Novel Computational Approach to Predict Off-Target Interactions for Small Molecules

Computational analysis of potential drug-like compounds from Solanum torvum - A promising phytotherapeutics approach for the treatment of diabetes

Diabetes mellitus (DM) is a global pandemic that is characterized by high blood glucose levels. Conventional treatments have limitations, leading to the search for natural alternatives. This study focused on Solanum torvum (STV), a medicinal plant, to identify potential anti-diabetic compounds using molecular docking and molecular dynamics simulations. We focused on identifying natural inhibitors of two key enzymes involved in glucose metabolism: α-amylase (1HNY) and α-glucosidase (4J5T). In our preliminary docking study, rutin showed the highest binding affinity (-11.58 kcal/mol) to α-amylase, followed by chlorogenin (-7.58 kcal/mol) and myricetin (-5.82 kcal/mol). For α-glucosidase, rutin had the highest binding affinity (-11.78 kcal/mol), followed by chlorogenin (-7.11 kcal/mol) and fisetin (-6.44 kcal/mol). Hence, chlorogenin and rutin were selected for further analysis and compared with acarbose, an FDA-approved antidiabetic drug. Comparative docking revealed that chlorogenin had the highest binding affinity of (-9.9 kcal/mol) > rutin (-8.7 kcal/mol) and > acarbose (-7.7 kcal/mol) for α-amylase. While docking with α-glucosidase, chlorogenin again had the highest binding affinity of (-9.8 kcal/mol) > compared to rutin (-9.5 kcal/mol) and acarbose (-7.9 kcal/mol). Molecular dynamics (MD) simulations were conducted to assess their stability. We simulated 100 nanoseconds (ns) trajectories to analyze their stability on various parameters, including RMSD, RMSF, RG, SASA, H-bond analysis, PCA, FEL, and MM-PBSA on the six docked proteins. In conclusion, our study suggests that chlorogenin and rutin derived from STV may be effective natural therapeutic agents for diabetes management because of their strong binding affinities for the α-amylase and α-glucosidase enzymes.Communicated by Ramaswamy H. Sarma.

Establishing a Pharmacoinformatics Repository of Approved Medicines: A Database to Support Drug Product Development

Advanced predictive modeling approaches have harnessed data to fuel important innovations at all stages of drug development. However, the need for a machine-readable drug product library which consolidates many aspects of formulation design and performance remains largely unmet. This study presents a scripted, reproducible approach to database curation and explores its potential to streamline oral medicine development. The Product Information files for all centrally authorized drug products containing a small molecule active ingredient were retrieved programmatically from the European Medicines Agency Web site. Text processing isolated relevant information, including the maximum clinical dose, dosage form, route of administration, excipients, and pharmacokinetic performance. Chemical and bioactivity data were integrated through automated linking to external curated databases. The capability of this database to inform oral medicine development was assessed in the context of drug-likeness evaluation, excipient selection, and prediction of oral fraction absorbed. Existing filters of drug-likeness, such as the Rule of Five, were found to poorly capture the chemical space of marketed oral drug products. Association rule learning identified frequent patterns in tablet formulation compositions that can be used to establish excipient combinations that have seen clinical success. Binary prediction models of oral fraction absorbed constructed exclusively from regulatory data achieved acceptable performance (balanced accuracytest = 0.725), demonstrating its modelability and potential for use during early stage molecule prioritization tasks. This study illustrates the impact of highly linked drug product data in accelerating clinical translation and underlines the ongoing need for accuracy and completeness of data reported in the regulatory datasphere.

Revealing novel protein interaction partners of glyphosate in Escherichia coli

Despite all debates about its safe use, glyphosate remains the most widely applied active ingredient in herbicide products, with renewed approval in the European Union until 2033. Non-target organisms are commonly exposed to glyphosate as a matter of its mode of application, with its broader environmental and biological impacts remaining under investigation. Glyphosate displays structural similarity to phosphoenolpyruvate (PEP), thereby competitively inhibiting the 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS), crucial for the synthesis of aromatic amino acids in plants, fungi, bacteria, and archaea. Most microbes, including the gut bacterium Escherichia coli (E. coli), possess a glyphosate-sensitive class I EPSPS, making them vulnerable to glyphosate's effects. Yet, little is known about glyphosate's interactions with other bacterial proteins or its broader modes of action at the proteome level. Here, we employed a quantitative proteomics and thermal proteome profiling (TPP) approach to identify novel protein binding partners of glyphosate in the E. coli proteome. Glyphosate exposure significantly altered amino acid synthesizing pathways. The abundance of shikimate pathway proteins was increased, suggesting a compensatory mechanism. Extracellular riboflavin concentrations were elevated upon glyphosate exposure, while intracellular levels remained stable. Beyond the target enzyme EPSPS, thermal proteome profiling indicated an effect of glyphosate on the thermal stability of certain proteins, including AroH and ProA, indicating interactions. Similar to the competitive binding between PEP and glyphosate at EPSPS, one reason for the interaction of AroH and ProA with the herbicide could be a high structural similarity between their substrates and glyphosate. Overall, glyphosate induced metabolic disturbances in E. coli, extending beyond its primary target, thereby providing new insights into glyphosate's broader impact on microbial systems.

ARCaDia: single-round screening of a DNA-type targeted covalent binder possessing a latent warhead

A covalent binder for a target protein was obtained by a direct single-round screening of a latent-warhead-modified DNA library via affinity/reactivity-based co-selection of aptameric deoxyribonucleic acid (ARCaDia), followed by a top k-mer analysis. The optimal position of the conjugated warhead on the selected aptamer was simultaneously identified.

Development of cancer-associated fibroblasts-targeting polymeric nanoparticles loaded with 8-O-methylfusarubin for breast cancer treatment

Cancer-associated fibroblasts (CAFs) are abundant stromal cells residing in a tumor microenvironment (TME) which are associated with the progression of tumor. Herein, we developed novel CAFs-targeting polymeric nanoparticles encapsulating a synthetic 8-O-methylfusarubin (OMF) compound (OMF@NPs-anti-FAP). Anti-FAP/fibroblast activation protein antibody was employed as a CAFs-targeting ligand. The physicochemical properties of the synthesized nanomaterials were firstly investigated with various techniques. The cytocompatibility of polymeric nanoparticles (NPs) was elicited through cell viability of CAFs and human breast epithelial cells, MCF-10A. Additionally, the anti-FAP-conjugated NPs displayed different degrees of cellular internalization regarding the FAP expression level on the CAFs' surface. However, CAFs exposed to NPs containing OMF demonstrated significant cell death which were associated with the apoptotic pathway as confirmed by caspase-3/7 activity. Upon OMF@NPs-anti-FAP treatment, an enhanced toxicity was clearly observed in 3D spheroid models. High FAP-expressed PC-B-132CAFs demonstrated a high percentage of cell death compared to other cells with a low level of FAP expression analyzed by flow cytometry (e.g. MCF-10A, HDFa, and PC-B-142CAFs). This result emphasized the importance of anti-FAP antibody as a targeting ligand. These findings suggest that the fabricated nanosystem of OMF-loaded polymeric NPs with CAFs' high specificity holds a potential NP-based platform for improvement in breast cancer treatment.

Artificial Intelligence and Machine Learning Models for Predicting Drug-Induced Kidney Injury in Small Molecules

BACKGROUND/OBJECTIVES

Drug-Induced Kidney Injury (DIKI) presents a significant challenge in drug development, often leading to clinical-stage failures. The early prediction of DIKI risk can improve drug safety and development efficiency. Existing models tend to focus on physicochemical properties alone, often overlooking drug-target interactions crucial for DIKI. This study introduces an AI/ML (artificial intelligence/machine learning) model that integrates both physicochemical properties and off-target interactions to enhance DIKI prediction.

METHODS

We compiled a dataset of 360 FDA-classified compounds (231 non-nephrotoxic and 129 nephrotoxic) and predicted 6064 off-target interactions, 59% of which were validated in vitro. We also calculated 55 physicochemical properties for these compounds. Machine learning (ML) models were developed using four algorithms: Ridge Logistic Regression (RLR), Support Vector Machine (SVM), Random Forest (RF), and Neural Network (NN). These models were then combined into an ensemble model for enhanced performance.

RESULTS

The ensemble model achieved an ROC-AUC of 0.86, with a sensitivity and specificity of 0.79 and 0.78, respectively. The key predictive features included 38 off-target interactions and physicochemical properties such as the number of metabolites, polar surface area (PSA), pKa, and fraction of Sp3-hybridized carbons (fsp3). These features effectively distinguished DIKI from non-DIKI compounds.

CONCLUSIONS

The integrated model, which combines both physicochemical properties and off-target interaction data, significantly improved DIKI prediction accuracy compared to models that rely on either data type alone. This AI/ML model provides a promising early screening tool for identifying compounds with lower DIKI risk, facilitating safer drug development.

Helix pomatia mucin alleviates DSS-induced colitis in mice: Unraveling the cross talk between microbiota and intestinal chemokine

Gut microbiota imbalance and alterations in the chemokine-chemokine receptor interactions are pivotal in the initiation and advancement of ulcerative colitis (UC). The current UC treatments are prolonged, exhibit high recurrence rates, and may lead to colorectal cancer. So, this study explores the efficacy of Helix pomatia (H. pomatia) mucin in preventing DSS-induced UC. This research focuses on investigating the underlying mechanisms, such as oxidative stress, inflammation, and alterations in gut microbiota and chemokine-chemokine receptor interactions, to understand the anti-inflammatory and antioxidant characteristics of the mucin. Using 4 % DSS in drinking water, UC was induced in C57BL/6 mice. For seven days, mice were given oral doses of either H. pomatia mucin or sulfasalazine. The study assessed changes in oxidative stress, gut microbiota, and histopathology, along with expression of IL-6, CXCR4, CCR7, CXCL9, and CXCL10. The H. pomatia mucin exhibited unique contents, including high glycolic acid (200 ± 2.08 mg/L), collagen (88 ± 2.52 mg/L), allantoin (20 ± 2 mg/L), and concentrated vitamins and minerals. Treatment with H. pomatia mucin in high dose demonstrated reduction in DAI, an increase in fecal Firmicutes, and elevated expression of colonic CCR7, CXCL9, and CXCL10, accompanied by enhanced CXCR4 (75 %) and diminished IL-6 (1.33 %) immunostaining. It also alleviated oxidative stress, reduced fecal Bacteroidetes, and mitigated inflammation, indicating its potential efficacy against DSS-induced UC. In conclusion, H. pomatia mucin is a promising candidate that could be an effective adjuvant in the management and prophylaxis of UC.

De novo drug design through gradient-based regularized search in information-theoretically controlled latent space

Over the last decade, automatic chemical design frameworks for discovering molecules with drug-like properties have significantly progressed. Among them, the variational autoencoder (VAE) is a cutting-edge approach that models the tractable latent space of the molecular space. In particular, the usage of a VAE along with a property estimator has attracted considerable interest because it enables gradient-based optimization of a given molecule. However, although successful results have been achieved experimentally, the theoretical background and prerequisites for the correct operation of this method have not yet been clarified. In view of the above, we theoretically analyze and rigorously reconstruct the entire framework. From the perspective of parameterized distribution and the information theory, we first describe how the previous model overcomes the limitations of the beta VAE in discovering molecules with the desired properties. Furthermore, we describe the prerequisites for training the above model. Next, from the log-likelihood perspective of each term, we reformulate the objectives for exploring latent space to generate drug-like molecules. The distributional constraints are defined in this study, which will break away from the invalid molecular search. We demonstrated that our model could discover a novel chemical compound for targeting BCL-2 family proteins in de novo approach. Through the theoretical analysis and practical implementation, the importance of the aforementioned prerequisites and constraints to operate the model was verified.

CRISPR challenges in clinical developments

CRISPR-Cas (clustered regularly interspaced short palindromic repeats and associated proteins) is a novel genome editing technology with potential applications in treating diseases. Currently, its use in humans is restricted to clinical trials, although its growth rate is significant, and some have received initial FDA approval. It is crucial to examine and address the challenges for this technology to be implemented in clinical settings. This review aims to identify and explore new research ideas to increase of CRISPR's efficiency in treating genetic diseases and cancer, as well as its future prospects. Given that a substantial amount of previous research has focused on CRISPR-Cas delivery strategies and materials, this overview introduces specific conditions and strategies. It also discusses some of the challenges and opportunities in this field, offering a unique perspective.

Safety Implications of Modulating Nuclear Receptors: A Comprehensive Analysis from Non-Clinical and Clinical Perspectives

The unintended modulation of nuclear receptor (NR) activity by drugs can lead to toxicities amongst the endocrine, gastrointestinal, hepatic cardiovascular, and central nervous systems. While secondary pharmacology screening assays include NRs, safety risks due to unintended interactions of small molecule drugs with NRs remain poorly understood. To identify potential nonclinical and clinical safety effects resulting from functional interactions with 44 of the 48 human-expressed NRs, we conducted a systematic narrative review of the scientific literature, tissue expression data, and used curated databases (OFF-X™) (Off-X, Clarivate) to organize reported toxicities linked to the functional modulation of NRs in a tabular and machine-readable format. The top five NRs associated with the highest number of safety alerts from peer-reviewed journals, regulatory agency communications, congresses/conferences, clinical trial registries, and company communications were the Glucocorticoid Receptor (GR, 18,328), Androgen Receptor (AR, 18,219), Estrogen Receptor (ER, 12,028), Retinoic acid receptors (RAR, 10,450), and Pregnane X receptor (PXR, 8044). Toxicities associated with NR modulation include hepatotoxicity, cardiotoxicity, endocrine disruption, carcinogenicity, metabolic disorders, and neurotoxicity. These toxicities often arise from the dysregulation of receptors like Peroxisome proliferator-activated receptors (PPARα, PPARγ), the ER, PXR, AR, and GR. This dysregulation leads to various health issues, including liver enlargement, hepatocellular carcinoma, heart-related problems, hormonal imbalances, tumor growth, metabolic syndromes, and brain function impairment. Gene expression analysis using heatmaps for human and rat tissues complemented the functional modulation of NRs associated with the reported toxicities. Interestingly, certain NRs showed ubiquitous expression in tissues not previously linked to toxicities, suggesting the potential utilization of organ-specific NR interactions for therapeutic purposes.

In silico ADME/tox comes of age: twenty years later

In the early 2000s pharmaceutical drug discovery was beginning to use computational approaches for absorption, distribution, metabolism, excretion and toxicity (ADME/Tox, also known as ADMET) prediction. This emphasis on prediction was an effort to reduce the risk of later stage failures from ADME/Tox.Much has been written in the intervening twenty plus years and significant expenditure has occurred in companies developing these in silico capabilities which can be gleaned from publications. It is therefore an appropriate time to briefly reflect on what was proposed then and what the reality is today.20 years ago, we tended to optimise bioactivity and perhaps one ADME/Tox property at a time. Previously pharmaceutical companies needed a whole infrastructure for models - in silico and in vitro experts, IT, champions on a project team, educators and management support. Now we are in the age of generative de novo design where bioactivity and many ADME/Tox properties can be optimised and large language model technologies are available.There are also some challenges such as the focus on very large molecules which may be outside of current ADME/Tox models.We provide an opportunity to look forward with the increasing public data for ADME/Tox as well as expanded types of algorithms available.

Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction

The attrition rate of drugs in clinical trials is generally quite high, with estimates suggesting that approximately 90% of drugs fail to make it through the process. The identification of unexpected toxicity issues during preclinical stages is a significant factor contributing to this high rate of failure. These issues can have a major impact on the success of a drug and must be carefully considered throughout the development process. These late-stage rejections or withdrawals of drug candidates significantly increase the costs associated with drug development, particularly when toxicity is detected during clinical trials or after market release. Understanding drug-biological target interactions is essential for evaluating compound toxicity and safety, as well as predicting therapeutic effects and potential off-target effects that could lead to toxicity. This will enable scientists to predict and assess the safety profiles of drug candidates more accurately. Evaluation of toxicity and safety is a critical aspect of drug development, and biomolecules, particularly proteins, play vital roles in complex biological networks and often serve as targets for various chemicals. Therefore, a better understanding of these interactions is crucial for the advancement of drug development. The development of computational methods for evaluating protein-ligand interactions and predicting toxicity is emerging as a promising approach that adheres to the 3Rs principles (replace, reduce, and refine) and has garnered significant attention in recent years. In this review, we present a thorough examination of the latest breakthroughs in drug toxicity prediction, highlighting the significance of drug-target binding affinity in anticipating and mitigating possible adverse effects. In doing so, we aim to contribute to the development of more effective and secure drugs.

Alternatives of Animal Models for Biomedical Research: a Comprehensive Review of Modern Approaches

Biomedical research has long relied on animal models to unravel the intricacies of human physiology and pathology. However, concerns surrounding ethics, expenses, and inherent species differences have catalyzed the exploration of alternative avenues. The contemporary alternatives to traditional animal models in biomedical research delve into three main categories of alternative approaches: in vitro models, in vertebrate models, and in silico models. This unique approach to artificial intelligence and machine learning has been a keen interest to be used in different biomedical research. The main goal of this review is to serve as a guide to researchers seeking novel avenues for their investigations and underscores the importance of considering alternative models in the pursuit of scientific knowledge and medical breakthroughs, including showcasing the broad spectrum of modern approaches that are revolutionizing biomedical research and leading the way toward a more ethical, efficient, and innovative future. Models can insight into cellular processes, developmental biology, drug interaction, assessing toxicology, and understanding molecular mechanisms.

Small-Molecule Regulators for Gene Switches to Program Mammalian Cell Behaviour

Synthetic or natural small molecules have been extensively employed as trigger signals or inducers to regulate engineered gene circuits introduced into living cells in order to obtain desired outputs in a controlled and predictable manner. Here, we provide an overview of small molecules used to drive synthetic-biology-based gene circuits in mammalian cells, together with examples of applications at different levels of control, including regulation of DNA manipulation, RNA synthesis and editing, and protein synthesis, maturation, and trafficking. We also discuss the therapeutic potential of these small-molecule-responsive gene circuits, focusing on the advantages and disadvantages of using small molecules as triggers, the mechanisms involved, and the requirements for selecting suitable molecules, including efficiency, specificity, orthogonality, and safety. Finally, we explore potential future directions for translation of these devices to clinical medicine.

Sunset Yellow protects against oxidative damage and exhibits chemoprevention in chemically induced skin cancer model

Skin cancer and other skin-related inflammatory pathologies are rising due to heightened exposure to environmental pollutants and carcinogens. In this context, natural products and repurposed compounds hold promise as novel therapeutic and preventive agents. Strengthening the skin's antioxidant defense mechanisms is pivotal in neutralizing reactive oxygen species (ROS) and mitigating oxidative stress. Sunset Yellow (SY) exhibits immunomodulatory characteristics, evidenced by its capacity to partially inhibit the secretion of proinflammatory cytokines, regulate immune cell populations, and modulate the activation of lymphocytes. This study aimed to investigate the antioxidant and anti-genotoxic properties of SY using in-silico, in vitro, and physiochemical test systems, and to further explore its potential role in 7,12-dimethylbenz(a) anthracene (DMBA)/ 12-o-tetradecanoylphorbol-13-acetate (TPA)-induced two-stage skin carcinogenesis. In vitro experiments showed that pre-treatment of SY significantly enhanced the cell viability of HaCaT cells when exposed to tertiary-Butyl Hydrogen Peroxide (tBHP). This increase was accompanied by reduced ROS levels, restoration of mitochondrial membrane potential, and notable reduction in DNA damage in (SY + tBHP) treated cells. Mechanistic investigations using DPPH chemical antioxidant activity test and potentiometric titrations confirmed SY's antioxidant properties, with a standard reduction potential (E o ) of 0.211 V. Remarkably, evaluating the effect of topical application of SY in DMBA/TPA-induced two-step skin carcinogenesis model revealed dose-dependent decreases in tumor latency, incidence, yield, and burden over 21-weeks. Furthermore, computational analysis and experimental validations identified GSK3β, KEAP1 and EGFR as putative molecular targets of SY. Collectively, our findings reveal that SY enhances cellular antioxidant defenses, exhibits anti-genotoxic effects, and functions as a promising chemopreventive agent.

Molecular docking, characterization, ADME/toxicity prediction, and anti-ulcer activity of new quercetin derivatives on indomethacin-induced gastric ulcer in mice

Gastric ulcer (GU) is a serious upper gastrointestinal tract disorder that affects people worldwide. The drugs now available for GU treatment have a high rate of relapses and drug interactions, as well as mild to severe side effects. As a result, new natural therapeutic medications for treating GU with fewer negative side effects are desperately needed. Because of quercetin's (QCT) diverse pharmacological effects and unique structural features, we decided to semi-synthesize new QCT derivatives and test them for antiulcer activity. Docking assays were performed on the synthesized compounds to determine their affinity for TLR-4/MD-2, MyD88/TIR, and NF-κB domains, an important inflammatory pathway involved in GU development and progression. Mice were given oral famotidine (40 mg/kg/day), QCT, QCT pentamethyl (QPM), or QCT pentaacetyl (QPA) (50 mg/kg/day) for 5 days before GU induction by a single intraperitoneal injection of indomethacin (INDO; 18 mg/kg). QPM and QPA have a stronger binding affinity for TLR-4/MD-2, MyD88/TIR and NF-κB domains than QCT. In comparison, they demonstrated the greatest reduction in ulcer score and index, gastric MDA and nitric oxide (NO) contents, MyD88 and NF-κB expressions, and gastric TLR-4 immunostaining. They also enhanced the levels of GSH, CAT, COX-1, and COX-2 in the gastric mucosa, as well as HO-1 and Nrf2 expression, with histological regression in gastric mucosal lesions, with QPA-treated mice demonstrating the best GU healing. QPA is safe against all of the target organs and adverse pathways studied, with good ADME properties. However, further in vitro experiments are necessary to demonstrate the inhibitory effects of QPM and QPA on the protein targets of interest. In addition, preclinical research on its bioavailability and safety is essential before clinical management can be undertaken. Overall, the new QPA derivative could one day serve as the basis for a new class of potential antiulcer drugs.

Therapeutic Implications of Ceritinib in Cholangiocarcinoma beyond ALK Expression and Mutation

Cholangiocarcinoma (CCA) is a difficult-to-treat cancer, with limited therapeutic options and surgery being the only curative treatment. Standard chemotherapy involves gemcitabine-based therapies combined with cisplatin, oxaliplatin, capecitabine, or 5-FU with a dismal prognosis for most patients. Receptor tyrosine kinases (RTKs) are aberrantly expressed in CCAs encompassing potential therapeutic opportunity. Hence, 112 RTK inhibitors were screened in KKU-M213 cells, and ceritinib, an approved targeted therapy for ALK-fusion gene driven cancers, was the most potent candidate. Ceritinib's cytotoxicity in CCA was assessed using MTT and clonogenic assays, along with immunofluorescence, western blot, and qRT-PCR techniques to analyze gene expression and signaling changes. Furthermore, the drug interaction relationship between ceritinib and cisplatin was determined using a ZIP synergy score. Additionally, spheroid and xenograft models were employed to investigate the efficacy of ceritinib in vivo. Our study revealed that ceritinib effectively killed CCA cells at clinically relevant plasma concentrations, irrespective of ALK expression or mutation status. Ceritinib modulated multiple signaling pathways leading to the inhibition of the PI3K/Akt/mTOR pathway and activated both apoptosis and autophagy. Additionally, ceritinib and cisplatin synergistically reduced CCA cell viability. Our data show ceritinib as an effective treatment of CCA, which could be potentially explored in the other cancer types without ALK mutations.

Identification of Antagonistic Action of Pyrrolizidine Alkaloids in Muscarinic Acetylcholine Receptor M1 by Computational Target Prediction Analysis

Pyrrolizidine alkaloids (PAs) are one of the largest distributed classes of toxins in nature. They have a wide range of toxicity, such as hepatotoxicity, pulmonary toxicity, neuronal toxicity, and carcinogenesis. Yet, biological targets responsible for these effects are not well addressed. Using methods of computational biology for target identification, we tested more than 200 PAs. We used a machine-learning approach that applies structural similarity for target identification, ChemMapper, and SwissTargetPrediction. The predicted target with high probability was muscarinic acetylcholine receptor M1. The predicted interactions between this target and PAs were further studied by molecular docking-based binding energies using AutoDock and VinaLC, which revealed good binding affinities. The PAs are bound to the same binding pocket as pirenzepine, a known M1 antagonist. These results were confirmed by in vitro assays showing that PAs increased the levels of intracellular calcium. We conclude that PAs are potential acetylcholine receptor M1 antagonists. This elucidates for the first time the serious neuro-oncological toxicities exerted by PA consumption.

1,10-phenanthroline inhibits sumoylation and reveals that yeast SUMO modifications are highly transient

The steady-state levels of protein sumoylation depend on relative rates of conjugation and desumoylation. Whether SUMO modifications are generally long-lasting or short-lived is unknown. Here we show that treating budding yeast cultures with 1,10-phenanthroline abolishes most SUMO conjugations within one minute, without impacting ubiquitination, an analogous post-translational modification. 1,10-phenanthroline inhibits the formation of the E1~SUMO thioester intermediate, demonstrating that it targets the first step in the sumoylation pathway. SUMO conjugations are retained after treatment with 1,10-phenanthroline in yeast that express a defective form of the desumoylase Ulp1, indicating that Ulp1 is responsible for eliminating existing SUMO modifications almost instantly when de novo sumoylation is inhibited. This reveals that SUMO modifications are normally extremely transient because of continuous desumoylation by Ulp1. Supporting our findings, we demonstrate that sumoylation of two specific targets, Sko1 and Tfg1, virtually disappears within one minute of impairing de novo sumoylation. Altogether, we have identified an extremely rapid and potent inhibitor of sumoylation, and our work reveals that SUMO modifications are remarkably short-lived.

Preparation of protein-loaded nanoparticles based on poly(succinimide)-oleylamine for sustained protein release: a two-step nanoprecipitation method

Currently, the treatment for acute disease encompasses the use of various biological drugs (BDs). However, the utilisation of BDs is limited due to their rapid clearance and non-specific accumulation in unwanted sites, resulting in a lack of therapeutic efficacy together with adverse effects. While nanoparticles are considered good candidates to resolve this problem, some available polymeric carriers for BDs were mainly designed for long-term sustained release. Thus, there is a need to explore new polymeric carriers for the acute disease phase that requires sustained release of BDs over a short period, for example for thrombolysis and infection. Poly(succinimide)-oleylamine (PSI-OA), a biocompatible polymer with a tuneable dissolution profile, represents a promising strategy for loading BDs for sustained release within a 48-h period. In this work, we developed a two-step nanoprecipitation method to load the model protein (e.g. bovine serum albumin and lipase) on PSI-OA. The characteristics of the nanoparticles were assessed based on various loading parameters, such as concentration, stirring rate, flow rate, volume ratio, dissolution and release of the protein. The optimised NPs displayed a size within 200 nm that is suitable for vasculature delivery to the target sites. These findings suggest that PSI-OA can be employed as a carrier for BDs for applications that require sustained release over a short period.

Artificial Intelligence/Machine Learning-Driven Small Molecule Repurposing via Off-Target Prediction and Transcriptomics

The process of discovering small molecule drugs involves screening numerous compounds and optimizing the most promising ones, both in vitro and in vivo. However, approximately 90% of these optimized candidates fail during trials due to unexpected toxicity or insufficient efficacy. Current concepts with respect to drug-protein interactions suggest that each small molecule interacts with an average of 6-11 targets. This implies that approved drugs and even discontinued compounds could be repurposed by leveraging their interactions with unintended targets. Therefore, we developed a computational repurposing framework for small molecules, which combines artificial intelligence/machine learning (AI/ML)-based and chemical similarity-based target prediction methods with cross-species transcriptomics information. This repurposing methodology incorporates eight distinct target prediction methods, including three machine learning methods. By using multiple orthogonal methods for a "dataset" composed of 2766 FDA-approved drugs targeting multiple therapeutic target classes, we identified 27,371 off-target interactions involving 2013 protein targets (i.e., an average of around 10 interactions per drug). Relative to the drugs in the dataset, we identified 150,620 structurally similar compounds. The highest number of predicted interactions were for drugs targeting G protein-coupled receptors (GPCRs), enzymes, and kinases with 10,648, 4081, and 3678 interactions, respectively. Notably, 17,283 (63%) of the off-target interactions have been confirmed in vitro. Approximately 4000 interactions had an IC50 of <100 nM for 1105 FDA-approved drugs and 1661 interactions had an IC50 of <10 nM for 696 FDA-approved drugs. Together, the confirmation of numerous predicted interactions and the exploration of tissue-specific expression patterns in human and animal tissues offer insights into potential drug repurposing for new therapeutic applications.

Cathepsins and SARS-CoV-2 infection: From pathogenic factors to potential therapeutic targets

Coronavirus disease-19 (COVID-19) is caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection. The COVID-19 pandemic began in March 2020 and has wrought havoc on health and economic systems worldwide. Efficacious treatment for COVID-19 is lacking: Only preventive measures as well as symptomatic and supportive care are available. Preclinical and clinical studies have indicated that lysosomal cathepsins might contribute to the pathogenesis and disease outcome of COVID-19. Here, we discuss cutting-edge evidence on the pathological roles of cathepsins in SARS-CoV-2 infection, host immune dysregulations, and the possible underlying mechanisms. Cathepsins are attractive drug targets because of their defined substrate-binding pockets, which can be exploited as binding sites for pharmaceutical enzyme inhibitors. Accordingly, the potential modulatory strategies of cathepsin activity are discussed. These insights could shed light on the development of cathepsin-based interventions for COVID-19.

Predicting binding between 55 cannabinoids and 4,799 biological targets by in silico methods

Recently, there has been an increase in cannabis-derived products being marketed as foods, dietary supplements, and other consumer products. Cannabis contains over a hundred cannabinoids, many of which have unknown physiological effects. Since there are large numbers of cannabinoids, and many are not commercially available for in vitro testing, an in silico tool (Chemotargets Clarity software) was used to predict binding between 55 cannabinoids and 4,799 biological targets (enzymes, ion channels, receptors, and transporters). This tool relied on quantitative structure activity relationships (QSAR), structural similarity, and other approaches to predict binding. From this screening, 827 cannabinoid-target binding pairs were predicted, which included 143 unique targets. Many cannabinoids sharing core structures (cannabinoid "types") had similar binding profiles, whereas most cannabinoids containing carboxylic acid groups were similar without regards to their core structure. For some of the binding predictions (43), in vitro binding data were available, and they agreed well with in silico binding data (median fourfold difference in binding concentrations). Finally, clinical adverse effects associated with 22 predicted targets were identified from an online database (Clarivate Off-X), providing important insights on potential human health hazards. Overall, in silico biological target predictions are a rapid means to identify potential hazards due to cannabinoid-target interactions, and the data can be used to prioritize subsequent in vitro and in vivo testing.

Positive and negative impacts of elexacaftor/tezacaftor/ivacaftor: Healthcare providers' observations across US centers

BACKGROUND

Elexacaftor/tezacaftor/ivacaftor (ETI) has been associated with unprecedented clinical improvements, transforming the management of cystic fibrosis (CF). However, side effects with implications for safety and well-being have been reported, including neuropsychiatric changes. This study aimed to better characterize the emerging positive and negative impacts of ETI.

METHODS

The Cystic Fibrosis Foundation's Mental Health Advisory Committee distributed a 26-item survey to US CF care teams to assess clinician observations of patient-reported experiences with ETI. Survey responses measured the prevalence of these effects in five domains: (1) positive physical and psychological effects, (2) sleep difficulties, (3) cognitive difficulties, (4) worsening mental health, and (5) concerns about the future and finances.

RESULTS

Seventy-five healthcare providers responded from a pediatric, adult, and combined centers. Positive physical effects of ETI and increased optimism were reported in the upper quartiles (50%-100%) and rated as having a significant impact on daily functioning. Sleep and cognitive difficulties were reported in 1%-24%, with slight impacts on functioning, and psychological symptoms (e.g., increased stress, depression, anxiety) and new psychiatric medications were reported in 1%-24%, with moderate impacts. Concerns about the future were reported in 1%-24%, with minimal impacts.

CONCLUSION

Across US centers, providers most often observed positive physical effects of ETI. However, a variety of negative side effects were also reported, including sleep disruptions and worsening psychological functioning, which should be systematically monitored by CF teams. These national-level data are a first step in evaluating the prevalence and consequences of these side effects and can directly inform future studies.

The recent progress of peptide regulators for the Wnt/β-catenin signaling pathway

Wnt signaling plays an important role in many biological processes such as stem cell self-renewal, cell proliferation, migration, and differentiation. The β-catenin-dependent signaling pathway mainly regulates cell proliferation, differentiation, and migration. In the Wnt/β-catenin signaling pathway, the Wnt family ligands transduce signals through LRP5/6 and Frizzled receptors to the Wnt/β-catenin signaling cascades. Wnt-targeted therapy has garnered extensive attention. The most commonly used approach in targeted therapy is small-molecule regulators. However, it is difficult for small-molecule regulators to make great progress due to their inherent defects. Therapeutic peptide regulators targeting the Wnt signaling pathway have become an alternative therapy, promising to fill the gaps in the clinical application of small-molecule regulators. In this review, we describe recent advances in peptide regulators for Wnt/β-catenin signaling.

ProfhEX: AI-based platform for small molecules liability profiling

Off-target drug interactions are a major reason for candidate failure in the drug discovery process. Anticipating potential drug's adverse effects in the early stages is necessary to minimize health risks to patients, animal testing, and economical costs. With the constantly increasing size of virtual screening libraries, AI-driven methods can be exploited as first-tier screening tools to provide liability estimation for drug candidates. In this work we present ProfhEX, an AI-driven suite of 46 OECD-compliant machine learning models that can profile small molecules on 7 relevant liability groups: cardiovascular, central nervous system, gastrointestinal, endocrine, renal, pulmonary and immune system toxicities. Experimental affinity data was collected from public and commercial data sources. The entire chemical space comprised 289'202 activity data for a total of 210'116 unique compounds, spanning over 46 targets with dataset sizes ranging from 819 to 18896. Gradient boosting and random forest algorithms were initially employed and ensembled for the selection of a champion model. Models were validated according to the OECD principles, including robust internal (cross validation, bootstrap, y-scrambling) and external validation. Champion models achieved an average Pearson correlation coefficient of 0.84 (SD of 0.05), an R² determination coefficient of 0.68 (SD = 0.1) and a root mean squared error of 0.69 (SD of 0.08). All liability groups showed good hit-detection power with an average enrichment factor at 5% of 13.1 (SD of 4.5) and AUC of 0.92 (SD of 0.05). Benchmarking against already existing tools demonstrated the predictive power of ProfhEX models for large-scale liability profiling. This platform will be further expanded with the inclusion of new targets and through complementary modelling approaches, such as structure and pharmacophore-based models. ProfhEX is freely accessible at the following address: https://profhex.exscalate.eu/ .

AI/ML Models to Predict the Severity of Drug-Induced Liver Injury for Small Molecules

Drug-induced liver injury (DILI), believed to be a multifactorial toxicity, has been a leading cause of attrition of small molecules during discovery, clinical development, and postmarketing. Identification of DILI risk early reduces the costs and cycle times associated with drug development. In recent years, several groups have reported predictive models that use physicochemical properties or in vitro and in vivo assay endpoints; however, these approaches have not accounted for liver-expressed proteins and drug molecules. To address this gap, we have developed an integrated artificial intelligence/machine learning (AI/ML) model to predict DILI severity for small molecules using a combination of physicochemical properties and off-target interactions predicted in silico. We compiled a data set of 603 diverse compounds from public databases. Among them, 164 were categorized as Most DILI (M-DILI), 245 as Less DILI (L-DILI), and 194 as No DILI (N-DILI) by the FDA. Six machine learning methods were used to create a consensus model for predicting the DILI potential. These methods include k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), Naïve Bayes (NB), artificial neural network (ANN), logistic regression (LR), weighted average ensemble learning (WA) and penalized logistic regression (PLR). Among the analyzed ML methods, SVM, RF, LR, WA, and PLR identified M-DILI and N-DILI compounds, achieving a receiver operating characteristic area under the curve of 0.88, sensitivity of 0.73, and specificity of 0.9. Approximately 43 off-targets, along with physicochemical properties (fsp³, log S, basicity, reactive functional groups, and predicted metabolites), were identified as significant factors in distinguishing between M-DILI and N-DILI compounds. The key off-targets that we identified include: PTGS1, PTGS2, SLC22A12, PPARγ, RXRA, CYP2C9, AKR1C3, MGLL, RET, AR, and ABCC4. The present AI/ML computational approach therefore demonstrates that the integration of physicochemical properties and predicted on- and off-target biological interactions can significantly improve DILI predictivity compared to chemical properties alone.

Rewiring Drug Research and Development through Human Data-Driven Discovery (HD3)

In an era of unparalleled technical advancement, the pharmaceutical industry is struggling to transform data into increased research and development efficiency, and, as a corollary, new drugs for patients. Here, we briefly review some of the commonly discussed issues around this counterintuitive innovation crisis. Looking at both industry- and science-related factors, we posit that traditional preclinical research is front-loading the development pipeline with data and drug candidates that are unlikely to succeed in patients. Applying a first principles analysis, we highlight the critical culprits and provide suggestions as to how these issues can be rectified through the pursuit of a Human Data-driven Discovery (HD3) paradigm. Consistent with other examples of disruptive innovation, we propose that new levels of success are not dependent on new inventions, but rather on the strategic integration of existing data and technology assets. In support of these suggestions, we highlight the power of HD3, through recently published proof-of-concept applications in the areas of drug safety analysis and prediction, drug repositioning, the rational design of combination therapies and the global response to the COVID-19 pandemic. We conclude that innovators must play a key role in expediting the path to a largely human-focused, systems-based approach to drug discovery and research.

New Insights on the Activity and Selectivity of MAO-B Inhibitors through In Silico Methods

To facilitate the identification of novel MAO-B inhibitors, we elaborated a consolidated computational approach, including a pharmacophoric atom-based 3D quantitative structure-activity relationship (QSAR) model, activity cliffs, fingerprint, and molecular docking analysis on a dataset of 126 molecules. An AAHR.2 hypothesis with two hydrogen bond acceptors (A), one hydrophobic (H), and one aromatic ring (R) supplied a statistically significant 3D QSAR model reflected by the parameters: R² = 0.900 (training set); Q² = 0.774 and Pearson's R = 0.884 (test set), stability s = 0.736. Hydrophobic and electron-withdrawing fields portrayed the relationships between structural characteristics and inhibitory activity. The quinolin-2-one scaffold has a key role in selectivity towards MAO-B with an AUC of 0.962, as retrieved by ECFP4 analysis. Two activity cliffs showing meaningful potency variation in the MAO-B chemical space were observed. The docking study revealed interactions with crucial residues TYR:435, TYR:326, CYS:172, and GLN:206 responsible for MAO-B activity. Molecular docking is in consensus with and complementary to pharmacophoric 3D QSAR, ECFP4, and MM-GBSA analysis. The computational scenario provided here will assist chemists in quickly designing and predicting new potent and selective candidates as MAO-B inhibitors for MAO-B-driven diseases. This approach can also be used to identify MAO-B inhibitors from other libraries or screen top molecules for other targets involved in suitable diseases.

Picamilon, a γ-aminobutyric acid (GABA) analogue and marketed nootropic, is inactive against 50 biological targets

Picamilon is an analogue of the neurotransmitter γ-aminobutyric acid (GABA), which is marketed as a nootropic claiming to enhance cognition. There is a lack of in silico, in vitro and in vivo data on the safety of picamilon. Therefore, to ascertain potential physiological effects of picamilon, it was screened against 50 safety-related biological targets (receptors, ion channels, enzymes and transporters) by in silico and in vitro methods. Using two in silico tools, picamilon was not predicted to bind to the targets. Similarly, picamilon exhibited weak or no binding to the targets when measured in vitro at 10 μM. Overall, this data shows that picamilon, although structurally similar to other GABA analogues, has a different biological target binding profile. Picamilon's lack of binding to the 50 targets fills important data gaps among GABA analogues, a group of structurally related substances found in drugs and other consumer products.

bioTCIs: Middle-to-Macro Biomolecular Targeted Covalent Inhibitors Possessing Both Semi-Permanent Drug Action and Stringent Target Specificity as Potential Antibody Replacements

Monoclonal antibody therapies targeting immuno-modulatory targets such as checkpoint proteins, chemokines, and cytokines have made significant impact in several areas, including cancer, inflammatory disease, and infection. However, antibodies are complex biologics with well-known limitations, including high cost for development and production, immunogenicity, a limited shelf-life because of aggregation, denaturation, and fragmentation of the large protein. Drug modalities such as peptides and nucleic acid aptamers showing high-affinity and highly selective interaction with the target protein have been proposed alternatives to therapeutic antibodies. The fundamental limitation of short in vivo half-life has prevented the wide acceptance of these alternatives. Covalent drugs, also known as targeted covalent inhibitors (TCIs), form permanent bonds to target proteins and, in theory, eternally exert the drug action, circumventing the pharmacokinetic limitation of other antibody alternatives. The TCI drug platform, too, has been slow in gaining acceptance because of its potential prolonged side-effect from off-target covalent binding. To avoid the potential risks of irreversible adverse drug effects from off-target conjugation, the TCI modality is broadening from the conventional small molecules to larger biomolecules possessing desirable properties (e.g., hydrolysis resistance, drug-action reversal, unique pharmacokinetics, stringent target specificity, and inhibition of protein-protein interactions). Here, we review the historical development of the TCI made of bio-oligomers/polymers (i.e., peptide-, protein-, or nucleic-acid-type) obtained by rational design and combinatorial screening. The structural optimization of the reactive warheads and incorporation into the targeted biomolecules enabling a highly selective covalent interaction between the TCI and the target protein is discussed. Through this review, we hope to highlight the middle to macro-molecular TCI platform as a realistic replacement for the antibody.

Targeting the Brain with Single-Domain Antibodies: Greater Potential Than Stated So Far?

Treatments for central nervous system diseases with therapeutic antibodies have been increasingly investigated over the last decades, leading to some approved monoclonal antibodies for brain disease therapies. The detection of biomarkers for diagnosis purposes with non-invasive antibody-based imaging approaches has also been explored in brain cancers. However, antibodies generally display a low capability of reaching the brain, as they do not efficiently cross the blood-brain barrier. As an alternative, recent studies have focused on single-domain antibodies (sdAbs) that correspond to the antigen-binding fragment. While some reports indicate that the brain uptake of these small antibodies is still low, the number of studies reporting brain-penetrating sdAbs is increasing. In this review, we provide an overview of methods used to assess or evaluate brain penetration of sdAbs and discuss the pros and cons that could affect the identification of brain-penetrating sdAbs of therapeutic or diagnostic interest.

Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond

The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research and development has to date focused on research: target identification; docking-, fragment-, and motif-based generation of compound libraries; modeling of synthesis feasibility; rank-ordering likely hits according to structural and chemometric similarity to compounds having known activity and affinity to the target(s); optimizing a smaller library for synthesis and high-throughput screening; and combining evidence from screening to support hit-to-lead decisions. Applying AI/ML methods to lead optimization and lead-to-candidate (L2C) decision-making has shown slower progress, especially regarding predicting absorption, distribution, metabolism, excretion, and toxicology properties. The present review surveys reasons why this is so, reports progress that has occurred in recent years, and summarizes some of the issues that remain. Effective AI/ML tools to derisk L2C and later phases of development are important to accelerate the pharmaceutical development process, ameliorate escalating development costs, and achieve greater success rates.

Fatty Acid Metabolism in Endothelial Cell

The endothelium is a monolayer of cells lining the inner blood vessels. Endothelial cells (ECs) play indispensable roles in angiogenesis, homeostasis, and immune response under normal physiological conditions, and their dysfunction is closely associated with pathologies such as cardiovascular diseases. Abnormal EC metabolism, especially dysfunctional fatty acid (FA) metabolism, contributes to the development of many diseases including pulmonary hypertension (PH). In this review, we focus on discussing the latest advances in FA metabolism in ECs under normal and pathological conditions with an emphasis on PH. We also highlight areas of research that warrant further investigation.

Angiotensin II receptor blockers (ARBs) and manufacturing contamination: A retrospective National Register Study into suspected associated adverse drug reactions

AIMS

The aim of this study was to determine if any suspected adverse drug reactions (ADRs) observed with the use of angiotensin II receptor blockers (ARBs) could be linked to either (a) their unique respective physicochemical and pharmacological profiles and (b) the recently disclosed suspected carcinogenic manufacturing contaminants found in certain sartan drug class batches.

METHODS

The pharmacology profiles of ARBs were data-mined from the Chemical Database of bioactive molecules with drug-like properties, European Molecular Biology Laboratory (ChEMBL). Suspected ADR data (from 01/2016-10/2022, inclusive) and prescribing rates of ARBs over a 5-year prescribing window (from 09/2016 to 08/2021, inclusive) were obtained via analysis of the United Kingdom Medicines and Healthcare products Regulatory Authority (MHRA) Yellow Card drug analysis profile and Open prescribing databases, respectively.

RESULTS

The overall suspected ADRs and fatalities per 100 000 prescriptions identified across the ARBs studied were found to be different between the sartan drug class members (chi-squared test, P < .05). There is a greater relative rate of reports for valsartan across all investigated organ classes of ADRs, than other ARBs, despite valsartan's more limited pharmacological profile and similar physicochemical properties to other sartans. The disparity in ADR reporting rates with valsartan vs other ARBs could be due to the dissimilarity in formulation excipients, patient factors and publicity surrounding batch contaminations, amongst others. Cancer-related ADRs and fatalities per 100 000 prescriptions identified across the ARBs studied are not statistically significant (chi-squared test, P > .05) based on the datasets used over the 5-year period.

CONCLUSION

No connection between ARB pharmacology and their suspected ADRs could be found. No conclusion between sartan batch contaminations and increased suspected cancer-related ADRs was found.

Combining DELs and machine learning for toxicology prediction

DNA-encoded libraries (DELs) allow starting chemical matter to be identified in drug discovery. The volume of experimental data generated also makes DELs an attractive resource for machine learning (ML). ML allows modeling complex relationships between compounds and numerical endpoints, such as the binding to a target measured by DELs. DELs could also empower other areas of drug discovery. Here, we propose that DELs and ML could be combined to model binding to off-targets, enabling better predictive toxicology. With enough data, ML models can make accurate predictions across a vast chemical space, and they can be reused and expanded across projects. Although there are limitations, more general toxicology models could be applied earlier during drug discovery, illuminating safety liabilities at a lower cost.

Advancements in antimicrobial nanoscale materials and self-assembling systems

Antimicrobial resistance is directly responsible for more deaths per year than either HIV/AIDS or malaria and is predicted to incur a cumulative societal financial burden of at least $100 trillion between 2014 and 2050. Already heralded as one of the greatest threats to human health, the onset of the coronavirus pandemic has accelerated the prevalence of antimicrobial resistant bacterial infections due to factors including increased global antibiotic/antimicrobial use. Thus an urgent need for novel therapeutics to combat what some have termed the 'silent pandemic' is evident. This review acts as a repository of research and an overview of the novel therapeutic strategies being developed to overcome antimicrobial resistance, with a focus on self-assembling systems and nanoscale materials. The fundamental mechanisms of action, as well as the key advantages and disadvantages of each system are discussed, and attention is drawn to key examples within each field. As a result, this review provides a guide to the further design and development of antimicrobial systems, and outlines the interdisciplinary techniques required to translate this fundamental research towards the clinic.

A novel HSP90 inhibitor SL-145 suppresses metastatic triple-negative breast cancer without triggering the heat shock response

Despite recent advances, there remains a significant unmet need for the development of new targeted therapies for triple-negative breast cancer (TNBC). Although the heat shock protein HSP90 is a promising target, previous inhibitors have had issues during development including undesirable induction of the heat shock response (HSR) and off-target effects leading to toxicity. SL-145 is a novel, rationally-designed C-terminal HSP90 inhibitor that induces apoptosis in TNBC cells via the suppression of oncogenic AKT, MEK/ERK, and JAK2/STAT3 signaling and does not trigger the HSR, in contrast to other inhibitors. In an orthotopic allograft model incorporating breast cancer stem cell-enriched TNBC tumors, SL-145 potently suppressed tumor growth, angiogenesis, and metastases concomitant with dysregulation of the JAK2/STAT3 signaling pathway. Our findings highlight the potential of SL-145 in suppressing metastatic TNBC independent of the HSR.

A Comprehensive Evaluation of Sdox, a Promising H2S-Releasing Doxorubicin for the Treatment of Chemoresistant Tumors

Sdox is a hydrogen sulfide (H₂S)-releasing doxorubicin effective in P-glycoprotein-overexpressing/doxorubicin-resistant tumor models and not cytotoxic, as the parental drug, in H9c2 cardiomyocytes. The aim of this study was the assessment of Sdox drug-like features and its absorption, distribution, metabolism, and excretion (ADME)/toxicity properties, by a multi- and transdisciplinary in silico, in vitro, and in vivo approach. Doxorubicin was used as the reference compound. The in silico profiling suggested that Sdox possesses higher lipophilicity and lower solubility compared to doxorubicin, and the off-targets prediction revealed relevant differences between Dox and Sdox towards several cancer targets, suggesting different toxicological profiles. In vitro data showed that Sdox is a substrate with lower affinity for P-glycoprotein, less hepatotoxic, and causes less oxidative damage than doxorubicin. Both anthracyclines inhibited CYP3A4, but not hERG currents. Unlike doxorubicin, the percentage of zebrafish live embryos at 72 hpf was not affected by Sdox treatment. In conclusion, these findings demonstrate that Sdox displays a more favorable drug-like ADME/toxicity profile than doxorubicin, different selectivity towards cancer targets, along with a greater preclinical efficacy in resistant tumors. Therefore, Sdox represents a prototype of innovative anthracyclines, worthy of further investigations in clinical settings.

Innovative approaches for treatment of osteosarcoma

Osteosarcoma (OS) is the most common primary malignant bone tumor, which usually occurs in children and adolescents. It is generally a high-grade malignancy presenting with extreme metastases to the lungs or other bones. The etiology of the disease is multifaceted and still remains obscure. A combination of surgery and chemotherapy has played a major role in the treatment of OS over the past three decades, and consequently, the overall survival rates for the disease have remained unchanged. Therefore, there is an urgent need to employ new comprehensive analyses and technologies to develop significantly more informative classification systems, with the aim of developing more effective and less toxic therapies for OS patients. This review discusses the existing knowledge of OS therapy and potential methods to develop novel therapeutic agents for the disease.

Increasing the Value of Data Within a Large Pharmaceutical Company Through In Silico Models

The present contribution describes how in silico models and methods are applied at different stages of the drug discovery process in the pharmaceutical industry. A description of the most relevant computational methods and tools is given along with an evaluation of their performance in the assessment of potential genotoxic impurities and the prediction of off-target in vitro pharmacology. The challenges of predicting the outcome of highly complex in vivo studies are discussed followed by considerations on how novel ways to manage, store, exchange, and analyze data may advance knowledge and facilitate modeling efforts. In this context, the current status of broad data sharing initiatives, namely, eTOX and eTransafe, will be described along with related projects that could significantly reduce the use of animals in drug discovery in the future.

Peptide Multimerization as Leads for Therapeutic Development

Multimerization of peptide structures has been a logical evolution in their development as potential therapeutic molecules. The multivalent properties of these assemblies have attracted much attention from researchers in the past and the development of more complex branching dendrimeric structures, with a wide array of biocompatible building blocks is revealing previously unseen properties and activities. These branching multimer and dendrimer structures can induce greater effect on cellular targets than monomeric forms and act as potent antimicrobials, potential vaccine alternatives and promising candidates in biomedical imaging and drug delivery applications. This review aims to outline the chemical synthetic innovations for the development of these highly complex structures and highlight the extensive capabilities of these molecules to rival those of natural biomolecules.

CNS pharmacology of NKCC1 inhibitors

The Na-K-2Cl cotransporter NKCC1 and the neuron-specific K-Cl cotransporter KCC2 are considered attractive CNS drug targets because altered neuronal chloride regulation and consequent effects on GABAergic signaling have been implicated in numerous CNS disorders. While KCC2 modulators are not yet clinically available, the loop diuretic bumetanide has been used off-label in attempts to treat brain disorders and as a tool for NKCC1 inhibition in preclinical models. Bumetanide is known to have anticonvulsant and neuroprotective effects under some pathophysiological conditions. However, as shown in several species from neonates to adults (mice, rats, dogs, and by extrapolation in humans), at the low clinical doses of bumetanide approved for diuresis, this drug has negligible access into the CNS, reaching levels that are much lower than what is needed to inhibit NKCC1 in cells within the brain parenchyma. Several drug discovery strategies have been initiated over the last ∼15 years to develop brain-permeant compounds that, ideally, should be selective for NKCC1 to eliminate the diuresis mediated by inhibition of renal NKCC2. The strategies employed to improve the pharmacokinetic and pharmacodynamic properties of NKCC1 blockers include evaluation of other clinically approved loop diuretics; development of lipophilic prodrugs of bumetanide; development of side-chain derivatives of bumetanide; and unbiased high-throughput screening approaches of drug discovery based on large chemical compound libraries. The main outcomes are that (1), non-acidic loop diuretics such as azosemide and torasemide may have advantages as NKCC1 inhibitors vs. bumetanide; (2), bumetanide prodrugs lead to significantly higher brain levels than the parent drug and have lower diuretic activity; (3), the novel bumetanide side-chain derivatives do not exhibit any functionally relevant improvement of CNS accessibility or NKCC1 selectivity vs. bumetanide; (4) novel compounds discovered by high-throughput screening may resolve some of the inherent problems of bumetanide, but as yet this has not been achieved. Thus, further research is needed to optimize the design of brain-permeant NKCC1 inhibitors. In parallel, a major challenge is to identify the mechanisms whereby various NKCC1-expressing cellular targets of these drugs within (e.g., neurons, oligodendrocytes or astrocytes) and outside the brain parenchyma (e.g., the blood-brain barrier, the choroid plexus, and the endocrine system), as well as molecular off-target effects, might contribute to their reported therapeutic and adverse effects.

Direct screening of a target-specific covalent binder: stringent regulation of warhead reactivity in a matchmaking environment

A peptide-type covalent binder for a target protein was obtained by direct and stringent screening of a warhead-modified peptide library on the robust T7 phage. The aryl fluorosulfate (fosylate) warhead was activated only in a matchmaking microenvironment created between the target protein and an appropriate peptide during the reactivity/affinity-based co-selection process of extended phage display.

Optimization of the clofazimine structure leads to a highly water-soluble C3-aminopyridinyl riminophenazine endowed with improved anti-Wnt and anti-cancer activity in vitro and in vivo

Triple-negative breast cancer (TNBC) is a cancer subtype critically dependent upon excessive activation of Wnt pathway. The anti-mycobacterial drug clofazimine is an efficient inhibitor of canonical Wnt signaling in TNBC, reducing tumor cell proliferation in vitro and in animal models. These properties make clofazimine a candidate to become first targeted therapy against TNBC. In this work, we optimized the clofazimine structure to enhance its water solubility and potency as a Wnt inhibitor. After extensive structure-activity relationships investigations, the riminophenazine 5-(4-(chlorophenyl)-3-((2-(piperazin-1-yl)ethyl)imino)-N-(pyridin-3-yl)-3,5-dihydrophenazin-2-amine (MU17) was identified as the new lead compound for the riminophenazine-based targeted therapy against TNBC and Wnt-dependent cancers. Compared to clofazimine, the water-soluble MU17 displayed a 7-fold improved potency against Wnt signaling in TNBC cells resulting in on-target suppression of tumor growth in a patient-derived mouse model of TNBC. Moreover, allowing the administration of reduced yet effective dosages, MU17 displayed no adverse effects, most notably no clofazimine-related skin coloration.

A Molecular Modeling Approach to Identify Potential Antileishmanial Compounds Against the Cell Division Cycle (cdc)-2-Related Kinase 12 (CRK12) Receptor of Leishmania donovani

The huge burden of leishmaniasis caused by the trypanosomatid protozoan parasite Leishmania is well known. This illness was included in the list of neglected tropical diseases targeted for elimination by the World Health Organization. However, the increasing evidence of resistance to existing antimonial drugs has made the eradication of the disease difficult to achieve, thus warranting the search for new drug targets. We report here studies that used computational methods to identify inhibitors of receptors from natural products. The cell division cycle-2-related kinase 12 (CRK12) receptor is a plausible drug target against Leishmania donovani. This study modelled the 3D molecular structure of the L. donovani CRK12 (LdCRK12) and screened for small molecules with potential inhibitory activity from African flora. An integrated library of 7722 African natural product-derived compounds and known inhibitors were screened against the LdCRK12 using AutoDock Vina after performing energy minimization with GROMACS 2018. Four natural products, namely sesamin (NANPDB1649), methyl ellagic acid (NANPDB1406), stylopine (NANPDB2581), and sennecicannabine (NANPDB6446) were found to be potential LdCRK12 inhibitory molecules. The molecular docking studies revealed two compounds NANPDB1406 and NANPDB2581 with binding affinities of -9.5 and -9.2 kcal/mol, respectively, against LdCRK12 which were higher than those of the known inhibitors and drugs, including GSK3186899, amphotericin B, miltefosine, and paromomycin. All the four compounds were predicted to have inhibitory constant (Ki) values ranging from 0.108 to 0.587 μM. NANPDB2581, NANPDB1649 and NANPDB1406 were also predicted as antileishmanial with Pa and Pi values of 0.415 and 0.043, 0.391 and 0.052, and 0.351 and 0.071, respectively. Molecular dynamics simulations coupled with molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) computations reinforced their good binding mechanisms. Most compounds were observed to bind in the ATP binding pocket of the kinase domain. Lys488 was predicted as a key residue critical for ligand binding in the ATP binding pocket of the LdCRK12. The molecules were pharmacologically profiled as druglike with inconsequential toxicity. The identified molecules have scaffolds that could form the backbone for fragment-based drug design of novel leishmanicides but warrant further studies to evaluate their therapeutic potential.

Computational Selectivity Assessment of Protease Inhibitors against SARS-CoV-2

The pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a serious global health threat. Since no specific therapeutics are available, researchers around the world screened compounds to inhibit various molecular targets of SARS-CoV-2 including its main protease (Mpro) essential for viral replication. Due to the high urgency of these discovery efforts, off-target binding, which is one of the major reasons for drug-induced toxicity and safety-related drug attrition, was neglected. Here, we used molecular docking, toxicity profiling, and multiple molecular dynamics (MD) protocols to assess the selectivity of 33 reported non-covalent inhibitors of SARS-CoV-2 Mpro against eight proteases and 16 anti-targets. The panel of proteases included SARS-CoV Mpro, cathepsin G, caspase-3, ubiquitin carboxy-terminal hydrolase L1 (UCHL1), thrombin, factor Xa, chymase, and prostasin. Several of the assessed compounds presented considerable off-target binding towards the panel of proteases, as well as the selected anti-targets. Our results further suggest a high risk of off-target binding to chymase and cathepsin G. Thus, in future discovery projects, experimental selectivity assessment should be directed toward these proteases. A systematic selectivity assessment of SARS-CoV-2 Mpro inhibitors, as we report it, was not previously conducted.

β2 -Adrenoceptor agonist activity of higenamine

Higenamine was included in the World Anti-Doping Agency (WADA) Prohibited Substances and Methods List as a β2 -adrenoceptor agonist in 2017, thereby resulting in its prohibition both in and out of competition. The present mini review describes the physiology and pharmacology of adrenoceptors, summarizes the literature addressing the mechanism of action of higenamine and extends these findings with previously unpublished in silico and in vitro work. Studies conducted in isolated in vitro systems, whole-animal preparations and a small number of clinical studies suggest that higenamine acts in part as a β2 -adrenoceptor agonist. In silico predictive tools indicated that higenamine and possibly a metabolite have a high probability of interacting with the β2 -receptor as an agonist. Stable expression of human β2 -receptors in Chinese hamster ovary (CHO) cells to measure agonist activity not only confirmed the activity of higenamine at β2 but also closely agreed with the in silico prediction of potency for this compound. These data confirm and extend literature findings supporting the inclusion of higenamine in the Prohibited List.