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

PayloadGenX, a multi-stage hybrid virtual screening approach for payload design: A microtubule inhibitor case study

Due to the rapid emergence of treatment-resistant cancers, there is a growing need to discover new anticancer therapies. Antibody-drug conjugates (ADCs) are aimed at solving this problem by specifically targeting and delivering cytotoxic payloads directly to cancer cells, thereby minimizing damage to healthy cells and enhancing treatment efficacy. Therefore, it is highly important to find an effective cytotoxic payload to ensure maximum therapeutic benefit and overcome cancer resistance. To address this challenge, we have developed a multi-stage hybrid virtual screening (VS) approach for payload design. We collected approximately 900 million molecules from databases such as ZINC12, ChEMBL, PubChem, and QM9. Additionally, 220 approved small molecule anticancer drugs were collected. Initially, these molecules were screened based on the Lipinski Rule of Five (RO5) criteria, resulting in 20 million molecules that met the drug-like properties criteria. Subsequently, fragments being key factor in this approach were generated from approved small molecule cancer drugs. This fragment-based screening approach resulted in identifying 6500, 36770, and 150,000 anticancer-like drugs with a similarity threshold greater than 0.6, 0.5, and 0.4. Similarity threshold when increased near to 1 bears better chance of discovering cancer like drugs. Further molecular docking of these anticancer-like drugs with β-tubulin resulted in identifying the top 1000 ranked drugs as microtubule inhibitors. ADMET analysis and synthetic validation followed by cell cytotoxicity further helps in shortlisting the 5 most effective payloads for further confirmation in preclinical setting. Additionally, molecular dynamics simulation was performed to confirm the structural stability and conformational dynamics of the Beta-tubulin-ligand complexes over a 100 ns simulation. In conclusion, this study effectively utilizes extensive compound databases and multi-stage screening methods to identify potent payloads, demonstrating promising advancements in discovering effective anticancer therapies.

A review: From old drugs to new solutions: The role of repositioning in alzheimer's disease treatment

Drug repositioning or drug reprofiling, involves identifying novel indications for approved and previously abandoned drugs in the treatment of other diseases. The traditional drug discovery process is tedious, time-consuming, risky, and challenging. Fortunately, the inception of the drug repositioning concept has expedited the process by using compounds with established safety profiles in humans, and thereby significantly reducing costs. Alzheimer's disease (AD) is a severe neurological disorder characterized by progressive degeneration of the brain with limited and less effective therapeutic interventions. Researchers have attempted to identify potential treatment of AD from existing drug however, the success of drug repositioning strategy in AD remains uncertain. This article briefly discusses the importance and effectiveness of drug repositioning strategies, the major obstacles in the development of drugs for AD, approaches to address these challenges, and the role of machine learning in identifying early markers of AD for improved management.

Practical progress towards the development of recombinant antivenoms for snakebite envenoming

INTRODUCTION

Snakebite envenoming is a neglected tropical disease that affects millions globally each year. In recent years, research into the potential production of recombinant antivenoms, formulated using mixtures of highly defined anti-toxin monoclonal antibodies, has rapidly moved from a theoretical concept to demonstrations of practical feasibility.

AREAS COVERED

This article examines the significant practical advancements in transitioning recombinant antivenoms from concept to potential clinical translation. The authors have based their review on literature obtained from Google Scholar and PubMed between September and November 2024. Coverage includes the development and validation of recombinant antivenom antibody discovery strategies, the characterization of the first broadly neutralizing toxin class antibodies, and recent translational proof-of-concept experiments.

EXPERT OPINION

The transition of recombinant antivenoms from a 'concept' to the current situation where high-throughput anti-venom mAb discovery is becoming routine, accompanied by increasing evidence of their broad neutralizing capacity in vivo, has been extraordinary. It is now important to build on this momentum by expanding the discovery of broadly neutralizing mAbs to encompass as many toxin classes as possible. It is anticipated that key demonstrations of whether recombinant antivenoms can match or surpass existing conventional polyvalent antivenoms in terms of neutralizing scope and capacity will be achieved in the next few years.

Morpholino nicotinamide analogs of ponatinib, dual MNK, p70S6K inhibitors, display efficacy against lung and breast cancers

Therapeutic options for aggressive cancer types such as breast and lung remain limited; disease relapse and death occur in 30-60% of non-small cell lung cancer (NSCLC) patients, whereas in triple-negative breast cancer or TNBC, recurrence-free survival occurs in less than 30% patients. The kinases, MNK and p70S6K have been proposed as targets for the potential treatment of breast cancer (BC) and lung cancer but currently, no drug that was purposely designed to inhibit these kinases have been approved by the FDA for the treatment of BC or NSCLC. In this study, we have identified HSND80 (a morpholino nicotinamide analog of ponatinib) as a potent MNK/p70S6K inhibitor that has excellent activity against TNBC and NSCLC cell lines. HSND80 has a longer target residence time (τ) of 45 mins and 58 mins against MNK1 and MNK2 respectively, compared to τ of eFT508 (tomivosertib) against MNK1 and MNK2 (τ = 1 min and 5 min, respectively). Molecular dynamics simulation was used to provide some insights into the binding of HSND80 to MNK and p70S6K kinases. Western blotting analysis and phosphoproteomics analysis of the TNBC cell line, MDA-MB-231, revealed that phosphorylations of elF4E (MNK target) and elF4B and S6 (p70S6K targets) were reduced upon compound treatment, which is in line with the proposed mechanism of action; dual MNK/p70S6K targeting. HSND80 could be dosed orally at 15 and 30 mg/kg and at such doses, could reduce tumor volume in a syngeneic NSCLC mouse model.

Advancing Alzheimer's Therapy: Computational strategies and treatment innovations

Alzheimer's disease (AD) is a multifaceted neurodegenerative condition distinguished by the occurrence of memory impairment, cognitive deterioration, and neuronal impairment. Despite extensive research efforts, conventional treatment strategies primarily focus on symptom management, highlighting the need for innovative therapeutic approaches. This review explores the challenges of AD treatment and the integration of computational methodologies to advance therapeutic interventions. A comprehensive analysis of recent literature was conducted to elucidate the broad scope of Alzheimer's etiology and the limitations of conventional drug discovery approaches. Our findings underscore the critical role of computational models in elucidating disease mechanisms, identifying therapeutic targets, and expediting drug discovery. Through computational simulations, researchers can predict drug efficacy, optimize lead compounds, and facilitate personalized medicine approaches. Moreover, machine learning algorithms enhance early diagnosis and enable precision medicine strategies by analyzing multi-modal datasets. Case studies highlight the application of computational techniques in AD therapeutics, including the suppression of crucial proteins implicated in disease progression and the repurposing of existing drugs for AD management. Computational models elucidate the interplay between oxidative stress and neurodegeneration, offering insights into potential therapeutic interventions. Collaborative efforts between computational biologists, pharmacologists, and clinicians are essential to translate computational insights into clinically actionable interventions, ultimately improving patient outcomes and addressing the unmet medical needs of individuals affected by AD. Overall, integrating computational methodologies represents a promising paradigm shift in AD therapeutics, offering innovative solutions to overcome existing challenges and transform the landscape of AD treatment.

Comparative effects of extracellular vesicles and liposomal nanocarriers on bleomycin-induced stress in A549 human adenocarcinoma cells

Lung cancer and chronic respiratory diseases are among the leading causes of death worldwide. Key factors in their pathogenesis include reactive oxygen species (ROS), transforming growth factor-β1 (TGF-β1) and epithelial-mesenchymal transition (EMT). Exogenous antioxidants can mitigate the oxidative stress that drives TGF-β1-mediated respiratory pathologies. Given their role in cellular communication and natural biocompatibility, extracellular vesicles (EVs) are emerging as promising candidates for the delivery of therapeutic cargo to pathological cells. Notably, microalgal-derived EVs (i.e., nanoalgosomes) have been shown to exhibit antioxidant and anti-inflammatory activity. In this study, the bioactivity of EVs derived from Tetraselmis chuii (CCAP 66/21B) was investigated in a bleomycin-stressed (8 µg mL-1) human adenocarcinoma alveolar epithelial cell model (A549). Moreover, the effects of these EVs were compared to liposomes loaded with established therapeutics (pirfenidone and quercetin), synthesised using the lipid film hydration method. In vitro assessments included cell viability (MTS), intracellular ROS, morphological changes, cell migration, EMT-related mRNA expression (qPCR), and TGF-β1 release (ELISA). Both the EVs (nanoalgosomes) and pirfenidone- and quercetin-loaded liposomal nanocarriers (1-4 µg mL-1) effectively attenuated bleomycin-induced EMT, inhibited cell migration, suppressed profibrotic TGF-β1, lowered intracellular ROS and upregulated glutathione peroxidase 4 (GPX4). Importantly, the innate bioactive cargo of the naturally derived nanoalgosomes exhibited comparable effects to the liposome therapeutic formulations in mitigating bleomycin-induced stress in A549 cells.

Framework to identify innovative sources of value creation from platform technologies

Platform technologies are fundamentally reshaping the pharmaceutical industry, offering unprecedented potential for innovation across multiple therapeutic areas. However, traditional valuation models, focused on single-asset metrics, struggle to capture the full spectrum of value these technologies create. This paper presents a comprehensive framework for evaluating the innovative sources of value creation enabled by platform technologies throughout the drug development lifecycle. Through a systematic literature review, in-depth case studies, and framework development, we provide a structured methodology for capturing the diverse benefits of these technologies. Our findings reveal that platform technologies generate value across strategic, technical, and adaptive dimensions, requiring a multifaceted valuation approach. The proposed Platform Value Identification across Strategic, Technical, and Adaptive domains Framework defines key value drivers, specifies quantitative assessment metrics, and provides implementation guidance to inform strategic decision-making in research and development investment, portfolio management, and business development. Application of the framework to case studies of Alnylam's RNAi platform, Genentech's therapeutic antibody platform, and Moderna's mRNA platform demonstrates its broad utility and impact potential. By adopting this holistic, data-driven approach, stakeholders can better assess the long-term value and competitive advantages of well-implemented platform technologies, accelerating the development of transformative therapies for patients.

Comprehensive Drug-Likeness Prediction Using a Pretrained Transformer Model and Multitask Learning

Drug-likeness is essential in drug discovery, indicating the potential of a compound to become a successful therapeutic. However, existing rule-based and machine learning methods are limited by their reliance on hand-crafted features, poor generalizability across chemical spaces, and insufficient adaptability to the diverse contexts of drug development. To overcome these limitations, we introduce an innovative framework that integrates molecular pretrained transformer models with multitask learning. This approach enables the simultaneous capture of complex chemical features and facilitates knowledge sharing across related prediction tasks. Our framework features two models: SpecDL, tailored for specialized drug-likeness assessments, and GeneralDL, designed for comprehensive, cross-data set evaluation. SpecDL achieved an average ROC-AUC of 0.836 across four tasks, while GeneralDL reached an average ROC-AUC of 0.781 on six internal and external test sets, both surpassing the leading existing methods. Furthermore, GeneralDL demonstrated robust generalization to toxicity and biological activity predictions and provided interpretable outputs via attention weight analysis. These results establish our framework as a powerful, generalizable tool for drug-likeness prediction with significant potential to enhance early-stage drug discovery.

Computational Hit Finding: An Industry Perspective

Computational hit finding, particularly virtual screening, has been a mainstay of drug discovery campaigns for decades, providing a cost-efficient complement to wet experiments. Innovation in this space slowed considerably as these approaches converged around mature software programs and stock chemical libraries up to ∼10 million in size. Recently, however, powered by massive increases in computational power, the emergence of ultralarge make-on-demand virtual libraries, the development of large capacity neural networks, the expansion of the domain of applicability of free energy calculations, and advances in protein structure prediction, the virtual screening field is currently seeing major change. We present a guide from industry practitioners summarizing key aspects on the changing computational hit finding landscape including practical recommendations for building a performant end-to-end screening workflow, strategies to mitigate risk by avoiding common pitfalls, determining success criteria, and a brief discussion of emerging technologies likely to impact drug discovery in the near future.

Human intestinal enteroids: Nonclinical applications for predicting oral drug disposition, toxicity, and efficacy

The application of human enteroid systems presents a significant opportunity within the drug development pipeline, highlighting considerable potential for advancements in the characterization and evaluation of new molecular entities. Derived from LGR5+ crypt-based columnar cells, enteroid systems more accurately recapitulate the microanatomy and physiological processes of the human intestinal mucosa compared to traditionally used systems. They contain the complement of major mucosal epithelial cell types, maintain the genetic identity of the donor and intestinal segment they were derived from, and exhibit biological functions and specific activities that are seen in vivo. In this review, we examine the applications of human enteroid systems in nonclinical drug development and compare findings to existing and emerging in vitro models of the small intestine. Specifically, we explore enteroid systems in the context of predicting oral drug disposition, focusing on apparent permeability, intestinal first-pass metabolism, and drug interactions, as well as their utility in assessing drug-induced gastrointestinal toxicity and screening therapeutic efficacy against enteric diseases. Additionally, we highlight aspects of enteroid systems that warrant further study.

Exploring a kinase inhibitor targeting PI3KCA mutant cancer cells

The PI3K/mTOR signaling pathway is often disrupted in human cancers, with PI3Kα being one of the most mutated kinases. There has been considerable interest in developing small-molecule inhibitors aimed at blocking the mutant PI3Kα-driven phosphatidylinositol 3-kinase (PI3K) signaling pathway as a potential treatment for cancer. In this study, we describe our effort to identify a compound, phenylacetamide-1H-imidazol-5-one (KIM-161), from our in-house oncogenic kinase-targeting inhibitors. KIM-161 showed excellent anti-proliferative activities at sub-nanomolar concentrations, primarily against mutant PI3Kα breast cancer cell lines, when compared with wild-type PI3Kα breast cancer cell lines, producing both dose- and time-dependent effects with an IC50 range of 1.42 - 0.064 µM. Next, we observed that KIM-161 was able to induce ROS production by modulating breast cancer metabolism, suggesting its broad effects on mutant PI3Kα regulated downstream pathways. We also computationally analyzed the binding interactions between KIM-161 and PI3K-alpha (PDB ID: 8EXL). Molecular docking showed that KIM-161 had a docking score of -7.44 Kcal/mol, compared to the reference compound, which had a docking score of -7.67 Kcal/mol. Moreover, molecular dynamics simulation studies demonstrated that the PI3Ka-KIM-161 complex remained stable throughout the 100 ns simulation, when compared to the PI3Ka complex with the co-crystallized inhibitor. These findings present KIM-161 as a promising lead, providing valuable insights into treatment approaches and resistance mechanisms associated with PI3K inhibitors in specific PIK3CA-mutant cancer subtypes.

Integration of structural study and machine learning to elucidate the RNA-SFs interaction atlas in eukaryotic cells

Alternative splicing (AS) occupies a central position in plant growth and development, stress response, and animal growth and disease processes. Mutations in SF (splicing factor) trigger aberrant AS activities that disrupt these fine biological processes. Although cryo electron microscopy (cryoEM) technology has successfully revealed the fine structure of multiple spliceosomes, the dynamic and complex network of RNA-SFs remains to be fully resolved. This review summarizes the binding patterns of RNA and SFs through machine learning's powerful computational capabilities, the deep structural analysis using cryoEM, and experimental validation of RNA protein binding. Connect RNA protein interaction experiments, high-resolution imaging capabilities of cryoEM, and powerful analytical capabilities of machine learning to jointly construct a detailed RNA-SFs interaction map, forming a powerful toolkit. These knowledge help us better understand the complexity and working mechanisms of biological systems. This article not only has profound significance in revealing the molecular mechanisms of diseases and developing multi-target efficient drugs but also provides in-depth insights into molecular breeding and plant resistance enhancement.

Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World

Machine learning (ML) is increasingly valuable for predicting molecular properties and toxicity in drug discovery. However, toxicity-related end points have always been challenging to evaluate experimentally with respect to in vivo translation due to the required resources for human and animal studies; this has impacted data availability in the field. ML can augment or even potentially replace traditional experimental processes depending on the project phase and specific goals of the prediction. For instance, models can be used to select promising compounds for on-target effects or to deselect those with undesirable characteristics (e.g., off-target or ineffective due to unfavorable pharmacokinetics). However, reliance on ML is not without risks, due to biases stemming from nonrepresentative training data, incompatible choice of algorithm to represent the underlying data, or poor model building and validation approaches. This might lead to inaccurate predictions, misinterpretation of the confidence in ML predictions, and ultimately suboptimal decision-making. Hence, understanding the predictive validity of ML models is of utmost importance to enable faster drug development timelines while improving the quality of decisions. This perspective emphasizes the need to enhance the understanding and application of machine learning models in drug discovery, focusing on well-defined data sets for toxicity prediction based on small molecule structures. We focus on five crucial pillars for success with ML-driven molecular property and toxicity prediction: (1) data set selection, (2) structural representations, (3) model algorithm, (4) model validation, and (5) translation of predictions to decision-making. Understanding these key pillars will foster collaboration and coordination between ML researchers and toxicologists, which will help to advance drug discovery and development.

Cryopreservation of human lung tissue for 3D ex vivo analysis

Ex vivo culture techniques have assisted researchers in narrowing the translational gap between the lab and the clinic by allowing the study of biology in human tissues. In pulmonary biology, however, the availability of such tissues is a limiting factor in experimental design and constrains the reproducibility and replicability of these models as scientifically rigorous complements to in vitro or in vivo methods. Cryopreservation of human lung tissue is a strategy to address these limitations by generating cryopreserved biobanks of donors in the ex vivo study of pulmonary biology. Modern cryopreservation solutions, incorporating blends of cryoprotective extracellular macromolecules and cell-permeant non-toxic small molecules, have enabled the long-term storage of human lung tissue, allowing repeated experiments in the same donors and the simultaneous study of the same hypothesis across multiple donors, therefore granting the qualities of reproducibility and replicability to ex vivo systems. Specific considerations are required to properly maintain fundamental aspects of tissue structure, properties, and function throughout the cryopreservation process. The examples of existing cryopreservation systems successfully employed to amass cryobanks, and ex vivo culture techniques compatible with cryopreservation, are discussed herein, with the goal of indicating the potential of cryopreservation in ex vivo human lung tissue culture and highlighting opportunities for cryopreservation to expand the utility of ex vivo human lung culture systems in the pursuit of clinically relevant discoveries.

Dynamic Tracking of In Vivo Receptor Availability in Tumor Using Paired-Agent Imaging

Quantitative assessment of receptor availability (RA) provides valuable insight into therapeutic outcomes in drug development and clinical practice. Here, paired-agent imaging (PAI) is used to dynamically track the availability of the epidermal growth factor receptor (EGFR) in response to in vivo ligand or inhibitor binding in individual mice with head and neck cancer (HNC). Naïve (n = 3) or xenograft HNC tumor-bearing (n = 21) mice were coadministered 0.15, 0.3, or 0.9 nmol ABY-029, and 2.5 nmol of IRDye 700DX. Fluorescence images were acquired for 300 min and then for an additional 60 min after administration of Z03115 (test group), human EGF (positive control), or PBS (vehicle control). Kinetic fluorescence and PAI curves were evaluated to determine the effects of the ABY-029 dose and EGFR blocking on tumor RA estimation. Nonquantifiable increases in ABY-029 fluorescence in tumor and muscle were observed after in vivo blocking, while PAI produced the expected decrease in RA. No statistically significant difference in preblocking RA was observed with different doses of ABY-029. RA decreased in response to blocking in positive control and test group animals, while the vehicle group exhibited no significant change in RA. This study demonstrated that RA can be monitored dynamically in individual animals using PAI regardless of imaging agent dose, while fluorescence from the receptor-targeted imaging agent alone could not. These results demonstrate PAI as a simple imaging strategy that could allow dose optimization in pharmaceutical development and patient-specific dosing for molecular therapeutics.

Construction and cytotoxicity evaluation of peptide nanocarriers based on coiled-coil structures with a cyclic β-amino acid at the knob-into-hole interaction site

Peptides are highly attractive as nanocarriers for drug delivery and other biomedical applications due to their unique combination of biocompatibility, efficacy, safety, and versatility-qualities that are difficult to achieve with other nanocarrier types. Particularly promising in this context are peptide foldamers containing non-canonical residues, which can yield nanostructures with diverse physicochemical properties. Additionally, the introduction of non-proteinogenic amino acids into the sequence enhances conformational stability and resistance to proteolysis, critical features for bioapplications. In this article, we report the development of novel foldameric bundles based on a coiled-coil structure incorporating trans-(1S,2S)-2-aminocyclopentanecarboxylic acid (trans-ACPC) at the key interacting site. We also provide both theoretical and experimental analyses of how this cyclic β-residue affects the thermodynamic and proteolytic stability, oligomerization state, and encapsulation properties of the resulting foldamers compared to standard coiled-coils. Additionally, we assessed the cytotoxicity of these foldamers using the MTT assay on 3T3 cells. The results demonstrate that neither the foldamers nor trans-ACPC exhibit toxic effects on the 3T3 cell line, highlighting their potential as safe and effective nanocarriers.

MHNfs: Prompting In-Context Bioactivity Predictions for Low-Data Drug Discovery

Today's drug discovery increasingly relies on computational and machine learning approaches to identify novel candidates, yet data scarcity remains a significant challenge. To address this limitation, we present MHNfs, an application specifically designed to predict molecular activity in low-data scenarios. At its core, MHNfs leverages a state-of-the-art few-shot activity prediction model, named MHNfs, which has demonstrated strong performance across a large set of prediction tasks in the benchmark data set FS-Mol. The application features an intuitive interface that enables users to prompt the model for precise activity predictions based on a small number of known active and inactive molecules, akin to interactive interfaces for large language models. To evaluate its efficacy, we simulate real-world scenarios by recasting PubChem bioassays as few-shot prediction tasks. MHNfs offers a streamlined and accessible solution for deploying advanced few-shot learning models, providing a valuable tool for accelerating drug discovery.

A self-conformation-aware pre-training framework for molecular property prediction with substructure interpretability

The major challenges in drug development stem from frequent structure-activity cliffs and unknown drug properties, which are expensive and time-consuming to estimate, contributing to a high rate of failures and substantial unavoidable costs in the clinical phases. Herein, we propose the self-conformation-aware graph transformer (SCAGE), an innovative deep learning architecture pretrained with approximately 5 million drug-like compounds for molecular property prediction. Notably, we develop a multitask pretraining framework, which incorporates four supervised and unsupervised tasks: molecular fingerprint prediction, functional group prediction using chemical prior information, 2D atomic distance prediction, and 3D bond angle prediction, covering aspects from molecular structures to functions. It enables learning comprehensive conformation-aware prior knowledge, thereby enhancing its generalization across various molecular property tasks. Moreover, we design a data-driven multiscale conformational learning strategy that effectively guides the model in understanding and representing atomic relationships at the molecular conformational scale. SCAGE achieves significant performance improvements across 9 molecular properties and 30 structure-activity cliff benchmarks. Case studies demonstrate that SCAGE accurately captures crucial functional groups at the atomic level, which are closely associated with molecular activity, providing valuable insights into quantitative structure-activity relationships.

Challenging the "Undruggable"─Targeting STAT3 but Identifying Potent TrkA-Targeted Inhibitors

Signal transducer and activator of transcription 3 (STAT3) is a promising yet challenging anticancer drug target due to its complex signaling and limited "druggability". To this end, we herein highlight a target engagement-focused screening and optimization pipeline pursuing the discovery of novel STAT3 inhibitors. From a STAT3 differential scanning fluorimetry high-throughput screen, we identified compounds that appeared to stabilize STAT3 toward thermal aggregation and moderately inhibited cellular STAT3 activity. Subsequent evaluation using complementary and orthogonal assays revealed their high affinity for tropomyosin receptor kinase A (TrkA). Applying a similar target engagement-inspired approach, we refined inhibitor binding and selectivity toward TrkA, showing efficacy in cellular TrkA cancer models. Top compound, PI-15, demonstrated successful target engagement in a cellular thermal shift assay and potently inhibited TrkA activity in cancer cells. These approaches highlight the importance of prioritizing rigorous target engagement validation early in the drug discovery pipeline, resulting in promising new inhibitors.

Discovery of a small molecule secreted clusterin enhancer that improves memory in Alzheimer's disease mice

Despite substantial research and drug discovery efforts, Alzheimer's Disease (AD) remains the sixth leading cause of death in the United States, underscoring the urgent need for novel therapeutic targets. A mutation in the clusterin (CLU) gene that hinders expression of the cyto-protective secreted isoform of clusterin (sCLU) that affects the aggregation and clearance of two key proteins implicated in AD, Aβ and tau, is the third most significant genetic risk factor for late-onset AD. Here, we present findings from our drug discovery program to identify small molecules that enhance sCLU levels and assess their impact on AD pathology and cognition in a murine model of AD. A high-throughput screening campaign identified two classes of epigenetic modulators that increase sCLU levels with subsequent medicinal chemistry efforts leading to bromodomain and extra-terminal (BET) inhibitor new chemical entities (NCEs) with enhanced potency, drug-like properties, and oral brain bioavailability. The lead candidate NCE, DDL-357, increased brain sCLU in the murine ApoE4TR-5XFAD model of AD in a subchronic study. In a follow-up chronic study in the murine 3xTg-AD model, DDL-357 reduced phospho-tau in brain and led to improvements in mouse performance and memory in the Barnes maze testing paradigm. Proteomic analysis of brain tissue from both AD models revealed changes in proteins involved in mitochondrial function and synaptic plasticity. These findings reveal the potential of sCLU enhancement as a target for therapeutic development in AD and support the continued development of the preclinical lead candidate.

Development and Characterization of pFluor50, a Fluorogenic-Based Kinetic Assay System for High-Throughput Inhibition Screening and Characterization of Time-Dependent Inhibition and Inhibition Type for Six Human CYPs

Cytochrome P450s (CYPs) play an integral role in drug and xenobiotic metabolism in humans, and thus, understanding CYP inhibition and/or activation by new therapeutic candidates is an important step in the drug development process. Ideally, CYP inhibition/activation assays should be high-throughput, use commercially available components, allow for analysis of metabolism by the majority of human CYPs, and allow for kinetic analysis of inhibition type and time-dependent inhibition. Here, we developed pFluor50, a 384-well microtiter plate-based fluorogenic kinetic enzyme assay system using substrates metabolized by six human CYPs to generate fluorescent products and determined the Michaelis-Menten kinetics constants (KM) and product formation rates (Vmax) for each substrate-CYP pair. The pFluor50 assay was also used to elucidate inhibition type and time-dependent inhibition for some inhibitors, demonstrating its utility for characterizing the observed inhibition, even mechanism-based inhibition. The pFluor50 assay system developed in this study using commercially available components should be very useful for high-throughput screening and further characterization of potential therapeutic candidates for inhibition/activation with the most prevalent human CYPs.

Startups and the next frontier of inflammatory bowel disease therapy: a guide for the brave

PURPOSE OF REVIEW

This review explores the evolving landscape of inflammatory bowel disease (IBD) therapy, particularly through the lens of startups that are pushing the boundaries of current treatment paradigms. By discussing the challenges and opportunities faced by startups, this review seeks to provide insights for aspiring entrepreneurs and innovators in the IBD space.

RECENT FINDINGS

The landscape of IBD is rapidly evolving, with innovative solutions ranging from novel therapeutics to digital health platforms. An analysis of recent SBIR award winners highlights emerging trends, including microbiome-based therapies, targeted small molecules, and advanced drug delivery systems like hydrogels. Digital health solutions, such as smart monitoring tools and AI-assisted treatment selection are gaining traction. IBD startups are playing a crucial role in cost reduction through competition, streamlining drug development, and treatment personalization. Despite regulatory, financial, and funding challenges, startups are driving the next phase of IBD innovation.

SUMMARY

The future of IBD therapy is being driven by innovative start-ups that are challenging the status quo in IBD treatment. These companies are addressing critical gaps in therapy by focusing on novel drug targets, improved drug delivery, and precision medicine. While startups face many challenges including high research and development (R&D) costs, regulatory hurdles, and funding, they continue to be at the forefront of IBD innovation. Their success could potentially lead to more affordable and effective therapies. By drawing on examples like the nutraceutical company, Evinature, my own personal experience as technical lead of Edulis, a startup focused on localized IBD therapy, and perspective from the head of the Crohn's and Colitis Foundation's IBD Ventures, this review aims to provide insights for those looking to innovate in IBD.

Repurposing FDA-approved drugs as NLRP3 inhibitors against inflammatory diseases: machine learning and molecular simulation approaches

Activation of NLRP3 (NOD-like receptor family, pyrin domain-containing protein 3) has been associated with multiple chronic pathologies, including diabetes, atherosclerosis, and rheumatoid arthritis. Moreover, histone deacetylases (HDACs), specifically HDAC6 is required for the NLRP3 inflammasome to assemble and activate. Thus, NLRP3 serves as an attractive target for the development of novel therapeutic approaches. Several companies are now attempting to develop specific modulators of the NLRP3 inflammasome, but only a handful of small molecules of NLRP3 inflammasome inhibitors, such as MCC950 and Tranilast, are currently available for clinical use. However, their use is limited due to severe side effects and short half-lives. Thus, the repurposing of FDA-approved drugs with NLRP3 inhibitory activity is needed. The present study was aimed at repurposing preexisting drugs that might act as safe and effective NLRP3 inhibitors. A library of 2,697 FDA-approved drugs was screened for binding with NLRP3 (PDB: 7ALV) using Glide (Schrödinger). The top seven FDA-approved drugs with potential binding affinities were selected based on docking scores and subjected to ADMET profiling using pkCSM and SwissADME. The binding of the ADMET-favorable FDA-approved drugs to NLRP3 was validated using MMGBSA (Prime) and Molecular Dynamics (Desmond) in the Schrödinger suite. ADMET profiling revealed that of the seven best docking drugs, empagliflozin and citicoline had good drug-likeness properties. Moreover, MMGBSA analysis and molecular dynamics demonstrated that empagliflozin and citicoline exhibited stable ligand-NLRP3 interactions in the presence of solvents. This study sheds light on the ability of various FDA-approved drugs to act as NLRP3 inhibitors.

Discovery of new dual butyrylcholinesterase (BuChE) inhibitors and 5-HT7 receptor antagonists as compounds used to treat Alzheimer's disease symptoms

Alzheimer's disease is a neurodegenerative condition with no effective cure, and current therapies, like donepezil, only alleviate symptoms. Research has explored cholinesterase inhibitors and strategies targeting tau protein, often combining inhibitors with 5-HT receptor antagonists, particularly 5-HT6. However, dual-action BuChE inhibitors and 5-HT7 antagonists have not been studied until now. This study evaluated such compounds in an animal model, focusing on two candidates: compound 18 (BuChE IC50 = 4.75 μM; 5-HT7Ki = 7 nM) and compound 50 (BuChE IC50 = 2.53 μM; 5-HT7Ki = 1 nM). Compound 50 showed robust cognitive improvements, enhancing memory consolidation and acquisition, particularly in reversing scopolamine-induced deficits. In contrast, compound 18 exhibited limited or dose-dependent efficacy, potentially limiting its applicability. These findings highlight the strong potential of compound 50 for cognitive enhancement therapies and suggest it warrants further investigation.

Modulation of OGG1 enzymatic activities by small molecules, promising tools and current challenges

Oxidative DNA damage, resulting from endogenous cellular processes and external sources plays a significant role in mutagenesis, cancer progression, and the pathogenesis of neurological disorders. Base Excision Repair (BER) is involved in the repair of base modifications such as oxidations or alkylations as well as single strand breaks. The DNA glycosylase OGG1, initiates the BER pathway by the recognition and excision of 8oxoG, the most common oxidative DNA lesion, in both nuclear and mitochondrial DNA. Beyond DNA repair, OGG1 modulates transcription, particularly pro-inflammatory genes, linking oxidative DNA damage to broader biological processes like inflammation and aging. In cancer therapy, BER inhibition has emerged as a promising strategy to enhance treatment efficacy. Targeting OGG1 sensitizes cells to chemotherapies, radiotherapies, and PARP inhibitors, presenting opportunities to overcome therapy resistance. Additionally, OGG1 activators hold potential in mitigating oxidative damage associated with aging and neurological disorders. This review presents the development of several inhibitors and activators of OGG1 and how they have contributed to advance our knowledge in the fundamental functions of OGG1. We also discuss the new opportunities they provide for clinical applications in treating cancer, inflammation and neurological disorders. Finally, we also highlight the challenges in targeting OGG1, particularly regarding the off-target effects recently reported for some inhibitors and how we can overcome these limitations.

KNU-DTI: KNowledge United Drug-Target Interaction prediction

MOTIVATION

Accurately predicting drug-target protein interactions (DTI) is a cornerstone of drug discovery, enabling the identification of potential therapeutic compounds. Sequence-based prediction models, despite their simplicity, hold great promise in extracting essential information directly from raw sequences. However, the focus in recent DTI studies has increasingly shifted toward enhancing algorithmic complexity, often at the expense of fully leveraging robust sequence representation learning methods. This shift has led to the underestimation and gradual neglect of methodologies aimed at effectively capturing discriminative features from sequences. Our work seeks to address this oversight by emphasizing the value of well-constructed sequence representation algorithms, demonstrating that even with simple interaction mapping algorithm techniques, accurate DTI models can be achieved. By prioritizing meaningful information extraction over excessive model complexity, we aim to advance the development of practical and generalizable DTI prediction frameworks.

RESULTS

We developed the KNowledge Uniting DTI model (KNU-DTI), which retrieves structural information and unites them. Protein structural properties were obtained using structural property sequence (SPS). Extended-connectivity fingerprint (ECFP) was used to estimate the structure-activity relationship in molecules. Including these two features, a total of five latent vectors were derived from protein and molecule via various neural networks and integrated by elemental-wise addition to predict binding interactions or affinity. Using four test concepts to evaluate the model, we show that the model outperforms recently published competitors. Finally, a case study indicated that our model has a competitive edge over existing docking simulations in some cases.

Modeling potential drugs for Zika virus in animal and in vitro platforms: what is the current state of the art?

INTRODUCTION

The Zika virus (ZIKV) poses a significant public health threat due to its association with congenital Zika syndrome (CZS) and severe neurological disorders. Since its discovery, ZIKV has transitioned from sporadic outbreaks to a major epidemic in Brazil in 2015, which highlighted the urgent need for effective therapies, especially for vulnerable groups like pregnant women and newborns.

AREAS COVERED

This review provides a comprehensive overview of recent advancements in ZIKV drug discovery and their current stage of development, with a particular focus on those tested in animal models from 2018 to date, excluding vaccine candidates. Repurposed drugs, such as molnupiravir and sofosbuvir, have shown the potential to inhibit viral replication and mitigate disease. Novel compounds targeting viral proteins and host-directed therapies are also discussed. Furthermore, advanced in vitro models, including brain organoids, have offered critical insights into therapeutic efficacy.

EXPERT OPINION

Although some preclinical models have identified potential drugs ready for human translation, no protocol has yet been established for treating ZIKV infection. Currently, the treatment involves supportive care, managing symptoms, and preventing complications, especially for pregnant women. Ongoing research aims to develop specific antiviral therapies and vaccines; however, no such options are currently available.

Scaling factors to inform in vitro-in vivo extrapolation from preclinical and livestock animals: state of the field and recommendations for development of missing data

The use of in-vitro-in-vivo physiologically based pharmacokinetic (IVIVE-PBPK) modeling approaches assists for prediction of first-in animal or human trials. These approaches are underpinned by the scaling factors: microsomal protein per gram (MPPG) and cytosolic protein per gram (CPPG). In addition, IVIVE-PBPK has significant application in the reduction and refinement of live animal models in research. While human scaling factors are well-defined, many preclinical and livestock species remain poorly elucidated or uncharacterized. The MPPG parameter for liver (MPPGL) is the best characterized across all species and is well-defined for mouse, rat, and dog models. The MPPG parameters for Kidney (MPPGK) and intestine (MPPGI), are however; relatively indefinite for most species. Similarly, CPPG scaling factors for liver, kidney, and intestine (CPPGL/CPPGK/CPPGI) are generally sparse in all species. In addition to generation of mathematical values for scaling factors, methodological and animal-specific considerations, such as age, sex, and strain differences, have not yet been comprehensively described. Here, we review the current state-of-the-field for microsomal and cytosolic scaling factors, including highlighting areas that may need further description and development, with the intention of drawing attention to key knowledge gaps. The intention is to promote improved accuracy and precision in IVIVE-PBPK, concordance between laboratories, and stimulate work in underserved, but increasingly vital areas.

Integrating natural commensals and pathogens into preclinical mouse models

Fundamental discoveries in many aspects of mammalian physiology have been made using laboratory mice as research models. These studies have been facilitated by the genetic tractability and inbreeding of such mice, the large set of immunological reagents that are available, and the establishment of environmentally controlled, high-throughput facilities. Such facilities typically include barriers to keep the mouse colonies free of pathogens and the frequent re-derivation of the mice severely limits their commensal flora. Because humans have co-evolved with microorganisms and are exposed to a variety of pathogens, a growing community of researchers posits that preclinical disease research can be improved by studying mice in the context of the microbiota and pathogens that they would encounter in the natural world. Here, we provide a perspective of how these different approaches can be combined and integrated to improve existing mouse models to enhance our understanding of disease mechanisms and develop new therapies for humans. We also propose that the term 'mice with natural microbiota' is more appropriate for describing these models than existing terms such as 'dirty mice'.

Patient-derived mini-colons enable long-term modeling of tumor-microenvironment complexity

Existing organoid models fall short of fully capturing the complexity of cancer because they lack sufficient multicellular diversity, tissue-level organization, biological durability and experimental flexibility. Thus, many multifactorial cancer processes, especially those involving the tumor microenvironment, are difficult to study ex vivo. To overcome these limitations, we herein implemented tissue-engineering and microfabrication technologies to develop topobiologically complex, patient-specific cancer avatars. Focusing on colorectal cancer, we generated miniature tissues consisting of long-lived gut-shaped human colon epithelia ('mini-colons') that stably integrate cancer cells and their native tumor microenvironment in a format optimized for real-time, high-resolution evaluation of cellular dynamics. We demonstrate the potential of this system through several applications: a comprehensive evaluation of drug effectivity, toxicity and resistance in anticancer therapies; the discovery of a mechanism triggered by cancer-associated fibroblasts that drives cancer invasion; and the identification of immunomodulatory interactions among different components of the tumor microenvironment. Similar approaches should be feasible for diverse tumor types.

Advances in Therapeutics for Chronic Lung Diseases: From Standard Therapies to Emerging Breakthroughs

Background: The global health burden of chronic respiratory diseases, such as chronic obstructive pulmonary disease (COPD), asthma, idiopathic pulmonary fibrosis (IPF), and acute respiratory distress syndrome (ARDS) affects billions of people and is associated with high levels of healthcare expenditure. Conventional therapies (bronchodilators and corticosteroids) provide symptomatic benefit but take no effect on disease progression, demonstrating the need to develop new therapies. Emerging therapies treat the underlying mechanisms of these chronic diseases, which provide symptomatic relief and benefit the underlying disease. Methods: This review assesses the evolution of therapeutic interventions for chronic lung diseases from a series of established inhaled combination therapies to biologics, gene therapy, and even AI-based stratification of therapies for patients. In addressing these issues, we review the mechanisms of action, evidence of efficacy, and clinical trial evidence, while discussing access issues affecting the implementation of these therapies and ethical issues in relation to their use. Results: The review highlights recent developments in treatment approaches, such as gene therapies aimed at cystic fibrosis mutations, advanced drug delivery pathways for more accurate targeting, and stem cell-based therapies designed to replace damaged lung tissue. These developments have the potential to improve outcomes for chronic lung diseases, but the challenges, including a lack of access, adequate patient selection, and long-term safety, need to be addressed. Conclusions: New therapies offer tremendous potential, but their transition from laboratory to clinic still face numerous barriers including access, regulation, and a need for personalized therapy approaches. The review indicates that future research should develop strategies to reduce barriers to access, improve distribution, and improve clinical guidelines to successfully implement these new therapies.

Orodispersible films prepared by hot-melt extrusion versus solvent casting

This study investigated the influence of solvent casting and hot-melt extrusion manufacturing methods on the physical, chemical, and functional properties of orodispersible films with the same composition and incorporating a poorly soluble active pharmaceutical ingredient (API). Both techniques produced films that met pharmaceutical standards for disintegration and dissolution times. Solvent casting, the most used method, yielded films with homogeneous distribution of plasticizer, smoother textures, and greater flexibility. In contrast, hot melt extrusion, a solvent-free process, resulted in slightly brittle films due to uneven plasticizer integration, highlighting the impact of manufacturing parameters on film structure. Despite these differences, both methods exhibited similar chemical stability under varying humidity conditions, with API recrystallization occurring at higher humidity, particularly in films prepared by solvent casting. Increased humidity significantly reduced tensile strength, as water acted as a plasticizer, promoting API recrystallization and weakening the structure. Stability tests revealed that hot melt extrusion films retained their structural and chemical integrity over 12 months when stored in impermeable packaging bags. This study confirms the suitability of hot melt extrusion for industrial-scale ODF production, offering advantages such as a solvent-free process, reduced environmental impact, and adaptability for modern pharmaceutical manufacturing, provided formulation and process parameters could be carefully optimized.

Seamless integration of CMOS microsensors into open microfluidic systems

As traditional two-dimensional (2D) cell cultures offer limited predictive capabilities for drug development, three-dimensional (3D) tissue models, such as spherical microtissues, have been introduced to better reproduce physiological conditions. The hanging-drop method, used to cultivate microtissues at an air-liquid interface, proves to be effective for microtissue formation and maintenance. Using that technology, it is possible to fluidically interconnect several hanging drops hosting different models of human organs to recapitulate relevant tissue interactions. Here, we combine microfluidics with microelectronics (i.e., complementary metal-oxide-semiconductor (CMOS) technology) and present a novel multifunctional CMOS microelectrode array (MEA) integrated into an open microfluidic system. The device can be used in hanging-drop mode for in situ microtissue readouts and in standing-drop mode like a conventional MEA. The CMOS-MEA chip features two reconfigurable electrode arrays with 1024 electrodes each, and enables electrophysiology, impedance spectroscopy, and electrochemical sensing to acquire a broad spectrum of biologically relevant information. We fabricated the chip using a 0.18 μm CMOS process and developed a strategy to integrate the CMOS-MEA chip into the open microfluidic system within a larger overall effort to incorporate discrete CMOS sensors into microfluidic devices. Proof-of-concept experiments demonstrate the capability to perform electrophysiology and impedance spectroscopy of human induced pluripotent stem cell (hiPSC)-derived cardiac microtissues, as well as electrochemical sensing of different analytes including hydrogen peroxide and epinephrine.

Electrochemical-Genetic Programming of Protein-Based Magnetic Soft Robots for Active Drug Delivery

Magnetic soft robots have the potential to revolutionize the field of drug delivery owing to their capability to execute tasks in hard-to-reach regions of living organisms. Advancing their functionality to perform active drug delivery and related tasks necessitates the innovation of smart substrate materials that satisfy both mechanical and biocompatibility requirements while offering stimuli-responsive properties. Optimization of the interaction between the substrate and magnetic components is also critical as it ensures robust actuation of the robot in complex physiological environments. To address these issues, a facile strategy is presented that synergistically combines genetic programming and electrochemical engineering to achieve on-demand drug release with protein-magnetite soft robots. As the substrate of the robot, genetically engineered silk-elastin-like protein (SELP) is encoded with thermo-responsive motifs, serving as the dynamic unit to respond to temperature changes. Ultrafine magnetite (Fe3O4) nanocrystals are electrochemically nucleated in situ and grown on Fe-protein coordination sites within the SELP hydrogel network, endowing reinforced mechanical strength, superparamagnetic property, and photothermal conversion capability. These soft robots can navigate confined spaces, target specific sites, and release drug payloads ex vivo in an intestinal model. Taken together, the proposed strategy offers an innovative approach to tailoring protein-based soft robots toward precision drug delivery systems.

The induction of quinone oxidoreductases NQO1 and NQO2 by clozapine: Potential implications for clozapine-induced agranulocytosis

Clozapine exhibits superior efficacy in the management of treatment-resistant schizophrenia. However, clozapine is currently considered under-prescribed as it carries a risk of idiosyncratic drug reactions (including severe neutropenia or agranulocytosis). The mechanisms of clozapine-induced agranulocytosis mechanisms are evolving. Reports of polymorphisms with NADPH: Quinone Oxidoreductase 2 (NQO2) being associated with clozapine-induced agranulocytosis prompted the studies described herein. Clozapine is known to produce reactive, electrophilic metabolites. It is not known if the latter can interact with NQO2 or with NQO1, its more well-characterized isoform, as quinoid electrophiles do. We hypothesized that clozapine or its metabolites can induce both NQO1 and NQO2 expression via the Nrf2 signaling pathway, as observed with quinoid electrophiles. HL-60 cells were used in this study as they are similar to granulocytes/neutrophils and contain enzymes to metabolize clozapine. A UV-Vis spectrophotometric assay was performed to determine NQO1 and NQO2 enzymatic activity using selective substrates and inhibitors. Immunoblotting was used to investigate NQO1, NQO2, and Nrf2 protein expression. NQO1 and NQO2 gene expression was determined using RT-PCR. Clozapine treatment induced NQO1 and NQO2 enzyme activity, mRNA, and protein expression significantly more than vehicle control accompanied by translocation of the transcription factor, Nrf2, from cytoplasm to nucleus. A structurally related antipsychotic, quetiapine, did not show these effects. The upregulation of both NQO1 and NQO2 enzymatic activity, protein, and gene expression indicated that these enzymes may be involved in the biological response to clozapine toxicity. The translocation of the Nrf2 suggested that the Nrf2 signaling pathway is involved in these response pathways. Further studies are required to determine if NQO2 expression levels and activity are protective mechanisms against clozapine-induced agranulocytosis or if NQO2 levels are a prognostic risk factor for clozapine-induced agranulocytosis.

From past to future: Digital approaches to success of clinical drug trials for Parkinson's disease

Recent years have seen successes in symptomatic drugs for Parkinson's disease, but the development of treatments for stopping disease progression continues to fail in clinical drug trials, largely due to the lack of clinical efficacy of drugs. This may be related to limited understanding of disease mechanisms, data heterogeneity, poor target screening and candidate selection, challenges in determining optimal dosage levels, reliance on animal models, insufficient patient participation, and lack of drug adherence in trials. Most of the recent applications of digital health technologies and artificial intelligence (AI)-based tools focused mainly on stages before clinical drug trials. Recent applications used AI-based algorithms or models to discover novel targets, inhibitors and indications, recommend drug candidates and drug dosage, and promote remote data collection. This paper reviews the state of the literature and highlights strengths and limitations in digital approaches to drug discovery and development for Parkinson's disease from 2021 to 2024, and offers recommendations for future research and practice for the success of drug clinical trials.

Efficacy of Endophytic Bacterium Serratia marcescens B.SB 1.1 associated with Sea Fern (Acrostichum aureum L.) as an Antidiabetic Agent

Diabetes mellitus (DM) is a primary global health concern, often progressing unnoticed until complications arise. Current antidiabetic therapies primarily aim to inhibit the α-amylase enzyme, thereby reducing blood glucose levels. Some medicinal plants are proven to be symbiotic with endophytic bacteria that produce bioactive compounds capable of inhibiting α-amylase activity. This study investigated the potential of endophytic bacteria isolated from the stem of the sea fern (Acrostichum aureum L.) to act as α-amylase inhibitors, using both in vitro and in silico studies. Phytochemical analysis of both the stem extract and cultured bacterial isolates showed the presence of alkaloids, flavonoids, and saponins. Isolate B.SB 1.1 was identified as Serratia marcescens based on 16S rRNA sequencing. The α-amylase inhibition assay demonstrated the strain as showing significant inhibitory activity, with 32.57% inhibition at 2% starch substrate concentration. In silico docking studies using LC-MS data predicted 4-propylbiphenyl and benzoin as compounds with the lowest binding energy to α-amylase, suggesting their potential as effective inhibitors. These findings highlight the efficacy and therapeutic potential of endophytic strain S. marcescens B.SB 1.1 as a novel antidiabetic agent.

Exploring the tissue distribution propensity of active alkaloids in normal and stomach heat syndrome rats following oral administration of Zuojin Pill based on pharmacokinetics and mass spectrometry imaging

ETHNOPHARMACOLOGICAL RELEVANCE

Zuojin Pill (ZJP) is a traditional Chinese medicine (TCM) formula composed of Coptidis Rhizoma and Euodiae Fructus in a ratio of 6:1 (w/w), which has been widely used for treating gastrointestinal disorders, especially stomach heat syndrome (SHS). However, the active alkaloids in ZJP showed low plasma exposure in rats following oral administration, which failed to explain their potent pharmacological effects, thereby limiting further mechanism studies.

AIM OF THE STUDY

This study aimed to investigate the in vivo exposure and tissue distribution propensities of the active alkaloids in normal and SHS rats following oral administration of ZJP.

MATERIAL AND METHODS

A rat model of SHS was induced by oral administration of chili pepper decoction and anhydrous ethanol. Then, the plasma and tissue pharmacokinetics of active alkaloids, including four protoberberine alkaloids (PBAs) and three indole alkaloids (IDAs), were investigated following oral administration of ZJP. Furthermore, desorption electrospray ionization mass spectrometry imaging (DESI-MSI) was employed to characterize the spatial distribution of active alkaloids in the stomach and liver. Western blot and immunofluorescence were used to evaluate the gastric mucosal barrier integrity.

RESULTS

Based on the tissue-to-plasma partition coefficient (Kp) values, the in vivo exposure levels of berberine (BBR), palmatine (PAL), coptisine (COP), and dehydroevodiamine (DHE) were found to be higher in tissues than in plasma, indicating a distinct tissue distribution propensity. Each alkaloid displayed the highest exposure in the gastrointestinal tissues, due to local penetration facilitated by its direct contact with the mucosal lining. Pathological states reduced the overall exposure of PBAs in the gastric mucosa. In non-gastrointestinal tissues, most alkaloids, especially BBR and COP, exhibited a potent liver distribution propensity with minimal impact from pathological states. According to DESI-MSI results, PBAs showed high exposure in the damaged regions of gastric mucosa, which was attributed to mucosal barrier damage and enhanced permeability. In the liver, PBAs were primarily localized in the parenchyma surrounding the central vein and portal area.

CONCLUSION

This study demonstrated the stomach and liver distribution propensity of the active alkaloids in ZJP, providing a scientific basis for these alkaloids as the pharmacodynamic material basis of ZJP against SHS from the perspective of drug exposure.

Discovery of Naphthyridinone Derivatives as Selective and Potent PKMYT1 Inhibitors with Antitumor Efficacy

PKMYT1 is a crucial regulator of the cell cycle, particularly involved in the G2/M transition through the inhibitory phosphorylation of CDK1, and is a promising therapeutic target for cancer therapy. Data mining in the Roche kinome screen database identified a hit characterized by 100% PKMYT1 inhibitory activity at a 10 μM concentration, which was further validated with a PKMYT1 enzymatic assay showing double-digit nanomolar potency. The hit featured a quinolinone central core and a phenol headgroup. The replacement of the problematic phenol headgroup with an indazole moiety induced a flip in the kinase hinge cysteine and glycine residues, resulting in a series of derivatives with enhanced potency, superior kinome selectivity, and no GSH flag. Further structural fine-tuning led to the discovery of compound 36, a novel, selective, and potent PKMYT1 inhibitor with favorable oral pharmacokinetic profiles and promising in vivo antitumor efficacy.

The Missing Enzymes: A Call to Update Pharmacological Profiling Practices for Better Drug Safety Assessment

Pharmacological profiling is critical for the development of safe drugs. With increasing awareness of its significance and attempts to share best practices, here we aimed to understand how pharmacological profiling is implemented and reported in the primary literature by analyzing the representation of nonkinase enzymes in selectivity screens. This aspect has been overlooked in previous publications, despite enzymes constituting a significant portion of the pharmacological targets for currently marketed drugs. Our analysis shows that while industry recommendations for improved pharmacological profiling have been widely adopted, enzymes remain largely underrepresented: about a quarter of studies did not include enzymes, and on average, enzymes comprise only 11% of all targets in pharmacological screens. We discuss possible reasons for this shortcoming and provide examples of critical enzymes missing from current screens. We conclude with the notion that selectivity screens should be expanded to include more enzymes to improve drug development and safety.

Sorption and release of small molecules in PDMS and COC for Organs on chip

Accurate risk assessment in drug development is crucial, as conventional in vitro and in vivo models often fail to predict human-specific responses. Organs on chips offer a promising alternative, but widespread use of polydimethylsiloxane introduces challenges due to its sorption of small lipophilic molecules, distorting pharmacokinetic and pharmacodynamic data. Cyclic olefin copolymer, a chemically stable alternative with minimal sorption, has emerged as a potential solution. This study investigates the sorption behavior of seven pharmaceutically active compounds in microfluidic devices and washout of these compounds, using high-performance liquid chromatography-mass spectrometry to evaluate recovery of compounds. Lipophilic molecules exhibited substantial sorption in polydimethylsiloxane and lower retention in cyclic olefin copolymer. Imipramine (logP = 4.80) decreased from 100 µM to 0.0384 µM for polydimethylsiloxane and 31.5 µM for cyclic olefin copolymer after 24 h incubation. Sorption was governed by multiple factors - lipophilicity and rotatable bond count were critical for both materials, hydrogen bond acceptors and molecular weight played a larger role in cyclic olefin copolymer, whereas topological polar surface area was critical for polydimethylsiloxane. Washout studies revealed that polydimethylsiloxane retains lipophilic compounds through bulk absorption, causing slow release, while cyclic olefin copolymer facilitated easier desorption. The cumulative sum of the first 5 h washout of loperamide (logP = 5.13) is 37.8% for polydimethylsiloxane and 71.5% for cyclic olefin copolymer. These findings highlight the importance of material selection and molecular properties in minimizing sorption and ensuring reliable experimental outcomes especially in microfluidic systems with distinctly different surface to volume ratios to other models.

Identification of Microbial-Based Natural Products as Potential CYP51 Inhibitors for Eumycetoma Treatment: Insights from Molecular Docking, MM-GBSA Calculations, ADMET Analysis, and Molecular Dynamics Simulations

Background/Objectives: Eumycetoma, caused by Madurella mycetomatis, is a chronic fungal infection with limited treatment options and increasing drug resistance. CYP51, a key enzyme in ergosterol biosynthesis, is a well-established target for azole antifungals. However, existing azole drugs demonstrate limited efficacy in treating eumycetoma. Microbial-based natural products, with their structural diversity and bioactivity, offer a promising source for novel CYP51 inhibitors. This study aimed to identify potential Madurella mycetomatis CYP51 inhibitors from microbial natural products using molecular docking, MM-GBSA calculations, ADMET analysis, and molecular dynamics (MD) simulations. Methods: Virtual screening was conducted on a library of microbial-based natural products using an in-house homology model of Madurella mycetomatis CYP51, with itraconazole as the reference drug. The top compounds from initial docking were refined through Standard and Extra Precision docking. MM-GBSA calculations assessed binding affinities, and ADMET analysis evaluated drug-like properties. Compounds with favorable properties underwent MD simulations. Results: The computational investigations identified 34 compounds with better docking scores and binding affinity than itraconazole. Of these, 9 compounds interacted with the heme group and key residues in the active site of Madurella mycetomatis CYP51. In silico pharmacokinetic profiling identified 3 compounds as promising candidates, and MD simulations confirmed their potential as CYP51 inhibitors. Conclusions: The study highlights microbial-derived natural products, particularly monacyclinone G, H, and I, as promising candidates for Madurella mycetomatis CYP51 inhibition, with the potential for treating eumycetoma, requiring further experimental validation.

Discovering genetically-supported drug targets for multisite chronic pain through multi-omics Mendelian randomization and single-cell RNA-sequencing

Multisite chronic pain (MCP) is a highly prevalent disorder with substantial unmet therapeutic needs.We conducted multi-omics Mendelian randomization and Bayesian colocalization to identify potential therapeutic targets for MCP. Summary-level data of gene expressions and protein abundance levels were obtained from corresponding quantitative trait loci studies, respectively. Summary-level data for MCP was leveraged from the UK Biobank. The transcriptome-wide association study (TWAS), Mendelian randomization, and Bayesian colocalization approaches were applied to investigate the potential causal effects of gene expressions and protein levels on MCP in both blood and brain tissues. Phenome-wide Mendelian randomization analysis (MR-PheWAS), single-cell sequencing data, protein-protein interaction (PPI), and reaction pathway analysis were further conducted to digging the underlying mechanisms. Our analysis identified and validated two plasma targets for MCP, namely KLC1 and LANCL1, at both gene expression levels and protein levels across multi-methodologies. Moreover, MR-PheWAS observed additional benefits associated with these two targets. Through analyses based on single-cell sequencing data, we identified critical cell types for KLC1, primarily megakaryocytes, and neurons, notably linked to the axon guidance pathway, while LANCL1 showed associations with B lymphocytes, neurons, and the electron transport pathway. In dorsal root ganglions, we identified enrichments of both LANCL1 and KLC1 in putative silent nociceptors. The effects are possibly mediated through axonal transport and the activation of NMDARs, supported by PPI and reaction pathway analysis. Our multi-dimensional analysis suggests that genetically determined KLC1 and LANCL1 are causally linked to MCP risk, holding promise as appealing drug targets for MCP.

DrugGen enhances drug discovery with large language models and reinforcement learning

Traditional drug design faces significant challenges due to inherent chemical and biological complexities, often resulting in high failure rates in clinical trials. Deep learning advancements, particularly generative models, offer potential solutions to these challenges. One promising algorithm is DrugGPT, a transformer-based model, that generates small molecules for input protein sequences. Although promising, it generates both chemically valid and invalid structures and does not incorporate the features of approved drugs, resulting in time-consuming and inefficient drug discovery. To address these issues, we introduce DrugGen, an enhanced model based on the DrugGPT structure. DrugGen is fine-tuned on approved drug-target interactions and optimized with proximal policy optimization. By giving reward feedback from protein-ligand binding affinity prediction using pre-trained transformers (PLAPT) and a customized invalid structure assessor, DrugGen significantly improves performance. Evaluation across multiple targets demonstrated that DrugGen achieves 100% valid structure generation compared to 95.5% with DrugGPT and produced molecules with higher predicted binding affinities (7.22 [6.30-8.07]) compared to DrugGPT (5.81 [4.97-6.63]) while maintaining diversity and novelty. Docking simulations further validate its ability to generate molecules targeting binding sites effectively. For example, in the case of fatty acid-binding protein 5 (FABP5), DrugGen generated molecules with superior docking scores (FABP5/11, -9.537 and FABP5/5, -8.399) compared to the reference molecule (Palmitic acid, -6.177). Beyond lead compound generation, DrugGen also shows potential for drug repositioning and creating novel pharmacophores for existing targets. By producing high-quality small molecules, DrugGen provides a high-performance medium for advancing pharmaceutical research and drug discovery.

Maternal transfer of anti-GAPDH IgG prevents neonatal infections caused by Staphylococcus aureus and group B Streptococcus

Group B Streptococcus (GBS) and Staphylococcus aureus cause 200.000 neonatal deaths every year and no vaccine has been developed yet. Here, we described that extracellular glyceraldehyde-3-phosphate dehydrogenase (GAPDH) from S. aureus is an immunomodulatory protein. Antibody mediated neutralization of S. aureus extracellular GAPDH promotes a protective inflammatory response by inhibiting an early and abnormal production of IL-10 in infected neonatal mice. As an immunomodulatory role for extracellular GAPDH was already described for GBS, we selected peptides exposed on bacterial GAPDH from both bacteria but completely absent from human GAPDH. These peptides were chemically synthesized and conjugated to a carrier protein. Maternal vaccination with these conjugated peptides induced an increased survival of mouse pups from infection with GBS or S. aureus, when compared to controls. The addition of anti-bacterial GAPDH IgG into infected human cord-blood cells caused a significant reduction in bacterial replication, suggesting a putative efficacy for humans.

HNF-DDA: subgraph contrastive-driven transformer-style heterogeneous network embedding for drug-disease association prediction

BACKGROUND

Drug-disease association (DDA) prediction aims to identify potential links between drugs and diseases, facilitating the discovery of new therapeutic potentials and reducing the cost and time associated with traditional drug development. However, existing DDA prediction methods often overlook the global relational information provided by other biological entities, and the complex association structure between drug diseases, limiting the potential correlations of drug and disease embeddings.

RESULTS

In this study, we propose HNF-DDA, a subgraph contrastive-driven transformer-style heterogeneous network embedding model for DDA prediction. Specifically, HNF-DDA adopts all-pairs message passing strategy to capture the global structure of the network, fully integrating multi-omics information. HNF-DDA also proposes the concept of subgraph contrastive learning to capture the local structure of drug-disease subgraphs, learning the high-order semantic information of nodes. Experimental results on two benchmark datasets demonstrate that HNF-DDA outperforms several state-of-the-art methods. Additionally, it shows superior performance across different dataset splitting schemes, indicating HNF-DDA's capability to generalize to novel drug and disease categories. Case studies for breast cancer and prostate cancer reveal that 9 out of the top 10 predicted candidate drugs for breast cancer and 8 out of the top 10 for prostate cancer have documented therapeutic effects.

CONCLUSIONS

HNF-DDA incorporates all-pairs message passing and subgraph capture strategies into heterogeneous network embedding, enabling effective learning of drug and disease representations enriched with heterogeneous information, while also demonstrating significant potential for applications in drug repositioning.

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.