Integration of pan-cancer cell line and single-cell transcriptomic profiles enables inference of therapeutic vulnerabilities in heterogeneous tumors.

Single-cell RNA-sequencing (scRNA-seq) greatly advanced the understanding of intratumoral heterogeneity by identifying distinct cancer cell subpopulations. However, translating biological differences into treatment strategies is challenging due to a lack of tools to facilitate efficient drug discovery that tackles heterogeneous tumors. Developing such approaches requires accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we developed a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual-cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening datasets. This method achieved high accuracy in separating cells into their correct cellular drug response statuses. In three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), the predicted results using scIDUC were accurate and mirrored biological expectations. In the first two tests, the framework identified drugs for cell subpopulations that were resistant to standard-of-care (SOC) therapies due to intrinsic resistance or tumor microenvironmental effects, and the results showed high consistency with experimental findings from the original studies. In the third test using newly generated SOC therapy resistant cell lines, scIDUC identified efficacious drugs for the resistant line, and the predictions were validated with in vitro experiments. Together, this study demonstrates the potential of scIDUC to quickly translate scRNA-seq data into drug responses for individual cells, displaying the potential as a tool to improve treatment of heterogenous tumors.

A Web Application for Predicting Drug Combination Efficacy Using Monotherapy Data and IDACombo.

We recently reported a computational method (IDACombo) designed to predict the efficacy of cancer drug combinations using monotherapy response data and the assumptions of independent drug action. Given the strong agreement between IDACombo predictions and measured drug combination efficacy in vitro and in clinical trials, we believe IDACombo can be of immediate use to researchers who are working to develop novel drug combinations. While we previously released our method as an R package, we have now created an R Shiny application to allow researchers without programming experience to easily utilize this method. The app provides a graphical interface which enables users to easily generate efficacy predictions with IDACombo using provided data from several high-throughput cell line screens or using custom, user-provided data.

Sex-related differences in delayed doxorubicin-induced cardiac dysfunction in C57BL/6 mice.

Cancer survivors may experience long-term cardiovascular complications due to chemotherapeutic drugs such as doxorubicin (DOX). The exact mechanism of delayed DOX-induced cardiotoxicity has not been fully elucidated. Sex is an important risk factor for DOX-induced cardiotoxicity. In the current study, we identified sex differences in delayed DOX-induced cardiotoxicity and determined the underlying molecular determinants of the observed sexual dimorphism. Five-week-old male and female mice were administered intraperitoneal injections of DOX (4 mg/kg/week) or saline for 6 weeks. Echocardiography was performed 5 weeks after the last dose of DOX to evaluate cardiac function. Thereafter, mice were sacrificed and gene expression of markers of apoptosis, senescence, and inflammation was measured by PCR in hearts and livers. Proteomic profiling of the heart from both sexes was conducted to determine differentially expressed proteins (DEPs). Only DOX-treated male, but not female, mice demonstrated cardiac dysfunction, cardiac atrophy, and upregulated cardiac expression of Nppb and Myh7. No sex-related differences were observed in DOX-induced expression of most apoptotic, senescence, and pro-inflammatory markers. However, the gene expression of Trp53 was significantly reduced in hearts of DOX-treated female mice only. The anti-inflammatory marker Il-10 was significantly reduced in hearts of DOX-treated male mice only, while the pro-inflammatory marker Il-1α was significantly reduced in livers of DOX-treated female mice only. Gene expression of Tnf-α was reduced in hearts of both DOX-treated male and female mice. Proteomic analysis identified several DEPs after DOX treatment in a sex-specific manner, including anti-inflammatory acute phase proteins. This is the first study to assess sex-specific proteomic changes in a mouse model of delayed DOX-induced cardiotoxicity. Our proteomic analysis identified several sexually dimorphic DEPs, many of which are associated with the anti-inflammatory marker Il-10.

Adaptive neural identification and non-singular control of pure-feedback nonlinear systems.

This paper proposes a new constructive identification and adaptive control method for nonlinear pure-feedback systems, which remedies the 'explosion of complexity' and potential control singularity encountered in the traditional adaptive backstepping controllers. First, to avoid using the backstepping recursive design, alternative state variables and the corresponding coordinate transformation are introduced to reformulate the pure-feedback system into an equivalent canonical model. Then, a high-order sliding mode (HOSM) observer is used to reconstruct the unknown states for this canonical model. To remedy the potential singularity in the control, the unknown system dynamics are lumped to derive an alternative identification structure and one-step control synthesis, where two radial basis function neural networks (RBFNN) are adopted to online estimate these lumped dynamics. In this framework, the online estimation of control gain is not in the denominator of controller, and thus the division by zero in the controllers is avoided. Finally, a new online learning algorithm is constructed to obtain the RBFNNs' weights, ensuring the convergence to the neighborhood of true values and allowing accurate identification of unknown dynamics. Theoretical analysis elaborates that the convergence of both the tracking error and the estimation error is obtained simultaneously. Simulations and practical experiments on a hydraulic servo test-rig verify the effectiveness and utility of the suggested methods.

A review of computational methods for predicting cancer drug response at the single-cell level through integration with bulk RNAseq data.

Cancer treatment failure is often attributed to tumor heterogeneity, where diverse malignant cell clones exist within a patient. Despite a growing understanding of heterogeneous tumor cells depicted by single-cell RNA sequencing (scRNA-seq), there is still a gap in the translation of such knowledge into treatment strategies tackling the pervasive issue of therapy resistance. In this review, we survey methods leveraging large-scale drug screens to generate cellular sensitivities to various therapeutics. These methods enable efficient drug screens in scRNA-seq data and serve as the bedrock of drug discovery for specific cancer cell groups. We envision that they will become an indispensable tool for tailoring patient care in the era of heterogeneity-aware precision medicine.

Inferring therapeutic vulnerability within tumors through integration of pan-cancer cell line and single-cell transcriptomic profiles.

Single-cell RNA sequencing greatly advanced our understanding of intratumoral heterogeneity through identifying tumor subpopulations with distinct biologies. However, translating biological differences into treatment strategies is challenging, as we still lack tools to facilitate efficient drug discovery that tackles heterogeneous tumors. One key component of such approaches tackles accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we present a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual-cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening datasets. Our method achieves high accuracy, with predicted sensitivities easily able to separate cells into their true cellular drug resistance status as measured by effect size (Cohen's d > 1.0). More importantly, we examine our method's utility with three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), and in each our predicted results are accurate and mirrored biological expectations. In the first two, we identified drugs for cell subpopulations that are resistant to standard-of-care (SOC) therapies due to intrinsic resistance or effects of tumor microenvironments. Our results showed high consistency with experimental findings from the original studies. In the third test, we generated SOC therapy resistant cell lines, used scIDUC to identify efficacious drugs for the resistant line, and validated the predictions with in-vitro experiments. Together, scIDUC quickly translates scRNA-seq data into drug response for individual cells, displaying the potential as a first-line tool for nuanced and heterogeneity-aware drug discovery.

Simplicity: web-based visualization and analysis of high-throughput cancer cell line screens.

High-throughput drug screens are a powerful tool for cancer drug development. However, the results of such screens are often made available only as raw data, which is intractable for researchers without informatic skills, or as highly processed summary statistics, which can lack essential information for translating screening results into clinically meaningful discoveries. To improve the usability of these datasets, we developed Simplicity, a robust and user-friendly web interface for visualizing, exploring, and summarizing raw and processed data from high-throughput drug screens. Importantly, Simplicity allows for easy recalculation of summary statistics at user-defined drug concentrations. This allows Simplicity's outputs to be used with methods that rely on statistics being calculated at clinically relevant doses. Simplicity can be freely accessed at https://oncotherapyinformatics.org/simplicity/.

Understanding Statin-Roxadustat Drug-Drug-Disease Interaction Using Physiologically-Based Pharmacokinetic Modeling.

A different drug-drug interaction (DDI) scenario may exist in patients with chronic kidney disease (CKD) compared with healthy volunteers (HVs), depending on the interplay between drug-drug and disease (drug-drug-disease interaction (DDDI)). Physiologically-based pharmacokinetic (PBPK) modeling, in lieu of a clinical trial, is a promising tool for evaluating these complex DDDIs in patients. However, the prediction confidence of PBPK modeling in the severe CKD population is still low when nonrenal pathways are involved. More mechanistic virtual disease population and robust validation cases are needed. To this end, we aimed to: (i) understand the implications of severe CKD on statins (atorvastatin, simvastatin, and rosuvastatin) pharmacokinetics (PK) and DDI; and (ii) predict untested clinical scenarios of statin-roxadustat DDI risks in patients to guide suitable dose regimens. A novel virtual severe CKD population was developed incorporating the disease effect on both renal and nonrenal pathways. Drug and disease PBPK models underwent a four-way validation. The verified PBPK models successfully predicted the altered PKs in patients for substrates and inhibitors and recovered the observed statin-rifampicin DDIs in patients and the statin-roxadustat DDIs in HVs within 1.25- and 2-fold error. Further sensitivity analysis revealed that the severe CKD effect on statins PK is mainly mediated by hepatic BCRP for rosuvastatin and OATP1B1/3 for atorvastatin. The magnitude of statin-roxadustat DDI in patients with severe CKD was predicted to be similar to that in HVs. PBPK-guided suitable dose regimens were identified to minimize the risk of side effects or therapeutic failure of statins when co-administered with roxadustat.

A systematic review on sex differences in adverse drug reactions related to psychotropic, cardiovascular, and analgesic medications.

Background and objective: Adverse drug reactions (ADRs) are the main safety concerns of clinically used medications. Accumulating evidence has shown that ADRs can affect men and women differently, which suggests sex as a biological predictor in the risk of ADRs. This review aims to summarize the current state of knowledge on sex differences in ADRs with the focus on the commonly used psychotropic, cardiovascular, and analgesic medications, and to aid clinical decision making and future mechanistic investigations on this topic. Methods: PubMed search was performed with combinations of the following terms: over 1,800 drugs of interests, sex difference (and its related terms), and side effects (and its related terms), which yielded over 400 unique articles. Articles related to psychotropic, cardiovascular, and analgesic medications were included in the subsequent full-text review. Characteristics and the main findings (male-biased, female-biased, or not sex biased ADRs) of each included article were collected, and the results were summarized by drug class and/or individual drug. Results: Twenty-six articles studying sex differences in ADRs of six psychotropic medications, ten cardiovascular medications, and one analgesic medication were included in this review. The main findings of these articles suggested that more than half of the ADRs being evaluated showed sex difference pattern in occurrence rate. For instance, lithium was found to cause more thyroid dysfunction in women, and amisulpride induced prolactin increase was more pronounced in women than in men. Some serious ADRs were also found to exert sex difference pattern, such as clozapine induced neutropenia was more prevalent in women whereas simvastatin/atorvastatin-related abnormal liver functions were more pronounced in men.

Genetic association of lipids and lipid-lowering drug target genes with non-alcoholic fatty liver disease.

BACKGROUND: Some observational studies found that dyslipidaemia is a risk factor for non-alcoholic fatty liver disease (NAFLD), and lipid-lowering drugs may lower NAFLD risk. However, it remains unclear whether dyslipidaemia is causative for NAFLD. This Mendelian randomisation (MR) study aimed to explore the causal role of lipid traits in NAFLD and evaluate the potential effect of lipid-lowering drug targets on NAFLD. METHODS: Genetic variants associated with lipid traits and variants of genes encoding lipid-lowering drug targets were extracted from the Global Lipids Genetics Consortium genome-wide association study (GWAS). Summary statistics for NAFLD were obtained from two independent GWAS datasets. Lipid-lowering drug targets that reached significance were further tested using expression quantitative trait loci data in relevant tissues. Colocalisation and mediation analyses were performed to validate the robustness of the results and explore potential mediators. FINDINGS: No significant effect of lipid traits and eight lipid-lowering drug targets on NAFLD risk was found. Genetic mimicry of lipoprotein lipase (LPL) enhancement was associated with lower NAFLD risks in two independent datasets (OR1 = 0.60 [95% CI 0.50-0.72], p1 = 2.07 × 10-8; OR2 = 0.57 [95% CI 0.39-0.82], p2 = 3.00 × 10-3). A significant MR association (OR = 0.71 [95% CI, 0.58-0.87], p = 1.20 × 10-3) and strong colocalisation association (PP.H4 = 0.85) with NAFLD were observed for LPL expression in subcutaneous adipose tissue. Fasting insulin and type 2 diabetes mediated 7.40% and 9.15%, respectively, of the total effect of LPL on NAFLD risk. INTERPRETATION: Our findings do not support dyslipidaemia as a causal factor for NAFLD. Among nine lipid-lowering drug targets, LPL is a promising candidate drug target in NAFLD. The mechanism of action of LPL in NAFLD may be independent of its lipid-lowering effects. FUNDING: Capital's Funds for Health Improvement and Research (2022-4-4037). CAMS Innovation Fund for Medical Sciences (CIFMS, grant number: 2021-I2M-C&T-A-010).

Computational drug discovery for castration-resistant prostate cancers through in vitro drug response modeling.

Prostate cancer (PC) is the most frequently diagnosed malignancy and a leading cause of cancer deaths in US men. Many PC cases metastasize and develop resistance to systemic hormonal therapy, a stage known as castration-resistant prostate cancer (CRPC). Therefore, there is an urgent need to develop effective therapeutic strategies for CRPC. Traditional drug discovery pipelines require significant time and capital input, which highlights a need for novel methods to evaluate the repositioning potential of existing drugs. Here, we present a computational framework to predict drug sensitivities of clinical CRPC tumors to various existing compounds and identify treatment options with high potential for clinical impact. We applied this method to a CRPC patient cohort and nominated drugs to combat resistance to hormonal therapies including abiraterone and enzalutamide. The utility of this method was demonstrated by nomination of multiple drugs that are currently undergoing clinical trials for CRPC. Additionally, this method identified the tetracycline derivative COL-3, for which we validated higher efficacy in an isogenic cell line model of enzalutamide-resistant vs. enzalutamide-sensitive CRPC. In enzalutamide-resistant CRPC cells, COL-3 displayed higher activity for inhibiting cell growth and migration, and for inducing G1-phase cell cycle arrest and apoptosis. Collectively, these findings demonstrate the utility of a computational framework for independent validation of drugs being tested in CRPC clinical trials, and for nominating drugs with enhanced biological activity in models of enzalutamide-resistant CRPC. The efficiency of this method relative to traditional drug development approaches indicates a high potential for accelerating drug development for CRPC.

Deciphering genetic causes for sex differences in human health through drug metabolism and transporter genes.

Sex differences have been widely observed in human health. However, little is known about the underlying mechanism behind these observed sex differences. We hypothesize that sex-differentiated genetic effects are contributors of these phenotypic differences. Focusing on a collection of drug metabolism enzymes and transporters (DMET) genes, we discover sex-differentiated genetic regulatory mechanisms between these genes and human complex traits. Here, we show that sex-differentiated genetic effects were present at genome-level and at DMET gene regions for many human complex traits. These sex-differentiated regulatory mechanisms are reflected in the levels of gene expression and endogenous serum biomarkers. Through Mendelian Randomization analysis, we identify putative sex-differentiated causal effects in each sex separately. Furthermore, we identify and validate sex differential gene expression of a subset of DMET genes in human liver samples. We observe higher protein abundance and enzyme activity of CYP1A2 in male-derived liver microsomes, which leads to higher level of an active metabolite formation of clozapine, a commonly prescribed antipsychotic drug. Taken together, our results demonstrate the presence of sex-differentiated genetic effects on DMET gene regulation, which manifest in various phenotypic traits including disease risks and drug responses.

Multi-H∞ Controls for Unknown Input-Interference Nonlinear System With Reinforcement Learning.

This article studies the multi- [Formula: see text] controls for the input-interference nonlinear systems via adaptive dynamic programming (ADP) method, which allows for multiple inputs to have the individual selfish component of the strategy to resist weighted interference. In this line, the ADP scheme is used to learn the Nash-optimization solutions of the input-interference nonlinear system such that multiple [Formula: see text] performance indices can reach the defined Nash equilibrium. First, the input-interference nonlinear system is given and the Nash equilibrium is defined. An adaptive neural network (NN) observer is introduced to identify the input-interference nonlinear dynamics. Then, the critic NNs are used to learn the multiple [Formula: see text] performance indices. A novel adaptive law is designed to update the critic NN weights by minimizing the Hamiltonian-Jacobi-Isaacs (HJI) equation, which can be used to directly calculate the multi- [Formula: see text] controls effectively by using input-output data such that the actor structure is avoided. Moreover, the control system stability and updated parameter convergence are proved. Finally, two numerical examples are simulated to verify the proposed ADP scheme for the input-interference nonlinear system.

Simplicity: Web-Based Visualization and Analysis of High-Throughput Cancer Cell Line Screens.

High-throughput drug screens are a powerful tool for cancer drug development. However, the results of such screens are often made available only as raw data, which is intractable for researchers without informatics skills, or as highly processed summary statistics, which can lack essential information for translating screening results into clinically meaningful discoveries. To improve the usability of these datasets, we developed Simplicity, a robust and user-friendly web interface for visualizing, exploring, and summarizing raw and processed data from high- throughput drug screens. Importantly, Simplicity allows for easy recalculation of summary statistics at user-defined drug concentrations. This allows Simplicity's outputs to be used with methods that rely on statistics being calculated at clinically relevant doses. Simplicity can be freely accessed at https://oncotherapyinformatics.org/simplicity/.

Sex dimorphism in response to targeted therapy and immunotherapy in non-small cell lung cancer patients: a narrative review.

Background and Objective: Multiple agents have been developed for treating non-small cell lung cancer (NSCLC). However, patients' response to these therapies vary drastically, which indicates a need to tailor therapy. Sex is a readily usable clinical characteristic that has been shown to impact patients' response to drugs. The main objective of this narrative review is to summarize the current state of knowledge, compiled from meta-analyses, on sex differences in treatment efficacy for targeted therapy and immunotherapy in NSCLC. We discuss the interplay of patient characteristics, both molecular and demographic, with sex on how they impact therapeutic response. Methods: PubMed search was performed with the term "sex/gender differences" with currently FDA approved targeting therapy and immunotherapy agents in treating NSCLC. Key Content and Findings: For targeted therapy, women tend to benefit more in terms of progression-free survival upon receiving first-generation anti-epidermal growth factor receptor (EGFR) treatment than men. On the other hand, there is an ongoing debate on sex differences in response to immunotherapy. Although preliminary, whether sex differences were observed depends on treatment settings, patient characteristics, and molecular features. Importantly, incorporating sex as a biological component in the biomarker discovery seems to reveal novel insights in immunotherapy response. Conclusions: Taken together, sex differences in responding to standard care have been observed in clinical settings for NSCLC patients. A better understanding of sex-associated treatment response and the underlying biology will improve cancer prognosis and eliminate these sex differences.

Unknown System Dynamics Estimator for Active Vehicle Suspension Control Systems With Time-Varying Delay.

This article proposes a novel control method for vehicle active suspension systems in the presence of time-varying input delay and unknown nonlinearities. An unknown system dynamics estimator (USDE), which employs first-order low-pass filter operations and has only one tuning parameter, is constructed to deal with unknown nonlinearities. With this USDE, the widely used function approximators (e.g., neural networks and fuzzy-logic systems) are not needed, and the intermediate variables and observer used in the traditional estimators are not required. This estimator has a reduced computational burden, trivial parameter tuning and guaranteed convergence. Moreover, a predictor-based compensation strategy is developed to handle the time-varying input delay. Finally, we combine the suggested USDE and predictor to design a feedback controller to attenuate the vibrations of vehicle body and retain the required suspension performances. Theoretical analysis is carried out via the Lyapunov-Krasovkii functional to prove the stability of the closed-loop system. Simulation results based on professional vehicle simulation software Carsim are provided to show the efficiency of the proposed control scheme.

A Long Noncoding RNA, GAS5 Can Be a Biomarker for Docetaxel Response in Castration Resistant Prostate Cancer.

While functional studies of long noncoding RNAs (lncRNAs) have mostly focused on how they influence disease diagnosis and prognosis, the pharmacogenomic relevance of lncRNAs remains largely unknown. Here, we test the hypothesis that the expression of a lncRNA, grow arrest-specific 5 (GAS5) can be a biomarker for docetaxel response in castration resistant prostate cancer (CRPC) using both prostate cancer (PCa) cell lines and CRPC patient datasets. Our results suggest that lower GAS5 expression is associated with docetaxel resistance in both PCa cell lines and CRPC patients. Further experiments also suggest that GAS5 is downregulated in docetaxel resistant CRPC cell lines, which reinforces its potential as a biomarker for docetaxel response. To examine the underlying biological mechanisms, we transiently knockdown GAS5 expression in PCa cell lines and then subject the cells to docetaxel treatment overtime. We did not observe a decrease in docetaxel induced growth inhibition or apoptosis in the siRNA treated cells. The findings suggest that there is no direct causal relationship between change in GAS5 expression and docetaxel response. Subsequently, we explored the indirect regulation among GAS5, ATP binding cassette subfamily B member 1 (ABCB1), and docetaxel sensitivity. We showed that transient knockdown GAS5 did not lead to significant changes in ABCB1 expression. Therefore, we rule out the hypothesis that GAS5 directly down regulate ABCB1 that lead to docetaxel sensitivity. In conclusion, our work suggests that GAS5 can serve as a predictive biomarker for docetaxel response in CRPC; however, the exact mechanism behind the observed correlation remain to be elucidated.

Oncogene or tumor suppressor? Long noncoding RNAs role in patient's prognosis varies depending on disease type.

Functional studies of long noncoding RNAs (lncRNAs) are often performed in the context of only a single cancer type. However, the tissue-specific expression patterns of lncRNAs raise the question of whether lncRNA associations identified in one cancer type are relevant to other cancer types. Here, we examine the relationships between the expression levels of 50 cancer-related lncRNAs and survival data from 24 types of cancer in The Cancer Genome Atlas (TCGA) with the goal of identifying prognosis related lncRNAs. Our results suggest that high expression levels of certain lncRNAs are consistently associated with worse/better survival in a number of cancers, while other lncRNAs have different prognostic roles in different types of cancer. Our analysis also identifies 20 novel unadjusted associations that have not been reported before. In addition, in low-grade glioma (LGG), prognostic-related lncRNAs are identified after conditioning on known clinical biomarker and common therapy, revealing that 2 lncRNAs, FOXP4-AS1, and NEAT1, are associated with temozolomide response-a standard-of-care in LGG. Pathway analysis suggests NF-kB/STAT3 signaling pathway enrichment in LGG patients with high NEAT1 expression and DNA repair/myc gene set enrichment in LGG patients with high expression of FOXP4-AS1. Our work demonstrates the context dependency of lncRNAs across cancer types and highlights a number of lncRNAs as potential novel cancer prognosis markers.

Intrapulmonary inoculation of multicellular spheroids to construct an orthotopic lung cancer xenograft model that mimics four clinical stages of non-small cell lung cancer.

INTRODUCTION: Lung cancer leads in mortality among all types of cancer in US and Non-small cell lung cancer (NSCLC) is the major type of lung cancer. Mice models of lung cancer based on subcutaneous or orthotopic inoculation of cancer cell suspension do not adequately mimic the progression of lung cancer in clinic. METHODS: A549-iRFP cells (human NSCLC adenocarcinoma) were cultured to form multicellular spheroids (MCS), which were then inoculated intrapulmonarily into male athymic nude mice. The xenograft cancer development was monitored by in vivo fluorescent imaging and validated by open-chest anatomy, ex vivo fluorescent imaging, and histological studies. RESULTS: The newly developed orthotopic xenograft model of lung cancer simulated all four clinical stages of NSCLC progression over one month: Stage 1) localized tumor at the inoculation site, Stage 2) multiple tumor nodules or larger tumor nodule on the same side of the lung, Stage 3) cancer growth on heart surface, and Stage 4) metastatic cancer on both sides of the lung. The model yielded high rates of postoperative survival (100%) and parenchymal tumor establishment (88.9%). The roughness of the inoculated MCS associated negatively with the time needed to develop metastatic cancer (p = .0299). DISCUSSION: This new orthotopic xenograft model of NSCLC would facilitate the development of medications to treat lung cancer.

Adaptive Finite-Time Fuzzy Control of Nonlinear Active Suspension Systems With Input Delay.

This paper presents a new adaptive fuzzy control scheme for active suspension systems subject to control input time delay and unknown nonlinear dynamics. First, a predictor-based compensation scheme is constructed to address the effect of input delay in the closed-loop system. Then, a fuzzy logic system (FLS) is employed as the function approximator to address the unknown nonlinearities. Finally, to enhance the transient suspension response, a novel parameter estimation error-based finite-time (FT) adaptive algorithm is developed to online update the unknown FLS weights, which differs from traditional estimation methods, for example, gradient algorithm with e -modification or σ -modification. In this framework, both the suspension and estimation errors can achieve convergence in FT. A Lyapunov-Krasovskii functional is constructed to prove the closed-loop system stability. Comparative simulation results based on a dynamic simulator built in a professional vehicle simulation software, Carsim, are provided to demonstrate the validity of the proposed control approach, and show its effectiveness to operate active suspension systems safely and reliably in various road conditions.