Université de Montréal

About: Université de Montréal is a education organization based out in Montreal, Quebec, Canada. It is known for research contribution in the topics: Population & Poison control. The organization has 45641 authors who have published 100476 publications receiving 4004007 citations. The organization is also known as: University of Montreal & UdeM.

Automated design of ligands to polypharmacological profiles

Abstract: The clinical efficacy and safety of a drug is determined by its activity profile across many proteins in the proteome. However, designing drugs with a specific multi-target profile is both complex and difficult. Therefore methods to design drugs rationally a priori against profiles of several proteins would have immense value in drug discovery. Here we describe a new approach for the automated design of ligands against profiles of multiple drug targets. The method is demonstrated by the evolution of an approved acetylcholinesterase inhibitor drug into brain-penetrable ligands with either specific polypharmacology or exquisite selectivity profiles for G-protein-coupled receptors. Overall, 800 ligand-target predictions of prospectively designed ligands were tested experimentally, of which 75% were confirmed to be correct. We also demonstrate target engagement in vivo. The approach can be a useful source of drug leads when multi-target profiles are required to achieve either selectivity over other drug targets or a desired polypharmacology.

Deep Learning: A Primer for Radiologists

Abstract: Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data. With the advent of large datasets and increased computing power, these methods can produce models with exceptional performance. These models are multilayer artificial neural networks, loosely inspired by biologic neural systems. Weighted connections between nodes (neurons) in the network are iteratively adjusted based on example pairs of inputs and target outputs by back-propagating a corrective error signal through the network. For computer vision tasks, convolutional neural networks (CNNs) have proven to be effective. Recently, several clinical applications of CNNs have been proposed and studied in radiology for classification, detection, and segmentation tasks. This article reviews the key concepts of deep learning for clinical radiologists, discusses technical requirements, describes emerging applications in clinical radiology, and outlines limitations and future directions in this field. Radiologists should become familiar with the principles and potential applications of deep learning in medical imaging. ©RSNA, 2017.

Roles of G-protein-coupled receptor dimerization.

Abstract: The classical idea that G-protein-coupled receptors (GPCRs) function as monomeric entities has been unsettled by the emerging concept of GPCR dimerization. Recent findings have indicated not only that many GPCRs exist as homodimers and heterodimers, but also that their oligomeric assembly could have important functional roles. Several studies have shown that dimerization occurs early after biosynthesis, suggesting that it has a primary role in receptor maturation. G-protein coupling, downstream signalling and regulatory processes such as internalization have also been shown to be influenced by the dimeric nature of the receptors. In addition to raising fundamental questions about GPCR function, the concept of dimerization could be important in the development and screening of drugs that act through this receptor class. In particular, the changes in ligand-binding and signalling properties that accompany heterodimerization could give rise to an unexpected pharmacological diversity that would need to be considered.

Panobinostat plus bortezomib and dexamethasone versus placebo plus bortezomib and dexamethasone in patients with relapsed or relapsed and refractory multiple myeloma: a multicentre, randomised, double-blind phase 3 trial

Abstract: Summary Background Panobinostat is a potent oral pan-deacetylase inhibitor that in preclinical studies has synergistic anti-myeloma activity when combined with bortezomib and dexamethasone. We aimed to compare panobinostat, bortezomib, and dexamethasone with placebo, bortezomib, and dexamethasone in patients with relapsed or relapsed and refractory multiple myeloma. Methods PANORAMA1 is a multicentre, randomised, placebo-controlled, double-blind phase 3 trial of patients with relapsed or relapsed and refractory multiple myeloma who have received between one and three previous treatment regimens. Patients were randomly assigned (1:1) via an interactive web-based and voice response system, stratified by number of previous treatment lines and by previous use of bortezomib, to receive 21 day cycles of placebo or panobinostat (20 mg; on days 1, 3, 5, 8, 10, 12, orally), both in combination with bortezomib (1·3 mg/m 2 on days 1, 4, 8, 11, intravenously) and dexamethasone (20 mg on days 1, 2, 4, 5, 8, 9, 11, 12, orally). Patients, physicians, and the investigators who did the data analysis were masked to treatment allocation; crossover was not permitted. The primary endpoint was progression-free survival (in accordance with modified European Group for Blood and Marrow Transplantation criteria and based on investigators' assessment) and was analysed by intention to treat. The study is ongoing, but no longer recruiting, and is registered at ClinicalTrials.gov, number NCT01023308. Findings 768 patients were enrolled between Jan 21, 2010, and Feb 29, 2012, with 387 randomly assigned to panobinostat, bortezomib, and dexamethasone and 381 to placebo, bortezomib, and dexamethasone. Median follow-up was 6·47 months (IQR 1·81–13·47) in the panobinostat group and 5·59 months (2·14–11·30) in the placebo group. Median progression-free survival was significantly longer in the panobinostat group than in the placebo group (11·99 months [95% CI 10·33–12·94] vs 8·08 months [7·56–9·23]; hazard ratio [HR] 0·63, 95% CI 0·52–0·76; p vs 208 [54·6%, 49·4–59·7] for placebo; p=0·09); however, the proportion of patients with a complete or near complete response was significantly higher in the panobinostat group than in the placebo group (107 [27·6%, 95% CI 23·2–32·4] vs 60 [15·7%, 12·2–19·8]; p=0·00006). Minimal responses were noted in 23 (6%) patients in the panobinostat group and in 42 (11%) in the placebo group. Median duration of response (partial response or better) was 13·14 months (95% CI 11·76–14·92) in the panobinostat group and 10·87 months (9·23–11·76) in the placebo group, and median time to response (partial response or better) was 1·51 months (1·41–1·64) in the panobinostat group and 2·00 months (1·61–2·79) in the placebo group. Serious adverse events were reported in 228 (60%) of 381 patients in the panobinostat group and 157 (42%) of 377 patients in the placebo group. Common grade 3–4 laboratory abnormalities and adverse events (irrespective of association with study drug) included thrombocytopenia (256 [67%] in the panobinostat group vs 118 [31%] in the placebo group), lymphopenia (202 [53%] vs 150 [40%]), diarrhoea (97 [26%] vs 30 [8%]), asthenia or fatigue (91 [24%] vs 45 [12%]), and peripheral neuropathy (67 [18%] vs 55 [15%]). Interpretation Our results suggest that panobinostat could be a useful addition to the treatment armamentarium for patients with relapsed or relapsed and refractory multiple myeloma. Longer follow up will be necessary to determine whether there is any effect on overall survival. Funding Novartis Pharmaceuticals.