Drug Discovery Research Articles

Metabolic Objectives and Trade-Offs: Inference and Applications

Background/Objectives: Determining appropriate cellular objectives is crucial for the system-scale modeling of biological networks for metabolic engineering, cellular reprogramming, and drug discovery applications. The mathematical representation of metabolic objectives can describe how cells manage limited resources to achieve biological goals within mechanistic and environmental constraints. While rapidly proliferating cells like tumors are often assumed to prioritize biomass production, mammalian cell types can exhibit objectives beyond growth, such as supporting tissue functions, developmental processes, and redox homeostasis. Methods: This review addresses the challenge of determining metabolic objectives and trade-offs from multiomics data. Results: Recent advances in single-cell omics, metabolic modeling, and machine/deep learning methods have enabled the inference of cellular objectives at both the transcriptomic and metabolic levels, bridging gene expression patterns with metabolic phenotypes. Conclusions: These in silico models provide insights into how cells adapt to changing environments, drug treatments, and genetic manipulations. We further explore the potential application of incorporating cellular objectives into personalized medicine, drug discovery, tissue engineering, and systems biology.

The role of electroencephalography in epilepsy research-From seizures to interictal activity and comorbidities.

Electroencephalography (EEG) has been instrumental in epilepsy research for the past century, both for basic and translational studies. Its contributions have advanced our understanding of epilepsy, shedding light on the pathophysiology and functional organization of epileptic networks, and the mechanisms underlying seizures. Here we re-examine the historical significance, ongoing relevance, and future trajectories of EEG in epilepsy research. We describe traditional approaches to record brain electrical activity and discuss novel cutting-edge, large-scale techniques using micro-electrode arrays. Contemporary EEG studies explore brain potentials beyond the traditional Berger frequencies to uncover underexplored mechanisms operating at ultra-slow and high frequencies, which have proven valuable in understanding the principles of ictogenesis, epileptogenesis, and endogenous epileptogenicity. Integrating EEG with modern techniques such as optogenetics, chemogenetics, and imaging provides a more comprehensive understanding of epilepsy. EEG has become an integral element in a powerful suite of tools for capturing epileptic network dynamics across various temporal and spatial scales, ranging from rapid pathological synchronization to the long-term processes of epileptogenesis or seizure cycles. Advancements in EEG recording techniques parallel the application of sophisticated mathematical analyses and algorithms, significantly augmenting the information yield of EEG recordings. Beyond seizures and interictal activity, EEG has been instrumental in elucidating the mechanisms underlying epilepsy-related cognitive deficits and othercomorbidities. Although EEG remains a cornerstone in epilepsy research, persistent challenges such as limited spatial resolution, artifacts, and the difficulty of long-term recording highlight the ongoing need for refinement. Despite these challenges, EEG continues to be a fundamental research tool, playing a central role in unraveling disease mechanisms and drug discovery.

Synthesis of the 3,3'-bipyrrole scaffold from diyne.

3,3'-Bipyrrole units often occur in biologically significant and materials-relevant compounds. However, the current efficient routes to afford these compounds are underdeveloped. Herein, a two-step approach for assembling substituted bipyrroles starting from 1,3-diyne and N-benzyl azomethine ylide has been demonstrated. In the first step, tungsten catalyzed twofold 1,3-dipolar cycloaddition gave rise to tetrahydrobipyrrole products in 42-85% yields. In the second step, a range of tetrahydrobipyrroles smoothly underwent copper-catalyzed dehydrogenative aromatization and provided 3,3'-bipyrroles in 54-80% yields. In addition, synthetic utilization and preliminary mechanistic studies were also performed. These bipyrroles might have potential utility in drug discovery and materials science.

Proteome selectivity profiling of photoaffinity probes derived from imidazopyrazine-kinase inhibitors

Kinases are attractive drug targets, but the design of highly selective kinase inhibitors remains challenging. Selectivity may be evaluated against a panel of kinases, or – preferred – in a complex proteome. Probes that allow photoaffinity-labeling of their targets can facilitate this process. Here, we report photoaffinity probes based on the imidazopyrazine scaffold, which is found in several kinase inhibitors and drugs or drug candidates. By chemical proteomics experiments, we find a range of off-targets, which vary between the different probes. In silico analysis suggests that differences between probes may be related to the size, spatial arrangement and rigidity of the imidazopyrazine and its substituent at the 1-position.

Computational Modeling of Pharmaceuticals with an Emphasis on Crossing the Blood–Brain Barrier

The discovery and development of new pharmaceutical drugs is a costly, time-consuming, and highly manual process, with significant challenges in ensuring drug bioavailability at target sites. Computational techniques are highly employed in drug design, particularly to predict the pharmacokinetic properties of molecules. One major kinetic challenge in central nervous system drug development is the permeation through the blood–brain barrier (BBB). Several different computational techniques are used to evaluate both BBB permeability and target delivery. Methods such as quantitative structure–activity relationships, machine learning models, molecular dynamics simulations, end-point free energy calculations, or transporter models have pros and cons for drug development, all contributing to a better understanding of a specific characteristic. Additionally, the design (assisted or not by computers) of prodrug and nanoparticle-based drug delivery systems can enhance BBB permeability by leveraging enzymatic activation and transporter-mediated uptake. Neuroactive peptide computational development is also a relevant field in drug design, since biopharmaceuticals are on the edge of drug discovery. By integrating these computational and formulation-based strategies, researchers can enhance the rational design of BBB-permeable drugs while minimizing off-target effects. This review is valuable for understanding BBB selectivity principles and the latest in silico and nanotechnological approaches for improving CNS drug delivery.

Dysregulated Pathways During Pregnancy Predict Drug Candidates in Neurodevelopmental Disorders.

Maternal health during pregnancy has a direct impact on the risk and severity of neurodevelopmental disorders (NDDs) in the offspring, especially in the case of drug exposure. However, little progress has been made to assess the risk of drug exposure during pregnancy due to ethical constraints and drug use factors. We collected and manually curated sub-pathways and pathways (sub-/pathways) and drug information to propose an analytical framework for predicting drug candidates. This framework linked sub-/pathway activity and drug response scores derived from gene transcription data and was applied to human fetal brain development and six NDDs. Further, specific and pleiotropic sub-/pathways/drugs were identified using entropy, and sex bias was analyzed in conjunction with logistic regression and random forest models. We identified 19 disorder-associated and 256 regionally pleiotropic and specific candidate drugs that targeted risk sub-/pathways in NDDs, showing temporal or spatial changes across fetal development. Moreover, 5443 differential drug-sub-/pathways exhibited sex-biased differences after filling in the gender labels. A user-friendly NDDP visualization website ( https://ndd-lab.shinyapps.io/NDDP ) was developed to allow researchers and clinicians to access and retrieve data easily. Our framework overcame data gaps and identified numerous pleiotropic and specific candidates across six disorders and fetal developmental trajectories. This could significantly contribute to drug discovery during pregnancy and can be applied to a wide range of traits.

Simplicity within biological complexity

Abstract Heterogeneous, interconnected, systems-level, molecular (multi-omic) data have become increasingly available and key in precision medicine. We need to utilize them to better stratify patients into risk groups, discover new biomarkers and targets, repurpose known and discover new drugs to personalize medical treatment. Existing methodologies are limited and a paradigm shift is needed to achieve quantitative and qualitative breakthroughs. In this perspective paper, we survey the literature and argue for the development of a comprehensive, general framework for embedding of multi-scale molecular network data that would enable their explainable exploitation in precision medicine in linear time. Network embedding methods (also called graph representation learning) map nodes to points in low-dimensional space, so that proximity in the learned space reflects the network’s topology-function relationships. They have recently achieved unprecedented performance on hard problems of utilizing few omic data in various biomedical applications. However, research thus far has been limited to special variants of the problems and data, with the performance depending on the underlying topology-function network biology hypotheses, the biomedical applications and evaluation metrics. The availability of multi-omic data, modern graph embedding paradigms and compute power call for a creation and training of efficient, explainable and controllable models, having no potentially dangerous, unexpected behaviour, that make a qualitative breakthrough. We propose to develop a general, comprehensive embedding framework for multi-omic network data, from models to efficient and scalable software implementation, and to apply it to biomedical informatics, focusing on precision medicine and personalized drug discovery. It will lead to a paradigm shift in computational and biomedical understanding of data and diseases that will open up ways to solving some of the major bottlenecks in precision medicine and other domains.

Development of 3D Muscle Cell Culture-Based Screening System for Metabolic Syndrome Drug Research.

Developing effective drug screening methods for type 2 diabetes requires physiologically relevant models. Traditional 2D cell cultures have limitations in replicating invivo conditions, leading to challenges in assessing drug efficacy. To overcome these issues, we developed a 3D artificial muscle model that induces insulin resistance, a hallmark of type 2 diabetes. Using C2C12 myoblasts cultured in a scaffold of 1% alginate and 1 mg/mL collagen type 1, we optimized conditions for differentiation and structural stability. Insulin resistance was induced using palmitic acid (PA), and glucose uptake was assessed using the fluorescent glucose analog 2-NBDG. The 3D model demonstrated superior glucose uptake responses compared with 2D cultures, with a threefold increase in insulin-stimulated glucose uptake on days 4 and 8 of differentiation. Induced insulin resistance was observed with 0.1 mM PA, which maintained cell viability and differentiation capacity. The model was validated through comparative drug screening using rosiglitazone and metformin, as well as 165 candidate compounds provided by Korea Chemical Bank. Drug screening revealed that three out of five hit compounds identified in both 2D and 3D models exhibited greater efficacy in 3D cultures, with results consistent with ex vivo assays using mouse soleus muscle. This model closely mimics invivo conditions, offering a robust platform for type 2 diabetes drug discovery while supporting ethical research practices.

Antioxidative Potential of Telfairia occidentalis Aqueous Leaves Extract in Mitigating Cyclophosphamide-Induced Neurotoxicity in Wistar Rats

Aim: This study sought to investigate the antioxidative potentials of Telfairia occidentalis aqueous leaves extract (TOAE) on cyclophosphamide (CP) induced neurotoxicity in rats. Study Design: This study sought to investigate the antioxidative potentials of Telfairia occidentalis aqueous leaf extract (TOAE) on cyclophosphamide induced neurotoxicity in rats. Place and duration of the study: This study was carried out in the Department of Anatomy, Olabisi Onabanjo University, Sagamu, Ogun State, Nigeria between October, 2023 and September, 2024 Methods: 25 Adult wistar rats were assigned into five different groups I-V of (n=5) which were respectively exposed for 14 days as follow to Normal saline, TOAE only (400 mg/kg b.w), 400 mg/kg b.w TOAE with single dose of 100 mg/kg CP on 14th day, single dose 100 mg/kg CP only on day 1 and single dose of 100 mg/kg CP only on day 1 followed by 400 mg/kg b.w of TOAE for 14 days. CP was administered intraperitoneally and TOAE was administered orally. All the animals were sacrificed at the end of designated exposure and processed for biochemical evaluations using standard protocols. Results: CP mediated inflammation in cerebellum of rats with a significant increase in MDA, CAT, IL-1β and TNF-α significant decrease in SOD and Gpx levels compared with a control group. Lipid peroxidation was also induced following exposure of rats to CP. There is a marked decrease in antioxidant enzymes activities in CP group. However, treatment with TOAE markedly inhibited the inflammation by reversing the elevated levels of inflammatory markers. Furthermore, TOAE suppressed the lipid peroxidation chain reaction by reducing the level of malondialdehyde (MDA) and catalase CAT. TOAE attenuates CP-induced oxidative stress by increase the level of GPx and superoxide dismutase enhancing the activities of the cytoprotective enzymes (catalase and glutathione-S- transferase). Serum levels of Interleukin-2/1-beta and Tumor Necrosis Factor-alpha (TNF-α), were measured on the 15th day after exposure. The effect of TOAE was deduced on anti-inflammatory and antioxidants properties. Taken together, TOAE inhibited inflammation, suppressed lipid peroxidation, attenuated oxidative stress, and enhanced the antioxidant enzymes activities. Conclusion: These therapeutic effects of TOAE might be due to its phytochemicals. The findings of this study indicate that aqueous leaf extract of T. occidentalis has could be a drug candidate for the treatment of the immunosuppressive effect of cyclophosphamide on neurotoxicity.

Effects of Steroidal Compounds on Viruses.

Viral infections are ubiquitous, and their prevention and treatment have become a great challenge. Steroids have different biological activities, including antiviral activity, which is related to steroid structural diversity. With the intensive study of steroids, it has been found that steroids can interfere with almost any step of the viral life cycle to exert antiviral activity. In this article, we review the antiviral activity and mechanism of action of steroids and their derivatives against a range of human viruses and conclude that natural steroids and their derivatives are very promising antiviral drug candidates that deserve further study to elucidate their pharmacological potential.

Design of Rigid Compounds to Enhance Selectivity for Carbonic Anhydrase IX.

High affinity and selectivity for intended targets is an important goal of small molecule design in drug discovery, yet balancing molecular flexibility and rigidity remains a challenge. While flexible compounds can increase target affinity, they often result in non-specific interactions and reduced selectivity. In contrast, rigid compounds may recognize their target more precisely and have lower off-target effects. In this study, we incorporated a 1,1-dioxido-1,4-thiazine ring into fluorinated benzenesulfonamide derivatives with bulky meta-substituents to enhance selectivity for human carbonic anhydrase IX (CAIX), an important cancer-associated target. Due to the structural similarities of CAIX with other carbonic anhydrase isozymes, selective inhibition remains a significant challenge. A series of 3,4-substituted trifluorobenzenesulfonamides containing oxidized thiazine rings were synthesized using a novel synthetic pathway. Although the potency against CAIX was modestly reduced compared to more flexible analogs, selectivity increased significantly, with lead compounds 7d and 7e exhibiting over 1000-fold selectivity for CAIX over most other isozymes. X-ray crystallography revealed the structural basis for this selectivity, confirming the advantageous positioning of rigidified compounds within some CA isozyme active sites. These findings highlight the potential of molecular rigidity in the design of highly selective inhibitors for therapeutic applications.

Bone microphysiological models for biomedical research.

Bone related disorders are highly prevalent, and many of these pathologies still do not have curative and definitive treatment methods. This is due to a complex interplay of multiple factors, such as the crosstalk between different tissues and cellular components, all of which are affected by microenvironmental factors. Moreover, these bone pathologies are specific, and current treatment results vary from patient to patient owing to their intrinsic biological variability. Current approaches in drug development to deliver new drug candidates against common bone disorders, such as standard two-dimensional (2D) cell culture and animal-based studies, are now being replaced by more relevant diseases modelling, such as three-dimension (3D) cell culture and primary cells under human-focused microphysiological systems (MPS) that can resemble human physiology by mimicking 3D tissue organization and cell microenvironmental cues. In this review, various technological advancements for in vitro bone modeling are discussed, highlighting the progress in biomaterials used as extracellular matrices, stem cell biology, and primary cell culture techniques. With emphasis on examples of modeling healthy and disease-associated bone tissues, this tutorial review aims to survey current approaches of up-to-date bone-on-chips through MPS technology, with special emphasis on the scaffold and chip capabilities for mimicking the bone extracellular matrix as this is the key environment generated for cell crosstalk and interaction. The relevant bone models are studied with critical analysis of the methods employed, aiming to serve as a tool for designing new and translational approaches. Additionally, the features reported in these state-of-the-art studies will be useful for modeling bone pathophysiology, guiding future improvements in personalized bone models that can accelerate drug discovery and clinical translation.

Establishing the hybrid rat diversity program: a resource for dissecting complex traits.

Rat models have been a major model for studying complex disease mechanisms, behavioral phenotypes, environmental factors, and for drug development and discovery. Inbred rat strains control for genetic background and allow for repeated, reproducible, cellular and whole animal phenotyping. The Hybrid Rat Diversity Panel (HRDP) was designed to be a powerful panel of inbred rats with genomic, physiological, and behavioral data to serve as a resource for systems genetics. The HRDP consists of 96-98 inbred rat strains aimed to maximize power to detect specific genetic loci associated with complex traits while maximizing the genetic diversity among strains. The panel consists of 32-34 genetically diverse inbred strains and two panels of recombinant inbred panels. To establish the HRDP program, embryo resuscitation and breeding were done to establish colonies for distribution. Whole genome sequencing was performed to achieve 30X coverage. Genomic, phenotype, and strain information is available through the Hybrid Rat Diversity Panel Portal at the Rat Genome Database ( http://rgd.mcw.edu ).

Selection and prioritization of candidate combination regimens for the treatment of tuberculosis

Accelerated tuberculosis drug discovery has increased the number of plausible multidrug regimens. Testing every drug combination in vivo is impractical, and varied experimental conditions make it challenging to compare results between experiments. Using published treatment efficacy data from a mouse tuberculosis model treated with candidate combination regimens, we trained and externally validated integrative mathematical models to predict relapse in mice and to rank both previously experimentally studied and unstudied regimens by their sterilization potential. We generated 18 datasets of 18 candidate regimens (comprising 11 drugs of six classes, including fluoroquinolone, nitroimidazole, diarylquinolines, and oxazolidinones), with 2965 relapse and 1544 colony-forming unit (CFU) observations for analysis. Statistical and machine learning techniques were applied to predict the probability of relapse in mice. The locked down mathematical model had an area under the receiver operating characteristic curve (AUROC) of 0.910 and showed that bacterial kill measured by longitudinal CFU cannot account for relapse alone and that sterilization is drug dependent. The diarylquinolines had the highest predicted sterilizing activity in the mouse model, and the addition of pyrazinamide to drug regimens provided the shortest estimated tuberculosis treatment duration to cure in mice. The mathematical model predicted the effect of treatment combinations, and these predictions were validated by conducting 11 experiments on previously unstudied regimens, achieving an AUROC of 0.829. We surmise that the next generation of tuberculosis drugs are highly effective at treatment shortening and suggest that there are several promising three- and four-drug regimens that should be advanced to clinical trials.

Barlow Twins deep neural network for advanced 1D drug–target interaction prediction

Accurate prediction of drug–target interactions is critical for advancing drug discovery. By reducing time and cost, machine learning and deep learning can accelerate this laborious discovery process. In a novel approach, BarlowDTI, we utilise the powerful Barlow Twins architecture for feature-extraction while considering the structure of the target protein. Our method achieves state-of-the-art predictive performance against multiple established benchmarks using only one-dimensional input. The use of our hybrid approach of deep learning and gradient boosting machine as the underlying predictor ensures fast and efficient predictions without the need for substantial computational resources. We also propose the use of an influence method to investigate how the model reaches its decision based on individual training samples. By comparing co-crystal structures, we find that BarlowDTI effectively exploits catalytically active and stabilising residues, highlighting the model’s ability to generalise from one-dimensional input data. In addition, we further benchmark new baselines against existing methods. Together, these innovations improve the efficiency and effectiveness of drug–target interactions predictions, providing robust tools for accelerating drug development and deepening the understanding of molecular interactions. Therefore, we provide an easy-to-use web interface that can be freely accessed at https://www.bio.nat.tum.de/oc2/barlowdti.Scientific contributionOur computationally efficient and effective hybrid approach, combining the deep learning model Barlow Twins and gradient boosting machines, outperforms state-of-the-art methods across multiple splits and benchmarks using only one-dimensional input. Furthermore, we advance the field by proposing an influence method that elucidates model decision-making, thereby providing deeper insights into molecular interactions and improving the interpretability of drug-target interactions predictions.Graphical

Screening of multi deep learning-based de novo molecular generation models and their application for specific target molecular generation

Traditional virtual screening methods need to explore expanse and vast chemical spaces and need to be based on existing chemical libraries. With the development of deep learning techniques for the de novo generation of molecules, also known as inverse molecular design, the increasingly widespread application of various types of deep learning algorithms has led to revolutionary changes in de novo molecular generation research. In particular, the emergence of a novel natural language processing (NLP) architecture called the transformer has improved the state-of-the-art performance of existing AI technologies. In this study, we modified one top-performing molecular generation model on the basis of the generative pretraining transformer (GPT) architecture in three directions. Moreover, we propose an integrated end-to-end neural network learning framework based on one complete encoder-decoder architecture transformer model: Transfer Text-to-Text Transformer (T5), by learning the embedding vector representation space of conditional molecular properties to encode and guide the vector representation of SMILES sequences, resulting in the output of the final decoder block with a softmax output (maximum likelihood objective). Moreover, we evaluated the performance of these NLP-based generation models and another new model architecture based on a selective state space and selected the best approach jointing a transfer learning strategy for de novo drug discovery to target L858R/T790M/C797S-mutant EGFR in non-small cell lung cancer.

Mitophagy in Neurodegenerative Diseases: Mechanisms of Action and the Advances of Drug Discovery

Mitophagy in Neurodegenerative Diseases: Mechanisms of Action and the Advances of Drug Discovery

Toward the Prediction of Binding Events in Very Flexible, Allosteric, Multidomain Proteins.

Knowledge of the structures formed by proteins and small molecules is key to understand the molecular principles of chemotherapy and for designing new and more effective drugs. During the early stage of a drug discovery program, it is customary to predict ligand-protein complexes in silico, particularly when screening large compound databases. While virtual screening based on molecular docking is widely used for this purpose, it generally fails in mimicking binding events associated with large conformational changes in the protein, particularly when the latter involve multiple domains. In this work, we describe a new methodology to generate bound-like conformations of very flexible and allosteric proteins bearing multiple binding sites by exploiting only information on the unbound structure and the putative binding sites. The protocol is validated on the paradigm enzyme adenylate kinase, for which we generated a significant fraction of bound-like structures. A fraction of these conformations, employed in ensemble-docking calculations, allowed to find native-like poses of substrates and inhibitors (binding to the active form of the enzyme), as well as catalytically incompetent analogs (binding the inactive form). Our protocol provides a general framework for the generation of bound-like conformations of challenging drug targets that are suitable to host different ligands, demonstrating high sensitivity to the fine chemical details that regulate protein's activity. We foresee applications in virtual screening, in the prediction of the impact of amino acid mutations on structure and dynamics, and in protein engineering.

Borrelia burgdorferi Tolerance-Associated Changes in Gene Expression of Murine Macrophages

Lyme disease, caused by the spirochete Borrelia burgdorferi (Bb), is the most prevalent vector-borne disease in the United States. Macrophages, key cellular players in the innate immune response, exhibit diminished functionality over time during Bb infection, potentially leading to chronic infection. This study explores the transcriptional changes associated with macrophages that develop tolerance to Bb. Using RNA sequencing of murine bone marrow-derived macrophages exposed to Bb, we identified differentially expressed genes and dysregulated pathways between productively stimulated and tolerized macrophages. Key findings revealed significant downregulation of type-I interferon signaling and associated immune responses, suggesting mechanisms of immune tolerance. Additionally, connectivity analysis identified potential drug candidates for repurposing to enhance macrophage activity. Our results underscore the complexity of macrophage responses to Bb and provide a foundation for future research to develop targeted therapies aimed at modulating immune responses and improving treatment outcomes for Lyme disease patients. Ultimately, these findings offer new insights into the pathogenesis and potential treatment strategies for Lyme disease.

Insilico targeting of virus entry facilitator NRP1 to block SARS-CoV2 entry.

The entry and infectivity of a virus are determined by its interaction with the host. SARS-CoV-2, the virus responsible for COVID-19, utilizes the spike (S) protein to attach to and enter host cells. Recent studies have identified neuropilin-1 (NRP1) as a crucial facilitator for the entry of SARS-CoV-2. The binding of the spike protein to the b1 domain of NRP1 has been shown to enhance viral infection twofold. Consequently, targeting NRP1 to disrupt this interaction represents a promising strategy to mitigate viral infection. In this study, a small molecule library of approximately 10,000 compounds was screened to identify those that could inhibit the interaction between NRP1 and the spike protein by targeting the b1 domain of NRP1. The crystallographic structure of the b1 domain of human NRP1 (PDB entry: 7JJC) was used for this purpose. Following virtual screening, docking studies, and evaluation of binding affinity and ADMET properties, 10 compounds were shortlisted. The top two candidates, AZD3839 and LY2090314, were selected for molecular dynamics simulation studies over 100 ns to assess binding stability. MM/GBSA calculations indicated that both AZD3839 and LY2090314 exhibited strong and stable binding to the b1 domain of NRP1. Computational modeling of the interaction between the b1 domain of NRP1 and the receptor-binding domain of the spike protein suggested that AZD3839 and LY2090314 could effectively hinder the NRP1-spike interaction. Therefore, these compounds may serve as potential drug candidates to reduce SARS-CoV-2 infectivity.