The convergence of artificial intelligence (AI) and genomics is redefining cancer drug discovery by facilitating the development of personalized and effective therapies. This review examines the trans Show more
The convergence of artificial intelligence (AI) and genomics is redefining cancer drug discovery by facilitating the development of personalized and effective therapies. This review examines the transformative role of AI technologies, including deep learning and advanced data analytics, in accelerating key stages of the drug discovery process: target identification, drug design, clinical trial optimization, and drug response prediction. Cutting-edge tools such as DrugnomeAI and PandaOmics have made substantial contributions to therapeutic target identification, while AI's predictive capabilities are driving personalized treatment strategies. Additionally, advancements like AlphaFold highlight AI's capacity to address intricate challenges in drug development. However, the field faces significant challenges, including the management of large-scale genomic datasets and ethical concerns surrounding AI deployment in healthcare. This review underscores the promise of data-centric AI approaches and emphasizes the necessity of continued innovation and interdisciplinary collaboration. Together, AI and genomics are charting a path toward more precise, efficient, and transformative cancer therapeutics. Show less
Non-small cell lung cancer (NSCLC) is one of the most prevalent and lethal types of cancers worldwide and its high incidence and mortality rates pose a significant public health challenge. Despite sig Show more
Non-small cell lung cancer (NSCLC) is one of the most prevalent and lethal types of cancers worldwide and its high incidence and mortality rates pose a significant public health challenge. Despite significant advances in targeted therapy and immunotherapy, the overall prognosis of patients with NSCLC remains poor. Hypoxia is a critical driving factor in tumor progression, influencing the biological behavior of tumor cells through complex molecular mechanisms. The present review systematically examined the role of the hypoxic microenvironment in NSCLC, demonstrating its crucial role in promoting tumor cell growth, invasion and metastasis. Additionally, it has been previously reported that the hypoxic microenvironment enhances tumor cell resistance by activating hypoxia-inducible factor and regulating exosome secretion. The hypoxic microenvironment also enables tumor cells to adapt to low oxygen and nutrient-deficient conditions by enhancing metabolic reprogramming, such as through upregulating glycolysis. Further studies have shown that the hypoxic microenvironment facilitates immune escape by modulating tumor-associated immune cells and suppressing the antitumor response of the immune system. Moreover, the hypoxic microenvironment increases tumor resistance to radiotherapy, chemotherapy and other types of targeted therapy through various pathways, significantly reducing the therapeutic efficacy of these treatments. Therefore, it could be suggested that early detection of cellular hypoxia and targeted therapy based on hypoxia may offer new therapeutic approaches for patients with NSCLC. The present review not only deepened the current understanding of the mechanisms of action and role of the hypoxic microenvironment in NSCLC but also provided a solid theoretical basis for the future development of precision treatments for patients with NSCLC. Show less
Introduction: Drugs targeting mitochondria are emerging as promising antitumor therapeutics in preclinical models. However, a few of these drugs have shown clinical toxicity. Developing mitochondria- Show more
Introduction: Drugs targeting mitochondria are emerging as promising antitumor therapeutics in preclinical models. However, a few of these drugs have shown clinical toxicity. Developing mitochondria-targeted modified natural compounds and US FDA-approved drugs with increased therapeutic index in cancer is discussed as an alternative strategy. Areas Covered: Triphenylphosphonium cation (TPP + )-based drugs selectively accumulate in the mitochondria of cancer cells due to their increased negative membrane potential, target the oxidative phosphorylation proteins, inhibit mitochondrial respiration, and inhibit tumor proliferation. TPP + -based drugs exert minimal toxic side effects in rodents and humans. These drugs can sensitize radiation and immunotherapies. Expert Opinion: TPP + -based drugs targeting the tumor mitochondrial electron transport chain are a new class of oxidative phosphorylation inhibitors with varying antiproliferative and antimetastatic potencies. Some of these TPP + -based agents, which are synthesized from naturally occurring molecules and FDA-approved drugs, have been tested in mice and did not show notable toxicity, including neurotoxicity, when used at doses under the maximally tolerated dose. Thus, more effort should be directed toward the clinical translation of TPP + -based OXPHOS-inhibiting drugs in cancer prevention and treatment. Show less
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potenti Show more
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation â and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration. Show less