Discovery of allosteric regulators with clinical potential to stabilize alpha-L-iduronidase in mucopolysaccharidosis type I.

Mucopolysaccharidosis type I (MPS I) is an inherited lysosomal disease caused by lowered activity of the enzyme alpha-L-iduronidase (IDUA). Current therapeutic options show limited efficacy and do not treat some important aspects of the disease. Therefore, it may be advantageous to identify strategies that could improve the efficacy of existing treatments. Pharmacological chaperones are small molecules that protect proteins from degradation, and their use in combination with enzyme replacement therapy (ERT) has been proposed as an alternative therapeutic strategy. Using the SEE-Tx® proprietary computational drug discovery platform, a new allosteric ligand binding cavity in IDUA was identified distal from the active site. Virtual high-throughput screening of approximately 5 million compounds using the SEE-Tx® docking platform identified a subset of small molecules that bound to the druggable cavity and functioned as novel allosteric chaperones of IDUA. Experimental validation by differential scanning fluorimetry showed an overall hit rate of 11.4%. Biophysical studies showed that one exemplary hit molecule GT-01803 bound to (Kd = 22 µM) and stabilized recombinant human IDUA (rhIDUA) in a dose-dependent manner. Co-administration of rhIDUA and GT-01803 increased IDUA activity in patient-derived fibroblasts. Preliminary in vivo studies have shown that GT-01803 improved the pharmacokinetic (PK) profile of rhIDUA, increasing plasma levels in a dose-dependent manner. Furthermore, GT-01803 also increased IDUA enzymatic activity in bone marrow tissue, which benefits least from standard ERT. Oral bioavailability of GT-01803 was found to be good (50%). Overall, the discovery and validation of a novel allosteric chaperone for rhIDUA presents a promising strategy to enhance the efficacy of existing treatments for MPS I. The compound's ability to increase rhIDUA activity in patient-derived fibroblasts and its good oral bioavailability underscore its potential as a potent adjunct to ERT, particularly for addressing aspects of the disease less responsive to standard treatment.

Research progress of autoimmune diseases based on induced pluripotent stem cells.

Autoimmune diseases can damage specific or multiple organs and tissues, influence the quality of life, and even cause disability and death. A 'disease in a dish' can be developed based on patients-derived induced pluripotent stem cells (iPSCs) and iPSCs-derived disease-relevant cell types to provide a platform for pathogenesis research, phenotypical assays, cell therapy, and drug discovery. With rapid progress in molecular biology research methods including genome-sequencing technology, epigenetic analysis, '-omics' analysis and organoid technology, large amount of data represents an opportunity to help in gaining an in-depth understanding of pathological mechanisms and developing novel therapeutic strategies for these diseases. This paper aimed to review the iPSCs-based research on phenotype confirmation, mechanism exploration, drug discovery, and cell therapy for autoimmune diseases, especially multiple sclerosis, inflammatory bowel disease, and type 1 diabetes using iPSCs and iPSCs-derived cells.

Ensemble-Based Virtual Screening Led to the Discovery of Novel Lead Molecules as Potential NMBAs.

Neuromuscular blocking agents (NMBAs) are routinely used during anesthesia to relax skeletal muscle. Nicotinic acetylcholine receptors (nAChRs) are ligand-gated ion channels; NMBAs can induce muscle paralysis by preventing the neurotransmitter acetylcholine (ACh) from binding to nAChRs situated on the postsynaptic membranes. Despite widespread efforts, it is still a great challenge to find new NMBAs since the introduction of cisatracurium in 1995. In this work, an effective ensemble-based virtual screening method, including molecular property filters, 3D pharmacophore model, and molecular docking, was applied to discover potential NMBAs from the ZINC15 database. The results showed that screened hit compounds had better docking scores than the reference compound d-tubocurarine. In order to further investigate the binding modes between the hit compounds and nAChRs at simulated physiological conditions, the molecular dynamics simulation was performed. Deep analysis of the simulation results revealed that ZINC257459695 can stably bind to nAChRs' active sites and interact with the key residue Asp165. The binding free energies were also calculated for the obtained hits using the MM/GBSA method. In silico ADMET calculations were performed to assess the pharmacokinetic properties of hit compounds in the human body. Overall, the identified ZINC257459695 may be a promising lead compound for developing new NMBAs as an adjunct to general anesthesia, necessitating further investigations.

Phytochemical evaluation of Ziziphus mucronata and Xysmalobium undulutum towards the discovery and development of anti-malarial drugs.

BACKGROUND: The development of resistance by Plasmodium falciparum is a burdening hazard that continues to undermine the strides made to alleviate malaria. As such, there is an increasing need to find new alternative strategies. This study evaluated and validated 2 medicinal plants used in traditional medicine to treat malaria. METHODS: Inspired by their ethnobotanical reputation of being effective against malaria, Ziziphus mucronata and Xysmalobium undulutum were collected and sequentially extracted using hexane (HEX), ethyl acetate (ETA), Dichloromethane (DCM) and methanol (MTL). The resulting crude extracts were screened for their anti-malarial and cytotoxic potential using the parasite lactate dehydrogenase (pLDH) and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, respectively. This was followed by isolating the active compounds from the DCM extract of Z. mucronata using silica gel chromatography and structural elucidation using spectroscopic techniques (NMR: 1H, 12C, and DEPT). The active compounds were then targeted against P. falciparum heat shock protein 70-1 (PfHsp70-1) using Autodock Vina, followed by in vitro validation assays using ultraviolet-visible (UV-VIS) spectroscopy and the malate dehydrogenase (MDH) chaperone activity assay. RESULTS: The extracts except those of methanol displayed anti-malarial potential with varying IC50 values, Z. mucronata HEX (11.69 ± 3.84 µg/mL), ETA (7.25 ± 1.41 µg/mL), DCM (5.49 ± 0.03 µg/mL), and X. undulutum HEX (4.9 ± 0.037 µg/mL), ETA (17.46 ± 0.024 µg/mL) and DCM (19.27 ± 0.492 µg/mL). The extracts exhibited minimal cytotoxicity except for the ETA and DCM of Z. mucronata with CC50 values of 10.96 and 10.01 µg/mL, respectively. Isolation and structural characterization of the active compounds from the DCM extracts revealed that betulinic acid (19.95 ± 1.53 µg/mL) and lupeol (7.56 ± 2.03 µg/mL) were responsible for the anti-malarial activity and had no considerable cytotoxicity (CC50 > µg/mL). Molecular docking suggested strong binding between PfHsp70-1, betulinic acid (- 6.8 kcal/mol), and lupeol (- 6.9 kcal/mol). Meanwhile, the in vitro validation assays revealed the disruption of the protein structural elements and chaperone function. CONCLUSION: This study proves that X undulutum and Z. mucronata have anti-malarial potential and that betulinic acid and lupeol are responsible for the activity seen on Z. mucronata. They also make a case for guided purification of new phytochemicals in the other extracts and support the notion of considering medicinal plants to discover new anti-malarials.

Targeting the cysteine biosynthesis pathway in microorganisms: Mechanism, structure, and drug discovery.

Owing to the global health crisis of resistant pathogenic infections, researchers are emphasizing the importance of novel prevention and control strategies. Existing antimicrobial drugs predominantly target a few pathways, and their widespread use has pervasively increased drug resistance. Therefore, it is imperative to develop new antimicrobial drugs with novel targets and chemical structures. The de novo cysteine biosynthesis pathway, one of the microbial metabolic pathways, plays a crucial role in pathogenicity and drug resistance. This pathway notably differs from that in humans, thereby representing an unexplored target for developing antimicrobial drugs. Herein, we have presented an overview of cysteine biosynthesis pathways and their roles in the pathogenicity of various microorganisms. Additionally, we have investigated the structure and function of enzymes involved in these pathways as well as have discussed drug design strategies and structure-activity relationships of the enzyme inhibitors. This review provides valuable insights for developing novel antimicrobials and offers new avenues to combat drug resistance.

De novo generation of multi-target compounds using deep generative chemistry.

Polypharmacology drugs-compounds that inhibit multiple proteins-have many applications but are difficult to design. To address this challenge we have developed POLYGON, an approach to polypharmacology based on generative reinforcement learning. POLYGON embeds chemical space and iteratively samples it to generate new molecular structures; these are rewarded by the predicted ability to inhibit each of two protein targets and by drug-likeness and ease-of-synthesis. In binding data for >100,000 compounds, POLYGON correctly recognizes polypharmacology interactions with 82.5% accuracy. We subsequently generate de-novo compounds targeting ten pairs of proteins with documented co-dependency. Docking analysis indicates that top structures bind their two targets with low free energies and similar 3D orientations to canonical single-protein inhibitors. We synthesize 32 compounds targeting MEK1 and mTOR, with most yielding >50% reduction in each protein activity and in cell viability when dosed at 1-10 µM. These results support the potential of generative modeling for polypharmacology.

A deep learning based multi-model approach for predicting drug-like chemical compound's toxicity.

Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection.

Innovative On-DNA Synthesis of Sulfides and Sulfoximines: Enriching the DEL Synthesis Toolbox.

DNA-encoded library (DEL) technologies enable the fast exploration of gigantic chemical space to identify ligands for the target protein of interest and have become a powerful hit finding tool for drug discovery projects. However, amenable DEL chemistry is restricted to a handful of reactions, limiting the creativity of drug hunters. Here, we describe a new on-DNA synthetic pathway to access sulfides and sulfoximines. These moieties, usually contemplated as challenging to achieve through alkylation and oxidation, can now be leveraged in routine DEL selection campaigns.

Discovery of sesquiterpenoids from the roots of Chloranthus henryi Hemsl. var. hupehensis (Pamp.) K. F. Wu and their anti-inflammatory activity by IKBα/NF-κB p65 signaling pathway suppression.

Phytochemical analysis of Chloranthus henryi var. hupehensis roots led to the identification of a new eudesmane sesquiterpenoid dimer, 18 new sesquiterpenoids, and three known sesquiterpenoids. Among the isolates, 1 was a rare sesquiterpenoid dimer that is assembled by a unique oxygen bridge (C11-O-C8') of two highly rearranged eudesmane-type sesquiterpenes with the undescribed C16 carbon framework. (+)-2 and (-)-2 were a pair of new skeleton dinorsesquiterpenoids with a remarkable 6/6/5 tricyclic ring framework including one γ-lactone ring and the bicyclo[3.3.1]nonane core. Their structures were elucidated using spectroscopic data, single-crystal X-ray diffraction analysis, and quantum chemical computations. In the LPS-induced BV-2 microglial cell model, 17 suppressed IL-1ß and TNF-α expression with EC50 values of 6.81 and 2.76 µM, respectively, indicating its excellent efficacy in inhibiting inflammatory factors production in a dose dependent manner and without cytotoxicity. In subsequent mechanism studies, compounds 3, 16, and 17 could reduce IL-1ß and TNF-α production by inhibiting IKBα/p65 pathway activation.

Multicriteria decision making attributes and estimation of physicochemical properties of kidney cancer drugs via topological descriptors.

Based on topological descriptors, QSPR analysis is an incredibly helpful statistical method for examining many physical and chemical properties of compounds without demanding costly and time-consuming laboratory tests. Firstly, we discuss and provide research on kidney cancer drugs using topological indices and done partition of the edges of kidney cancer drugs which are based on the degree. Secondly, we examine the attributes of nineteen drugs casodex, eligard, mitoxanrone, rubraca, and zoladex, etc and among others, using linear QSPR model. The study in the article not only demonstrates a good correlation between TIs and physical characteristics with the QSPR model being the most suitable for predicting complexity, enthalpy, molar refractivity, and other factors and a best-fit model is attained in this study. This theoretical approach might benefit chemists and professionals in the pharmaceutical industry to forecast the characteristics of kidney cancer therapies. This leads towards new opportunities to paved the way for drug discovery and the formation of efficient and suitable treatment options in therapeutic targeting. We also employed multicriteria decision making techniques like COPRAS and PROMETHEE-II for ranking of said disease treatment drugs and physicochemical characteristics.

DCGAN-DTA: Predicting drug-target binding affinity with deep convolutional generative adversarial networks.

BACKGROUND: In recent years, there has been a growing interest in utilizing computational approaches to predict drug-target binding affinity, aiming to expedite the early drug discovery process. To address the limitations of experimental methods, such as cost and time, several machine learning-based techniques have been developed. However, these methods encounter certain challenges, including the limited availability of training data, reliance on human intervention for feature selection and engineering, and a lack of validation approaches for robust evaluation in real-life applications. RESULTS: To mitigate these limitations, in this study, we propose a method for drug-target binding affinity prediction based on deep convolutional generative adversarial networks. Additionally, we conducted a series of validation experiments and implemented adversarial control experiments using straw models. These experiments serve to demonstrate the robustness and efficacy of our predictive models. We conducted a comprehensive evaluation of our method by comparing it to baselines and state-of-the-art methods. Two recently updated datasets, namely the BindingDB and PDBBind, were used for this purpose. Our findings indicate that our method outperforms the alternative methods in terms of three performance measures when using warm-start data splitting settings. Moreover, when considering physiochemical-based cold-start data splitting settings, our method demonstrates superior predictive performance, particularly in terms of the concordance index. CONCLUSION: The results of our study affirm the practical value of our method and its superiority over alternative approaches in predicting drug-target binding affinity across multiple validation sets. This highlights the potential of our approach in accelerating drug repurposing efforts, facilitating novel drug discovery, and ultimately enhancing disease treatment. The data and source code for this study were deposited in the GitHub repository, https://github.com/mojtabaze7/DCGAN-DTA . Furthermore, the web server for our method is accessible at https://dcgan.shinyapps.io/bindingaffinity/ .

Elucidating the semantics-topology trade-off for knowledge inference-based pharmacological discovery.

Leveraging AI for synthesizing the deluge of biomedical knowledge has great potential for pharmacological discovery with applications including developing new therapeutics for untreated diseases and repurposing drugs as emergent pandemic treatments. Creating knowledge graph representations of interacting drugs, diseases, genes, and proteins enables discovery via embedding-based ML approaches and link prediction. Previously, it has been shown that these predictive methods are susceptible to biases from network structure, namely that they are driven not by discovering nuanced biological understanding of mechanisms, but based on high-degree hub nodes. In this work, we study the confounding effect of network topology on biological relation semantics by creating an experimental pipeline of knowledge graph semantic and topological perturbations. We show that the drop in drug repurposing performance from ablating meaningful semantics increases by 21% and 38% when mitigating topological bias in two networks. We demonstrate that new methods for representing knowledge and inferring new knowledge must be developed for making use of biomedical semantics for pharmacological innovation, and we suggest fruitful avenues for their development.

Patient-derived tumoroids and proteomic signatures: tools for early drug discovery.

Onco-virotherapy is an emergent treatment for cancer based on viral vectors. The therapeutic activity is based on two different mechanisms including tumor-specific oncolysis and immunostimulatory properties. In this study, we evaluated onco-virotherapy in vitro responses on immunocompetent non-small cell lung cancer (NSCLC) patient-derived tumoroids (PDTs) and healthy organoids. PDTs are accurate tools to predict patient's clinical responses at the in vitro stage. We showed that onco-virotherapy could exert specific antitumoral effects by producing a higher number of viral particles in PDTs than in healthy organoids. In the present work, we used multiplex protein screening, based on proximity extension assay to highlight different response profiles. Our results pointed to the increase of proteins implied in T cell activation, such as IFN-γ following onco-virotherapy treatment. Based on our observation, oncolytic viruses-based therapy responders are dependent on several factors: a high PD-L1 expression, which is a biomarker of greater immune response under immunotherapies, and the number of viral particles present in tumor tissue, which is dependent to the metabolic state of tumoral cells. Herein, we highlight the use of PDTs as an alternative in vitro model to assess patient-specific responses to onco-virotherapy at the early stage of the preclinical phases.

Computer-aided pattern scoring - A multitarget dataset-driven workflow to predict ligands of orphan targets.

The identification of lead molecules and the exploration of novel pharmacological drug targets are major challenges of medical life sciences today. Genome-wide association studies, multi-omics, and systems pharmacology steadily reveal new protein networks, extending the known and relevant disease-modifying proteome. Unfortunately, the vast majority of the disease-modifying proteome consists of 'orphan targets' of which intrinsic ligands/substrates, (patho)physiological roles, and/or modulators are unknown. Undruggability is a major challenge in drug development today, and medicinal chemistry efforts cannot keep up with hit identification and hit-to-lead optimization studies. New 'thinking-outside-the-box' approaches are necessary to identify structurally novel and functionally distinctive ligands for orphan targets. Here we present a unique dataset that includes critical information on the orphan target ABCA1, from which a novel cheminformatic workflow - computer-aided pattern scoring (C@PS) - for the identification of novel ligands was developed. Providing a hit rate of 95.5% and molecules with high potency and molecular-structural diversity, this dataset represents a suitable template for general deorphanization studies.

Discovery of BIO-8169─A Highly Potent, Selective, and Brain-Penetrant IRAK4 Inhibitor for the Treatment of Neuroinflammation.

Interleukin receptor associated kinase 4 (IRAK4) plays an important role in innate immune signaling through Toll-like and interleukin-1 receptors and represents an attractive target for the treatment of inflammatory diseases and cancer. We previously reported the development of a potent, selective, and brain-penetrant imidazopyrimidine series of IRAK4 inhibitors. However, lead molecule BIO-7488 (1) suffered from low solubility which led to variable PK, compound accumulation, and poor in vivo tolerability. Herein, we describe the discovery of a series of pyridone analogs with improved solubility which are highly potent, selective and demonstrate desirable PK profiles including good oral bioavailability and excellent brain penetration. BIO-8169 (2) reduced the in vivo production of pro-inflammatory cytokines, was well tolerated in safety studies in rodents and dog at margins well above the predicted efficacious exposure and showed promising results in a mouse model for multiple sclerosis.