The genetic basis of many epilepsies is increasingly understood, giving rise to the possibility of precision treatments tailored to specific genetic etiologies. Despite this, current medical therapy for most epilepsies remains imprecise, aimed primarily at empirical seizure reduction rather than targeting specific disease processes. Intellectual and technological leaps in diagnosis over the past 10 years have not yet translated to routine changes in clinical practice. However, the epilepsy community is poised to make impressive gains in precision therapy, with continued innovation in gene discovery, diagnostic ability, and bioinformatics; increased access to genetic testing and counseling; fuller understanding of natural histories; agility and rigor in preclinical research, including strategic use of emerging model systems; and engagement of an evolving group of stakeholders (including patient advocates, governmental resources, and clinicians and scientists in academia and industry). In each of these areas, we highlight notable examples of recent progress, new or persistent challenges, and future directions. The future of precision medicine for genetic epilepsy looks bright if key opportunities on the horizon can be pursued with strategic and coordinated effort.
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http://dx.doi.org/10.1111/epi.17332 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
Clinic for Autism and Neurodevelopmental research, Brain and Mind Centre, Children's Hospital Westmead Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia.
Proc Natl Acad Sci U S A
January 2025
Laboratory of Precision Medicine and Biopharmaceuticals, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
Recurrent missense mutations in the human epidermal growth factor receptor 2 (HER2) have been identified across various human cancers. Among these mutations, the active S310F mutation in the HER2 extracellular domain stands out as not only oncogenic but also confers resistance to pertuzumab, an antibody drug widely used in clinical cancer therapy, by impeding its binding. In this study, we have successfully employed computational-aided rational design to undertake directed evolution of pertuzumab, resulting in the creation of an evolved pertuzumab variant named Ptz-SA.
View Article and Find Full Text PDFJ Am Med Inform Assoc
January 2025
Institute of Data Science, National University of Singapore, 117602, Singapore.
Objectives: This study introduces Smart Imitator (SI), a 2-phase reinforcement learning (RL) solution enhancing personalized treatment policies in healthcare, addressing challenges from imperfect clinician data and complex environments.
Materials And Methods: Smart Imitator's first phase uses adversarial cooperative imitation learning with a novel sample selection schema to categorize clinician policies from optimal to nonoptimal. The second phase creates a parameterized reward function to guide the learning of superior treatment policies through RL.
Natural killer (NK) cells have proven to be safe and effective immunotherapies, associated with favorable treatment responses in chronic myeloid leukemia (CML). Augmenting NK cell function with oncological drugs could improve NK cell-based immunotherapies. Here, we used a high-throughput drug screen consisting of over 500 small-molecule compounds to systematically evaluate the effects of oncological drugs on primary NK cells against CML cells.
View Article and Find Full Text PDFACS Nano
January 2025
NOVA Medical School|Faculdade de Ciências Médicas, NMS|FCM, Universidade NOVA de Lisboa, Lisbon 1169-056, Portugal.
The "" under this Perspective underline the importance of interdisciplinary collaboration and partnerships across several disciplines, such as medical science and technology, medicine, bioengineering, and computational approaches, in bridging the gap between research, manufacturing, and clinical applications. Effective communication is key to bridging team gaps, enhancing trust, and resolving conflicts, thereby fostering teamwork and individual growth toward shared goals. Drawing from the success of the COVID-19 vaccine development, we advocate the application of similar collaborative models in other complex health areas such as nanomedicine and biomedical engineering.
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