Biology subjects

Kabanov, A.

Publications and source records attributed to Kabanov, A..

3 recordsLinked to original sources

bioRxiv2022

High-Dose Paclitaxel and its Combination with CSF1R Inhibitor in Polymeric Micelles for Chemoimmunotherapy of Triple Negative Breast Cancer

The presence of immunosuppressive immune cells in cancer is a significant barrier to the generation of therapeutic immune responses. Similarly, in vivo triple-negative breast cancer (TNBC) models often contain prevalent tumor-associated macrophages in the tumor microenvironment (TME), resulting in breast cancer initiation, invasion, and metastasis by generating immunosuppressive environment. Here, we test systemic chemoimmunotherapy using small-molecule agents, paclitaxel (PTX), and colony-stimulating factor 1 receptor (CSF1R) inhibitor, PLX3397, to enhance the adaptive T cell immunity against TNBCs in immunocompetent mouse TNBC models. PTX and PLX3397 are very poorly soluble in water and shown poor therapeutic outcomes in TNBC animal models in conventional formulation. To address the challenge for the delivery of insoluble drugs to TNBC, we use high-capacity poly(2-oxazoline) (POx)-based polymeric micelles to greatly improve the solubility and widen the therapeutic index of such drugs. The results demonstrate that high-dose PTX in POx, even as a single agent, exerts strong effects on TME and induces the long-term immune memory. In addition, we demonstrate that the PTX and PLX3397 combination provides consistent therapeutic improvement across several TNBC models, resulting from the repolarization of the immunosuppressive TME and enhanced T cell immune response that suppress both the primary tumor growth and metastasis. Overall, the work emphasizes the benefit of drug reformulation and outlines potential translational path for both PTX and PTX with PLX3397 combination therapy using POx polymeric micelles for the treatment of TNBC.

cancer biology↗

bioRxiv2021

Efficient Design Of Peptide-Binding Polymers Using Active Learning Approaches

Active learning (AL) has become a subject of active recent research both in industry and academia as an efficient approach for rapid design and discovery of novel chemicals, materials, and polymers. The key advantages of this approach relate to its ability to (i) employ relatively small datasets for model development, (ii) iterate between model development and model assessment using small external datasets that can be either generated in focused experimental studies or formed from subsets of the initial training data, and (iii) progressively evolve models toward increasingly more reliable predictions and the identification of novel chemicals with the desired properties. Herein, we first compared various AL protocols for their effectiveness in finding biologically active molecules using synthetic datasets. We have investigated the dependency of AL performance on the size of the initial training set, the relative complexity of the task, and the choice of the initial training dataset. We found that AL techniques as applied to regression modeling offer no benefits over random search, while AL used for classification tasks performs better than models built for randomly selected training sets but still quite far from perfect. Using the best performing AL protocol, we have assessed the applicability of AL for the discovery of polymeric micelle formulations for poorly soluble drugs. Finally, the best performing AL approach was employed to discover and experimentally validate novel binding polymers for a case study of asialoglycoprotein receptor (ASGPR).

pharmacology and toxicology↗

bioRxiv2020

Poly(2-oxazoline) nanoparticle delivery enhances the therapeutic potential of vismodegib for medulloblastoma by improving CNS pharmacokinetics and reducing systemic toxicity

We report a novel, nanoparticle formulation of the SHH pathway inhibitor vismodegib that improves efficacy for medulloblastoma treatment while reducing toxicity. Systemic therapies for brain tumors are complicated by restricted blood-brain barrier (BBB) permeability and dose-limiting extraneural toxicity, therefore improved delivery approached are needed. Here we show how a nanoparticle delivery system addresses these obstacles, bringing new efficacy to previously ineffective therapy. Vismodegib has been a promising agent for patients with SHH- subgroup medulloblastoma and is FDA-approved for basal cell carcinoma. However, vismodegib has limited benefit for patients with SHH-driven medulloblastoma, due to off-target toxicities and the development of resistance during therapy. We encapsulated vismodegib in polyoxazoline block copolymer micelles (POx-vismo). We then evaluated POx-vismo using transgenic mice engineered to develop endogenous medulloblastomas, testing the novel agent in a preclinical model with native vasculature and tumor microenvironment. POx-vismo showed improved CNS pharmacokinetics and reduced systemic and bone toxicity. Mechanistic studies show that POx nanoparticles did not enter the CNS, but rather acted within the vascular compartment to improve drug delivery by decreasing drug binding to serum proteins and reducing the volume of distribution. POx-vismo demonstrated improved efficacy, extending the survival of medulloblastoma-bearing mice. Our results show the potential for a simple, non-targeted nanoparticle formulation to improve systemic brain tumor therapy, and specifically to enhance vismodegib therapy for SHH-driven cancers.

neuroscience↗