The baculovirus expression vector system (BEVS) has been widely used for over-expressing eukaryotic proteins due to a close resemblance in post-translational modification, processing, and transportation properties of the expressed protein, to that of the mammalian cells. In comparison to the bacterial expression system, protein yield from BEVS is relatively low, resulting in higher cost of production. To improve the existing recombinant protein expression levels, baculovirus homologous region1 (hr1) was strategically integrated into the bacmid-based transfer vectors. Luciferase reporter, human Protein Kinase B-alpha (PKB-A), and N-terminal-modified CYP-1A2 genes were independently cloned in non-hr1 and hr1 constructs for generating respective bacmids and baculoviruses. These recombinant baculoviruses were utilized for comparing the expression levels at varying multiplicity of infections (MOI) and time intervals in Spodoptera frugiperda (Sf21) or Trichoplusia ni (Tni) insect cell lines. Targeted insertion of hr1 upstream to CYP-1A2, PKB-A, and Luciferase genes, compared to the non-hr1 sets, led to 3-, 3.5-, and 4.5-fold increase in the resultant protein levels, respectively. Moreover, at equal protein concentration, the corresponding activity and inhibition characteristics of these high expression hr1 sets were comparable to that of the respective non-hr1 sets. Utilization of this modified baculovirus expression construct offers significant advantage of producing recombinant proteins in a cost-effective manner for various biotechnological and therapeutic applications.

Using the proprietary Big Data PharmGPS® Discovery platform, BioXcel has created a comprehensive relationship map between immune-evasion and immune-activation pathways, comprising interacting genes and all overlapping pharmacological agents and tumors. This map was used to identify clinically validated compounds that would act synergistically in combination with immune-checkpoint inhibitors (ICI) by remodelling the tumor micro-enviroment and transforming cold, non-inflamed tumors into hot immune-sensitive tumors. One of the several compounds thus identified is BXCL701, previously known as Talabostat/PT-100, a DPP inhibitor that by inducing a wide panel of cytokines and chemokines stimulates both the innate and acquired immune system. BXCL701 has a dual immuno-oncology related MOA. Via the Fibroblast Activator Protein (FAP) target, it inhibits the activation of immuno-suppressive fibroblasts and through an angiogenic related effect, it increases immune cell extravasation into the tumor tissue. Via the DPP8/9 targets, it depresses the immuno-suppressive activity of MDSCs by inducing a granulocytic differentiation while it stimulates the priming, migration and cytotoxicity of T-cells and NK cells and the formation of memory T-cells. The hypothesis that BXCL701 immune-mediated MOA would complement the action of ICIs was validated in-vivo in the syngeneic MC38 mouse model of colon adenocarcinoma. Co-administration of BXCL701 combined with anti-PD1 showed a synergistic inhibition of tumor growth as well as synergistic up-regulation of immuno-stimulatory cytokines, IL-2, IL12 and GM-CSF. The effects of the combination on the immune-phenotyping of the circulating and tumor infiltrated immune cells will also be presented. The findings support BXCL701 ability to transform the immune-suppressive tumour microenvironment to an immuno-permissive milieu sensitive to immune-checkpoint inhibitors. Further supporting the therapeutic potential of BXCL701, an analysis of genomic alterations in FAP, DPP8 and DPP9 across a wide range of tumors singled out castration-resistant prostate cancer with a high level of DPP9 amplification (14%) and overexpression of DPP8 in 50% of the patients which could make this patient population uniquely sensitive to the combination as shown by in-vitro and in-vivo experiments. This study provides further evidence of the capability of Big data analytics to generate in-silico hypothesis of synergistic combination effects that can be converted in validated therapeutic opportunities to benefit patients non- responsive to ICI therapy. Citation Format: Luca Rastelli, Snigdha Gupta, Akhil Dahiya, Zeenia Jagga, Krishnan Nandabalan, Sanatan Upmanyu. The synergy between BXCL701, a DPP inhibitor, and immune checkpoint inhibitors discovered using AI and Big Data analytics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2629. doi:10.1158/1538-7445.AM2017-2629

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Tumor cells have long been known to successfully evade immune surveillance mechanisms.In order to curtail the tumor progression as well as to develop effective therapeutic anti-tumor strategies, key immune regulators,or so-called immune checkpoints, havebeen recognizedas playing an important role. To prevent excess tissue damage,immune checkpoints that refer to a number of inhibitory players involved in immune responses,are crucial for maintaining self-tolerance and modulating the duration and amplitude of physiological immune responses.Therapies targeting such immune checkpoints such as CTLA-4 and PD-1 have shown great clinical successas they boost the already existing immune response working against the tumorprogression. To identify novel immune checkpoints it becomes crucial to comprehensively analyze the wealth of existing knowledge regarding key signaling pathways, theircross-talk with immune regulators, protein expression profiles in tumor tissues, and patient outcomes data. These data silos require a big data analytics approach to successfully hypothesize, query and derive meaningful conclusions from this data. With a proprietary big data analytics platform PharmGPS™, we have identified a number of immune checkpoints that have clear therapeutic potential for solid tumor regenesis. First, second and third degree associations between tumorigenic pathways and immune checkpoints were identified and preliminary findings will be presented. Citation Format: Sheetal Kaw, Sanatan Upmanyu, Himani Sharma, Krishnan Nandabalan. Targeting immune checkpoints: using a big data approach for their identification, prioritization and application. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 64. doi:10.1158/1538-7445.AM2015-64

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