Network Biology and Artificial Intelligence Drive the Understanding of the Multidrug Resistance Phenotype in Cancer

Accurate Predictions of Molecular Properties of Proteins via Graph Neural Networks and Transfer Learning

Machine learning has emerged as a promising approach for predicting molecular properties of proteins, as it addresses limitations of experimental and traditional computational methods. Here, we introduce GSnet, a graph neural network (GNN) trained to predict physicochemical and geometric properties including solvation-free energies, diffusion constants, and hydrodynamic radii, based on three-dimensional protein structures. By leveraging transfer learning, pretrained GSnet embeddings were adapted to predict solvent-accessible surface area (SASA) and residue-specific pKa values, achieving high accuracy and generalizability. Notably, GSnet outperformed existing protein embeddings for SASA prediction and a locally charge-aware variant, aLCnet, approached the accuracy of simulation-based and empirical methods for pKa prediction. Our GNN framework demonstrated robustness across diverse data sets, including intrinsically disordered peptides, and scalability for high-throughput applications. These results highlight the potential of GNN-based embeddings and transfer learning to advance protein structure analysis, providing a foundation for integrating predictive models into proteome-wide studies and structural biology pipelines.

Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges

Drug resistance is one of the main reasons for cancer treatment failure, leading to a rapid recurrence/disease progression of the cancer. Recently, artificial intelligence (AI) has empowered physicians to use its powerful data processing and pattern recognition capabilities to extract and mine valuable drug resistance information from large amounts of clinical or omics data, to study drug resistance mechanisms, to evaluate and predict drug resistance, and to develop innovative therapeutic strategies to reduce drug resistance. In this review, we proposed a feasible workflow for incorporating AI into tumor drug resistance research, highlighted current AI-driven tumor drug resistance applications, and discussed the opportunities and challenges encountered in the process. Based on a comprehensive literature analysis, we systematically summarized the role of AI in tumor drug resistance research, including drug development, resistance mechanism elucidation, drug sensitivity prediction, combination therapy optimization, resistance phenotype identification, and clinical biomarker discovery. With the continuous advancement of AI technology and rigorous validation of clinical data, AI models are expected to fuel the development of precision oncology by improving efficacy, guiding therapeutic decisions, and optimizing patient prognosis. In summary, by leveraging clinical and omics data, AI models are expected to pioneer new therapy strategies to mitigate tumor drug resistance, improve efficacy and patient survival, and provide novel perspectives and tools for oncology treatment.

Advanced materials for cancer treatment and beyond

Conservative anti-cancer treatment represented by chemotherapy and surgery lacks tumor-specificity and could hardly resolve the problems associated with multidrug resistance (MDR) in cancers. Novel therapeutic materials in cancer treatment, such as those with anti-MDR or controllable treatment features, represent a significant trend due to their advantages of high and specific efficacy and timely intervention of cancer progress. In addition to their excellent biocompatibility and specificity, they can be utilized in therapies that require ease of operation, provided they are designed with high detection sensitivity. In this review, we summarize a series of recently developed materials that exhibit these advantages, including immune-enhancing and tumor microenvironment (TME)- responsive materials, and those with integrated therapeutic and imaging capabilities. We also introduce advanced modification approaches that can impart essential targeting functionalities to these materials.

Insight into the structure, oligomerization, and the role in drug resistance of human UDP-glucuronosyltransferases

Human UDP-glucuronosyltransferases (UGTs) are pivotal phase II metabolic enzymes facilitating the transfer of glucuronic acid from UDP-glucuronic acid (UDPGA) to various substrates. UGTs are classic type I transmembrane glycoproteins, mainly localized in the endoplasmic reticulum (ER) membrane. This review comprehensively explores UGTs, encompassing gene expression, functional characteristics, substrate specificity, and metabolic mechanisms. A recent analysis of C-terminal structures, compared with original data, underscores the pivotal role of α3, α4, and β4 functional domains in selectively recognizing diverse glycosyl donors. Accumulating evidence suggests that UGTs function as homo- and heterodimers, with oligomers likely stabilizing UGTs and modulating their activity. The review sheds light on the implications of UGT oligomerization on substrate glucuronidation and the interplay between protein-protein interaction and glucuronidation activity. UGT-mediated drug resistance, often underestimated, emerges as a clinically relevant form of chemical resistance, with delineated outcomes in tumors and other diseases. This review provides a multifaceted exploration of the physiological significance of UGTs, spanning genetics, proteins, oligomerization, drug resistance, and more, offering insights into their metabolic mechanisms. Understanding interactions between UGT isoforms is crucial for predicting drug-drug interactions, preventing drug toxicity, and enabling precision treatment.

System level network data and models attack cancer drug resistance

Drug resistance is responsible for >90% of cancer related deaths. Cancer drug resistance is a system level network phenomenon covering the entire cell. Small-scale interactomes and signalling network models of drug resistance guide directed drug development. Recently, proteome-wide human interactome and signalling network data have become available, which have been extended by drug-target interactions, drug resistance-inducing mutations, as well as by several cancer and drug resistance-related multi-omics datasets. System level signalling network models have become available examining therapy resistance, performing in silico clinical trials, and conducting large, in silico drug combination screens. Drug resistance network data and models have become interoperable and reliable. These advances paved the road for building proteome-wide drug resistance models.

Cancer drug resistance as learning of signaling networks

Drug resistance is a major cause of tumor mortality. Signaling networks became useful tools for driving pharmacological interventions against cancer drug resistance. Signaling datasets now cover the entire human cell. Recently, network adaptation became understood as a learning process. We review rapidly increasing evidence showing that the development of cancer drug resistance can be described as learning of signaling networks. During drug adaptation, the network forgets drug-affected pathways by desensitization and relearns by strengthening alternative pathways. Thus, resistant cancer cells develop a drug resistance memory. We show that all key players of cellular learning (i.e., IDPs, protein translocation, microRNAs/lncRNAs, scaffolding proteins and epigenetic/chromatin memory) have important roles in the development of cancer drug resistance. Moreover, all of them are central components of the epithelial-mesenchymal transition leading to metastases and resistance. Phenotypic plasticity was recently listed as a hallmark of cancer. We review how network plasticity induces rare, pre-existent drug-resistant cells in the absence of drug treatment. Key network methods assessing the development of drug resistance and network pharmacological interventions against drug resistance are summarized. Finally, we highlight the class of cellular memory drugs affecting cellular learning and forgetting, and we summarize current challenges to prevent or break drug resistance using network models.

Global insights into MRSA bacteremia: a bibliometric analysis and future outlook

Background

Methicillin-resistant Staphylococcus aureus (MRSA) bloodstream infections (BSIs) pose a significant challenge to global public health, characterized by high morbidity and mortality rates, particularly in immunocompromised patients. Despite extensive research, the rapid development of MRSA antibiotic resistance has outpaced current treatment methods, increasing the difficulty of treatment. Therefore, reviewing research on MRSA BSIs is crucial.

Methods

This study conducted a bibliometric analysis, retrieving and analyzing 1,621 publications related to MRSA BSIs from 2006 to 2024. The literature was sourced from the Web of Science Core Collection (WoSCC), and data visualization and trend analysis were performed using VOSviewer, CiteSpace, and Bibliometrix software packages.

Results

The bibliometric analysis showed that research on MRSA BSIs was primarily concentrated in the United States, China, and Japan. The United States leads in research output and influence, with significant contributions from institutions such as the University of California system and the University of Texas system. The journal with the most publications is Antimicrobial Agents and Chemotherapy, while the most cited global publication is Vincent JL's article "Sepsis in European Intensive Care Units: Results of the SOAP Study" published in Critical Care Medicine in 2006. Cosgrove SE's article "Comparison of Mortality Associated with Methicillin-Resistant and Methicillin-Susceptible Staphylococcus aureus Bacteremia: A Meta-analysis" had the most co-citations. Key trends in the research include MRSA's antibiotic resistance mechanisms, the application of new diagnostic technologies, and the impact of COVID-19 on MRSA studies. Additionally, artificial intelligence (AI) and machine learning are increasingly applied in MRSA diagnosis and treatment, and phage therapy and vaccine development have become future research hotspots.

Conclusion

Methicillin-resistant Staphylococcus aureus BSIs remain a major global public health challenge, especially with the increasing severity of antibiotic resistance. Although progress has been made in new treatments and diagnostic technologies, further validation is required. Future research will rely on integrating genomics, AI, and machine learning to drive personalized treatment. Strengthening global cooperation, particularly in resource-limited countries, will be key to effectively addressing MRSA BSIs.

Accurate Predictions of Molecular Properties of Proteins via Graph Neural Networks and Transfer Learning

Machine learning has emerged as a promising approach for predicting molecular properties of proteins, as it addresses limitations of experimental and traditional computational methods. Here, we introduce GSnet, a graph neural network (GNN) trained to predict physicochemical and geometric properties including solvation free energies, diffusion constants, and hydrodynamic radii, based on three-dimensional protein structures. By leveraging transfer learning, pre-trained GSnet embeddings were adapted to predict solvent-accessible surface area (SASA) and residue-specific pKa values, achieving high accuracy and generalizability. Notably, GSnet outperformed existing protein embeddings for SASA prediction, and a locally charge-aware variant, aLCnet, approached the accuracy of simulation-based and empirical methods for pKa prediction. Our GNN framework demonstrated robustness across diverse datasets, including intrinsically disordered peptides, and scalability for high-throughput applications. These results highlight the potential of GNN-based embeddings and transfer learning to advance protein structure analysis, providing a foundation for integrating predictive models into proteome-wide studies and structural biology pipelines.

Computer-Aided Identification and Design of Ligands for Multi-Targeting Inhibition of a Molecular Acute Myeloid Leukemia Network

BACKGROUND/OBJECTIVES

Acute myeloid leukemia (AML) is characterized by therapeutic failure and long-term risk for disease relapses. As several therapeutic targets participate in networks, they can rewire to eventually evade single-target drugs. Hence, multi-targeting approaches are considered on the expectation that interference with many different components could synergistically hinder activation of alternative pathways and demolish the network one-off, leading to complete disease remission.

METHODS

Herein, we established a network-based, computer-aided approach for the rational design of drug combinations and de novo agents that interact with many AML network components simultaneously.

RESULTS

A reconstructed AML network guided the selection of suitable protein hubs and corresponding multi-targeting strategies. For proteins responsive to existing drugs, a greedy algorithm identified the minimum amount of compounds targeting the maximum number of hubs. We predicted permissible combinations of amiodarone, artenimol, fostamatinib, ponatinib, procaine, and vismodegib that interfere with 3-8 hubs, and we elucidated the pharmacological mode of action of procaine on DNMT3A. For proteins that do not respond to any approved drugs, namely cyclins A1, D2, and E1, we used structure-based de novo drug design to generate a novel triple-targeting compound of the chemical formula C15H15NO5, with favorable pharmacological and drug-like properties.

CONCLUSIONS

Overall, by integrating network and structural pharmacology with molecular modeling, we determined two complementary strategies with the potential to annihilate the AML network, one in the form of repurposable drug combinations and the other as a de novo synthesized triple-targeting agent. These target-drug interactions could be prioritized for preclinical and clinical testing toward precision medicine for AML.

Exploring the role of ADAMTSL2 across multiple cancer types: A pan-cancer analysis and validated in colorectal cancer

BACKGROUND

Recent studies have established a correlation between ADAMTSL2 (ADAMTS-like 2) and the development of various cancers. This study aims to conduct a comprehensive pan-cancer analysis in 37 cancer types and investigate its potential role in colon and rectal adenocarcinoma (COADREAD).

METHOD

Pan-cancer and mutation data were sourced from The Cancer Genome Atlas (TCGA) database and analyzed using Sangerbox analysis platform. We explored the expression patterns and prognostic implications of ADAMTSL2, and investigated its relationships with tumor heterogeneity, stemness, immune checkpoint genes, immune cell infiltration, RNA modifications, and mutational profiles across different cancers. Additionally, with Ethics Committee approval, we conducted immunohistochemical (IHC) analysis on 120 COADEAD samples to evaluate ADAMTSL2 expression and its association with clinicopathological parameters.

RESULTS

ADAMTSL2 expression was positively correlated with the hazard ratio of OS, DSS, DFI and PFI for ESCA and COADREAD. A negative correlation was observed between ADAMTSL2 expression and NEO levels in COAD. Gene alterations in ADAMTSL2 were observed, with a mutation frequency of 5.0% in COAD. There is a significant correlation between ADAMTSL2 expression and immune cell infiltration in a variety of cancers. The expression level of ADAMTSL2 protein was associated with T stage, N stage, M stage (p < 0.05). Kaplan‒Meier survival curves demonstrated that the high ADAMTSL2 group had a shorter OS time (p = 0.047) and progression free survival time (p = 0.026) than the low ADAMTSL2 group.

CONCLUSION

In summary, we conducted a comprehensive pan-cancer analysis of ADAMTSL2 and we demonstrated that ADAMTSL2 may serve as a novel prognostic biomarker and immunotherapy target in COADREAD.

Harnessing machine learning potential for personalised drug design and overcoming drug resistance

Drug resistance in cancer treatment presents a significant challenge, necessitating innovative approaches to improve therapeutic efficacy. Integrating machine learning (ML) in cancer research is promising as ML algorithms outrival in analysing complex datasets, identifying patterns, and predicting treatment outcomes. Leveraging diverse data sources such as genomic profiles, clinical records, and drug response assays, ML uncovers molecular mechanisms of drug resistance, enabling personalised treatment, maximising efficacy and minimising adverse effects. Various ML algorithms contribute to the drug discovery process - Random Forest and Decision Trees predict drug-target interactions and aid in virtual screening, and SVM classify leads on bioactivity data. Neural Networks model QSAR to optimise lead compounds and K-means clustering group compounds with similar chemical properties aiding compound selection. Gaussian Processes predict drug responses, Bayesian Networks infer causal relationships, Autoencoders generate novel compounds, and Genetic Algorithms optimise molecular structures. These algorithms collectively enhance efficiency and success rates in drug design endeavours, from lead identification to optimisation and are cost-effective, empowering clinicians with real-time treatment monitoring and improving patient outcomes. This review highlights the immense potential of ML in revolutionising cancer care through effective drug design to reduce drug resistance, and we have also discussed various limitations and research gaps to understand better.

Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction

The attrition rate of drugs in clinical trials is generally quite high, with estimates suggesting that approximately 90% of drugs fail to make it through the process. The identification of unexpected toxicity issues during preclinical stages is a significant factor contributing to this high rate of failure. These issues can have a major impact on the success of a drug and must be carefully considered throughout the development process. These late-stage rejections or withdrawals of drug candidates significantly increase the costs associated with drug development, particularly when toxicity is detected during clinical trials or after market release. Understanding drug-biological target interactions is essential for evaluating compound toxicity and safety, as well as predicting therapeutic effects and potential off-target effects that could lead to toxicity. This will enable scientists to predict and assess the safety profiles of drug candidates more accurately. Evaluation of toxicity and safety is a critical aspect of drug development, and biomolecules, particularly proteins, play vital roles in complex biological networks and often serve as targets for various chemicals. Therefore, a better understanding of these interactions is crucial for the advancement of drug development. The development of computational methods for evaluating protein-ligand interactions and predicting toxicity is emerging as a promising approach that adheres to the 3Rs principles (replace, reduce, and refine) and has garnered significant attention in recent years. In this review, we present a thorough examination of the latest breakthroughs in drug toxicity prediction, highlighting the significance of drug-target binding affinity in anticipating and mitigating possible adverse effects. In doing so, we aim to contribute to the development of more effective and secure drugs.

DeepKa Web Server: High-Throughput Protein pKa Prediction

DeepKa is a deep-learning-based protein pKa predictor proposed in our previous work. In this study, a web server was developed that enables online protein pKa prediction driven by DeepKa. The web server provides a user-friendly interface where a single step of entering a valid PDB code or uploading a PDB format file is required to submit a job. Two case studies have been attached in order to explain how pKa's calculated by the web server could be utilized by users. Finally, combining the web server with post processing as described in case studies, this work suggests a quick workflow of investigating the relationship between protein structure and function that are pH dependent. The web server of DeepKa is freely available at http://www.computbiophys.com/DeepKa/main.

Schiff bases and their metal complexes to target and overcome (multidrug) resistance in cancer

Overcoming multidrug resistance (MDR) is one of the major challenges in cancer therapy. In this respect, Schiff base-related compounds (bearing a R1R2CNR3 bond) gained high interest during the past decades. Schiff bases are considered privileged ligands for various reasons, including the easiness of their preparation and the possibility to form complexes with almost all transition metal ions. Schiff bases and their metal complexes exhibit many types of biological activities and are used for the treatment and diagnosis of various diseases. Until now, 13 Schiff bases have been investigated in clinical trials for cancer treatment and hypoxia imaging. This review represents the first collection of Schiff bases and their complexes which demonstrated MDR-reversal activity. The areas of drug resistance covered in this article involve: 1) Modulation of ABC transporter function, 2) Targeting lysosomal ABCB1 overexpression, 3) Circumvention of ABC transporter-mediated drug efflux by alternative routes of drug uptake, 4) Selective activity against MDR cancer models (collateral sensitivity), 5) Targeting GSH-detoxifying systems, 6) Overcoming apoptosis resistance by inducing necrosis and paraptosis, 7) Reactivation of mutated p53, 8) Restoration of sensitivity to DNA-damaging anticancer therapy, and 9) Overcoming drug resistance through modulation of the immune system. Through this approach, we would like to draw attention to Schiff bases and their metal complexes representing highly interesting anticancer drug candidates with the ability to overcome MDR.

Machine learning based on optimal VOI of multi-sequence MR images to predict lymphovascular invasion in invasive breast cancer

Objectives

Lymphovascular invasion serves as a crucial prognostic indicator in invasive breast cancer, influencing treatment decisions. We aimed to develop a machine learning model utilizing optimal volumes of interest extracted from multisequence magnetic resonance images to predict lymphovascular invasion in patients with invasive breast cancer.

Materials and methods

This study comprised 191 patients postoperatively diagnosed with invasive breast cancer through multi-sequence magnetic resonance imaging. Independent predictors were identified through univariate and multivariate logistic regression analyses, culminating in the construction of a clinical model. Radiomic features were extracted from multi-sequence magnetic resonance imaging images across various volume of interest scales (-2 mm, entire, +2 mm, +4 mm, and +6 mm). Subsequently, various radiomic models were developed using machine learning model algorithms, including logistic regression, support vector machine, k-nearest neighbor, gradient boosting machine, classification and regression tree, and random forest. A hybrid model was then formulated, amalgamating optimal radiomic and clinical models.

Results

The area under the curve of the clinical model was 0.757. Among the radiomic models, the most efficient diagnosis was achieved by the k-nearest neighbor-based radiomics-volume of interest (+2 mm), resulting in an area under the curve of 0.780. The hybrid model, integrating the k-nearest neighbor-based radiomics-volume of interest (+2 mm), and the clinical model surpassed the individual clinical and radiomics models, exhibiting a superior area under the curve of 0.864.

Conclusion

Utilizing a hybrid approach integrating clinical data and multi-sequence magnetic resonance imaging-derived radiomics models based on the multiscale tumor region volume of interest (+2 mm) proved effective in determining lymphovascular invasion status in patients with invasive breast cancer. This innovative methodology may offer valuable insights for treatment planning and disease management.

Treatment resistance in pancreatic and biliary tract cancer: molecular and clinical pharmacology perspectives

INTRODUCTION

Treatment resistance poses a significant obstacle in oncology, especially in biliary tract cancer (BTC) and pancreatic cancer (PC). Current therapeutic options include chemotherapy, targeted therapy, and immunotherapy. Resistance to these treatments may arise due to diverse molecular mechanisms, such as genetic and epigenetic modifications, altered drug metabolism and efflux, and changes in the tumor microenvironment. Identifying and overcoming these mechanisms is a major focus of research: strategies being explored include combination therapies, modulation of the tumor microenvironment, and personalized approaches.

AREAS COVERED

We provide a current overview and discussion of the most relevant mechanisms of resistance to chemotherapy, target therapy, and immunotherapy in both BTC and PC. Furthermore, we compare the different strategies that are being implemented to overcome these obstacles.

EXPERT OPINION

So far there is no unified theory on drug resistance and progress is limited. To overcome this issue, individualized patient approaches, possibly through liquid biopsies or single-cell transcriptome studies, are suggested, along with the potential use of artificial intelligence, to guide effective treatment strategies. Furthermore, we provide insights into what we consider the most promising areas of research, and we speculate on the future of managing treatment resistance to improve patient outcomes.

The Role of Intracellular and Extracellular Vesicles in the Development of Therapy Resistance in Cancer

Cancer is the second leading cause of global mortality and claims approximately 10 million lives annually. Despite advances in treatments such as surgery, chemotherapy, and immunotherapy, resistance to these methods has emerged. Multidrug resistance (MDR), where cancer cells resist diverse treatments, undermines therapy effectiveness, escalating mortality rates. MDR mechanisms include, among others, drug inactivation, reduced drug uptake, enhanced DNA repair, and activation of survival pathways such as autophagy. Moreover, MDR mechanisms can confer resistance to other therapies like radiotherapy. Understanding these mechanisms is crucial for improving treatment efficacy and identifying new therapeutic targets. Extracellular vesicles (EVs) have gathered attention for their role in cancer progression, including MDR development through protein transfer and genetic reprogramming. Autophagy, a process balancing cellular resources, also influences MDR. The intersection of EVs and autophagy further complicates the understanding of MDR. Both extracellular (exosomes, microvesicles) and intracellular (autophagic) vesicles contribute to therapy resistance by regulating the tumor microenvironment, facilitating cell communication, and modulating drug processing. While much is known about these pathways, there is still a need to explore their potential for predicting treatment responses and understanding tumor heterogeneity.

LC-ESI QToF MS Non-Targeted Screening of Latex Extracts of Euphorbia seguieriana ssp. seguieriana Necker and Euphorbia cyparissias and Determination of Their Potential Anticancer Activity

Euphorbia seguieriana ssp. seguieriana Necker (ES) and Euphorbia cyparissias (EC) with a habitat in the Deliblato Sands were the subject of this examination. The latexes of these so far insufficiently investigated species of the Euphorbia genus are used in traditional medicine for the treatment of wounds and warts on the skin. To determine their chemical composition, non-targeted screening of the latexes' chloroform extracts was performed using liquid chromatography coupled with quadrupole time-of-flight mass spectrometry employing an electrospray ionization source (LC-ESI QTOF MS). The analysis of the obtained results showed that the latexes of ES and EC represent rich sources of diterpenes, tentatively identified as jatrophanes, ingenanes, tiglianes, myrsinanes, premyrsinanes, and others. Examination of the anticancer activity of the ES and EC latex extracts showed that both extracts significantly inhibited the growth of the non-small cell lung carcinoma NCI-H460 and glioblastoma U87 cell lines as well as of their corresponding multi-drug resistant (MDR) cell lines, NCI-H460/R and U87-TxR. The obtained results also revealed that the ES and EC extracts inhibited the function of P-glycoprotein (P-gp) in MDR cancer cells, whose overexpression is one of the main mechanisms underlying MDR.

The Involvement of Peroxiporins and Antioxidant Transcription Factors in Breast Cancer Therapy Resistance

Breast cancer is still the leading cause of death in women of all ages. The reason for this is therapy resistance, which leads to the progression of the disease and the formation of metastases. Multidrug resistance (MDR) is a multifactorial process that leads to therapy failure. MDR involves multiple processes and many signaling pathways that support each other, making it difficult to overcome once established. Here, we discuss cellular-oxidative-stress-modulating factors focusing on transcription factors NRF2, FOXO family, and peroxiporins, as well as their possible contribution to MDR. This is significant because oxidative stress is a consequence of radiotherapy, chemotherapy, and immunotherapy, and the activation of detoxification pathways could modulate the cellular response to therapy and could support MDR. These proteins are not directly responsible for MDR, but they support the survival of cancer cells under stress conditions.

Enhanced Uptake of Anticancer C6-Pep Dimer in a Model Membrane Caused by Differential pKa in Acidic Microenvironment

Acidic tumor microenvironment (TME) presents a challenge for the action of antitumor drugs by acting as an additional barrier for the passive crossing of the cell membrane by chemotherapic agents playing a critical role in the proliferation of tumor cells. Anticancer lipopeptide C6-Pep dimer containing the leucine zipper motif shows an increased uptake into the model tumor membrane in TME, and application of external heat might lead to the uncoiling of the zipper, which could result in cell lysis. This work investigated the cause of this increased uptake of C6-Pep dimer into the bilayer model in TME. Accurate protonation states of all the titratable residues of the C6-Pep dimer in TME were determined using constant pH molecular dynamics. In TME, except for two terminal Glu5 residues, all other Glu residues in the C6-Pep dimer were permanently protonated. The remaining Glu5 residues had differential pKa values, leading to the construction of four possible dimers with different fixed protonation states, and molecular dynamics was used to study their interaction with the anionic bilayer. Except for the dimer at a physiological pH, the other dimers were positively charged and could readily adsorb on the membrane surface. The free energy of insertion of these dimers in the bilayer was lower for single and double protonated Glu5-containing dimers than for the others. After the insertion of the lipopeptides into the membrane, thinning of the bilayer in the vicinity of dimers and an increase in area per lipid of the bilayer were observed for all systems, indicating destabilization of the bilayer due to this intercalation. This study shows that the anticancer lipopeptide C6-Pep utilizes the TME around a tumor cell for insertion into the membrane.

Cell membrane-camouflaged bufalin targets NOD2 and overcomes multidrug resistance in pancreatic cancer

AIMS

Multidrug resistance in pancreatic cancer poses a significant challenge in clinical treatment. Bufalin (BA), a compound found in secretions from the glands of toads, may help overcome this problem. However, severe cardiotoxicity thus far has hindered its clinical application. Hence, the present study aimed to develop a cell membrane-camouflaged and BA-loaded polylactic-co-glycolic acid nanoparticle (CBAP) and assess its potential to counter chemoresistance in pancreatic cancer.

METHODS

The toxicity of CBAP was evaluated by electrocardiogram, body weight, distress score, and nesting behavior of mice. In addition, the anticarcinoma activity and underlying mechanism were investigated both in vitro and in vivo.

RESULTS

CBAP significantly mitigated BA-mediated acute cardiotoxicity and enhanced the sensitivity of pancreatic cancer to several clinical drugs, such as gemcitabine, 5-fluorouracil, and FOLFIRINOX. Mechanistically, CBAP directly bound to nucleotide-binding and oligomerization domain containing protein 2 (NOD2) and inhibited the expression of nuclear factor kappa-light-chain-enhancer of activated B cells. This inhibits the expression of ATP-binding cassette transporters, which are responsible for chemoresistance in cancer cells.

CONCLUSIONS

Our findings indicate that CBAP directly inhibits NOD2. Combining CBAP with standard-of-care chemotherapeutics represents a safe and efficient strategy for the treatment of pancreatic cancer.

Perspectives and mechanisms for targeting mitotic catastrophe in cancer treatment

Mitotic catastrophe is distinct from other cell death modes due to unique nuclear alterations characterized as multi and/or micronucleation. Mitotic catastrophe is a common and virtually unavoidable consequence during cancer therapy. However, a comprehensive understanding of mitotic catastrophe remains lacking. Herein, we summarize the anticancer drugs that induce mitotic catastrophe, including microtubule-targeting agents, spindle assembly checkpoint kinase inhibitors, DNA damage agents and DNA damage response inhibitors. Based on the relationships between mitotic catastrophe and other cell death modes, we thoroughly evaluated the roles played by mitotic catastrophe in cancer treatment as well as its advantages and disadvantages. Some strategies for overcoming its shortcomings while fully utilizing its advantages are summarized and proposed in this review. We also review how mitotic catastrophe regulates cancer immunotherapy. These summarized findings suggest that the induction of mitotic catastrophe can serve as a promising new therapeutic approach for overcoming apoptosis resistance and strengthening cancer immunotherapy.

Understanding and targeting resistance mechanisms in cancer

Resistance to cancer therapies has been a commonly observed phenomenon in clinical practice, which is one of the major causes of treatment failure and poor patient survival. The reduced responsiveness of cancer cells is a multifaceted phenomenon that can arise from genetic, epigenetic, and microenvironmental factors. Various mechanisms have been discovered and extensively studied, including drug inactivation, reduced intracellular drug accumulation by reduced uptake or increased efflux, drug target alteration, activation of compensatory pathways for cell survival, regulation of DNA repair and cell death, tumor plasticity, and the regulation from tumor microenvironments (TMEs). To overcome cancer resistance, a variety of strategies have been proposed, which are designed to enhance the effectiveness of cancer treatment or reduce drug resistance. These include identifying biomarkers that can predict drug response and resistance, identifying new targets, developing new targeted drugs, combination therapies targeting multiple signaling pathways, and modulating the TME. The present article focuses on the different mechanisms of drug resistance in cancer and the corresponding tackling approaches with recent updates. Perspectives on polytherapy targeting multiple resistance mechanisms, novel nanoparticle delivery systems, and advanced drug design tools for overcoming resistance are also reviewed.

Basis for Accurate Protein pKa Prediction with Machine Learning

pH regulates protein structures and the associated functions in many biological processes via protonation and deprotonation of ionizable side chains where the titration equilibria are determined by pK_a's. To accelerate pH-dependent molecular mechanism research in the life sciences or industrial protein and drug designs, fast and accurate pK_a prediction is crucial. Here we present a theoretical pK_a data set PHMD549, which was successfully applied to four distinct machine learning methods, including DeepKa, which was proposed in our previous work. To reach a valid comparison, EXP67S was selected as the test set. Encouragingly, DeepKa was improved significantly and outperforms other state-of-the-art methods, except for the constant-pH molecular dynamics, which was utilized to create PHMD549. More importantly, DeepKa reproduced experimental pK_a orders of acidic dyads in five enzyme catalytic sites. Apart from structural proteins, DeepKa was found applicable to intrinsically disordered peptides. Further, in combination with solvent exposures, it is revealed that DeepKa offers the most accurate prediction under the challenging circumstance that hydrogen bonding or salt bridge interaction is partly compensated by desolvation for a buried side chain. Finally, our benchmark data qualify PHMD549 and EXP67S as the basis for future developments of protein pK_a prediction tools driven by artificial intelligence. In addition, DeepKa built on PHMD549 has been proven an efficient protein pK_a predictor and thus can be applied immediately to, for example, pK_a database construction, protein design, drug discovery, and so on.

Intersection of network medicine and machine learning towards investigating the key biomarkers and pathways underlying amyotrophic lateral sclerosis: a systematic review

BACKGROUND

Network medicine is an emerging area of research that focuses on delving into the molecular complexity of the disease, leading to the discovery of network biomarkers and therapeutic target discovery. Amyotrophic lateral sclerosis (ALS) is a complicated rare disease with unknown pathogenesis and no available treatment. In ALS, network properties appear to be potential biomarkers that can be beneficial in disease-related applications when explored independently or in tandem with machine learning (ML) techniques.

OBJECTIVE

This systematic literature review explores recent trends in network medicine and implementations of network-based ML algorithms in ALS. We aim to provide an overview of the identified primary studies and gather details on identifying the potential biomarkers and delineated pathways.

METHODS

The current study consists of searching for and investigating primary studies from PubMed and Dimensions.ai, published between 2018 and 2022 that reported network medicine perspectives and the coupling of ML techniques. Each abstract and full-text study was individually evaluated, and the relevant studies were finally included in the review for discussion once they met the inclusion and exclusion criteria.

RESULTS

We identified 109 eligible publications from primary studies representing this systematic review. The data coalesced into two themes: application of network science to identify disease modules and promising biomarkers in ALS, along with network-based ML approaches. Conclusion This systematic review gives an overview of the network medicine approaches and implementations of network-based ML algorithms in ALS to determine new disease genes, and identify critical pathways and therapeutic target discovery for personalized treatment.

From single-omics to interactomics: How can ligand-induced perturbations modulate single-cell phenotypes?

Cells suffer from perturbations by different stimuli, which, consequently, rise to individual alterations in their profile and function that may end up affecting the tissue as a whole. This is no different if we consider the effect of a therapeutic agent on a biological system. As cells are exposed to external ligands their profile can change at different single-omics levels. Detecting how these changes take place through different sequencing technologies is key to a better understanding of the effects of therapeutic agents. Single-cell RNA-sequencing stands out as one of the most common approaches for cell profiling and perturbation analysis. As a result, single-cell transcriptomics data can be integrated with other omics data sources, such as proteomics and epigenomics data, to clarify the perturbation effects and mechanism at the cell level. Appropriate computational tools are key to process and integrate the available information. This chapter focuses on the recent advances on ligand-induced perturbation and single-cell omics computational tools and algorithms, their current limitations, and how the deluge of data can be used to improve the current process of drug research and development.