Why 90% of clinical drug development fails and how to improve it?

An application of deep learning model InceptionTime to predict nausea, vomiting, diarrhoea, and constipation using the gastro-intestinal pacemaker activity drug database (GIPADD)

The accurate preclinical prediction of adverse drug reactions (ADRs), such as nausea and vomiting, remains a challenge. The Gastro-Intestinal Pacemaker Activity Drug Database (GIPADD) ( http://www.gutrhythm.com/public_database ) is a new source of electrophysiological big data for drug research. Over the past 2 years, the database has doubled in size, and now contains the electrophysiological profiles of 172 drugs across 11,943 datasets. This study used a state-of-the-art deep-learning model with time-series classification to explore the feasibility of using raw electrophysiological recordings from tissues to predict ADRs. The GIPADD contains the recordings of the electrical activity of various gastrointestinal tissues (stomach, duodenum, ileum, and colon) exposed to a drug at three or more different concentrations, representing the effects of the drug on gastrointestinal pacemaker activity. Each drug in the database is associated with at least 60 recordings. The datasets are divided in a ratio of 8:2 for training and validation. A modified InceptionTime classifier (ICT) was used to predict whether a drug induces ADRs, using data from the SIDER database as the target. Concentrations and tissues were added as covariates and added to the input of the model during forward propagation. We also established a negative control with shuffled target labels, and external validation was conducted using time-shifted recording predictions. The best model for predicting nausea, vomiting, diarrhoea, and constipation achieved by-drug accuracies of 0.87, 0.89, 0.85, and 0.91, respectively; by-drug precision (class 1) of 0.88, 0.90, 0.99, and 0.89, respectively; and area under the receiver operating characteristic curve (AUROC) values of 0.84, 0.87, 0.94, and 0.96, respectively. The best model was an ensemble of five independent ICT classifiers trained on the same dataset. Models trained using shuffled labels (negative controls) exhibited significantly lower accuracy, precision, and AUROC values than models trained using correctly labelled datasets, indicating that ICT classifiers successfully identified latent features in the raw recordings associated with ADRs. The combined benefits of the GIPADD and deep learning may accelerate drug safety testing and drug development by enabling the reliable analysis of electrophysiological drug profiles during the preclinical stage.

Daidzin Enhances the Anticonvulsion Effects of Carbamazepine and Diazepam, Possibly Through Voltage-Gated Sodium Channels and GABAA-Dependent Pathways

Epilepsy is a neurological disorder characterized by recurrent seizures, affecting approximately 50 million people globally. Daidzin (DZN), a naturally occurring isoflavone, has shown various pharmacological effects, including neuroprotective activities in animals. This study investigated the anticonvulsant effects of DZN with possible mechanisms of action using behavioral studies using experimental animals and in silico approaches. For this, a pentylenetetrazole (PTZ, 80 mg/kg, i.p.)-induced seizure model was applied in young broiler chicks. Treatment groups included DZN (5, 10, 20 mg/kg, p.o.), carbamazepine (CAR: 80 mg/kg, p.o.), and diazepam (DZP: 5 mg/kg, p.o.) alone and in combinations. After PTZ administration, convulsion onset, frequency, duration, and mortality rates were recorded. We also performed an in vitro study to check GABAergic activity of DZN and DZP. Additionally, molecular docking studies were performed against the GABAA receptor and voltage-gated sodium channel, along with pharmacokinetics and toxicity assessments of the test compound and the reference drugs. Results showed that DZN dose-dependently increased convulsion onset and significantly reduced convulsion frequency and duration compared to the control group (p < 0.05). The combination of DZN- 20 with CAR- 80 and DZP- 5 significantly enhanced convulsion onset and protection rates while reducing convulsion frequency and durations compared to their individual treatment groups. Both DZP and DZN also showed a concentration-dependent GABA activity inhibition capacity. DZN showed the highest binding affinities with GABAA receptor (- 7.8 kcal/mol) and voltage-gated sodium channel (- 9.1 kcal/mol) than the standard drugs. It also supported acceptable pharmacokinetic and toxicity profiles in in silico studies. Taken together, DZN exerted and enhanced the anticonvulsant effects of CAR and DZP, possibly through GABAA receptor and voltage-gated sodium channel interaction pathways.

Inorganic nanoparticle-based nanogels and their biomedical applications

The advent of nanotechnology has brought tremendous progress in the field of biomedical science and opened avenues for advanced diagnostics and therapeutics applications. Several nanocarriers such as nanoparticles, liposomes, and nanogels have been designed to increase the drug efficiency and targeting ability in patients. Nanoparticles based on gold, silver, and iron are dominantly used for biomedical purposes owing to their biocompatibility properties. Nanoparticles offer an enhanced permeation into tissue vessels; however, their short half-life, toxicity, and off-site accumulations limit their functionality. The above shortcomings could be prevented by employing an integrated system combining nanoparticles with a nanogel-based system. These nanogels are 3D polymeric networks formed by physical and chemical crosslinking and are capable of incorporating nanoparticles, drugs, proteins, and genetic materials. Modification, functionalization, and introduction of inorganic nanoparticles have been shown to enhance the properties of nanogels, such as biocompatibility, stimuli responsiveness, stability, and selectivity. This review paper is focused on the design, synthesis, and biomedical application of inorganic nanoparticle-based nanogels. Current challenges and future perspectives will be briefly discussed to emphasize the versatile role of these multifunctional nanogels for therapeutic and diagnostic purposes.

Employing Automated Machine Learning (AutoML) Methods to Facilitate the In Silico ADMET Properties Prediction

The rationale for using ADMET prediction tools in the early drug discovery paradigm is to guide the design of new compounds with favorable ADMET properties and ultimately minimize the attrition rates of drug failures. Artificial intelligence (AI) in in silico ADMET modeling has gained momentum due to its high-throughput and low-cost attributes. In this study, we developed a machine learning model capable of predicting 11 ADMET properties of chemical compounds. Each model was constructed by combining one of 40 classification algorithms including random forest (RF), extreme gradient boosting (XGB), support vector machine (SVM), and gradient boosting (GB) with one of three predefined hyperparameter configurations. This process can be efficiently performed using automated machine learning (AutoML) methods, which automatically search for the best combination of model algorithms and optimized hyperparameters. We developed optimal predictive models for 11 different ADMET properties using the Hyperopt-sklearn AutoML method. All of the developed models depicted an area under the ROC curve (AUC) >0.8. Furthermore, our developed models outperformed most of the ADMET properties and showed comparable performance in other properties when evaluated on external data sets and compared with published predictive models. Our results support the applicability of AutoML in ADMET prediction and will be helpful for ADMET prediction in early-stage drug discovery.

M01 tool: an automated, comprehensive computational tool for generating small molecule-peptide hybrids and docking them into curated protein structures

BACKGROUND

The field of computational drug design is undergoing rapid advancements, highlighting the need for innovative methods to enhance the efficiency and accuracy of calculating ligand-receptor interactions. In this context, we introduce the M01 tool, a comprehensive computational package designed to facilitate the generation and docking of small molecule-peptide hybrids. M01 integrates several established tools, such as RDKit and EasyDock, into a user-friendly platform that automates the workflow from hybrid generation to docking simulations. This tool is particularly beneficial for researchers with limited chemistry expertise, helping them leverage advanced computational techniques.

RESULTS

The M01 tool features an intuitive interface for visualizing molecules and selecting connection points in generating new ligands. It also offers automated receptor preparation using UniProt or PDB IDs and generates default docking configuration files. Furthermore, it includes ligand preparation and docking capabilities through EasyDock and calculates molecular descriptors relevant to drug-likeness properties. Validation studies with peptide-alkoxyamine hybrids demonstrated the tool's effectiveness, generating over 14,000 unique hybrid molecules and showcasing its versatility in drug design applications.

CONCLUSIONS

The M01 tool represents a significant advancement in computational drug design, streamlining the process of creating hybrid molecules and conducting docking studies. Its ability to automate complex workflows and provide essential molecular insights can empower researchers and enhance the development of novel therapeutics, ultimately contributing to more efficient drug discovery efforts.

Global alliances in translational cancer research

Translating basic cancer biology into effective clinical therapies remains a major challenge due to differences in research models, communication gaps, and limited funding. This commentary underscores the transformative potential of international collaborations, which integrate diverse resources, multidisciplinary talents, and innovative trial designs to bridge the gap between laboratory discoveries and clinical applications. By fostering global alliances, sharing knowledge, and harmonizing regulatory and funding frameworks, we can accelerate breakthroughs in cancer treatment, improving patient outcomes worldwide.

Off-Target Interactions of Vancomycin with Vascular Wall Involving Elastin-Induced Self-Assembly

Off-target effects, which arise from drug interactions in nontarget tissues, can lead to unfavored side effects. The treatment efficacy of vancomycin (Vanco) in Gram-positive bacterial infections is often compromised by the frequent occurrence of Vanco-induced vascular injury. However, the potential targets and underlying molecular mechanisms of this phenomenon remain unclear. Here, we developed multidimensional two-photon imaging for dynamic tracking of fluorescently labeled Vanco in vivo, characterizing the molecular behavior of Vanco in situ after administration and providing the first direct evidence of its interactions with vascular wall. Morphological analysis combined with colocalization imaging identified elastin within the vascular wall as the molecular target. After binding, Vanco underwent self-assembly into forming irregular nanoaggregates, primarily driven by electrostatic and hydrophobic forces. This persistent binding and self-assembly on the elastic lamina resulted in significant endothelial cytotoxicity and subsequent apoptosis, suggesting a mechanistic link to the vascular injury observed in clinical settings. Taken together, our findings revealed off-target molecular interactions between Vanco and vascular elastin in situ, highlighting the importance of considering unintended drug-vascular interactions.

Phenogenomic resources immortalized in a panel of wild-derived strains of five species of house mice

The house mouse, Mus musculus, is a widely used animal model in biomedical research, with classical laboratory strains (CLS) being the most frequently employed. However, the limited genetic variability in CLS hinders their applicability in evolutionary studies. Wild-derived strains (WDS), on the other hand, provide a suitable resource for such investigations. This study quantifies genetic and phenotypic data of 101 WDS representing 5 species, 3 subspecies, and 8 natural Y consomic strains and compares them with CLS. Genetic variability was estimated using whole mtDNA sequences, the Prdm9 gene, and copy number variation at two sex chromosome-linked genes. WDS exhibit a large natural variation with up to 2173 polymorphic sites in mitogenomes, whereas CLS display 92 sites. Moreover, while CLS have two Prdm9 alleles, WDS harbour 46 different alleles. Although CLS resemble M. m. domesticus and M. m. musculus WDS, they differ from them in 10 and 14 out of 16 phenotypic traits, respectively. The results suggest that WDS can be a useful tool in evolutionary and biomedical studies with great potential for medical applications.

Miniaturized High-Throughput Amorphous Solid Dispersion Screening via Picoliter Volume 2D-Inkjet Printing of Formulation Microarrays

Many new drug substances exhibit poor physicochemical properties and therefore require significant time and material resources to develop into safe and efficacious medicinal products. This typically involves exploring a large amount of compositional space and may require excessive amounts of drug compounds, which may not be adequate at the early stage of drug development. Scaled-down screening methods have been used as a cost-effective approach to the early-stage formulation. However, even the most material-efficient methods used in product development require milligrams or grams of drug material, which is often not available until relatively late in the lead optimization process. Herein, we report the application of picoliter inkjet printing of drugs and polymers from solution to create addressable formulation microarrays. This allows the efficient screening of drug-polymer compositions while only requiring micrograms or less of the drug substance. A total of eight model compounds, namely, carbamazepine, griseofulvin, saccharin, theophylline, 4-aminobenzoic acid, caffeine, salicylic acid, and benzocaine, were screened against seven commonly used amorphous solid dispersion (ASD) matrix polymers at 5% w/w composition intervals in the range of 5-80% w/w, with five replicates each. Each dispensed spot contains a total of only 1 μg of material (model compound and/or polymer). Across the tested ASD formulations, we ranked the different polymers based on their ability to hinder drug recrystallization across different compositions. Also, we identified distinct physicochemical behaviors in their crystallization kinetics, such as moisture resolubilization. We expect this approach to enable the rapid time- and material-efficient development of new amorphous solid dispersion formulations in an industrial setting.

A Bright Future for Photopharmaceuticals Addressing Central Nervous System Disorders: State of the Art and Challenges Toward Clinical Translation

Photopharmacology is an innovative approach that uses light to activate drugs. This method offers the potential for highly localized and precise drug activation, making it particularly promising for the treatment of neurological disorders. Despite the enticing prospects of photopharmacology, its application to treat human central nervous system (CNS) diseases remains to be demonstrated. In this review, we provide an overview of prominent strategies for the design and activation of photopharmaceutical agents in the field of neuroscience. Photocaged and photoswitchable drugs and bioactive molecules are discussed, and an instructive list of examples is provided to highlight compound design strategies. Special emphasis is placed on photoactivatable compounds for the modulation of glutamatergic, GABAergic, dopaminergic, and serotonergic neurotransmission for the treatment of neurological conditions, as well as various photoresponsive molecules with potential for improved pain management. Compounds holding promise for clinical translation are discussed in-depth and their potential for future applications is assessed. Neurophotopharmaceuticals have yet to achieve breakthrough in the clinic, as both light delivery and drug design have not reached full maturity. However, by describing the current state of the art and providing illustrative case studies, we offer a perspective on future opportunities in the field of neurophotopharmacology focused on addressing CNS disorders.

From organoids to organoids-on-a-chip: Current applications and challenges in biomedical research

ABSTRACT

The high failure rates in clinical drug development based on animal models highlight the urgent need for more representative human models in biomedical research. In response to this demand, organoids and organ chips were integrated for greater physiological relevance and dynamic, controlled experimental conditions. This innovative platform-the organoids-on-a-chip technology-shows great promise in disease modeling, drug discovery, and personalized medicine, attracting interest from researchers, clinicians, regulatory authorities, and industry stakeholders. This review traces the evolution from organoids to organoids-on-a-chip, driven by the necessity for advanced biological models. We summarize the applications of organoids-on-a-chip in simulating physiological and pathological phenotypes and therapeutic evaluation of this technology. This section highlights how integrating technologies from organ chips, such as microfluidic systems, mechanical stimulation, and sensor integration, optimizes organoid cell types, spatial structure, and physiological functions, thereby expanding their biomedical applications. We conclude by addressing the current challenges in the development of organoids-on-a-chip and offering insights into the prospects. The advancement of organoids-on-a-chip is poised to enhance fidelity, standardization, and scalability. Furthermore, the integration of cutting-edge technologies and interdisciplinary collaborations will be crucial for the progression of organoids-on-a-chip technology.

Exploring bioactive natural products for treating neurodegenerative diseases: a computational network medicine approach targeting the estrogen signaling pathway in amyotrophic lateral sclerosis and Parkinson's disease

Amyotrophic lateral sclerosis (ALS) and Parkinson's disease (PD) share overlapping molecular mechanisms, including estrogen signaling dysregulation, oxidative stress, and neuroinflammation. Standard treatments often lead to adverse effects due to unintended cross-talk with the estrogen signaling pathway. Identifying key regulatory genes and bioactive plant-derived compounds that modulate estrogen signaling without interfering with standard therapies offers a promising neuroprotective strategy. A network medicine and systems biology approach was used, beginning with the screening of 29 medicinal plants for ALS and 49 for PD, identifying 12 shared plants with neuroprotective potential. Bioactive compounds were screened for gene, protein, and pathway interactions, leading to target prediction (846 ALS-related and 690 PD-related targets) and disease association mining, which identified 93 overlapping genes (OGs). Protein-protein interaction (PPI) network analysis and MCODE clustering revealed ESR1, EGFR, and SRC as key hub-bottleneck (HB) genes, further validated via differential gene expression analysis. Gene ontology (GO) and pathway enrichment analyses revealed significant enrichment in estrogen signaling confirming the involvement of HB genes in neurodegenerative disease progression. Differential expression analysis confirmed ESR1 upregulation in ALS but downregulation in PD, suggesting a converse disease-specific regulatory pattern. Gene regulatory network (GRN) analysis identified hsa-miR-145-5p (ALS) and hsa-miR-181a-5p (PD) as key regulators, while FOXC1, GATA2, and TP53 emerged as crucial transcription factors (TFs) influencing disease progression. Molecular docking and MD simulations validated strong and stable interactions of Eupalitin (CYP19A1, -9.0 kcal/mol), Hesperetin (ESR1, -8.1 kcal/mol), and Sumatrol (PIK3CA, -8.9 kcal/mol). These phytochemicals, derived from Rosmarinus officinalis, Artemisia scoparia, Ocimum tenuiflorum, and Indigofera tinctoria, maintained stable hydrogen bonding and hydrophobic interactions for over 30% of a 25 ns simulation, supporting their therapeutic potential. The identification of ESR1, EGFR, and SRC as key targets, alongside estrogen signaling involvement, highlights the need for targeted nutraceutical interventions. These findings pave the way for safer, plant-based therapies that mitigate neurodegeneration while preserving estrogen signaling integrity, offering a promising adjuvant strategy alongside existing treatments.

Barriers to publishing early phase clinical trials: the oncologists' perspective

INTRODUCTION

Findings from early phase studies are not always placed in the public domain. This study aims to explore why many early phase clinical oncology studies are not published, as well as identify the potential barriers investigators encountered in the publication process.

METHODS

Semi-structured interviews were conducted among investigators with experience in early phase clinical oncology studies. Interviews were analyzed using reflexive thematic analysis.

RESULTS

Twenty-one investigators were interviewed. The majority worked in Europe (n = 13), while other investigators were based in North America (n = 4), Asia (n = 2) or Oceania (n = 2). We identified three reasons why investigators believed publishing early phase clinical trial results was important: (1) there is an ethical and moral responsibility; (2) there should be no loss of knowledge to society; and (3) there should be no waste of resources. Four main barriers in the publication process of early phase clinical trials were identified: (1) practical barriers (eg, an increased complexity of number of trials/trial sites), (2) insufficient resources (eg, money, time and human), (3) limited motivation (eg, limited intrinsic motivation of the investigator or limited prospect of return for the sponsor), and (4) inadequate collaboration (eg, different interests between industry partners and investigators). Finally, five major stakeholders were identified that can potentially contribute to improving the publication process: (1) journal editors, (2) sponsors, (3) investigators, (4) regulatory bodies, and (5) society. Investigator suggestions for improving this process, for each stakeholder, are presented.

CONCLUSIONS

This study highlights the barriers experienced in publishing early phase clinical trials. Recognizing and acknowledging these barriers is crucial to devise effective strategies to improve the publishing and public sharing of early phase clinical trials.

Biomarker-guided decision making in clinical drug development for neurodegenerative disorders

Neurodegenerative disorders are characterized by complex neurobiological changes that are reflected in biomarker alterations detectable in blood, cerebrospinal fluid (CSF) and with brain imaging. As accessible proxies for processes that are difficult to measure, biomarkers are tools that hold increasingly important roles in drug development and clinical trial decision making. In the past few years, biomarkers have been the basis for accelerated approval of new therapies for Alzheimer disease and amyotrophic lateral sclerosis as surrogate end points reasonably likely to predict clinical benefit.Blood-based biomarkers are emerging for Alzheimer disease and other neurodegenerative disorders (for example, Parkinson disease, frontotemporal dementia), and some biomarkers may be informative across multiple disease states. Collection of CSF provides access to biomarkers not available in plasma, including markers of synaptic dysfunction and neuroinflammation. Molecular imaging is identifying an increasing array of targets, including amyloid plaques, neurofibrillary tangles, inflammation, mitochondrial dysfunction and synaptic density. In this Review, we consider how biomarkers can be implemented in clinical trials depending on their context of use, including providing information on disease risk and/or susceptibility, diagnosis, prognosis, pharmacodynamic outcomes, monitoring, prediction of response to therapy and safety. Informed choice of increasingly available biomarkers and rational deployment in clinical trials support drug development decision making and de-risk the drug development process for neurodegenerative disorders.

Virtual screening and molecular dynamics of anti-Alzheimer compounds from Cardiospermum halicacabum via GC-MS

Background

Ayurveda is an ancient Indian medicinal system that uses medicinal plants for their neuroprotective effects. Ayurveda claims that the (C. halicacabum) leaves possess significant neuroprotective properties. Alzheimer's is characterized by the accumulation of amyloid-β, acetylcholinesterase, and tau tangles that interfere with neural transmission and impair cognitive abilities.

Objectives

This study aimed to identify novel potential anti-Alzheimer phytoconstituents of C. halicacabum leaves using in silico methods.

Methods

This study utilized the Box-Behnken design within the response surface methodology (RSM) to optimize and combine the effects of process variables, namely powder weight, solvent volume, and extraction time, on the microwave-assisted extraction (MAE) of C. halicacabum leaves. The optimization process revealed that these variables, along with microwave usage, significantly influenced the extraction yield. The ethanolic extract was examined using gas chromatography-mass spectrometry (GC-MS) analysis, and the identified phytoconstituents were further analyzed through computer-based simulations, including docking, absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies, assessment of drug-likeness, molecular dynamics, LigPlot analysis, and density functional theory (DFT) analysis.

Results

Gas chromatography-mass spectrometry (GC-MS) analysis identified 40 phytoconstituents and 37 were successfully characterized. Molecular docking and dynamics simulations revealed two lead compounds, acetic acid (dodecahydro-7-hydroxy-1,4b,8,8-tetramethyl-10-oxo-2(1H)-phenanthrenylidene)-,2-(dimethylamino)ethyl ester, [1R-(1. alpha)], and 1-(2-hydroxyethoxy)-2-methyldodecane, which exhibited superior stability in the docked complex compared to galantamine.

Conclusion

Based on computational predictions and observed pharmacological properties, these findings suggest that phytoconstituents may have therapeutic effects against selected AD targets.

The Changing Landscape of Heart Failure Drug Clinical Trials in China, 2013-2023

Objective

This review aimed to delineate the changing landscape of heart failure (HF) drug clinical trials conducted in China during 2013-2023.

Methods

Detailed information on HF drug trials registered on the National Medical Products Administration Clinical Trial Information Disclosure Platform from January 1, 2013, to December 31, 2023, was collected. The characteristics, drug mechanisms, data safety, participant protection, geographical locations, and scales of HF drug clinical trials were analyzed.

Results

China initiated 354 hF drug clinical trials during 2013-2023, encompassing eight acute heart failure (AHF) trials and 346 trials for chronic heart failure (CHF). The overall number of HF trials continued to increase, whereas the number of AHF trials remained consistently low. Significant differences were observed between AHF and CHF trials regarding trial phases, drug types, trial designs, blinding methods, and geographical coverage. 85.8% CHF trials were bioequivalence studies, whereas AHF trials were exclusively Phase I-III studies. Most trial drugs were chemical drugs, with renin-angiotensin-aldosterone system inhibitors accounting for the highest proportion. Sixteen new drug studies involved 13 different new drugs. The proportion of studies establishing independent data monitoring committees annually remained generally low, whereas the proportion of studies purchasing clinical trial insurance for participants annually exhibited an overall upward trend. The 354 trials were led by principal investigators from 27 provinces, autonomous regions, or municipalities in China. 47.2% leading units for these studies were distributed in the eastern coastal regions of China. There were 30 drug clinical trials with more than ten participating centers and 16 drug clinical trials with a target number of participants of over 1000 individuals.

Conclusion

Over the past decade, China has experienced rapid development in HF drug trials, particularly in drug consistency evaluations. All stakeholders involved in drug trials should carefully consider the inadequate innovations in first-in-class drugs.

Robust and reproducible human intestinal organoid-derived monolayer model for analyzing drug absorption

Predicting the absorption of orally administered drugs is crucial to drug development. Current in vitro models lack physiological relevance, robustness, and reproducibility, thus hindering reliable predictions. In this study, we developed a reproducible and robust culture method to generate a human intestinal organoid-derived monolayer model that can be applied to study drug absorption through a step-by-step approach. Our model showed similarity to primary enterocytes in terms of the drug absorption-related gene expression profile, tight barrier function, tolerability toward artificial bile juice, drug transporter and metabolizing enzyme function, and nuclear receptor activity. This method can be applied to organoids derived from multiple donors. The permeability of launched 19 drugs in our model demonstrated a correlation with human Fa values, with an R2 value of 0.88. Additionally, by combining the modeling and simulation approaches, the estimated FaFg values for seven out of nine drugs, including CYP3A substrates, fell within 1.5 times the range of the human FaFg values. Applying this method to the drug discovery process might bridge the gap between preclinical and clinical research and increase the success rates of drug development.

A Probe-Based Target Engagement Assay for Kinases in Live Cells

The efficacy and safety of kinase inhibitor drugs are largely influenced by their selectivity. Available profiling technologies are primarily based on overexpressed or endogenously expressed kinases in cell extracts. We compared kinase capture with the cell penetrant covalent probe XO44 to three derivatives and found that replacing the alkyne handle with a trans-cyclooctene group allowed the development of a more robust kinase capture and enrichment protocol. An intracellular chemoproteomics target profiling and engagement assay was devised by optimizing probe concentration and incubation time and using an isobaric mass tag-based strategy for relative quantification. Comparing intracellular kinase profiles of the marketed drug dasatinib and the tool compound dinaciclib with the lysate-based kinobeads assay revealed excellent agreement in rank-order of binding. Dinaciclib showed a systematic shift to higher IC50s, suggesting that intracellular cosubstrate concentrations, cell penetration of the compound, as well as kinase localization and complexes in live cells influence target profiles. Further, we show that sepiapterin reductase SPR and multidrug resistance protein 1 ABCC1 are off-targets of kinase inhibitor scaffolds with potential implications on efficacy and safety.

Identification of a TNIK-CDK9 Axis as a Targetable Strategy for Platinum-Resistant Ovarian Cancer

Up to 90% of patients with high-grade serous ovarian cancer (HGSC) will develop resistance to platinum-based chemotherapy, posing substantial therapeutic challenges due to a lack of universally druggable targets. Leveraging BenevolentAI's artificial intelligence (AI)-driven approach to target discovery, we screened potential AI-predicted therapeutic targets mapped to unapproved tool compounds in patient-derived 3D models. This identified TNIK, which is modulated by NCB-0846, as a novel target for platinum-resistant HGSC. Targeting by this compound demonstrated efficacy across both in vitro and ex vivo organoid platinum-resistant models. Additionally, NCB-0846 treatment effectively decreased Wnt activity, a known driver of platinum resistance; however, we found that these effects were not solely mediated by TNIK inhibition. Comprehensive AI, in silico, and in vitro analyses revealed CDK9 as another key target driving NCB-0846's efficacy. Interestingly, TNIK and CDK9 co-expression positively correlated, and chromosomal gains in both served as prognostic markers for poor patient outcomes. Combined knockdown of TNIK and CDK9 markedly diminished downstream Wnt targets and reduced chemotherapy-resistant cell viability. Furthermore, we identified CDK9 as a novel mediator of canonical Wnt activity, providing mechanistic insights into the combinatorial effects of TNIK and CDK9 inhibition and offering a new understanding of NCB-0846 and CDK9 inhibitor function. Our findings identified the TNIK-CDK9 axis as druggable targets mediating platinum resistance and cell viability in HGSC. With AI at the forefront of drug discovery, this work highlights how to ensure that AI findings are biologically relevant by combining compound screens with physiologically relevant models, thus supporting the identification and validation of potential drug targets.

Enhancing throughput and robustness of the fibroblast to myofibroblast transition assay

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive age-related lung disease with an average survival of 3-5 years post-diagnosis if left untreated. It is characterized by lung fibrosis, inflammation, and destruction of lung architecture, leading to worsening respiratory symptoms and physiological impairment, ultimately culminating in progressive respiratory failure. The development of novel therapeutics for the treatment of IPF represents a significant unmet medical need. Fibroblast to myofibroblast transition (FMT) in response to fibrogenic mediators such as transforming growth factor beta 1 (TGF-β1) has been identified as a key cellular phenotype driving the formation of myofibroblasts and lung fibrosis in IPF. Establishing a robust and high-throughput in vitro human FMT assay is crucial for uncovering new disease targets and for efficiently screening compounds for the advancement of novel therapeutics aimed at targeting myofibroblast activity. However, creating a robust FMT assay suitable for high-throughput drug screening has proven challenging due to the requisite level of automation. In this study, we focus on evaluating different automation approaches for liquid exchange and compound dosing in the human FMT assay. A semi-automated assay, capable of screening a large number of compounds that inhibit TGF-β1-induced FMT in both Normal Human Lung Fibroblasts (NHLF) and IPF-patient derived Disease Human Lung Fibroblasts (IPF-DHLF), has been successfully developed and optimized. We demonstrate that the optimized FMT assay using liquid handling automation exhibits great assay reproducibility, shows good assay translation using human lung fibroblasts from normal healthy versus IPF-patients, and demonstrates acceptable human primary donor variability. This allows for the standardization of comparisons of compound anti-fibrotic potency across IPF projects.

SAIAME: Semi-Parameter Adaptation Information-Assisted Multi-Objective Evolutionary for Protein-Ligand Docking

Molecular docking, which simulates the binding pose of a drug molecule to target proteins and predicts the binding affinity, is an important computational tool in structure-based drug discovery. However, the difficulties of high ligand connectivity and dimensionality reduce the search ability of the conformational sampling. To this end, a semi-parameter adaptation information-assisted multi-objective evolution method named SAIAME is proposed for protein-ligand docking optimization. SAIAME employs a staged and dynamic semi-parameter adaptive updating strategy, in which the crossover rate is updated by a weighted arithmetic average algorithm in the exploration phase, as well as the scaling factor is updated by the Lehmer mean in the exploitation phase. It integrates a gradient enhancement based on infinity norms to smooth the decay of the weights of the learning rate during gradient descent to enhance the handling of outliers. It introduces a population size reduction strategy that combines linear and bilateral symmetric sawtooth functions to enhance its execution efficiency. The experimental results demonstrate that SAIAME not only achieves the accuracies of 87.02% for the best poses and 72.98% for the top-score poses within an RMSD of 2 Å, but also has certain advantages in execution efficiency.

Compound screening in human airway basal cells identifies Wnt pathway activators as potential pro-regenerative therapies

Regeneration of the airway epithelium restores barrier function and mucociliary clearance following lung injury and infection. The mechanisms regulating the proliferation and differentiation of tissue-resident airway basal stem cells remain incompletely understood. To identify compounds that promote human airway basal cell proliferation, we performed phenotype-based compound screening of 1429 compounds (from the ENZO and Prestwick Chemical libraries) in 384-well format using primary cells transduced with lentiviral luciferase. A total of 17 pro-proliferative compounds were validated in independent donor cell cultures, including the antiretroviral therapy agent abacavir and several Wnt signalling pathway-activating compounds. The effects of compounds on proliferation were further explored in colony formation and 3D organoid assays. Structurally and functionally related compounds that more potently induced Wnt pathway activation were investigated. One such compound, 1-azakenpaullone, induced Wnt target gene activation and basal cell proliferation in mice. Our results demonstrate the pro-proliferative effect of small-molecule Wnt pathway activators on airway basal cells. These findings contribute to the rationale to develop novel approaches to modulate Wnt signalling during airway epithelial repair.

Screening of phytochemicals from Clerodendrum inerme (L.) Gaertn as potential anti-breast cancer compounds targeting EGFR: an in-silico approach

Breast cancer (BC) is the most prevalent malignancy among women around the world. The epidermal growth factor receptor (EGFR) is a tyrosine kinase receptor (RTK) of the ErbB/HER family. It is essential for triggering the cellular signaling cascades that control cell growth and survival. However, perturbations in EGFR signaling lead to cancer development and progression. Hence, EGFR is regarded as a prominent therapeutic target for breast cancer. Therefore, in the current investigation, EGFR was targeted with phytochemicals from Clerodendrum inerme (L.) Gaertn (C. inerme). A total of 121 phytochemicals identified by gas chromatography-mass spectrometry (GC-MS) analysis were screened against EGFR through molecular docking, ADMET analyses (Absorption, Distribution, Metabolism, Excretion, and Toxicity), PASS predictions, and molecular dynamics simulation, which revealed three potential hit compounds with CIDs 10586 [i.e. alpha-bisabolol (-6.4 kcal/mol)], 550281 [i.e. 2,(4,4-Trimethyl-3-hydroxymethyl-5a-(3-methyl-but-2-enyl)-cyclohexene) (-6.5 kcal/mol)], and 161271 [i.e. salvigenin (-7.4 kcal/mol)]. The FDA-approved drug gefitinib was used to compare the inhibitory effects of the phytochemicals. The top selected compounds exhibited good ADMET properties and obeyed Lipinski's rule of five (ROF). The molecular docking analysis showed that salvigenin was the best among the three compounds and formed bonds with the key residue Met 793. Furthermore, the molecular mechanics generalized born surface area (MMGBSA) calculations, molecular dynamics simulation, and normal mode analysis validated the binding affinity of the compounds and also revealed the strong stability and compactness of phytochemicals at the docked site. Additionally, DFT and DOS analyses were done to study the reactivity of the compounds and to further validate the selected phytochemicals. These results suggest that the identified phytochemicals possess high inhibitory potential against the target EGFR and can treat breast cancer. However, further in vitro and in vivo investigations are warranted towards the development of these constituents into novel anti-cancer drugs.

Isoform-specific vs. isoform-universal drug targeting: a new targeting paradigm illustrated by new anti-ICAM-1 antibodies

Drug targeting can be achieved by coupling drugs or their carriers to affinity molecules, mostly antibodies (Abs), which recognise specific protein targets. However, most proteins are not expressed in an exclusive configuration but as various isoforms. Hence, selected targeting molecules may fail to target with enough efficiency in clinical trials, which is overlooked. We illustrate this by targeting intercellular adhesion molecule 1 (ICAM-1), a cell-surface protein overexpressed in many pathologies. Most ICAM-1 targeting studies used Ab R6.5, which binds ICAM-1 domain 2 (D2). Yet, literature and our data show that D2 is frequently absent among ICAM-1 isoforms. We thus produced a battery of five new Abs (B4, B6, B11, C12 and G2) and tested their ability to recognise both full-length and -D2 ICAM-1. In solution, all Abs recognised both ICAM-1 forms (from 5.3 × 1011 to 4.2 × 1012 sum intensity/well). Coating them on nanocarriers (NCs) rendered G2 specific against -D2 ICAM-1 (4.2 × 106 NCs/well) while other Abs kept their dual recognition (from 6.4 × 106 to 2.2 × 107 NCs/well). All Abs induced NC intracellular uptake in respective cells (from 42% to 85%) and displayed good cross-species reactivity (from 4.4 × 1011 to 2.6 × 1012 sum intensity/well). These Abs represent valuable tools to target ICAM-1 and illustrate a new targeting paradigm that may improve classical strategies.

Revolutionizing prostate cancer therapy: Artificial intelligence - Based nanocarriers for precision diagnosis and treatment

Prostate cancer is one of the major health challenges in the world and needs novel therapeutic approaches to overcome the limitations of conventional treatment. This review delineates the transformative potential of artificial intelligence (AL) in enhancing nanocarrier-based drug delivery systems for prostate cancer therapy. With its ability to optimize nanocarrier design and predict drug delivery kinetics, AI has revolutionized personalized treatment planning in oncology. We discuss how AI can be integrated with nanotechnology to address challenges related to tumor heterogeneity, drug resistance, and systemic toxicity. Emphasis is placed on strong AI-driven advancements in the design of nanocarriers, structural optimization, targeting of ligands, and pharmacokinetics. We also give an overview of how AI can better predict toxicity, reduce costs, and enable personalized medicine. While challenges persist in the way of data accessibility, regulatory hurdles, and interactions with the immune system, future directions based on explainable AI (XAI) models, integration of multimodal data, and green nanocarrier designs promise to move the field forward. Convergence between AI and nanotechnology has been one key step toward safer, more effective, and patient-tailored cancer therapy.

Synthesis, structural studies, and inhibitory potential of selected sulfonamide analogues: insights from in silico and in vitro analyses

Antimicrobial resistance is a growing public health threat worldwide, and the current drug development pipeline has thus far been inadequate in addressing this impending crisis. Further research into antibiotic agents, both existing and novel, is therefore paramount for identifying suitable candidates to combat antibiotic-resistant pathogens. Sulfonamides, the first class of synthetic antibiotics, target dihydropteroate synthase (DHPS), a key bacterial enzyme. While this class of antibiotics has historically demonstrated great utility, their use has diminished due to resistance and undesired side effects. In the present study, we synthesized a selection of four sulfonamide analogues (FQ5, FQ6, FQ7 and FQ12), validated their structures through NMR spectroscopy, and evaluated their inhibitory potential through computational docking and MIC assays against four bacterial strains: Staphylococcus aureus ATCC 25923, Pseudomonas aeruginosa ATCC 27853, Escherichia coli ATCC 35401 and Bacillus subtilis ATCC 6633. Each compound exhibited antibacterial activity; FQ5 demonstrated the most potent activity, with an MIC of 32, 16, 16, and 16 µg/mL against aforementioned strains, respectively. FQ6, FQ7 and FQ12, on the other hand, exhibited moderate activity against P. aeruginosa and E. coli (MIC = 128 µg/mL each) and low activity against S. aureus and B. subtilis (MIC = 256 µg/mL each). Molecular docking studies indicated that FQ5 captures multiple hydrogen bonding, ionic, and π-π interactions with key binding pocket residues of DHPS, and FQ5 also demonstrated superior predicted drug-likeness in in silico ADMET studies compared to other compounds. FQ5 is therefore a favorable starting point for further optimization.

Microfluidic organoid-slice-on-a-chip system for studying anti-cholangiocarcinoma drug efficacy and hepatorenal toxicity

Organ-chip technology, in contrast to cell culture and animal models, offers a promising platform for accelerating drug development. However, current chip designs simulate human organ functions and there is a lack of multi-organ chip designs that can simultaneously study drug efficacy and hepatorenal toxicity. Here, we developed a novel microfluidic multi-organ chip that integrated cholangiocarcinoma organoids (CCOs) with recellularized liver slices (RLS) and recellularized kidney slices (RKS), to simultaneously assess anti-cholangiocarcinoma drug efficacy and hepatorenal toxicity. Co-culture of patient-derived CCOs with RLS and RKS was successfully achieved for 7 days under flow conditions with enhanced liver and renal cell functions. Furthermore, an in vitro biomimetic model showed IC50 values of trastuzumab emtansine (T-DM1) of around 6.42 ± 7.34 μg mL-1 in four clinical cases, with one outlier of 77.77 μg mL-1 due to patient variability. Post-treatment, RLS and RKS cell viability remained high at 75.67% and 81.03%, respectively, suggesting low hepatorenal toxicity of T-DM1 for treating cholangiocarcinoma. Our study demonstrates the use of an organoid-slice-on-a-chip (OSOC) platform for personalized drug efficacy and toxicity assessment, particularly aiming at leveraging anticancer drugs for off-label use to save patient lives.

Fighting Cancer with Photodynamic Therapy and Nanotechnologies: Current Challenges and Future Directions

Photodynamic therapy (PDT) is an innovative treatment that has recently been approved for clinical use and holds promise for cancer patients. It offers several benefits, such as low systemic toxicity, minimal invasiveness, and the ability to stimulate antitumor immune responses. For certain types of cancer, it has shown positive results with few side effects. However, PDT still faces some challenges, including limited light penetration into deeper tumor tissues, uneven distribution of the photosensitizer (PS) that can also affect healthy cells, and the difficulties posed by the hypoxic tumor microenvironment (TME). In hypoxic conditions, PDT's effectiveness is reduced due to insufficient production of reactive oxygen species, which limits tumor destruction and can lead to relapse. This review highlights recent advances in photosensitizers and nanotechnologies that are being developed to improve PDT. It focuses on multifunctional nanoplatforms and nanoshuttles that have shown promise in preclinical studies, especially for treating solid tumors. One of the key areas of focus is the development of PSs that specifically target mitochondria to treat deep-seated malignant tumors. New mitochondria-targeting nano-PSs are designed with better water solubility and extended wavelength ranges, allowing them to target tumors more effectively, even in challenging, hypoxic environments. These advancements in PDT are opening new doors for cancer treatment, especially when combined with other therapeutic strategies. Moving forward, research should focus on optimizing PDT, creating more efficient drug delivery systems, and developing smarter PDT platforms. Ultimately, these efforts aim to make PDT a first-choice treatment option for cancer patients.

Healing of ischemic injury in the retina

Neuro- and retinal degenerative diseases, including Alzheimer's, age-related macular degeneration, stroke, and central retinal artery occlusion, rob millions of their independence. Studying these diseases in human retinas has been hindered by the immediate loss of neuronal activity postmortem. While recent studies restored limited activity in postmortem CNS tissues, synchronized neuronal transmission >30 minutes postmortem remained elusive. Our study overcomes this barrier by reviving and sustaining light signal transmission in human retinas recovered up to four hours and stored 48 hours postmortem. We also establish infrared-based ex vivo imaging for precise sampling, a closed perfusion system for drug testing, and an ex vivo ischemia-reperfusion model in mouse and human retina. This platform enables testing of neuroprotective and neurotoxic effects of drugs targeting oxidative stress and glutamate excitotoxicity. Our advances question the irreversibility of ischemic injury, support preclinical vision restoration studies, offer new insights into treating ischemic CNS injuries, and pave the way for transplantation of human donor eyes.

Teaser

Reviving light signaling in postmortem human retinas challenges the irreversibility of ischemic injury and advances research to restore vision.

Use of Bayesian approaches in oncology clinical trials: A cross-sectional analysis

Purpose

Bayesian approaches may improve the efficiency of trials and accelerate decision-making, but reluctance to depart from traditional frequentist statistics may limit their use. Because oncology trials generally involve severe conditions with no or limited therapeutic options, they are well-suited to applying Bayesian methodologies and are perceived as using these methods often in early phases.

Objectives

In this study, we aim to describe the use of Bayesian methods and designs in oncology clinical trials in the last 20 years.

Method

A cross-sectional observational study was conducted to identify oncology clinical trials using Bayesian approaches registered in clinicaltrials.gov between 2004 and 2024. Trials were searched in clinicaltrials.gov, PubMed, and through manual search of cross-references.

Results

Bayesian trials were retrieved, and their main characteristics were extracted using R and verified manually. Between 2004 and 2024, 384,298 trials were registered in clinicaltrials.gov; we identified 84,850 oncology clinical trials (22%), of which 640 (0.75%) used Bayesian approaches. The adoption of Bayesian trials increased significantly after 2011, but while half of all Bayesian studies started in the last 5 years, this paralleled the overall increase in oncology research rather than an increase in the proportion of Bayesian trials. The majority of Bayesian trials were phase 1 and phase 2 studies, and two-thirds of Bayesian trials with efficacy objectives had single-arm designs, often utilizing binary endpoints, such as overall response, as the primary measure.

Conclusion

The uptake of Bayesian methods in oncology clinical trials has flattened and is still scarce, and is mostly applied to the analysis of treatment efficacy in single-arm trials with binary endpoints. There is room for further uptake and use of their potential advantages in settings with small populations and severe conditions with unmet needs.

Real-time and regional analysis of the efficacy of anticancer drugs in a patient-derived intratumoral heterogeneous tumor microenvironment

Preclinical evaluation of anticancer drug efficacy utilizes 2D cell culture systems, tumoroids or experimental animal models, but it suffers from limitations such as inaccurate simulation of tumor microenvironments in living tumors, difficulty in regional analysis, and low throughput. Therefore, in this study, we developed a system named tumor-microenvironment-on-chip (TMoC) comprising a 3D dynamic tumor tissue culture system, which recreated diverse and heterogeneous cellular tumor microenvironments. In addition to the culture with a dynamic circulation, TMoC allowed users to perform real-time regional analysis, independently assessing the drug response from the normoxic area to the hypoxic area in a gradient manner. Through cell composition analysis and gene analysis, we proved that TMoC has a tumor environment with close resemblance to the original tumor environment. By comparing 15 drug testing results with animal experiments, we proved that TMoC is 93% consistent with the response results of animal experiments. In addition, we confirmed that either mouse- or patient-derived tumor cell lines can be cultured and tested in TMoC, indicating its immense potential for all aspects of preclinical drug evaluation.

Academic entrepreneurship: promise and perils

As academic institutions strive to meet financial demands, a tantalizing opportunity posits that faculty have untapped treasures emanating from laboratories or astute clinical observations awaiting commercialization. 'If only this alchemy could be turned into gold', administrators muse. Understanding the complexities of academic entrepreneurship should provide words of caution and encouragement.

A gut-brain axis on-a-chip platform for drug testing challenged with donepezil

Current drug development pipelines are time-consuming and prone to a significant percentage of failure, partially due to the limited availability of advanced human preclinical models able to better replicate the in vivo complexity of our body. To contribute to an advancement in this field, we developed an in vitro multi-organ-on-a-chip system, that we named PEGASO platform, which enables the dynamic culturing of human cell-based models relevant for drug testing. The PEGASO platform is composed of five independent connected units, which are based on a previously developed millifluidic organ-on-a-chip device (MINERVA 2.0), hosting human primary cells and iPSC-derived cells recapitulating key biological features of the gut, immune system, liver, blood-brain-barrier and brain that were fluidically connected and challenged to model the physiological passage of donepezil, a drug prescribed for Alzheimer's disease. The nutrient medium flow rate of the connected units was tuned to obtain suitable oxygenation and shear stress values for the cells cultured in dynamic condition. A computational model was at first developed to simulate donepezil transport within the platform and to assess the drug amount reaching the last organ-on-a-chip. Then, we demonstrated that after 24 hours of donepezil administration, the drug was actually transported though the cell-based models of the platform which in turn were found viable and functional. Donepezil efficacy was confirmed by the decreased acetylcholinesterase activity at the brain model and by the increased expression of a donepezil-relevant multi-drug transporter (P-gp). Overall, the PEGASO platform is an innovative in vitro tool for drug screening and personalized medicine applications which holds the potential to be translated to preclinical research and improve new drug development pipelines.

Design, Synthesis, and Biological Evaluation of New Ureido (Thioureido) Anthranilic Acid Isosteres: Molecular Docking, In Silico ADMET Predictions, and In Vivo Anti-Inflammatory Activity

A novel series of anthranilic acid isosteres were designed and synthesized as antiinflammatory agents. The in silico absorption, distribution, metabolism, excretion, and toxicity (ADMET) study predicted a favorable pharmacokinetic profile and respect for Lipinski's rule of five. Density functional theory (DFT) calculations revealed an improvement in some target compounds' electronic parameters compared to diclofenac (DCF) and aspirin (ASA), predicting an improvement in their biological activity. Docking investigations demonstrated a strong affinity toward the cyclooxygenase (COX)-1 and COX-2 enzymes, with a relative preference for COX-2, predicting antiinflammatory activity. The MolDock scores were between -140.59 and -91.81 kcal/mol for COX-1 and between -148.10 and -108.9 kcal/mol for COX-2. The experimental pharmacological investigation confirmed these theoretical findings. Indeed, target compounds demonstrated a significant inhibition of the carrageenan-induced paw edema in rats and probable inhibition of COX. Particularly, compounds 4e and 4h devoid of COOH group, which provoke serious gastrointestinal irritation, exhibited antiinflammatory activity comparable to that of salicylic acid (ASA) and surpassed the effectiveness of DCF. Cpmpounds 4e and 4h showed 91.72% inhibition after 3h, against 91.03% and 83.44% for ASA and DCF, respectively, with a greater onset effect, and also surpassing the reference compounds after 1 and 2 h. The results also indicate good pharmacokinetic profile of the target compounds similar to ASA and DCF.

Revolutionizing Biomedical Research: Unveiling the Power of Microphysiological Systems with Advanced Assays, Integrated Sensor Technologies, and Real-Time Monitoring

The limitation of animal models to imitate a therapeutic response in humans is a key problem that challenges their use in fundamental research. Organ-on-a-chip (OOC) devices, also called microphysiological systems (MPS), are devices containing a lining of living cells grown under dynamic flow to recapitulate the important features of human physiology and pathophysiology with high precision. Recent advances in microfabrication and tissue engineering techniques have led to the wide adoption of OOC in next-generation experimental platforms. This review presents a comprehensive analysis of the OOC systems, categorizing them by flow types (single-pass and multipass), operational mechanisms (pumpless and pump-driven), and configurations (single-organ and multiorgan systems), along with their respective advantages and limitations. Furthermore, it explores the integration of qualitative and quantitative assay techniques, providing a comparative evaluation of systems with and without sensor integration. This review aims to fill essential knowledge gaps, driving the progress of the development of OOC systems and paving the way for breakthroughs in biomedical research, pharmaceutical innovation, and tissue engineering.

Nanobody-Targeted Conditional Antimicrobial Therapeutics

Conditional therapeutics that rely on disease microenvironment-specific triggers for activation are a promising strategy to improve therapeutic cargos. Among the investigated triggers, protease activity is used most often because of its dysregulation in several diseases. How to optimally fine-tune protease activation for different therapeutic cargos remains a challenge. Here, we designed nanobody-targeted conditional antimicrobial therapeutics to deliver a model therapeutic peptide and protein to the site of bacterial infection. We explored several parameters that influence proteolytic activation. We report the use of targeting nanobodies to enhance the activation of therapeutics that are otherwise activated inefficiently despite extensive optimization of the cleavable linker. Specifically, the pairing of Ly6G/C or ADAM10-targeting nanobodies with ADAM10-cleavable linkers improved activation via proximity-enabled reactivity. This study demonstrates a distinct role of active targeting in conditional therapeutic activation. More broadly, this optimization framework provides a guideline for the development of conditional therapeutics to treat various diseases in which protease activity is dysregulated.

Impact of Glucagon-like Peptide-1 Receptor Agonists on Mental Illness: Evidence from a Mendelian Randomization Study

Emerging evidence suggests that glucagon-like peptide-1 receptor (GLP1R) agonists may have potential benefits for mental illnesses. However, their exact effects remain unclear. This study investigated the causal relationship between glucagon-like peptide-1 receptor agonist (GLP1RA) and the risk of 10 common mental illnesses, including attention deficit and hyperactivity disorder, anorexia nervosa, anxiety disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, post-traumatic stress disorder, schizophrenia, cannabis use disorder, and alcohol use disorder. We selected GLP1RA as the exposure and conducted a Mendelian randomization (MR) analysis. The cis-eQTLs of the drug target gene GLP1R, provided by eQTLGen, were used to simulate the pharmacological effects of GLP1RA. Type 2 diabetes and BMI were included as positive controls. Using data from both the Psychiatric Genomic Consortium and FinnGen, we conducted separate MR analyses for the same disease across these two independent databases. Meta-analysis was used to pool the results. We found genetic evidence suggesting a causal relationship between GLP1RA and a reduced risk of schizophrenia [OR (95% CI) = 0.84 (0.71-0.98), I2 = 0.0%, common effects model]. Further mediation analysis indicated that this effect might be unrelated to improvements in glycemic control but rather mediated by BMI. However, the findings of this study provide insufficient evidence to support a causal relationship between GLP1RA and other mental illnesses. Sensitivity analyses did not reveal any potential bias due to horizontal pleiotropy or heterogeneity in the above results (p > 0.05). This study suggests that genetically proxied activation of glucagon-like peptide-1 receptor is associated with a lower risk of schizophrenia. GLP1R is implicated in schizophrenia pathogenesis, and its agonists may exert potential benefits through weight management. Our study provides useful information for understanding the neuropsychiatric effects of GLP1RA, which may contribute to refining future research designs and guiding clinical management. Moreover, our findings could have significant implications for overweight individuals at high risk of schizophrenia when selecting weight-loss medications. Future research should further investigate the potential mechanisms underlying the relationship between GLP1RA and schizophrenia.

Unlocking the microbial treasure trove: advances in Streptomyces derived secondary metabolites in the battle against cancer

Streptomyces is widely recognized as the "biological factory" of specialized metabolites comprising a huge variety of bioactive molecules with diverse chemical properties. The potential of this Gram-positive soil bacteria to produce such diversified secondary metabolites with significant biological properties positions them as an ideal candidate for anticancer drug discovery. Some of the Streptomyces-derived secondary metabolites include siderophores (enterobactin, desferrioxamine), antibiotics (xiakemycin, dinactin) pigments (prodigiosin, melanin), and enzymes (L-methioninase, L-asperginase, cholesterol oxidase) which exhibit a pronounced anticancer effect on both in vitro and in vivo system. These secondary metabolites are endowed with antiproliferative, pro-apoptotic, antimetastatic, and antiangiogenic properties, presenting several promising characteristics that make them suitable candidates in the battle against this deadly disease. In this comprehensive review, we have dived deep and explored their history of discovery, their role as anticancer agents, underlying mechanisms, the approaches for the discovery of anticancer molecules from the secondary metabolites of Streptomyces (isolation of Streptomyces, characterization of bacterial strain, screening for anticancer activity and determination of in vitro and in vivo toxicity, structure-activity relationship studies, clinical translation, and drug development studies). The hurdles and challenges associated with this process and their future prospect were also illustrated. This review highlights the efficacy of Streptomyces as a "microbial treasure island" for novel anticancer agents, which warrants sustained research and exploration in this field to disclose more molecules from Streptomyces that are unidentified and to translate the clinical application of these secondary metabolites for cancer patients.

Drug Discovery and Development for Heart Failure Using Multi-Omics Approaches

Heart failure (HF) is a complex, heterogeneous syndrome with rising prevalence and high morbidity and mortality. The pathophysiology and diverse etiologies of HF present significant challenges for developing effective therapies. Omics technologies-including genomics, proteomics, transcriptomics, metabolomics, and epigenomics-have reshaped our understanding of HF at the molecular level, uncovering new biomarkers and potential therapeutic targets. Omics also enable insights into individualized treatment responses, the risks of adverse drug effects, and patient stratification for clinical trials. This review explores how multi-omics can enhance heart failure drug discovery and development across all stages of the therapeutic pipeline: (1) target selection and lead identification, (2) preclinical studies, and (3) clinical trials. By integrating omics approaches throughout the drug development process, we can accelerate the discovery of more effective and personalized therapies for heart failure.

Bone-brain interaction: mechanisms and potential intervention strategies of biomaterials

Following the discovery of bone as an endocrine organ with systemic influence, bone-brain interaction has emerged as a research hotspot, unveiling complex bidirectional communication between bone and brain. Studies indicate that bone and brain can influence each other's homeostasis via multiple pathways, yet there is a dearth of systematic reviews in this area. This review comprehensively examines interactions across three key areas: the influence of bone-derived factors on brain function, the effects of brain-related diseases or injuries (BRDI) on bone health, and the concept of skeletal interoception. Additionally, the review discusses innovative approaches in biomaterial design inspired by bone-brain interaction mechanisms, aiming to facilitate bone-brain interactions through materiobiological effects to aid in the treatment of neurodegenerative and bone-related diseases. Notably, the integration of artificial intelligence (AI) in biomaterial design is highlighted, showcasing AI's role in expediting the formulation of effective and targeted treatment strategies. In conclusion, this review offers vital insights into the mechanisms of bone-brain interaction and suggests advanced approaches to harness these interactions in clinical practice. These insights offer promising avenues for preventing and treating complex diseases impacting the skeleton and brain, underscoring the potential of interdisciplinary approaches in enhancing human health.

Current Approaches and Strategies Applied in First-in-class Drug Discovery

First-in-class drug discovery (FICDD) offers novel therapies, new biological targets and mechanisms of action (MOAs) toward targeting various diseases and provides opportunities to understand unexplored biology and to target unmet diseases. Current screening approaches followed in FICDD for discovery of hit and lead molecules can be broadly categorized and discussed under phenotypic drug discovery (PDD) and target-based drug discovery (TBDD). Each category has been further classified and described with suitable examples from the literature outlining the current trends in screening approaches applied in small molecule drug discovery (SMDD). Similarly, recent applications of functional genomics, structural biology, artificial intelligence (AI), machine learning (ML), and other such advanced approaches in FICDD have also been highlighted in the article. Further, some of the current medicinal chemistry strategies applied during discovery of hits and optimization studies such as hit-to-lead (HTL) and lead optimization (LO) have been simultaneously overviewed in this article.

The effect of Polybrominated diphenyl ethers at the fetal blood-brain-barrier: evaluation using a microphysiological system

Background

Glutamate dysregulation leading to neuronal excitotoxicity and neuroinflammation are associated with neurological disorders, specifically autism spectrum disorders (ASD) in preterm neonates. The lack of physiologically relevant in vitro models has limited mechanistic understanding of glutamate dysregulation and neuroinflammation during pregnancy. This study evaluated the effect of environmental pollutant and flame retardant, Polybrominated Diphenyl Ethers (PBDE) 99 and 47, on cell viability, glutamate dysregulation, and neuroinflammation using a microphysiologic system (MPS) of human fetal blood-brain-barrier organ on a chip (FB-OOC).

Methods

The FB-OOC is composed of 3-cell culture chambers, connected by microchannels, containing 1) human brain microvessel endothelial cells (HBMEC), 2) human vascular pericytes (HBVP), and 3) a triculture of neurons, astrocytes, and microglia in a 5:2:1 ratio, respectively. To assess the effect of toxicants on glutamate dysregulation and neuroinflammation, control (standard media) endothelial cells were exposed to PBDE 99 and 47 (150 ng/mL). To mimic the passage of PBDE through the placenta, endothelial cells in FB-OOC were exposed to conditioned PDBE media (1:1) derived from a placenta-OOC. In parallel, triculture cells were directly treated in a 96-well plate. Dextran propagation over 72 h confirmed FB barrier function. The activation status of microglia was determined using immunocytochemistry for CD11 b and Iba1, respectively. Cell morphology (microscopy), cell cytotoxicity (Lactate Dehydrogenase and glutamate assays), and cytokines (multiplex assay) were measured.

Results

Physiologic FB-OOCs were characterized by 1) viable cell cultures expressing standard cell morphologies and cell-specific markers, 2) barrier formation confirmed by decreased dextran propagation over 72 h, and 3) baseline glutamate and pro-inflammatory cytokine production. On-chip PBDE and placenta-derived metabolites of PBDE treatment in the endothelial chamber induced cell cytotoxicity and significant upregulation of glutamate in the triculture but did not induce neuroinflammation nor microglia activation compared to the controls. Conversely, 2D triculture experiments showed direct PBDE treatment-induced significant neuroinflammation (TNF-α, GM-CSF, IL-8) compared to PBDE placenta-derived metabolites or controls.

Conclusion

This study established an FB model that recreated intercellular interactions. We report PBDE-induced glutamate dysregulation, often associated with the development of ASD, independent of neuroinflammation.

A structure-based framework for selective inhibitor design and optimization

Structure-based drug design aims to create active compounds with favorable properties by analyzing target structures. Recently, deep generative models have facilitated structure-specific molecular generation. However, many methods are limited by inadequate pharmaceutical data, resulting in suboptimal molecular properties and unstable conformations. Additionally, these approaches often overlook binding pocket interactions and struggle with selective inhibitor design. To address these challenges, we developed a framework called Coarse-grained and Multi-dimensional Data-driven molecular generation (CMD-GEN). CMD-GEN bridges ligand-protein complexes with drug-like molecules by utilizing coarse-grained pharmacophore points sampled from diffusion model, enriching training data. Through a hierarchical architecture, it decomposes three-dimensional molecule generation within the pocket into pharmacophore point sampling, chemical structure generation, and conformation alignment, mitigating instability issues. CMD-GEN outperforms other methods in benchmark tests and controls drug-likeness effectively. Furthermore, CMD-GEN excels in cases across three synthetic lethal targets, and wet-lab validation with PARP1/2 inhibitors confirms its potential in selective inhibitor design.

Advances of dual-organ and multi-organ systems for gut, lung, skin and liver models in absorption and metabolism studies

Drug development is a costly and timely process with high risks of failure during clinical trials. Although in vitro tissue models have significantly advanced over the years, thus fostering a transition from animal-derived models towards human-derived models, failure rates still remain high. Current cell-based assays are still not able to provide an accurate prediction of the clinical success or failure of a drug candidate. To overcome the limitations of current methods, a variety of microfluidic systems have been developed as powerful tools that are capable of mimicking (micro)physiological conditions more closely by integrating physiological fluid flow conditions, mechanobiological cues and concentration gradients, to name only a few. One major advantage of these biochip-based tissue cultures, however, is their ability to seamlessly connect different organ models, thereby allowing the study of organ-crosstalk and metabolic byproduct effects. This is especially important when assessing absorption, distribution, metabolism, and excretion (ADME) processes of drug candidates, where an interplay between various organs is a prerequisite. In the current review, a number of in vitro models as well as microfluidic dual- and multi-organ systems are summarized with a focus on absorption (skin, lung, gut) and metabolism (liver). Additionally, the advantage of multi-organ chips in identifying a drug's on and off-target toxicity is discussed. Finally, the potential high-throughput implementation and modular chip design of multi-organ-on-a-chip systems within the pharmaceutical industry is highlighted, outlining the necessity of reducing handling complexity.

Mechanisms of developmental neurotoxicity mediated by perturbed thyroid hormone homeostasis in the brain: an adverse outcome pathway network

Thyroid hormone (TH) is crucial for proper neurodevelopment. Insufficient TH concentrations in early life are associated with lower IQ and delayed motor development in children. Intracellular levels of TH are modulated via the transmembrane transport of TH and intracellular deiodination, and can mediate gene transcription via binding to the nuclear TH receptor. Chemical exposure can disrupt TH homeostasis via modes of action targeting intracellular mechanisms, thereby potentially influencing TH transport, deiodination or signaling. Understanding the cause and effect relationships of chemical hazards interfering with TH homeostasis in the developing brain is necessary to identify how chemicals might disturb brain development and result in neurodevelopmental disorders. Adverse Outcome Pathways (AOPs) can provide a template for mapping these relationships, and so far multiple AOPs have been developed for TH homeostasis and adverse effects on cognition. The present review aims to expand current AOP networks by (1) summarizing the most important factors in the regulation of brain development under influence of TH, (2) integrating human-based mechanistic information of biological pathways which can be disturbed by TH disrupting chemicals, and (3) by incorporating brain-specific TH-mediated physiology, including barriers and cell specificity, as well as clinical knowledge. TH-specific pathways in the fetal brain are highlighted and supported by distinguishing cell type specific Molecular Initiating Events (MIEs) and downstream Key Events (KEs) for astrocytes, neurons and oligodendrocytes. Two main pathways leading to adverse outcomes (AOs) in the areas of 'cognition' and 'motor function' are decreased myelination due to oligodendrocyte dysfunction, and decreased synaptogenesis and network formation via the neurons. The proposed AOP framework can form a basis for selecting developmental neurotoxic in vitro and in silico test systems for an innovative human-focused hazard testing strategy and risk assessment of chemical exposure.

Bone-on-a-Chip Systems for Hematological Cancers

Hematological malignancies originating from blood, bone marrow, and lymph nodes include leukemia, lymphoma, and myeloma, which necessitate the use of a distinct chemotherapeutic approach. Drug resistance frequently complicates their treatment, highlighting the need for predictive tools to guide therapeutic decisions. Conventional 2D/3D cell cultures do not fully encompass in vivo criteria, and translating disease models from mice to humans proves challenging. Organ-on-a-chip technology presents an avenue to surmount genetic disparities between species, offering precise design, concurrent manipulation of various cell types, and extrapolation of data to human physiology. The development of bone-on-a-chip (BoC) systems is crucial for accurately representing the in vivo bone microenvironment, predicting drug responses for hematological cancers, mitigating drug resistance, and facilitating personalized therapeutic interventions. BoC systems for modeling hematological cancers and drug research can encompass intricate designs and integrated platforms for analyzing drug response data to simulate disease scenarios. This review provides a comprehensive examination of BoC systems applicable to modeling hematological cancers and visualizing drug responses within the intricate context of bone. It thoroughly discusses the materials pertinent to BoC systems, suitable in vitro techniques, the predictive capabilities of BoC systems in clinical settings, and their potential for commercialization.

Drug target assessments: classifying target modulation and associated health effects using multi-level BERT-based classification models

Motivation

Drug target selection determines the success of the drug development pipeline. Therefore, novel drug targets need to be assessed for their therapeutic benefits/risks at the earliest stage possible. Where manual risk/benefit analyses are often user-biased and time-consuming, Large Language Models can offer a systematic and efficient approach to curating and analysing literature. Currently, publicly available Large Language Models are lacking for this task, while public platforms for target assessments are limited to co-occurrences.

Results

BERT-models for multi-level classification of drug target-health effect relationships described in PubMed were developed. Relationships were classified based on (i) causality; (ii) direction of target modulation; (iii) direction of the associated health effect. The models showed competitive performances with F1 scores between 0.86 and 0.92 and their applicability was demonstrated using ADAM33 and OSM as case study. The developed classification pipeline is the first to allow detailed classification of drug target-health effect relationships. The models provide mechanistic insight into how target modulation affects health and disease, both from an efficacy and safety perspective. The models, deployed on the whole of PubMed and available through the TargetTri platform, are expected to offer a significant advancement in artificial intelligence-assisted target identification and evaluation.

Availability and implementation

https://www.targettri.com.

Open Source Repurposing Reveals Broad-Spectrum Antiviral Activity of Diphenylureas

The pandemic threat from newly emerging viral diseases constitutes a major unsolved issue for global health. Antiviral therapy can play an important role in treating and preventing the spread of unprecedented viral infections. A repository of compounds exhibiting broad-spectrum antiviral activity against a series of different viral families would be an invaluable asset to be prepared for future pandemic threats. Utilizing an open innovation crowd-sourcing paradigm, we were able to identify a compound class of diphenylureas that exhibits in vitro antiviral activity against multiple viruses, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), adenovirus, dengue virus, herpes, and influenza viruses. Compound 4 among the series exhibits strong activity against dengue virus, a growing global health problem with high medical need and no approved antiviral drug. The compounds are active against SARS-CoV-2 in a primary human stem cell-based mucociliary airway epithelium model and also active in vivo, as shown in a murine SARS-CoV-2 infection model. These results demonstrate the potential of the chemical class as antivirals on the one hand and the power of open innovation, crowd-sourcing, and repurposing on the other hand.

Absorption of Toxicants from the Ocular Surface: Potential Applications in Toxicology

In relation to the eye, the body can absorb substances from the ocular surface fluid (OSF) in a few ways: directly through the conjunctival sac, through the nasal mucosa as the fluid drains into the nose, or through ingestion. Regardless of the absorption method, fluid from the conjunctival sac should be used as a toxicological matrix, even though only small quantities are needed. Contemporary analytical techniques make it a suitable matrix for toxicological research. Analyzing small quantities of the matrix and nano-quantities of the analyte requires high-cost, sophisticated tools, which is particularly relevant in the high-throughput environment of new drug or cosmetics testing. Environmental toxicology also presents a challenge, as many pollutants can enter the system using the same ocular surface route. A review of the existing literature was conducted to assess potential applications in clinical and forensic toxicology related to the absorption of toxicants from the ocular surface. The selection of the studies used in this review aimed to identify new, more efficient, and cost-effective analytical technology and diagnostic methods.

CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability

The link between in vitro hERG ion channel inhibition and subsequent in vivo QT interval prolongation, a critical risk factor for the development of arrythmias such as Torsade de Pointes, is so well established that in vitro hERG activity alone is often sufficient to end the development of an otherwise promising drug candidate. It is therefore of tremendous interest to develop advanced methods for identifying hERG-active compounds in the early stages of drug development, as well as for proposing redesigned compounds with reduced hERG liability and preserved primary pharmacology. In this work, we present CardioGenAI, a machine learning-based framework for re-engineering both developmental and commercially available drugs for reduced hERG activity while preserving their pharmacological activity. The framework incorporates novel state-of-the-art discriminative models for predicting hERG channel activity, as well as activity against the voltage-gated NaV1.5 and CaV1.2 channels due to their potential implications in modulating the arrhythmogenic potential induced by hERG channel blockade. We applied the complete framework to pimozide, an FDA-approved antipsychotic agent that demonstrates high affinity to the hERG channel, and generated 100 refined candidates. Remarkably, among the candidates is fluspirilene, a compound which is of the same class of drugs as pimozide (diphenylmethanes) and therefore has similar pharmacological activity, yet exhibits over 700-fold weaker binding to hERG. Furthermore, we demonstrated the framework's ability to optimize hERG, NaV1.5 and CaV1.2 profiles of multiple FDA-approved compounds while maintaining the physicochemical nature of the original drugs. We envision that this method can effectively be applied to developmental compounds exhibiting hERG liabilities to provide a means of rescuing drug development programs that have stalled due to hERG-related safety concerns. Additionally, the discriminative models can also serve independently as effective components of virtual screening pipelines. We have made all of our software open-source at https://github.com/gregory-kyro/CardioGenAI to facilitate integration of the CardioGenAI framework for molecular hypothesis generation into drug discovery workflows.Scientific contributionThis work introduces CardioGenAI, an open-source machine learning-based framework designed to re-engineer drugs for reduced hERG liability while preserving their pharmacological activity. The complete CardioGenAI framework can be applied to developmental compounds exhibiting hERG liabilities to provide a means of rescuing drug discovery programs facing hERG-related challenges. In addition, the framework incorporates novel state-of-the-art discriminative models for predicting hERG, NaV1.5 and CaV1.2 channel activity, which can function independently as effective components of virtual screening pipelines.