Machine learning driven drug repurposing strategy for identification of potential RET inhibitors against non-small cell lung cancer

Screening of phytochemicals from Clerodendrum inerme (L.) Gaertn as potential anti-breast cancer compounds targeting EGFR: an in-silico approach

Breast cancer (BC) is the most prevalent malignancy among women around the world. The epidermal growth factor receptor (EGFR) is a tyrosine kinase receptor (RTK) of the ErbB/HER family. It is essential for triggering the cellular signaling cascades that control cell growth and survival. However, perturbations in EGFR signaling lead to cancer development and progression. Hence, EGFR is regarded as a prominent therapeutic target for breast cancer. Therefore, in the current investigation, EGFR was targeted with phytochemicals from Clerodendrum inerme (L.) Gaertn (C. inerme). A total of 121 phytochemicals identified by gas chromatography-mass spectrometry (GC-MS) analysis were screened against EGFR through molecular docking, ADMET analyses (Absorption, Distribution, Metabolism, Excretion, and Toxicity), PASS predictions, and molecular dynamics simulation, which revealed three potential hit compounds with CIDs 10586 [i.e. alpha-bisabolol (-6.4 kcal/mol)], 550281 [i.e. 2,(4,4-Trimethyl-3-hydroxymethyl-5a-(3-methyl-but-2-enyl)-cyclohexene) (-6.5 kcal/mol)], and 161271 [i.e. salvigenin (-7.4 kcal/mol)]. The FDA-approved drug gefitinib was used to compare the inhibitory effects of the phytochemicals. The top selected compounds exhibited good ADMET properties and obeyed Lipinski's rule of five (ROF). The molecular docking analysis showed that salvigenin was the best among the three compounds and formed bonds with the key residue Met 793. Furthermore, the molecular mechanics generalized born surface area (MMGBSA) calculations, molecular dynamics simulation, and normal mode analysis validated the binding affinity of the compounds and also revealed the strong stability and compactness of phytochemicals at the docked site. Additionally, DFT and DOS analyses were done to study the reactivity of the compounds and to further validate the selected phytochemicals. These results suggest that the identified phytochemicals possess high inhibitory potential against the target EGFR and can treat breast cancer. However, further in vitro and in vivo investigations are warranted towards the development of these constituents into novel anti-cancer drugs.

Drug repurposing through Biophysical Insights: Focus on Indoleamine 2,3-Dioxygenase and Tryptophan 2,3-Dioxygenase Dual Inhibitors

The kynurenine pathway (KP) plays a pivotal role in dampening the immune response in many types of cancer, including TNBC. The intricate involvement of tryptophan degradation via KP serves as a critical regulator in mediating immunosuppression in the tumor microenvironment. The key enzymes that facilitate this mechanism and contribute to tumor progression are indoleamine 2,3-dioxygenase (IDO1) and tryptophan 2,3-dioxygenase (TDO). Despite attempts to use navoximod as a dual-specific inhibitor, its poor bioavailability and lack of clinical efficacy have hampered its utility. To date, no FDA-approved drugs have advanced for dual targeting of these enzymes. Therefore, this study aimed to repurpose the approved drugs from the DrugBank database as novel IDO1/TDO inhibitors. Initially, 2588 FDA-approved compounds were screened by employing molecular docking and pharmacokinetic profiling. Subsequently, methods such as MM-GBSA calculations and machine learning based analysis precisely identified 20 potential lead compounds. The resultant compounds were then assessed for various toxicity endpoints and anticancer activity. The PaccMann server revealed potent anticancer activity, with sensitivities ranging from 0.203 to 24.119 μM against MDA-MB-231 TNBC cell lines. Alongside, the interaction profile with critical residues, strongly reinforced DB06292 (Dapagliflozin) as a compelling hit candidate. Finally, the reliability of the result was corroborated through a rigorous 200 ns molecular dynamics simulation, ensuring the stable binding of the hit against the target proteins. Considering the promising outcomes, we speculate that the proposed hit compound holds strong potential for the management of TNBC.

ResisenseNet hybrid neural network model for predicting drug sensitivity and repurposing in breast Cancer

Breast cancer remains a leading cause of mortality among women worldwide, with drug resistance driven by transcription factors and mutations posing significant challenges. To address this, we present ResisenseNet, a predictive model for drug sensitivity and resistance. ResisenseNet integrates transcription factor expression, genomic markers, drugs, and molecular descriptors, employing a hybrid architecture of 1D-CNN + LSTM and DNN to effectively learn long-range and temporal patterns from amino acid sequences and transcription factor data. The model demonstrated exceptional predictive accuracy, achieving a validation accuracy of 0.9794 and a loss value of 0.042. Comprehensive validation included comparisons with state-of-the-art models and ablation studies, confirming the robustness of the developed architecture. ResisenseNet has been applied to repurpose existing anticancer drugs across 14 different cancers, with a focus on breast cancer. Among the malignancies studied, drugs targeting Low-grade Glioma (LGG) and Lung Adenocarcinoma (LUAD) showed increased sensitivity to breast cancer as per ResisenseNet's assessment. Further evaluation of the predicted sensitive drugs revealed that 14 had no prior history of anticancer activity against breast cancer. These drugs target key signaling pathways involved in breast cancer, presenting novel therapeutic opportunities. ResisenseNet addresses drug resistance by filtering ineffective compounds and enhancing chemotherapy for breast cancer. In vitro studies on sensitive drugs provide valuable insights into breast cancer prognosis, contributing to improved treatment strategies.

An extensive review on lung cancer therapeutics using machine learning techniques: state-of-the-art and perspectives

There are over 100 types of human cancer, accounting for millions of deaths every year. Lung cancer alone claims over 1.8 million lives per year and is expected to surpass 3.2 million by 2050, which underscores the urgent need for rapid drug development and repurposing initiatives. The application of AI emerges as a pivotal solution to developing anti-cancer therapeutics. This state-of-the-art review aims to explore the various applications of AI in lung cancer therapeutics. Predictive models can analyse large datasets, including clinical data, genetic information, and treatment outcomes, for novel drug design and to generate personalised treatment recommendations, potentially optimising therapeutic strategies, enhancing treatment efficacy, and minimising adverse effects. A thorough literature review study was conducted based on articles indexed in PubMed and Scopus. We compiled the use of various machine learning approaches, including CNN, RNN, GAN, VAEs, and other AI techniques, enhancing efficiency with accuracy exceeding 95%, which is validated through a computer-aided drug design process. AI can revolutionise lung cancer therapeutics, streamlining processes and saving biological scientists' time and effort-however, further research is needed to overcome challenges and fully unlock AI's potential in Lung Cancer Therapeutics.

Orally Administered Drugs and Their Complicated Relationship with Our Gastrointestinal Tract

Orally administered compounds represent the great majority of all pharmaceutical compounds produced for human use and are the most popular among patients since they are practical and easy to self-administer. Following ingestion, orally administered drugs begin a "perilous" journey down the gastrointestinal tract and their bioavailability is modulated by numerous factors. The gastrointestinal (GI) tract anatomy can modulate drug bioavailability and accounts for interpatient drug response heterogeneity. Furthermore, host genetics is a contributor to drug bioavailability modulation. Importantly, a component of the GI tract that has been gaining notoriety with regard to drug treatment interactions is the gut microbiota, which shares a two-way interaction with pharmaceutical compounds in that they can be influenced by and are able to influence administered drugs. Overall, orally administered drugs are a patient-friendly treatment option. However, during their journey down the GI tract, there are numerous host factors that can modulate drug bioavailability in a patient-specific manner.

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.