMPs is a widely used strategy by medicinal chemists, it offers limited capability in terms of exploring the space of structural modifications, therefore does not cover the complete space of solutions. Often more general transformations beyond the nature of MMPs are feasible and/or necessary, e.g. simultaneous modifications of the starting molecule at different places including the core scaffold. This study aims to provide a general methodology that offers more general structural modifications beyond MMPs. In particular, the same Transformer architecture is trained on different datasets. These datasets consist of a set of molecular pairs which reflect different types of transformations. Beyond MMP transformation, datasets reflecting general structural changes are constructed from ChEMBL based on two approaches: Tanimoto similarity (allows for multiple modifications) and scaffold matching (allows for multiple modifications but keep the scaffold constant) respectively. We investigate how the model behavior can be altered by tailoring the dataset while using the same model architecture. Our results show that the models trained on differently prepared datasets transform a given starting molecule in a way that it reflects the nature of the dataset used for training the model. These models could complement each other and unlock the capability for the chemists to pursue different options for improving a starting molecule.

How well does molecular simulation reproduce environment-specific conformations of the intrinsically disordered peptides PLP, TP2 and ONEG?

Understanding the conformational ensembles of intrinsically disordered proteins and peptides (IDPs) in their various biological environments is essential for understanding their mechanisms and functional roles in the proteome, leading to a greater knowledge of, and potential treatments for, a broad range of diseases. To determine whether molecular simulation is able to generate accurate conformational ensembles of IDPs, we explore the structural landscape of the PLP peptide (an intrinsically disordered region of the proteolipid membrane protein) in aqueous and membrane-mimicking solvents, using replica exchange with solute scaling (REST2), and examine the ability of four force fields (ff14SB, ff14IDPSFF, CHARMM36 and CHARMM36m) to reproduce literature circular dichroism (CD) data. Results from variable temperature (VT) 1H and Rotating frame Overhauser Effect SpectroscopY (ROESY) nuclear magnetic resonance (NMR) experiments are also presented and are consistent with the structural observations obtained from the simulations and CD. We also apply the optimum simulation protocol to TP2 and ONEG (a cell-penetrating peptide (CPP) and a negative control peptide, respectively) to gain insight into the structural differences that may account for the observed difference in their membrane-penetrating abilities. Of the tested force fields, we find that CHARMM36 and CHARMM36m are best suited to the study of IDPs, and accurately predict a disordered to helical conformational transition of the PLP peptide accompanying the change from aqueous to membrane-mimicking solvents. We also identify an α-helical structure of TP2 in the membrane-mimicking solvents and provide a discussion of the mechanistic implications of this observation with reference to the previous literature on the peptide. From these results, we recommend the use of CHARMM36m with the REST2 protocol for the study of environment-specific IDP conformations. We believe that the simulation protocol will allow the study of a broad range of IDPs that undergo conformational transitions in different biological environments.

Automated high-throughput in vitro assays to identify metabolic hotspots and protease stability of structurally diverse, pharmacologically active peptides for inhalation.

The inhalation of peptides comes with the advantage of directly targeting the lung as tissue of interest. However, peptides are often rapidly metabolized in lung tissue through proteolytic cleavage. We have developed an assay workflow to obtain half-life and metabolite ID data for peptides incubated with four proteases abundant in lungs of asthma and COPD patients. The assay system has been validated using 28 structurally diverse linear and cyclic peptides with a molecular weight between 708 and 5808 Da. Experimental conditions for incubation, sample preparation, chromatography, data acquisition and analysis are compatible with the required throughput in early stage peptide projects. Together with co-crystal structures and Ala scans, we are using the described assay workflow to guide the first chemical modifications of peptide hits in early respiratory drug discovery projects.

Molecular optimization by capturing chemist's intuition using deep neural networks.

A main challenge in drug discovery is finding molecules with a desirable balance of multiple properties. Here, we focus on the task of molecular optimization, where the goal is to optimize a given starting molecule towards desirable properties. This task can be framed as a machine translation problem in natural language processing, where in our case, a molecule is translated into a molecule with optimized properties based on the SMILES representation. Typically, chemists would use their intuition to suggest chemical transformations for the starting molecule being optimized. A widely used strategy is the concept of matched molecular pairs where two molecules differ by a single transformation. We seek to capture the chemist's intuition from matched molecular pairs using machine translation models. Specifically, the sequence-to-sequence model with attention mechanism, and the Transformer model are employed to generate molecules with desirable properties. As a proof of concept, three ADMET properties are optimized simultaneously: logD, solubility, and clearance, which are important properties of a drug. Since desirable properties often vary from project to project, the user-specified desirable property changes are incorporated into the input as an additional condition together with the starting molecules being optimized. Thus, the models can be guided to generate molecules satisfying the desirable properties. Additionally, we compare the two machine translation models based on the SMILES representation, with a graph-to-graph translation model HierG2G, which has shown the state-of-the-art performance in molecular optimization. Our results show that the Transformer can generate more molecules with desirable properties by making small modifications to the given starting molecules, which can be intuitive to chemists. A further enrichment of diverse molecules can be achieved by using an ensemble of models.

Pyrimidin-6-yl Trifluoroborate Salts as Versatile Templates for Heterocycle Synthesis.

We report a novel and general method to access a highly under-studied privileged scaffold-pyrimidines bearing a trifluoroborate at C4, and highlight the broad utility of these intermediates in a rich array of downstream functionalization reactions. This chemistry is underpinned by the unique features of the trifluoroborate group; its robustness provides an opportunity to carry out chemoselective reactions at other positions on the pyrimidine while providing a pathway for elaboration at the C-B bond when suitably activated.

Design and Synthesis of Fused Pyridine Building Blocks for Automated Library Generation.

We demonstrate that a diboration-electrocyclization sequence provides access to a range of pyridine-fused, small-molecule boronic ester building blocks, and that these are amenable to high-throughput synthesis leading to biaryl and ether-linked compound libraries. Moreover, the implementation of an integrated physicochemical and ADME profiling workflow allows accelerated design of novel lead compounds for application in drug-discovery projects.

N-Trifluoromethyl Amines and Azoles: An Underexplored Functional Group in the Medicinal Chemist's Toolbox.

Introducing trifluoromethyl groups is a common strategy to improve the properties of biologically active compounds. However, N-trifluoromethyl moieties on amines and azoles are very rarely used. To evaluate their suitability in drug design, we synthesized a series of N-trifluoromethyl amines and azoles, determined their stability in aqueous media, and investigated their properties. We show that N-trifluoromethyl amines are prone to hydrolysis, whereas N-trifluoromethyl azoles have excellent aqueous stability. Compared to their N-methyl analogues, N-trifluoromethyl azoles have a higher lipophilicity and can show increased metabolic stability and Caco-2 permeability. Furthermore, N-trifluoromethyl azoles can serve as bioisosteres of N-iso-propyl and N-tert-butyl azoles. Consequently, we suggest that N-trifluoromethyl azoles are valuable substructures to be considered in medicinal chemistry.

A Palladium-Catalyzed Domino Reaction To Access 3-Amino-2H-indazoles from Hydrazines and 2-Halobenzonitriles.

The development of a novel selective synthesis of 3-amino-2H-indazoles from readily available 2-halobenzonitriles is presented. The reaction proceeds through a domino reaction sequence, consisting of a regioselective palladium-catalyzed coupling of monosubstituted hydrazines with 2-halobenzonitriles, followed by an intramolecular hydroamination through a 5-exo-dig cyclization and subsequent isomerization to directly afford a wide variety of substituted 2H-indazole analogues in good to excellent yields.

Design and Synthesis of New Pyrazole-Based Heterotricycles and their Derivatization by Automated Library Synthesis.

Small-molecule heterocycles bearing orthogonal functionality have the potential to deliver diverse structural motifs that aid the drug-discovery effort. This work highlights how a readily assembled N-hydroxyethyl pyrazole trifluoroborate offers rapid access to architecturally distinct 5-6-6- and 5-7-6-fused tricyclic compounds. This chemistry is not only amenable to single compound synthesis, but also to high-throughput experimentation. It gives easy access to diverse compound arrays with various physicochemical and ADME profiles by fully automated library synthesis. The combination of the high-throughput experimentation with rapid testing of the compounds in an integrated physicochemical and ADME profiling workflow allows accelerated design of novel lead compounds in drug-discovery projects.

Exploiting Hydrazones To Improve the Efficiency of 6π-Electrocyclization Reactions of 1-Azatrienes.

The greater geometric lability of hydrazones compared to that of oxime ethers is used as a basis to overcome the reluctance of Z-oxime ether azatrienes to undergo electrocyclization toward the synthesis of borylated (heteroaromatic) pyridines and ring-fused analogues. Such hydrazones now allow access to previously inaccessible tri- and tetrasubstituted 3-borylpyridines in high yields.