Faculty Bibliography

BACKGROUND:Clinical trials are vital for developing new therapies but can also delay drug development. Efficient trial data management, optimized trial protocol, and accurate patient identification are critical for reducing trial timelines. Natural language processing (NLP) has the potential to achieve these objectives. OBJECTIVE:This study aims to assess the feasibility of using data-driven approaches to optimize clinical trial protocol design and identify eligible patients. This involves creating a comprehensive eligibility criteria knowledge base integrated within electronic health records using deep learning-based NLP techniques. METHODS:We obtained data of 3281 industry-sponsored phase 2 or 3 interventional clinical trials recruiting patients with non-small cell lung cancer, prostate cancer, breast cancer, multiple myeloma, ulcerative colitis, and Crohn disease from ClinicalTrials.gov, spanning the period between 2013 and 2020. A customized bidirectional long short-term memory- and conditional random field-based NLP pipeline was used to extract all eligibility criteria attributes and convert hypernym concepts into computable hyponyms along with their corresponding values. To illustrate the simulation of clinical trial design for optimization purposes, we selected a subset of patients with non-small cell lung cancer (n=2775), curated from the Mount Sinai Health System, as a pilot study. RESULTS:-score (0.83, range 0.67-1), enabling the efficient extraction of granular criteria entities and relevant attributes from 3281 clinical trials. A standardized eligibility criteria knowledge base, compatible with electronic health records, was developed by transforming hypernym concepts into machine-interpretable hyponyms along with their corresponding values. In addition, an interface prototype demonstrated the practicality of leveraging real-world data for optimizing clinical trial protocols and identifying eligible patients. CONCLUSIONS:Our customized NLP pipeline successfully generated a standardized eligibility criteria knowledge base by transforming hypernym criteria into machine-readable hyponyms along with their corresponding values. A prototype interface integrating real-world patient information allows us to assess the impact of each eligibility criterion on the number of patients eligible for the trial. Leveraging NLP and real-world data in a data-driven approach holds promise for streamlining the overall clinical trial process, optimizing processes, and improving efficiency in patient identification.

The only FDA-approved oral immunotherapy for a food allergy provides protection against accidental exposure to peanuts. However, this therapy often causes discomfort or side effects and requires long-term commitment. Better preventive and therapeutic solutions are urgently needed. We develop a tolerance-inducing vaccine technology that utilizes glycosylation-modified antigens to induce antigen-specific non-responsiveness. The glycosylation-modified antigens are administered intravenously (i.v.) or subcutaneously (s.c.) and traffic to the liver or lymph nodes, respectively, leading to preferential internalization by antigen-presenting cells, educating the immune system to respond in an innocuous way. In a mouse model of cow's milk allergy, treatment with glycosylation-modified β-lactoglobulin (BLG) is effective in preventing the onset of allergy. In addition, s.c. administration of glycosylation-modified BLG shows superior safety and potential in treating existing allergies in combination with anti-CD20 co-therapy. This platform provides an antigen-specific immunomodulatory strategy to prevent and treat food allergies.

Atherosclerosis is a chronic inflammatory disease associated with the accumulation of low-density lipoprotein (LDL) in arterial walls. Higher levels of the anti-inflammatory cytokine IL-10 in serum are correlated with reduced plaque burden. However, cytokine therapies have not translated well to the clinic, partially due to their rapid clearance and pleiotropic nature. Here, we engineered IL-10 to overcome these challenges by hitchhiking on LDL to atherosclerotic plaques. Specifically, we constructed fusion proteins in which one domain is IL-10 and the other is an antibody fragment (Fab) that binds to protein epitopes of LDL. In murine models of atherosclerosis, we show that systemically administered Fab-IL-10 constructs bind circulating LDL and traffic to atherosclerotic plaques. One such construct, 2D03-IL-10, significantly reduces aortic immune cell infiltration to levels comparable to healthy mice, whereas non-targeted IL-10 has no therapeutic effect. Mechanistically, we demonstrate that 2D03-IL-10 preferentially associates with foamy macrophages and reduces pro-inflammatory activation markers. This platform technology can be applied to a variety of therapeutics and shows promise as a potential targeted anti-inflammatory therapy in atherosclerosis.

Butyrate-a metabolite produced by commensal bacteria-has been extensively studied for its immunomodulatory effects on immune cells, including regulatory T cells, macrophages and dendritic cells. However, the development of butyrate as a drug has been hindered by butyrate's poor oral bioavailability, owing to its rapid metabolism in the gut, its low potency (hence, necessitating high dosing), and its foul smell and taste. Here we report that the oral bioavailability of butyrate can be increased by esterifying it to serine, an amino acid transporter that aids the escape of the resulting odourless and tasteless prodrug (O-butyryl-L-serine, which we named SerBut) from the gut, enhancing its systemic uptake. In mice with collagen-antibody-induced arthritis (a model of rheumatoid arthritis) and with experimental autoimmune encephalomyelitis (a model of multiple sclerosis), we show that SerBut substantially ameliorated disease severity, modulated key immune cell populations systemically and in disease-associated tissues, and reduced inflammatory responses without compromising the global immune response to vaccination. SerBut may become a promising therapeutic for autoimmune and inflammatory diseases.

Therapeutic vaccination has long been a promising avenue for cancer immunotherapy but is often limited by tumor heterogeneity. The genetic and molecular diversity between patients often results in variation in the antigens present on cancer cell surfaces. As a result, recent research has focused on personalized cancer vaccines. Although promising, this strategy suffers from time-consuming production, high cost, inaccessibility, and targeting of a limited number of tumor antigens. Instead, we explore an antigen-agnostic polymeric in situ cancer vaccination platform for treating blood malignancies, in our model here with acute myeloid leukemia (AML). Rather than immunizing against specific antigens or targeting adjuvant to specific cell-surface markers, this platform leverages a characteristic metabolic and enzymatic dysregulation in cancer cells that produces an excess of free cysteine thiols on their surfaces. These thiols increase in abundance after treatment with cytotoxic agents such as cytarabine, the current standard of care in AML. The resulting free thiols can undergo efficient disulfide exchange with pyridyl disulfide (PDS) moieties on our construct and allow for in situ covalent attachment to cancer cell surfaces and debris. PDS-functionalized monomers are incorporated into a statistical copolymer with pendant mannose groups and TLR7 agonists to target covalently linked antigen and adjuvant to antigen-presenting cells in the liver and spleen after IV administration. There, the compound initiates an anticancer immune response, including T-cell activation and antibody generation, ultimately prolonging survival in cancer-bearing mice.

Immunoengineering is a rapidly evolving field that has been driving innovations in manipulating immune system for new treatment tools and methods. The need for materials for immunoengineering applications has gained significant attention in recent years due to the growing demand for effective therapies that can target and regulate the immune system. Biologics and biomaterials are emerging as promising tools for controlling immune responses, and a wide variety of materials, including proteins, polymers, nanoparticles, and hydrogels, are being developed for this purpose. In this review article, we explore the different types of materials used in immunoengineering applications, their properties and design principles, and highlight the latest therapeutic materials advancements. Recent works in adjuvants, vaccines, immune tolerance, immunotherapy, and tissue models for immunoengineering studies are discussed.

Sexual minority clients report experiencing frequent microaggressions during therapy, however, therapists may not recognize those microaggressions or may be reluctant to self-report them. The main aim of the present study was thus to develop an observational measure of in-session therapist-committed microaggressions related to the sexual orientation of sexual minority individuals (e.g., those who identify as lesbian, gay, bisexual, or queer). The present study further examined the association between therapist-committed sexual orientation microaggressions and ruptures in the therapeutic alliance. We hypothesized that clinically significant microaggressions would be positively associated with withdrawal ruptures in the alliance. The sample consisted of 44 gay and bisexual men who participated in a cognitive behavioral treatment designed to reduce depression, anxiety, human immunodeficiency virus-transmission-risk behaviors, and substance use. An observer-based coding measure designed for this study, the Sexual Orientation Microaggression Rating Scale (SOMRS), was utilized to capture sexual minority microaggressions in the initial sessions of treatment. Good interrater reliability was achieved for the SOMRS. Microaggressions were coded in 34% of the sessions. Within the subset of sessions with coded microaggressions, a significant association was found between withdrawal ruptures and microaggression significance ratings. The SOMRS holds potential for supporting research on microaggression as well as future efforts to help clinicians recognize and repair in-session behaviors that negatively impact sexual minority clients. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Nerve growth factor (NGF) monoclonal antibodies inhibit chronic pain, yet failed to gain approval due to worsened joint damage in osteoarthritis patients. We report that neuropilin-1 (NRP1) is a coreceptor for NGF and tropomyosin-related kinase A (TrkA) pain signaling. NRP1 was coexpressed with TrkA in human and mouse nociceptors. NRP1 inhibitors suppressed NGF-stimulated excitation of human and mouse nociceptors and NGF-evoked nociception in mice. NRP1 knockdown inhibited NGF/TrkA signaling, whereas NRP1 overexpression enhanced signaling. NGF bound NRP1 with high affinity and interacted with and chaperoned TrkA from the biosynthetic pathway to the plasma membrane and endosomes, enhancing TrkA signaling. Molecular modeling suggested that the C-terminal R/KXXR/K NGF motif interacts with the extracellular "b" NRP1 domain within a plasma membrane NGF/TrkA/NRP1 of 2:2:2 stoichiometry. G α interacting protein C-terminus 1 (GIPC1), which scaffolds NRP1 and TrkA to myosin VI, colocalized in nociceptors with NRP1/TrkA. GIPC1 knockdown abrogated NGF-evoked excitation of nociceptors and pain-like behavior. Thus, NRP1 is a nociceptor-enriched coreceptor that facilitates NGF/TrkA pain signaling. NRP binds NGF and chaperones TrkA to the plasma membrane and signaling endosomes via the GIPC1 adaptor. NRP1 and GIPC1 antagonism in nociceptors offers a long-awaited nonopioid alternative to systemic antibody NGF sequestration for the treatment of chronic pain.