Disease Drug Development Research Articles

Recent Progress in DNA Biosensors for Detecting Biomarkers in Living Cells.

Analysis of biomarkers in living cells is crucial for deciphering the dynamics of cells as well as for precise diagnosis of diseases. DNA biosensors employ DNA sequences as probes to offer insights into living cells, and drive progress in disease diagnosis and drug development. In this review, we present recent advances in DNA biosensors for detecting biomarkers in living cells. The basic structural components of DNA biosensors and the signal output method are presented. The strategies of DNA biosensors crossing the cell membrane are also described, including coincubation, nanocarriers, and nanoelectroporation techniques. Based on biomarker categorization, we detail recent applications of DNA biosensors for detecting small molecules, RNAs, proteins, and integrated targets in living cells. Furthermore, the future development directions of DNA biosensors are summarized to encourage further research in this growing field.

Unlocking the full potential of rare disease drug development: exploring the not-for-profit sector's contributions to drug development and access.

This commentary provides a comprehensive overview of the challenges and opportunities in the field of drug development for rare diseases and especially of gene therapy products for ultra-rare diseases. It discusses the limited market size, reimbursement and scientific complexities that deter pharmaceutical investment in this field. Highlighting the pivotal role of charitable organizations like Fondazione Telethon, it showcases their efforts in funding research and ensuring access to innovative therapies. This commentary also addresses the challenges in therapy distribution, particularly regarding sustainability and global access. It outlines Fondazione Telethon's operational model to try to address these challenges. Finally, it appeals to governments and regulatory bodies to implement policies and incentives aimed at further fostering innovation and accessibility in rare disease drug development and access.

High Surface Area Metal Oxide Enhanced Self-Powered Microfluidic Device for Real-Time Pathological Detection

Biomarkers based sensing in the realm of disease prevention, its diagnosis, and drug development has shown its unique importance in health care. In particular, the platelet level in blood is strongly linked with the development of several kind of severe diseases like coronavirus disease 2019, diabetes, and microbial infections as well as individuals undergoing cancer diagnosis. Currently, commercial methods employed for platelet level determination in whole-blood are based on Coulter principle, flow cytometry, and image analysis. However, these technologies are intricate, time consuming, expensive, and require a trained operator as well as lack of portability. Considering these issues, we have developed a self-powered portable device for real-time platelet level monitoring, aimed towards point-of-care applications. The fabricated self-powered-microfluidic triboelectric nanogenerator (SPM-TENG) utilized test sample flow-resistance for sensing of platelet level and triboelectric voltage output as sensing signal. For the fine tuning of fabricated SPM-TENG, we compared the triboelectric voltage output from gold electrode measurement setup and terminal electrode measurement setup. In view of further improvement in stability, voltage output and to minimize the noise in sensing signals, the geometrical optimization of microfluidic channel was performed. In conclusion we have optimized the device as a terminal electrode measurement setup with copper tubes on terminals of microfluidic channel and copper oxide nanowires grown on the interior of copper tubes. These copper oxide nanowires provide a high surface area for solid-liquid contact separation and is responsible for enhanced sensing signal. The fabricated SPM-TENG has shown its great potential in platelet level sensing with plasma-based samples as well as whole-blood samples. The proposed device demonstrated its practicality towards real-time rapid sensing of platelet level and portability.

(Invited) Photoluminescence Modulation of Quantum Well Defects for Biomedicine

Improved measurements of biomarkers, drugs, and metabolites in live cells and organisms would allow for improvements in disease detection, drug development, and biomedical research. Quantum well-modified carbon nanotubes (QWNTs) have exciting optical properties for applications as sensors for use in live cells and in vivo, including the potential to increase quantum yield over pristine SWCNTs, and to improve control over photoluminescence modulation in response to the local environment. To develop QWNTs into sensors, we devised new methods to encapsulate them for biocompatibility, as well as new instrumentation for measurements. We also investigated mechanisms of QWNT photoluminescence environmental responsivity, including the dependence of electrostatic charge on spectral red-shifting. We found that this mechanism can amplify measurements of biomarkers, enhancing the development of sensors.

Feasibility of disease terminology systems for mapping orphan conditions and therapeutic indications of designated orphan medicines in the European Union

BackgroundIn the European Union, rare diseases are defined as diseases that affect maximum 5 in 10,000 citizens. These diseases are typically associated with a high unmet medical need. To stimulate development and authorisation of medicines for rare diseases (‘orphan conditions’), the European Commission (EC) can grant orphan designations. In order to enable systematic evaluation and communication of the diseases for which designated orphan medicines have (not) been developed and authorised, we aimed to investigate the feasibility of important disease terminology systems for mapping orphan conditions and therapeutic indications. MethodsWe selected all designated orphan medicines that were authorised by the EC during 2022–2023 from the EC's Union Register of medicinal products. For these medicines, we extracted orphan conditions and associated therapeutic indications at initial marketing authorisation. The orphan conditions and separate elements of therapeutic indications such as target disease or condition, severity criteria and target population were assessed for availability in six major disease terminology systems: ICD-10, ICD-11, MedDRA, MeSH, Orphanet nomenclature of rare diseases, and SNOMED CT. Descriptive statistics were used to describe the ability of each disease terminology system to map orphan conditions and elements of therapeutic indications. ResultsDuring 2022–2023, 37 designated orphan medicines were authorised that were designated for 40 orphan conditions (of which 37 unique) and granted 39 therapeutic indications (of which 37 unique). Overall, SNOMED CT covered most descriptions of orphan conditions (33/37, 89 %) and target diseases or conditions within therapeutic indications (28/37, 76 %). However, when allowing descriptions to be partly included and/or complemented by additional words, SNOMED CT, the Orphanet nomenclature, ICD-11 and MedDRA all had high coverage (92–97 %). Other elements than target diseases or conditions within therapeutic indications were mostly lacking. ConclusionsRegulatory data concerning orphan conditions and therapeutic indications of designated orphan medicines seem to be best covered by SNOMED CT. However, which disease terminology system best facilitates systematic evaluation and communication about development and authorisation of designated orphan medicines also depends on the specific use case. Given the frequent use of SNOMED CT in healthcare settings, it may also facilitate interoperability between regulatory and healthcare data, while for example ICD-11 may be better suited to generate statistics concerning drug development for rare diseases.

Stacking-Kcr: A Stacking Model for Predicting the Crotonylation Sites of Lysine by Fusing Serial and Automatic Encoder

Background: Protein lysine crotonylation (Kcr), a newly discovered important posttranslational modification (PTM), is typically localized at the transcription start site and regulates gene expression, which is associated with a variety of pathological conditions such as developmental defects and malignant transformation. Objective: Identifying Kcr sites is advantageous for the discovery of its biological mechanism and the development of new drugs for related diseases. However, traditional experimental methods for identifying Kcr sites are expensive and inefficient, necessitating the development of new computational techniques. Methods: In this work, to accurately identify Kcr sites, we propose a model for ensemble learning called Stacking-Kcr. Firstly, extract features from sequence information, physicochemical properties, and sequence fragment similarity. Then, the two characteristics of sequence information and physicochemical properties are fused using automatic encoder and serial, respectively. Finally, the fused two features and sequence fragment similarity features are then respectively input into the four base classifiers, a meta classifier is constructed using the first level prediction results, and the final forecasting results are obtained. Results: The five-fold cross-validation of this model has achieved an accuracy of 0.828 and an AUC of 0.910. This shows that the Stacking-Kcr method has obvious advantages over traditional machine learning methods. On independent test sets, Stacking-Kcr achieved an accuracy of 84.89% and an AUC of 92.21%, which was higher than 1.7% and 0.8% of other state-of-the-art tools. Additionally, we trained Stacking-Kcr on the phosphorylation site, and the result is superior to the current model. Conclusion: These outcomes are additional evidence that Stacking-Kcr has strong application potential and generalization performance.

Characterization of the Structural Requirements for the NADase Activity of Bacterial Toll/IL-1R domains in a Course-based Undergraduate Research Experience.

TLRs initiate innate immune signaling pathways via Toll/IL-1R (TIR) domains on their cytoplasmic tails. Various bacterial species also express TIR domain-containing proteins that contribute to bacterial evasion of the innate immune system. Bacterial TIR domains, along with the mammalian sterile α and TIR motif-containing protein 1 and TIRs from plants, also have been found to exhibit NADase activity. Initial X-ray crystallographic studies of the bacterial TIR from Acinetobacter baumannii provided insight into bacterial TIR structure but were unsuccessful in cocrystallization with the NAD+ ligand, leading to further questions about the TIR NAD binding site. In this study, we designed a Course-Based Undergraduate Research Experience (CURE) involving 16-20 students per year to identify amino acids crucial for NADase activity of A. baumannii TIR domain protein and the TIR from Escherichia coli (TIR domain-containing protein C). Students used structural data to identify amino acids that they hypothesized would play a role in TIR NADase activity, and created plasmids to express mutated TIRs through site-directed mutagenesis. Mutant TIRs were expressed, purified, and tested for NADase activity. The results from these studies provide evidence for a conformational change upon NAD binding, as was predicted by recent cryogenic electron microscopy and hydrogen-deuterium exchange mass spectrometry studies. Along with corroborating recent characterization of TIR NADases that could contribute to drug development for diseases associated with dysregulated TIR activity, this work also highlights the value of CURE-based projects for inclusion of a diverse group of students in authentic research experiences.

AutoMitoNetwork: Software for analyzing mitochondrial networks in autofluorescence images to enable label-free cell classification.

High-resolution mitochondria imaging in combination with image analysis tools have significantly advanced our understanding of cellular function in health and disease. However, most image analysis tools for mitochondrial studies have been designed to work with fluorescently labeled images only. Additionally, efforts to integrate features describing mitochondrial networks with machine learning techniques for the differentiation of cell types have been limited. Herein, we present AutoMitoNetwork software for image-based assessment of mitochondrial networks in label-free autofluorescence images using a range of interpretable morphological, intensity, and textural features. To demonstrate its utility, we characterized unstained mitochondrial networks in healthy retinal cells and in retinal cells exposed to two types of treatments: rotenone, which directly inhibited mitochondrial respiration and ATP production, and iodoacetic acid, which had a milder impact on mitochondrial networks via the inhibition of anaerobic glycolysis. For both cases, our multi-dimensional feature analysis combined with a support vector machine classifier distinguished between healthy cells and those treated with rotenone or iodoacetic acid. Subtle changes in morphological features were measured including increased fragmentation in the treated retinal cells, pointing to an association with metabolic mechanisms. AutoMitoNetwork opens new options for image-based machine learning in label-free imaging, diagnostics, and mitochondrial disease drug development.

A Phytochemical Analysis and the Pharmacological Implications of the Seagrass Halodule uninervis: An Overview.

Seagrasses are marine angiosperms that inhabit tropical and subtropical regions around the world. They play a vital role in marine biodiversity and the ecosystem by providing habitats and food for several marine organisms, stabilizing sediments, and improving water quality. Halodule uninervis from the family Cymodoceaceae has been used in traditional folk medicine for the treatment of many ailments. Additionally, several identified bioactive metabolites have been shown to contribute to its pharmacological activities, including anticancer, anti-inflammatory, and antioxidant. As such, H. uninervis could contribute to the development of novel drugs for various diseases. This review aims to compile the phytochemical composition and pharmacological activities of H. uninervis. Furthermore, details about its botanical characteristics and ecological significance are also discussed. By providing valuable insights into the role of H. uninervis in both the marine ecosystem and biomedicine, this review helps to highlight its potential as a therapeutic agent for future drug discovery and development.

Detection of pharmacolipidodynamic effects following the intravenous and oral administration of gefitinib to C57Bl/6JRj mice by rapid UHPLC-MS analysis of plasma

Omics-based biomarker technologies, including metabolic profiling (metabolomics/metabonomics) and lipidomics, are making a significant impact on disease understanding, drug development, and translational research. A wide range of patho-physiological processes involve lipids and monitoring changes in lipid abundance can give valuable insights into mechanisms of drug action, off target pharmacology and toxicity. Here we report changes, detected by untargeted LC–MS, in the plasma lipid profiles of male C57Bl/6JRj mice following the PO and IV administration of the epidermal growth factor receptor (EGFR) inhibitor gefitinib. Statistical analysis of the data obtained for both the IV and PO samples showed time-related changes in the amounts of lipids from several different classes. The largest effects were associated with a rapid onset of these changes following gefitinib administration followed by a gradual return by 24 h post dose to the type of lipid profile seen in predose samples. Investigation of the lipids responsible for the variance observed in the data showed that the PI, PC, LPC, PE and TG were subject to the largest disruption with both transient increases and decreases in relative amounts seen in response to administration of the drug. The pattern of the changes in the relative abundances of those lipids subject to variation appeared to be correlated to the pharmacokinetics of gefitinib (and its major metabolites). These observations support the concept of a distinct pharmacolipidodynamic relationship between drug exposure and plasma lipid abundance.

Developing public–private R&D consortia to accelerate Alzheimer’s disease drug development

Efforts to accelerate Alzheimer's disease (AD) drug development have been spurred on by the creation of open science, public-private R&D consortia. An R&D consortium provides an improved structure for generating and disseminating AD knowledge across a range of organizations while also aligning their interests. Drawing from archival and interview data collected on 46 public-private R&D consortia focused wholly or in part on AD, we uncover two important innovations: the creation of novel consortium types that facilitate coordination beyond the individual consortium, and the practice of organizations joining multiple consortia. Collectively these innovations provide member organizations with different pathways for advancing AD research. These findings have significant implications for how member organizations should approach collaboration in the AD drug development process.

Development of orphan drugs for rare diseases.

Most rare diseases (orphan diseases) still lack approved treatment options despite major advances in research providing the necessary tools to understand their molecular basis and legislation providing regulatory and economic incentives to expedite the development of specific therapies. Addressing this translational gap is a multifaceted challenge, a key aspect of which is the selection of an optimal therapeutic modality to translate advances in rare disease knowledge to potential medicines known as orphan drugs. There are several strategies for developing orphan drugs for rare genetic disorders, including protein replacement therapies, small-molecule therapies (e.g., substrate reduction, chemical chaperone, cofactor, expression modification, and read-through therapies), monoclonal antibodies, antisense oligonucleotides, small interfering RNA or exon skipping therapies, gene replacement and direct genome-editing therapies, mRNA therapy, cell therapy, and drug repurposing. Each strategy has its own strengths and limitations in orphan drug development. Furthermore, numerous hurdles are present in clinical trials of rare genetic diseases because of difficulty with patient recruitment, unknown molecular physiology, the natural history of the disease, ethical concerns regarding pediatric patients, and regulatory challenges. To address these barriers, the rare genetic diseases community, including academic institutions, industry, patient advocacy groups, foundations, payers, and government regulatory and research organizations, must become engaged in discussions about these issues.

Prioritizing drug targets in systemic lupus erythematosus from a genetic perspective: a druggable genome-wide Mendelian randomization study

ObjectivesSystemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease with an unsatisfactory state of treatment. We aim to explore novel targets for SLE from a genetic standpoint. MethodsCis-expression quantitative trait loci (eQTLs) for whole blood from 31,684 samples provided by the eQTLGen Consortium as well as two large SLE cohorts were utilized for screening and validating genes causally associated with SLE. Colocalization analysis was employed to further investigate whether changes in the expression of risk genes, as indicated by GWAS signals, influence the occurrence and development of SLE. Targets identified for drug development were evaluated for potential side effects using a phenome-wide association study (PheWAS). Based on the multiple databases, we explored the interactions between drugs and genes for drug prediction and the assessment of current medications.ResultsThe analysis comprised 5427 druggable genes in total. The two-sample Mendelian randomization (MR) in the discovery phase identified 20 genes causally associated with SLE and validated 8 genes in the replication phase. Colocalization analysis ultimately identified five genes (BLK, HIST1H3H, HSPA1A, IL12A, NEU1) with PPH4 > 0.8. PheWAS further indicated that drugs acting on BLK and IL12A are less likely to have potential side effects, while HSPA1A and NEU1 were associated with other traits. Four genes (BLK, HSPA1A, IL12A, NEU1) have been targeted for drug development in autoimmune diseases and other conditions.Conclusions.This study identified five genes as therapeutic targets for SLE. Repurposing and developing drugs targeting these genes is anticipated to improve the existing treatment state for SLE.Key Points• We identified five gene targets of priority for the treatment of SLE, with BLK and IL12A indicating fewer side effects.• Among the existing drugs that target these candidate genes, Ustekinumab, Ebdarokimab, and Briakinumab (targeting the IL12 gene) and CD24FC (targeting HSPA1A) may potentially be repurposed for the treatment of SLE.

New Horizons of Model Informed Drug Development in Rare Diseases Drug Development.

Model-informed approaches provide a quantitative framework to integrate all available nonclinical and clinical data, thus furnishing a totality of evidence approach to drug development and regulatory evaluation. Maximizing the use of all available data and information about the drug enables a more robust characterization of the risk-benefit profile and reduces uncertainty in both technical and regulatory success. This offers the potential to transform rare diseases drug development, where conducting large well-controlled clinical trials is impractical and/or unethical due to a small patient population, a significant portion of which could be children. Additionally, the totality of evidence generated by model-informed approaches can provide confirmatory evidence for regulatory approval without the need for additional clinical data. In the article, applications of novel quantitative approaches such as quantitative systems pharmacology, disease progression modeling, artificial intelligence, machine learning, modeling of real-world data using model-based meta-analysis and strategies such as external control and patient-reported outcomes as well as clinical trial simulations to optimize trials and sample collection are discussed. Specific case studies of these modeling approaches in rare diseases are provided to showcase applications in drug development and regulatory review. Finally, perspectives are shared on the future state of these modeling approaches in rare diseases drug development along with challenges and opportunities for incorporating such tools in the rational development of drug products.

Prion protein E219K polymorphism: from the discovery of the KANNO blood group to interventions for human prion disease.

KANNO is a new human blood group that was recently discovered. The KANNO antigen shares the PRNP gene with the prion protein and the prion protein E219K polymorphism determines the presence or absence of the KANNO antigen and the development of anti-KANNO alloantibodies. These alloantibodies specifically react with prion proteins, which serve as substrates for conversion into pathological isoforms in some prion diseases and may serve as effective targets for resisting prion infection. These findings establish a potential link between the KANNO blood group and human prion disease via the prion protein E219K polymorphism. We reviewed the interesting correlation between the human PRNP gene's E219K polymorphism and the prion proteins it expresses, as well as human red blood cell antigens. Based on the immune serological principles of human blood cells, the prion protein E219K polymorphism may serve as a foundation for earlier molecular diagnosis and future drug development for prion diseases.

The potential therapeutic value of the natural plant compounds matrine and oxymatrine in cardiovascular diseases.

Matrine (MT) and Oxymatrine (OMT) are two natural alkaloids derived from plants. These bioactive compounds are notable for their diverse pharmacological effects and have been extensively studied and recognized in the treatment of cardiovascular diseases in recent years. The cardioprotective effects of MT and OMT involve multiple aspects, primarily including antioxidative stress, anti-inflammatory actions, anti-atherosclerosis, restoration of vascular function, and inhibition of cardiac remodeling and failure. Clinical pharmacology research has identified numerous novel molecular mechanisms of OMT and MT, such as JAK/STAT, Nrf2/HO-1, PI3 K/AKT, TGF-β1/Smad, and Notch pathways, providing new evidence supporting their promising therapeutic potential against cardiovascular diseases. Thus, this review aims to investigate the potential applications of MT and OMT in treating cardiovascular diseases, encompassing their mechanisms, efficacy, and safety, confirming their promise as lead compounds in anti-cardiovascular disease drug development.

Cerebral Ischemic Coma Model Induced by Modified Four-Vessel Occlusion.

Coma caused by cerebral ischemia is the most serious complication of cerebral ischemia. Four-vessel occlusion can establish a cerebral ischemic coma model for disease research and drug development. However, the commonly used four-vessel occlusion method mainly involves inserting an electrocoagulation pen into the bilateral pterygoid foramen of the first cervical vertebra behind the neck to electrocoagulate the vertebral arteries. This process carries the risk of incomplete electrocoagulation, bleeding, and damage to the brainstem and spinal cord. Twenty-four hours after surgery, re-anesthetized rats undergo carotid artery ligation in front of the neck. Two surgeries expose the rats to a higher risk of infection and increase the experimental period. In this study, during a single surgical procedure, an anterior cervical incision was used to locate the key site where the vertebral artery penetrates the first cervical vertebra. The bilateral vertebral arteries were electrocauterized under visual conditions, while the bilateral common carotid arteries were separated to place loose knots. When the rats showed consciousness of the inversion reaction, the bilateral common carotid arteries were quickly ligated to induce ischemic coma. This method can avoid the risk of infection caused by two surgical operations and is easy to perform with a high success rate, providing a useful reference for relevant practitioners.

Discovery of an EP300 Inhibitor using Structure-based Virtual Screening and Bioactivity Evaluation.

EP300 (E1A binding protein p300) played a significant role in serial diseases such as cancer, neurodegenerative disease. Therefore, it became a significant target. Targeting EP300 discovery of a novel drug to alleviate these diseases. In this paper, 17 candidate compounds were obtained using a structure-based virtual screening approach, 4449-0460, with an IC50 of 5.89 ± 2.08 uM, which was identified by the EP300 bioactivity test. 4449-0460 consisted of three rings. The middle benzene ring connected the 5-ethylideneimidazolidine-2,4-dione group and the 3-F-Phenylmethoxy group. Furthermore, the interaction mechanism between 4449-0460 and EP300 was explored by combining molecular dynamics (MD) simulations and binding free energy calculation methods. The binding free energy of EP300 with 4449-0460 was -10.93 kcal/mol, and mainly came from the nonpolar energy term (ΔGnonpolar). Pro1074, Phe1075, Val1079, Leu1084, and Val1138 were the key residues in EP300/4449-0460 binding with more -1 kcal/mol energy contribution. 4449-0460 was a promising inhibitor targeting EP300, which had implications for the development of drugs for EP300-related diseases.

MRCat: A Novel CatBoost Predictor for the Binary Classification of mRNA Subcellular Localization by Fusing Large Language Model Representation and Sequence Features.

The subcellular localization of messenger RNAs (mRNAs) is a pivotal aspect of biomolecules, tightly linked to gene regulation and protein synthesis, and offers innovative insights into disease diagnosis and drug development in the field of biomedicine. Several computational methods have been proposed to predict the subcellular localization of mRNAs within cells. However, there remains a deficiency in the accuracy of these predictions. In this study, we propose an mRCat predictor based on the gradient boosting tree algorithm specifically to predict whether mRNAs are localized in the nucleus or in the cytoplasm. This predictor firstly uses large language models to thoroughly explore hidden information within sequences and then integrates traditional sequence features to collectively characterize mRNA gene sequences. Finally, it employs CatBoost as the base classifier for predicting the subcellular localization of mRNAs. The experimental validation on an independent test set demonstrates that mRCat obtained accuracy of 0.761, F1 score of 0.710, MCC of 0.511, and AUROC of 0.751. The results indicate that our method has higher accuracy and robustness compared to other state-of-the-art methods. It is anticipated to offer deep insights for biomolecular research.

Advantages and Challenges of Platform Trials for Disease Modifying Therapies in Parkinson's Disease.

Traditional drug development in Parkinson's disease (PD) faces significant challenges because of its protracted timeline and high costs. In response, innovative master protocols are emerging and designed to address multiple research questions within a single overarching protocol. These trials may offer advantages such as increased efficiency, agility in adding new treatment arms, and potential cost savings. However, they also present organizational, methodological, funding, regulatory, and sponsorship challenges. We review the potential of master protocols, focusing on platform trials, for disease modifying therapies in PD. These trials share a common control group and allow for the termination or addition of treatment arms during a trial with non-predetermined end. Specific issues exist for a platform trial in the PD field considering the heterogeneity of patients in terms of phenotype, genotype and staging, the confounding effects of symptomatic treatments, and the choice of outcome measures with no consensus on a non-clinical biomarker to serve as a surrogate and the slowness of PD progression. We illustrate these aspects using the examples of the main PD platform trials currently in development with each one targeting distinct goals, populations, and outcomes. Overall, platform trials hold promise in expediting the evaluation of potential therapies for PD. However, it remains to be proven whether these theoretical benefits will translate into increased production of high-quality trial data. Success also depends on the willingness of pharmaceutical companies to engage in such trials and whether this approach will ultimately hasten the identification and licensing of effective disease-modifying drugs. © 2024 International Parkinson and Movement Disorder Society.