Detoxified synthetic bacterial membrane vesicles as a vaccine platform against bacteria and SARS-CoV-2.

The development of vaccines based on outer membrane vesicles (OMV) that naturally bud off from bacteria is an evolving field in infectious diseases. However, the inherent inflammatory nature of OMV limits their use as human vaccines. This study employed an engineered vesicle technology to develop synthetic bacterial vesicles (SyBV) that activate the immune system without the severe immunotoxicity of OMV. SyBV were generated from bacterial membranes through treatment with detergent and ionic stress. SyBV induced less inflammatory responses in macrophages and in mice compared to natural OMV. Immunization with SyBV or OMV induced comparable antigen-specific adaptive immunity. Specifically, immunization with Pseudomonas aeruginosa-derived SyBV protected mice against bacterial challenge, and this was accompanied by significant reduction in lung cell infiltration and inflammatory cytokines. Further, immunization with Escherichia coli-derived SyBV protected mice against E. coli sepsis, comparable to OMV-immunized group. The protective activity of SyBV was driven by the stimulation of B-cell and T-cell immunity. Also, SyBV were engineered to display the SARS-CoV-2 S1 protein on their surface, and these vesicles induced specific S1 protein antibody and T-cell responses. Collectively, these results demonstrate that SyBV may be a safe and efficient vaccine platform for the prevention of bacterial and viral infections.

On the Value of Using 3D Shape and Electrostatic Similarities in Deep Generative Methods.

Multiparameter optimization, the heart of drug design, is still an open challenge. Thus, improved methods for automated compound design with multiple controlled properties are desired. Here, we present a significant extension to our previously described fragment-based reinforcement learning method (DeepFMPO) for the generation of novel molecules with optimal properties. As before, the generative process outputs optimized molecules similar to the input structures, now with the improved feature of replacing parts of these molecules with fragments of similar three-dimensional (3D) shape and electrostatics. We developed and benchmarked a new python package, ESP-Sim, for the comparison of the electrostatic potential and the molecular shape, allowing the calculation of high-quality partial charges (e.g., RESP with B3LYP/6-31G**) obtained using the quantum chemistry program Psi4. By performing comparisons of 3D fragments, we can simulate 3D properties while overcoming the notoriously difficult step of accurately describing bioactive conformations. The new improved generative (DeepFMPO v3D) method is demonstrated with a scaffold-hopping exercise identifying CDK2 bioisosteres. The code is open-source and freely available.

A High-Throughput Method for Screening for Genes Controlling Bacterial Conjugation of Antibiotic Resistance.

The rapid horizontal transmission of antibiotic resistance genes on conjugative plasmids between bacterial host cells is a major cause of the accelerating antibiotic resistance crisis. There are currently no experimental platforms for fast and cost-efficient screening of genetic effects on antibiotic resistance transmission by conjugation, which prevents understanding and targeting conjugation. We introduce a novel experimental framework to screen for conjugation-based horizontal transmission of antibiotic resistance between >60,000 pairs of cell populations in parallel. Plasmid-carrying donor strains are constructed in high-throughput. We then mix the resistance plasmid-carrying donors with recipients in a design where only transconjugants can reproduce, measure growth in dense intervals, and extract transmission times as the growth lag. As proof-of-principle, we exhaustively explore chromosomal genes controlling F-plasmid donation within Escherichia coli populations, by screening the Keio deletion collection in high replication. We recover all seven known chromosomal gene mutants affecting conjugation as donors and identify many novel mutants, all of which diminish antibiotic resistance transmission. We validate nine of the novel genes' effects in liquid mating assays and complement one of the novel genes' effect on conjugation (rseA). The new framework holds great potential for exhaustive disclosing of candidate targets for helper drugs that delay resistance development in patients and societies and improve the longevity of current and future antibiotics. Further, the platform can easily be adapted to explore interspecies conjugation, plasmid-borne factors, and experimental evolution and be used for rapid construction of strains.IMPORTANCE The rapid transmission of antibiotic resistance genes on conjugative plasmids between bacterial host cells is a major cause of the accelerating antibiotic resistance crisis. There are currently no experimental platforms for fast and cost-efficient screening of genetic effects on antibiotic resistance transmission by conjugation, which prevents understanding and targeting conjugation. We introduce a novel experimental framework to screen for conjugation-based horizontal transmission of antibiotic resistance between >60,000 pairs of cell populations in parallel. As proof-of-principle, we exhaustively explore chromosomal genes controlling F-plasmid donation within E. coli populations. We recover all previously known and many novel chromosomal gene mutants that affect conjugation efficiency. The new framework holds great potential for rapid screening of compounds that decrease transmission. Further, the platform can easily be adapted to explore interspecies conjugation, plasmid-borne factors, and experimental evolution and be used for rapid construction of strains.

Can easy chemistry produce complex, diverse, and novel molecules?

Easy-to-make compounds are often perceived to be inferior compared with molecules obtained through elaborate reaction schemes. This study evaluates in depths whether this perception is true.

Design of a small molecule that stimulates vascular endothelial growth factor A enabled by screening RNA fold-small molecule interactions.

Vascular endothelial growth factor A (VEGFA) stimulates angiogenesis in human endothelial cells, and increasing its expression is a potential treatment for heart failure. Here, we report the design of a small molecule (TGP-377) that specifically and potently enhances VEGFA expression by the targeting of a non-coding microRNA that regulates its expression. A selection-based screen, named two-dimensional combinatorial screening, revealed preferences in small-molecule chemotypes that bind RNA and preferences in the RNA motifs that bind small molecules. The screening program increased the dataset of known RNA motif-small molecule binding partners by 20-fold. Analysis of this dataset against the RNA-mediated pathways that regulate VEGFA defined that the microRNA-377 precursor, which represses Vegfa messenger RNA translation, is druggable in a selective manner. We designed TGP-377 to potently and specifically upregulate VEGFA in human umbilical vein endothelial cells. These studies illustrate the power of two-dimensional combinatorial screening to define molecular recognition events between 'undruggable' biomolecules and small molecules, and the ability of sequence-based design to deliver efficacious structure-specific compounds.

Identification and analyses of inhibitors targeting apolipoprotein(a) kringle domains KIV-7, KIV-10, and KV provide insight into kringle domain function.

Increased plasma concentrations of lipoprotein(a) (Lp(a)) are associated with an increased risk for cardiovascular disease. Lp(a) is composed of apolipoprotein(a) (apo(a)) covalently bound to apolipoprotein B of low-density lipoprotein (LDL). Many of apo(a)'s potential pathological properties, such as inhibition of plasmin generation, have been attributed to its main structural domains, the kringles, and have been proposed to be mediated by their lysine-binding sites. However, available small-molecule inhibitors, such as lysine analogs, bind unselectively to kringle domains and are therefore unsuitable for functional characterization of specific kringle domains. Here, we discovered small molecules that specifically bind to the apo(a) kringle domains KIV-7, KIV-10, and KV. Chemical synthesis yielded compound AZ-05, which bound to KIV-10 with a Kd of 0.8 µm and exhibited more than 100-fold selectivity for KIV-10, compared with the other kringle domains tested, including plasminogen kringle 1. To better understand and further improve ligand selectivity, we determined the crystal structures of KIV-7, KIV-10, and KV in complex with small-molecule ligands at 1.6-2.1 Å resolutions. Furthermore, we used these small molecules as chemical probes to characterize the roles of the different apo(a) kringle domains in in vitro assays. These assays revealed the assembly of Lp(a) from apo(a) and LDL, as well as potential pathophysiological mechanisms of Lp(a), including (i) binding to fibrin, (ii) stimulation of smooth-muscle cell proliferation, and (iii) stimulation of LDL uptake into differentiated monocytes. Our results indicate that a small-molecule inhibitor targeting the lysine-binding site of KIV-10 can combat the pathophysiological effects of Lp(a).

Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design.

In medicinal chemistry programs it is key to design and make compounds that are efficacious and safe. This is a long, complex, and difficult multiparameter optimization process, often including several properties with orthogonal trends. New methods for the automated design of compounds against profiles of multiple properties are thus of great value. Here we present a fragment-based reinforcement learning approach based on an actor-critic model, for the generation of novel molecules with optimal properties. The actor and the critic are both modeled with bidirectional long short-term memory (LSTM) networks. The AI method learns how to generate new compounds with desired properties by starting from an initial set of lead molecules and then improving these by replacing some of their fragments. A balanced binary tree based on the similarity of fragments is used in the generative process to bias the output toward structurally similar molecules. The method is demonstrated by a case study showing that 93% of the generated molecules are chemically valid and more than a third satisfy the targeted objectives, while there were none in the initial set.

Deep Convolutional Neural Networks for the Prediction of Molecular Properties: Challenges and Opportunities Connected to the Data.

We present a flexible deep convolutional neural network method for the analysis of arbitrary sized graph structures representing molecules. This method, which makes use of the Lipinski RDKit module, an open-source cheminformatics software, enables the incorporation of any global molecular (such as molecular charge and molecular weight) and local (such as atom hybridization and bond orders) information. In this paper, we show that this method significantly outperforms another recently proposed method based on deep convolutional neural networks on several datasets that are studied. Several best practices for training deep convolutional neural networks on chemical datasets are also highlighted within the article, such as how to select the information to be included in the model, how to prevent overfitting and how unbalanced classes in the data can be handled.

Expanding the medicinal chemistry synthetic toolbox.

This corrects the article DOI: 10.1038/nrd.2018.116.

Expanding the medicinal chemistry synthetic toolbox.

The key objectives of medicinal chemistry are to efficiently design and synthesize bioactive compounds that have the potential to become safe and efficacious drugs. Most medicinal chemistry programmes rely on screening compound collections populated by a range of molecules derived from a set of known and robust chemistry reactions. Analysis of the role of synthetic organic chemistry in subsequent hit and lead optimization efforts suggests that only a few reactions dominate. Thus, the uptake of new synthetic methodologies in drug discovery is limited. Starting from the known limitations of reaction parameters, synthesis design tools, synthetic strategies and innovative chemistries, here we highlight opportunities for the expansion of the medicinal chemists' synthetic toolbox. More intense crosstalk between synthetic and medicinal chemists in industry and academia should enable enhanced impact of new methodologies in future drug discovery.

Where Do Recent Small Molecule Clinical Development Candidates Come From?

An analysis of 66 published clinical candidates from Journal of Medicinal Chemistry has been conducted to shed light on which lead generation strategies are most frequently employed in identifying drug candidates. The most frequent lead generation strategy (producing a drug candidate) was based on starting points derived from previously known compounds (43%) followed by random high throughput screening (29%). The remainder of approaches included focused screening, structure-based drug design (SBDD), fragment-based lead generation (FBLG), and DNA-encoded library screening (DEL). An analysis of physicochemical properties on the hit-to-clinical pairs shows an average increase in molecular weight (ΔMW = +85) but no change in lipophilicity (ΔclogP = -0.2), although exceptions are noted. The majority (>50%) of clinical candidates were found to be structurally very different from their starting point and were more complex. Finally, several reports of noncovalent scaffolds modified by a covalent warhead using SBDD approaches are discussed.

ReFlex3D: Refined Flexible Alignment of Molecules Using Shape and Electrostatics.

We present an algorithm, ReFlex3D, for the refinement of flexible molecular alignments based on their three-dimensional shape and electrostatic properties. The algorithm is designed to be used with fast conformer generators to refine an initial overlay between two molecules and thus to obtain improved overlaps as judged by an increase in calculated similarity values. ReFlex3D is open-source and built as a python package working in combination with the OEChem Toolkit. As such it can readily be implemented in existing workflows ranging from the selection of compounds from a virtual screening campaign to the construction of similarity based prediction models to estimate binding affinities. We evaluate ReFlex3D against the AstraZeneca Validation Test Set and illustrate its potential within a predictive model compared to an established method (Posit).

Discovery and Evaluation of Anti-Fibrinolytic Plasmin Inhibitors Derived from 5-(4-Piperidyl)isoxazol-3-ol (4-PIOL).

Inhibition of plasmin has been found to effectively reduce fibrinolysis and to avoid hemorrhage. This can be achieved by addressing its kringle 1 domain with the known drug and lysine analogue tranexamic acid. Guided by shape similarities toward a previously discovered lead compound, 5-(4-piperidyl)isoxazol-3-ol, a set of 16 structurally similar compounds was assembled and investigated. Successfully, in vitro measurements revealed one compound, 5-(4-piperidyl)isothiazol-3-ol, superior in potency compared to the initial lead. Furthermore, a strikingly high correlation (R2 = 0.93) between anti-fibrinolytic activity and kringle 1 binding affinity provided strong support for the hypothesized inhibition mechanism, as well as revealing opportunities to fine-tune biological effects through minor structural modifications. Several different ligand-based (Freeform, shape, and electrostatic-based similarities) and structure-based methods (e.g., Posit, MM/GBSA, FEP+) were used to retrospectively predict the binding affinities. A combined method, molecular alignment using Posit and scoring with Tcombo, lead to the highest coefficient of determination (R2 = 0.6).

Women's Experiences of Undergoing Total Knee Joint Replacement Surgery.

PURPOSE: The purpose of the study was to describe women's experiences of undergoing total knee joint replacement surgery. DESIGN: A qualitative approach was used. METHOD: A content analysis of the text from interviews with five women was conducted. FINDINGS: The time before surgery was marked by the experience of constant pain, which affected the women negatively in their everyday lives. During surgery, the information provided by the staff gave each woman a sense of security; the women handed over responsibility to the staff and experienced a sensation of relief. The postoperative period was characterized by a feeling of joy when the surgery was over, although a rough and tedious rehabilitation phase then began. Challenges in everyday life were a factor for motivation and confidence, although postoperative pain was experienced as discouraging. CONCLUSION: Support from health care staff is an important factor for coping with everyday life during the preoperative, perioperative and postoperative phases of undergoing knee joint replacement.

3D-Lab: a collaborative web-based platform for molecular modeling.

AIM: The use of 3D information has shown impact in numerous applications in drug design. However, it is often under-utilized and traditionally limited to specialists. We want to change that, and present an approach making 3D information and molecular modeling accessible and easy-to-use 'for the people'. METHODOLOGY/RESULTS: A user-friendly and collaborative web-based platform (3D-Lab) for 3D modeling, including a blazingly fast virtual screening capability, was developed. 3D-Lab provides an interface to automatic molecular modeling, like conformer generation, ligand alignments, molecular dockings and simple quantum chemistry protocols. 3D-Lab is designed to be modular, and to facilitate sharing of 3D-information to promote interactions between drug designers. Recent enhancements to our open-source virtual reality tool Molecular Rift are described. CONCLUSION: The integrated drug-design platform allows drug designers to instantaneously access 3D information and readily apply advanced and automated 3D molecular modeling tasks, with the aim to improve decision-making in drug design projects.

Analysis of Past and Present Synthetic Methodologies on Medicinal Chemistry: Where Have All the New Reactions Gone?

An analysis of chemical reactions used in current medicinal chemistry (2014), three decades ago (1984), and in natural product total synthesis has been conducted. The analysis revealed that of the current most frequently used synthetic reactions, none were discovered within the past 20 years and only two in the 1980s and 1990s (Suzuki-Miyaura and Buchwald-Hartwig). This suggests an inherent high bar of impact for new synthetic reactions in drug discovery. The most frequently used reactions were amide bond formation, Suzuki-Miyaura coupling, and SNAr reactions, most likely due to commercial availability of reagents, high chemoselectivity, and a pressure on delivery. We show that these practices result in overpopulation of certain types of molecular shapes to the exclusion of others using simple PMI plots. We hope that these results will help catalyze improvements in integration of new synthetic methodologies as well as new library design.

Molecular Rift: Virtual Reality for Drug Designers.

Recent advances in interaction design have created new ways to use computers. One example is the ability to create enhanced 3D environments that simulate physical presence in the real world--a virtual reality. This is relevant to drug discovery since molecular models are frequently used to obtain deeper understandings of, say, ligand-protein complexes. We have developed a tool (Molecular Rift), which creates a virtual reality environment steered with hand movements. Oculus Rift, a head-mounted display, is used to create the virtual settings. The program is controlled by gesture-recognition, using the gaming sensor MS Kinect v2, eliminating the need for standard input devices. The Open Babel toolkit was integrated to provide access to powerful cheminformatics functions. Molecular Rift was developed with a focus on usability, including iterative test-group evaluations. We conclude with reflections on virtual reality's future capabilities in chemistry and education. Molecular Rift is open source and can be downloaded from GitHub.

Understanding our love affair with p-chlorophenyl: present day implications from historical biases of reagent selection.

We report here an unexpectedly strong preference toward para substitution in phenyl rings within drug discovery programs. A population analysis of aromatic rings in various drug databases demonstrated that para substitution is favored over meta and ortho regioisomers, with p-chlorophenyl (p-ClPh) being one of the most predominant examples. We speculate that the frequency of p-ClPh is traced back to historical models of medicinal chemistry where para-substituted regioisomers were perhaps more easily accessed, and further reinforced by Topliss in 1972 that if Ph was active, the p-ClPh should be made because of ease of synthesis and hydrophobicity driven potency effects. On the basis of our analysis, the para bias has become useful conventional wisdom but perhaps so much so that a perception has been created that druglike space favors a linear aromatic structure. It is hoped this analysis will catalyze a new look at design of reagent databases and screening collections.

Discovery of the Fibrinolysis Inhibitor AZD6564, Acting via Interference of a Protein-Protein Interaction.

A class of novel oral fibrinolysis inhibitors has been discovered, which are lysine mimetics containing an isoxazolone as a carboxylic acid isostere. As evidenced by X-ray crystallography the inhibitors bind to the lysine binding site in plasmin thus preventing plasmin from binding to fibrin, hence blocking the protein-protein interaction. Optimization of the series, focusing on potency in human buffer and plasma clotlysis assays, permeability, and GABAa selectivity, led to the discovery of AZD6564 (19) displaying an in vitro human plasma clot lysis IC50 of 0.44 µM, no detectable activity against GABAa, and with DMPK properties leading to a predicted dose of 340 mg twice a day oral dosing in humans.