Conditional PD-1/PD-L1 Probody Therapeutics Induce Comparable Antitumor Immunity but Reduced Systemic Toxicity Compared with Traditional Anti-PD-1/PD-L1 Agents

Virtual clinical trials via a QSP immuno-oncology model to simulate the response to a conditionally activated PD-L1 targeting antibody in NSCLC

Recently, immunotherapies for antitumoral response have adopted conditionally activated molecules with the objective of reducing systemic toxicity. Amongst these are conditionally activated antibodies, such as PROBODY® activatable therapeutics (Pb-Tx), engineered to be proteolytically activated by proteases found locally in the tumor microenvironment (TME). These PROBODY® therapeutics molecules have shown potential as PD-L1 checkpoint inhibitors in several cancer types, including both effectiveness and locality of action of the molecule as shown by several clinical trials and imaging studies. Here, we perform an exploratory study using our recently published quantitative systems pharmacology model, previously validated for triple-negative breast cancer (TNBC), to computationally predict the effectiveness and targeting specificity of a PROBODY® therapeutics drug compared to the non-modified antibody. We begin with the analysis of anti-PD-L1 immunotherapy in non-small cell lung cancer (NSCLC). As a first contribution, we have improved previous virtual patient selection methods using the omics data provided by the iAtlas database portal compared to methods previously published in literature. Furthermore, our results suggest that masking an antibody maintains its efficacy while improving the localization of active therapeutic in the TME. Additionally, we generalize the model by evaluating the dependence of the response to the tumor mutational burden, independently of cancer type, as well as to other key biomarkers, such as CD8/Treg Tcell and M1/M2 macrophage ratio. While our results are obtained from simulations on NSCLC, our findings are generalizable to other cancer types and suggest that an effective and highly selective conditionally activated PROBODY® therapeutics molecule is a feasible option.

NSCLC: from tumorigenesis, immune checkpoint misuse to current and future targeted therapy

Non-small cell lung cancer (NSCLC) is largely promoted by a multistep tumorigenesis process involving various genetic and epigenetic alterations, which essentially contribute to the high incidence of mortality among patients with NSCLC. Clinical observations revealed that NSCLC also co-opts a multifaceted immune checkpoint dysregulation as an important driving factor in NSCLC progression and development. For example, a deregulated PI3K/AKT/mTOR pathway has been noticed in 50-70% of NSCLC cases, primarily modulated by mutations in key oncogenes such as ALK, EGFR, KRAS, and others. Additionally, genetic association studies containing patient-specific factors and local reimbursement criteria expose/reveal mutations in EGFR/ALK/ROS/BRAF/KRAS/PD-L1 proteins to determine the suitability of available immunotherapy or tyrosine kinase inhibitor therapy. Thus, the expression of such checkpoints on tumors and immune cells is pivotal in understanding the therapeutic efficacy and has been extensively studied for NSCLC treatments. Therefore, this review summarizes current knowledge in NSCLC tumorigenesis, focusing on its genetic and epigenetic intricacies, immune checkpoint dysregulation, and the evolving landscape of targeted therapies. In the context of current and future therapies, we emphasize the significance of antibodies targeting PD-1/PD-L1 and CTLA-4 interactions as the primary therapeutic strategy for immune system reactivation in NSCLC. Other approaches involving the promising potential of nanobodies, probodies, affibodies, and DARPINs targeting immune checkpoints are also described; these are under active research or clinical trials to mediate immune regulation and reduce cancer progression. This comprehensive review underscores the multifaceted nature, current state and future directions of NSCLC research and treatment.

A Generic Antibody-Blocking Protein That Enables pH-Switchable Activation of Antibody Activity

Molecular strategies that allow for reversible control of antibody activity have drawn considerable interest for both therapeutic and diagnostic applications. Protein M is a generic antibody-binding protein that binds to the Fv domain of IgGs and, in doing so, blocks antigen binding. However, the dissociation of protein M is essentially irreversible, which has precluded its use as an antibody affinity reagent and molecular mask to control antibody activity. Here, we show that introduction of 8 histidine residues on the Fv binding interface of protein M results in a variant that shows pH-switchable IgG binding. This protein M-8his variant provides an attractive and universal affinity resin for the purification of IgGs, antibody fragments (Fab and single-chain variable fragments (scFv)), and antibody conjugates. Moreover, protein M-8his enables the pH-dependent blocking of therapeutic antibodies, allowing the selective targeting of cells at pH 6.0.

Development of antibody-drug conjugates in cancer: Overview and prospects

In recent years, remarkable breakthroughs have been reported on antibody-drug conjugates (ADCs), with 15 ADCs successfully entering the market over the past decade. This substantial development has positioned ADCs as one of the fastest-growing domains in the realm of anticancer drugs, demonstrating their efficacy in treating a wide array of malignancies. Nonetheless, there is still an unmet clinical need for wider application, better efficacy, and fewer side effects of ADCs. An ADC generally comprises an antibody, a linker and a payload, and the combination has profound effects on drug structure, pharmacokinetic profile and efficacy. Hence, optimization of the key components provides an opportunity to develop ADCs with higher potency and fewer side effects. In this review, we comprehensively reviewed the current development and the prospects of ADC, provided an analysis of marketed ADCs and the ongoing pipelines globally as well as in China, highlighted several ADC platforms and technologies specific to different pharmaceutical enterprises and biotech companies, and also discussed the new related technologies, possibility of next-generation ADCs and the directions of clinical research.

Eliciting the antitumor immune response with a conditionally activated PD-L1 targeting antibody analyzed with a quantitative systems pharmacology model

Conditionally activated molecules, such as Probody therapeutics (PbTx), have recently been investigated to improve antitumoral response while reducing systemic toxicity. PbTx are engineered to be proteolytically activated by proteases that are preferentially active locally in the tumor microenvironment (TME). Here, we perform an exploratory study using our recently published quantitative systems pharmacology model, previously validated for other drugs, to evaluate the effectiveness and targeting specificity of an anti-PD-L1 PbTx compared to the non-modified antibody. We have informed the model using the PbTx dynamics and pharmacokinetics published in the literature for anti-PD-L1 in patients with triple-negative breast cancer (TNBC). Our results suggest masking of the antibody slightly decreases its efficacy, while increasing the localization of active therapeutic component in the TME. We also perform a parameter optimization for the PbTx design and drug dosing regimens to maximize the response rate. Although our results are specific to the case of TNBC, our findings are generalizable to any conditionally activated PbTx molecule in solid tumors and suggest that design of a highly effective and selective PbTx is feasible.

In silico evolution of autoinhibitory domains for a PD-L1 antagonist using deep learning models

There has been considerable progress in the development of computational methods for designing protein-protein interactions, but engineering high-affinity binders without extensive screening and maturation remains challenging. Here, we test a protein design pipeline that uses iterative rounds of deep learning (DL)-based structure prediction (AlphaFold2) and sequence optimization (ProteinMPNN) to design autoinhibitory domains (AiDs) for a PD-L1 antagonist. With the goal of creating an anticancer agent that is inactive until reaching the tumor environment, we sought to create autoinhibited (or masked) forms of the PD-L1 antagonist that can be unmasked by tumor-enriched proteases. Twenty-three de novo designed AiDs, varying in length and topology, were fused to the antagonist with a protease-sensitive linker, and binding to PD-L1 was measured with and without protease treatment. Nine of the fusion proteins demonstrated conditional binding to PD-L1, and the top-performing AiDs were selected for further characterization as single-domain proteins. Without any experimental affinity maturation, four of the AiDs bind to the PD-L1 antagonist with equilibrium dissociation constants (KDs) below 150 nM, with the lowest KD equal to 0.9 nM. Our study demonstrates that DL-based protein modeling can be used to rapidly generate high-affinity protein binders.

Crosstalk of ferroptosis regulators and tumor immunity in pancreatic adenocarcinoma: novel perspective to mRNA vaccines and personalized immunotherapy

Pancreatic adenocarcinoma (PAAD) is the eighth leading cause of cancer-related mortality that causes serious physical and mental burden to human. Reactive oxygen species accumulation and iron overload might enable ferroptosis-mediated cancer therapies. This study was to elusive novel ferroptosis regulator and its association with immune microenvironment and PD-L1 in PAAD. RNA-seq data and relevant information were obtained from The Cancer Genome Atlas and Genotype-Tissue Expression. The R packages "ggplot2" and "pheatmap" were used to the expression of 20 ferroptosis regulators between PAAD and normal tissues. The R package "ConsensusClusterPlus", "survival", "survminer", "immunedeconv", and TIDE algorithm performed consensus clustering, overall survival, progression-free survival, disease free survival, immune infiltration level, and immunotherapy responses between cluster 1 and cluster 2. The prognostic value was confirmed by the Kaplan-Meier curves, receiver operating characteristic curve, univariate and multivariate cox regression, and nomogram. Moreover, the relationship of FANCD2 and immunity, drug sensitivity was investigated by R package "ggstatsplot", "immunedeconv", "ggalluvial" and "pRRophetic". Besides, the qRT-PCR, immunohistochemistry and western blotting detected the expression of FANCD2 in PAAD cell lines. Most ferroptosis regulators were up-regulated in PAAD, while the expression of LPCAT3, MT1G, and GLS2 was down-regulated in PAAD (P < 0.05), indicting there was a positively correlation among ferroptosis regulators. Based on clustering parameter, we identified cluster 1 and cluster 2, and cluster 2 had a better prognosis for patients with PAAD. The immune infiltration level of cluster 1 was higher in macrophage M1, myeloid dendritic cell, T cell CD4 + Th2, B cell, T cell CD8 + central memory, immune score, and microenvironment score than cluster 2 in PAAD. Moreover, FANCD2 was up-regulated in PAAD by public databases, immunohistochemistry, qRT-PCR and Western blotting, which had closely related to overall survival, immune microenvironment, and drug sensitivity. A novel crosstalk of ferroptosis exhibits a favourable prognostic performance and builds a robust theoretical foundation for mRNA vaccine and personalized immunotherapy. FANCD2 could be an effective for prognostic recognition, immune efficacy evaluation, and mRNA vaccine for patients with PAAD, providing a vital guidance for further study of regulating tumor immunity and vaccine development.

Conditionally Active, pH-Sensitive Immunoregulatory Antibodies Targeting VISTA and CTLA-4 Lead an Emerging Class of Cancer Therapeutics

Immune checkpoints and other immunoregulatory targets can be difficult to precisely target due to expression on non-tumor immune cells critical to maintaining immune homeostasis in healthy tissues. On-target/off-tumor binding of therapeutics results in significant pharmacokinetic and pharmacodynamic problems. Target-mediated drug disposition (TMDD) significantly limits effective intratumoral drug levels and adversely affects anti-tumor efficacy. Target engagement outside the tumor environment may lead to severe immune-related adverse events (irAEs), resulting in a narrowing of the therapeutic window, sub-optimal dosing, or cessation of drug development altogether. Overcoming these challenges has become tractable through recent advances in antibody engineering and screening approaches. Here, we review the discovery and development of conditionally active antibodies with minimal binding to target at physiologic pH but high-affinity target binding at the low pH of the tumor microenvironment by focusing on the discovery and improved properties of pH-dependent mAbs targeting two T cell checkpoints, VISTA and CTLA-4.

In silico evolution of protein binders with deep learning models for structure prediction and sequence design

There has been considerable progress in the development of computational methods for designing protein-protein interactions, but engineering high-affinity binders without extensive screening and maturation remains challenging. Here, we test a protein design pipeline that uses iterative rounds of deep learning (DL)-based structure prediction (AlphaFold2) and sequence optimization (ProteinMPNN) to design autoinhibitory domains (AiDs) for a PD-L1 antagonist. Inspired by recent advances in therapeutic design, we sought to create autoinhibited (or masked) forms of the antagonist that can be conditionally activated by proteases. Twenty-three de novo designed AiDs, varying in length and topology, were fused to the antagonist with a protease sensitive linker, and binding to PD-L1 was tested with and without protease treatment. Nine of the fusion proteins demonstrated conditional binding to PD-L1 and the top performing AiDs were selected for further characterization as single domain proteins. Without any experimental affinity maturation, four of the AiDs bind to the PD-L1 antagonist with equilibrium dissociation constants (KDs) below 150 nM, with the lowest KD equal to 0.9 nM. Our study demonstrates that DL-based protein modeling can be used to rapidly generate high affinity protein binders.

Design of a protease-activated PD-L1 inhibitor

Immune checkpoint inhibitors that bind to the cell surface receptor PD-L1 are effective anti-cancer agents but suffer from immune-related adverse events as PD-L1 is expressed on both healthy and cancer cells. To mitigate toxicity, researchers are testing prodrugs that have low affinity for checkpoint targets until activated with proteases enriched in the tumor microenvironment. Here, we engineer a prodrug form of a PD-L1 inhibitor. The inhibitor is a soluble PD-1 mimetic that was previously engineered to have high affinity for PD-L1. In the basal state, the binding surface of the PD-1 mimetic is masked by fusing it to a soluble variant of its natural ligand, PD-L1. Proteolytic cleavage of the linker that connects the mask to the inhibitor activates the molecule. To optimize the mask so that it effectively blocks binding to PD-L1 but releases upon cleavage, we tested a set of mutants with varied affinity for the inhibitor. The top-performing mask reduces the affinity of the prodrug for PD-L1 120-fold, and binding is nearly fully recovered upon cleavage. In a cell-based assay measuring inhibition of the PD-1:PD-L1 interaction on the surface of cells, the IC50s of the masked inhibitors were up to 40-fold higher than their protease-treated counterparts. The changes in activity we observe upon protease treatment are comparable to systems currently tested in the clinic and provide evidence that natural binding partners are an excellent starting point for creating a prodrug.

Strategies for Conditional Regulation of Proteins

Design of the next-generation of therapeutics, biosensors, and molecular tools for basic research requires that we bring protein activity under control. Each protein has unique properties, and therefore, it is critical to tailor the current techniques to develop new regulatory methods and regulate new proteins of interest (POIs). This perspective gives an overview of the widely used stimuli and synthetic and natural methods for conditional regulation of proteins.

Immunotherapy in non-small cell lung cancer: Past, present, and future directions

Many decades in the making, immunotherapy has demonstrated its ability to produce durable responses in several cancer types. In the last decade, immunotherapy has shown itself to be a viable therapeutic approach for non-small cell lung cancer (NSCLC). Several clinical trials have established the efficacy of immune checkpoint blockade (ICB), particularly in the form of anti-programmed death 1 (PD-1) antibodies, anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) antibodies and anti-programmed death 1 ligand (PD-L1) antibodies. Many trials have shown progression free survival (PFS) and overall survival (OS) benefit with either ICB alone or in combination with chemotherapy when compared to chemotherapy alone. The identification of biomarkers to predict response to immunotherapy continues to be evaluated. The future of immunotherapy in lung cancer continues to hold promise with the development of combination therapies, cytokine modulating therapies and cellular therapies. Lastly, we expect that innovative advances in technology, such as artificial intelligence (AI) and machine learning, will begin to play a role in the future care of patients with lung cancer.

Exploiting protease activation for therapy

Proteases have crucial roles in homeostasis and disease; and protease inhibitors and recombinant proteases in enzyme replacement therapy have become key therapeutic applications of protease biology across several indications. This review briefly summarises therapeutic approaches based on protease activation and focuses on how recent insights into the spatial and temporal control of the proteolytic activation of growth factors and interleukins are leading to unique strategies for the discovery of new medicines. In particular, two emerging areas are covered: the first is based on antibody therapies that target the process of proteolytic activation of the pro-form of proteins rather than their mature form; the second covers a potentially new class of biopharmaceuticals using engineered, proteolytically activable and initially inactive pro-forms of antibodies or effector proteins to increase specificity and improve the therapeutic window.

Targeting Syndecan-1: New Opportunities in Cancer Therapy

Syndecan-1 (SDC1, CD138) is one of the heparan sulfate proteoglycans and is essential for maintaining normal cell morphology, interacting with extracellular and intracellular protein repertoire as well as mediating signaling transduction upon environmental stimuli. The critical role of SDC1 in promoting tumorigenesis and metastasis has been increasingly recognized in various cancer types, implying a promising potential of utilizing SDC1 as a novel target for cancer therapy. This review summarizes the current knowledge on SDC1 structure and functions, including its role in tumor biology. We also discuss the highlights and limitations of current SDC1-targeted therapies as well as the obstacles in developing new therapeutic methods, offering our perspective on the future directions to target SDC1 for cancer treatment.

The prognostic value of prognostic nutritional index in advanced cancer receiving PD-1/L1 inhibitors: A meta-analysis

Purpose

To study the prognostic value of the prognostic nutritional index (PNI) in advanced cancers receiving programmed death‐1/programmed death‐ligand 1 (PD‐1/L1) inhibitors.

Methods

Online electronic databases were comprehensively searched and available literature was retrieved. We extracted available data from included researches and pooled the hazard ratios (HRs) and 95% confidence intervals (CIs) to learn the prognostic value of PNI on overall survival (OS) or progression‐free survival (PFS); and meanwhile calculated the relative risk (RR) with 95% CI to study the relationship between PNI and treatment efficacy (objective response rate [ORR] or disease control rate [DCR]) in late staged cancer receiving PD‐1/L1 inhibitors.

Results

Nine studies were finally selected for this meta‐analysis. We obtained data regarding PNI on OS from all nine studies, and the pooled HR was 2.31 (95% CI 1.81–2.94, p = 0.000), showing a correlation between low PNI and worse OS. Eight studies reported a relationship between PNI and PFS, and combined results revealed shorter PFS in patients with lower PNI, with an HR of 1.75 (95% CI 1.40–2.18, p = 0.000). Four studies explored the association between PNI and ORR and two studies explored the influence of PNI on DCR. An association between PNI and ORR (RR = 0.47, p = 0.003) was observed, while no association between PNI and DCR (RR = 0.49, p = 0.103) was observed by pooling these studies.

Conclusion

In summary, this meta‐analysis indicated that a lower PNI was significantly correlated with decreased OS and PFS and played adverse roles in ORR in advanced cancer patients receiving PD‐1/L1 inhibitors. Therefore, PNI could be promising for predicting prognosis and treatment response in advanced malignancies treated with PD‐1/L1 inhibitors.