AI-Based Drug Discovery of TKIs Targeting L858R/T790M/C797S-Mutant EGFR in Non-small Cell Lung Cancer

A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer

According to data from the World Health Organization (WHO), lung cancer is becoming a global epidemic. It is particularly high in the list of the leading causes of death not only in developed countries, but also worldwide; furthermore, it holds the leading place in terms of cancer-related mortality. Nevertheless, many breakthroughs have been made the last two decades regarding its management, with one of the most prominent being the implementation of artificial intelligence (AI) in various aspects of disease management. We included 473 papers in this thorough review, most of which have been published during the last 5-10 years, in order to describe these breakthroughs. In screening programs, AI is capable of not only detecting suspicious lung nodules in different imaging modalities-such as chest X-rays, computed tomography (CT), and positron emission tomography (PET) scans-but also discriminating between benign and malignant nodules as well, with success rates comparable to or even better than those of experienced radiologists. Furthermore, AI seems to be able to recognize biomarkers that appear in patients who may develop lung cancer, even years before this event. Moreover, it can also assist pathologists and cytologists in recognizing the type of lung tumor, as well as specific histologic or genetic markers that play a key role in treating the disease. Finally, in the treatment field, AI can guide in the development of personalized options for lung cancer patients, possibly improving their prognosis.

Computational design of CDK1 inhibitors with enhanced target affinity and drug-likeness using deep-learning framework

Cyclin Dependent Kinase 1 (CDK1) plays a crucial role in cell cycle regulation, and dysregulation of its activity has been implicated in various cancers. Although several CDK1 inhibitors are currently in clinical trials, none have yet been approved for therapeutic use. This research utilized deep learning techniques, specifically Recurrent Neural Networks with Long Short-Term Memory (LSTM), to generate potential CDK1 inhibitors. Molecular docking, evaluation of molecular properties, and molecular dynamics simulations were conducted to identify the most promising candidates. The results showed that the generated ligands exhibited substantial improvements in target affinity and drug-likeness. Molecular docking results showed that the generated ligands had an average binding affinity of -10.65 ± 0.877 kcal/mol towards CDK1. The Quantitative Estimate of Drug-likeness (QED) values for the generated ligands averaged 0.733 ± 0.10, significantly higher than the 0.547 ± 0.15 observed for known CDK1 inhibitors (p < 0.001). Molecular dynamics simulations further confirmed the stability and favorable interactions of the selected ligands with the CDK1 complex. The identification of novel CDK1 inhibitors with improved binding affinities and drug-likeness properties could potentially fill the gap in the ongoing development of CDK inhibitors. However, it is imperative to note that extensive experimental validation is required prior to advancing these generated ligands to subsequent stages of drug development.

Fused in silico and bioactivity evaluation method for drug discovery: T001-10027877 was identified as an antiproliferative agent that targets EGFRT790M/C797S/L858R and EGFRT790M/L858R

BACKGROUND

Facing the significant challenge of overcoming drug resistance in cancer treatment, particularly resistance caused by mutations in epidermal growth factor receptor (EGFR), the aim of our study was to identify potent EGFR inhibitors effective against the T790M/C797S/L858R mutant, a key player in resistance mechanisms.

METHODS

Our integrated in silico approach harnessed machine learning, virtual screening, and activity evaluation techniques to screen 5105 compounds from three libraries, aiming to find candidates capable of overcoming the resistance conferred by the T790M and C797S mutations within EGFR. This methodical process narrowed the search down to six promising compounds for further examination.

RESULTS

Kinase assays identified three compounds to which the T790M/C797S/L858R mutant exhibited increased sensitivity compared to the T790M/L858R mutant, highlighting the potential efficacy of these compounds against resistance mechanisms. Among them, T001-10027877 exhibited dual inhibitory effects, with IC50 values of 4.34 µM against EGFRT790M/C797S/L858R and 1.27 µM against EGFRT790M/L858R. Further investigations into the antiproliferative effects in H1975, A549, H460 and Ba/F3-EGFRL858/T790M/C797S cancer cells revealed that T001-10027877 was the most potent anticancer agent among the tested compounds. Additionally, the induction of H1975 cell apoptosis and cell cycle arrest by T001-10027877 were confirmed, elucidating its mechanism of action.

CONCLUSIONS

This study highlights the efficacy of combining computational techniques with bioactivity assessments in the quest for novel antiproliferative agents targeting complex EGFR mutations. In particular, T001-10027877 has great potential for overcoming EGFR-mediated resistance and merits further in vivo exploration. Our findings contribute valuable insights into the development of next-generation anticancer therapies, demonstrating the power of an integrated drug discovery approach.

Mogrol-mediated enhancement of radiotherapy sensitivity in non-small cell lung cancer: a mechanistic study

This study investigated mogrol's impact on non-small cell lung cancer (NSCLC) radiosensitivity and underlying mechanisms, using various methods including assays, bioinformatics, and xenograft models. CCK-8, clonogenic, flow cytometry, TUNEL, and Western blot assays evaluated mogrol and radiation effects on NSCLC viability and apoptosis. Ubiquitin-specific protease 22 (USP22) expression in NSCLC patient tissues was determined by RT-qPCR and Western blot. A xenograft model validated mogrol's effects on tumor growth. Bioinformatics identified four ubiquitin-specific proteases, including USP22, in NSCLC. Kaplan-Meier analysis confirmed USP22's value in lung cancer survival. Human Protein Atlas (HPA) database analysis indicated higher USP22 expression in lung cancer tissues. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis implicated ERK1/2 in NSCLC progression, and molecular docking showed stability between mogrol and ERK1/2. Further in vivo and in vitro experiments have demonstrated that mogrol enhances the inhibitory effect of radiation on NSCLC cell viability and clonogenic capacity. Cell viability and clonogenic capacity are reduced by >50%, and an increase in cellular apoptosis is observed, with apoptotic levels reaching 10%. USP22 expression was significantly elevated in NSCLC tissues, particularly in radiotherapy-resistant patients. Mogrol downregulated USP22 expression by inhibiting the ERK/CREB pathway, lowering COX2 expression. Mogrol also enhanced radiation's inhibition of tumor growth in mice. Mogrol enhances NSCLC radiosensitivity by downregulating USP22 via the ERK/CREB pathway, leading to reduced COX2 expression.NEW & NOTEWORTHY Mogrol enhances non-small cell lung cancer (NSCLC) cell sensitivity to radiotherapy by downregulating USP22 through the ERK/CREB pathway, reducing COX2 expression. These findings highlight mogrol's potential as an adjunct to improve NSCLC radiotherapy and open avenues for further research and clinical applications.

Advances and challenges in thyroid cancer: The interplay of genetic modulators, targeted therapies, and AI-driven approaches

Thyroid cancer continues to exhibit a rising incidence globally, predominantly affecting women. Despite stable mortality rates, the unique characteristics of thyroid carcinoma warrant a distinct approach. Differentiated thyroid cancer, comprising most cases, is effectively managed through standard treatments such as thyroidectomy and radioiodine therapy. However, rarer variants, including anaplastic thyroid carcinoma, necessitate specialized interventions, often employing targeted therapies. Although these drugs focus on symptom management, they are not curative. This review delves into the fundamental modulators of thyroid cancers, encompassing genetic, epigenetic, and non-coding RNA factors while exploring their intricate interplay and influence. Epigenetic modifications directly affect the expression of causal genes, while long non-coding RNAs impact the function and expression of micro-RNAs, culminating in tumorigenesis. Additionally, this article provides a concise overview of the advantages and disadvantages associated with pharmacological and non-pharmacological therapeutic interventions in thyroid cancer. Furthermore, with technological advancements, integrating modern software and computing into healthcare and medical practices has become increasingly prevalent. Artificial intelligence and machine learning techniques hold the potential to predict treatment outcomes, analyze data, and develop personalized therapeutic approaches catering to patient specificity. In thyroid cancer, cutting-edge machine learning and deep learning technologies analyze factors such as ultrasonography results for tumor textures and biopsy samples from fine needle aspirations, paving the way for a more accurate and effective therapeutic landscape in the near future.

The role of angiogenesis in melanoma: Clinical treatments and future expectations

The incidence of melanoma has increased rapidly over the past few decades, with mortality accounting for more than 75% of all skin cancers. The high metastatic potential of Melanoma is an essential factor in its high mortality. Vascular angiogenic system has been proved to be crucial for the metastasis of melanoma. An in-depth understanding of angiogenesis will be of great benefit to melanoma treatment and may promote the development of melanoma therapies. This review summarizes the recent advances and challenges of anti-angiogenic agents, including monoclonal antibodies, tyrosine kinase inhibitors, human recombinant Endostatin, and traditional Chinese herbal medicine. We hope to provide a better understanding of the mechanisms, clinical research progress, and future research directions of melanoma.

Adaptive therapy to circumvent drug resistance to tyrosine kinase inhibitors in cancer: is it clinically relevant?

INTRODUCTION

Cancer is highly adaptable and is constantly evolving against current targeted therapies such as tyrosine kinase inhibitors. Despite advances in recent decades, the emergence of drug resistance to tyrosine kinase inhibitors constantly hampers therapeutic efficacy of cancer treatment. Continuous therapy versus intermittent clinical regimen has been a debate in drug administration of cancer patients. An ecologically-inspired shift in cancer treatment known as 'adaptive therapy' intends to improve the drug administration of drugs to cancer patients that can delay emergence of drug resistance.

AREAS COVERED

We discuss improved understanding of the concept of drug resistance, the basis of continuous therapy, intermittent clinical regimens, and adaptive therapy will be reviewed. In addition, we discuss how adaptive therapy provides guidance for future cancer treatment.

EXPERT OPINION

The current understanding of drug resistance in cancer leads to poor prognosis and limited treatment options in patients. Fighting drug resistance mutants is constantly followed by new forms of resistance. In most reported cases, continuous therapy leads to drug resistance and an intermittent clinical regimen vaguely delays it. However, adaptive therapy, conceptually, exploits multiple parameters that can suppress the growth of drug resistance and provides safe treatment for cancer patients in the future.

A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence

Through the revolutionization of artificial intelligence (AI) technologies in clinical research, significant improvement is observed in diagnosis of cancer. Utilization of these AI technologies, such as machine and deep learning, is imperative for the discovery of novel anticancer drugs and improves existing/ongoing cancer therapeutics. However, building a model for complicated cancers and their types remains a challenge due to lack of effective therapeutics that hinder the establishment of effective computational tools. In this review, we exploit recent approaches and state-of-the-art in implementing AI methods for anticancer drug discovery, and discussed how advances in these applications need to be considered in the current cancer therapeutics. Considering the immense potential of AI, we explore molecular docking and their interactions to recognize metabolic activities that support drug design. Finally, we highlight corresponding strategies in applying machine and deep learning methods to various types of cancer with their pros and cons.

Investigating the Retained Inhibitory Effect of Cobimetinib against p.P124L Mutated MEK1: A Combined Liquid Biopsy and in Silico Approach

The systemic treatment of metastatic melanoma has radically changed, due to an improvement in the understanding of its genetic landscape and the advent of targeted therapy. However, the response to BRAF/MEK inhibitors is transitory, and big efforts were made to identify the mechanisms underlying the resistance. We exploited a combined approach, encompassing liquid biopsy analysis and molecular dynamics simulation, for tracking tumor evolution, and in parallel defining the best treatment option. The samples at different time points were collected from a BRAF-mutant melanoma patient who developed an early resistance to dabrafenib/trametinib. The analysis of the circulating tumor DNA (ctDNA) identified the MEK1 p.P124L mutation that confers resistance to trametinib. With an in silico modeling, we identified cobimetinib as an alternative MEK inhibitor, and consequently suggested a therapy switch to vemurafenib/cobimetinib. The patient response was followed by ctDNA tracking and circulating melanoma cell (CMC) count. The cobimetinib administration led to an important reduction in the BRAF p.V600E and MEK1 p.P124L allele fractions and in the CMC number, features suggestive of a putative response. In summary, this study emphasizes the usefulness of a liquid biopsy-based approach combined with in silico simulation, to track real-time tumor evolution while assessing the best treatment option.

Mechanism of Lethal Skin Toxicities Induced by Epidermal Growth Factor Receptor Inhibitors and Related Treatment Strategies

Epidermal growth factor receptor (EGFR) inhibitors are widely used to treat various types of cancers such as non-small cell lung cancer, head and neck cancer, breast cancer, pancreatic cancer. Adverse reactions such as skin toxicity, interstitial lung disease, hepatotoxicity, ocular toxicity, hypomagnesemia, stomatitis, and diarrhea may occur during treatment. Because the EGFR signaling pathway is important for maintaining normal physiological skin function. Adverse skin reactions occurred in up to 90% of cancer patients treated with EGFR inhibitors, including common skin toxicities (such as papulopustular exanthemas, paronychia, hair changes) and rare fatal skin toxicities (e.g., Stevens–Johnson syndrome, toxic epidermal necrolysis, acute generalized exanthematous pustulosis). This has led to the dose reduction or discontinuation of EGFR inhibitors in the treatment of cancer. Recently, progress has been made about research on the skin toxicity of EGFR inhibitors. Here, we summarize the mechanism of skin toxicity caused by EGFR inhibitors, measures to prevent severe fatal skin toxicity, and provide reference for medical staff how to give care and treatment after adverse skin reactions.