Antisense Therapy for Infectious Diseases

Enabling safer, more potent oligonucleotide therapeutics with bottlebrush polymer conjugates

Oligonucleotide therapeutics have the unique ability to address traditionally undruggable targets through various target engagement pathways. However, despite advances in chemically modified oligonucleotides and carrier-assisted delivery systems such as lipid nanoparticles and protein/peptide conjugates, the development of oligonucleotide drugs is still plagued with lackluster potency, narrow therapeutic window, poor delivery to non-liver target sites, and/or high potential for toxicity and unwanted immune system activation. In this perspective, we discuss an unconventional delivery solution based upon bottlebrush polymers, which overcomes many key challenges in oligonucleotide drug development. We address the molecular basis of the polymer's ability to enhance tissue bioavailability and drug potency, reduce side effects, and suppress anti-carrier immunity. Furthermore, we discuss the potential of the technology in advancing oligonucleotide-based therapies for non-liver targets.

BIR: Biomedical Information Retrieval System for Cancer Treatment in Electronic Health Record Using Transformers

The rapid growth of electronic health records (EHRs) has led to unprecedented biomedical data. Clinician access to the latest patient information can improve the quality of healthcare. However, clinicians have difficulty finding information quickly and easily due to the sheer data mining volume. Biomedical information retrieval (BIR) systems can help clinicians find the information required by automatically searching EHRs and returning relevant results. However, traditional BIR systems cannot understand the complex relationships between EHR entities. Transformers are a new type of neural network that is very effective for natural language processing (NLP) tasks. As a result, transformers are well suited for tasks such as machine translation and text summarization. In this paper, we propose a new BIR system for EHRs that uses transformers for predicting cancer treatment from EHR. Our system can understand the complex relationships between the different entities in an EHR, which allows it to return more relevant results to clinicians. We evaluated our system on a dataset of EHRs and found that it outperformed state-of-the-art BIR systems on various tasks, including medical question answering and information extraction. Our results show that Transformers are a promising approach for BIR in EHRs, reaching an accuracy and an F1-score of 86.46%, and 0.8157, respectively. We believe that our system can help clinicians find the information they need more quickly and easily, leading to improved patient care.