Background Checkpoint inhibition (CPI) therapies have led to large successes in treating certain cancer types, but they are largely ineffective in immunologically 'cold' tumors, with an immunosuppressive microenvironment. The immunosuppressive tumor microenvironment (TME) is dominated by Treg and suppressive myeloid cells. Patients who respond well to CPI therapies exhibit a Th1-biased TME driven by IFNg and related genes. Thus, approaches that can safely transform an immunosuppressive TME would be beneficial. Methods mRNA-based therapies are a rapidly growing class of medications that can redefine how many diseases are treated. These therapies enable the production of biologics directly in the patient and mRNA LNPs are straightforward to manufacture at scale. Despite these key advantages, mRNA therapeutics are yet to show their true potential in oncology. Dose limiting toxicities of mRNA immunotherapies are driven, in part, by systemic payload (encoded protein) toxicity, which can be avoided by engineering the mRNA to improve its onco-selectivity and thereby reduce its on-target off-tumor toxicity. To tackle this challenge, Kernal Biologics has developed onco-selective mRNA LNP therapies that utilize a computational pipeline enabled by machine learning. The aim is to overcome the immunosuppressive conditions within the tumor microenvironment. Results Previously, we have shown that local onco-selective mRNA LNP therapy via intratumoral injections can results in complete responses in various preclinical models, including those that are resistant to checkpoint inhibitors. Here we report the outcome of in vivo proof-of-concept studies for systemic mRNA LNP administration. In a syngeneic MC38 tumor model, KR-336 was administered intravenously as a monotherapy, resulting in impressive anti-tumor effectiveness. This was evidenced by tumor regression, multiple complete responses, and enhanced overall survival rates. The mRNA LNPs were well-tolerated and prompted immune activation, fostering a pro-inflammatory tumor microenvironment. Conclusions These findings collectively indicate the viability of systemic onco-selective mRNA LNP therapy as a potential treatment option for cancers characterized by an immunosuppressive tumor microenvironment. Additionally, it has the potential to drastically expand the patient population that can benefit from cancer immunotherapy. Ethics Approval All in vivo animal studies were performed in accordance with the IACUC number 2021–1309 at CRADL, Cambridge MA

Novel improved cancer immunotherapies are needed, since most cancer patients exhibit a treatment resistant tumor microenvironment to currently available immunotherapeutic modalities. Due to primary resistance or the development of acquired resistance to therapeutic approaches these patients unfortunately do not benefit from a long-lasting overall survival benefit. Combination treatments are a promising approach to induce a sustained anti-tumor immune response. However, dose limiting adverse events in healthy tissues and/or the complex expensive production requirements have limited the full therapeutic potential of combinatorial applications. mRNA-based combinatorial therapy options have evolved by recent advancements in the production, purification, and delivery of mRNA to cells. Today, mRNA therapies are a rapidly growing class of medications that can redefine how many diseases are treated. These therapies enable the production of biologics directly in the patient and mRNA LNPs are easy to manufacture at scale. Despite these key advantages, mRNA therapeutics are yet to show their true potential in oncology. Dose limiting toxicities of mRNA immunotherapies are driven, in part, by systemic payload (encoded protein) toxicity, which can be avoided by engineering the mRNA to improve its onco-selectivity and thereby reduce systemic target-mediated adverse events. Kernal Biologics develops novel onco-selective mRNA therapies directed to breach the immunosuppressive tumor microenvironment. Based on our proprietary machine learning-enabled computational pipeline, we designed our next generation mRNA therapeutics. These mRNAs have the potential to increase the depth and breadth of anti-PD1/PD-L1 treatment plus enable responses in patients that are currently non-responders or refractory to the clinically approved immune checkpoint blockade therapies. Here, we describe combination therapies of tumor-selective mRNA LNPs that achieve strong and lasting anti-tumor efficacy in syngeneic tumor models. We observed regression of established tumors, complete responses (CRs) and improved overall survival. At efficacious doses the mRNA LNPs were well tolerated while driving anti-tumor immune activation and modulation of the tumor microenvironment. Mouse blood hematology and chemistry analyses were within a normal range. Similarly, pathological immunohistochemistry analysis of liver, spleen and bone marrow revealed no findings. In summary, our data support the feasibility of onco-selective mRNA combination treatment of a variety of cancers with poor T cell infiltration and immunosuppressive TME, major obstacles in cancer immunotherapy. Citation Format: Manfred Kraus, Rudy Christmas, Tom A. Addison, Yulia Rybakova, Leona Lee, Jieni Xu, Mark Krimmer, Cafer Ozdemir, Burak Yilmaz, Yusuf Erkul. Combination therapy with onco-selective mRNA LNPs targets the complex immunosuppressive tumor microenvironment and is well tolerated at efficacious doses [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3232.

Background Cancer immunotherapy has made significant advancements in revolutionizing cancer treatment, but the presence of T regulatory (Treg) cells within tumors presents a major hurdle. These cells can suppress the activation of effector immune cells induced by the immunotherapy, thus hindering the desired anti-tumor response. Specific depletion of tumor Tregs is proven to be an efficient strategy to improve efficacy of anti-PD-1/PD-L1 therapies. Methods mRNA-based combinatorial therapy options have evolved by recent advancements in the production, purification, and delivery of mRNA to cells. Today, mRNA therapies are a rapidly growing class of medications that can redefine how many diseases are treated. These therapies enable the production of biologics directly in the patient and mRNA LNPs are straightforward to manufacture at scale. Despite these key advantages, mRNA therapeutics are yet to show their true potential in oncology. Dose limiting toxicities of mRNA immunotherapies are driven, in part, by systemic payload (encoded protein) toxicity, which can be avoided by engineering the mRNA to improve its onco-selectivity and thereby reduce systemic target-mediated adverse events. To address that challenge, Kernal Biologics has developed onco-selective mRNA LNP therapies (KR-505) using a machine learning-enabled computational pipeline, targeting Treg cells within the immunosuppressive tumor microenvironment. Results In preclinical studies using a syngeneic tumor model (C57BL6/MC38), KR-505 demonstrated strong anti-tumor efficacy, leading to tumor regression, complete responses, and improved overall survival. These mRNA LNPs (lipid nanoparticles) were well-tolerated, promoting anti-tumor immune activation and modulating the tumor microenvironment. Furthermore, combination treatment with anti-PD-1 therapies showed even better efficacy. Notably, treatment with KR-505, but not the inactive drug analog, led to a significant reduction in Treg cell numbers within the tumors. Reimplantation of complete responders resulted in no tumor growth, indicating the development of anti-tumor immunity. Conclusions Our findings support the feasibility of onco-selective mRNA combination therapy as a potential solution for cancers characterized by an immunosuppressive tumor microenvironment and can help broaden the number of patients who can benefit from anti-PD-1 treatment.

Genomic studies have significantly improved our understanding of hepatocellular carcinoma (HCC) biology and have led to the discovery of multiple protein-coding genes driving hepatocarcinogenesis. In addition, these studies have identified thousands of new non-coding transcripts deregulated in HCC. We hypothesize that some of these transcripts may be involved in disease progression. Long non-coding RNAs are a large class of non-coding transcripts which participate in the regulation of virtually all cellular functions. However, a majority of lncRNAs remain dramatically understudied. Here, we applied a pooled shRNA-based screen to identify lncRNAs essential for HCC cell survival. We validated our screening results using RNAi, CRISPRi, and antisense oligonucleotides. We found a lncRNA, termed ASTILCS, that is critical for HCC cell growth and is overexpressed in tumors from HCC patients. We demonstrated that HCC cell death upon ASTILCS knockdown is associated with apoptosis induction and downregulation of a neighboring gene, protein tyrosine kinase 2 (PTK2), a crucial protein for HCC cell survival. Taken together, our study describes a new, non-coding RNA regulator of HCC.

Nanoformulations of therapeutic drugs are transforming our ability to effectively deliver and treat a myriad of conditions. Often, however, they are complex to produce and exhibit low drug loading, except for nanoparticles formed via co-assembly of drugs and small molecular dyes, which display drug-loading capacities of up to 95%. There is currently no understanding of which of the millions of small-molecule combinations can result in the formation of these nanoparticles. Here we report the integration of machine learning with high-throughput experimentation to enable the rapid and large-scale identification of such nanoformulations. We identified 100 self-assembling drug nanoparticles from 2.1 million pairings, each including one of 788 candidate drugs and one of 2,686 approved excipients. We further characterized two nanoparticles, sorafenib–glycyrrhizin and terbinafine–taurocholic acid both ex vivo and in vivo. We anticipate that our platform can accelerate the development of safer and more efficacious nanoformulations with high drug-loading capacities for a wide range of therapeutics. Self-assembly of small drugs with organic dyes represents a facile route to synthesize nanoparticles with high drug-loading capability. Here the authors combine a machine learning approach with high-throughput experimental validation to identify which combinations of drugs and excipient lead to successful nanoparticle formation and characterize the therapeutic efficacy of two of them in vitro and in animal models.

Antibody-based drugs are a leading class of biologics used to treat a variety of diseases, including cancer. However, wide antibody implementation is hindered by manufacturing challenges and high production cost. Use of in-vitro-transcribed mRNA (IVT-mRNA) for endogenous protein expression has the potential to circumvent many of the shortcomings of antibody production and therapeutic application. Here, we describe the development of an IVT-mRNA system for in vivo delivery of a humanized anti-HER2 (also known as ERBB2) antibody, trastuzumab, and demonstrate its anticancer activity. We engineered the IVT-mRNA sequence to maximize expression, then formulated the IVT-mRNA into lipid-based nanoparticles (LNPs) to protect the mRNA from degradation and enable efficient in vivo delivery. Systemic delivery of the optimized IVT-mRNA loaded into LNPs resulted in antibody serum concentrations of 45 ± 8.6 μg/mL for 14 days after LNP injection. Further studies demonstrated an improved pharmacokinetic profile of the produced protein compared to injection of trastuzumab protein. Finally, treatment of tumor-bearing mice with trastuzumab IVT-mRNA LNPs selectively reduced the volume of HER2-positive tumors and improved animal survival. Taken together, the results of our study demonstrate that using IVT-mRNA LNPs to express full-size therapeutic antibodies in the liver can provide an effective strategy for cancer treatment and offers an alternative to protein administration.

Carnosine is an endogenous dipeptide with antiproliferative properties. Here we show that carnosine selectively inhibits proliferation of human glioblastoma cells (U-118-MG) compared to breast (MB231) and head and neck carcinoma (FaDu and Cal27) cells. Carnosine-induced inhibition of U-118-MG proliferation is associated with a significant: decrease in cellular reactive oxygen species levels, increase in manganese superoxide dismutase (MnSOD) and increase in cyclin B1 expression resulting in G2-block. We conclude that the antiproliferative property of carnosine is due to its ability to enhance MnSOD and cyclin B1 expression. These results will be of significance to the potential application of carnosine in brain cancer therapy.

A primary culture of murine cerebellar neurons was used to induce oxidative stress resulting in the accumulation of reactive oxygen species (ROS) and activation of ERK 1/2 kinase. Short-term incubation (15 min) of cerebellar neurons with homocysteine (HC) or N-methyl-D-aspartate (NMDA) induced partial ERK 1/2 phosphorylation thus providing the activation of the enzyme. Inhibitors of NMDA receptors, MK-801 or D-AP5, both prevented the activation of cells by HC or NMDA. Another receptor-dependent means of oxidative stress stimulation is exposure of cells to the cardiac glycoside ouabain, a specific inhibitor of Na/K-ATPase. Ouabain induces ROS accumulation and substantial ERK1/2 activation in neuronal cells at concentrations as low as 1 nM - 1 M, which corresponds to participation of Na/K-ATPase in intracellular signalling. Neuropeptide carnosine added to the cells 2 hours before oxidative stress prevented both ROS accumulation and ERK1/2 activation. As ERK1/2 kinase plays a key role in gene expression responsible for either cell adaptation or cell death, the model used gives a useful tool to characterize the effect of natural and synthetic anti-cancer drugs on cellular life. The data presented show that carnosine is a natural modulator of oxidative stress in neuronal cells, providing regulation of ERK1/2 activity via buffering intracellular ROS levels.