Chemogenomics involves the combination of a compound's effect on biological targets together with modern genomics technologies. The merger of these two methodologies is creating a new way to screen for compound-target interactions, as well as map chemical and biological space in a parallel fashion. The challenge associated with mining complex databases has initiated the development of many novel in silico tools to profile and analyze data in a systematic way. The ability to analyze the combinatorial effects of chemical libraries on biological systems will aid the discovery of new therapeutic entities. Chemogenomics provides a tool for the rapid validation of novel targeted therapeutics, where a specific molecular target is modulated by a small molecule. Along with targeted therapies comes the ability to discovery pathway nodes where a single molecular target might be an essential component of more than one disease. Several disease areas will benefit directly from the chemogenomics approach, the most advanced being cancer. A genetic loss-of-function screen can be modulated in the presence of a compound to search for genes or pathways involved in the compound's activity. Several recent papers highlight how chemogenomics is changing with RNA interference-based screening and shaping the discovery of new targeted therapies. Together, chemical and RNA interference-based screens open the door for a new way to discovery disease-associated genes and novel targeted therapies.
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http://dx.doi.org/10.1586/14789450.4.3.411 | DOI Listing |
JAMA Cardiol
January 2025
Ifakara Health Institute, Ifakara Branch, Ifakara, United Republic of Tanzania.
Importance: Hypertension is the primary cardiovascular risk factor in Africa. Recently revised World Health Organization guidelines recommend starting antihypertensive dual therapy; clinical efficacy and tolerability of low-dose triple combination remain unclear.
Objectives: To compare the effect of 3 treatment strategies on blood pressure control among persons with untreated hypertension in Africa.
J Vis Exp
January 2025
Center for Gender-Specific Medicine, Istituto Superiore di Sanità.
Transgender (TG) people are individuals whose gender identity and sex assigned at birth do not match. They often undergo gender-affirming hormone therapy (GAHT), a medical intervention that allows the acquisition of secondary sex characteristics more aligned with their individual gender identity, providing consistent results in the improvement of numerous socio-psychological variables. However, GAHT targets different body systems, and some side effects are recorded, although not yet fully identified and characterized.
View Article and Find Full Text PDFPain Ther
January 2025
Department of Trauma and Orthopaedic Surgery, Faculty of Medicine and Psychology, University La Sapienza, 00185, Rome, Italy.
Introduction: Elbow ailments are common, but conventional treatment modalities have shortcomings, offering only interim pain relief rather than targeting the underlying pathophysiology. The last two decades have seen a marked increase in the use of autologous peripheral blood-derived orthobiologics (APBOs), such as platelet-rich plasma (PRP), to manage elbow disorders. Platelet-rich plasma (PRP) is the most widely used APBO, but its efficacy remains debatable.
View Article and Find Full Text PDFEur J Nucl Med Mol Imaging
January 2025
Departments of Radiology and Medical Physics, University of Wisconsin - Madison, Madison, WI, 53705, USA.
Purpose: Trophoblast cell-surface antigen 2 (Trop2) is overexpressed in various solid tumors and contributes to tumor progression, while its expression remains low in normal tissues. Trop2-targeting antibody-drug conjugate (ADC), sacituzumab govitecan-hziy (Trodelvy), has shown efficacy in targeting this antigen. Leveraging the enhanced specificity of ADCs, we conducted the first immunoPET imaging study of Trop2 expression in gastric cancer (GC) and triple-negative breast cancer (TNBC) models using Zr-labeled Trodelvy ([Zr]Zr-DFO-Trodelvy).
View Article and Find Full Text PDFEur J Nucl Med Mol Imaging
January 2025
Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Spitalgasse 23, Vienna, 1090, Austria.
Purpose: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.
Methods: We trained a generative model on Tc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis.
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