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Systematic Development and Optimization of Inhalable Pirfenidone Liposomes for Non-Small Cell Lung Cancer Treatment.

ABSTRACT: Non-small cell lung cancer (NSCLC) is a global disorder, treatment options for which remain limited with resistance development by cancer cells and off-target events being major roadblocks for current therapies. The discovery of new drug molecules remains time-consuming, expensive, and prone to failure in safety/efficacy studies. Drug repurposing (i.e., investigating FDA-approved drug molecules for use against new indications) provides an opportunity to shorten the drug development cycle. In this project, we propose to repurpose pirfenidone (PFD), an anti-fibrotic drug, for NSCLC treatment by encapsulation in a cationic liposomal carrier. Liposomal formulations were optimized and evaluated for their physicochemical properties, in-vitro aerosol deposition behavior, cellular internalization capability, and therapeutic potential against NSCLC cell lines in-vitro and ex-vivo. Anti-cancer activity of PFD-loaded liposomes and molecular mechanistic efficacy was determined through colony formation (1.5- to 2-fold reduction in colony growth compared to PFD treatment in H4006, A549 cell lines, respectively), cell migration, apoptosis and angiogenesis assays. Ex-vivo studies using 3D tumor spheroid models revealed superior efficacy of PFD-loaded liposomes against NSCLC, as compared to plain PFD. Hence, the potential of inhalable liposome-loaded pirfenidone in NSCLC treatment has been established in-vitro and ex-vivo, where further studies are required to determine their efficacy through in vivo preclinical studies followed by clinical studies.

SUBMITTER: Parvathaneni V

PROVIDER: S-EPMC7150896 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Systematic Development and Optimization of Inhalable Pirfenidone Liposomes for Non-Small Cell Lung Cancer Treatment.

Parvathaneni Vineela V Kulkarni Nishant S NS Shukla Snehal K SK Farrales Pamela T PT Kunda Nitesh K NK Muth Aaron A Gupta Vivek V

Pharmaceutics 20200228 3

Non-small cell lung cancer (NSCLC) is a global disorder, treatment options for which remain limited with resistance development by cancer cells and off-target events being major roadblocks for current therapies. The discovery of new drug molecules remains time-consuming, expensive, and prone to failure in safety/efficacy studies. Drug repurposing (i.e., investigating FDA-approved drug molecules for use against new indications) provides an opportunity to shorten the drug development cycle. In thi ...[more]

PMID: 32121070

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Project description:Objective. Traditional radiotherapy (RT) treatment planning of non-small cell lung cancer (NSCLC) relies on population-wide estimates of organ tolerance to minimize excess toxicity. The goal of this study is to develop a personalized treatment planning based on patient-specific lung radiosensitivity, by combining machine learning and optimization.Approach. Sixty-nine non-small cell lung cancer patients with baseline and mid-treatment [18]F-fluorodeoxyglucose (FDG)-PET images were retrospectively analyzed. A probabilistic Bayesian networks (BN) model was developed to predict the risk of radiation pneumonitis (RP) at three months post-RT using pre- and mid-treatment FDG information. A patient-specific dose modifying factor (DMF), as a surrogate for lung radiosensitivity, was estimated to personalize the normal tissue toxicity probability (NTCP) model. This personalized NTCP was then integrated into a NTCP-based optimization model for RT adaptation, ensuring tumor coverage and respecting patient-specific lung radiosensitivity. The methodology was employed to adapt the treatment planning of fifteen NSCLC patients.Main results. The magnitude of the BN predicted risks corresponded with the RP severity. Average predicted risk for grade 1-4 RP were 0.18, 0.42, 0.63, and 0.76, respectively (p< 0.001). The proposed model yielded an average area under the receiver-operating characteristic curve (AUROC) of 0.84, outperforming the AUROCs of LKB-NTCP (0.77), and pre-treatment BN (0.79). Average DMF for the radio-tolerant (RP grade = 1) and radiosensitive (RP grade ≥ 2) groups were 0.8 and 1.63,p< 0.01. RT personalization resulted in five dose escalation strategies (average mean tumor dose increase = 6.47 Gy, range = [2.67-17.5]), and ten dose de-escalation (average mean lung dose reduction = 2.98 Gy [0.8-5.4]), corresponding to average NTCP reduction of 15% [4-27].Significance. Personalized FDG-PET-based mid-treatment adaptation of NSCLC RT could significantly lower the RP risk without compromising tumor control. The proposed methodology could help the design of personalized clinical trials for NSCLC patients.

Dataset Information

Systematic Development and Optimization of Inhalable Pirfenidone Liposomes for Non-Small Cell Lung Cancer Treatment.

Publications

Systematic Development and Optimization of Inhalable Pirfenidone Liposomes for Non-Small Cell Lung Cancer Treatment.

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