Unraveling the Multifunctional and Translational Paradigm of Nanoparticulate Systems against Colorectal Cancer.

3627 Background: Colorectal cancer (CRC) ranks as the second leading cause of cancer-related mortality worldwide, and its incidence is increasing in younger populations. Detection of early-stage CRC and its precursor lesions, such as advanced adenomas (AA), is crucial for successful treatment and reduces CRC-related mortality. Although non-invasive methods for early detection are available and increase screening compliance, their performance with respect to detection of advanced adenomas and early-stage cancers is limited. Here, we describe a novel and non-invasive stool-based approach combining diagnostic biomarkers with an algorithm generated by machine learning/artificial intelligence (ML/AI) resulting in significantly improved diagnostic performance for the detection of not only CRC, but especially precancerous lesions like AA. Methods: Data were generated from a combined cohort of stool samples collected at 10 clinical sites in Europe (COLOFUTURE study) and 21 clinical sites in the US (eAArly DETECT study). The evaluable study cohort consists of 690 subjects, including 78 CRC, 146 AA, 147 with non-advanced adenoma (AD) and 319 normal colonoscopy negative control (NC) subjects (49% female, 51% male, average age 61.8 years). Results were compared with colonoscopy and pathology findings to determine sensitivity and specificity for detection of early-stage CRC and AA vs. AD and NC. Nucleic acids were extracted from stabilized stool samples using a silica bead-based extraction method. Expression of mRNA biomarkers was analyzed utilizing Real-Time PCR. Human hemoglobin was quantified using FIT. The Emerge Quantitative Evolution ML/AI platform was leveraged to develop classifiers capable of distinguishing CRC and AA from AD and NC. Results: Applying the combined approach of non-invasive diagnostic testing with an AI/ML generated algorithm, the sensitivity for detection of CRC overall was 92.3%. In addition, this method enabled AA detection with a sensitivity of 82.2%. Specificity turned out to be 90.1 % (AD+NC). Conclusions: This innovative, non-invasive, multimodal screening strategy based on self-collected stool samples which combines mRNA expression patterns and FIT analysis with an AI/ML-generated algorithm represents a substantial improvement in the effectiveness of non-invasive CRC screening. Such improvements in usability and performance leading to reliable detection of early-stage CRC and precursor lesions, such as advanced adenomas, are required for wide-spread availability and adaption of non-invasive screening tests to accepted clinically relevant usage and to prevent the development of CRC effectively.

Colorectal cancer (CRC) is still one of the most common cancers and a leading cause of cancer morbidity worldwide. A significant issue that should be paid much attention to is its resistance to anticancer agents. Non-coding RNAs (ncRNAs), such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), have been identified as important regulators of drug response and drug resistance in CRC. In this review, we provide a comprehensive assessment of the studies conducted in the field of investigating the role of ncRNAs in drug resistance to prominent anticancer agents used in CRC, including 5-fluorouracil (5-FU), oxaliplatin, cetuximab, bevacizumab, and regorafenib. We focussed specifically on the miRNAs, lncRNAs, and circRNAs associated with resistance to each drug individually, even within the context of combination therapies, such as FOLFOX. We also reviewed competing endogenous RNA (ceRNAs) networks and their relationship with drug sensitivity and resistance and new therapeutic strategies to manage drug resistance in CRC and also analyzed the role of ceRNA networks in modulating drug response and its implications for new approaches to overcome drug resistance in CRC. This article also discusses findings from human clinical trials, highlighting evidence from patient studies that validate the role of targeting ceRNA and ncRNA networks in improving drug sensitivity, overcoming resistance, and enhancing therapeutic outcomes in CRC. It supports the targeting of ncRNA and ceRNA networks as potential therapies to improve treatment outcomes and drug resistance in CRC.

Differentiating Lynch-Like From Lynch Syndrome

Design and in vivo evaluation of solid lipid nanoparticulate systems of Olanzapine for acute phase schizophrenia treatment: Investigations on antipsychotic potential and adverse effects

Genome-Wide Identification and Validation of a Novel Methylation Biomarker, SDC2, for Blood-Based Detection of Colorectal Cancer

Acquired drug resistance represents a major clinical problem and one of the biggest limitations of chemotherapeutic regimens in colorectal cancer. Combination regimens using standard chemotherapeutic agents, together with bioactive natural compounds derived from diet or plants, may be one of the most valuable strategies to overcome drug resistance and re-sensitize chemoresistant cells. In this review, we highlight the effect of combined regimens based on conventional chemotherapeutics in conjunction with well-tolerated plant-derived bioactive compounds, mainly curcumin, resveratrol, and EGCG, with emphasis on the molecular mechanisms associated with the acquired drug resistance.

The present study focuses on the effect of material used for the preparation of nanoparticulate (NP) systems and surface modification on the pharmacokinetics and biodistribution of atypical antipsychotic, olanzapine (OLN). NP carriers of OLN were prepared from two different materials such as polymer (polycaprolactone) and solid lipid (Glyceryl monostearate). These systems were further surface modified with surfactant, Polysorbate 80 and studied for pharmacokinetics–biodistribution in Wistar rats using in-house developed bioanalytical methods. The pharmacokinetics and biodistribution studies resulted in a modified and varied distribution of NP systems with higher area under curve (AUC) values along with prolonged residence time of OLN in the rat blood circulation. The distribution of OLN to the brain was significantly enhanced with surfactant surface-modified NP systems, followed by nonsurface-modified NP formulations as compared with pure OLN solution. Biodistribution study demonstrated a low uptake of obtained NP systems by kidney and heart, thereby decreasing the nephrotoxicity and adverse cardiovascular effects. By coating the NP with surfactant, uptake of macrophage was found to be reduced. Thus, our studies confirmed that the biodistribution OLN could be modified effectively by incorporating in NP drug delivery systems prepared from different materials and surface modifications. A judicious selection of materials used for the preparation of delivery carriers and surface modifications would help to design a most efficient drug delivery system with better therapeutic efficacy.

Colorectal cancer (CRC) is one of the common types of cancer affecting humans. The treatment of CRC involves surgery and chemotherapy. CRC treatment using the conventional chemotherapeutics has a negative burden on the patient's health as a result of high toxicity, occurrence of side effects, and drug resistance. Therefore, there is a pressing need to discover more effective and efficient approaches and drugs for treating CRC. This chapter will shed more light on the conventional treatment of colorectal cancer. This chapter discusses the natural products that have anti-CRC effects such as the polyphenols (curcumin, resveratrol), irinotecan, Ganoderma lucidum, cannabinoids, flavonoids, and terpenes. Furthermore, this chapter also highlights the importance of combination chemotherapy (conventional therapy and natural products) in treating CRC. It is believed that this area of research could be a promising approach to minimize side effects and drug resistance linked to the conventional chemotherapy.

Hypoxia Induces Drug Resistance in Colorectal Cancer through the HIF-1α/miR-338-5p/IL-6 Feedback Loop.

FDA-regulated AI Algorithms: Trends, Strengths, and Gaps of Validation Studies

Colorectal cancer (CRC) is a significant public health challenge, with 5-fluorouracil (5-FU) resistance being a major obstacle to effective treatment. Despite advancements, resistance to 5-FU remains formidable due to complex mechanisms such as alterations in drug transport, evasion of apoptosis, dysregulation of cell cycle dynamics, tumor microenvironment (TME) interactions, and extracellular vesicle (EV)-mediated resistance pathways. Traditional chemotherapy often results in high toxicity, highlighting the need for alternative approaches with better efficacy and safety. Phytochemicals (PCs) and EVs offer promising CRC therapeutic strategies. PCs, derived from natural sources, often exhibit lower toxicity and can target multiple pathways involved in cancer progression and drug resistance. EVs can facilitate targeted drug delivery, modulate the immune response, and interact with the TME to sensitize cancer cells to treatment. However, the potential of PCs and engineered EVs in overcoming 5-FU resistance and reshaping the immunosuppressive TME in CRC remains underexplored. Addressing this gap is crucial for identifying innovative therapies with enhanced efficacy and reduced toxicities. This review explores the multifaceted mechanisms of 5-FU resistance in CRC and evaluates the synergistic effects of combining PCs with 5-FU to improve treatment efficacy while minimizing adverse effects. Additionally, it investigates engineered EVs in overcoming 5-FU resistance by serving as drug delivery vehicles and modulating the TME. By synthesizing the current knowledge and addressing research gaps, this review enhances the academic understanding of 5-FU resistance in CRC, highlighting the potential of interdisciplinary approaches involving PCs and EVs for revolutionizing CRC therapy. Further research and clinical validation are essential for translating these findings into improved patient outcomes.

Colorectal cancer (CRC) is one of the most fatal types of cancers that is seen in both men and women. CRC is the third most common type of cancer worldwide. Over the years, several drugs are developed for the treatment of CRC; however, patients with advanced CRC can be resistant to some drugs. P-glycoprotein (P-gp) (also known as Multidrug Resistance 1, MDR1) is a well-identified membrane transporter protein expressed by ABCB1 gene. The high expression of MDR1 protein found in several cancer types causes chemotherapy failure owing to efflux drug molecules out of the cancer cell, decreases the drug concentration, and causes drug resistance. As same as other cancers, drug-resistant CRC is one of the major obstacles for effective therapy and novel therapeutic strategies are urgently needed. Network-based approaches can be used to determine specific biomarkers, potential drug targets, or repurposing approved drugs in drug-resistant cancers. Drug repositioning is the approach for using existing drugs for a new therapeutic purpose; it is a highly efficient and low-cost process. To improve current understanding of the MDR-1-related drug resistance in CRC, we explored gene co-expression networks around ABCB1 gene with different network sizes (50, 100, 150, 200 edges) and repurposed candidate drugs targeting the ABCB1 gene and its co-expression network by using drug repositioning approach for the treatment of CRC. The candidate drugs were also assessed by using molecular docking for determining the potential of physical interactions between the drug and MDR1 protein as a drug target. We also evaluated these four networks whether they are diagnostic or prognostic features in CRC besides biological function determined by functional enrichment analysis. Lastly, differentially expressed genes of drug-resistant (i.e., oxaliplatin, methotrexate, SN38) HT29 cell lines were found and used for repurposing drugs with reversal gene expressions. As a result, it is shown that all networks exhibited high diagnostic and prognostic performance besides the identification of various drug candidates for drug-resistant patients with CRC. All these results can shed light on the development of effective diagnosis, prognosis, and treatment strategies for drug resistance in CRC.

Neurodegenerative diseases are characterized by slowly progressing neuronal cell death. Conventional drug treatment strategies often fail because of poor solubility, low bioavailability, and the inability of the drugs to effectively cross the blood-brain barrier. Therefore, the development of new neurodegenerative disease drugs (NDDs) requires immediate attention. Nanoparticle (NP) systems are of increasing interest for transporting NDDs to the central nervous system. However, discovering effective nanoparticle neuronal disease drug delivery systems (N2D3Ss) is challenging because of the vast number of combinations of NP and NDD compounds, as well as the various assays involved. Artificial intelligence/machine learning (AI/ML) algorithms have the potential to accelerate this process by predicting the most promising NDD and NP candidates for assaying. Nevertheless, the relatively limited amount of reported data on N2D3S activity compared to assayed NDDs makes AI/ML analysis challenging. In this work, the IFPTML technique, which combines information fusion (IF), perturbation theory (PT), and machine learning (ML), was employed to address this challenge. Initially, we conducted the fusion into a unified dataset comprising 4403 NDD assays from ChEMBL and 260 NP cytotoxicity assays from journal articles. Through a resampling process, three new working datasets were generated, each containing 500,000 cases. We utilized linear discriminant analysis (LDA) along with artificial neural network (ANN) algorithms, such as multilayer perceptron (MLP) and deep learning networks (DLN), to construct linear and non-linear IFPTML models. The IFPTML-LDA models exhibited sensitivity (Sn) and specificity (Sp) values in the range of 70% to 73% (>375,000 training cases) and 70% to 80% (>125,000 validation cases), respectively. In contrast, the IFPTML-MLP and IFPTML-DLN achieved Sn and Sp values in the range of 85% to 86% for both training and validation series. Additionally, IFPTML-ANN models showed an area under the receiver operating curve (AUROC) of approximately 0.93 to 0.95. These results indicate that the IFPTML models could serve as valuable tools in the design of drug delivery systems for neurosciences.

1559 Background: Timely access to specialty Palliative Care (SPC) services provides significant benefits to hospitalized patients with cancer, including improvements in quality of life. However, many patients with cancer who could benefit from SPC suffer from lack of timely referral or do not receive SPC services. A previously published trial of an artificial intelligence/machine learning (AI/ML) model to predict need for SPC at Mayo Clinic increased timely SPC consultation of hospitalized patients overall. We sought to review the performance of this algorithm in patients with cancer across an expanded hospitalized population. Methods: The study population consisted of all patients admitted into a Mayo Clinic hospital in Minnesota, Wisconsin, Arizona and Florida between January 2020 and September 2023. Due to the size of the cohort a case-control sample of three controls for every case was created. Patients were considered to be a patient with cancer if they had at least one billing diagnosis up to 1 year prior to their index hospitalization from any of the 5 HCC categories denoting cancer: Metastatic Cancer and Acute Leukemia (HCC 8); Lung and Other Severe Cancers (HCC 9); Lymphoma and Other Cancers (HCC 10); Colorectal, Bladder, and Other Cancers (HCC 11); and Breast, Prostate, and Other Cancers and Tumors (HCC 12). The training data set consisted of 107,076 patient encounters with a total of 8,355,090 time periods of constant risk. An AI/ML model using gradient boosting methods which contained 269 variables (both static and time-varying) of various classes with SPC consultation treated as a time-to-event outcome was trained. Results: Due to the longitudinal nature of the prediction, performance was assessed using the max score AUC (using the max score a patient received during a given encounter prior to an event or discharge) to produce the AUC. The model had an overall AUC of 0.932 (0.929, 0.935 – 95% CI). We saw a decrease in performance of the AI/ML model in the Oncology population with an overall performance of 0.818 (0.806, 0.83). Some of the most influential variables were previous SPC visit, age, metastatic disease, acute leukemia diagnosis, and pain scores. Conclusions: An AI/ML model can effectively predict the need for an inpatient PC consult in a population of hospitalized patients with cancer. [Table: see text]

Colorectal cancer is the third most common malignancy in the United States. Modest advances with therapeutic approaches that include oxaliplatin (L-OHP) have brought the median survival rate to 22 months, with drug resistance remaining a significant barrier. Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is undergoing clinical evaluation. Although human colon carcinomas express TRAIL receptors, they can also demonstrate TRAIL resistance. Constitutive NF-kappaB activation has been implicated in resistance to TRAIL and to cytotoxic agents. We have demonstrated constitutive NF-kappaB activation in five of six human colon carcinoma cell lines; this activation is inhibited by quinacrine. Quinacrine induced apoptosis in colon carcinomas and potentiated the cytotoxic activity of TRAIL in RKO and HT29 cells and that of L-OHP in HT29 cells. Similarly, overexpression of IkappaBalpha mutant (IkappaBalphaM) or treatment with the IKK inhibitor, BMS-345541, also sensitized these cells to TRAIL and L-OHP. Importantly, 2 h of quinacrine pretreatment resulted in decreased expression of c-FLIP and Mcl-1, which were determined to be transcriptional targets of NF-kappaB. Extended exposure for 24 h to quinacrine did not further sensitize these cells to TRAIL- or L-OHP-induced cell death; however, exposure caused the down-regulation of additional NF-kappaB-dependent survival factors. Short hairpin RNA-mediated knockdown of c-FLIP or Mcl-1 significantly sensitized these cells to TRAIL and L-OHP. Taken together, data demonstrate that NF-kappaB is constitutively active in colon cancer cell lines and NF-kappaB, and its downstream targets may constitute an important target for the development of therapeutic approaches against this disease.