Computational Approaches in Theranostics: Mining and Predicting Cancer Data

AIScholar: An OpenFaaS-enhanced cloud platform for intelligent medical data analytics

This paper presents AIScholar, an intelligent research cloud platform developed based on artificial intelligence analysis methods and the OpenFaaS serverless framework, designed for intelligent analysis of clinical medical data with high scalability. AIScholar simplifies the complex analysis process by encapsulating a wide range of medical data analytics methods into a series of customizable cloud tools that emphasize ease of use and expandability, within OpenFaaS's serverless computing framework. As a multifaceted auxiliary tool in medical scientific exploration, AIScholar accelerates the deployment of computational resources, enabling clinicians and scientific personnel to derive new insights from clinical medical data with unprecedented efficiency. A case study focusing on breast cancer clinical data underscores the practicality that AIScholar offers to clinicians for diagnosis and decision-making. Insights generated by the platform have a direct impact on the physicians' ability to identify and address clinical issues, signifying its real-world application significance in clinical practice. Consequently, AIScholar makes a meaningful impact on medical research and clinical practice by providing powerful analytical tools to clinicians and scientific personnel, thereby promoting significant advancements in the analysis of clinical medical data.

Circulating Tumor Cells in Cancer Diagnosis, Therapy, and Theranostics Applications: An Overview of Emerging Materials and Technologies

In recent years, immunotherapy, namely immune checkpoint inhibitor therapy, has significantly transformed the approach to treating various forms of cancer. Simultaneously, the adoption of clinical oncology has been sluggish due to the exorbitant expense of therapy, the adverse effects experienced by patients, and the inconsistency in treatment response among individuals. As a reaction, individualized methods utilizing predictive biomarkers have arisen as novel strategies for categorizing patients to achieve successful immunotherapy. Recently, the identification and examination of circulating tumor cells (CTCs) have gained attention as predictive indicators for the treatment of cancer patients undergoing chemotherapy and for personalized targeted therapy. CTCs have been found to exhibit immunological checkpoints in several types of solid tumors, which has contributed to our understanding of managing cancer immunotherapy. Circulating tumor cells (CTCs) present in the bloodstream have a crucial function in the formation of metastases. Nevertheless, the practical usefulness of existing CTC tests is mostly restricted by methodological limitations.

Knockout cancer by nano-delivered immunotherapy using perfusion-aided scaffold-based tumor-on-a-chip

Cancer is a multifactorial disease produced by mutations in the oncogenes and tumor suppressor genes, which result in uncontrolled cell proliferation and resistance to cell death. Cancer progresses due to the escape of altered cells from immune monitoring, which is facilitated by the tumor's mutual interaction with its microenvironment. Understanding the mechanisms involved in immune surveillance evasion and the significance of the tumor microenvironment might thus aid in developing improved therapies. Although in vivo models are commonly utilized, they could be better for time, cost, and ethical concerns. As a result, it is critical to replicate an in vivo model and recreate the cellular and tissue-level functionalities. A 3D cell culture, which gives a 3D architecture similar to that found in vivo, is an appropriate model. Furthermore, numerous cell types can be cocultured, establishing cellular interactions between TME and tumor cells. Moreover, microfluidics perfusion can provide precision flow rates, thus simulating tissue/organ function. Immunotherapy can be used with the perfused 3D cell culture technique to help develop successful therapeutics. Immunotherapy employing nano delivery can target the spot and silence the responsible genes, ensuring treatment effectiveness while minimizing adverse effects. This study focuses on the importance of 3D cell culture in understanding the pathophysiology of 3D tumors and TME, the function of TME in drug resistance, tumor progression, and the development of advanced anticancer therapies for high-throughput drug screening.

Spatiotemporal dissection of tumor microenvironment via in situ sensing and monitoring in tumor-on-a-chip

Real-time monitoring in the tumor microenvironment provides critical insights of cancer progression and mechanistic understanding of responses to cancer treatments. However, clinical challenges and significant questions remain regarding assessment of limited clinical tissue samples, establishment of validated, controllable pre-clinical cancer models, monitoring of static versus dynamic markers, and the translation of insights gained from in vitro tumor microenvironments to systematic investigation and understanding in clinical practice. State-of-art tumor-on-a-chip strategies will be reviewed herein, and emerging real-time sensing and monitoring platforms for on-chip analysis of tumor microenvironment will also be examined. The integration of the sensors with tumor-on-a-chip platforms to provide spatiotemporal information of the tumor microenvironment and the associated challenges will be further evaluated. Though optimal integrated systems for in situ monitoring are still in evolution, great promises lie ahead that will open new paradigm for rapid, comprehensive analysis of cancer development and assist clinicians with powerful tools to guide the diagnosis, prognosis and treatment course in cancer.

Automation: A revolutionary vision of artificial intelligence in theranostics

The last two decades have witnessed an extraordinary evolution of automation and artificial intelligence (AI), which has become an integral part of our daily lives. Lately, AI has also been assimilated in the field of medicine to upgrade overall healthcare system and encourage personalized treatment. Theranostics literally meaning combination of diagnosis and therapeutics, is a targeted pharmacotherapy, based on specific targeted diagnostic tests. Numerous theranostic agents/biomarkers are available which can identify the most beneficial treatment, correct dose or predict response to a medicine, thus, maximizing drug efficacy, minimizing toxicity and providing informed treatment choice. For instance, a statistics based Cluster-FLIM technology provides precise data on drug-receptor binding behavior in biological tissues using fluorescence real experimental imaging. Automated Idylla™ qPCR System is another approach in oncology to determine the EGFR mutations at initial stage as well as during the treatment and also assists the oncologist in designing the treatment protocol. Recent incorporation of automation and AI in theranostics has brought a drastic change in early detection and treatment protocols for various diseases such as cancer and diabetes. Also, it leads to quick analysis of number of diverse experimental datum with accuracy. The approach mainly uses computer algorithms to unveil relevant and significant information from clinical data, thereby assisting in making accurate, logical and pertinent decisions. This review highlights the emerging uses/role of automation and AI in theranostics, technical difficulties and focuses on its future prospects to facilitate a patient specific, reliable and efficient pharmacotherapy.

Artificial intelligence aids in development of nanomedicines for cancer management

Over the last decade, the nanomedicine has experienced unprecedented development in diagnosis and management of diseases. A number of nanomedicines have been approved in clinical use, which has demonstrated the potential value of clinical transition of nanotechnology-modified medicines from bench to bedside. The application of artificial intelligence (AI) in development of nanotechnology-based products could transform the healthcare sector by realizing acquisition and analysis of large datasets, and tailoring precision nanomedicines for cancer management. AI-enabled nanotechnology could improve the accuracy of molecular profiling and early diagnosis of patients, and optimize the design pipeline of nanomedicines by tuning the properties of nanomedicines, achieving effective drug synergy, and decreasing the nanotoxicity, thereby, enhancing the targetability, personalized dosing and treatment potency of nanomedicines. Herein, the advances in AI-enabled nanomedicines in cancer management are elaborated and their application in diagnosis, monitoring and therapy as well in precision medicine development is discussed.

Advanced Computational Methodologies Used in the Discovery of New Natural Anticancer Compounds

Natural chemical compounds have been widely investigated for their programmed necrosis causing characteristics. One of the conventional methods for screening such compounds is the use of concentrated plant extracts without isolation of active moieties for understanding pharmacological activity. For the last two decades, modern medicine has relied mainly on the isolation and purification of one or two complicated active and isomeric compounds. The idea of multi-target drugs has advanced rapidly and impressively from an innovative model when first proposed in the early 2000s to one of the popular trends for drug development in 2021. Alternatively, fragment-based drug discovery is also explored in identifying target-based drug discovery for potent natural anticancer agents which is based on well-defined fragments opposite to use of naturally occurring mixtures. This review summarizes the current key advancements in natural anticancer compounds; computer-assisted/fragment-based structural elucidation and a multi-target approach for the exploration of natural compounds.

BZD9L1 sirtuin inhibitor: Identification of key molecular targets and their biological functions in HCT 116 colorectal cancer cells

BZD9L1 was previously described as a SIRT1/2 inhibitor with anti-cancer activities in colorectal cancer (CRC), either as a standalone chemotherapy or in combination with 5-fluorouracil. BZD9L1 was reported to induce apoptosis in CRC cells; however, the network of intracellular pathways and crosstalk between molecular players mediated by BZD9L1 is not fully understood. This study aimed to uncover the mechanisms involved in BZD9L1-mediated cytotoxicity based on previous and new findings for the prediction and identification of related pathways and key molecular players. BZD9L1-regulated candidate targets (RCTs) were identified using a range of molecular, cell-based and biochemical techniques on the HCT 116 cell line. BZD9L1 regulated major cancer pathways including Notch, p53, cell cycle, NFκB, Myc/MAX, and MAPK/ERK signalling pathways. BZD9L1 also induced reactive oxygen species (ROS), regulated apoptosis-related proteins, and altered cell polarity and adhesion profiles. In silico analyses revealed that most RCTs were interconnected, and were involved in the modulation of catalytic activity, metabolism and transcription regulation, response to cytokines, and apoptosis signalling pathways. These RCTs were implicated in p53-dependent apoptosis pathway. This study provides the first assessment of possible associations of molecular players underlying the cytotoxic activity of BZD9L1, and establishes the links between RCTs and apoptosis through the p53 pathway.

Quantitative Molecular Positron Emission Tomography Imaging Using Advanced Deep Learning Techniques

The widespread availability of high-performance computing and the popularity of artificial intelligence (AI) with machine learning and deep learning (ML/DL) algorithms at the helm have stimulated the development of many applications involving the use of AI-based techniques in molecular imaging research. Applications reported in the literature encompass various areas, including innovative design concepts in positron emission tomography (PET) instrumentation, quantitative image reconstruction and analysis techniques, computer-aided detection and diagnosis, as well as modeling and prediction of outcomes. This review reflects the tremendous interest in quantitative molecular imaging using ML/DL techniques during the past decade, ranging from the basic principles of ML/DL techniques to the various steps required for obtaining quantitatively accurate PET data, including algorithms used to denoise or correct for physical degrading factors as well as to quantify tracer uptake and metabolic tumor volume for treatment monitoring or radiation therapy treatment planning and response prediction.This review also addresses future opportunities and current challenges facing the adoption of ML/DL approaches and their role in multimodality imaging.

Nanomedicine of tyrosine kinase inhibitors

Recent progress in nanomedicine and targeted therapy brings new breeze into the field of therapeutic applications of tyrosine kinase inhibitors (TKIs). These drugs are known for many side effects due to non-targeted mechanism of action that negatively impact quality of patients' lives or that are responsible for failure of the drugs in clinical trials. Some nanocarrier properties provide improvement of drug efficacy, reduce the incidence of adverse events, enhance drug bioavailability, helps to overcome the blood-brain barrier, increase drug stability or allow for specific delivery of TKIs to the diseased cells. Moreover, nanotechnology can bring new perspectives into combination therapy, which can be highly efficient in connection with TKIs. Lastly, nanotechnology in combination with TKIs can be utilized in the field of theranostics, i.e. for simultaneous therapeutic and diagnostic purposes. The review provides a comprehensive overview of advantages and future prospects of conjunction of nanotransporters with TKIs as a highly promising approach to anticancer therapy.

Recent advances in theranostic polymeric nanoparticles for cancer treatment: A review

Nanotheranostics is fast-growing pharmaceutical technology for simultaneously monitoring drug release and its distribution, and to evaluate the real time therapeutic efficacy through a single nanoscale for treatment and diagnosis of deadly disease such as cancers. In recent two decades, biodegradable polymers have been discovered as important carriers to accommodate therapeutic and medical imaging agents to facilitate construction of multi-modal formulations. In this review, we summarize various multifunctional polymeric nano-sized formulations such as polymer-based super paramagnetic nanoparticles, ultrasound-triggered polymeric nanoparticles, polymeric nanoparticles bearing radionuclides, and fluorescent polymeric nano-sized formulations for purpose of theranostics. The use of such multi-modal nano-sized formulations for near future clinical trials can assist clinicians to predict therapeutic properties (for instance, depending upon the quantity of drug accumulated at the cancerous site) and observed the progress of tumor growth in patients, thus improving tailored medicines.

Tumor microenvironment complexity and therapeutic implications at a glance

The dynamic interactions of cancer cells with their microenvironment consisting of stromal cells (cellular part) and extracellular matrix (ECM) components (non-cellular) is essential to stimulate the heterogeneity of cancer cell, clonal evolution and to increase the multidrug resistance ending in cancer cell progression and metastasis. The reciprocal cell-cell/ECM interaction and tumor cell hijacking of non-malignant cells force stromal cells to lose their function and acquire new phenotypes that promote development and invasion of tumor cells. Understanding the underlying cellular and molecular mechanisms governing these interactions can be used as a novel strategy to indirectly disrupt cancer cell interplay and contribute to the development of efficient and safe therapeutic strategies to fight cancer. Furthermore, the tumor-derived circulating materials can also be used as cancer diagnostic tools to precisely predict and monitor the outcome of therapy. This review evaluates such potentials in various advanced cancer models, with a focus on 3D systems as well as lab-on-chip devices. Video abstract.

Novel therapeutic targets for cancer metastasis

Introduction: Metastatic cancers are extremely difficult to treat, and account for the vast majority of cancer-related deaths. The dissemination of tumor cells to distant sites is highly dynamic, asynchronous, and involves both tumor and host intrinsic factors. Effective therapeutic targets to block metastasis will need to disrupt key pathways that are required for multiple stages of metastasis.Areas covered: This review discusses the heterogeneity of cancers and metastasis, with an emphasis on motility as a key driver trait of metastasis. Recent metastatic cancer studies that identified either host or cancer cell intrinsic factors important for metastasis, using single gene-deficient animal models or 3D intravital imaging of avian embryo models, are also discussed. Potential metastatic blocking targets are listed as they relate to metastatic cancer therapy.Expert opinion: The development of metastatic disease is a complex interplay of genetic and epigenetic factors from the host and cancer cells acting in a patient-specific manner. Inhibiting key driver traits of metastasis should yield survival benefit at any stage of the disease, and we look forward to the next generation of personalized medicines for cancer therapy that target cancer cell motility for increased therapeutic efficacy.

The effect of ligand amount, affinity and internalization on PSMA-targeted imaging and therapy: A simulation study using a PBPK model

The aim of this work was to investigate the effect of ligand amount, affinity and internalization of prostate-specific membrane antigen (PSMA)-specific ligands on the activity concentrations for PET/CT imaging and on the absorbed doses for therapy. A physiologically-based pharmacokinetic (PBPK) model for PSMA-specific ligands was implemented. Thirteen virtual patients with metastatic castration-resistant prostate cancer were analysed. Simulations were performed for different combinations of association rates k_on (0.1-0.01 L/nmol/min), dissociation rates k_off (0.1-0.0001 min^-1), internalization rates λ_int (0.01-0.0001 min^-1) and ligand amounts (1-1000 nmol). For imaging the activity was normalized to volume and injected activity (⁶⁸Ga-PSMA at 1 h). For therapy the absorbed dose was calculated for 7.3 ± 0.3 GBq ¹⁷⁷Lu-PSMA. The effect of the investigated parameters on therapy were larger compared to imaging. For imaging, the combination of properties leading to the highest tumour uptake was k_on = 0.1 L/nmol/min, k_off = 0.01 min^-1 for typical ligand amounts (1-10 nmol). For therapy, the higher the internalization rate, the larger was the required ligand amount for optimal tumour-to-kidney ratios. The higher the affinity, the more important was the choice of the optimal ligand amount. PBPK modelling provides insight into the pharmacokinetics of PSMA-specific ligands. Further in silico and in vivo studies are required to verify the influence of the analysed parameters.

Artificial intelligence and big data facilitated targeted drug discovery

Different kinds of biological databases publicly available nowadays provide us a goldmine of multidiscipline big data. The Cancer Genome Atlas is a cancer database including detailed information of many patients with cancer. DrugBank is a database including detailed information of approved, investigational and withdrawn drugs, as well as other nutraceutical and metabolite structures. PubChem is a chemical compound database including all commercially available compounds as well as other synthesisable compounds. Protein Data Bank is a crystal structure database including X-ray, cryo-EM and nuclear magnetic resonance protein three-dimensional structures as well as their ligands. On the other hand, artificial intelligence (AI) is playing an important role in the drug discovery progress. The integration of such big data and AI is making a great difference in the discovery of novel targeted drug. In this review, we focus on the currently available advanced methods for the discovery of highly effective lead compounds with great absorption, distribution, metabolism, excretion and toxicity properties.