scholarly journals A novel artificial lung organoid for simulating a patient derived adenocarcinoma of lung for personalized oncology

2021 ◽  
Author(s):  
Sally Esmail ◽  
Wayne R Danter
Keyword(s):  
Drug Screening ◽  
Large Scale ◽  
Disease Modeling ◽  
Standard Of Care ◽  
Cancer Drug ◽  
Genetic Profile ◽  
Precision Oncology ◽  
Mutational Profiling ◽  
Approved Drugs ◽  
The Individual

ABSTRACTOptimizing patient care based on precision oncology will inevitably become the standard of care. If we accept the principle that every persons’ cancer is different then the most effective therapies will have to be designed for the individual patient and for their tumors genetic profile. Access to tumor mutational profiling is now widely available but continues to be limited by cost and actionable information. For example, novel combinations of approved drugs are rarely considered. These considerations lead us to hypothesize that artificially induced Lung Adenocarcinoma (LUAD) derived lung organoids could provide a novel, alternate approach for LUAD disease modeling and large-scale targeted drug screening.In this project, we used data from a commercially available tumor mutation profile to generate and then validate the artificially induced LUAD-derived lung organoid simulations (aiLUNG-LUAD) to model LUAD and identify several drug combinations that effectively reverse the tumors’ genotypic and phenotypic features when compared with placebo. These results complement previous LUAD-derived lung organoids research and provide a novel and widely applicable cancer drug-screening approach for precision/individualized oncology.

scholarly journals Contrasting Medical and Legal Standards of Evidence: A Precision Medicine Case Study

2016 ◽  
Vol 44 (1) ◽  
pp. 194-204 ◽  
Author(s):  
Gary E. Marchant ◽  
Kathryn Scheckel ◽  
Doug Campos-Outcalt
Keyword(s):  
Precision Medicine ◽  
New Technologies ◽  
Clinical Care ◽  
Large Body ◽  
Standard Of Care ◽  
Genetic Profile ◽  
Legal Standards ◽  
Standards Of Evidence ◽  
The Individual

As the health care system transitions to a precision medicine approach that tailors clinical care to the genetic profile of the individual patient, there is a potential tension between the clinical uptake of new technologies by providers and the legal system's expectation of the standard of care in applying such technologies. We examine this tension by comparing the type of evidence that physicians and courts are likely to rely on in determining a duty to recommend pharmacogenetic testing of patients prescribed the oral anti-coagulant drug warfarin. There is a large body of inconsistent evidence and factors for and against such testing, but physicians and courts are likely to weigh this evidence differently. The potential implications for medical malpractice risk are evaluated and discussed.

scholarly journals Bimodality of gene expression in cancer patient tumors as interpretable biomarkers for drug sensitivity

2020 ◽  
Author(s):  
Wail Ba-Alawi ◽  
Sisira Kadambat Nair ◽  
Bo Li ◽  
Anthony Mammoliti ◽  
Petr Smirnov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Human Cancer ◽  
Predictive Biomarkers ◽  
Precision Oncology ◽  
Human Cancer Cell Lines ◽  
Validation Rate ◽  
Approved Drugs ◽  
Pharmacogenomic Studies

ABSTRACTIdentifying biomarkers predictive of cancer cells’ response to drug treatment constitutes one of the main challenges in precision oncology. Recent large-scale cancer pharmacogenomic studies have boosted the research for finding predictive biomarkers by profiling thousands of human cancer cell lines at the molecular level and screening them with hundreds of approved drugs and experimental chemical compounds. Many studies have leveraged these data to build predictive models of response using various statistical and machine learning methods. However, a common challenge in these methods is the lack of interpretability as to how they make the predictions and which features were the most associated with response, hindering the clinical translation of these models. To alleviate this issue, we develop a new machine learning pipeline based on the recent LOBICO approach that explores the space of bimodally expressed genes in multiple large in vitro pharmacogenomic studies and builds multivariate, nonlinear, yet interpretable logic-based models predictive of drug response. Using our method, we used a compendium of three of the largest pharmacogenomic data sets to build robust and interpretable models for 101 drugs that span 17 drug classes with high validation rate in independent datasets.

scholarly journals Tissue specificity of in vitro drug sensitivity

2017 ◽  
Vol 25 (2) ◽  
pp. 158-166 ◽  
Author(s):  
Fupan Yao ◽  
Seyed Ali Madani Tonekaboni ◽  
Zhaleh Safikhani ◽  
Petr Smirnov ◽  
Nehme El-Hachem ◽  
...  
Keyword(s):  
Drug Screening ◽  
Large Scale ◽  
Tissue Specificity ◽  
Drug Sensitivity ◽  
Cancer Drug ◽  
General Level ◽  
Response Curves ◽  
Tissue Specific ◽  
Clinical Indications

Abstract Objectives We sought to investigate the tissue specificity of drug sensitivities in large-scale pharmacological studies and compare these associations to those found in drug clinical indications. Materials and Methods We leveraged the curated cell line response data from PharmacoGx and applied an enrichment algorithm on drug sensitivity values’ area under the drug dose-response curves (AUCs) with and without adjustment for general level of drug sensitivity. Results We observed tissue specificity in 63% of tested drugs, with 8% of total interactions deemed significant (false discovery rate <0.05). By restricting the drug-tissue interactions to those with AUC > 0.2, we found that in 52% of interactions, the tissue was predictive of drug sensitivity (concordance index > 0.65). When compared with clinical indications, the observed overlap was weak (Matthew correlation coefficient, MCC = 0.0003, P > .10). Discussion While drugs exhibit significant tissue specificity in vitro, there is little overlap with clinical indications. This can be attributed to factors such as underlying biological differences between in vitro models and patient tumors, or the inability of tissue-specific drugs to bring additional benefits beyond gold standard treatments during clinical trials. Conclusion Our meta-analysis of pan-cancer drug screening datasets indicates that most tested drugs exhibit tissue-specific sensitivities in a large panel of cancer cell lines. However, the observed preclinical results do not translate to the clinical setting. Our results suggest that additional research into showing parallels between preclinical and clinical data is required to increase the translational potential of in vitro drug screening.

What Can a Urine Drug Screening Immunoassay Really Tell Us?

2016 ◽  
Vol 29 (5) ◽  
pp. 516-526 ◽  
Author(s):  
Zachary J. Nelson ◽  
Samuel J. Stellpflug ◽  
Kristin M. Engebretsen
Keyword(s):  
Drug Screening ◽  
Recent Literature ◽  
Negative Impact ◽  
Standard Of Care ◽  
Evidence Based ◽  
Clinical Knowledge ◽  
False Negatives ◽  
Urine Drug ◽  
The Individual ◽  
Urine Drug Screening

Urine drug screening has become standard of care in many medical practice settings to assess compliance, detect misuse, and/or to provide basis for medical or legal action. The antibody-based enzymatic immunoassays used for qualitative analysis of urine have significant drawbacks that clinicians are often not aware of. Recent literature suggests that there is a lack of understanding of the shortcomings of these assays by clinicians who are ordering and/or interpreting them. This article addresses the state of each of the individual immunoassays that are most commonly used today in order to help the reader become proficient in the interpretation and application of the results. Some literature already exists regarding sources of “false positives” and “false negatives,” but none seem to present the material with the practicing clinician in mind. This review aims to avoid overwhelming the reader with structures and analytical chemistry. The reader will be presented relevant clinical knowledge that will facilitate appropriate interpretation of immunoassays regardless of practice settings. Using this review as a learning tool and a reference, clinicians will be able to interpret the results of commonly used immunoassays in an evidence-based, informed manner and minimize the negative impact that misinterpretation has on patient care.

Voluntary patient registries

2020 ◽  
pp. 545-550
Author(s):  
Jeff Greenberg ◽  
Sheetal Patel
Keyword(s):  
Rheumatoid Arthritis ◽  
Clinical Trials ◽  
Large Scale ◽  
Standard Of Care ◽  
Population Based ◽  
Patient Registries ◽  
Research Findings ◽  
Comparative Safety ◽  
Approved Drugs ◽  
Shed Light

With the number of approved drugs now available for the treatment of rheumatoid arthritis, there is an ever-increasing need for real-world, objective data to shed light on the comparative effectiveness and comparative safety of newly approved drugs versus other standard of care older medications. This chapter will provide an overview of voluntary rheumatoid arthritis (RA) registries that currently recruit patients and actively publish research findings. It will also highlight some of the key clinical insights derived from these voluntary registries. Voluntary registries have their own strengths and limitations compared to population-based registries. Unlike pharmaceutical sponsored clinical trials, the majority of these voluntary registries do not exclude patients based on comorbidities. Additionally, clinical trials are not always feasible on a large scale and have limited duration. Observational registries do not typically have these limitations.

scholarly journals Bioengineered Skin Intended for Skin Disease Modeling

2019 ◽  
Vol 20 (6) ◽  
pp. 1407 ◽  
Author(s):  
Maria Sarkiri ◽  
Stephan Fox ◽  
Lidy Fratila-Apachitei ◽  
Amir Zadpoor
Keyword(s):  
Drug Screening ◽  
Skin Disease ◽  
Large Scale ◽  
Disease Modeling ◽  
Clinical Use ◽  
Ethical Considerations ◽  
Efficient Replication ◽  
Skin Conditions ◽  
Bioengineered Skin ◽  
Native Skin

Clinical use of bioengineered skin in reconstructive surgery has been established for more than 30 years. The limitations and ethical considerations regarding the use of animal models have expanded the application of bioengineered skin in the areas of disease modeling and drug screening. These skin models should represent the anatomical and physiological traits of native skin for the efficient replication of normal and pathological skin conditions. In addition, reliability of such models is essential for the conduction of faithful, rapid, and large-scale studies. Therefore, research efforts are focused on automated fabrication methods to replace the traditional manual approaches. This report presents an overview of the skin models applicable to skin disease modeling along with their fabrication methods, and discusses the potential of the currently available options to conform and satisfy the demands for disease modeling and drug screening.

scholarly journals Large-Scale Drug Screening in Patient-Derived IDHmut Glioma Stem Cells Identifies Several Efficient Drugs among FDA-Approved Antineoplastic Agents

Cells ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1389 ◽  
Author(s):  
Philip Dao Trong ◽  
Gerhard Jungwirth ◽  
Tao Yu ◽  
Stefan Pusch ◽  
Andreas Unterberg ◽  
...  
Keyword(s):  
Stem Cells ◽  
Drug Screening ◽  
Large Scale ◽  
Annexin V ◽  
Chemotherapeutic Agents ◽  
Glioma Stem Cells ◽  
Clinical Investigations ◽  
Ic50 Values ◽  
Induction Of Apoptosis ◽  
Approved Drugs

The discovery of the isocitrate dehydrogenase (IDH) mutation in glioma led to a paradigm shift on how we see glioma biology. Difficulties in cultivating IDH mutant glioma stem cells (IDHmut GSCs) resulted in a paucity of preclinical models in IDHmut glioma, limiting the discovery of new effective chemotherapeutic agents. To fill this gap, we used six recently developed patient-derived IDHmut GSC lines and performed a large-scale drug screening with 147 Food and Drug Administration (FDA)-approved anticancer drugs. GSCs were subjected to the test compounds for 72 h in concentrations ranging from 0.0001 to 1 µM. Cell viability was assessed by CellTiterGlo and the induction of apoptosis by flow cytometry with Annexin V/propidium iodide staining. The initial screen was performed with two IDHmut GSC lines and identified seven drugs (bortezomib, carfilzomib, daunorubicin, doxorubicin, epirubicin, omacetaxine, plicamycin) with a substantial antiproliferative activity, as reflected by half maximal inhibitory concentrations (IC50) below 1 µM and maximum inhibitory effects (Emax) below 25%. These findings were validated in an additional four IDHmut GSC lines. The candidate drugs, of which plicamycin and omacetaxine are known to cross the blood brain barrier, were used for subsequent cell death analyses. A significant induction of apoptosis was observed at IC50 values of the respective drugs. In summary, we were able to identify seven FDA-approved drugs that should be further taken into clinical investigations for the treatment of IDHmut gliomas.

scholarly journals An Integrated Deep Learning and Molecular Dynamics Simulation-Based Screening Pipeline Identifies Inhibitors of a New Cancer Drug Target TIPE2

2021 ◽  
Vol 12 ◽  
Author(s):  
Haiping Zhang ◽  
Junxin Li ◽  
Konda Mani Saravanan ◽  
Hao Wu ◽  
Zhichao Wang ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Deep Learning ◽  
Molecular Dynamics Simulation ◽  
Drug Screening ◽  
Large Scale ◽  
Inflammatory Diseases ◽  
Dynamics Simulation ◽  
Cancer Drug ◽  
Necrosis Factor Alpha ◽  
Simulation Based

The TIPE2 (tumor necrosis factor-alpha-induced protein 8-like 2) protein is a major regulator of cancer and inflammatory diseases. The availability of its sequence and structure, as well as the critical amino acids involved in its ligand binding, provides insights into its function and helps greatly identify novel drug candidates against TIPE2 protein. With the current advances in deep learning and molecular dynamics simulation-based drug screening, large-scale exploration of inhibitory candidates for TIPE2 becomes possible. In this work, we apply deep learning-based methods to perform a preliminary screening against TIPE2 over several commercially available compound datasets. Then, we carried a fine screening by molecular dynamics simulations, followed by metadynamics simulations. Finally, four compounds were selected for experimental validation from 64 candidates obtained from the screening. With surprising accuracy, three compounds out of four can bind to TIPE2. Among them, UM-164 exhibited the strongest binding affinity of 4.97 µM and was able to interfere with the binding of TIPE2 and PIP2 according to competitive bio-layer interferometry (BLI), which indicates that UM-164 is a potential inhibitor against TIPE2 function. The work demonstrates the feasibility of incorporating deep learning and MD simulation in virtual drug screening and provides high potential inhibitors against TIPE2 for drug development.

Models, Practices and Methods of Reading: Evolution in Time and Space

2009 ◽  
pp. 59-64
Author(s):  
Yulia P. Melentyeva
Keyword(s):  
Large Scale ◽  
Social Groups ◽  
National Program ◽  
Time And Space ◽  
Lack Of Information ◽  
The Individual

In recent years as public in general and specialist have been showing big interest to the matters of reading. According to discussion and launch of the “Support and Development of Reading National Program”, many Russian libraries are organizing the large-scale events like marathons, lecture cycles, bibliographic trainings etc. which should draw attention of different social groups to reading. The individual forms of attraction to reading are used much rare. To author’s mind the main reason of such an issue has to be the lack of information about forms and methods of attraction to reading.

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