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The discovery and facile synthesis of a new class of sartan-like arterial antihypertensive drugs (angiotensin receptor blockers [ARBs]), subsequently referred to as “bisartans” is reported. In vivo results and complementary molecular modelling presented in this communication indicate bisartans may be beneficial for the treatment of not only heart disease, diabetes, renal dysfunction, and related illnesses, but possibly COVID-19. Bisartans are novel bis-alkylated imidazole sartan derivatives bearing dual symmetric anionic biphenyl tetrazole moieties. In silico docking and molecular dynamics studies revealed bisartans exhibited higher binding affinities for the ACE2/spike protein complex (PDB 6LZG) compared to all other known sartans. They also underwent stable docking to the Zn2+ domain of the ACE2 catalytic site as well as the critical interfacial region between ACE2 and the SARS-CoV-2 receptor binding domain. Additionally, semi-stable docking of bisartans at the arginine-rich furin-cleavage site of the SARS-CoV-2 spike protein (residues 681–686) required for virus entry into host cells, suggest bisartans may inhibit furin action thereby retarding viral entry into host cells. Bisartan tetrazole groups surpass nitrile, the pharmacophoric “warhead” of PF-07321332, in its ability to disrupt the cysteine charge relay system of 3CLpro. However, despite the apparent targeting of multifunctional sites, bisartans do not inhibit SARS-CoV-2 infection in bioassays as effectively as PF-07321332 (Paxlovid). © 2022

Importance Older and/or frail patients are underrepresented in landmark cancer trials. Tailored research is needed to address this evidence gap. Objective The GO2 randomized clinical trial sought to optimize chemotherapy dosing in older and/or frail patients with advanced gastroesophageal cancer, and explored baseline geriatric assessment (GA) as a tool for treatment decision-making. Design, Setting, and Participants This multicenter, noninferiority, open-label randomized trial took place at oncology clinics in the United Kingdom with nurse-led geriatric health assessment. Patients were recruited for whom full-dose combination chemotherapy was considered unsuitable because of advanced age and/or frailty. Interventions There were 2 randomizations that were performed: CHEMO-INTENSITY compared oxaliplatin/capecitabine at Level A (oxaliplatin 130 mg/m2 on day 1, capecitabine 625 mg/m2 twice daily on days 1-21, on a 21-day cycle), Level B (doses 0.8 times A), or Level C (doses 0.6 times A). Alternatively, if the patient and clinician agreed the indication for chemotherapy was uncertain, the patient could instead enter CHEMO-BSC, comparing Level C vs best supportive care. Main Outcomes and Measures First, broad noninferiority of the lower doses vs reference (Level A) was assessed using a permissive boundary of 34 days reduction in progression-free survival (PFS) (hazard ratio, HR = 1.34), selected as acceptable by a forum of patients and clinicians. Then, the patient experience was compared using Overall Treatment Utility (OTU), which combines efficacy, toxic effects, quality of life, and patient value/acceptability. For CHEMO-BSC, the main outcome measure was overall survival. Results A total of 514 patients entered CHEMO-INTENSITY, of whom 385 (75%) were men and 299 (58%) were severely frail, with median age 76 years. Noninferior PFS was confirmed for Levels B vs A (HR = 1.09 [95% CI, 0.89-1.32]) and C vs A (HR = 1.10 [95% CI, 0.90-1.33]). Level C produced less toxic effects and better OTU than A or B. No subgroup benefited from higher doses: Level C produced better OTU even in younger or less frail patients. A total of 45 patients entered the CHEMO-BSC randomization: overall survival was nonsignificantly longer with chemotherapy: median 6.1 vs 3.0 months (HR = 0.69 [95% CI, 0.32-1.48], P = .34). In multivariate analysis in 522 patients with all variables available, baseline frailty, quality of life, and neutrophil to lymphocyte ratio were independently associated with OTU, and can be combined in a model to estimate the probability of different outcomes. Conclusions and Relevance This phase 3 randomized clinical trial found that reduced-intensity chemotherapy provided a better patient experience without significantly compromising cancer control and should be considered for older and/or frail patients. Baseline geriatric assessment can help predict the utility of chemotherapy but did not identify a group benefiting from higher-dose treatment. Trial Registration isrctn.org Identifier: ISRCTN44687907

Sakellaropoulos et al. designed a machine learning workflow to predict drug response and survival of cancer patients. All pipelines are trained on a large panel of cancer cell lines and tested in clinical cohorts. DNN outperforms other machine learning algorithms by capturing pathways that link gene expression with drug response. © 2019 The Author(s) A major challenge in cancer treatment is predicting clinical response to anti-cancer drugs on a personalized basis. Using a pharmacogenomics database of 1,001 cancer cell lines, we trained deep neural networks for prediction of drug response and assessed their performance on multiple clinical cohorts. We demonstrate that deep neural networks outperform the current state in machine learning frameworks. We provide a proof of concept for the use of deep neural network-based frameworks to aid precision oncology strategies. © 2019 The Author(s)

A major challenge in cancer treatment is predicting the clinical response to anti-cancer drugs on a personalized basis. The success of such a task largely depends on the ability to develop computational resources that integrate big “omic” data into effective drug-response models. Machine learning is both an expanding and an evolving computational field that holds promise to cover such needs. Here we provide a focused overview of: 1) the various supervised and unsupervised algorithms used specifically in drug response prediction applications, 2) the strategies employed to develop these algorithms into applicable models, 3) data resources that are fed into these frameworks and 4) pitfalls and challenges to maximize model performance. In this context we also describe a novel in silico screening process, based on Association Rule Mining, for identifying genes as candidate drivers of drug response and compare it with relevant data mining frameworks, for which we generated a web application freely available at: https://compbio.nyumc.org/drugs/. This pipeline explores with high efficiency large sample-spaces, while is able to detect low frequency events and evaluate statistical significance even in the multidimensional space, presenting the results in the form of easily interpretable rules. We conclude with future prospects and challenges of applying machine learning based drug response prediction in precision medicine. © 2019 Elsevier Inc.