Hasil untuk "Therapeutics. Pharmacology"

G. Sirugo, Scott M. Williams, S. Tishkoff

Giorgio Sirugo,1,2,6,* Scott M. Williams,5,6,* and Sarah A. Tishkoff3,4,6,* 1Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA 2Division of Translational Medicine and Human Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA 3Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA 4Department of Biology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA 5Departments of Population and Quantitative Health Sciences, and Genetics and Genome Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH 44106, USA 6All three authors contributed equally *Correspondence: giorgio.sirugo@pennmedicine.upenn.edu (G.S.), smw154@case.edu (S.M.W.), tishkoff@pennmedicine.upenn.edu (S.A.T.) https://doi.org/10.1016/j.cell.2019.02.048

M. Pettersson, C. Crews

The majority of currently used therapeutics are small molecule-based and utilize occupancy-driven pharmacology as the mode of action (MOA), in which the protein function is modulated via temporary inhibition. New modalities that operate using alternative MOAs are essential for tapping into the "undruggable" proteome. The PROteolysis Targeting Chimera (PROTAC) technology provides an attractive new approach that utilizes an event-driven MOA. Small molecule-based heterobifunctional PROTACs modulate protein target levels by hijacking the ubiquitin-proteasome system to induce degradation of the target. Here, we address important milestones in the development of the PROTAC technology, as well as emphasize key findings from this previous year and highlight future directions of this promising drug discovery modality.

C. Serhan, S. Brain, C. Buckley et al.

A recent focus meeting on Controlling Acute Inflammation was held in London, April 27–28, 2006, organized by D.W. Gilroy and S.D. Brain for the British Pharmacology Society. We concluded at the meeting that a consensus report was needed that addresses the rapid progress in this emerging field and details how the specific study of resolution of acute inflammation provides leads for novel anti‐inflammatory therapeutics, as well as defines the terms and key components of interest in the resolution process within tissues as appreciated today. The inflammatory response protects the body against infection and injury but can itself become dysregulated with deleterious consequences to the host. It is now evident that endogenous biochemical pathways activated during defense reactions can counter‐regulate inflammation and promote resolution. Hence, resolution is an active rather than a passive process, as once believed, which now promises novel approaches for the treatment of inflammation‐associated diseases based on endogenous agonists of resolution.—Serhan, C. N., Brain, S. D., Buckley, C. D., Gilroy, D. W., Haslett, C., O'Neill, L. A. J., Perretti, M., Rossi, A. G., Wallace, J. L. Resolution of inflammation: state of the art, definitions and terms. FASEB J. 21, 325–332 (2007)

Caroline A. MacCallum, Ethan B. Russo

B. Furr, V. Jordan

S. Dewire, D. Yamashita, D. Rominger et al.

H. Möhler, J. Fritschy, U. Rudolph

Celeste Seah, Yoke Chuan Lee, Jung-Joo Lee et al.

Reminiscence therapy (RT) is a common non-pharmacological intervention in dementia care. Recent technology-mediated interventions have largely focused on people with dementia through solutions that replace human facilitators with conversational agents. However, the relational work of facilitation is critical in the effectiveness of RT. Hence, we developed Rememo, a therapist-oriented tool that integrates Generative AI to support and enrich human facilitation in RT. Our tool aims to support the infrastructural and cultural challenges that therapists in Singapore face. In this research, we contribute the Rememo system as a therapist's tool for personalized RT developed through sociotechnically-aware research-through-design. Through studying this system in-situ, our research extends our understanding of human-AI collaboration for care work. We discuss the implications of designing AI-enabled systems that respect the relational dynamics in care contexts, and argue for a rethinking of synthetic imagery as a therapeutic support for memory rahter than a record of truth.

ShahZeb Khan, Chiara Pallara, Barbara Monti et al.

Alzheimers disease (AD), a progressive neuro-degenerative disorder, currently lacks effective therapeutic strategies that can modify disease progression. Recent studies have highlighted the circadian rhythm critical role in AD pathophysiology, implicating circadian clock kinases, such as the Salt-Inducible Kinase 3 (SIK3), as promising therapeutic target. Generative AI models have surpassed traditional methods of drug discovery, untapping the vast unexplored chemical space of drug-like molecules. We present a sequence-to-sequence Variational Autoencoder (Seq2Seq-VAE) model guided by an Active Learning (AL) approach to optimize molecular generation. Our pipeline iteratively guided a pre-trained Seq2Seq-VAE model towards the pharmacological landscape relevant to SIK3 using a two-step framework, an inner loop that iteratively improves physiochemical properties profile, drug likeliness and synthesizability, followed by an outer loop that steer the latent space towards high-affinity ligands for SIK3. Our approach introduces feedback-driven optimization without requiring large labeled datasets, making it particularly suited for early-stage drug discovery in under-explored therapeutic targets. Our results demonstrate the models convergence toward SIK3-specific small molecules with desired properties and high binding affinity. This work highlights the use of generative AI combined with AL for rational drug discovery that can be extended to other protein targets with minimal modifications, offering a scalable solution to the molecular design bottleneck in drug design.

Zhongheng Cai, Lian Ma, Jingjing Ye et al.

Development of effective treatments in pediatric population poses unique scientific and ethical challenges in addition to the small population. In this regard, both the U.S. and E.U. regulations suggest a complementary strategy, pediatric extrapolation, based on assessing the relevance of existing information in the adult population to the pediatric population. The pediatric extrapolation approach often relies on data extrapolation from adults, contingent upon evidence of similar disease progression, pharmacology and clinical response to treatment between adult and children. Similarity evaluation in pharmacology is usually characterized through the exposure-response relationship. Current methodologies for comparing exposure-response (E-R) curves between these groups are inadequate, typically focusing on isolated data points rather than the entire curve spectrum (Zhang et al., 2021). To overcome this limitation, we introduce an innovative Bayesian approach for a comprehensive evaluation of E-R curve similarities between adult and pediatric populations. This method encompasses the entire curve, employing logistic regression for binary endpoints. We have developed an algorithm to determine sample size and key design parameters, such as the Bayesian posterior probability threshold, and utilize the maximum curve distance as a measure of similarity. Integrating Bayesian and frequentist principles, our approach involves developing a method to simulate datasets under both null and alternative hypotheses, allowing for type I error and type II error control. Simulation studies and sensitivity analyses demonstrate that our method maintains a stable performance with type I error and type II error control.

Shijie Yuan, Jiaxin Liu, Zhihua Gong et al.

We consider basket trials in which a biomarker-targeting drug may be efficacious for patients across different disease indications. Patients are enrolled if their cells exhibit some levels of biomarker expression. The threshold level is allowed to vary by indication. The proposed SIMBA method uses a decision framework to identify optimal biomarker subgroups (OBS) defined by an optimal biomarker threshold for each indication. The optimality is achieved through minimizing a posterior expected loss that balances estimation accuracy and investigator preference for broadly effective therapeutics. A Bayesian hierarchical model is proposed to adaptively borrow information across indications and enhance the accuracy in the estimation of the OBS. The operating characteristics of SIMBA are assessed via simulations and compared against a simplified version and an existing alternative method, both of which do not borrow information. SIMBA is expected to improve the identification of patient sub-populations that may benefit from a biomarker-driven therapeutics.

Mehvish Aqil, Malik Istikhar Ali Sajjad , Mehr Muhammad Imran et al.

Background: Nontuberculous mycobacteria (NTM), a diverse group of environmental organisms rapidly proliferating in water, soil, and dust, are becoming a common cause of clinical disease. This study analyzed patient data from two major hospitals in Faisalabad, Pakistan, to improve early detection of NTM lung disease and to guide clinical practice in seeking earlier and quicker intervention.   Methods: A retrospective cross-sectional study was conducted from January 2020 to December 2021, using the records of 294 tuberculosis patients at Allied Hospital and DHQ Hospital, Faisalabad. Non-probability convenience sampling was used for sample collection and sample size was collected using OpenEpi 3.0.0.  Data from patients with NTM lung disease were checked. The diagnosis was based on criteria defined by the ATS/IDSA (a clinical, radiological and microbiological evidence). Testing of specimens (sputum, BAL fluid, puncture fluid) was conducted using AFB smear, culture (MGIT 960) and species identification by molecular techniques. Chi-square, Wilcoxon tests and logistic regression were performed using SPSS version 26.0.p<0.05 was considered as significant. Results: There were 294 patients (147 males; 147 females); median age 61 years, 77.2% had bronchiectasis. The most frequently identified species was the Mycobacterium avium-intracellulare complex (MAC 56.1%) followed by M. kansasii (19%) and M. abscessus (15.3%).   Sputum cultures had the highest positivity rate (87.4%), outperforming BAL fluid (80.3%) and puncture fluid (61.5%). Conclusion: The M. avium-intracellulare complex is the most common NTM species found in patients in these hospitals. The signs of expectoration, gender and bronchiectasis increased likelihood of BAL culture positivity, which aids in diagnosis.

I. Batinic-Haberle, J. Rebouças, I. Spasojević

Ziheng Wang, Xinhe Li, Haruki Momma et al.

To enhance therapeutic outcomes from a pharmacological perspective, we propose MiranDa, designed for medication recommendation, which is the first actionable model capable of providing the estimated length of stay in hospitals (ELOS) as counterfactual outcomes that guide clinical practice and model training. In detail, MiranDa emulates the educational trajectory of doctors through two gradient-scaling phases shifted by ELOS: an Evidence-based Training Phase that utilizes supervised learning and a Therapeutic Optimization Phase grounds in reinforcement learning within the gradient space, explores optimal medications by perturbations from ELOS. Evaluation of the Medical Information Mart for Intensive Care III dataset and IV dataset, showcased the superior results of our model across five metrics, particularly in reducing the ELOS. Surprisingly, our model provides structural attributes of medication combinations proved in hyperbolic space and advocated "procedure-specific" medication combinations. These findings posit that MiranDa enhanced medication efficacy. Notably, our paradigm can be applied to nearly all medical tasks and those with information to evaluate predicted outcomes. The source code of the MiranDa model is available at https://github.com/azusakou/MiranDa.

Leyao Wang, Rishab Pulugurta, Pranay Vure et al.

Peptide therapeutics, including macrocycles, peptide inhibitors, and bioactive linear peptides, play a crucial role in therapeutic development due to their unique physicochemical properties. However, predicting these properties remains challenging. While structure-based models primarily focus on local interactions, language models are capable of capturing global therapeutic properties of both modified and linear peptides. Protein language models like ESM-2, though effective for natural peptides, cannot however encode chemical modifications. Conversely, pre-trained chemical language models excel in representing small molecule properties but are not optimized for peptides. To bridge this gap, we introduce PepDoRA, a unified peptide representation model. Leveraging Weight-Decomposed Low-Rank Adaptation (DoRA), PepDoRA efficiently fine-tunes the ChemBERTa-77M-MLM on a masked language model objective to generate optimized embeddings for downstream property prediction tasks involving both modified and unmodified peptides. By tuning on a diverse and experimentally valid set of 100,000 modified, bioactive, and binding peptides, we show that PepDoRA embeddings capture functional properties of input peptides, enabling the accurate prediction of membrane permeability, non-fouling and hemolysis propensity, and via contrastive learning, target protein-specific binding. Overall, by providing a unified representation for chemically and biologically diverse peptides, PepDoRA serves as a versatile tool for function and activity prediction, facilitating the development of peptide therapeutics across a broad spectrum of applications.

Ali Y. Benkherouf

Insomnia often requires pharmacological interventions, with benzodiazepines and Z-drugs enhancing GABA's inhibitory effects by stabilizing GABAA receptor chloride ion channels. Prolonged use, however, raises dependency and cognitive concerns. Humulus lupulus (hops) is gaining attention as a natural relaxant and sleep aid, potentially modulating GABAA receptors differently. This study explores hops' neuroactive phytochemicals and their therapeutic mechanisms. The alpha-acid humulone and hop prenylflavonoids affect GABA-induced displacement of [3H]EBOB in the GABAA receptor, showing flumazenil-insensitive and subtype-selective effects. Molecular docking identifies binding sites, with humulone's activity confirmed electrophysiologically and in mouse studies, impacting sleep onset and duration. These findings suggest hops as positive modulators of GABAA receptors, offering insights for sleep aid optimization.

Rabah Dabouz, Pénélope Abram, Jose Carlos Rivera et al.

Abstract ‘Wet’ age-related macular degeneration (AMD) is characterized by pathologic choroidal neovascularization (CNV) that destroys central vision. Abundant evidence points to inflammation and immune cell dysfunction in the progression of CNV in AMD. Mast cells are resident immune cells that control the inflammatory response. Mast cells accumulate and degranulate in the choroid of patients with AMD, suggesting they play a role in CNV. Activated mast cells secrete various biologically active mediators, including inflammatory cytokines and proteolytic enzymes such as tryptase. We investigated the role of mast cells in AMD using a model of CNV. Conditioned media from activated mast cells exerts proangiogenic effects on choroidal endothelial cells and choroidal explants. Laser-induced CNV in vivo was markedly attenuated in mice genetically depleted of mast cells (KitW−sh/W−sh) and in wild-type mice treated with mast cell stabilizer, ketotifen fumarate. Tryptase was found to elicit pronounced choroidal endothelial cell sprouting, migration and tubulogenesis; while tryptase inhibition diminished CNV. Transcriptomic analysis of laser-treated RPE/choroid complex revealed collagen catabolism and extracellular matrix (ECM) reorganization as significant events correlated in clusters of mast cell activation. Consistent with these analyses, compared to wildtype mice choroids of laser-treated mast cell-deficient mice displayed less ECM remodelling evaluated using collagen hybridizing peptide tissue binding. Findings herein provide strong support for mast cells as key players in the progression of pathologic choroidal angiogenesis and as potential therapeutic targets to prevent pathological neovascularization in ‘wet’ AMD.

Olufunke Olufunmilola Olorundare, Nikita Zrelovs, Dennis Kabantiyok et al.

<i>Pseudomonas aeruginosa</i> is a bacteria responsible for many hospital-acquired infections. Phages are promising alternatives for treating <i>P. aeruginosa</i> infections, which are often intrinsically resistant. The combination of phage and antibiotics in clearing bacterial infection holds promise due to increasing reports of enhanced effectiveness when both are used together. The aim of the study is to isolate and characterize a novel <i>P. aeruginosa</i> phage and determine its effectiveness in in vitro combination with antibiotics in controlling <i>P. aeruginosa</i>. In this study, a novel jumbo myophage HPP-Temi infecting <i>P. aeruginosa</i> Pa9 (PP334386) was isolated from household sewage. Electron micrographs of the phage were obtained to determine the morphological features of HPP-Temi virions. Complete genome analysis and a combination of Pseudomonas phage HPP-Temi with antibiotics were examined. The phage HPP-Temi was able to productively infect <i>P. aeruginosa</i> ATCC 9027 but was unable to infect a closely related genus. The phage was stable at 4–37 °C, 0.5% NaCl, and pH 8 for at least one hour. The HPP-Temi genome is a 302,719-bp-long dsDNA molecule with a GC content of 46.46%. The genome was predicted to have 436 ORFs and 7 tRNA genes. No virulence factor-related genes, antimicrobial resistance, or temperate lifestyle-associated genes were found in the phage HPP-Temi genome. Phage HPP-Temi is most closely related to the known or tentative representatives of the <i>Pawinskivirus</i> genus and can be proposed as a representative for the creation of a novel phage species in that genus. The phage and antibiotics (Ciprofloxacin) combination at varying phage titers (10³, 10⁶, 10⁹) were used against <i>P. aeruginosa</i> Pa9 (PP334386) at 3.0 × 10⁸ CFU/mL, which was carried out in triplicate. The result showed that combining antibiotics with phage significantly reduced the bacteria count at 10³ and 10⁶ titers, while no growth was observed at 10⁹ PFU/mL. This suggests that the effect of phage HPP-Temi in combination with antibiotics is a potential and promising agent for the control of <i>P. aeruginosa</i> infections.

Kethely L. Marques, Victor Rodrigues, Cassiana T. N. Balduci et al.

Perinatal hypoxia-ischemia represents a significant risk to CNS development, leading to high mortality rates, diverse damages, and persistent neurological deficits. Despite advances in neonatal medicine in recent decades, the incidence of HIE remains substantial. Motor deficits can manifest early, while cognitive impairments may be diagnosed later, emphasizing the need for extended follow-up. This review aims to explore potential candidates for therapeutic interventions for hypoxic-ischemic encephalopathy (HIE), with a focus on cognitive deficits. We searched randomized clinical trials (RCT) that tested drug treatments for HIE and evaluated cognitive outcomes. The results included studies on erythropoietin, melatonin, magnesium sulfate, topiramate, and a combination of vitamin C and ibuprofen. Although there are several indications of the efficacy of these drugs among animal models, considering neuroprotective properties, the RCTs failed to provide complete effectiveness in the context of cognitive impairments derived from HIE. More robust RCTs are still needed to advance our knowledge and to establish standardized treatments for HIE.

N. Buss, Simon J. Henderson, M. McFarlane et al.