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1. Computational Approach for the Development of pH-Selective PD-1/PD-L1 Signaling Pathway Inhibition in Fight with Cancer

   https://doi.org/10.3390/cancers16132295  

   McDowell, Roderick C; Booth, Jordhan D; McGowan, Allyson; Kolodziejczyk, Wojciech; Hill, Glake A; Banerjee, Santanu; Feng, Manliang; Kapusta, Karina (July 2024, Cancers)

   Mok, Samuel C (Ed.)

   Immunotherapy, particularly targeting the PD-1/PD-L1 pathway, holds promise in cancer treatment by regulating the immune response and preventing cancer cells from evading immune destruction. Nonetheless, this approach poses a risk of unwanted immune system activation against healthy cells. To minimize this risk, our study proposes a strategy based on selective targeting of the PD-L1 pathway within the acidic microenvironment of tumors. We employed in silico methods, such as virtual screening, molecular mechanics, and molecular dynamics simulations, analyzing approximately 10,000 natural compounds from the MolPort database to find potential hits with the desired properties. The simulations were conducted under two pH conditions (pH = 7.4 and 5.5) to mimic the environments of healthy and cancerous cells. The compound MolPort-001-742-690 emerged as a promising pH-selective inhibitor, showing a significant affinity for PD-L1 in acidic conditions and lower toxicity compared to known inhibitors like BMS-202 and LP23. A detailed 1000 ns molecular dynamics simulation confirmed the stability of the inhibitor-PD-L1 complex under acidic conditions. This research highlights the potential of using in silico techniques to discover novel pH-selective inhibitors, which, after experimental validation, may enhance the precision and reduce the toxicity of immunotherapies, offering a transformative approach to cancer treatment. 

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2. Regulation of CTLA-4 and PD-L1 Expression in Relapsing-Remitting Multiple Sclerosis Patients after Treatment with Fingolimod, IFNβ-1α, Glatiramer Acetate, and Dimethyl Fumarate Drugs

   https://doi.org/10.3390/jpm11080721  

   Derakhshani, Afshin; Asadzadeh, Zahra; Safarpour, Hossein; Leone, Patrizia; Shadbad, Mahdi Abdoli; Heydari, Ali; Baradaran, Behzad; Racanelli, Vito (August 2021, Journal of Personalized Medicine)

   Multiple sclerosis (MS) is a chronic demyelinating disease of the central nervous system (CNS) that is characterized by inflammation which typically results in significant impairment in most patients. Immune checkpoints act as co-stimulatory and co-inhibitory molecules and play a fundamental role in keeping the equilibrium of the immune system. Cytotoxic T-lymphocyte antigen-4 (CTLA-4) and Programmed death-ligand 1 (PD-L1), as inhibitory immune checkpoints, participate in terminating the development of numerous autoimmune diseases, including MS. We assessed the CTLA-4 and PD-L1 gene expression in the different cell types of peripheral blood mononuclear cells of MS patients using single-cell RNA-seq data. Additionally, this study outlines how CTLA-4 and PD-L1 expression was altered in the PBMC samples of relapsing-remitting multiple sclerosis (RRMS) patients compared to the healthy group. Finally, it investigates the impact of various MS-related treatments in the CTLA-4 and PD-L1 expression to restrain autoreactive T cells and stop the development of MS autoimmunity. 

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3. Global stability and parameter analysis reinforce therapeutic targets of PD-L1-PD-1 and MDSCs for glioblastoma

   https://doi.org/10.1007/s00285-023-02027-y  

   Anderson, Hannah G.; Takacs, Gregory P.; Harris, Duane C.; Kuang, Yang; Harrison, Jeffrey K.; Stepien, Tracy L. (December 2023, Journal of Mathematical Biology)

   Abstract Glioblastoma (GBM) is an aggressive primary brain cancer that currently has minimally effective treatments. Like other cancers, immunosuppression by the PD-L1-PD-1 immune checkpoint complex is a prominent axis by which glioma cells evade the immune system. Myeloid-derived suppressor cells (MDSCs), which are recruited to the glioma microenviroment, also contribute to the immunosuppressed GBM microenvironment by suppressing T cell functions. In this paper, we propose a GBM-specific tumor-immune ordinary differential equations model of glioma cells, T cells, and MDSCs to provide theoretical insights into the interactions between these cells. Equilibrium and stability analysis indicates that there are unique tumorous and tumor-free equilibria which are locally stable under certain conditions. Further, the tumor-free equilibrium is globally stable when T cell activation and the tumor kill rate by T cells overcome tumor growth, T cell inhibition by PD-L1-PD-1 and MDSCs, and the T cell death rate. Bifurcation analysis suggests that a treatment plan that includes surgical resection and therapeutics targeting immune suppression caused by the PD-L1-PD1 complex and MDSCs results in the system tending to the tumor-free equilibrium. Using a set of preclinical experimental data, we implement the approximate Bayesian computation (ABC) rejection method to construct probability density distributions that estimate model parameters. These distributions inform an appropriate search curve for global sensitivity analysis using the extended fourier amplitude sensitivity test. Sensitivity results combined with the ABC method suggest that parameter interaction is occurring between the drivers of tumor burden, which are the tumor growth rate and carrying capacity as well as the tumor kill rate by T cells, and the two modeled forms of immunosuppression, PD-L1-PD-1 immune checkpoint and MDSC suppression of T cells. Thus, treatment with an immune checkpoint inhibitor in combination with a therapeutic targeting the inhibitory mechanisms of MDSCs should be explored. 

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4. Multiparatopic antibodies induce targeted downregulation of programmed death-ligand 1

   https://doi.org/10.1016/j.chembiol.2024.02.014  

   Ludwig, Seth D; Meksiriporn, Bunyarit; Tan, Jiacheng; Kureshi, Rakeeb; Mishra, Akhilesh; Kaeo, Kyle J; Zhu, Angela; Stavrakis, Georgia; Lee, Stephen J; Schodt, David J; et al (May 2024, Cell Chemical Biology)

   NA (Ed.)

   Programmed death-ligand 1 (PD-L1) drives inhibition of antigen-specific T cell responses through engage- ment of its receptor programmed death-1 (PD-1) on activated T cells. Overexpression of these immune checkpoint proteins in the tumor microenvironment has motivated the design of targeted antibodies that disrupt this interaction. Despite clinical success of these antibodies, response rates remain low, necessi- tating novel approaches to enhance performance. Here, we report the development of antibody fusion pro- teins that block immune checkpoint pathways through a distinct mechanism targeting molecular trafficking. By engaging multiple receptor epitopes on PD-L1, our engineered multiparatopic antibodies induce rapid clustering, internalization, and degradation in an epitope- and topology-dependent manner. The comple- mentary mechanisms of ligand blockade and receptor downregulation led to more durable immune cell acti- vation and dramatically reduced PD-L1 availability in mouse tumors. Collectively, these multiparatopic anti- bodies offer mechanistic insight into immune checkpoint protein trafficking and how it may be manipulated to reprogram immune outcomes. 

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5. Machine Learning of Single Cell Transcriptomic Data From anti-PD-1 Responders and Non-responders Reveals Distinct Resistance Mechanisms in Skin Cancers and PDAC

   https://doi.org/10.3389/fgene.2021.806457  

   Liu, Ryan; Dollinger, Emmanuel; Nie, Qing (February 2022, Frontiers in Genetics)

   Immune checkpoint therapies such as PD-1 blockade have vastly improved the treatment of numerous cancers, including basal cell carcinoma (BCC). However, patients afflicted with pancreatic ductal carcinoma (PDAC), one of the deadliest malignancies, overwhelmingly exhibit negative responses to checkpoint therapy. We sought to combine data analysis and machine learning to differentiate the putative mechanisms of BCC and PDAC non-response. We discover that increased MHC-I expression in malignant cells and suppression of MHC and PD-1/PD-L expression in CD8 + T cells is associated with nonresponse to treatment. Furthermore, we leverage machine learning to predict response to PD-1 blockade on a cellular level. We confirm divergent resistance mechanisms between BCC, PDAC, and melanoma and highlight the potential for rapid and affordable testing of gene expression in BCC patients to accurately predict response to checkpoint therapies. Our findings present an optimistic outlook for the use of quantitative cross-cancer analyses in characterizing immune responses and predicting immunotherapy outcomes. 

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