scholarly journals N-of-1 Trials in Pediatric Oncology: From a Population-Based Approach to Personalized Medicine—A Review

Cancers ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 5428
Author(s):  
Michal Kyr ◽  
Adam Svobodnik ◽  
Radka Stepanova ◽  
Renata Hejnova
Keyword(s):  
Personalized Medicine ◽  
Pediatric Oncology ◽  
Large Scale ◽  
Population Based ◽  
Modern Medicine ◽  
New Drugs ◽  
Small Samples ◽  
Personalized Oncology ◽  
New Treatments ◽  
Analytical Approaches

Pediatric oncology is a critical area where the more efficient development of new treatments is urgently needed. The speed of approval of new drugs is still limited by regulatory requirements and a lack of innovative designs appropriate for trials in children. Childhood cancers meet the criteria of rare diseases. Personalized medicine brings it even closer to the horizon of individual cases. Thus, not all the traditional research tools, such as large-scale RCTs, are always suitable or even applicable, mainly due to limited sample sizes. Small samples and traditional versus subject-specific evidence are both distinctive issues in personalized pediatric oncology. Modern analytical approaches and adaptations of the paradigms of evidence are warranted. We have reviewed innovative trial designs and analytical methods developed for small populations, together with individualized approaches, given their applicability to pediatric oncology. We discuss traditional population-based and individualized perspectives of inferences and evidence, and explain the possibilities of using various methods in pediatric personalized oncology. We find that specific derivatives of the original N-of-1 trial design adapted for pediatric personalized oncology may represent an optimal analytical tool for this area of medicine. We conclude that no particular N-of-1 strategy can provide a solution. Rather, a whole range of approaches is needed to satisfy the new inferential and analytical paradigms of modern medicine. We reveal a new view of cancer as continuum model and discuss the “evidence puzzle”.

Drug Development in the Era of Personalized Oncology: From Population-Based Trials to Enrichment and Prescreening Strategies

2012 ◽  
pp. 168-172
Author(s):  
Rodrigo Dienstmann ◽  
Jordi Rodon ◽  
Josep Tabernero
Keyword(s):  
Clinical Trials ◽  
Drug Development ◽  
Large Scale ◽  
Human Genetics ◽  
Tumor Biology ◽  
Drug Approval ◽  
Population Based ◽  
Tumor Subtypes ◽  
Recent Success ◽  
Personalized Oncology

Overview: Recent advances in tumor biology and human genetics along with the development of drugs for specific targets hold promise for an era of personalized oncology treatment. Routine use of modern technologies, such as large-scale genome sequencing, will help to unravel the specific biology of each tumor. Adding a rigorous genomic view could determine key genetic events, critical dependencies, and stratification of patients in early clinical trials. Integrating biomarker development into the early testing of novel agents might provide clinically relevant therapeutic opportunities for patients with advanced-stage cancer and also accelerate the drug-approval process. After recent success stories of therapies targeting driver molecular aberrations in genetically defined tumor subtypes, innovative clinical trials based on a strong biologic hypothesis are expected to bring further excitement to the field. In this article, we describe a new trend in biomarker-driven early drug development using enrichment and prescreening strategies. Technical and logistical obstacles that may hinder progress of this approach will be discussed, along with ethical and economic concerns.

scholarly journals Laboratory-Based Resources for COVID-19 Diagnostics: Traditional Tools and Novel Technologies. A Perspective of Personalized Medicine

2021 ◽  
Vol 11 (1) ◽  
pp. 42
Author(s):  
Boris G. Andryukov ◽  
Natalya N. Besednova ◽  
Tatyana A. Kuznetsova ◽  
Ludmila N. Fedyanina
Keyword(s):  
Personalized Medicine ◽  
Large Scale ◽  
Diagnostic Testing ◽  
Population Based ◽  
Diagnostic Tools ◽  
Rapid Diagnostics ◽  
Retrospective Diagnosis ◽  
Novel Technologies ◽  
Population Immunity ◽  
Number Of Patients

The coronavirus infection 2019 (COVID-19) pandemic, caused by the highly contagious SARS-CoV-2 virus, has provoked a global healthcare and economic crisis. The control over the spread of the disease requires an efficient and scalable laboratory-based strategy for testing the population based on multiple platforms to provide rapid and accurate diagnosis. With the onset of the pandemic, the reverse transcription polymerase chain reaction (RT-PCR) method has become a standard diagnostic tool, which has received wide clinical use. In large-scale and repeated examinations, these tests can identify infected patients with COVID-19, with their accuracy, however, dependent on many factors, while the entire process takes up to 6–8 h. Here we also describe a number of serological systems for detecting antibodies against SARS-CoV-2. These are used to assess the level of population immunity in various categories of people, as well as for retrospective diagnosis of asymptomatic and mild COVID-19 in patients. However, the widespread use of traditional diagnostic tools in the context of the rapid spread of COVID-19 is hampered by a number of limitations. Therefore, the sharp increase in the number of patients with COVID-19 necessitates creation of new rapid, inexpensive, sensitive, and specific tests. In this regard, we focus on new laboratory technologies such as loop mediated isothermal amplification (LAMP) and lateral flow immunoassay (LFIA), which have proven to work well in the COVID-19 diagnostics and can become a worthy alternative to traditional laboratory-based diagnostics resources. To cope with the COVID-19 pandemic, the healthcare system requires a combination of various types of laboratory diagnostic testing techniques, whodse sensitivity and specificity increases with the progress in the SARS-CoV-2 research. The testing strategy should be designed in such a way to provide, depending on the timing of examination and the severity of the infection in patients, large-scale and repeated examinations based on the principle: screening–monitoring–control. The search and development of new methods for rapid diagnostics of COVID-19 in laboratory, based on new analytical platforms, is still a highly important and urgent healthcare issue. In the final part of the review, special emphasis is made on the relevance of the concept of personalized medicine to combat the COVID-19 pandemic in the light of the recent studies carried out to identify the causes of variation in individual susceptibility to SARS-CoV-2 and increase the efficiency and cost-effectiveness of treatment.

The German Version of the Satisfaction With Life Scale (SWLS)

2011 ◽  
Vol 27 (2) ◽  
pp. 127-132 ◽  
Author(s):  
Heide Glaesmer ◽  
Gesine Grande ◽  
Elmar Braehler ◽  
Marcus Roth
Keyword(s):  
Convergent Validity ◽  
Satisfaction With Life ◽  
Age Groups ◽  
Population Based ◽  
Factorial Invariance ◽  
Satisfaction With Life Scale ◽  
Small Samples ◽  
Cross Sectional ◽  
Confirmatory Factor Analyses ◽  
Life Scale

The Satisfaction with Life Scale (SWLS) is the most commonly used measure for life satisfaction. Although there are numerous studies confirming factorial validity, most studies on dimensionality are based on small samples. A controversial debate continues on the factorial invariance across different subgroups. The present study aimed to test psychometric properties, factorial structure, factorial invariance across age and gender, and to deliver population-based norms for the German general population from a large cross-sectional sample of 2519 subjects. Confirmatory factor analyses supported that the scale is one-factorial, even though indications of inhomogeneity of the scale have been detected. Both findings show invariance across the seven age groups and both genders. As indicators of the convergent validity, a positive correlation with social support and negative correlation with depressiveness was shown. Population-based norms are provided to support the application in the context of individual diagnostics.

Permeability Through Bacterial Porins Dictates Whole Cell Compound Accumulation

2020 ◽  
Author(s):  
Silvia Acosta Gutiérrez ◽  
Igor Bodrenko ◽  
Matteo Ceccarelli
Keyword(s):  
Cell Envelope ◽  
Scoring Function ◽  
Modern Medicine ◽  
New Drugs ◽  
Prediction Ability ◽  
Whole Cell ◽  
Virtual Libraries ◽  
Major Bottleneck ◽  
Global Threat ◽  
Bacterial Porins

The lack of new drugs for Gram-negative pathogens is a global threat to modern medicine. The complexity of their cell envelope, with an additional outer membrane, hinders internal accumulation and thus, the access of molecules to targets. Our limited understanding of the molecular basis for compound influx and efflux from these pathogens is a major bottleneck for the discovery of effective antibacterial compounds. Here we analyse the correlation between the whole-cell compound accumulation of ~200 molecules and their predicted porin permeability coefficient (influx), using a recently developed scoring function. We found a strong linear relationship (75%) between the two, confirming porins key role in compound penetration. Further, the remarkable prediction ability of the scoring function demonstrates its potentiality to guide the optimization of hits to leads as well as the possibility of screening ultra-large virtual libraries. Eventually, the analysis of false positives, molecules with high-predicted influx but low accumulation, provides new hints on the molecular properties behind efflux.<br>

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

A Drug Target Interaction Prediction Based on LINE-RF Learning

2020 ◽  
Vol 15 (7) ◽  
pp. 750-757
Author(s):  
Jihong Wang ◽  
Yue Shi ◽  
Xiaodan Wang ◽  
Huiyou Chang
Keyword(s):  
Network Topology ◽  
Drug Target ◽  
Large Scale ◽  
Representation Learning ◽  
New Drugs ◽  
Combination Method ◽  
Learning Methods ◽  
Network Representation ◽  
On Line ◽  
Clinical Experiments

Background: At present, using computer methods to predict drug-target interactions (DTIs) is a very important step in the discovery of new drugs and drug relocation processes. The potential DTIs identified by machine learning methods can provide guidance in biochemical or clinical experiments. Objective: The goal of this article is to combine the latest network representation learning methods for drug-target prediction research, improve model prediction capabilities, and promote new drug development. Methods: We use large-scale information network embedding (LINE) method to extract network topology features of drugs, targets, diseases, etc., integrate features obtained from heterogeneous networks, construct binary classification samples, and use random forest (RF) method to predict DTIs. Results: The experiments in this paper compare the common classifiers of RF, LR, and SVM, as well as the typical network representation learning methods of LINE, Node2Vec, and DeepWalk. It can be seen that the combined method LINE-RF achieves the best results, reaching an AUC of 0.9349 and an AUPR of 0.9016. Conclusion: The learning method based on LINE network can effectively learn drugs, targets, diseases and other hidden features from the network topology. The combination of features learned through multiple networks can enhance the expression ability. RF is an effective method of supervised learning. Therefore, the Line-RF combination method is a widely applicable method.

scholarly journals Use of Antiplatelet Agents Decreases the Positive Predictive Value of Fecal Immunochemical Tests for Colorectal Cancer but Does Not Affect Their Sensitivity

2021 ◽  
Vol 11 (6) ◽  
pp. 497
Author(s):  
Yoonsuk Jung ◽  
Eui Im ◽  
Jinhee Lee ◽  
Hyeah Lee ◽  
Changmo Moon
Keyword(s):  
Colorectal Cancer ◽  
Large Scale ◽  
Screening Program ◽  
Antiplatelet Agents ◽  
Population Based ◽  
Small Sample ◽  
Antithrombotic Agents ◽  
Predictive Values ◽  
Prescription Rates ◽  
Small Sample Sizes

Previous studies have evaluated the effects of antithrombotic agents on the performance of fecal immunochemical tests (FITs) for the detection of colorectal cancer (CRC), but the results were inconsistent and based on small sample sizes. We studied this topic using a large-scale population-based database. Using the Korean National Cancer Screening Program Database, we compared the performance of FITs for CRC detection between users and non-users of antiplatelet agents and warfarin. Non-users were matched according to age and sex. Among 5,426,469 eligible participants, 768,733 used antiplatelet agents (mono/dual/triple therapy, n = 701,683/63,211/3839), and 19,569 used warfarin, while 4,638,167 were non-users. Among antiplatelet agents, aspirin, clopidogrel, and cilostazol ranked first, second, and third, respectively, in terms of prescription rates. Users of antiplatelet agents (3.62% vs. 4.45%; relative risk (RR): 0.83; 95% confidence interval (CI): 0.78–0.88), aspirin (3.66% vs. 4.13%; RR: 0.90; 95% CI: 0.83–0.97), and clopidogrel (3.48% vs. 4.88%; RR: 0.72; 95% CI: 0.61–0.86) had lower positive predictive values (PPVs) for CRC detection than non-users. However, there were no significant differences in PPV between cilostazol vs. non-users and warfarin users vs. non-users. For PPV, the RR (users vs. non-users) for antiplatelet monotherapy was 0.86, while the RRs for dual and triple antiplatelet therapies (excluding cilostazol) were 0.67 and 0.22, respectively. For all antithrombotic agents, the sensitivity for CRC detection was not different between users and non-users. Use of antiplatelet agents, except cilostazol, may increase the false positives without improving the sensitivity of FITs for CRC detection.

scholarly journals Multi-GPU approach to global induction of classification trees for large-scale data mining

2021 ◽  
Author(s):  
Krzysztof Jurczuk ◽  
Marcin Czajkowski ◽  
Marek Kretowski
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Real Life ◽  
Population Based ◽  
Tree Structure ◽  
Global Approach ◽  
Data Parallel ◽  
Large Scale Data ◽  
The Impact ◽  
Scale Data

AbstractThis paper concerns the evolutionary induction of decision trees (DT) for large-scale data. Such a global approach is one of the alternatives to the top-down inducers. It searches for the tree structure and tests simultaneously and thus gives improvements in the prediction and size of resulting classifiers in many situations. However, it is the population-based and iterative approach that can be too computationally demanding to apply for big data mining directly. The paper demonstrates that this barrier can be overcome by smart distributed/parallel processing. Moreover, we ask the question whether the global approach can truly compete with the greedy systems for large-scale data. For this purpose, we propose a novel multi-GPU approach. It incorporates the knowledge of global DT induction and evolutionary algorithm parallelization together with efficient utilization of memory and computing GPU’s resources. The searches for the tree structure and tests are performed simultaneously on a CPU, while the fitness calculations are delegated to GPUs. Data-parallel decomposition strategy and CUDA framework are applied. Experimental validation is performed on both artificial and real-life datasets. In both cases, the obtained acceleration is very satisfactory. The solution is able to process even billions of instances in a few hours on a single workstation equipped with 4 GPUs. The impact of data characteristics (size and dimension) on convergence and speedup of the evolutionary search is also shown. When the number of GPUs grows, nearly linear scalability is observed what suggests that data size boundaries for evolutionary DT mining are fading.

scholarly journals Prospects for new drugs to treat binge-eating disorder: Insights from psychopathology and neuropharmacology

2021 ◽  
pp. 026988112110324
Author(s):  
David J Heal ◽  
Sharon L Smith
Keyword(s):  
Eating Disorder ◽  
Binge Eating ◽  
Binge Eating Disorder ◽  
Clinical Findings ◽  
New Drugs ◽  
Dopaminergic Neurotransmission ◽  
Drug Candidates ◽  
Product Profile ◽  
Proven Efficacy ◽  
New Treatments

Background: Binge-eating disorder (BED) is a common psychiatric condition with adverse psychological and metabolic consequences. Lisdexamfetamine (LDX) is the only approved BED drug treatment. New drugs to treat BED are urgently needed. Methods: A comprehensive review of published psychopathological, pharmacological and clinical findings. Results: The evidence supports the hypothesis that BED is an impulse control disorder with similarities to ADHD, including responsiveness to catecholaminergic drugs, for example LDX and dasotraline. The target product profile (TPP) of the ideal BED drug combines treating the psychopathological drivers of the disorder with an independent weight-loss effect. Drugs with proven efficacy in BED have a common pharmacology; they potentiate central noradrenergic and dopaminergic neurotransmission. Because of the overlap between pharmacotherapy in attention deficit hyperactivity disorder (ADHD) and BED, drug-candidates from diverse pharmacological classes, which have already failed in ADHD would also be predicted to fail if tested in BED. The failure in BED trials of drugs with diverse pharmacological mechanisms indicates many possible avenues for drug discovery can probably be discounted. Conclusions: (1) The efficacy of drugs for BED is dependent on reducing its core psychopathologies of impulsivity, compulsivity and perseveration and by increasing cognitive control of eating. (2) The analysis revealed a large number of pharmacological mechanisms are unlikely to be productive in the search for effective new BED drugs. (3) The most promising areas for new treatments for BED are drugs, which augment noradrenergic and dopaminergic neurotransmission and/or those which are effective in ADHD.

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