Identification and characterization of M6903, an antagonistic anti-TIM-3 monoclonal antibody.

T cell immunoglobulin and mucin domain-3 (TIM-3) is an immune checkpoint that regulates normal immune responses but can be exploited by tumor cells to evade immune surveillance. TIM-3 is primarily expressed on immune cells, particularly on dysfunctional and exhausted T cells, and engagement of TIM-3 with its ligands promotes TIM-3-mediated T cell inhibition. Antagonistic ligand-blocking anti-TIM-3 antibodies have the potential to abrogate T cell inhibition, activate antigen-specific T cells, and enhance anti-tumor immunity. Here we describe M6903, a fully human anti-TIM-3 antibody without effector function and with high affinity and selectivity to TIM-3. We demonstrate that M6903 blocks the binding of TIM-3 to three of its ligands, phosphatidylserine (PtdSer), carcinoembryonic antigen cell adhesion-related molecule 1 (CEACAM1), and galectin 9 (Gal-9). These results are supported by an atomic resolution crystal structure and functional assays, which demonstrate that M6903 monotherapy enhanced T cell activation. This activation was further enhanced by the combination of M6903 with bintrafusp alfa, a bifunctional fusion protein that simultaneously blocks the transforming growth factor-ß (TGF-ß) and programmed death ligand 1 (PD-L1) pathways. M6903 and bintrafusp alfa combination therapy also enhanced anti-tumor efficacy in huTIM-3 knock-in mice, relative to either monotherapy. These in vitro and in vivo data, along with favorable pharmacokinetics in marmoset monkeys, suggest that M6903 as a monotherapy warrants further pre-clinical assessment and that M6903 and bintrafusp alfa may be a promising combination therapy in the clinic.

Prediction of active human dose: learnings from 20 years of Merck KGaA experience, illustrated by case studies.

High-quality dose predictions based on a good understanding of target engagement is one of the main translational goals in drug development. Here, we systematically evaluate active human dose predictions for 15 Merck KGaA/EMD Serono assets spanning several modalities and therapeutic areas. Using case studies, we illustrate the value of adhering to the translational best practices of having an exposure-response relationship in an appropriate animal model; having validated, translatable pharmacodynamic (PD) biomarkers measurable in Phase I populations in the right tissue; having a deeper understanding of biology; and capturing uncertainties in predictions. Given the gap in publications on the subject, we believe that the learnings from this unique diverse data set, which are generic to the industry, will trigger actions to improve future predictions.

Correction to: 33rd Annual Meeting & Pre-Conference Programs of the Society for Immunotherapy of Cancer (SITC 2018).

After publication of this supplement [1, 2], it was brought to our attention that due to an error authors were missing in the following abstracts. This has now been included in this correction.

Translational Medicine Guide transforms drug development processes: the recent Merck experience.

Merck is implementing a question-based Translational Medicine Guide (TxM Guide) beginning as early as lead optimization into its stage-gate drug development process. Initial experiences with the TxM Guide, which is embedded into an integrated development plan tailored to each development program, demonstrated opportunities to improve target understanding, dose setting (i.e., therapeutic index), and patient subpopulation selection with more robust and relevant early human-based evidence, and increased use of biomarkers and simulations. The TxM Guide is also helping improve organizational learning, costs, and governance. It has also shown the need for stronger external resources for validating biomarkers, demonstrating clinical utility, tracking natural disease history, and biobanking.

Digital analysis of cDNA abundance; expression profiling by means of restriction fragment fingerprinting.

BACKGROUND: Gene expression profiling among different tissues is of paramount interest in various areas of biomedical research. We have developed a novel method (DADA, Digital Analysis of cDNA Abundance), that calculates the relative abundance of genes in cDNA libraries. RESULTS: DADA is based upon multiple restriction fragment length analysis of pools of clones from cDNA libraries and the identification of gene-specific restriction fingerprints in the resulting complex fragment mixtures. A specific cDNA cloning vector had to be constructed that governed missing or incomplete cDNA inserts which would generate misleading fingerprints in standard cloning vectors. Double stranded cDNA was synthesized using an anchored oligo dT primer, uni-directionally inserted into the DADA vector and cDNA libraries were constructed in E. coli. The cDNA fingerprints were generated in a PCR-free procedure that allows for parallel plasmid preparation, labeling, restriction digest and fragment separation of pools of 96 colonies each. This multiplexing significantly enhanced the throughput in comparison to sequence-based methods (e.g. EST approach). The data of the fragment mixtures were integrated into a relational database system and queried with fingerprints experimentally produced by analyzing single colonies. Due to limited predictability of the position of DNA fragments on the polyacrylamid gels of a given size, fingerprints derived solely from cDNA sequences were not accurate enough to be used for the analysis. We applied DADA to the analysis of gene expression profiles in a model for impaired wound healing (treatment of mice with dexamethasone). CONCLUSIONS: The method proved to be capable of identifying pharmacologically relevant target genes that had not been identified by other standard methods routinely used to find differentially expressed genes. Due to the above mentioned limited predictability of the fingerprints, the method was yet tested only with a limited number of experimentally determined fingerprints and was able to detect differences in gene expression of transcripts representing 0.05% of the total mRNA population (e.g. medium abundant gene transcripts).