Modifying peptide/lipid-associated nucleic acids (PLANAs) for CRISPR/Cas9 ribonucleoprotein delivery.

With the first reports on the possibility of genome editing by Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and CRISPR-associated protein (Cas)9 surfacing in 2005, the enthusiasm for protein silencing via nucleic acid delivery experienced a resurgence following a period of diminished enthusiasm due to challenges in delivering small interfering RNAs (siRNA), especially in vivo. However, delivering the components necessary for this approach into the nucleus is challenging, maybe even more than the cytoplasmic delivery of siRNA. We previously reported the birth of peptide/lipid-associated nucleic acids (PLANAs) for siRNA delivery. This project was designed to investigate the efficiency of these nanoparticles for in vitro delivery of CRISPR/Cas9 ribonucleoproteins. Our initial experiments indicated higher toxicity for PLANAs with the more efficient reverse transfection method. Therefore, polyethylene glycol (PEG) was added to the composition for PEGylation of the nanoparticles by partially replacing two of the lipid components with the PEG-conjugated counterparts. The results indicated a more significant reduction in the toxicity of the nanoparticle, less compromise in encapsulation efficiency and more PEGylation of the surface of the nanoparticles using DOPE-PEG2000 at 50 % replacement of the naïve lipid. The cell internalization and transfection efficiency showed a comparable efficiency for the PEGylated and non-PEGylated PLANAs and the commercially available Lipofectamine™ CRISPRMAX™. Next Generation Sequencing of the cloned cells showed a variety of indels in the transfected cell population. Overall, our results indicate the efficiency and safety of PEGylated PLANAs for in vitro transfection with CRISPR/Cas9 ribonucleoproteins. PEGylation has been studied extensively for in vivo delivery, and PEGylated PLANAs will be candidates for future in vivo studies.

Targeting Breast Cancer: The Familiar, the Emerging, and the Uncharted Territories.

Breast cancer became the most diagnosed cancer in the world in 2020. Chemotherapy is still the leading clinical strategy in breast cancer treatment, followed by hormone therapy (mostly used in hormone receptor-positive types). However, with our ever-expanding knowledge of signaling pathways in cancer biology, new molecular targets are identified for potential novel molecularly targeted drugs in breast cancer treatment. While this has resulted in the approval of a few molecularly targeted drugs by the FDA (including drugs targeting immune checkpoints), a wide array of signaling pathways seem to be still underexplored. Also, while combinatorial treatments have become common practice in clinics, the majority of these approaches seem to combine molecularly targeted drugs with chemotherapeutic agents. In this manuscript, we start by analyzing the list of FDA-approved molecularly targeted drugs for breast cancer to evaluate where molecular targeting stands in breast cancer treatment today. We will then provide an overview of other options currently under clinical trial or being investigated in pre-clinical studies.

An Artificial Neural Network Approach to Predict the Effects of Formulation and Process Variables on Prednisone Release from a Multipartite System.

The impact of formulation and process variables on the in-vitro release of prednisone from a multiple-unit pellet system was investigated. Box-Behnken Response Surface Methodology (RSM) was used to generate multivariate experiments. The extrusion-spheronization method was used to produce pellets and dissolution studies were performed using United States Pharmacopoeia (USP) Apparatus 2 as described in USP XXIV. Analysis of dissolution test samples was performed using a reversed-phase high-performance liquid chromatography (RP-HPLC) method. Four formulation and process variables viz., microcrystalline cellulose concentration, sodium starch glycolate concentration, spheronization time and extrusion speed were investigated and drug release, aspect ratio and yield were monitored for the trained artificial neural networks (ANN). To achieve accurate prediction, data generated from experimentation were used to train a multi-layer perceptron (MLP) using back propagation (BP) and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) 57 training algorithm until a satisfactory value of root mean square error (RMSE) was observed. The study revealed that the in-vitro release profile of prednisone was significantly impacted by microcrystalline cellulose concentration and sodium starch glycolate concentration. Increasing microcrystalline cellulose concentration retarded dissolution rate whereas increasing sodium starch glycolate concentration improved dissolution rate. Spheronization time and extrusion speed had minimal impact on prednisone release but had a significant impact on extrudate and pellet quality. This work demonstrated that RSM can be successfully used concurrently with ANN for dosage form manufacture to permit the exploration of experimental regions that are omitted when using RSM alone.