Harnessing the power of artificial intelligence to advance cell therapy

Expanding the CAR toolbox with high throughput screening strategies for CAR domain exploration: a comprehensive review

Chimeric antigen receptor (CAR)-T-cell therapy has been highly successful in the treatment of B-cell hematological malignancies. CARs are modular synthetic molecules that can redirect immune cells towards target cells with antibody-like specificity. Despite their modularity, CARs used in the clinic are currently composed of a limited set of domains, mostly derived from IgG, CD8α, 4-1BB, CD28 and CD3ζ. The current low throughput CAR screening workflows are labor-intensive and time-consuming, and lie at the basis of the limited toolbox of CAR building blocks available. High throughput screening methods facilitate simultaneous investigation of hundreds of thousands of CAR domain combinations, allowing discovery of novel domains and increasing our understanding of how they behave in the context of a CAR. Here we review the growing body of reports that employ these high throughput screening and computational methods to advance CAR design. We summarize and highlight the important differences between the different studies and discuss their limitations and future considerations for further improvements. In conclusion, while still in its infancy, high throughput screening of CARs has the capacity to vastly expand the CAR domain toolbox and improve our understanding of CAR design. This knowledge could be foundational for translating CAR therapy beyond hematological malignancies and push the frontiers in personalized medicine.

Applications of artificial intelligence in regenerative dentistry: promoting stem cell therapy and the scaffold development

Tissue repair represents a critical concern within the domain of dentistry. On a daily basis, countless individuals seek dental clinic services due to inadequate dental care. Many of the treatments that patients receive have unfavorable side effects. The employment of innovative methodologies, including gene therapy, tissue engineering, and stem cell (SCs) applications for regenerative purposes, has garnered significant interest over the past years. In recent times, artificial intelligence, particularly neural networks, has emerged as a topic of considerable attention among many medical professionals. Artificial intelligence possesses the capability to analyze data patterns through learning algorithms. Research opportunities in the rapidly expanding field of health sciences have been made possible by the use of artificial intelligence (AI) technologies. Though its uses are not restricted to these situations, artificial intelligence (AI) has the potential to improve and accelerate many aspects of regenerative medicine research and development, especially when working with complicated patterns. This review article is to investigate how artificial intelligence might be used to enhance regenerative processes in dentistry by using scaffolds and stem cells, in light of the continuous advances in artificial intelligence in the fields of medicine and tissue regeneration. It highlights the difficulties that still exist in this developing sector and explores the possible uses of AI with a particular emphasis on dentistry practices.

AI-Based solutions for current challenges in regenerative medicine

The emergence of Artificial Intelligence (AI) and its usage in regenerative medicine represents a significant opportunity that holds the promise of tackling critical challenges and improving therapeutic outcomes. This article examines the ways in which AI, including machine learning and data fusion techniques, can contribute to regenerative medicine, particularly in gene therapy, stem cell therapy, and tissue engineering. In gene therapy, AI tools can boost the accuracy and safety of treatments by analyzing extensive genomic datasets to target and modify genetic material in a precise manner. In cell therapy, AI improves the characterization and optimization of cell products like mesenchymal stem cells (MSCs) by predicting their function and potency. Additionally, AI enhances advanced microscopy techniques, enabling accurate, non-invasive and quantitative analyses of live cell cultures. AI enhances tissue engineering by optimizing biomaterial and scaffold designs, predicting interactions with tissues, and streamlining development. This leads to faster and more cost-effective innovations by decreasing trial and error. The convergence of AI and regenerative medicine holds great transformative potential, promising effective treatments and innovative therapeutic strategies. This review highlights the importance of interdisciplinary collaboration and the continued integration of AI-based technologies, such as data fusion methods, to overcome current challenges and advance regenerative medicine.

Tuning CAR T-cell therapies for efficacy and reduced toxicity

Chimeric antigen receptor (CAR) T-cell therapies are a standard of care for certain relapsed or refractory B-cell cancers. However, many patients do not respond to CAR T-cell therapy or relapse later, short- and long-term toxicities are common, and current CAR T-cell therapies have limited efficacy for solid cancers. The gene engineering inherent in CAR T-cell manufacture offers an unprecedented opportunity to control cellular characteristics and design products that may overcome these limitations. This review summarises available methods to "tune" CAR T-cells for optimal efficacy and safety. The components of a typical CAR, and the modifications that can influence CAR T-cell function are discussed. Methods of engineering passive, inducible or autonomous control mechanisms into CAR T-cells, allowing selective limitation or enhancement of CAR T-cell activity are reviewed. The impact of manufacturing processes on CAR T-cell function are considered, including methods of limiting CAR T-cell terminal differentiation and exhaustion, and the use of specific T-cell subsets as the CAR T starting material. We discuss the use of multicistronic transgenes and multiplexed gene editing. Finally, we highlight the need for innovative clinical trial designs if we are to make the most of the opportunities offered by CAR T-cell therapies.

AI in cellular engineering and reprogramming

During the last decade, artificial intelligence (AI) has increasingly been applied in biophysics and related fields, including cellular engineering and reprogramming, offering novel approaches to understand, manipulate, and control cellular function. The potential of AI lies in its ability to analyze complex datasets and generate predictive models. AI algorithms can process large amounts of data from single-cell genomics and multiomic technologies, allowing researchers to gain mechanistic insights into the control of cell identity and function. By integrating and interpreting these complex datasets, AI can help identify key molecular events and regulatory pathways involved in cellular reprogramming. This knowledge can inform the design of precision engineering strategies, such as the development of new transcription factor and signaling molecule cocktails, to manipulate cell identity and drive authentic cell fate across lineage boundaries. Furthermore, when used in combination with computational methods, AI can accelerate and improve the analysis and understanding of the intricate relationships between genes, proteins, and cellular processes. In this review article, we explore the current state of AI applications in biophysics with a specific focus on cellular engineering and reprogramming. Then, we showcase a couple of recent applications where we combined machine learning with experimental and computational techniques. Finally, we briefly discuss the challenges and prospects of AI in cellular engineering and reprogramming, emphasizing the potential of these technologies to revolutionize our ability to engineer cells for a variety of applications, from disease modeling and drug discovery to regenerative medicine and biomanufacturing.

Beyond CAR-T: The rise of CAR-NK cell therapy in asthma immunotherapy

Asthma poses a major public health burden. While existing asthma drugs manage symptoms for many, some patients remain resistant. The lack of a cure, especially for severe asthma, compels exploration of novel therapies. Cancer immunotherapy successes with CAR-T cells suggest its potential for asthma treatment. Researchers are exploring various approaches for allergic diseases including membrane-bound IgE, IL-5, PD-L2, and CTLA-4 for asthma, and Dectin-1 for fungal asthma. NK cells offer several advantages over T cells for CAR-based immunotherapy. They offer key benefits: (1) HLA compatibility, meaning they can be used in a wider range of patients without the need for matching tissue types. (2) Minimal side effects (CRS and GVHD) due to their limited persistence and cytokine profile. (3) Scalability for "off-the-shelf" production from various sources. Several strategies have been introduced that highlight the superiority and challenges of CAR-NK cell therapy for asthma treatment including IL-10, IFN-γ, ADCC, perforin-granzyme, FASL, KIR, NCRs (NKP46), DAP, DNAM-1, TGF-β, TNF-α, CCL, NKG2A, TF, and EGFR. Furthermore, we advocate for incorporating AI for CAR design optimization and CRISPR-Cas9 gene editing technology for precise gene manipulation to generate highly effective CAR constructs. This review will delve into the evolution and production of CAR designs, explore pre-clinical and clinical studies of CAR-based therapies in asthma, analyze strategies to optimize CAR-NK cell function, conduct a comparative analysis of CAR-T and CAR-NK cell therapy with their respective challenges, and finally present established novel CAR designs with promising potential for asthma treatment.

Improving chimeric antigen receptor T-cell therapies by using artificial intelligence and internet of things technologies: A narrative review

Cancer poses a formidable challenge in the field of medical science, prompting the exploration of innovative and efficient treatment strategies. One revolutionary breakthrough in cancer therapy is Chimeric Antigen Receptor (CAR) T-cell therapy, an avant-garde method involving the customization of a patient's immune cells to combat cancer. Particularly successful in addressing blood cancers, CAR T-cell therapy introduces an unprecedented level of effectiveness, offering the prospect of sustained disease management. As ongoing research advances to overcome current challenges, CAR T-cell therapy stands poised to become an essential tool in the fight against cancer. Ongoing enhancements aim to improve its effectiveness and reduce time and cost, with the integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies. The synergy of AI and IoT could enable more precise tailoring of CAR T-cell therapy to individual patients, streamlining the therapeutic process. This holds the potential to elevate treatment efficacy, mitigate adverse effects, and expedite the overall progress of CAR T-cell therapies.

Comprehensive application of AI algorithms with TCR NGS data for glioma diagnosis

T-cell receptor (TCR) detection can examine the extent of T-cell immune responses. Therefore, the article analyzed characteristic data of glioma obtained by DNA-based TCR high-throughput sequencing, to predict the disease with fewer biomarkers and higher accuracy. We downloaded data online and obtained six TCR-related diversity indices to establish a multidimensional classification system. By comparing actual presence of the 602 correlated sequences, we obtained two-dimensional and multidimensional datasets. Multiple classification methods were utilized for both datasets with the classification accuracy of multidimensional data slightly less to two-dimensional datasets. This study reduced the TCR β sequences through feature selection methods like RFECV (Recursive Feature Elimination with Cross-Validation). Consequently, using only the presence of these three sequences, the classification AUC value of 96.67% can be achieved. The combination of the three correlated TCR clones obtained at a source data threshold of 0.1 is: CASSLGGNTEAFF_TRBV12_TRBJ1-1, CASSYSDTGELFF_TRBV6_TRBJ2-2, and CASSLTGNTEAFF_TRBV12_TRBJ1-1. At 0.001, the combination is: CASSLGETQYF_TRBV12_TRBJ2-5, CASSLGGNQPQHF_TRBV12_TRBJ1-5, and CASSLSGNTIYF_TRBV12_TRBJ1-3. This method can serve as a potential diagnostic and therapeutic tool, facilitating diagnosis and treatment of glioma and other cancers.

CAR-NKT Cells in Asthma: Use of NKT as a Promising Cell for CAR Therapy

NKT cells, unique lymphocytes bridging innate and adaptive immunity, offer significant potential for managing inflammatory disorders like asthma. Activating iNKT induces increasing IFN-γ, TGF-β, IL-2, and IL-10 potentially suppressing allergic asthma. However, their immunomodulatory effects, including granzyme-perforin-mediated cytotoxicity, and expression of TIM-3 and TRAIL warrant careful consideration and targeted approaches. Although CAR-T cell therapy has achieved remarkable success in treating certain cancers, its limitations necessitate exploring alternative approaches. In this context, CAR-NKT cells emerge as a promising approach for overcoming these challenges, potentially achieving safer and more effective immunotherapies. Strategies involve targeting distinct IgE-receptors and their interactions with CAR-NKT cells, potentially disrupting allergen-mast cell/basophil interactions and preventing inflammatory cytokine release. Additionally, targeting immune checkpoints like PDL-2, inducible ICOS, FASL, CTLA-4, and CD137 or dectin-1 for fungal asthma could further modulate immune responses. Furthermore, artificial intelligence and machine learning hold immense promise for revolutionizing NKT cell-based asthma therapy. AI can optimize CAR-NKT cell functionalities, design personalized treatment strategies, and unlock a future of precise and effective care. This review discusses various approaches to enhancing CAR-NKT cell efficacy and longevity, along with the challenges and opportunities they present in the treatment of allergic asthma.

Convergence of nanomedicine and neutrophils for drug delivery

Neutrophils have recently emerged as promising carriers for drug delivery due to their unique properties including rapid response toward inflammation, chemotaxis, and transmigration. When integrated with nanotechnology that has enormous advantages in improving treatment efficacy and reducing side effects, neutrophil-based nano-drug delivery systems have expanded the repertoire of nanoparticles employed in precise therapeutic interventions by either coating nanoparticles with their membranes, loading nanoparticles inside living cells, or engineering chimeric antigen receptor (CAR)-neutrophils. These neutrophil-inspired therapies have shown superior biocompatibility, targeting ability, and therapeutic robustness. In this review, we summarized the benefits of combining neutrophils and nanotechnologies, the design principles and underlying mechanisms, and various applications in disease treatments. The challenges and prospects for neutrophil-based drug delivery systems were also discussed.

Stem cell therapy for heart failure in the clinics: new perspectives in the era of precision medicine and artificial intelligence

Stem/progenitor cells have been widely evaluated as a promising therapeutic option for heart failure (HF). Numerous clinical trials with stem/progenitor cell-based therapy (SCT) for HF have demonstrated encouraging results, but not without limitations or discrepancies. Recent technological advancements in multiomics, bioinformatics, precision medicine, artificial intelligence (AI), and machine learning (ML) provide new approaches and insights for stem cell research and therapeutic development. Integration of these new technologies into stem/progenitor cell therapy for HF may help address: 1) the technical challenges to obtain reliable and high-quality therapeutic precursor cells, 2) the discrepancies between preclinical and clinical studies, and 3) the personalized selection of optimal therapeutic cell types/populations for individual patients in the context of precision medicine. This review summarizes the current status of SCT for HF in clinics and provides new perspectives on the development of computation-aided SCT in the era of precision medicine and AI/ML.

Advancing genome editing with artificial intelligence: opportunities, challenges, and future directions

Clustered regularly interspaced short palindromic repeat (CRISPR)-based genome editing (GED) technologies have unlocked exciting possibilities for understanding genes and improving medical treatments. On the other hand, Artificial intelligence (AI) helps genome editing achieve more precision, efficiency, and affordability in tackling various diseases, like Sickle cell anemia or Thalassemia. AI models have been in use for designing guide RNAs (gRNAs) for CRISPR-Cas systems. Tools like DeepCRISPR, CRISTA, and DeepHF have the capability to predict optimal guide RNAs (gRNAs) for a specified target sequence. These predictions take into account multiple factors, including genomic context, Cas protein type, desired mutation type, on-target/off-target scores, potential off-target sites, and the potential impacts of genome editing on gene function and cell phenotype. These models aid in optimizing different genome editing technologies, such as base, prime, and epigenome editing, which are advanced techniques to introduce precise and programmable changes to DNA sequences without relying on the homology-directed repair pathway or donor DNA templates. Furthermore, AI, in collaboration with genome editing and precision medicine, enables personalized treatments based on genetic profiles. AI analyzes patients' genomic data to identify mutations, variations, and biomarkers associated with different diseases like Cancer, Diabetes, Alzheimer's, etc. However, several challenges persist, including high costs, off-target editing, suitable delivery methods for CRISPR cargoes, improving editing efficiency, and ensuring safety in clinical applications. This review explores AI's contribution to improving CRISPR-based genome editing technologies and addresses existing challenges. It also discusses potential areas for future research in AI-driven CRISPR-based genome editing technologies. The integration of AI and genome editing opens up new possibilities for genetics, biomedicine, and healthcare, with significant implications for human health.

Broadening the horizon: potential applications of CAR-T cells beyond current indications

Engineering immune cells to treat hematological malignancies has been a major focus of research since the first resounding successes of CAR-T-cell therapies in B-ALL. Several diseases can now be treated in highly therapy-refractory or relapsed conditions. Currently, a number of CD19- or BCMA-specific CAR-T-cell therapies are approved for acute lymphoblastic leukemia (ALL), diffuse large B-cell lymphoma (DLBCL), mantle cell lymphoma (MCL), multiple myeloma (MM), and follicular lymphoma (FL). The implementation of these therapies has significantly improved patient outcome and survival even in cases with previously very poor prognosis. In this comprehensive review, we present the current state of research, recent innovations, and the applications of CAR-T-cell therapy in a selected group of hematologic malignancies. We focus on B- and T-cell malignancies, including the entities of cutaneous and peripheral T-cell lymphoma (T-ALL, PTCL, CTCL), acute myeloid leukemia (AML), chronic myeloid leukemia (CML), chronic lymphocytic leukemia (CLL), classical Hodgkin-Lymphoma (HL), Burkitt-Lymphoma (BL), hairy cell leukemia (HCL), and Waldenström's macroglobulinemia (WM). While these diseases are highly heterogenous, we highlight several similarly used approaches (combination with established therapeutics, target depletion on healthy cells), targets used in multiple diseases (CD30, CD38, TRBC1/2), and unique features that require individualized approaches. Furthermore, we focus on current limitations of CAR-T-cell therapy in individual diseases and entities such as immunocompromising tumor microenvironment (TME), risk of on-target-off-tumor effects, and differences in the occurrence of adverse events. Finally, we present an outlook into novel innovations in CAR-T-cell engineering like the use of artificial intelligence and the future role of CAR-T cells in therapy regimens in everyday clinical practice.