The final frontier for conquering cancer and how Mestastop can change the game.

Bengaluru, Karnataka Jan 4, 2023 (Issuewire.com) - Currently, there are almost no drugs to delay or prevent the process of cancer metastasis. Mestastop Solutions, based out of Marlton, New Jersey, and Bangalore, Karnataka, has created three proprietary platforms, both in vitro and in vivo, along with patient tumour translational validation, to unravel the complexity of metastasis drug discovery and early predictive diagnostics.

Founded in 2018, Mestastop has a senior advisory team of Dr. John Ellingboe (ex-Wyeth) and Dr. Ajith Kamath (ex-Pfizer) with a cumulative experience of 60 years. A medical advisory group of clinicians with a cumulative experience of over 120 years oversees all prospective and retrospective clinical studies. A recently ended retrospective clinical trial of one hundred colorectal cancer patients has validated the translational significance of their first platform METAssay, which thoroughly dissects the metastasis biology into multiple steps. Each step mimics a particular facet of metastasis, and the platform represents complete metastasis biology. Additionally, the platform differentiates the subtle functional differences between growing and moving tumour cells, enabling selective targeting of the latter.

The second platform, METSCAN integrates all the data generated from the METAssay platform and normalises it with actual patient sample data, thereby distributing weightages to the steps in the metastasis cascade that are more relevant for metastasis success vis-a-vis patient perspective. All experimental data acts as an input into METSCAN, after which machine learning algorithms are applied to identify the most weighted steps. Once these weightages are in place, patient samples are analysed to understand their respective position against the baseline of the moving and growing cell phenotypes, thereby attributing a summative score for each patient and determining their probability of metastasis. Together, METSCAN helps identify the critical rate-limiting steps of metastasis and the primary tumour patients at a higher risk of developing metastasis. The first trial with a small cohort of patients is currently ongoing. There have been no false negatives, and three metastasis predictions have already matched. The weightage-based critical step analysis has helped identify a few first-in-class novel targets, with genetic and pharmacological proof of concepts achieved for the first target.

The third platform, METVivo, addresses the bottleneck of biologically relevant metastasis animal models that have impaired drug discovery efforts. Mestastop used engineered cells for their orthotopic spontaneous metastasis model, observing hundred percent mesenteric lymph node and organ metastasis in only six weeks as kinetic or endpoint studies. The company claims this will reduce turnaround time and cost while increasing efficiency.

Preliminary data from the retrospective study suggest that medications given to cancer patients for other chronic indications, e.g., diabetes, hypertension etc., can also impact the survival of these patients, both positively and negatively. In an internal effort, the company screened ten approved drugs in the METAssay and METVivo platforms and eventually rank-ordered them. Four drugs were common in both the in vitro study and retrospective trial. Their rank ordering in vitro correlated with their effect on patient survival in the clinical setting, underscoring the importance of evaluating the impact of these drugs.

According to Dr. John Ellingboe, Director of Mestastop Inc., the company has the tools to delay cancer metastasis by screening for new drugs and evaluating the impact of existing drugs and is seeking collaboration partners, enabling patient outreach at the earliest.

https://news.topwirenews.com/2022/12/31/mestastop-solution-case-studies-can-change-cancer-treatment-paradigm_479729/

https://www.digitaljournal.com/pr/mestastop-solutions-inc-launches-breakthrough-platforms-to-improve-cancer-survival

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