Combinatorial therapies have been recently proposed to improve the efficacy of anticancer treatment. The SynergyFinder R package is a software used to analyze pre-clinical drug combination datasets. ...Here, we report the major updates to the SynergyFinder R package for improved interpretation and annotation of drug combination screening results. Unlike the existing implementations, the updated SynergyFinder R package includes five main innovations. 1) We extend the mathematical models to higher-order drug combination data analysis and implement dimension reduction techniques for visualizing the synergy landscape. 2) We provide a statistical analysis of drug combination synergy and sensitivity with confidence intervals and P values. 3) We incorporate a synergy barometer to harmonize multiple synergy scoring methods to provide a consensus metric for synergy. 4) We evaluate drug combination synergy and sensitivity to provide an unbiased interpretation of the clinical potential. 5) We enable fast annotation of drugs and cell lines, including their chemical and target information. These annotations will improve the interpretation of the mechanisms of action of drug combinations. To facilitate the use of the R package within the drug discovery community, we also provide a web server at www.synergyfinderplus.org as a user-friendly interface to enable a more flexible and versatile analysis of drug combination data.
The simple (linear) birth-and-death process is a widely used stochastic model for describing the dynamics of a population. When the process is observed discretely over time, despite the large amount ...of literature on the subject, little is known about formal estimator properties. Here we will show that its application to observed data is further complicated by the fact that numerical evaluation of the well-known transition probability is an ill-conditioned problem. To overcome this difficulty we will rewrite the transition probability in terms of a Gaussian hypergeometric function and subsequently obtain a three-term recurrence relation for its accurate evaluation. We will also study the properties of the hypergeometric function as a solution to the three-term recurrence relation. We will then provide formulas for the gradient and Hessian of the log-likelihood function and conclude the article by applying our methods for numerically computing maximum likelihood estimates in both simulated and real dataset.
High-throughput drug screening has facilitated the discovery of drug combinations in cancer. Many existing studies adopted a full matrix design, aiming for the characterization of drug pair effects ...for cancer cells. However, the full matrix design may be suboptimal as it requires a drug pair to be combined at multiple concentrations in a full factorial manner. Furthermore, many of the computational tools assess only the synergy but not the sensitivity of drug combinations, which might lead to false positive discoveries. We proposed a novel cross design to enable a more cost-effective and simultaneous testing of drug combination sensitivity and synergy. We developed a drug combination sensitivity score (CSS) to determine the sensitivity of a drug pair, and showed that the CSS is highly reproducible between the replicates and thus supported its usage as a robust metric. We further showed that CSS can be predicted using machine learning approaches which determined the top pharmaco-features to cluster cancer cell lines based on their drug combination sensitivity profiles. To assess the degree of drug interactions using the cross design, we developed an S synergy score based on the difference between the drug combination and the single drug dose-response curves. We showed that the S score is able to detect true synergistic and antagonistic drug combinations at an accuracy level comparable to that using the full matrix design. Taken together, we showed that the cross design coupled with the CSS sensitivity and S synergy scoring methods may provide a robust and accurate characterization of both drug combination sensitivity and synergy levels, with minimal experimental materials required. Our experimental-computational approach could be utilized as an efficient pipeline for improving the discovery rate in high-throughput drug combination screening, particularly for primary patient samples which are difficult to obtain.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Drug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of drug resistance. However, discovery of synergistic and effective drug ...combinations has been a laborious and often serendipitous process. In recent years, identification of combination therapies has been accelerated due to the advances in high-throughput drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal called DrugComb (https://drugcomb.fimm.fi) where the results of drug combination screening studies are accumulated, standardized and harmonized. Through the data portal, we provided a web server to analyze and visualize users' own drug combination screening data. The users can also effectively participate a crowdsourcing data curation effect by depositing their data at DrugComb. To initiate the data repository, we collected 437 932 drug combinations tested on a variety of cancer cell lines. We showed that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity of drug combinations. All the data and informatics tools are freely available in DrugComb to enable a more efficient utilization of data resources for future drug combination discovery.
Analysis of dose-response data is an important step in many scientific disciplines, including but not limited to pharmacology, toxicology, and epidemiology. The R package drda is designed to ...facilitate the analysis of dose-response data by implementing efficient and accurate functions with a familiar interface. With drda it is possible to fit models by the method of least squares, perform goodness-of-fit tests, and conduct model selection. Compared to other similar packages, drda provides in general more accurate estimates in the least-squares sense. This result is achieved by a smart choice of the starting point in the optimization algorithm and by implementing the Newton method with a trust region with analytical gradients and Hessian matrices. In this article, drda is presented through the description of its methodological components and examples of its user-friendly functions. Performance is evaluated using both synthetic data and a real, large-scale drug sensitivity screening dataset.
NAD+ is a redox-active metabolite, the depletion of which has been proposed to promote aging and degenerative diseases in rodents. However, whether NAD+ depletion occurs in patients with degenerative ...disorders and whether NAD+ repletion improves their symptoms has remained open. Here, we report systemic NAD+ deficiency in adult-onset mitochondrial myopathy patients. We administered an increasing dose of NAD+-booster niacin, a vitamin B3 form (to 750–1,000 mg/day; clinicaltrials.govNCT03973203) for patients and their matched controls for 10 or 4 months, respectively. Blood NAD+ increased in all subjects, up to 8-fold, and muscle NAD+ of patients reached the level of their controls. Some patients showed anemia tendency, while muscle strength and mitochondrial biogenesis increased in all subjects. In patients, muscle metabolome shifted toward controls and liver fat decreased even 50%. Our evidence indicates that blood analysis is useful in identifying NAD+ deficiency and points niacin to be an efficient NAD+ booster for treating mitochondrial myopathy.
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•Mitochondrial myopathy patients have NAD+ deficiency in muscle and blood•Niacin is an efficient NAD+ booster in humans•Niacin improves muscle strength and fatty liver in mitochondrial myopathy•Niacin boosts muscle mitochondrial biogenesis and respiratory chain activity in humans
Pirinen et al. report that niacin, a vitamin B3, can efficiently rescue NAD+ levels in the muscle and blood of patients with mitochondrial myopathy, improving disease signs and muscle strength. NAD+ levels increased also in healthy subjects. The evidence suggests that niacin is an effective NAD+ booster in humans.
Fluoropyrimidines are the first-line treatment for colorectal cancer, but their efficacy is highly variable between patients. We queried whether gut microbes, a known source of inter-individual ...variability, impacted drug efficacy. Combining two tractable genetic models, the bacterium E. coli and the nematode C. elegans, we performed three-way high-throughput screens that unraveled the complexity underlying host-microbe-drug interactions. We report that microbes can bolster or suppress the effects of fluoropyrimidines through metabolic drug interconversion involving bacterial vitamin B6, B9, and ribonucleotide metabolism. Also, disturbances in bacterial deoxynucleotide pools amplify 5-FU-induced autophagy and cell death in host cells, an effect regulated by the nucleoside diphosphate kinase ndk-1. Our data suggest a two-way bacterial mediation of fluoropyrimidine effects on host metabolism, which contributes to drug efficacy. These findings highlight the potential therapeutic power of manipulating intestinal microbiota to ensure host metabolic health and treat disease.
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•Drug-microbe-host high-throughput screens reveal new mechanisms for cancer drugs•Microbes integrate nutritional and drug cues regulating treatment efficacy in the host•Ribonucleotide co-metabolism of cancer pro-drugs exists between host and microbe•Imbalanced bacterial deoxynucleotides synergize 5-FU-induced autophagic cell death
A three-way high-throughput screen involving host-microbe-drug interactions reveals that the beneficial impact of some drugs can be due to effects of drug-dependent alterations by gut microbe composition rather than direct action of the therapeutic itself.
CD73 is an important ectoenzyme responsible for the production of extracellular adenosine. It is involved in regulating inflammatory responses and cell migration and is overexpressed in various ...cancers. The functions of CD73 in blood endothelial cells are understood in detail, but its role on afferent lymphatics remains unknown. Moreover, anti‐CD73 antibodies are now used in multiple clinical cancer trials, but their effects on different endothelial cell types have not been studied. This study reveals that a previously unknown role of CD73 on afferent lymphatics is to dampen immune responses. Knocking it out or suppressing it by siRNA leads to the upregulation of inflammation‐associated genes on lymphatic endothelial cells and a more pro‐inflammatory phenotype of interacting dendritic cells in vitro and in vivo. In striking contrast, anti‐CD73 antibodies had only negligible effects on the gene expression of lymphatic‐ and blood‐endothelial cells. Our data thus reveal new functions of lymphatic CD73 and indicate a low likelihood of endothelial cell–related adverse effects by CD73 targeting therapeutic antibodies.
Silencing CD73 increases the expression of MHCI and ICAM‐1 on lymphatic endothelial cells (LECs), resulting in elevated MHCII and CD83 levels on DCs. This is also the case for inflammatory genes such as TGFB1 or CD69. In contrast, modulating CD73 expression with antibodies showed no such effect with LECs or blood endothelial cells.
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