scholarly journals Transcriptomic signatures predict regulators of drug synergy and clinical regimen efficacy against Tuberculosis

2019 ◽  
Author(s):  
Shuyi Ma ◽  
Suraj Jaipalli ◽  
Jonah Larkins-Ford ◽  
Jenny Lohmiller ◽  
Bree B. Aldridge ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Computational Model ◽  
Drug Combinations ◽  
Broth Culture ◽  
Multiple Drug ◽  
P Value ◽  
Drug Synergy ◽  
Drug Regimens

ABSTRACTThe rapid spread of multi-drug resistant strains has created a pressing need for new drug regimens to treat tuberculosis (TB), which kills 1.8 million people each year. Identifying new regimens has been challenging due to the slow growth of the pathogen M. tuberculosis (MTB), coupled with large number of possible drug combinations. Here we present a computational model (INDIGO-MTB) that identified synergistic regimens featuring existing and emerging anti-TB drugs after screening in silico over 1 million potential drug combinations using MTB drug transcriptomic profiles. INDIGO-MTB further predicted the gene Rv1353c as a key transcriptional regulator of multiple drug interactions, and we confirmed experimentally that Rv1353c up-regulation reduces the antagonism of the bedaquiline-streptomycin combination. Retrospective analysis of 57 clinical trials of TB regimens using INDIGO-MTB revealed that synergistic combinations were significantly more efficacious than antagonistic combinations (p-value = 1 × 10−4) based on the percentage of patients with negative sputum cultures after 8 weeks of treatment. Our study establishes a framework for rapid assessment of TB drug combinations and is also applicable to other bacterial pathogens.IMPORTANCEMulti-drug combination therapy is an important strategy for treating tuberculosis, the world’s deadliest bacterial infection. Long treatment durations and growing rates of drug resistance have created an urgent need for new approaches to prioritize effective drug regimens. Hence, we developed a computational model called INDIGO-MTB, which identifies synergistic drug regimens from an immense set of possible drug combinations using pathogen response transcriptome elicited by individual drugs. Although the underlying input data for INDIGO-MTB was generated under in vitro broth culture conditions, the predictions from INDIGO-MTB correlated significantly with in vivo drug regimen efficacy from clinical trials. INDIGO-MTB also identified the transcription factor Rv1353c as a regulator of multiple drug interaction outcomes, which could be targeted for rationally enhancing drug synergy.

scholarly journals Transcriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis

mBio ◽  
2019 ◽  
Vol 10 (6) ◽  
Author(s):  
Shuyi Ma ◽  
Suraj Jaipalli ◽  
Jonah Larkins-Ford ◽  
Jenny Lohmiller ◽  
Bree B. Aldridge ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Computational Model ◽  
Drug Combinations ◽  
Broth Culture ◽  
Multiple Drug ◽  
Content Type ◽  
Drug Synergy ◽  
Drug Regimens

ABSTRACT The rapid spread of multidrug-resistant strains has created a pressing need for new drug regimens to treat tuberculosis (TB), which kills 1.8 million people each year. Identifying new regimens has been challenging due to the slow growth of the pathogen Mycobacterium tuberculosis (MTB), coupled with the large number of possible drug combinations. Here we present a computational model (INDIGO-MTB) that identified synergistic regimens featuring existing and emerging anti-TB drugs after screening in silico more than 1 million potential drug combinations using MTB drug transcriptomic profiles. INDIGO-MTB further predicted the gene Rv1353c as a key transcriptional regulator of multiple drug interactions, and we confirmed experimentally that Rv1353c upregulation reduces the antagonism of the bedaquiline-streptomycin combination. A retrospective analysis of 57 clinical trials of TB regimens using INDIGO-MTB revealed that synergistic combinations were significantly more efficacious than antagonistic combinations (P value = 1 × 10−4) based on the percentage of patients with negative sputum cultures after 8 weeks of treatment. Our study establishes a framework for rapid assessment of TB drug combinations and is also applicable to other bacterial pathogens. IMPORTANCE Multidrug combination therapy is an important strategy for treating tuberculosis, the world’s deadliest bacterial infection. Long treatment durations and growing rates of drug resistance have created an urgent need for new approaches to prioritize effective drug regimens. Hence, we developed a computational model called INDIGO-MTB that identifies synergistic drug regimens from an immense set of possible drug combinations using the pathogen response transcriptome elicited by individual drugs. Although the underlying input data for INDIGO-MTB was generated under in vitro broth culture conditions, the predictions from INDIGO-MTB correlated significantly with in vivo drug regimen efficacy from clinical trials. INDIGO-MTB also identified the transcription factor Rv1353c as a regulator of multiple drug interaction outcomes, which could be targeted for rationally enhancing drug synergy.

scholarly journals Importance of Confirming Data on theIn VivoEfficacy of Novel Antibacterial Drug Regimens against Various Strains of Mycobacterium tuberculosis

2011 ◽  
Vol 56 (2) ◽  
pp. 731-738 ◽  
Author(s):  
Mary A. De Groote ◽  
Veronica Gruppo ◽  
Lisa K. Woolhiser ◽  
Ian M. Orme ◽  
Janet C. Gilliland ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Animal Studies ◽  
Strain Difference ◽  
Drug Combinations ◽  
Antibacterial Drug ◽  
Preclinical Testing ◽  
Content Type ◽  
Drug Regimens

ABSTRACTIn preclinical testing of antituberculosis drugs, laboratory-adapted strains ofMycobacterium tuberculosisare usually used both forin vitroandin vivostudies. However, it is unknown whether the heterogeneity ofM. tuberculosisstocks used by various laboratories can result in different outcomes in tests of antituberculosis drug regimens in animal infection models. In head-to-head studies, we investigated whether bactericidal efficacy results in BALB/c mice infected by inhalation with the laboratory-adapted strains H37Rv and Erdman differ from each other and from those obtained with clinical tuberculosis strains. Treatment of mice consisted of dual and triple drug combinations of isoniazid (H), rifampin (R), and pyrazinamide (Z). The results showed that not all strains gave the samein vivoefficacy results for the drug combinations tested. Moreover, the ranking of HRZ and RZ efficacy results was not the same for the two H37Rv strains evaluated. The magnitude of this strain difference also varied between experiments, emphasizing the risk of drawing firm conclusions for human trials based on single animal studies. The results also confirmed that the antagonism seen within the standard HRZ regimen by some investigators appears to be anM. tuberculosisstrain-specific phenomenon. In conclusion, the specific identity ofM. tuberculosisstrain used was found to be an important variable that can change the apparent outcome ofin vivoefficacy studies in mice. We highly recommend confirmation of efficacy results in late preclinical testing against a differentM. tuberculosisstrain than the one used in the initial mouse efficacy study, thereby increasing confidence to advance potent drug regimens to clinical trials.

scholarly journals DTF: Deep Tensor Factorization for predicting anticancer drug synergy

2020 ◽  
Vol 36 (16) ◽  
pp. 4483-4489
Author(s):  
Zexuan Sun ◽  
Shujun Huang ◽  
Peiran Jiang ◽  
Pingzhao Hu
Keyword(s):  
Logistic Regression ◽  
Performance Metrics ◽  
Factorization Method ◽  
Drug Combinations ◽  
Supplementary Information ◽  
Tensor Factorization ◽  
Drug Synergy ◽  
Synergistic Drug Combinations

Abstract Motivation Combination therapies have been widely used to treat cancers. However, it is cost and time consuming to experimentally screen synergistic drug pairs due to the enormous number of possible drug combinations. Thus, computational methods have become an important way to predict and prioritize synergistic drug pairs. Results We proposed a Deep Tensor Factorization (DTF) model, which integrated a tensor factorization method and a deep neural network (DNN), to predict drug synergy. The former extracts latent features from drug synergy information while the latter constructs a binary classifier to predict the drug synergy status. Compared to the tensor-based method, the DTF model performed better in predicting drug synergy. The area under precision-recall curve (PR AUC) was 0.58 for DTF and 0.24 for the tensor method. We also compared the DTF model with DeepSynergy and logistic regression models, and found that the DTF outperformed the logistic regression model and achieved similar performance as DeepSynergy using several performance metrics for classification task. Applying the DTF model to predict missing entries in our drug–cell-line tensor, we identified novel synergistic drug combinations for 10 cell lines from the 5 cancer types. A literature survey showed that some of these predicted drug synergies have been identified in vivo or in vitro. Thus, the DTF model could be a valuable in silico tool for prioritizing novel synergistic drug combinations. Availability and implementation Source code and data are available at https://github.com/ZexuanSun/DTF-Drug-Synergy. Supplementary information Supplementary data are available at Bioinformatics online.

Modulation of Matrix Metalloproteinases by Plant-Derived Products

2020 ◽  
Vol 20 ◽  
Author(s):  
Nur Najmi Mohamad Anuar ◽  
Nurul Iman Natasya Zulkafali ◽  
Azizah Ugusman
Keyword(s):  
Clinical Trials ◽  
Natural Products ◽  
Matrix Metalloproteinases ◽  
Animal Studies ◽  
Current Knowledge ◽  
In Vitro Models ◽  
In Vivo Studies ◽  
Extracellular Matrix Components

: Matrix metalloproteinases (MMPs) are a group of zinc-dependent metallo-endopeptidase that are responsible towards the degradation, repair and remodelling of extracellular matrix components. MMPs play an important role in maintaining a normal physiological function and preventing diseases such as cancer and cardiovascular diseases. Natural products derived from plants have been used as traditional medicine for centuries. Its active compounds, such as catechin, resveratrol and quercetin, are suggested to play an important role as MMPs inhibitors, thereby opening new insights into their applications in many fields, such as pharmaceutical, cosmetic and food industries. This review summarises the current knowledge on plant-derived natural products with MMP-modulating activities. Most of the reviewed plant-derived products exhibit an inhibitory activity on MMPs. Amongst MMPs, MMP-2 and MMP-9 are the most studied. The expression of MMPs is inhibited through respective signalling pathways, such as MAPK, NF-κB and PI3 kinase pathways, which contribute to the reduction in cancer cell behaviours, such as proliferation and migration. Most studies have employed in vitro models, but a limited number of animal studies and clinical trials have been conducted. Even though plant-derived products show promising results in modulating MMPs, more in vivo studies and clinical trials are needed to support their therapeutic applications in the future.

scholarly journals Expression Profile of New Marker Genes Involved in Differentiation of Canine Adipose-Derived Stem Cells into Osteoblasts

2021 ◽  
Vol 22 (13) ◽  
pp. 6663
Author(s):  
Maurycy Jankowski ◽  
Mariusz Kaczmarek ◽  
Grzegorz Wąsiatycz ◽  
Claudia Dompe ◽  
Paul Mozdziak ◽  
...  
Keyword(s):  
Stem Cells ◽  
In Vitro Culture ◽  
Osteogenic Differentiation ◽  
Molecular Mechanisms ◽  
Marker Genes ◽  
P Value ◽  
Illumina Platform

Next-generation sequencing (RNAseq) analysis of gene expression changes during the long-term in vitro culture and osteogenic differentiation of ASCs remains to be important, as the analysis provides important clues toward employing stem cells as a therapeutic intervention. In this study, the cells were isolated from adipose tissue obtained during routine surgical procedures and subjected to 14-day in vitro culture and differentiation. The mRNA transcript levels were evaluated using the Illumina platform, resulting in the detection of 19,856 gene transcripts. The most differentially expressed genes (fold change >|2|, adjusted p value < 0.05), between day 1, day 14 and differentiated cell cultures were extracted and subjected to bioinformatical analysis based on the R programming language. The results of this study provide molecular insight into the processes that occur during long-term in vitro culture and osteogenic differentiation of ASCs, allowing the re-evaluation of the roles of some genes in MSC progression towards a range of lineages. The results improve the knowledge of the molecular mechanisms associated with long-term in vitro culture and differentiation of ASCs, as well as providing a point of reference for potential in vivo and clinical studies regarding these cells’ application in regenerative medicine.

scholarly journals Melatonin in Cancer Treatment: Current Knowledge and Future Opportunities

Molecules ◽  
2021 ◽  
Vol 26 (9) ◽  
pp. 2506
Author(s):  
Wamidh H. Talib ◽  
Ahmad Riyad Alsayed ◽  
Alaa Abuawad ◽  
Safa Daoud ◽  
Asma Ismail Mahmod
Keyword(s):  
Clinical Trials ◽  
Current Knowledge ◽  
Biological Activities ◽  
Experimental Studies ◽  
Therapeutic Effects ◽  
Cell Proliferation Inhibition ◽  
Different Types ◽  
Solid Foundation

Melatonin is a pleotropic molecule with numerous biological activities. Epidemiological and experimental studies have documented that melatonin could inhibit different types of cancer in vitro and in vivo. Results showed the involvement of melatonin in different anticancer mechanisms including apoptosis induction, cell proliferation inhibition, reduction in tumor growth and metastases, reduction in the side effects associated with chemotherapy and radiotherapy, decreasing drug resistance in cancer therapy, and augmentation of the therapeutic effects of conventional anticancer therapies. Clinical trials revealed that melatonin is an effective adjuvant drug to all conventional therapies. This review summarized melatonin biosynthesis, availability from natural sources, metabolism, bioavailability, anticancer mechanisms of melatonin, its use in clinical trials, and pharmaceutical formulation. Studies discussed in this review will provide a solid foundation for researchers and physicians to design and develop new therapies to treat and prevent cancer using melatonin.

scholarly journals In Vitro Effects of Sulforaphane on Interferon-Driven Inflammation and Exploratory Evaluation in Two Healthy Volunteers

Molecules ◽  
2021 ◽  
Vol 26 (12) ◽  
pp. 3602
Author(s):  
Elena Genova ◽  
Maura Apollonio ◽  
Giuliana Decorti ◽  
Alessandra Tesser ◽  
Alberto Tommasini ◽  
...  
Keyword(s):  
Healthy Volunteers ◽  
Supportive Therapy ◽  
P Value ◽  
Type I ◽  
Interferon Signature ◽  
Interferon Stimulated Genes ◽  
Immune System Activation ◽  
In Vitro Effects

Interferonopathies are rare genetic conditions defined by systemic inflammatory episodes caused by innate immune system activation in the absence of pathogens. Currently, no targeted drugs are authorized for clinical use in these diseases. In this work, we studied the contribution of sulforaphane (SFN), a cruciferous-derived bioactive molecule, in the modulation of interferon-driven inflammation in an immortalized human hepatocytes (IHH) line and in two healthy volunteers, focusing on STING, a key-component player in interferon pathway, interferon signature modulation, and GSTM1 expression and genotype, which contributes to SFN metabolism and excretion. In vitro, SFN exposure reduced STING expression as well as interferon signature in the presence of the pro-inflammatory stimulus cGAMP (cGAMP 3 h vs. SFN+cGAMP 3 h p value < 0.0001; cGAMP 6 h vs. SFN+cGAMP 6 h p < 0.001, one way ANOVA), restoring STING expression to the level of unstimulated cells. In preliminary experiments on healthy volunteers, no appreciable variations in interferon signature were identified after SFN assumption, while only in one of them, presenting the GSTM1 wild type genotype related to reduced SFN excretion, could a downregulation of STING be recorded. This study confirmed that SFN inhibits STING-mediated inflammation and interferon-stimulated genes expression in vitro. However, only a trend towards the downregulation of STING could be reproduced in vivo. Results obtained have to be confirmed in a larger group of healthy individuals and in patients with type I interferonopathies to define if the assumption of SFN could be useful as supportive therapy.

scholarly journals Effects of Vitrification on the Blastocyst Gene Expression Profile in a Porcine Model

2021 ◽  
Vol 22 (3) ◽  
pp. 1222
Author(s):  
Cristina Cuello ◽  
Cristina A. Martinez ◽  
Josep M. Cambra ◽  
Inmaculada Parrilla ◽  
Heriberto Rodriguez-Martinez ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Analysis ◽  
Survival Rates ◽  
Control Group ◽  
P Value ◽  
Transcriptome Profile ◽  
Embryo Viability ◽  
The Impact

This study was designed to investigate the impact of vitrification on the transcriptome profile of blastocysts using a porcine (Sus scrofa) model and a microarray approach. Blastocysts were collected from weaned sows (n = 13). A total of 60 blastocysts were vitrified (treatment group). After warming, vitrified embryos were cultured in vitro for 24 h. Non-vitrified blastocysts (n = 40) were used as controls. After the in vitro culture period, the embryo viability was morphologically assessed. A total of 30 viable embryos per group (three pools of 10 from 4 different donors each) were subjected to gene expression analysis. A fold change cut-off of ±1.5 and a restrictive threshold at p-value < 0.05 were used to distinguish differentially expressed genes (DEGs). The survival rates of vitrified/warmed blastocysts were similar to those of the control (nearly 100%, n.s.). A total of 205 (112 upregulated and 93 downregulated) were identified in the vitrified blastocysts compared to the control group. The vitrification/warming impact was moderate, and it was mainly related to the pathways of cell cycle, cellular senescence, gap junction, and signaling for TFGβ, p53, Fox, and MAPK. In conclusion, vitrification modified the transcriptome of in vivo-derived porcine blastocysts, resulting in minor gene expression changes.

scholarly journals Antitumor mechanism of evodiamine, a constituent from Chinese herb Evodiae fructus , in human multiple-drug resistant breast cancer NCI/ADR-RES cells in vitro and in vivo

2005 ◽  
Vol 26 (5) ◽  
pp. 968-975 ◽  
Author(s):  
Cho-Hwa Liao ◽  
Shiow-Lin Pan ◽  
Jih-Hwa Guh ◽  
Ya-Ling Chang ◽  
Hui-Chen Pai ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Chinese Herb ◽  
Multiple Drug ◽  
Drug Resistant ◽  
Multiple Drug Resistant

scholarly journals Modelling human skull growth: a validated computational model

2017 ◽  
Vol 14 (130) ◽  
pp. 20170202 ◽  
Author(s):  
Joseph Libby ◽  
Arsalan Marghoub ◽  
David Johnson ◽  
Roman H. Khonsari ◽  
Michael J. Fagan ◽  
...  
Keyword(s):  
Computational Model ◽  
Three Dimensional ◽  
Basic Knowledge ◽  
Fe Model ◽  
Adult Size ◽  
Fe Method ◽  
Percentage Difference ◽  
Skull Growth

During the first year of life, the brain grows rapidly and the neurocranium increases to about 65% of its adult size. Our understanding of the relationship between the biomechanical forces, especially from the growing brain, the craniofacial soft tissue structures and the individual bone plates of the skull vault is still limited. This basic knowledge could help in the future planning of craniofacial surgical operations. The aim of this study was to develop a validated computational model of skull growth, based on the finite-element (FE) method, to help understand the biomechanics of skull growth. To do this, a two-step validation study was carried out. First, an in vitro physical three-dimensional printed model and an in silico FE model were created from the same micro-CT scan of an infant skull and loaded with forces from the growing brain from zero to two months of age. The results from the in vitro model validated the FE model before it was further developed to expand from 0 to 12 months of age. This second FE model was compared directly with in vivo clinical CT scans of infants without craniofacial conditions ( n = 56). The various models were compared in terms of predicted skull width, length and circumference, while the overall shape was quantified using three-dimensional distance plots. Statistical analysis yielded no significant differences between the male skull models. All size measurements from the FE model versus the in vitro physical model were within 5%, with one exception showing a 7.6% difference. The FE model and in vivo data also correlated well, with the largest percentage difference in size being 8.3%. Overall, the FE model results matched well with both the in vitro and in vivo data. With further development and model refinement, this modelling method could be used to assist in preoperative planning of craniofacial surgery procedures and could help to reduce reoperation rates.

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