Sequence-defined polymers for the delivery of oligonucleotides

Machine learning strategies for the structure-property relationship of copolymers

Establishing the structure-property relationship is extremely valuable for the molecular design of copolymers. However, machine learning (ML) models can incorporate both chemical composition and sequence distribution of monomers, and have the generalization ability to process various copolymer types (e.g., alternating, random, block, and gradient copolymers) with a unified approach are missing. To address this challenge, we formulate four different ML models for investigation, including a feedforward neural network (FFNN) model, a convolutional neural network (CNN) model, a recurrent neural network (RNN) model, and a combined FFNN/RNN (Fusion) model. We use various copolymer types to systematically validate the performance and generalizability of different models. We find that the RNN architecture that processes the monomer sequence information both forward and backward is a more suitable ML model for copolymers with better generalizability. As a supplement to polymer informatics, our proposed approach provides an efficient way for the evaluation of copolymers.

Sequence-defined oligomer as a modular platform for selective sub-picomolar detection and removal of Hg2+

A modular synthetic platform for selective sensing and removal of Hg2+ was developed.

Sequence-defined non-natural polymers: synthesis and applications

Sequence-defined polymer: A promising gateway for the next generation polymeric materials and vast opportunities for new synthetic strategies, functional diversity and its material and biomedical applications.

Nanomaterial-Enabled Cancer Therapy

While cancer remains the major cause of death worldwide, nanomaterial (NM)-based diagnosis and treatment modalities are showing remarkable potential to better tackle clinical oncology by effectively targeting therapeutic agents to tumors. NMs can selectively accumulate in solid tumors, and they can improve the bioavailability and reduce the toxicity of encapsulated cytotoxic agents. Additional noteworthy functions of NMs in cancer treatment include the delivery of contrast agents to image tumor sites, delivery of genetic materials for gene therapy, and co-delivery of multiple agents to achieve combination therapy or simultaneous diagnostic and therapeutic outcomes. Although several NM therapeutics have been successfully translated to clinical applications, the gap between the bench and the bedside remains ominously wide. Tumor heterogeneity and the disparity between pre-clinical and clinical studies have been identified as two of the major translational challenges of NM-based cancer therapies. Herein, we review a handful of recent research studies on the use of NMs in cancer therapy and imaging, with a limited discussion on the consequences of tumor heterogeneity and pre-clinical studies on translational research of NM-based delivery systems and propositions in the literature to overcome these challenges.

Peptides for nucleic acid delivery

Nucleic acids and their synthetic oligonucleotide (ON) analogs are a group of gene therapeutic compounds which hold enormous clinical potential. Despite their undoubted potential, clinical translation of these molecules, however, has been largely held back by their limited bioavailability in the target tissues/cells. To overcome this, many different drug delivery systems have been devised. Among others, short delivery peptides, called cell-penetrating peptides (CPPs), have been demonstrated to allow for efficient delivery of nucleic acids and their ON analogs, in both cell culture and animal models. In this review, we provide brief overview of the latest advances in nucleic acid delivery with CPPs, covering the two main vectorization strategies, covalent conjugation and nanoparticle formation-based approach. In conclusion, CPP-based drug delivery systems have the capacity to overcome the hurdle of delivery and thus have the potential to facilitate the clinical translation of nucleic acid-based therapeutics.

Tumor-targeted Delivery of Anti-microRNA for Cancer Therapy: pHLIP is Key

pHLIP opens the door to the cell: An improved cytosolic transfer of anti-microRNAs (anti-miRs) against onco-miRs paves the way for future cancer therapies. The employed anti-miR-peptide conjugates are based on peptide nucleic acids (PNAs), which are connected with the membrane translocation peptide pHLIP through a disulfide bond. The PNAs are thus transferred into the cell and released by the cleavage of the S-S bond.