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How mRNA therapeutics are entering the monoclonal antibody field.

ABSTRACT: In 1975, Milstein and Köhler revolutionized the medical world with the development of the hybridoma technique to produce monoclonal antibodies. Since then, monoclonal antibodies have entered almost every branch of biomedical research. Antibodies are now used as frontline therapeutics in highly divergent indications, ranging from autoimmune disease over allergic asthma to cancer. Wider accessibility and implementation of antibody-based therapeutics is however hindered by manufacturing challenges and high development costs inherent to protein-based drugs. For these reasons, alternative ways are being pursued to produce and deliver antibodies more cost-effectively without hampering safety. Over the past decade, messenger RNA (mRNA) based drugs have emerged as a highly appealing new class of biologics that can be used to encode any protein of interest directly in vivo. Whereas current clinical efforts to use mRNA as a drug are mainly situated at the level of prophylactic and therapeutic vaccination, three recent preclinical studies have addressed the feasibility of using mRNA to encode therapeutic antibodies directly in vivo. Here, we highlight the potential of mRNA-based approaches to solve several of the issues associated with antibodies produced and delivered in protein format. Nonetheless, we also identify key hurdles that mRNA-based approaches still need to take to fulfill this potential and ultimately replace the current protein antibody format.

SUBMITTER: Van Hoecke L

PROVIDER: S-EPMC6387507 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Project description:Patents of some therapeutic monoclonal antibodies (mAb) used for cancer treatment are ending soon, allowing for the entry of similar analogs (biosimilars) onto the market. To have a biosimilar approved by health agencies, the manufacturer must typically demonstrate functional and physicochemical similarity between the biosimilar and the approved reference product. Functional similarity is often validated with cell-based assays, but the information gained is limited. In contrast, RNA-seq methods enable a sensitive transcriptomic analysis, providing detailed information on pathways and cellular responses. Trastuzumab inhibits signaling of the cell-surface receptor HER2, which is overexpressed in approximately 30% of all breast cancer patients. Here, we compare the functional effect of the mAb Trastuzumab and a corresponding biosimilar. The BT-474 breast cancer cell line was treated with the reference product, Trastuzumab (Herceptin®), or a proposed biosimilar (ApoTras), and the cellular transcriptomes were analyzed by RNA-seq. Functional similarity was assessed using two statistical contrasts. One contrast evaluated the mechanism of action by comparing treated samples (Her and ApoTras, n = 19) vs. untreated controls (n = 10), which identified 2623 differentially expressed genes (DEG) at a minimum fold change of 1.25. The other contrast directly compared ApoTras (n = 9) vs. Her (n = 10) to determine differences in expression between treatments and identified 24 DEG using a 1.25 fold- change threshold. This low DEG number reveals a very high similarity of effect of both mAb treatments on the cancer cells. A gene set overrepresentation analysis of DEG for mAb treatments revealed mechanisms of action, which were consistent with known trastuzumab effects. Supporting the small number of changes between both treatments, a post-hoc power analysis for the between-treatment comparison estimated power of 0.88 to detect a gene expression fold-changes of 2.0. To summarize these data, we introduce an overall similarity index (SI) to quantify treatment similarity based on changes in gene expression. Using statistical contrasts with RNA-seq analysis provides a powerful tool for the comparison of biological effects of biosimilar and originator compounds and can be broadly used for functional comparisons of drug treatments.

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How mRNA therapeutics are entering the monoclonal antibody field.

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