THPdb (http://crdd.osdd.net/raghava/thpdb/) is a manually curated repository of Food and Drug Administration (FDA) approved therapeutic peptides and proteins. The information in THPdb has been compiled from 985 research publications, 70 patents and other resources like DrugBank. The current version of the database holds a total of 852 entries, providing comprehensive information on 239 US-FDA approved therapeutic peptides and proteins and their 380 drug variants. The information on each peptide and protein includes their sequences, chemical properties, composition, disease area, mode of activity, physical appearance, category or pharmacological class, pharmacodynamics, route of administration, toxicity, target of activity, etc. In addition, we have annotated the structure of most of the protein and peptides. A number of user-friendly tools have been integrated to facilitate easy browsing and data analysis. To assist scientific community, a web interface and mobile App have also been developed.
Neutrophil extracellular traps (NETs), a unique DNA framework decorated with antimicrobial peptides, have been in the scientific limelight for their role in a variety of pathologies ranging from cystic fibrosis to cancer. The formation of NETs, as well as relevant regulatory mechanisms, physiological factors, and pharmacological agents have not been systematically discussed in the context of their beneficial and pathological aspects. Novel forms of NET formation including vital NET formation continue to be uncovered, however, there remain fundamental questions around established mechanisms such as NADPH-oxidase (Nox)-dependent and Nox-independent NET formation. Whether NET formation takes place in the tissue versus the bloodstream, internal factors (e.g. reactive oxygen species (ROS) production and transcription factor activation), and external factors (e.g. alkaline pH and hypertonic conditions), have all been demonstrated to influence specific NET pathways. Elements of neutrophil biology such as transcription and mitochondria, which were previously of unknown significance, have been identified as critical mediators of NET formation through facilitating chromatin decondensation and generating ROS, respectively. While promising therapeutics inhibiting ROS, transcription, and gasdermin D are being investigated, neutrophil phagocytosis plays a critical role in host defense and any therapies targeting NET formation must avoid impairing the physiological functions of these cells. This review summarizes what is known in the many domains of NET research, highlights the most relevant challenges in the field, and inspires new questions that can bring us closer to a unified model of NET formation.
The recognition of invading pathogens and endogenous molecules from damaged tissues by toll-like receptors (TLRs) triggers protective self-defense mechanisms. However, excessive TLR activation disrupts the immune homeostasis by sustained pro-inflammatory cytokines and chemokines production and consequently contributes to the development of many inflammatory and autoimmune diseases, such as systemic lupus erythematosus (SLE), infection-associated sepsis, atherosclerosis, and asthma. Therefore, inhibitors/antagonists targeting TLR signals may be beneficial to treat these disorders. In this article, we first briefly summarize the pathophysiological role of TLRs in the inflammatory diseases. We then focus on reviewing the current knowledge in both preclinical and clinical studies of various TLR antagonists/inhibitors for the prevention and treatment of inflammatory diseases. These compounds range from conventional small molecules to therapeutic biologics and nanodevices. In particular, nanodevices are emerging as a new class of potent TLR inhibitors for their unique properties in desired bio-distribution, sustained circulation, and preferred pharmacodynamic and pharmacokinetic profiles. More interestingly, the inhibitory activity of these nanodevices can be regulated through precise nano-functionalization, making them the next generation therapeutics or “nano-drugs.” Although, significant efforts have been made in developing different kinds of new TLR inhibitors/antagonists, only limited numbers of them have undergone clinical trials, and none have been approved for clinical uses to date. Nevertheless, these findings and continuous studies of TLR inhibition highlight the pharmacological regulation of TLR signaling, especially on multiple TLR pathways, as future promising therapeutic strategy for various inflammatory and autoimmune diseases.
The Ribonucleic Acid (RNA) inverse folding problem, designing nucleotide sequences that fold into specific tertiary structures, is a fundamental computational biology problem with important applications in synthetic biology and bioengineering. The design of complex three-dimensional RNA architectures remains computationally demanding and mostly unresolved, as most existing approaches focus on secondary structures. In order to address tertiary RNA inverse folding, we present BeeRNA, a bio-inspired method that employs the Artificial Bee Colony (ABC) optimization algorithm. Our approach combines base-pair distance filtering with RMSD-based structural assessment using RhoFold for structure prediction, resulting in a two-stage fitness evaluation strategy. To guarantee biologically plausible sequences with balanced GC content, the algorithm takes thermodynamic constraints and adaptive mutation rates into consideration. In this work, we focus primarily on short and medium-length RNAs ($<$ 100 nucleotides), a biologically significant regime that includes microRNAs (miRNAs), aptamers, and ribozymes, where BeeRNA achieves high structural fidelity with practical CPU runtimes. The lightweight, training-free implementation will be publicly released for reproducibility, offering a promising bio-inspired approach for RNA design in therapeutics and biotechnology.
AmirAli Kalbasi, Shole Jamali, Mahdi Aliyari Shoorehdeli
et al.
Accurate prediction of neural dynamics in the brain's reward circuitry is crucial for elucidating how natural and pharmacological rewards influence neural activity and connectivity. Traditional linear models, such as autoregressive (AR) and vector autoregressive (VAR), often inadequately capture the inherent nonlinear interactions in neural data. This study develops and benchmarks both linear and advanced deep learning models for predicting local field potentials (LFPs) in the rat hippocampus (HIP) and nucleus accumbens (NAc) across morphine, food, and saline conditions. We compared AR, VAR, long short-term memory (LSTM), and wavelet-based deep learning model (WCLSA). Additionally, a novel wavelet coherence-enhanced model (WCOH CLSA) was introduced to capture cross-region connectivity. Results indicate that WCLSA achieves superior predictive accuracy (up to 0.97 for HIP in food, 0.96 for NAc in morphine), while VAR performs competitively in the food group due to significant HIP-NAc correlation. Wavelet coherence analysis reveals robust connectivity in natural reward contexts and disrupted or nonlinear relationships under pharmacological influence. These findings highlight the differential engagement of HIP and NAc in reward processing and underscore the importance of advanced nonlinear models for capturing complex neural dynamics. The study provides a robust framework for predictive neuroscience and elucidates functional interactions within the reward circuitry.
Prostate cancer (PCa) remains a significant global health concern among men, particularly due to the lethality of its more aggressive variants. Despite therapeutic advancements that have enhanced survival for many patients, high grade PCa continues to contribute substantially to cancer related mortality. Emerging evidence points to the MYB proto-oncogene as a critical factor in promoting tumor progression, therapeutic resistance, and disease relapse. Notably, differential expression patterns have been observed, with markedly elevated MYB levels in tumor tissues from Black men relative to their White counterparts potentially offering insight into documented racial disparities in clinical outcomes. This study investigates the association between MYB expression and key oncogenic features, including androgen receptor (AR) signaling, disease progression, and the risk of biochemical recurrence. Employing a multimodal approach that integrates histopathological examination, quantitative digital imaging, and analyses of public transcriptomic datasets, our findings suggest that MYB overexpression is strongly linked to adverse prognosis. These results underscore MYB's potential as a prognostic biomarker and as a candidate for the development of individualized therapeutic strategies.
Maria Francesca Abbate, Pierre Toxe, Nicolas Maestrali
et al.
The identification and validation of therapeutic antibodies is critical for developing effective treatments for many diseases. We present a computational approach for identifying antibodies targeting GFRAL-specific receptors, receptors implicated in appetite regulation. Using humanized Trianni mice, we conducted a longitudinal study with repeated blood sampling and splenic analysis. We applied the STAR computational method for antibody discovery on bulk antibody repertoire data sampled at key time points. By mapping the output from STAR to single-cell data taken at the last time point, we successfully identified a pool of antibodies, of which 50% demonstrated binding capabilities. We observed convergent selection, where responding sequences with identical amino acid complementarity determining regions 3 (CDR3) were found in different mice. We provide a catalog of 67 experimentally validated antibodies against GFRAL. The potential of these antibodies as antagonists or agonists against GFRAL suggests therapeutic solutions for conditions like cancer cachexia, anorexia, obesity, and diabetes. This study underscores the utility of integrating computational methods and experimental validation for antibody discovery in therapeutic contexts by reducing time and increasing efficiency.
Shinichi Kumagai, Tomoyo Isoguchi Shiramatsu, Kensuke Kawai
et al.
Vagus nerve stimulation (VNS) has emerged as a promising therapeutic intervention across various neurological and psychiatric conditions, including epilepsy, depression, and stroke rehabilitation; however, its mechanisms of action on neural circuits remain incompletely understood. Here, we present a novel theoretical framework based on predictive coding that conceptualizes VNS effects through differential modulation of feedforward and feedback neural circuits. Based on recent evidence, we propose that VNS shifts the balance between feedforward and feedback processing through multiple neuromodulatory systems, resulting in enhanced feedforward signal transmission. This framework integrates anatomical pathways, receptor distributions, and physiological responses to explain the influence of the VNS on neural dynamics across different spatial and temporal scales. VNS may facilitate neural plasticity and adaptive behavior through acetylcholine and noradrenaline (norepinephrine), which differentially modulate feedforward and feedback signaling. This mechanistic understanding serves as a basis for interpreting the cognitive and therapeutic outcomes across different clinical conditions. Our perspective provides a unified theoretical framework for understanding circuit-specific VNS effects and suggests new directions for investigating their therapeutic mechanisms.
Recent progress in large language models (LLMs) has opened new possibilities for mental health support, yet current approaches lack realism in simulating specialized psychotherapy and fail to capture therapeutic progression over time. Narrative therapy, which helps individuals transform problematic life stories into empowering alternatives, remains underutilized due to limited access and social stigma. We address these limitations through a comprehensive framework with two core components. First, INT (Interactive Narrative Therapist) simulates expert narrative therapists by planning therapeutic stages, guiding reflection levels, and generating contextually appropriate expert-like responses. Second, IMA (Innovative Moment Assessment) provides a therapy-centric evaluation method that quantifies effectiveness by tracking "Innovative Moments" (IMs), critical narrative shifts in client speech signaling therapy progress. Experimental results on 260 simulated clients and 230 human participants reveal that INT consistently outperforms standard LLMs in therapeutic quality and depth. We further demonstrate the effectiveness of INT in synthesizing high-quality support conversations to facilitate social applications.
Consider two similar drug companies with access to similar chemical libraries and synthesis methods, who each run an R&D program. The programs have the same number of stages, which each take the same amount of time, with the same costs, with the same historic stepwise progression rates, and which aim to address the same therapeutic indication. Now let us suppose one of these companies invests in new scientific tools that make it unusually good at critical progression decisions, while the other company does not. How do we assess the difference in value between the two programs? Surprisingly, standard discounted cash flow valuation methods, such as risk-adjusted net present value (rNPV), ubiquitous in drug industry portfolio management and venture capital, are largely useless in this case. They fail to value the decisions that make drug candidates more or less valuable because rNPV conflates wrong decisions to progress bad candidates with right decisions to progress good ones. The purpose of this paper is to set out a new class of valuation model that logically links the value of therapeutic assets with the value of "decisions tools" that are used to design, optimize, and test those assets. Our model makes clear the interaction between asset value and decision tool value. It also makes clear the downstream consequences of better, or worse, upstream decisions. This new approach may support more effective allocation of R&D capital; helping fund therapeutic assets that are developed using good decision tools, and funding better decision tools to distinguish between good and bad therapeutic assets.
Persis E Mathew, Archa Susan Cherian, Rakshith S
et al.
Background Post-operative pain is experienced by the
majority of patients and can be managed using a single analgesic agent or
through multimodal analgesia.Objective To evaluate the prescription patterns
of analgesics and assess pain management among post-operative
patients.Method This was a prospective observational study conducted on 400
patients over a six months period. Data collected included patient
demographics age gender chief complaints diagnosis laboratory test results
and details of the drugs prescribed such as genericbrand name dose frequency
route of administration and duration of treatment. Assessment of pain was
done using the Numerical Rating Scale.Result Out of 400 patients 243 were
male 60.75 and 167 were female 39.25. The largest proportion of subjects
belonged to the 46-60 years age group 33.75. Majority of subjects reported
mild pain 52.75. Most drugs were prescribed by brand name 88.1. Intravenous
administration was the most common route 97.14. Tramadol was the frequently
prescribed monotherapy on the day of surgery 16.25 whereas paracetamol was
the most commonly prescribed monotherapy on postoperative days 1 42.96 2
61.61 and 3 69.37. The combination of tramadol and paracetamol was the most
commonly prescribed combination therapy on postoperative day 0 40.25 day 1
26.04 day 2 15.16 and day 3 7.5.Conclusion This study highlights the need to
curb irrational prescribing to reduce morbidity and ease the public health
burden. Promoting the judicious use of all medications including analgesics
can lower costs limit adverse effects and improve care delivery.
Pharmacy and materia medica, Therapeutics. Pharmacology
Peter Kipkurui Mashep, Roseline Ochieng, Jesse Coleman
et al.
IntroductionIntermittent hypoxemia (IH) is defined as oxygen saturation (SpO2) drop ≥5% from the baseline (set at 90 s preceding the event) to a level less than 90% lasting for ≥5 s. Caffeine citrate, the standard of care for apnea of prematurity, reduces IH events. IH contributes to both short and long-term adverse neurologic outcomes. Standard patient monitors cannot detect IH events due to long averaging times.ObjectiveDescribe change in patterns of IH events in preterms <34 weeks before and after cessation of caffeine citrate and factors associated.MethodsInterrupted time series study design. Data was collected from 1 December 2022 to 30 June 2023. MASIMO RAD-G oximeter was used, and analysis done using Trace software V3028 output was desaturation frequency, duration, and time. Data exported, stored, and analyzed using Excel 2016. Change in slope compared visually in two time periods interrupted at 34 weeks and objective statistical analysis done using the Student-T-test, CI 95% with p-value <0.05 considered significant.Results49 patients medical records available for secondary analysis. Frequency of IH events increased from 7.94 to 40.94 events/hour (5-fold). IH events of durations lasting between 0 and 10 s, and >20 s decreased by 12.3% and 6.8%, respectively, while those lasting 10–20 s increased by 17%. The mildly severe IH events decreased by nearly half, 46.9% (78.4% to 31.5%), while both the moderately severe and severe IH events increased by 17.4% (18.5%–35.9%), and 26% (6.7% to 32.7%) respectively. The time spent in hypoxemia increased by 2.3 h/week/patient, while the cumulative time in hypoxemia increased by 1.6 h/patient. Preterms exposure to ACS (antenatal corticosteroids) was associated with decrease in IH events.ConclusionCaffeine citrate cessation leads to worsening of IH events with increased frequency, duration, severity and cumulative time spent in hypoxemia. Exposure to ACS was associated with decrease in IH events.RecommendationCaffeine citrate therapy use beyond 34 weeks is likely to be beneficial especially in the context of LMIC where antenatal steroid is not always administered, and monitoring of preterm babies is suboptimal. Safe cessation of caffeine therapy requires monitoring to detect IH events.
Notum, a negative feedback regulator of the Wnt signaling, has emerged as a promising target for treating glucocorticoid-induced osteoporosis (GIOP). This study showcases an efficient strategy for discovering the anti-Notum constituents from herbal medicines (HMs) as novel anti-GIOP agents. Firstly, a rapid-responding near-infrared fluorogenic substrate for Notum was rationally engineered for high-throughput identifying the anti-Notum HMs. The results showed that Bu-Gu-Zhi (BGZ), a known anti-osteoporosis herb, potently inhibited Notum in a competitive-inhibition manner. To uncover the key anti-Notum constituents in BGZ, an efficient strategy was adapted via integrating biochemical, phytochemical, computational, and pharmacological assays. Among all identified BGZ constituents, three furanocoumarins were validated as strong Notum inhibitors, while 5-methoxypsoralen (5-MP) showed the most potent anti-Notum activity and favorable safety profiles. Mechanistically, 5-MP acted as a competitive inhibitor of Notum via creating strong hydrophobic interactions with Trp128 and Phe268 in the catalytic cavity of Notum. Cellular assays showed that 5-MP remarkably promoted osteoblast differentiation and activated Wnt signaling in dexamethasone (DXMS)-challenged MC3T3-E1 osteoblasts. In dexamethasone-induced osteoporotic mice, 5-MP strongly elevated bone mineral density (BMD) and improved cancellous and cortical bone thickness. Collectively, this study constructs a high-efficient platform for discovering key anti-Notum constituents from HMs, while 5-MP emerges as a promising anti-GIOP agent.