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Background: Malaria is a leading cause of mortality, particularly in sub-Saharan Africa and among children. Prompt and efficacious treatment is important; if ineffective drugs are given or treatment is delayed, a patient may progress within a few hours to severe disease, which if untreated is almost always fatal. Chlorproguanil-Dapsone+Artesunate (CD+A) is an artemisinin-based combination treatment (ACT), with the potential risk of triggering a haemolytic anaemia in glucose-6-phosphate (G6PD) deficient patients because of the oxidative properties of dapsone. We assessed the haemolytic risk related to G6PD deficiency and to CDA intake in African children with uncomplicated malaria.
Methods: This is a nested study of a phase IV multicentre study comparing safety and efficacy of several ACTs in children. In a matched case-control design we compared children with a haemoglobin drop ≥2g/dl in the three days after the first treatment dose with those with a drop <2g/dl. Cases and controls were matched for study site, gender, age and haemoglobin at baseline. Data were analyzed with a conditional logistic regression model.
Results: G6PD deficiency, homo- and hemizygote, was present in 8.5% (10/117) of cases and 6.8% (16/234) of controls. After adjusting, neither G6PD deficiency (OR: 0.36; p=0.25) nor CDA (OR: 1.20; p=0.50) were independent risk factors for a haemoglobin drop. However, CDA and G6PD deficiency interacted (p=0.02) leading to a higher risk estimate (OR 5.71; IC95% 0.15 to 225.79).
Conclusion: Administering CDA to patients with uncomplicated malaria and with a G6PD deficiency could result in a higher risk of haemolytic anaemia Contexte : Le paludisme est une cause de mortalité importante, particulièrement en Afrique sub-saharienne et chez les enfants. Un traitement rapide et efficace est nécessaire; si le traitement est inefficace ou retardé, la maladie peut progresser en quelques heures vers un état grave, presque toujours fatal si non traité. Le Chlorproguanil-Dapsone+Artesunate (CD+A), un traitement combiné à base d’artémisinine (ACT), est susceptible de déclencher une anémie hémolytique chez les patients déficients en glucose-6-phosphate déshydrogénase (G6PD) à cause des propriétés oxydatives du dapsone. Nous avons évalué le risque hémolytique lié à la déficience en G6PD et à la prise du CDA chez les enfants africains avec un paludisme non compliqué.
Méthodes : L’étude est une sous-analyse d’une étude multicentrique de phase IV qui compare la tolérance et l’efficacité de plusieurs ACTs chez les enfants. Dans un schéma cas-témoins, nous avons comparé les enfants dont l’hémoglobine a chuté de ≥2g/dl après la première dose du traitement avec ceux dont la chute était <2g/dl. Les cas et les témoins ont été appariés sur le site de l’étude, le genre, l’âge et l’hémoglobinémie initiale. Les données ont été analysées avec un modèle de régression logistique conditionnelle.
Résultats : La déficience en G6PD, homo- et hémizygote, était présente chez 8,5% (10/117) des cas et 6,8% (16/234) des témoins. Après ajustement, ni la déficience en G6PD (OR: 0,36; p=0,25), ni le CDA (OR:1,20; p=0.50) n’étaient des facteurs de risque indépendants pour une chute de l’hémoglobine. Cependant, le CDA et la déficience en G6PD interagissaient (p=0,02), induisant une estimation plus élevée du risque (OR: 6,88; IC95% 1,06 à 44,88).
Conclusion: L’administration du CDA chez les patients avec un paludisme non compliqué et déficients en G6PD peut mener à un risque plus élevé d’anémie hémolytique

Malaria --- Glucosephosphate Dehydrogenase --- chloroguanil, dapsone drug combination --- artesunate --- Dapsone --- Artemisinins --- Hemolysis --- Child

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Finding a treatment for cancer is a major challenge of our time. In the ongoing research, combination therapies (the use of several drugs together) are of high interest. In comparison with the use of a single drug, combinations of synergistic drugs (i.e. drugs that are more effective together than alone) can be as effective while allowing to overcome the drug resistance, to reduce the doses at which the drugs are used, and consequently decrease their toxic effect and multiply their targets. However, the space of all potentially effective combinations is too large to experimentally test all of them and assess their effectiveness, this is known as the combinatorial explosion problem. To overcome that, the identification of interesting combinations requires the help of computational tools. In recent years, machine learning models have been successfully used in biomedical applications. They are typically used in order to determine which combinations would be interesting to be experimentally tested. Since some models aiming at predicting the responses of pairwise combinations already exist, there are only a few machine learning models able to predict responses of higher order drug combinations (the order of a drug combination is defined as the number of drugs in the combination). In addition to the response of a drug combination (typically expressed as a growth percentage), the synergy score of this combination is of high interest. The synergy score allow to answer the question: how much are those drugs more effective together than individually? This work is a step towards the use of machine learning to predict the effect of higher order (order larger than two) cancer drug combinations. It has been made in collaboration with Aalto University (Finland), where a machine learning tool called ComboFM has been developed. ComboFM is able to efficiently predict pairwise responses of cancer drugs. The goal of this work is to extend the use of ComboFM to the predictions of higher order drug combinations. To that end, we propose to combine ComboFM with another model, called the Dose model. The Dose model computes the responses of any order drug combinations, based on all the pairwise responses existing in the combination. This work investigates how those two models can be combined together in order to predict responses of higher order drug combinations while decreasing the amount of required experimental data (pairwise responses). This combination of models gives rise to several issues that are tackled and investigated. The experiments made in this thesis showed that ComboFM and the Dose model can efficiently be combined, as long as the parameters of both models are optimized specifically for this application.

Combination therapy --- Cancer treatment --- Machine learning --- Higher order drug combination --- Ingénierie, informatique & technologie > Multidisciplinaire, généralités & autres

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The next frontier in pharmacology is the development of multi-target strategies in which pathological processes are controlled by pharmacologically manipulating them at many different points at once. Designing multi-target strategies will require deep understanding of the complex physiology that underlies pathological processes. It will also require the development of single drugs with multiple targets, or combinations of drugs with compatible pharmacokinetics that work synergistically to maximize desirable effects while minimizing unwanted side effects. This e-Book contains ten original articles, each addressing a different aspect of this challenge. Together they open new perspectives and show the way forward in the development of multi-target therapeutics.

systems biology --- computer modeling --- drug repurposing --- synergy --- drug discovery --- drug combination --- high throughput screening --- multidrug therapeutics --- polypharmacy --- multifactorial process

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The Special Issue "In Vitro and In Vivo Models of Colorectal Cancer for Clinical Application", edited by Marta Baiocchi and Ann Zeuner for Cancers, collects original research papers and reviews, depicting the current state and the perspectives of CRC models for preclinical and translational research. Original research papers published in this issue focus on some of the hottest topics in CRC research, such as circulating tumor cells, epigenetic regulation of stemness states, new therapeutic targets, molecular CRC classification and experimental CRC models such as organoids and PDXs. Additionally, four reviews on CRC stem cells, immunotherapy and drug discovery provide an updated viewpoint on key topics linking benchtop to bedside research in CRC.

colorectal cancer --- organoids --- 3D bioprinting --- patient-derived xenograft --- cancer-on-chip --- drug combination --- cancer stem cells --- drug resistance --- clinical trials --- tumor-initiating cells --- tumor heterogeneity --- patient-derived cancer models --- single-cell RNA-sequencing --- tumor metabolism --- transcriptional programs --- tumor cell differentiation --- immunotherapy --- methods --- chromosomal instability --- DNA damage --- targeted therapy --- decitabine --- colon cancer --- DNA methylation --- clinical translation study --- machine learning --- patient-derived tumor organoid --- precision medicine --- radiation response --- rectal cancer --- PDX model --- CRC --- mutation analysis --- histological examination --- animal models --- in vitro culture --- cancer stem cell methods --- SATB2 --- colorectal carcinoma --- prognosis --- CDX2 --- circulating tumor cells --- CTC cluster --- size-based method --- ScreenCell® --- epithelial mesenchymal transition --- hypoxia --- HIF-1α --- immunofluorescence analysis --- sequential filtration

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Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis and still represents one of the global health threats to mankind. The World Health Organization estimated more than 10 million new cases and reported more than 1.5 million deaths in 2019, thus ranking TB among the main causes of death due to a single pathogen. Standard anti-TB therapy includes four first-line antibiotics that should be administered for at least six months. However, in the case of multi- and extensively drug-resistant TB, second-line medications must be used and these frequently cause severe side effects resulting in poor compliance. Developing new anti-TB drug candidates is therefore of outmost importance. In this Special Issue dedicated to Tuberculosis Drug Discovery and Development, we present the main and latest achievements in the fields of drug and target discovery, host-directed therapy, anti-virulence drugs, and describe the development of two advanced compounds: macozinone and delpazolid. In addition, this Special Issue provides an historical perspective focused on Carlo Forlanini, the inventor of pneumothorax for TB treatment, and includes an overview of the state-of-the-art technologies which are being exploited nowadays in TB drug development. Finally, a summary of TB vaccines that are either approved or undergoing clinical trials concludes the Special Issue.

Research & information: general --- Biology, life sciences --- mycobacteria --- tuberculosis --- multi-drug resistance --- drug discovery --- promiscuous targets --- Mycobacterium tuberculosis --- rifampin --- isoniazid --- mechanisms of resistance --- mutations --- granulomas --- caseum --- cell envelope --- dormancy --- delpazolid --- macozinone --- DprE1 inhibitor --- clinical studies --- discovery --- mode of action --- drug resistance --- toxicity --- target --- energy metabolism --- electron transport chain --- oxidative phosphorylation --- bedaquiline --- Q203 --- MID3 --- pharmacokinetics --- pharmacodynamics --- drug-drug interactions --- in vitro --- in vivo --- drug development --- tuberculosis treatment --- biomarkers --- drug combination --- clinical trial --- BCG --- tuberculosis vaccines --- TBVI --- EDCTP --- IAVI --- CTVD --- host-directed therapy --- anti-virulence compounds --- TB --- post-treatment sequelae --- surgery --- pulmonary rehabilitation --- Carlo Forlanini --- artificial pneumothorax --- n/a --- structure-based drug design --- target-based drug design --- PknB --- PknG --- DNA gyrase --- antibiotic --- mycobacterium --- genomics --- transcriptomics --- proteomics --- metabolomics --- lipidomics --- target identification --- mechanism of action --- antimicrobial drug resistance (AMR) --- target-based screening --- phenotypic screening --- antituberculosis agents --- antimycobacterial --- anti-TB drug pipeline --- privileged targets --- lead generation