Target Binding Research Articles

Role of lncRNA TPRG1-AS1 in the development of cervical squamous cell carcinoma and its prognostic value

ObjectiveCervical squamous cell carcinoma (CSCC) has a poor prognosis due to persistent HPV infection. LncRNA TPRG1-AS1 is linked to regulating the development of many cancers, so the regulatory mechanism and prognostic value of TPRG1-AS1 in CSCC were explored.Methods138 patients with cervical cancer were included. TPRG1-AS1 expression and miR-590-3p were analyzed by qRT-PCR. The association between TPRG1-AS1 and clinicopathological features was investigated. Independent prognostic factors of CSCC were analyzed by multifactorial Cox regression. Patient survival was analyzed using Kaplan–Meier plotter curves. CCK-8 was employed to evaluate the proliferative capacity of the cells. Transwell assays were performed to evaluate the effects of TPRG1-AS1 and miR-590-3p on cell migration and invasion performance, and the target of both was reported by DLR assay.ResultsTPRG1-AS1 levels were ascended in CSCC, and miR-590-3p levels were reduced. TPRG1-AS1 and miR-590-3p target binding and expression correlated negatively. Knockdown of TPRG1-AS1 expression could facilitate high miR-590-3p expression, which reduced cell proliferation, migration, and invasion ability. TPRG1-AS1 is an independent prognostic factor.ConclusionTPRG1-AS1 has potential as a prognostic marker for CSCC. Silencing the expression of TPRG1-AS1 could contribute to the high miR-590-3p expression thereby slowing down the progression of CSCC.

Disulfide-Directed Multicyclic Peptides with N-Terminally Extendable α-Helices for Recognition and Activation of G Protein-Coupled Receptors.

Many peptide hormones adopt long α-helical structures upon interacting with their cognate receptors but often exhibit flexible conformations when unbound. Strategies that can stabilize long α-helices without disrupting their binding to receptors are still lacking, which hinders progress in their biological applications and drug development. Here, we present an approach that combines rational design with library screening to create and identify a unique disulfide-directed multicyclic peptide (DDMP) scaffold, which could effectively stabilize N-terminally extendable α-helices while displaying exceptional efficiency in disulfide pairing and oxidative folding. This DDMP scaffold was then utilized for stabilizing the α-helical structure of glucagon-like peptide-1 (GLP-1), resulting in a potent GLP-1 receptor (GLP-1R) agonist with a significantly improved α-helicity and proteolytic stability. By incorporating external α-helices into the DDMP scaffold, we can effectively preserve the native N-terminal α-helical structures while allowing for extensive evolution of the C-terminal disulfide-rich domain for enhancing target binding, as demonstrated by the generation of the DDMP-stabilized GLP-1 (g1:Ox). The cryo-electron microscopy structure of the g1:Ox-GLP-1R in complex with heterotrimeric Gs reveals the molecular basis for the potent binding between g1:Ox and GLP-1R. Specifically, the DDMP moiety establishes additional interactions with the extracellular domain of GLP-1R, which are absent in the case of GLP-1. Thus, this work offers a novel and effective approach for engineering therapeutic peptides and other peptide α-helices, ensuring that both the N- and C-terminal regions remain essential for target recognition and activation.

IR Pulsed Laser Ablation of Carbon Materials in High Vacuum

This work aimed to understand how the energy released by short laser pulses can produce different effects in carbon targets with different allotropic states. The IR pulse laser ablation, operating at 1064 nm wavelength, 3 ns pulse duration, and 100 mJ pulse energy, has been used to irradiate different types of carbon targets in a high vacuum. Graphite, highly oriented pyrolytic graphite, glassy carbon, active carbon, and vegetable carbon have exhibited different mass densities and have been laser irradiated. Time-of-flight (TOF) measurements have permitted the evince of the maximum carbon ion acceleration in the generated plasma (of about 200 eV per charge state) and the maximum yield emission (96 μg/pulse in the case of vegetal carbon) along the direction normal to the irradiated surface. The ion energy analyzer measured the carbon charge states (four) and their energy distributions. Further plasma investigations have been performed using a fast CCD camera image and surface profiles of the generated craters to calculate the angular emission and the ablation yield for each type of target. The effects as a function of the target carbon density and binding energy have been highlighted. Possible applications for the generation of thin films and carbon nanoparticles are discussed.

SeqDPI: A 1D-CNN approach for predicting binding affinity of kinase inhibitors.

Predicting drug target binding affinity has huge relevance in Modern drug discovery and drug repositioning processes which assist doctors to come up with new drugs or even use the existing drugs for new target proteins. In silico models, using advanced deep learning techniques could further assist these prediction tasks by providing most prominent drug target pairs. Considering these factors, a deep learning based algorithmic framework is developed in this study to support drug target interaction prediction. The proposed SeqDPI model extract the relevant drug and protein features from the one dimensional Sequential representation of the dataset considered using optimized CNN networks that deploy convolutions on varying length of amino acid subsequence's to capture hidden pattern, the convolved drug- protein features obtained are then used as an input to L2 penalized feed forward neural network which matches the local residue patterns in protein classes with molecular fingerprints of drugs to predict the binding strength for all drug target pairs. The proposed model reduces the convolution strain typically encountered in existing in silico models that utilize complex 3D structures of drug protein datasets. The result shows that the SeqDPI model achieves a mean square error MSE of (0.167) across cross validation folds, outperforming baseline models such as KronRLS (0.406), Simboost (0.226), and DeepPS (0.214). Additionally, SeqDPI attains a high CI score of 0.9114 on the benchmark KIBA dataset, demonstrating its statistical significance and computational efficiency compared to existing methods. This gives the relevance and effectiveness of SeqDPI model in accurately predicting binding affinities while working with simpler one-dimensional data, making it a robust and computationally cost-effective solution for drug-target interaction prediction.

Translational population target binding model for the anti-FcRn fragment antibody efgartigimod

Efgartigimod is a human IgG1 antibody Fc-fragment that lowers IgG levels through blockade of the neonatal Fc receptor (FcRn) and is being evaluated for the treatment of patients with severe autoimmune diseases mediated by pathogenic IgG autoantibodies. Engineered for increased FcRn affinity at both acidic and physiological pH, efgartigimod can outcompete endogenous IgG binding, preventing FcRn-mediated recycling of IgGs and resulting in increased lysosomal degradation. A population pharmacokinetic-pharmacodynamic (PKPD) model including FcRn binding was developed based on data from two healthy volunteer studies after single and repeated administration of efgartigimod. This model was able to simultaneously describe the serum efgartigimod and total IgG profiles across dose groups, using drug-induced FcRn receptor occupancy as driver of total IgG suppression. The model was expanded to describe the PKPD of efgartigimod in cynomolgus monkeys, rabbits, rats and mice. Most species differences were explainable by including the species-specific in vitro affinity for FcRn binding at pH 7.4 and by allometric scaling of the physiological parameters. In vitro-in vivo scaling proved crucial for translation success: the drug effect was over/underpredicted in rabbits/mice when ignoring the lower/higher binding affinity of efgartigimod for these species versus human, respectively. Given the successful model prediction of the PK and total IgG dynamics across species, it was concluded that the PKPD of efgartigimod can be characterized by target binding. From the model, it is suggested that the initial fast decrease of measurable unbound efgartigimod following dosing is the result of combined clearance of free drug and high affinity target binding, while the relatively slow terminal PK phase reflects release of bound drug from the receptor. High affinity target binding protects the drug from elimination and results in a sustained PD effect characterized by an increase in the IgG degradation rate constant with increasing target receptor occupancy.

Role of Computer-Aided Design and Development in Modern Pharmaceuticals

The integration of molecular biology and computational technologies has transformed modern drug discovery and development processes. Advanced computational methodologies, particularly artificial intelligence (AI) and machine learning (ML), have reshaped traditional drug development approaches through unprecedented access to ligand property data, target binding interactions, three-dimensional protein structures, and virtual libraries containing billions of drug-like molecules. AI and deep learning (DL) implementations have enhanced multiple stages of drug discovery, from target identification to lead optimization. These computational advances, backed by improved hardware capabilities and sophisticated algorithms, now enable targeting of previously "undruggable" proteins. This review presents modern computational approaches in pharmaceutical development, including strategies for challenging protein targets through covalent regulation, allosteric inhibition, protein-protein interaction modulation, and targeted protein degradation. AI-driven methods have accelerated drug discovery pipelines, reduced development costs, and improved clinical trial success rates. The transition from traditional broad-spectrum approaches to precision medicine, supported by computational tools, has enabled personalized therapeutic strategies. Current limitations in computer-aided drug design persist, yet the combination of computational predictions with experimental validation continues to advance therapeutic development. Recent developments in quantum computing and advanced neural networks promise to further enhance drug discovery efficiency and success rates in the coming decades.

MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction

BackgroundThe development of drug–target binding affinity (DTA) prediction tasks significantly drives the drug discovery process forward. Leveraging the rapid advancement of artificial intelligence, DTA prediction tasks have undergone a transformative shift from wet lab experimentation to machine learning-based prediction. This transition enables a more expedient exploration of potential interactions between drugs and targets, leading to substantial savings in time and funding resources. However, existing methods still face several challenges, such as drug information loss, lack of calculation of the contribution of each modality, and lack of simulation regarding the drug–target binding mechanisms.ResultsWe propose MTAF–DTA, a method for drug–target binding affinity prediction to solve the above problems. The drug representation module extracts three modalities of features from drugs and uses an attention mechanism to update their respective contribution weights. Additionally, we design a Spiral-Attention Block (SAB) as drug–target feature fusion module based on multi-type attention mechanisms, facilitating a triple fusion process between them. The SAB, to some extent, simulates the interactions between drugs and targets, thereby enabling outstanding performance in the DTA task. Our regression task on the Davis and KIBA datasets demonstrates the predictive capability of MTAF–DTA, with CI and MSE metrics showing respective improvements of 1.1% and 9.2% over the state-of-the-art (SOTA) method in the novel target settings. Furthermore, downstream tasks further validate MTAF–DTA’s superiority in DTA prediction.ConclusionsExperimental results and case study demonstrate the superior performance of our approach in DTA prediction tasks, showing its potential in practical applications such as drug discovery and disease treatment.

Usenamine A: a potential therapeutic agent for rheumatoid arthritis and ankylosing spondylitis through its anti-inflammatory activity.

Usenamine A (UA) is a natural compound isolated from the lichen Usnea diffracta, and its therapeutic effects on rheumatic diseases are not well understood. This study aimed to evaluate the potential anti-inflammatory effects of UA and its therapeutic effects on rheumatoid arthritis (RA) and ankylosing spondylitis (AS). Molecular docking was performed between the 3D structure of UA and the TNF-TNFR2 complex. Peripheral blood mononuclear cells (PBMCs) from RA and AS patients were treated with UA, and cell viability was measured using the MTS assay and flow cytometry. The in vitro effects of co-culture with UA were determined by measuring inflammatory cytokines, including IFN-γ, IL-17A, and GM-CSF, using flow cytometry and enzyme-linked immunosorbent assay (ELISA). The in vivo effects of UA were evaluated using an arthritis mouse model. The docking complex of UA bound to the TNF-TNFR2 complex exhibited docking scores of -5.251 kcal/mol and -6.274 kcal/mol, confirming their active sites. UA did not affect cell viability and suppressed the production of inflammatory cytokines in the PBMCs of RA (IFN-γ, IL-17A, and GM-CSF) and AS (GM-CSF) patients. The ELISA also confirmed reduced cytokine levels in the co-culture of UA and PBMCs from RA or AS patients. In the arthritis mouse model, significantly reduced clinical and histological scores were observed in the UA treatment group. Our findings suggest that UA has potential as a binding target for TNF, suppresses inflammatory cytokines in PBMCs, and exhibits anti-inflammatory effects on arthritis in a mouse model.

Enhancement of the signal resolution in aptamer assay upon target binding by employing DNA probes and dual fluorophores

In this study, we aimed to significantly enhance the resolution of aptamer assays by using DNA probes and dual fluorophores. The resolution is defined as the smallest change in the target concentration that causes a reliable and distinguishable signal change. Therefore, resolution enhancement in the aptamer assay ensures improved analytical performance. We first examined four aptamer-probe combinations in the aptamer assay and investigated the resolution by observing a percentage signal response difference (% ΔAAD/SYBR). A large % ΔAAD/SYBR means a discernable signal change for a certain concentration difference, which indicates a high-resolution assay. Consequently, significant resolution enhancement (approximately 3–7 times) was achieved using two DNA probes. The improved analytical performance of the resolution-enhanced aptamer assay was further demonstrated by a wide working range of quantification (i.e., 10−2–102 mg/L, four orders of magnitude), a regression curve with improved fitness, and a lower limit of detection (LOD) response (2–6 times lower).

Optical-fiber sensor for 17β-Estradiol-binding aptamer evaluation and specific detection of 17β-Estradiol in serum at physiological concentrations.

Methods for evaluation of immobilized-small molecule-binding aptamers are rare. In this study, taking the evaluation of 17β-Estradiol (E2) aptamers as an example, we first summarized the reported affinity and specificity results of 16 E2 aptamers, highlighting the issues of insufficient and inconsistent results and the lacking of evaluation of immobilized aptamers. We further exemplified the limited application scope of current affinity techniques by testing the two most widely-applied E2 aptamers, Kim76 and Alsa35, using the three label-free fluorescence assays and two nuclease protection assays. Subsequently, we evaluated the affinity of immobilized-E2 aptamers, Alsa35 and E09, using fiber optic evanescent wave aptasensor (FOEW) based on the competitive binding of target and fluorophore-labeled complementary strand with the fiber surface immobilized-aptamer. The results revealed that Alsa35 had the better affinity and specificity than E09. Using Alsa35-based FOEW, the enzyme-free detection of E2 spiked in river water and human serum was respectively realized with the unprecedented limits of detection (LOD, S/N=3) of 4.75 (undiluted river water) and 206 pM (undiluted serum). FOEW is a valuable addition to analytical approaches for evaluation of immobilized-aptamers and a general platform for ultrasensitive target detection.

Generative adversarial network (GAN) model-based design of potent SARS-CoV-2 Mpro inhibitors using the electron density of ligands and 3D binding pockets: insights from molecular docking, dynamics simulation, and MM-GBSA analysis.

Deep learning-based generative adversarial network (GAN) frameworks have recently been developed to expedite the drug discovery process. These models generate novel molecules from scratch and validate them through molecular docking simulation to identify the most promising candidates for a given drug target. In this study, the SARS-CoV-2 main protease (Mpro) was selected as the drug target. Two distinct GAN algorithms were employed to generate novel small molecules. One approach utilized experimental electron density (ED-based) data of ligands for training to generate drug-like molecules, while the second approach leveraged the target binding pocket to capture spatial and bonding relationship between atoms within the binding pockets. The ED-based approach generated approximately 26,000 molecules, whereas the binding pocket-based method produced around 100 molecules. These generated molecules were subsequently ranked based on molecular docking results using the glide XP score (both flexible and rigid docking) and AutoDock Vina. To identify the most potent GAN-derived molecules, molecular docking was also performed on co-crystallized inhibitor molecules of Mpro. The six most promising molecules from these GAN approaches were further evaluated for stability, interactions, and MM-GBSA binding free energy through molecular dynamics simulations. This analysis led to the identification of four potent Mpro inhibitor molecules, all featuring a 2-benzyl-6-bromophenol scaffold. The binding free energies of these compounds were compared with those of other Mpro inhibitors, revealing that our compounds demonstrated better affinity for Mpro than some broad-spectrum protease inhibitors. The dynamic cross-correlation matrix plot indicated strongly correlated and anti-correlated regions, potentially linked to ligand binding.

Histone-binding protein RBBP4 is necessary to promote neurogenesis in the developing mouse neocortical progenitors.

Chromatin regulation plays a crucial role in neocortical neurogenesis, and mutations in chromatin modifiers are linked to neurodevelopmental disorders. RBBP4 is a core subunit of several chromatin-modifying complexes; however, its functional role and genome-wide occupancy profile in the neocortical primordium are unknown. To address this, we performed RBBP4 knockdown using CRISPR/Cas9 on neocortical progenitors derived from mice of both sexes at embryonic age 12.5 during deep-layer neurogenesis. Our study demonstrates that downregulation of RBBP4 in the E12.5 neocortical progenitors reduced neuronal output, specifically affecting CTIP2-expressing neurons. We demonstrate that RBBP4 plays an essential role in regulating neocortical progenitor proliferation. However, overexpression of RBBP4 alone was not sufficient to regulate neuronal fate.Genome-wide occupancy analysis revealed that RBBP4 primarily binds to distal regulatory elements, and neuron differentiation is a significant GO biological pathway of RBBP4-bound genes. Interestingly, we found that RBBP4 binds to Cdon, a receptor protein in the Shh signaling pathway, and knockdown of Cdon phenocopies RBBP4 knockdown resulting in a significant reduction in neurogenesis, particularly CTIP2-expressing neurons. CDON overexpression could rescue the phenotype caused upon loss of RBBP4 in the neocortex, thereby suggesting the functional link between RBBP4 and its target gene CDON. Our results shed light on the cellular role of RBBP4 and identify CDON as a novel regulator of deep-layer neurogenesis in the neocortical progenitors. Our findings are significant in the context of understanding how dysregulated chromatin regulation impacts cellular mechanisms in neurodevelopmental disorders.Significance Statement Chromatin modifier RBBP4 regulates chromatin structure and, thereby, gene expression. It is expressed in the dorsal telencephalon progenitors during deep-layer neurogenesis. In this study, we unveil a novel role for RBBP4 in regulating deep-layer neurogenesis in the neocortical progenitors. Our research underscores RBBP4's critical role in governing progenitor proliferation and neuronal subtype specification in the neocortex while identifying its genome-wide binding occupancy profile. Moreover, we identify Cdon as a novel binding target of RBBP4, also involved in regulating deep-layer neurogenesis. These findings illuminate the mechanisms by which chromatin modifiers influence neocortical development, offering insights into how mutations in chromatin modifiers could impact cortical development and contribute to neurodevelopmental disorders.

Integrative pharmacological analysis of modified Zuojin formula: Inhibiting the HIF-1α-mediated glycolytic pathway in chronic atrophic gastritis

Ethnopharmacological relevanceZuojin formula (ZJF) is a well-known herbal medicine in Pharmacopoeia of China, which is widely used for gastritis. Modified Zuojin formula (MZJF) was adapted based on traditional Chinese medicine (TCM) theories concerning gastric atrophy and dysplasia, along with extensive clinical experience, has been clinically employed to treat chronic atrophic gastritis (CAG). However, the underlying mechanisms by which MZJF intervenes in CAG remain to be fully elucidated. Aim of the studyThe aim of this study was to evaluate the effects of MZJF intervention in CAG and explore its potential mechanisms. MethodsFour induction factors were used to establish a CAG rat model. HE and AB-PAS staining was utilized to assess the effects of MZJF in the intervention of CAG. The stomach weight index and gastric acid pH was used to assess the overall state of stomach. ELISA was used to assess the gastric mucosal inflammatory response. Using transmission electron microscopy to observe chief cells and parietal cells, we evaluated the improvement of ultrastructure by MZJF. Through network pharmacology analysis, the possible regulatory mechanism of MZJF in CAG was preliminarily explored. Binding interactions between MZJF components and predicted targets were explored using molecular docking. Subsequently, quantitative real-time PCR (qRT-PCR), Western blot, biochemical analysis and TUNEL staining were applied to validate the effect of MZJF on predicted pathway. ResultsMZJF treatment ameliorated gastric mucosal pathology, inflammation, cellular ultrastructural damage and PG levels, halted the exacerbation of CAG in rats, along with a reduction in stomach weight index and gastric acid pH. A total of 79 compounds in MZJF targeting 203 CAG-related molecules were identified through network pharmacology. Enrichment analysis of the core targets was focused on the hypoxia inducible factor-1α (HIF-1α) signaling pathway. Molecular docking results identified HIF-1α as stable binding targets for MZJF primary components. Subsequently, PCR, WB, and biochemical results showed that MZJF suppressed the gene and protein expression levels of HIF-1α and its downstream molecules including glycolytic enzymes and transporters, modulated glucose, pyruvic acid and lactate levels in gastric mucosal tissue. Moreover, MZJF induced apoptosis of gastric epithelial cells, as evidenced by the upregulation of cleaved caspase-3, Bax, Bax/Bcl-2 and TUNEL positive cells ratio. ConclusionsMZJF suppressed the HIF-1α-mediated glycolytic pathway, and promoted cell apoptosis, thereby halting the malignant transformation of CAG. The study provides a valuable reference point for applying TCM in preventing and treating CAG.

Programming the Dynamic Range of Nanochannel Biosensors for MicroRNA Detection Through Allosteric DNA Probes

AbstractSolid‐state nanochannel biosensors are extensively utilized for microRNA (miRNA) detection owing to their high sensitivity and rapid response. However, conventional nanochannel biosensors face limitations in their fixed dynamic range, restricting their versatility and efficacy. Herein, we introduce tunable triblock DNA probes with varying affinities for target miRNA to engineer solid‐state nanochannel biosensors capable of customizable dynamic range adjustment. The triblock DNA architecture comprises a poly‐adenine (polyA) block for adjustable surface density anchoring, alongside stem and loop blocks for modulating structural stability. Through systematic manipulation of these blocks, we demonstrate the ability to achieve diverse target binding affinities and detection limits, achieving an initial 81‐fold dynamic range. By combining probes with various affinities, we extend this dynamic range significantly to 10,900‐fold. Furthermore, by implementing a sequestration mechanism, the effective dynamic range of the nanochannel biosensor is narrowed to only a 3‐fold span of target concentrations. The customizable dynamic range of these advanced nanochannel biosensors makes them highly promising for a broad spectrum of biomedical and clinical applications.

A Brief Review of Radiolabelling Nucleic Acid-Based Molecules for Tracking and Monitoring.

The rise of nucleic acid-based therapeutics continues apace. At the same time, the need for radiolabelled oligonucleotides for determination of spatial distribution is increasing. Complex molecular structures with mostly multiple charges and low solubility in organic solvents increase the challenge of integrating radionuclides. In preclinical research, it is important to understand the fate of new drug candidates in biodistribution studies, target binding or biotransformation studies. Depending on a specific question, the selection of a respective radiolabelling strategy is crucial. Radiometals for molecular imaging with positron emission tomography or single-photon computed tomography generally require an attached chelating agent for stable complexation of the metal with the oligonucleotide, whereas labelling using carbon-11/-14 or tritium allows incorporation of the radioisotope into the native structure without altering it. Moreover, the suitability of direct radiolabelling of the oligonucleotide of interest or indirect radiolabelling, for example, by a two-step pretargeting approach, for the study design requires consideration. This review focuses on the challenges of radiolabelling nucleic acid-based molecules with beta-plus, gamma and beta-minus emitters and their use for tracking and monitoring.

Stereorandomized Oncocins with Preserved Ribosome Binding and Antibacterial Activity.

We recently showed that solid-phase peptide synthesis using racemic amino acids yields stereorandomized peptides comprising all possible diastereomers as homogeneous, single-mass products that can be purified by HPLC and that stereorandomization modulates activity, toxicity, and stability of membrane-disruptive cyclic and linear antimicrobial peptides (AMPs) and dendrimers. Here, we tested if stereorandomization might be compatible with target binding peptides with the example of the proline-rich AMP oncocin, which inhibits the bacterial ribosome. Stereorandomization of up to nine C-terminal residues preserved ribosome binding and antibacterial effects including activities against drug-resistant bacteria and protected against serum degradation. Surprisingly, fully stereorandomized oncocin was as active as L-oncocin in dilute growth media stimulating peptide uptake, although it did not bind the ribosome, indicative of an alternative mechanism of action. These experiments show that stereorandomization can be compatible with target binding peptides and can help understand their mechanism of action.

Abstract A030: Rapid extraction and detection of extracellular vesicle-derived PD-L1 in a microfluidic platform

Abstract Background. Extracellular vesicles (EV) are emerging as new biomarkers for cancer diagnostics and therapeutic monitoring1. For example, in patients that receive PD1/PD-L1 immune checkpoint inhibitors (a key pillar of cancer immunotherapy), circulating EV-derived PD-L1 is gaining attention as a non-invasive biomarker for therapeutic efficacy. Standard methodologies to measure EV-derived PD-L1 requires ultracentrifugation to first separate EVs and then quantify those that express PD-L1 using nano flow cytometry. These are tedious processes that require trained technologists in centralized facilities, which is a key barrier to frequently monitoring EV-based biomarkers. Yet, frequent monitoring is crucial to quickly identify and stop an ineffective therapy while more fruitful options are still available. Method. To bridge the gap, we develop a proof-of-concept digital microfluidic (DMF) device that can separate EVs directly from biofluids and quantify PD-L1+ EVs automatedly. Briefly, DMF device supports automatedly droplet handling, including droplet transport, splitting and mixing, based on electrowetting principles2. Immunomagnetic beads were used to capture EVs from biofluids, and EVs were then detected by electrochemical sensors inserted into the device. These tips of the electrochemical sensors were modified with gold nanoparticle self-assembled layers to enhance target binding, and CD9 antibodies were immobilized on the tips to bind to EVs. We then used anti-PD-L1 as secondary antibodies for target detection. Differential Pulse Voltammetry (DPV)3 were performed to obtain the results. Results. We used the DMF device to extract 109- 1011 #/mL EVs secreted by a human breast cancer cell line (MDA-MB-231). Briefly, 10 mL of cell culture media was loaded into the DMF device, and the sample was mixed and incubated with a 10 mL droplet of immunomagnetic beads to capture EVs. After 5 min, EVs were eluted in 20 mL of elution buffer, and the beads were discarded. We estimated the number of EVs using Nanoparticle Tracking Analysis; the number of EVs extracted on-chip was comparable with those that were extracted using standard in-tube method. We also verified the purity of the EVs extracted on-chip using Western Blot. Our results showed that the electrochemical sensors can detect as low as 104 #/mL PD-L1+ EVs, with a linear range of 104 – 107 #/mL (R2=0.9701). The entire process can be completed within 1 hour. Conclusion. We integrated automated EV extraction and their surface marker detection in a single DMF device. Our next step is to extract and detect EV-based biomarkers from plasma samples. This platform holds promise for the regular monitoring of EV-based biomarkers that are indicators of therapeutic responses in patients that receive cancer immunotherapies.

Local depletion of large molecule drugs due to target binding in tissue interstitial space.

Drug-target binding determines a drug's pharmacodynamics but can also have a profound impact on a drug's pharmacokinetics, known as target-mediated drug disposition (TMDD). TMDD models describe the influence of drug-target binding and target turnover on unbound drug concentrations and are frequently used for biologics and drugs with nonlinear plasma pharmacokinetics. For drug targets expressed in tissues, the effect of TMDD may not be detected when analyzing plasma concentration curves, but it might still affect tissue concentrations and occupancy. This review aimed to investigate the likeliness of such a scenario by reviewing the literature for a typical range of TMDD parameter values and their impact on local drug concentrations and target occupancy in a whole-body PBPK model with TMDD. Our analysis demonstrated that tissue drug concentrations are impacted and significantly depleted in many physiological scenarios. In contrast, the effect on plasma concentrations is much lower, specifically for smaller organs with lower perfusion. Moreover, in scenarios with fast internalization of the drug-target complex, the distribution of large molecules from plasma to tissue interstitial space emerges as a rate-limiting step for the drug-target interaction. These factors may lead to overpredicting local drug concentrations when considering only plasma pharmacokinetics. A sensitivity analysis revealed the high and not always intuitive impact of drug-specific parameters, including the drug molecule hydrodynamic radius, dissociation constant (Kd), drug-target complex internalization rate constant (kint), and target dissociation rate constant (koff), on the drug's pharmacokinetics. Our analysis demonstrated that tissue TMDD needs to be considered even if plasma pharmacokinetics are linear.

Enhanced Prenatal and Postnatal Development Study in Marmoset Monkeys Following Administration of Felzartamab.

Felzartamab is a recombinant fully human immunoglobulin G1 anti-CD38 monoclonal antibody under clinical investigation for immune-mediated diseases. In support of felzartamab clinical development, toxicology studies were conducted in marmoset monkeys, which was the most relevant species based on CD38 binding affinity, pharmacologic activity, and target expression. The felzartamab toxicology program included an enhanced prenatal and postnatal development (ePPND) study to identify potential reproductive and postnatal development risks. In this ePPND study, pregnant marmoset monkeys were randomized to receive vehicle (0mg/kg) or felzartamab at two dose levels (15mg/kg and 75mg/kg) twice per week until parturition, and maternal animals and infants were evaluated for 6 months thereafter. Felzartamab exposure was confirmed in maternal animals and infants in both dosing groups. Overall, felzartamab was well tolerated by pregnant animals at the evaluated doses, with no effect on body weight or body weight gain during pregnancy. No felzartamab-related effects on pregnancy loss or stillbirth rate were observed, and litter counts and numbers of liveborn infants were similar between the vehicle and felzartamab groups. Among infants, there were no felzartamab-related malformations or variations in external anatomy or skeletal morphology and no felzartamab-related observations in histopathology, hematologic and immune cell development, or humoral immune response to vaccination. In conclusion, among pregnant marmoset monkeys dosed with felzartamab, the lack of reproductive toxicity and felzartamab-related effects on offspring supports the clinical evaluation of felzartamab in women of childbearing potential and further demonstrates the suitability of the marmoset monkey for ePPND studies.

Engineering DNA Nanocube SAM Scaffolds for FRET-Based Biosensing: Interfacial Characterization and Sensor Demonstration.

Decorating a gold surface with molecular-level control over the positioning of DNA probes was demonstrated using a self-assembled monolayer (SAM) of wireframe DNA nanocube structures. The DNA nanocubes were specifically adsorbed and oriented using thiol-modified DNA on one face of the cube. The DNA nanocube SAM had a uniform coverage over the gold single crystal bead electrode with a separation of 20-30 nm measured by AFM. The face of the nanocube furthest from the gold surface was designed to hybridize with two different sequences of a 50 base single-stranded DNA probe that was modified with a fluorophore. The first 20 bases were hybridized with the DNA nanocube. One of a pair of FRET fluorophores was used for each probe strand. The dimensions of the nanocube controlled the relative spacing between these fluorophores. When the DNA probes were single-stranded, a FRET signal was observed. FRET decreased to background levels when a complementary DNA target was hybridized to either probe, resulting in a turn-off sensor with little cross-talk between the individual hybridization events. Hybridization isotherms for one target gave KA = 170 pM and a detection limit <50 pM. In addition, the DNA nanocube SAM was configured to be used as a turn-on NeutrAvidin sensor using biotinylated DNA targets hybridized to each probe resulting in an increase in FRET. We show that the wireframe DNA nanocube can be an effective scaffold for preparing biosensors with controlled separation between surface-bound probes facilitating precise sensor surface design and enabling a wide range of sensing modalities with more than one signal available for correlative confirmation of the target binding.