# Application of mucus modulating multipurpose trypsin nanoparticles to overcome the mucus barrier and deliver mitochondria-targeted anticancer drugs in mucinous carcinoma peritonei

> **NIH NIH R21** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2022 · $185,831

## Abstract

PROJECT ABSTRACT
Mucinous colorectal and appendiceal cancers (MCAC) are unique histologic subtypes that frequently
metastasize to the peritoneal cavity (known as mucinous carcinoma peritonei [MCP]). MCP is frequently
unresectable, responds poorly to standard intravenous chemotherapy, and often recurs after “curative” surgery
with intraperitoneal (IP) chemotherapy, resulting in poor oncologic outcomes. Intraperitoneal chemotherapy
for MCP faces two major challenges. First, MCP is characterized by abundant extracellular mucus that forms
a protective barrier around cancer cells, hindering IP chemotherapeutic drug delivery. We have previously
demonstrated robust mucolysis, in patient-derived in vitro and in vivo models of MCP, using mucolytic drugs
(e.g. bromelain [BRO], N-acetylcysteine [NAC] and trypsin [TRYP]). We also found that the baseline net negative
charge of mucus was significantly increased after mucolysis (ζ-potential in our studies: undigested mucus -1.93
mV; digested mucus -17.2 mV). Second, commonly administered IP drugs for MCP (e.g. doxorubicin [DOX] and
mitomycin C [MITO]) are rapidly absorbed across the peritoneal membrane, resulting in short IP retention time,
low intra-tumoral (IT) penetration, and systemic toxicity. Therapeutic nanoparticle formulations have longer IP
retention and IT penetration than free drugs because of enhanced permeability and retention effect and provide
protection from early degradation and pre-absorption. The aim of this proposal is to leverage nanotechnology
and the significant negative charge of mucus following mucolysis to enhance IP retention, IT penetration, and
delivery of positively charged anticancer drugs in MCP. To this end, we have synthesized mucus modulating
multipurpose TRYP nanoparticles (MTN) comprised of three components; (a) a core of negatively charged TRYP
clusters, consisting of 4 arms of polyethylene glycol (PEG) and TRYP, for enzymatic mucolysis and drug delivery;
(b) nanoparticle-conjugated NAC, for mucus disruption and mucoadhesion; and (c) nanoparticle-loaded and
positively-charged mitochondria-targeted anticancer drugs (mitocans), specifically triphenyl phosphonium (TPP)-
doxorubicin (TPP-DOX) and TPP-mitomycin C (TPP-MITO), for anti-cancer effect. We hypothesize that our
MTN will disrupt the structural integrity of mucus, enhance IP/IT retention and penetration of loaded drugs, and
deliver positively charged TPP-DOX or TPP-MITO across a progressively higher negative charge-gradient from
the nanoparticle surface to digested mucus to mitochondria (ζ-potential: digested mucus -17.2 mV; cell
membranes -30 to -60 mV; mitochondrial membranes -160 mV). Our research proposal provides a novel
therapeutic strategy to overcome the cytoprotective mucus barrier and improve drug delivery in MCP. It is
expected that the proposed MTN will provide a pharmacokinetic and pharmacodynamic advantage over non-
nanocarrier formulations of the drugs. Notably, the proposed MTN are synthesized from bioc...

## Key facts

- **NIH application ID:** 10510536
- **Project number:** 1R21CA273630-01
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Mohammad Haroon Asif Choudry
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $185,831
- **Award type:** 1
- **Project period:** 2022-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10510536

## Citation

> US National Institutes of Health, RePORTER application 10510536, Application of mucus modulating multipurpose trypsin nanoparticles to overcome the mucus barrier and deliver mitochondria-targeted anticancer drugs in mucinous carcinoma peritonei (1R21CA273630-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10510536. Licensed CC0.

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